Search

Environmental enteric dysfunction (EED) is a subclinical syndrome of altered small intestinal function postulated to be an important contributor to childhood undernutrition. The role of small intestinal bacterial communities in the pathophysiology of EED is poorly defined due to a paucity of studies where there has been a direct collection of small intestinal samples from undernourished children. Sixty-three members of a Pakistani cohort identified as being acutely malnourished between 3 and 6 months of age and whose wasting (weight-for-length Z -score [WLZ]) failed to improve after a 2-month nutritional intervention underwent esophagogastroduodenoscopy (EGD). Paired duodenal luminal aspirates and duodenal mucosal biopsies were obtained from 43 children. Duodenal microbiota composition was characterized by sequencing bacterial 16S rRNA gene amplicons. Levels of bacterial taxa (amplicon sequence variants [ASVs]) were referenced to anthropometric indices, histopathologic severity in biopsies, expression of selected genes in the duodenal mucosa, and fecal levels of an immunoinflammatory biomarker (lipocalin-2). A "core" group of eight bacterial ASVs was present in the duodenal samples of 69% of participants. Streptococcus anginosus was the most prevalent, followed by Streptococcus sp., Gemella haemolysans , Streptococcus australis , Granulicatella elegans , Granulicatella adiacens, and Abiotrophia defectiva . At the time of EGD, none of the core taxa were significantly correlated with WLZ. Statistically significant correlations were documented between the abundances of Granulicatella elegans and Granulicatella adiacens and the expression of duodenal mucosal genes involved in immune responses (dual oxidase maturation factor 2, serum amyloid A, and granzyme H). These results suggest that a potential role for members of the oral microbiota in pathogenesis, notably Streptococcus , Gemella, and Granulicatella species, warrants further investigation.IMPORTANCEUndernutrition among women and children is a pressing global health problem. Environmental enteric dysfunction (EED) is a disease of the small intestine (SI) associated with impaired gut mucosal barrier function and reduced capacity for nutrient absorption. The cause of EED is ill-defined. One emerging hypothesis is that alterations in the SI microbiota contribute to EED. We performed a culture-independent analysis of the SI microbiota of a cohort of Pakistani children with undernutrition who had failed a standard nutritional intervention, underwent upper gastrointestinal tract endoscopy, and had histologic evidence of EED in their duodenal mucosal biopsies. The results revealed a shared group of bacterial taxa in their duodenums whose absolute abundances were correlated with levels of the expression of genes in the duodenal mucosa that are involved in inflammatory responses. A number of these bacterial taxa are more typically found in the oral microbiota, a finding that has potential physiologic and therapeutic implications.

Patient-specific fabrication of scaffold/implant requires an engineering approach to manufacture the ideal scaffold. Herein, we design and 3D print scaffolds comprised of polyether-ether-ketone (PEEK) and sodium-carboxymethyl cellulose (Na-CMC). The fabricated scaffold was dip coated with Zn and Mn doped bioactive glass nanoparticles (Zn-Mn MBGNs). The synthesized ink exhibit suitable shear-thinning behavior for direct ink write (DIW) 3D printing. The scaffolds were crafted with precision, featuring 85% porosity, 0.3 mm layer height, and 1.5 mm/s printing speed at room temperature. Scanning electron microscopy images reveal a well-defined scaffold with an average pore size of 600 ± 30 μm. The energy dispersive X-ray spectroscopy analysis confirmed a well dispersed/uniform coating of Zn-Mn MBGNs on the PEEK/Na-CMC scaffold. Fourier transform infrared spectroscopy approved the presence of PEEK, CMC, and Zn-Mn MBGNs. The tensile test revealed a Young's modulus of 2.05 GPa. Antibacterial assays demonstrate inhibition zone against Staphylococcus aureus and Escherichia Coli strains. Chick Chorioallantoic Membrane assays also present significant angiogenesis potential, owing to the antigenic nature of Zn-Mn MBGNs. WST-8 cell viability assays depicted cell proliferation, with a 103% viability after 7 days of culture. This study suggests that the PEEK/Na-CMC scaffolds coated with Zn-Mn MBGNs are an excellent candidate for osteoporotic fracture treatment. Thus, the fabricated scaffold can offer multifaceted properties for enhanced patient outcomes in the bone tissue regeneration., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction . To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/., (© 2024. The Author(s).)

This study explores the evaluation of knowledge regarding neonatal danger signs (NDS) among first-time mothers in Pakistan during their discharge from healthcare facilities. The investigation aimed to establish connections between their understanding of NDS and factors such as sociodemographic background, prenatal check-ups, and educational measures. Considering the persistently high neonatal mortality rates in low- and middle-income nations, recognizing maternal NDS awareness becomes crucial for promoting early medical attention and reducing neonatal health risks. Existing research highlights the role of maternal knowledge in shaping neonatal well-being; however, gaps in knowledge remain prevalent, especially among first-time mothers. Using a cross-sectional approach, data were gathered through structured questionnaires, revealing significant relationships among maternal NDS awareness, sociodemographic aspects, prenatal care, and educational interventions. It is noteworthy that first-time mothers demonstrated lower NDS knowledge than mothers with multiple childbirth experiences, while those with higher education displayed greater awareness The effects of educational interventions were diverse, and antenatal visits were linked to enhanced knowledge. This study highlights the significance of focused treatments that target knowledge deficiencies, which can empower women and contribute to the reduction of infant health problems and mortality. encouraging further exploration of effective strategies to augment maternal knowledge and proactive healthcare-seeking behaviors., Competing Interests: The Authors declared no conflict of interest, (African Journal of Reproductive Health © 2024.)

Post-translational modifications (PTMs) either enhance a protein's activity in various sub-cellular processes, or degrade their activity which leads toward failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein's activity that initiates and propagates various diseases including neurodegenerative, cardiovascular, autoimmune diseases and carcinogenesis. Identification of NT modification supports development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. This paper presents the NTpred framework that is competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, which is used to train a Logistic Regression classifier. On the BD1 benchmark dataset, the proposed framework outperforms the existing best-performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on the BD2 benchmark dataset, the proposed framework outperforms the existing best-performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC. NTpred is publicly available for further experimentation and predictive use at: https://sds_genetic_analysis.opendfki.de/PredNTS/., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Stroke, a major cause of disability worldwide, necessitates comprehensive rehabilitation, with nutrition playing a pivotal role in recovery. Our systematic review assesses the impact of nutritional interventions on stroke survivors' rehabilitation. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched PubMed, Embase, Web of Science, and Scopus, using keywords related to stroke rehabilitation and nutrition. Studies were selected based on criteria emphasizing dietary interventions and their effect on functional recovery in stroke patients. The review involved detailed data extraction and synthesis, covering study design, participant characteristics, interventions, and outcomes. Five studies met our inclusion criteria, encompassing longitudinal and prospective studies, retrospective cohorts, and randomized controlled trials. These studies highlighted the importance of early nutritional assessment, particularly for sarcopenic patients, and the role of energy and protein intake soon after a stroke. Findings indicated high nutritional risk correlated with poorer functional outcomes and increased inflammation. Tailored dietary support appeared beneficial for muscle mass maintenance and overall functional recovery, especially in older patients. Our review emphasizes the critical role of nutritional interventions in stroke rehabilitation. It suggests that personalized nutritional strategies can positively impact functional recovery, notably in older and nutritionally vulnerable stroke survivors. These insights underscore the necessity of integrating dietary assessments and interventions into standard stroke rehabilitation protocols, advocating a holistic approach to patient care., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Kolanu et al.)

