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Background: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. Methods: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. Results: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. Conclusion: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity. [ABSTRACT FROM AUTHOR]

Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation. Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction. Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative. [ABSTRACT FROM AUTHOR]

The effects of heat exposure on negative affect are thought to be central to the observed relationships between hot summer days and deleterious outcomes, such as violent crime or mental health crises. As these relationships are likely to be magnified by the effects of climate change, a better understanding of how consistent or variable the effects of hot weather on affective states is required. The current work combines data gathered from an ecological momentary assessment (EMA) study on individuals' thermal perceptions, comfort, and affective states in outdoor environments during their daily lives with high spatiotemporal resolution climate-modeled weather variables. Using these data, associations between objective weather variables (temperature, humidity, etc.), perceived heat (thermal perception and comfort), and affective states are examined. Overall, objective weather data reasonably predicted perception and comfort, but only comfort predicted negative affective states. The variance explained across individuals was generally very low in predicting negative affect or comfort, but within-person variance explained was high. In other words, while there may be a relatively consistent relationship between temperature and psychological experience for any given person, there are significant individual differences across people. Age and gender were examined as moderators of these relationships, and while gender had no impact, participant age showed several significant interactions. Specifically, while older adults tended to experience more thermal discomfort and perceived higher temperatures as hotter, the relationship between discomfort and negative affect was lower in older adults. Taken together, these results emphasize the importance of thermal discomfort specifically in predicting negative affect, as well as the high inter-individual variability in thermal perceptions and comfort for the same ambient temperatures. [ABSTRACT FROM AUTHOR]

Background: The backbone of the eukaryotic tree of life contains taxa only found in molecular surveys, of which we still have a limited understanding. Such is the case of Picozoa, an enigmatic lineage of heterotrophic picoeukaryotes within the supergroup Archaeplastida, which has emerged as a significant component of marine microbial planktonic communities. To enhance our understanding of the diversity, distribution, and ecology of Picozoa, we conduct a comprehensive assessment at different levels, from assemblages to taxa, employing phylogenetic analysis, species distribution modeling, and ecological niche characterization. Results: Picozoa was among the ten most abundant eukaryotic groups, found almost exclusively in marine environments. The phylum was represented by 179 Picozoa's OTU (pOTUs) placed in five phylogenetic clades. Picozoa community structure had a clear latitudinal pattern, with polar assemblages tending to cluster separately from non-polar ones. Based on the abundance and occupancy pattern, the pOTUs were classified into four categories: Low-abundant, Widespread, Polar, and Non-polar. We calculated the ecological niche of each of these categories. Notably, pOTUs sharing similar ecological niches were not closely related species, indicating a phylogenetic overdispersion in Picozoa communities. This could be attributed to competitive exclusion and the strong influence of the seasonal amplitude of variations in environmental factors, such as temperature, shaping physiological and ecological traits. Conclusions: Overall, this work advances our understanding of uncharted protists' evolutionary dynamics and ecological strategies. Our results highlight the importance of understanding the species-level ecology of marine heteroflagellates like Picozoa. The observed phylogenetic overdispersion challenges the concept of phylogenetic niche conservatism in protist communities, suggesting that closely related species do not necessarily share similar ecological niches. 9DjsWS8J_yUMTxizMfQ8ty Video Abstract [ABSTRACT FROM AUTHOR]

Background: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. Methods: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. Results: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. Conclusions: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options. [ABSTRACT FROM AUTHOR]