This study aim to investigate the diagnostic potential of Raman spectroscopy in comparison with rapid urease test and histopathology in diagnosis of H. pylori infection. A comparative study was conducted at Pathology Laboratory and a total of 94 samples were collected from patients based on Rome IV criteria. Sensitivity, specificity and accuracy of histopathology, rapid urease test and for Raman spectroscopy were investigated. Rapid urease test showed 23 false negative results of H. pylori as compared to Raman spectroscopy and histopathology. We concluded that Raman spectroscopy showed sensitivity (94.5%), accuracy (94.0%) and specificity of (87.5%) in the diagnosis of H. pylori infection. However rapid urease test showed specificity of 92.5% while low sensitivity 75%, and 78% accuracy as compared to Raman spectroscopy and histopathology . This study illustrates the applicability of Raman spectroscopy as a potent innovative detection tool for the molecular detection of H. pylori infection in gastritis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Additive manufacturing (also known as 3D printing) is a promising method for producing patient-specific implants. In the present study, sodium alginate (Na-ALG)/poly(vinyl alcohol) (PVA) polymer blends of varying ratios (1:0, 3:1, 1:1, and 1:3) were used to produce tailored-designed skin scaffolds using a 3D bioprinter. Samples of skin scaffolds were printed at 20 layers with a layer height of 0.15 mm using a needle with an inner diameter of 330 μm while maintaining the extrusion speed, extrusion width, and fill density at 10 mm/s, 0.2 mm, and 85%, respectively. The Na-ALG/PVA blend with a 3:1 ratio showed the best printability due to its good viscosity and minimal nozzle leakage, allowing for the fabrication of skin scaffolds with high fidelity and the desired morphological characteristics. Then, copper-silver doped mesoporous bioactive glass nanoparticles (Cu-Ag MBGNs) were incorporated into the Na-ALG/PVA blend (which had already been prepared by using a Na-ALG:PVA ratio of 3:1) in order to obtain therapeutic (angiogenic and antibacterial) effects. The fabricated Na-ALG/PVA/Cu-Ag MBGNs biocomposite scaffolds with dimensions of 20 mm× 20 × 3 mm 3 and pore size of 400 ± 60 μm exhibited a promising fidelity. The presence of chemical bonds attributed to Na-ALG, PVA, and Cu-Ag MBGNs and the uniform distribution of Na, C, and O elements within the microstructure of the scaffolds were confirmed by EDX, SEM, and FTIR analyses. The scaffolds were hydrophilic and exhibited proper swelling and degradation behavior for skin tissue engineering. According to the inhibition halo test, the scaffolds exhibited strong antibacterial activity against Staphylococcus aureus and Escherichia coli . The cytocompatibility to human-derived fibroblast cells was confirmed by the WST-8 assay and in vivo Chorioallantoic Membrane Assay. In addition, Na-ALG/PVA/Cu-Ag MBGNs showed angiogenic potential, exhibiting favorable wound healing properties.

The acquisition of antimicrobial resistance (AR) genes has rendered important pathogens nearly or fully unresponsive to antibiotics. It has been suggested that pathogens acquire AR traits from the gut microbiota, which collectively serve as a global reservoir for AR genes conferring resistance to all classes of antibiotics. However, only a subset of AR genes confers resistance to clinically relevant antibiotics, and, although these AR gene profiles are well-characterized for common pathogens, less is known about their taxonomic associations and transfer potential within diverse members of the gut microbiota. We examined a collection of 14,850 human metagenomes and 1666 environmental metagenomes from 33 countries, in addition to nearly 600,000 isolate genomes, to gain insight into the global prevalence and taxonomic range of clinically relevant AR genes. We find that several of the most concerning AR genes, such as those encoding the cephalosporinase CTX-M and carbapenemases KPC, IMP, NDM, and VIM, remain taxonomically restricted to Proteobacteria. Even cfiA, the most common carbapenemase gene within the human gut microbiome, remains tightly restricted to Bacteroides, despite being found on a mobilizable plasmid. We confirmed these findings in gut microbiome samples from India, Honduras, Pakistan, and Vietnam, using a high-sensitivity single-cell fusion PCR approach. Focusing on a set of genes encoding carbapenemases and cephalosporinases, thus far restricted to Bacteroides species, we find that few mutations are required for efficacy in a different phylum, raising the question of why these genes have not spread more widely. Overall, these data suggest that globally prevalent, clinically relevant AR genes have not yet established themselves across diverse commensal gut microbiota., (© 2023. The Author(s).)

Background: The reduction in severe and moderate acute malnutrition (SAM and MAM) rates in Pakistan has been sub-optimal compared to other low-and middle-income countries (LMICs). Specially-formulated products have been designed globally to manage SAM and MAM, such as ready-to-use therapeutic food (RUTF) and ready-to-use supplementary food (RUSF), with variable efficacies. RUTF is primarily produced and patented in industrialized countries, raising supply challenges in resource-constrained regions with a high burden of acute malnutrition. RUSF minimizes costs by using locally-available ingredients while providing similar nutritional value. In this study, we compared the efficacy, side effects, and compliance of two months of supplementation with either RUTF or RUSF., Methods: Children aged nine months in the rural district of Matiari, Pakistan, with a weight-for-height z-score (WHZ) <-2 received either RUTF (500 kcal sachet) for two months in 2015 or RUSF (520 kcal sachet) for two months in 2018., Results: The RUSF group had a higher height gain and mid-upper arm circumferences (MUAC) score. Higher compliance was noted with lower side effects in the RUSF group. A higher compliance rate did correlate with the growth parameters in respective groups., Conclusion: Our study found that both RUTF and RUSF partially improve the anthropometric status of acutely malnourished children, with neither being superior to the other., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Sarfraz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Artificial Intelligence (AI) has achieved remarkable success in image generation, image analysis, and language modeling, making data-driven techniques increasingly relevant in practical real-world applications, promising enhanced creativity and efficiency for human users. However, the deployment of AI in high-stakes domains such as infrastructure and healthcare still raises concerns regarding algorithm accountability and safety. The emerging field of explainable AI (XAI) has made significant strides in developing interfaces that enable humans to comprehend the decisions made by data-driven models. Among these approaches, concept-based explainability stands out due to its ability to align explanations with high-level concepts familiar to users. Nonetheless, early research in adversarial machine learning has unveiled that exposing model explanations can render victim models more susceptible to attacks. This is the first study to investigate and compare the impact of concept-based explanations on the privacy of Deep Learning based AI models in the context of biomedical image analysis. An extensive privacy benchmark is conducted on three different state-of-the-art model architectures (ResNet50, NFNet, ConvNeXt) trained on two biomedical (ISIC and EyePACS) and one synthetic dataset (SCDB). The success of membership inference attacks while exposing varying degrees of attribution-based and concept-based explanations is systematically compared. The findings indicate that, in theory, concept-based explanations can potentially increase the vulnerability of a private AI system by up to 16% compared to attributions in the baseline setting. However, it is demonstrated that, in more realistic attack scenarios, the threat posed by explanations is negligible in practice. Furthermore, actionable recommendations are provided to ensure the safe deployment of concept-based XAI systems. In addition, the impact of differential privacy (DP) on the quality of concept-based explanations is explored, revealing that while negatively influencing the explanation ability, DP can have an adverse effect on the models' privacy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lucieri, Dengel and Ahmed.)

This study assessed the impact of gross domestic product (GDP), education, natural resources, remittances, and financial inclusion on carbon emissions in G-11 countries from 1990 to 2021. Based on the negative impact of pollution and the need for sustainable development, this study examined factors affecting CO 2 emissions in G-11 countries using non-linear panel ARDL model. The study found that a positive GDP shock increases CO 2 emissions in the short and long term, while a negative shock decreases emissions in the short term and increases emissions in the long term. Education was found to increase CO 2 emissions in the long term but decrease them in the short term, emphasizing the need for education on combating emissions. Natural resources were also found to increase emissions in the long term, highlighting the need for government-defined institutions to minimize extraction effects and enforce transparency and accountability. Positive changes in personal remittances and financial inclusion were found to increase emissions in both the short and long term, suggesting the need for policies that encourage renewable energy sources and energy efficiency improvement. The study concludes that policymakers should prioritize efficient resource allocation, promote renewable energy usage, and enhance environmental awareness to achieve sustainable development goals in G-11 countries. The possible applications of this study include the use of the models to investigate the asymmetric effects on CO 2 emissions. This model can be applied in future studies to examine the relationship between GDP, education, natural resources, personal remittances, financial inclusion, and CO 2 emissions in other countries., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (© 2023 The Authors.)

Background: Diarrhoea and acute respiratory infections (ARI) are assumed to be major drivers of growth and likely contribute to environmental enteric dysfunction (EED), which is a precursor to childhood malnutrition. In the present study, we checked the correlation between diarrhoeal/ARI burden and EED using a novel duodenal histological index., Methods: Between November 2017 and July 2019, a total of 365 infants with weight-for-height Z scores (WHZ score) of <-2 were enrolled, and 51 infants with WHZ scores of >0 and height-for-age Z scores (HAZ scores) of >-1 were selected as age-matched healthy controls. Morbidity was assessed weekly and categorised as the total number of days with diarrhoea and acute respiratory infection (ARI) from enrolment until two years of age and was further divided into four quartiles in ascending order., Findings: The HAZ declined until two years of age regardless of morbidity burden, and WHZ and weight-for-age Z scores (WAZ scores) were at their lowest at six months. Sixty-three subjects who had a WHZ score <-2 and failed to respond to nutritional and educational interventions were further selected at 15 months to investigate their EED histological scores with endoscopy further. EED histological scores of the subjects were higher with increasing diarrhoeal frequency yet remained statistically insignificant (p = 0.810)., Interpretation: There was not a clear correlation between diarrhoea and ARI frequency with growth faltering, however, children with the highest frequency of diarrhoea had the highest EED histological scores and growth faltering., Funding: Bill and Melinda Gates Foundation and The National Institutes of Health., Competing Interests: This work was supported by the 10.13039/100000865Bill and Melinda Gates Foundation (grant number OPP1138727 to SAA and grant number OPP1144149 to SRM and The National Institutes of Health (grant number 2D43TW007585-2 to AA and SRM)., (© 2023 The Author(s).)

Objectives: Staphylococcus aureus and Streptococcus pneumoniae are common colonizers of the human nasopharynx. In this study, we describe S. aureus nasopharyngeal carriage and evaluate its association with S. pneumoniae carriage post-10-valent pneumococcal conjugate vaccine (PCV10) introduction in Pakistan., Methods: A serial cross-sectional study was undertaken from 2014 to 2018, children <2 years were randomly selected, and nasopharyngeal swabs were collected using standard WHO guidelines. S. aureus and S. pneumoniae isolates were identified using standard methods and tested for antimicrobial susceptibility by the standard Kirby-Bauer disk-diffusion method as per Clinical & Laboratory Standards Institute (CLSI) recommendations. Regression analysis was used to determine predictors associated with S. aureus carriage., Results: We enrolled 3140 children. S. aureus carriage prevalence was 5.6% (176/3140), and 50.1% (81/176) of the isolates were methicillin-resistant S. aureus (MRSA). S. aureus carriage was higher in the absence of pneumococcus compared to isolates in which pneumococcus was present (7.5% vs 5.0%). S. aureus carriage was negatively associated with pneumococcal carriage, being in 3rd and 4th year of enrollment, and vaccination with two and three PCV10 doses, in addition, fast breathing, ≥2 outpatients visits, and rainy season were positively associated. The following resistance rates were observed: 98.9% for penicillin, 74.4% for fusidic acid, and 23.3% for gentamicin, 10.2% for erythromycin, and 8.5% for cotrimoxazole. All isolates were susceptible to amikacin., Conclusions: Overall S. aureus carriage prevalence was low, PCV10 vaccine was protective against the carriage. The proportion of MRSA carriage and antimicrobial resistance was high in this community warranting continuous monitoring for invasive infections., Competing Interests: The authors have no relevant financial or non-financial interests to disclose., (© 2023 The Authors.)

Background: Pakistan has a well-established Expanded Program on Immunization (EPI) however vaccine-preventable diseases still account for high infant and child mortality rates. This study describes the differential vaccine coverage and determinants of vaccine uptake in rural Pakistan., Methods: From October 2014 to September 2018, we enrolled children younger than 2 years of age from the Matiari Demographic Surveillance System in Sindh, Pakistan. Socio-demographic and vaccination history were collected from all participants. Vaccine coverage rates and timeliness were reported. Socio-demographic variables for missed and untimely vaccination were studied in multivariable logistic regression., Results: Of the 3140 enrolled children, 48.4 % received all EPI recommended vaccines. Only 21.2 % of these were age appropriate. Around 45.4 % of the children were partially vaccinated, and 6.2 % were unvaccinated. Highest coverage was seen for the first dose of pentavalent (72.8 %), 10-valent Pneumococcal Conjugate Vaccine (PCV10) (70.4 %) and Oral Polio Vaccine (OPV) (69.2 %) and the lowest coverage was for measles (29.3 %) and rotavirus (1.8 %) vaccines. Primary caretakers and wage earners with a higher level of education were protective against missed and untimely vaccination. Enrollment in the 2nd, 3rd and 4th study year was negatively associated with being unvaccinated whereas distance from a major road was positively associated with non-adherence to schedule., Conclusion: Vaccine coverage was low among children in Matiari, Pakistan, and majority received delayed doses. Parents' education status and year of study enrollment was protective against vaccine dropout and delayed vaccination whereas geographical distance from a major road was a predictor. Vaccine promotion and outreach efforts may have had a beneficial impact on vaccine coverage and timeliness., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Environmental enteric dysfunction (EED) is characterized by intestinal inflammation, malabsorption and growth-faltering in children with heightened exposure to gut pathogens. The aim of this study was to characterize serum non-esterified fatty acids (NEFA), in association with childhood undernutrition and EED, as potential biomarkers to predict growth outcomes. The study comprised a cohort of undernourished rural Pakistani infants ( n = 365) and age-matched controls followed prospectively up to 24 months of age. Serum NEFA were quantified at ages 3-6 and 9 months and correlated with growth outcomes, serum bile acids and EED histopathological biomarkers. Serum NEFA correlated with linear growth-faltering and systemic and gut biomarkers of EED. Undernourished children exhibited essential fatty acid deficiency (EFAD), with low levels of linoleic acid and total n-6 polyunsaturated fatty acids, compensated by increased levels of oleic acid and increased elongase and desaturase activities. EFAD correlated with reduced anthropometric Z scores at 3-6 and 9 months of age. Serum NEFA also correlated with elevated BA and liver dysfunction. Essential fatty acid depletion and altered NEFA metabolism were highly prevalent and associated with acute and chronic growth-faltering in EED. The finding suggests that targeting early interventions to correct EFAD and promote FA absorption in children with EED may facilitate childhood growth in high-risk settings.