Objectives: Capsicum baccatum and C. chinense are domesticated pepper species originating from Latin America recognized for their unique flavor and taste and widely diffused as spicy food for fresh uses or for processing. Owing to their capacity for adaptation to diverse habitats in tropical regions, these species serve as a valuable resource for agronomic traits and tolerance to both biotic and abiotic challenges in breeding projects. This study aims to dissect the genetic diversity of C. baccatum and C. chinense germplasm and to detect candidate genes underlying the variation of plant morphological and fruit size and shape traits. To that goal, SNP data from genotyping by sequencing have been used to investigate the genetic diversity and population structure of 103 accessions belonging to the two species. Further, plants have been assessed with main plant descriptors and fruit imaging analysis and association between markers and traits has been performed. Results: The population structure based on 29,820 SNPs revealed 4 subclusters separating C. chinense and C. baccatum individuals. A deeper analysis within each species highlighted three subpopulations in C. chinense and two in C. baccatum. Phenotypic characterization of 54 traits provided approximately 125 thousand datapoints highlighting main differences between species for flower and fruit traits rather than plant architecture. Marker-traits association, performed with the CMLM model, revealed a total of 6 robust SNPs responsible for change in flower traits and fruit shape. This is the first attempt for mapping morphological traits and fruit features in the two domesticated species, paving the way for further genomic assisted breeding. [ABSTRACT FROM AUTHOR]

Background: Omicron variant impacts populations with its rapid contagiousness, and part of patients suffered from persistent symptoms termed as long COVID. The molecular and immune mechanisms of this currently dominant global variant leading to long COVID remain unclear, due to long COVID heterogeneity across populations. Methods: We recruited 66 participants in total, 22 out of 66 were healthy control without COVID-19 infection history, and 22 complaining about long COVID symptoms 6 months after first infection of Omicron, referred as long COVID (LC) Group. The left ones were defined as non-long COVID (NLC) Group. We profiled them via plasma neutralizing antibody titer, SARS-CoV-2 viral load, transcriptomic and proteomics screening, and machine learning. Results: No serum residual SARS-CoV-2 was observed in the participants 6 months post COVID-19 infection. No significant difference in neutralizing antibody titers was found between the long COVID (LC) Group and the non-long COVID (NLC) Group. Transcriptomic and proteomic profiling allow the stratification of long COVID into neutrophil function upregulated (NU-LC) and downregulated types (ND-LC). The NU-LC, identifiable through a refined set of 5 blood gene markers (ABCA13, CEACAM6, CRISP3, CTSG and BPI), displays evidence of relatively higher neutrophil counts and function of degranulation than the ND-LC at 6 months after infection, while recovered at 12 months post COVID-19. Conclusion: The transcriptomic and proteomic profiling revealed heterogeneity among long COVID patients. We discovered a subgroup of long COVID population characterized by neutrophil activation, which might associate with the development of psychiatric symptoms and indicate a higher inflammatory state. Meanwhile, a cluster of 5 genes was manually curated as the most potent discriminators of NU-LC from long COVID population. This study can serve as a foundational exploration of the heterogeneity in the pathogenesis of long COVID and assist in therapeutic targeting and detailed epidemiological investigation of long COVID. [ABSTRACT FROM AUTHOR]

Objective: Early diagnosis and prediction of organ dysfunction are critical for intervening and improving the outcomes of septic patients. The study aimed to find novel diagnostic and predictive biomarkers of organ dysfunction for perioperative septic patients. Method: This is a prospective, controlled, preliminary, and single-center study of emergency surgery patients. Mass spectrometry, Gene Ontology (GO) functional analysis, and the protein-protein interaction (PPI) network were performed to identify the differentially expressed proteins (DEPs) from sepsis patients, which were selected for further verification via enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was used to estimate the relative correlation of selected serum protein levels and clinical outcomes of septic patients. Calibration curves were plotted to assess the calibration of the models. Results: Five randomized serum samples per group were analyzed via mass spectrometry, and 146 DEPs were identified. GO functional analysis and the PPI network were performed to evaluate the molecular mechanisms of the DEPs. Six DEPs were selected for further verification via ELISA. Cathepsin B (CatB), vascular cell adhesion protein 1 (VCAM-1), neutrophil gelatinase-associated lipocalin (NGAL), protein S100-A9, prosaposin, and thrombospondin-1 levels were significantly increased in the patients with sepsis compared with those of the controls (p < 0.001). Logistic regression analysis showed that CatB, S100-A9, VCAM-1, prosaposin, and NGAL could be used for preoperative diagnosis and postoperative prediction of organ dysfunction. CatB and S100-A9 were possible predictive factors for preoperative diagnosis of renal failure in septic patients. Internal validation was assessed using the bootstrapping validation. The preoperative diagnosis of renal failure model displayed good discrimination with a C-index of 0.898 (95% confidence interval 0.843–0.954) and good calibration. Conclusion: Serum CatB, S100-A9, VCAM-1, prosaposin, and NGAL may be novel markers for preoperative diagnosis and postoperative prediction of organ dysfunction. Specifically, S100-A9 and CatB were indicators of preoperative renal dysfunction in septic patients. Combining these two biomarkers may improve the accuracy of predicting preoperative septic renal dysfunction. Trial registration: The study was registered at the Chinese Clinical Trials Registry (ChiCTR2200060418) on June 1, 2022. [ABSTRACT FROM AUTHOR]