Environmental enteric dysfunction (EED) is a subclinical enteropathy prevalent in resource-limited settings, hypothesized to be a consequence of chronic exposure to environmental enteropathogens, resulting in malnutrition, growth failure, neurocognitive delays, and oral vaccine failure. This study explored the duodenal and colonic tissues of children with EED, celiac disease, and other enteropathies using quantitative mucosal morphometry, histopathologic scoring indices, and machine learning-based image analysis from archival and prospective cohorts of children from Pakistan and the United States. We observed villus blunting as being more prominent in celiac disease than in EED, as shorter lengths of villi were observed in patients with celiac disease from Pakistan than in those from the United States, with median (interquartile range) lengths of 81 (73, 127) µm and 209 (188, 266) µm, respectively. Additionally, per the Marsh scoring method, celiac disease histologic severity was increased in the cohorts from Pakistan. Goblet cell depletion and increased intraepithelial lymphocytes were features of EED and celiac disease. Interestingly, the rectal tissue from cases with EED showed increased mononuclear inflammatory cells and intraepithelial lymphocytes in the crypts compared with controls. Increased neutrophils in the rectal crypt epithelium were also significantly associated with increased EED histologic severity scores in duodenal tissue. We observed an overlap between diseased and healthy duodenal tissue upon leveraging machine learning image analysis. We conclude that EED comprises a spectrum of inflammation in the duodenum, as previously described, and the rectal mucosa, warranting the examination of both anatomic regions in our efforts to understand and manage EED.

Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds&#95;genetic&#95;analysis.opendfki.de/DNA&#95;Modifications/., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Introduction: Environmental enteropathy is an important contributor to childhood malnutrition in the developing world. Chronic exposure to fecal pathogens leads to alteration in intestinal structure and function, resulting in impaired gut immune function, malabsorption, and growth faltering leading to environmental enteropathy., Methods: A community-based intervention study was carried out on children till 24 months of age in Matiari district, Pakistan. Blood and fecal specimens were collected from the enrolled children aged 3-6 and 9 months. A real-time PCR-based TaqMan array card (TAC) was used to detect enteropathogens., Results: Giardia , Campylobacter spp., enteroaggregative Escherichia coli (EAEC), Enteropathogenic Escherichia coli (EPEC), Enterotoxigenic Escherichia coli (ETEC), and Cryptosporidium spp. were the most prevailing enteropathogens in terms of overall positivity at both time points. Detection of protozoa at enrollment and 9 months was negatively correlated with rate of change in height-for-age Z (ΔHAZ) scores during the first and second years of life. A positive association was found between Giardia, fecal lipocalin (LCN), and alpha 1-Acid Glycoprotein (AGP), while Campylobacter spp. showed positive associations with neopterin (NEO) and myeloperoxidase (MPO)., Conclusion: Protozoal colonization is associated with a decline in linear growth velocity during the first 2 years of life in children living in Environmental enteric dysfunction (EED) endemic settings. Mechanistic studies exploring the role of cumulative microbial colonization, their adaptations to undernutrition, and their influence on gut homeostasis are required to understand symptomatic enteropathogen-induced growth faltering., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kabir, Iqbal, Jamil, Iqbal, Mallawaarachchi, Aziz, Kalam, Muneer, Hotwani, Ahmed, Umrani, Syed, Sadiq, Ma, Moore and Ali.)

Background: Pneumonia is the leading cause of mortality in under-five children and most of these deaths occur in South-East Asia and Africa. Fast breathing pneumonia if not treated can progress to lower chest indrawing pneumonia. Treatment recommendation by the World Health Organization (WHO) for fast-breathing pneumonia includes oral amoxicillin and cotrimoxazole (as an alternative). Due to limited access to health care facilities and skilled health care workers, many children are unable to receive antibiotics. Algorithm-based community case management of pneumonia through trained community health workers has resulted in a decline in morbidity and mortality in low- and middle-income countries (LMIC)., Methods: It was a cluster-randomized, unblinded, community-based trial conducted in the Matiari district of Sindh province, Pakistan. Lady Health Workers (LHWs) were trained in assessing, classifying, and managing fast-breathing pneumonia cases (Respiratory rate of >50 breaths/min) at home with oral amoxicillin for three days and with co-trimoxazole for five days in the intervention and control arms respectively. Children with fast-breathing pneumonia were screened by LHWs and were validated by the study by Community Health Workers (CHWs) within 48 hours. They were followed by the LHWs on days 2, 4, and 14 in intervention and on days 2, 6, and 14 in the control arm. Primary treatment failure was assessed on day 4 in intervention and day 6 in the control arm. A severe pneumonia trial was registered with ClinicalTrials.gov, number NCT01192789., Results: From February 2008 to March 2010, a total of 5876 children were enrolled by Lady Health Workers as fast breathing pneumonia. On validation visits of the CHWs, 728 (12%) children were excluded. A total of 4984 children were analysed as per protocol: 2480 in intervention and 2504 in control. There were 72 (2.9%) primary treatment failures in the intervention arm as compared to 102 (4%) in the control arm with a risk difference of -0.94 (-2.84%, 0.96%). Secondary treatment failures were almost equal in both arms (4 vs 7 cases). No deaths or serious adverse events were recorded., Conclusions: This study shows that amoxicillin can be as effective as cotrimoxazole to treat fast-breathing pneumonia cases at the domiciliary level., Registration: NCT01192789., Competing Interests: Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests., (Copyright © 2022 by the Journal of Global Health. All rights reserved.)

Background and Objective: Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences., Method: The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach "Bot-Net" which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA-miRNA interaction prediction., Results: BoT-Net outperforms the state-of-the-art lncRNA-miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA-protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%., Conclusion: In the benchmark lncRNA-miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA-protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introduces a significant level of bias which makes a large number of lncRNA sequences very much identical to each other and eventually derail classifier generalizeability. Empirical evaluation reveals that within 50 residues of only the starting region of long lncRNA sequences, a highly informative distribution for lncRNA-miRNA interaction prediction is contained, a crucial finding exploited by the proposed BoT-Net approach to optimize the lncRNA fixed length generation process., Availability: BoT-Net web server can be accessed at https://sds&#95;genetic&#95;analysis.opendfki.de/lncmiRNA/., (© 2022. The Author(s).)

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds&#95;genetic&#95;analysis.opendfki.de/MP-VHPPI/., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Asim, Fazeel, Ibrahim, Dengel and Ahmed.)

Protein-protein interaction (PPI) prediction is essential to understand the functions of proteins in various biological processes and their roles in the development, progression, and treatment of different diseases. To perform economical large-scale PPI analysis, several artificial intelligence-based approaches have been proposed. However, these approaches have limited predictive performance due to the use of in-effective statistical representation learning methods and predictors that lack the ability to extract comprehensive discriminative features. The paper in hand generates statistical representation of protein sequences by applying transfer learning in an unsupervised manner using FastText embedding generation approach. Furthermore, it presents "ADH-PPI" classifier which reaps the benefits of three different neural layers, long short-term memory, convolutional, and self-attention layers. Over two different species benchmark datasets, proposed ADH-PPI predictor outperforms existing approaches by an overall accuracy of 4%, and matthews correlation coefficient of 6%. In addition, it achieves an overall accuracy increment of 7% on four independent test sets. Availability : ADH-PPI web server is publicly available at https://sds&#95;genetic&#95;analysis.opendfki.de/PPI/., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Purpose: Diabetic foot is a common complication associated with diabetes mellitus (DM) leading to ulcerations in the feet. Due to diabetic neuropathy, most patients have reduced sensitivity to pain. As a result, minor injuries go unnoticed and progress into ulcers. The timely detection of potential ulceration points and intervention is crucial in preventing amputation. Changes in plantar temperature are one of the early signs of ulceration. Previous studies have focused on either binary classification or grading of DM severity, but neglect the holistic consideration of the problem. Moreover, multi-class studies exhibit severe performance variations between different classes., Methods: We propose a new convolutional neural network for discrimination between non-DM and five DM severity grades from plantar thermal images and compare its performance against pre-trained networks such as AlexNet and related works. We address the lack of data and imbalanced class distribution, prevalent in prior work, achieving well-balanced classification performance., Results: Our proposed model achieved the best performance with a mean accuracy of 0.9827, mean sensitivity of 0.9684 and mean specificity of 0.9892 in combined diabetic foot detection and grading., Conclusion: To the best of our knowledge, this study sets a new state-of-the-art in plantar foot thermogram detection and grading, while being the first to implement a holistic multi-class classification and grading solution. Reliable automatic thermogram grading is a first step towards the development of smart health devices for DM patients., Competing Interests: Conflict of interestThe authors have no competing interests to declare that are relevant to the content of this article., (© The Author(s) 2022.)

Background: Exclusive breastfeeding (EBF) of children until six months of age is considered one of the most critical interventions in tackling childhood undernutrition. EBF rates are suboptimal in Pakistan, particularly in rural areas where child undernutrition is most prevalent. This study aimed to explore barriers to EBF in a rural context of Pakistan., Methods: The study was conducted in the rural district Matiari of Sindh, Pakistan, during Jan-March 2020. We used a qualitative exploratory study design and conducted 36 focus group discussions (FGDs). Participants were purposively selected mothers who had not practiced EBF during their previous childbirth, their spouses and mothers-in-law, and lady health workers (LHWs) serving in the study catchment. FGDs were audio-recorded, transcribed, and translated into English from the local language and analysed using thematic content analysis., Results: Barriers to EBF included low awareness and cultural practices of prelacteal feeds, insufficient breast milk production, undernutrition of mothers, mothers' occupation as fieldworkers, less birth spacing, low awareness about the correct technique of breastfeeding, maternal and child ailments, abnormal breasts, and influence of in-laws to start top-up feeds. Several facilitators were identified: family support, appropriate maternal diet, maternal awareness, and support in the neighborhood., Conclusion: Barriers to EBF are multifaceted in rural areas, and interventions aiming to improve adherence to EBFshould be multipronged. Awareness-raising alone might not be sufficient, and other interventions should be designed to address the barriers of maternal malnutrition, insufficient milk production, and socio-cultural practices. In addition, safe alternatives to breast milk may be necessary if breastfeeding is truly not feasible. Lack of breast milk substitutes is particularly challenging for poor rural women who cannot afford infant formula milk., (© 2022. The Author(s).)

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network "EL-RMLocNet", predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds&#95;genetic&#95;analysis.opendfki.de/subcellular&#95;loc/)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs.

Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework "LGCA-VHPPI" is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets., Competing Interests: The authors have declared that no competing interests exist.

The 10-valent pneumococcal vaccine was introduced in Pakistan’s Expanded Program on Immunization (EPI) in 2013 as a 3 + 0 schedule without catchup. We conducted three annual cross-sectional surveys from 2014−2016 to measure vaccine-type (VT) carriage in infants from a rural part of Pakistan. Nasopharyngeal specimens were collected by random sampling of infants from two union councils of Matiari. Samples were then transported to the Infectious Disease Research Laboratory (IDRL) at the Aga Khan University within 6−8 h of collection. Serotypes were established using sequential multiplex PCR. Of the 665 children enrolled across three surveys, 547 were culture-positive for pneumococcus. VT carriage decreased from 21.8% in 2014 to 12.7% in 2016 (p-value for trend <0.001). Those who were not vaccinated or partially vaccinated were found to be at higher risk of carrying a VT serotype ((aOR 2.53, 95% CI 1.39, 4.63 for non-vaccinated) and (aOR 3.35, 95% CI 1.82, 6.16 for partially vaccinated)). On the other hand, being enrolled in the most recent survey was negatively associated with VT carriage (aOR 0.51, 95% CI 0.28, 0.93). We found that PCV10 was effective in decreasing the carriage of vaccine-type serotypes in Pakistani infants.

Background: The underperformance of oral vaccines in children of low- and middle-income countries is partly attributable to underlying environmental enteric dysfunction (EED)., Methodology: We conducted a longitudinal, community-based study to evaluate the association of oral rotavirus vaccine (Rotarix®) seroconversion with growth anthropometrics, EED biomarkers and intestinal enteropathogens in Pakistani infants. Children were enrolled between three to six months of their age based on their nutritional status. We measured serum anti-rotavirus immunoglobulin A (IgA) at enrollment and nine months of age with EED biomarkers and intestinal enteropathogens., Results: A total of 391 infants received two doses of rotavirus (RV) vaccine. 331/391 provided paired blood samples. Of these 331 children, 45% seroconverted at 9 months of age, 35% did not seroconvert and 20% were seropositive at baseline. Non-seroconverted children were more likely to be stunted, wasted and underweight at enrollment. In univariate analysis, insulin-like growth factor (IGF) concentration at 6 months were higher in seroconverters, median (25th, 75th percentile): 26.3 (16.5, 43.5) ng/ml vs. 22.5 (13.6, 36.3) ng/ml for non-seroconverters, p-value = 0.024. At nine months, fecal myeloperoxidase (MPO) concentrations were significantly lower in seroconverters, 3050(1250, 7587) ng/ml vs. 4623.3 (2189, 11650) ng/ml in non-seroconverted children, p-value = 0.017. In multivariable logistic regression analysis, alpha-1 acid glycoprotein (AGP) and IGF-1 concentrations were positively associated with seroconversion at six months. The presence of sapovirus and rotavirus in fecal samples at the time of rotavirus administration, was associated with non-seroconversion and seroconversion, respectively., Conclusion: We detected high baseline RV seropositivity and impaired RV vaccine immunogenicity in this high-risk group of children. Healthy growth, serum IGF-1 and AGP, and fecal shedding of rotavirus were positively associated with RV IgA seroconversion following immunization, whereas the presence of sapovirus was more common in non-seroconverters., Trial Registration: Clinical Trials ID: NCT03588013., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Deep neural networks are one of the most successful classifiers across different domains. However, their use is limited in safety-critical areas due to their limitations concerning interpretability. The research field of explainable artificial intelligence addresses this problem. However, most interpretability methods align to the imaging modality by design. The paper introduces TimeREISE, a model agnostic attribution method that shows success in the context of time series classification. The method applies perturbations to the input and considers different attribution map characteristics such as the granularity and density of an attribution map. The approach demonstrates superior performance compared to existing methods concerning different well-established measurements. TimeREISE shows impressive results in the deletion and insertion test, Infidelity, and Sensitivity. Concerning the continuity of an explanation, it showed superior performance while preserving the correctness of the attribution map. Additional sanity checks prove the correctness of the approach and its dependency on the model parameters. TimeREISE scales well with an increasing number of channels and timesteps. TimeREISE applies to any time series classification network and does not rely on prior data knowledge. TimeREISE is suited for any usecase independent of dataset characteristics such as sequence length, channel number, and number of classes.