Background: This study was aimed to determine the taxonomic position and delimitation of fifteen Lamiaceae taxa using leaf epidermal morpho-anatomical features in Lahore. A main objective of the study was also the revision and upgradation of Lamiaceae taxa in the flora of Pakistan, as no details of studied species are found in the flora of Pakistan. Methods: The examination of significant anatomical parameters, such as epidermal cell shape and size, stomatal types, guard and subsidiary cells shape and size, stomatal cavity size, trichome size and shape, oil droplets, crystals, and secretory cavity characteristics were studied using light microscopic (LM) and scanning electron microscopic (SEM) techniques. Among all the studied Lamiaceae species, these anatomical features varied significantly. Principal component analysis and correlation were done to distinguish the species' similarities. Results: Most species had pentagonal and hexagonal epidermal cells with straight anticlinal wall thickness. On the adaxial surface, paracytic stomata were found in Ocimum basilicum L. and Rosmarinus officinalis L. Diacytic stomata was observed in Ajuga reptans L. and anisocytic stomata in Galeopsis tetrahit L. In the abaxial surface, trichomes were present in five species, i.e., Mentha suaveolens Ehrh. A. reptans, Thymus vulgaris L., M. haplocalyx, and Salvia splendens Ewat. In S. splendens, peltate and glandular trichomes were seen whereas, in other species, trichomes were long, unbranched glandular and had tapering ends. In adaxial side trichomes were present only in M. suaveolens, A. reptans, S. bazyntina, O. basciculum, S. splendens, S. officinalis, S. rosemarinus. In other species, trichomes were absent on the adaxial surface. In abaxial view, M. suaveolens had the largest length of trichomes, and O. basciculum had the smallest. S. splendens L. had the largest trichome width, while T. vulgaris had the smallest. Conclusion: Hence, according to these findings, morpho-anatomical traits are useful for identifying Lamiaceae taxa. Also, there is a need of upgradation and addition of studied taxa in flora of Pakistan comprehensively. [ABSTRACT FROM AUTHOR]