Background and Objectives: One principal impediment in the successful deployment of Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday clinical workflows is their lack of transparent decision-making. Although commonly used eXplainable AI (XAI) methods provide insights into these largely opaque algorithms, such explanations are usually convoluted and not readily comprehensible. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is ambiguous in itself. This work presents ExAID (Explainable AI for Dermatology), a novel XAI framework for biomedical image analysis that provides multi-modal concept-based explanations, consisting of easy-to-understand textual explanations and visual maps, to justify the predictions., Methods: Our framework relies on Concept Activation Vectors to map human-understandable concepts to those learned by an arbitrary Deep Learning (DL) based algorithm, and Concept Localisation Maps to highlight those concepts in the input space. This identification of relevant concepts is then used to construct fine-grained textual explanations supplemented by concept-wise location information to provide comprehensive and coherent multi-modal explanations. All decision-related information is presented in a diagnostic interface for use in clinical routines. Moreover, the framework includes an educational mode providing dataset-level explanation statistics as well as tools for data and model exploration to aid medical research and education processes., Results: Through rigorous quantitative and qualitative evaluation of our framework on a range of publicly available dermoscopic image datasets, we show the utility of multi-modal explanations for CAD-assisted scenarios even in case of wrong disease predictions. We demonstrate that concept detectors for the explanation of pre-trained networks reach accuracies of up to 81.46%, which is comparable to supervised networks trained end-to-end., Conclusions: We present a new end-to-end framework for the multi-modal explanation of DL-based biomedical image analysis in Melanoma classification and evaluate its utility on an array of datasets. Since perspicuous explanation is one of the cornerstones of any CAD system, we believe that ExAID will accelerate the transition from AI research to practice by providing dermatologists and researchers with an effective tool that they can both understand and trust. ExAID can also serve as the basis for similar applications in other biomedical fields., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Water is a basic and primary resource which is required for sustenance of life on the Earth. The importance of water quality is increasing with the ascending water pollution owing to industrialization and depletion of fresh water sources. The countries having low control on reducing water pollution are likely to retain poor public health. Additionally, the methods being used in most developing countries are not effective and are based more on human intervention than on technological and automated solutions. Typically, most of the water samples and related data are monitored and tested in laboratories, which eventually consumes time and effort at the expense of producing fewer reliable results. In view of the above, there is an imperative need to devise a proper and systematic system to regularly monitor and manage the quality of water resources to arrest the related issues. Towards such ends, Internet of Things (IoT) is a great alternative to such traditional approaches which are complex and ineffective and it allows taking remote measurements in real-time with minimal human involvement. The proposed system consists of various water quality measuring nodes encompassing various sensors including dissolved oxygen, turbidity, pH level, water temperature, and total dissolved solids. These sensors nodes deployed at various sites of the study area transmit data to the server for processing and analysis using GSM modules. The data collected over months is used for water quality classification using water quality indices and for bacterial prediction by employing machine learning algorithms. For data visualization, a Web portal is developed which consists of a dashboard of Web services to display the heat maps and other related info-graphics. The real-time water quality data is collected using IoT nodes and the historic data is acquired from the Rawal Lake Filtration Plant. Several machine learning algorithms including neural networks (NN), convolutional neural networks (CNN), ridge regression (RR), support vector machines (SVM), decision tree regression (DTR), Bayesian regression (BR), and an ensemble of all models are trained for fecal coliform bacterial prediction, where SVM and Bayesian regression models have shown the optimal performance with mean squared error (MSE) of 0.35575 and 0.39566 respectively. The proposed system provides an alternative and more convenient solution for bacterial prediction, which otherwise is done manually in labs and is an expensive and time-consuming approach. In addition to this, it offers several other advantages including remote monitoring, ease of scalability, real-time status of water quality, and a portable hardware., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Objective: Pakistan was one of the first South-Asian countries to introduce the ten-valent pneumococcal conjugate vaccine (PCV10) at the national level, using a 3+0 schedule without catchup, in 2013., Methods: From 2014-18, fifteen children <2 years old were recruited every week in Matiari, Sindh, and nasopharyngeal swabs were collected. The samples were cultured, and pneumococcus was further serotyped through multiplex PCR at the Aga Khan University Hospital as per the method described by the Centers for Disease Control and Prevention, USA., Results: Pneumococcus was detected in 2370/3140 (75%) children. Vaccine type (VT) and non-vaccine type (NVT) serotypes were carried by 379 and 1990 children. There was a significant decline in VT carriage (by 40.3%, p-value <0.001), whereas overall NVT carriage remained the same. The prevalence of VT serotypes 6B, 9V/9A, and 19F showed a significant decline by 58.8%, 79.3%, and 56%, respectively. The prevalence of NVT serotypes 19A, 21, and 10A increased by 70%, 33.3%, and 65.6%, respectively, whereas serotypes 13 and 9N/9L decreased by 53.4% and 51.8%, respectively. Serotype-specific vaccine effectiveness estimates that reached statistical significance were for 9V/9A (VE = 65.0, 95% CI 26.0-83.5%), 19F (VE = 55.3, 95% CI 15.5-76.4%) and for the vaccine related serotype 6A (VE = 28.4, 95% CI 0.9-48.2%)., Conclusion: The emergence of NVT serotypes, primarily 19A replacing VT serotypes in this rural community, necessitates continuous monitoring of serotypes in the carriage and invasive disease to evaluate the utility of existing vaccine formulations., Competing Interests: The authors have declared that no competing interests exist.

This study investigates the relationship between daily returns of oil and ten European industrial equity indices for the period 2008-2017 using the spatial econometric technique. In our settings, the historical co-movements of sectoral returns are incorporated while modelling the contemporaneous relationships between oil and European sectoral returns. After controlling for regional and global equity risk factors, we find that oil returns pose a significantly positive effect on sectoral indices' returns in line with previous studies. However, the magnitude of economic impact found mostly by earlier studies was largely underestimated without considering the co-movements of sectoral indices. The use of spatial econometric technique allowed us to disaggregate the total impact into a direct (due to oil price) and an indirect (due to spillover effect because of intra-industry co-movements) effect of oil price changes. Our results indicate that the direct economic impact of changes in oil prices on industrial returns is almost 31% more than what has been found by earlier studies. The negative spatial dependence among sectoral indices provides a useful tool to identify the source of the indirect impact of oil on overall equity prices in Europe that in turn explains the overestimation of direct impact by 31%., Competing Interests: Conflict of interestThe authors declare there are no conflicts of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

Antimicrobial resistance is an emerging public health concern. Ten-valent pneumococcal vaccine (PCV10) was introduced in Pakistan's Expanded Program on Immunization (EPI) in 2012 as a 3 + 0 schedule without catchup. From 2014 to 2018, children <2 years were randomly selected in two rural union councils of Matiari, Pakistan. Nasopharyngeal swabs were collected using standard WHO guidelines by trained staff and processed at Infectious Disease Research Laboratory at The Aga Khan University, Karachi using culture on sheep blood agar and Multiplex PCR methods described by CDC, USA. Pneumococcal isolates were identified by optochin sensitivity and bile solubility tests. Isolates were then tested for antimicrobial susceptibility by standard Kirby-Bauer disk-diffusion method on Mueller-Hinton Agar (MHA) with 5% sheep blood agar as per Clinical & Laboratory Standards Institute (CLSI) recommendations. Of 3140 children enrolled, pneumococcal isolates were detected in 2370 (75%). Vaccine coverage improved from 41% to 68.4%. Out of the 2370 isolates, 88.4%, 37.6% and 25% were resistant to cotrimoxazole, tetracycline and erythromycin, respectively. There was no resistance to penicillin, ceftriaxone, and vancomycin. For erythromycin, resistance increased from 20% in 2014/15 to 30.8% in 2017/18 and for tetracycline it increased from 34.9% to 41.8% both of which were explained by an increase in prevalence of serotype 19A. Pneumococcal isolates were susceptible to penicillin, ceftriaxone, and vancomycin. They were largely resistant to cotrimoxazole and tetracycline. There was an increase in erythromycin and tetracycline resistance attributed to increasing prevalence of serotype 19A. Pneumococcal isolates from carriage and invasive disease should be closely monitored for antimicrobial susceptibility. IMPORTANCE Antimicrobial resistance is an emerging public health concern particularly in low- and middle-income countries where there is poor regulation and easy availability of antibiotics. This is the first study from Pakistan to report antimicrobial resistance patterns of pneumococcus after vaccine introduction in the community. Pakistan was the first South-Asian country to introduce PCV10 in its Expanded Program on Immunization (EPI) in 2012 as a 3 + 0 schedule without catchup. In this study, we describe the PCV10 impact on antimicrobial resistance patterns of pneumococcal nasopharyngeal carriage in children younger than 2 years of age in a rural district in Pakistan after the introduction of the vaccine.

Herbal drugs play an imperative role in healthcare programs in developing countries. Curry leaves have wide medicinal importance and are used to treat various diseases traditionally. The current study was carried out to estimate the extent of mercury toxicity and the potential effect of curry leaves against defined toxicity. The study group comprised 24 rats weighing between 130 and150 g. Group 1 was kept normal, and group 2 was exposed to mercury at 0.4 mg/kg of body weight in the form of mercuric chloride (HgCl 2 ). The group 3 animals were treated with curry leaves with a dosage of 300 mg/kg of body weight. Group 4 was treated with curry leaves along with mercury with a dosage of 300 and 0.4 mg/kg consecutively. After 28 days, the rats were killed. Blood sample of all groups were evaluated separately to determine the results of different parameters. The results show that ALP, AST, ALT, urea, bilirubin, and creatinine increased with mercury application and decreased with curry leaf exposure. SOD, CAT, GPx, and GR of the liver as well as the kidney depleted on mercury exposure whereas they increased with curry leaf application. HDL increased with curry leaf application and decreased with mercury treatment, while LDL, triglyceride, and cholesterol decreased with curry leaves and increased with mercury exposure. Organ index in mercury along with curry leaf application got close to normal., Competing Interests: The authors do not have any conflict of interest., (© 2021 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC.)

Background: Intestinal inflammation and malabsorption in environmental enteric dysfunction (EED) are associated with early childhood growth faltering in impoverished settings worldwide., Objectives: The goal of this study was to identify candidate biomarkers associated with inflammation, EED histology, and as predictors of later growth outcomes by focusing on the liver-gut axis by investigating the bile acid metabolome., Methods: Undernourished rural Pakistani infants (n = 365) with weight-for-height Z score (WHZ) < -2 were followed up to the age of 24 mo and monitored for growth, infections, and EED. Well-nourished local children (n = 51) were controls, based on consistent WHZ > 0 and height-for-age Z score (HAZ) > -1 on 2 consecutive visits at 3 and 6 mo. Serum bile acid (sBA) profiles were measured by tandem MS at the ages of 3-6 and 9 mo and before nutritional intervention. Biopsies and duodenal aspirates were obtained following upper gastrointestinal endoscopy from a subset of children (n = 63) that responded poorly to nutritional intervention. BA composition in paired plasma and duodenal aspirates was compared based on the severity of EED histopathological scores and correlated to clinical and growth outcomes., Results: Remarkably, >70% of undernourished Pakistani infants displayed elevated sBA concentrations consistent with subclinical cholestasis. Serum glycocholic acid (GCA) correlated with linear growth faltering (HAZ, r = -0.252 and -0.295 at the age of 3-6 and 9 mo, respectively, P <0.001) and biomarkers of inflammation. The proportion of GCA positively correlated with EED severity for both plasma (rs = 0.324 P = 0.02) and duodenal aspirates (rs = 0.307 P = 0.06) in children with refractory wasting that underwent endoscopy, and the proportion of secondary BA was low in both undernourished and EED children., Conclusions: Dysregulated bile acid metabolism is associated with growth faltering and EED severity in undernourished children. Restoration of intestinal BA homeostasis may offer a novel therapeutic target for undernutrition in children with EED. This trial was registered at clinicaltrials.gov as NCT03588013., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

The relationship between environmental factors and child health is not well understood in rural Pakistan. This study characterized the environmental factors related to the morbidity of acute respiratory infections (ARIs), diarrhea, and growth using geographical information systems (GIS) technology. Anthropometric, address and disease prevalence data were collected through the SEEM (Study of Environmental Enteropathy and Malnutrition) study in Matiari, Pakistan. Publicly available map data were used to compile coordinates of healthcare facilities. A Pearson correlation coefficient ( r ) was used to calculate the correlation between distance from healthcare facilities and participant growth and morbidity. Other continuous variables influencing these outcomes were analyzed using a random forest regression model. In this study of 416 children, we found that participants living closer to secondary hospitals had a lower prevalence of ARI ( r = 0.154, p < 0.010) and diarrhea (r = 0.228, p < 0.001) as well as participants living closer to Maternal Health Centers (MHCs): ARI ( r = 0.185, p < 0.002) and diarrhea ( r = 0.223, p < 0.001) compared to those living near primary facilities. Our random forest model showed that distance has high variable importance in the context of disease prevalence. Our results indicated that participants closer to more basic healthcare facilities reported a higher prevalence of both diarrhea and ARI than those near more urban facilities, highlighting potential public policy gaps in ameliorating rural health.

With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time series data has been neglected, with only a handful of methods tested due to their poor intelligibility. We approach the problem of interpretability in a novel way by proposing TSInsight, where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty. TSInsight learns to preserve features that are important for prediction by the classifier and suppresses those that are irrelevant, i.e., serves as a feature attribution method to boost the interpretability. In contrast to most other attribution frameworks, TSInsight is capable of generating both instance-based and model-based explanations. We evaluated TSInsight along with nine other commonly used attribution methods on eight different time series datasets to validate its efficacy. The evaluation results show that TSInsight naturally achieves output space contraction; therefore, it is an effective tool for the interpretability of deep time series models.

Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks., (© 2021. The Author(s).)

The formation of protein aggregates is a hallmark of neurodegenerative diseases. Observations on patient samples and model systems demonstrated links between aggregate formation and declining mitochondrial functionality, but causalities remain unclear. We used Saccharomyces cerevisiae to analyze how mitochondrial processes regulate the behavior of aggregation-prone polyQ protein derived from human huntingtin. Expression of Q97-GFP rapidly led to insoluble cytosolic aggregates and cell death. Although aggregation impaired mitochondrial respiration only slightly, it considerably interfered with the import of mitochondrial precursor proteins. Mutants in the import component Mia40 were hypersensitive to Q97-GFP, whereas Mia40 overexpression strongly suppressed the formation of toxic Q97-GFP aggregates both in yeast and in human cells. Based on these observations, we propose that the post-translational import of mitochondrial precursor proteins into mitochondria competes with aggregation-prone cytosolic proteins for chaperones and proteasome capacity. Mia40 regulates this competition as it has a rate-limiting role in mitochondrial protein import. Therefore, Mia40 is a dynamic regulator in mitochondrial biogenesis that can be exploited to stabilize cytosolic proteostasis., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.

Environmental enteric dysfunction (EED) is a subclinical condition of intestinal inflammation, barrier dysfunction and malabsorption associated with growth faltering in children living in poverty. This study explores association of altered duodenal permeability (lactulose, rhamnose and their ratio) with higher burden of enteropathogen in the duodenal aspirate, altered histopathological findings and higher morbidity (diarrhea) that is collectively associated with linear growth faltering in children living in EED endemic setting. In a longitudinal birth cohort, 51 controls (WHZ > 0, HAZ > -1.0) and 63 cases (WHZ< -2.0, refractory to nutritional intervention) were recruited. Anthropometry and morbidity were recorded on monthly bases up to 24 months of age. Dual sugar assay of urine collected after oral administration of lactulose and rhamnose was assessed in 96 children from both the groups. Duodenal histopathology (n = 63) and enteropathogen analysis of aspirate via Taqman array card (n = 60) was assessed in only cases. Giardia was the most frequent pathogen and was associated with raised L:R ratio (p = 0.068). Gastric microscopy was more sensitive than duodenal aspirate in H. pylori detection. Microscopically confirmed H. pylori negatively correlated with HAZ at 24 months (r = -0.313, p = 0.013). Regarding histopathological parameters, goblet cell reduction significantly correlated with decline in dual sugar excretion (p< 0.05). Between cases and controls, there were no significant differences in the median (25th, 75th percentile) of urinary concentrations (μg/ml) of lactulose [27.0 (11.50, 59.50) for cases vs. 38.0 (12.0, 61.0) for controls], rhamnose [66.0 (28.0, 178.0) vs. 86.5 (29.5, 190.5)] and L:R ratio [0.47 (0.24, 0.90) vs. 0.51 (0.31, 0.71)] respectively. In multivariable regression model, 31% of variability in HAZ at 24 months of age among cases and controls was explained by final model including dual sugars. In conclusion, enteropathogen burden is associated with altered histopathological features and intestinal permeability. In cases and controls living in settings of endemic enteropathy, intestinal permeability test may predict linear growth. However, for adoption as a screening tool for EED, further validation is required due to its complex intestinal pathophysiology., Competing Interests: The authors have declared that no competing interests exist.

Currently, prevention and control of the coronavirus disease pneumonia epidemic situation are grim globally. To cope with total sheer carriers and patients of COVID-19 requires intensive medical support and adjunctive therapies to overcome the disease. The epidemic can be controlled with the help of both, disease suppression via community health measures and adjunctive therapies for patients suffering from infection. Till date, we do not have any proper anti-COVID-19 therapy. In order to achieve the overall realization of this pandemic, there is a need to identify treatments depending upon their direct or indirect targets; like inhibition of polyprotein synthesis, transmembrane serine protease, inhibition of viral entry and endocytosis. This could be possible by turning the focus in the direction towards the development of numerous tentative drugs, particularly in the severe to badly ill. Though, majority of these off-label adjunctive medicines are being inspected in a lot of clinical trials at different stages, scientific organizations have endeavored to elucidate the situation where these adjunctive drugs might be practiced as off-label, open- label or compassionate. Our review compiles the adjunctive therapies adopted in COVID-19 infected patients according to clinical severity in conjugation with practicing recommendations from existing guidance rules issued by global professional bodies in healthcare.

Background & Aims: Environmental enteric dysfunction (EED) limits the Sustainable Development Goals of improved childhood growth and survival. We applied mucosal genomics to advance our understanding of EED., Methods: The Study of Environmental Enteropathy and Malnutrition (SEEM) followed 416 children from birth to 24 months in a rural district in Pakistan. Biomarkers were measured at 9 months and tested for association with growth at 24 months. The duodenal methylome and transcriptome were determined in 52 undernourished SEEM participants and 42 North American controls and patients with celiac disease., Results: After accounting for growth at study entry, circulating insulin-like growth factor-1 (IGF-1) and ferritin predicted linear growth, whereas leptin correlated with future weight gain. The EED transcriptome exhibited suppression of antioxidant, detoxification, and lipid metabolism genes, and induction of anti-microbial response, interferon, and lymphocyte activation genes. Relative to celiac disease, suppression of antioxidant and detoxification genes and induction of antimicrobial response genes were EED-specific. At the epigenetic level, EED showed hyper-methylation of epithelial metabolism and barrier function genes, and hypo-methylation of immune response and cell proliferation genes. Duodenal coexpression modules showed association between lymphocyte proliferation and epithelial metabolic genes and histologic severity, fecal energy loss, and wasting (weight-for-length/height Z < -2.0). Leptin was associated with expression of epithelial carbohydrate metabolism and stem cell renewal genes. Immune response genes were attenuated by giardia colonization., Conclusions: Children with reduced circulating IGF-1 are more likely to experience stunting. Leptin and a gene signature for lymphocyte activation and dysregulated lipid metabolism are implicated in wasting, suggesting new approaches for EED refractory to nutritional intervention. ClinicalTrials.gov, Number: NCT03588013. (https://clinicaltrials.gov/ct2/show/NCT03588013)., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Pneumonia is the leading cause of morbidity and mortality in children worldwide. The ten valent pneumococcal vaccine (PCV10) was introduced in Pakistan's Expanded Program on Immunization (EPI) in 2012 as a 3 + 0 schedule without catchup immunization. Nasopharyngeal carriage is taken as a surrogate marker to measure the impact of pneumococcal vaccine on populations. Carriage surveys are necessary to monitor the persistence of Vaccine Type (VT) serotypes, the emergence of Non-Vaccine Type (NVT) serotypes, and their role in both transmission and disease. This article describes various troubleshooting measures which we undertook to adopt the protocol to our setting. We also used an innovative approach to describe various epidemiological parameters of vaccine effectiveness against carriage. It is important to publish these methods to allow for valid regional and temporal comparisons of results in different settings. Thus, in this article, we describe the following methods for isolating upper airway pneumococcal carriage:•Methods for the collection, transport, and storage of nasopharyngeal samples.•Methods for identification and serotyping of pneumococci.•Methods for estimation of the direct and indirect effects of pneumococcal vaccines on nasopharyngeal carriage., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s). Published by Elsevier B.V.)

Background: Pakistan introduced Ten-valent pneumococcal-conjugate-vaccine PCV10 in 2012 as a 3 + 0 schedule without catch-up., Methods: Children <2 years old in Matiari, Sindh provided nasopharyngeal swabs between 2014 and 2018, which were cultured for pneumococcus and serotyped through multiplex PCR at the Aga Khan University Hospital. Carriage rates over time for Vaccine-Type (VT) and Non-VT (NVT) serotypes were used to estimate direct, indirect, total and overall effects of vaccination. Regression analysis was used to determine factors associated with VT carriage., Results: Pneumococcus was detected in 2370/3140 (75%). VT carriage decreased overall, 16.1-9.6% (p-trend <0.001); vaccinated (all 3 doses of PCV10 received) 11.3-8.1% (p-trend 0.031) and unvaccinated (no PCV10 dose received) 17.4-10.3% (p-trend 0.003) with a decline in serotypes 6B, 9V/9A and 19F. Immunization increased from 41.0% to 68.4% (p-trend 0.001). Direct effect of vaccine was 32.8% (95% CI 14.7-47.0%) and indirect effect 44.6%(95% CI 40.6-48.6%). Factors associated with decreased VT colonization were education 1-5 years (aOR 0.7, 95%CI 0.6-1.0), history of difficulty breathing (aOR 0.7, 95%CI 0.5-1.0), exposure to smoke (aOR 0.8, 95% CI 0.6-1.0), child fully immunized (aOR 0.7, 95%CI 0.5-1.0) and enrolled in 3rd (aOR 0.6, 95%CI 0.4-0.8) and 4th (aOR 0.6, 95%CI 0.5-0.9) year of the study whereas history of runny nose (aOR 1.5, 95% CI 1.2-1.9) was positively associated., Conclusions: Decrease in VT pneumococcal carriage in vaccinated and unvaccinated children indicates herd immunity. Sustained increase in vaccine coverage and close long-term surveillance is warranted., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The dataset described in this paper was collected for a time-series cross-sectional study exploring the impact of 10-valent Pneumococcal Conjugate Vaccine (PCV10) on nasopharyngeal (NP) carriage in children under 2 years of age from a rural population in Sindh, Pakistan. The study was carried out in two union councils of Matiari - Khyber and Shah Alam Shah Jee Wasi (Latitude 25.680298 / Longitude 68.502711). Data was collected on socio-demographics, clinical characteristics and vaccination status using android phone-based application. NP samples were collected using standard World Health Organisation (WHO) techniques, culture and serotyping was done using sequential Multiplex PCR described by Centre for Disease Control, USA. We looked at the carriage rate of vaccine type (VT) and non-vaccine type (NVT) serotypes over time in vaccinated and unvaccinated children. We additionally looked at the predictors for pneumococcal carriage. The uploaded dataset, available on Mendeley data repository (Nisar, Muhammad Imran (2021), "Impact of PCV10 on nasopharyngeal carriage in children in Pakistan", Mendeley Data, V1, doi:10.17632/t79h6g97gr.1), has 3140 observations in CSV format. Additional files uploaded include a data dictionary and the set of questionnaires. The dataset and accompanying files can be used by other interested researchers to replicate our analysis, carry similar analysis under varying set of assumptions or perform additional exploratory or metanalysis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article., (© 2021 The Author(s).)