Background: Chronic pelvic pain is a common disease that affects approximately 4% of women of reproductive age in developed countries. This number is estimated to be higher in developing countries, with a significant negative personal and socioeconomic impact on women. The lack of data on this condition in several countries, particularly those in development and in socially and biologically vulnerable populations such as the indigenous, makes it difficult to guide public policies. Objectives: To evaluate the prevalence of chronic pelvic pain (dysmenorrhea, dyspareunia, non-cyclical pain) and identify which variables are independently associated with the presence of the condition in indigenous women from Otavalo-Ecuador. Design: A cross-sectional study was carried out including a sample of 2429 women of reproductive age between 14 and 49 years old, obtained from April 2022 to March 2023. A directed questionnaire was used, collected by bilingual interviewers (Kichwa and Spanish) belonging to the community itself; the number of patients was selected by random sampling proportional to the number of women estimated by sample calculation. Data are presented as case prevalence, odds ratio, and 95% confidence interval, with p < 0.05. Results: The prevalence of primary dysmenorrhea, non-cyclic pelvic pain, and dyspareunia was, respectively, 26.6%, 8.9%, and 3.9%.all forms of chronic pain were independently associated with each other. Additionally, dysmenorrhoea was independently associated with hypertension, intestinal symptoms, miscegenation, long cycles, previous pregnancy, use of contraceptives and pear body shape. Pain in other sites, late menarche, exercise, and pear body shape were associated with non-cyclic pelvic pain. And, urinary symptoms, previous pregnancy loss, miscegenation, and pear body shape were associated with dyspareunia. Conclusion: The prevalence of primary dysmenorrhea and non-cyclical chronic pelvic pain was notably high, in contrast with the frequency of reported dyspareunia. Briefly, our results suggest an association between dysmenorrhoea and conditions related to inflammatory and/or systemic metabolic disorders, including a potential causal relationship with other manifestations of pelvic pain, and between non-cyclical pelvic pain and signs/symptoms suggesting central sensitization. The report of dyspareunia may be influenced by local cultural values and beliefs. [ABSTRACT FROM AUTHOR]

Background: The prevalence of metabolic syndrome (MetS) in American adults increased from 37.6% in the 2011–12 period to 41.8% in 2017–2018. Environmental exposure, particularly to common compounds such as glyphosate, has drawn increasing attention as a potential risk factor. Methods: We employed three cycles of data (2013–2018) from the National Health and Nutrition Examination Survey (NHANES) in a cross-sectional study to examine potential associations between urine glyphosate measurements and MetS incidence. We first created a MetS score using exploratory factor analysis (EFA) of the International Diabetes Federation (IDF) criteria for MetS, with data drawn from the 2013–2018 NHANES cycles, and validated this score independently on an additional associated metric, the albumin-to-creatinine (ACR) ratio. The score was validated via a machine learning approach in predicting the ACR score via binary classification and then used in multivariable regression to test the association between quartile-categorized glyphosate exposure and the MetS score. Results: In adjusted multivariable regressions, regressions between quartile-categorized glyphosate exposure and MetS score showed a significant inverted U-shaped or saturating dose‒response profile, often with the largest effect for exposures in quartile 3. Exploration of potential effect modification by sex, race, and age category revealed significant differences by race and age, with older people (aged > 65 years) and non-Hispanic African American participants showing larger effect sizes for all exposure quartiles. Conclusions: We found that urinary glyphosate concentration is significantly associated with a statistical score designed to predict MetS status and that dose–response coefficient is nonlinear, with advanced age and non-Hispanic African American, Mexican American and other Hispanic participants exhibiting greater effect sizes. [ABSTRACT FROM AUTHOR]

Highlights: 1. Gamma-aminobutyric acid (GABA) has been proven to promote seedling growth in wheat varieties exposed to salt stress. As a result, there was enhanced root growth, longer shoot length, and improved overall health of the seedlings. 2. GABA has been shown to enhance physiological indicators (chlorophyll levels and enhanced relative water content) in wheat varieties under salt stress conditions and minimize membrane impairment, all of which indicate improved stress resistance and general plant well-being. 3. GABA therapy has been found to increase gene expression in wheat cultivars subjected to salt stress. This includes the upregulation of stress-responsive genes and the downregulation of genes associated with negative stress responses, ultimately leading to improved resilience and adaptation to harsh growing conditions. 4. The impact of GABA on seedling growth, physiological biomarkers, and gene expression can change depending on the unique wheat cultivar. Each wheat variety may show distinct reactions to GABA therapy, emphasizing the need for cultivar-specific studies and customized strategies to optimize the advantages of GABA in reducing salt stress in wheat farming. Plants spontaneously accumulate γ-aminobutyric acid (GABA), a nonprotein amino acid, in response to various stressors. Nevertheless, there is limited knowledge regarding the precise molecular mechanisms that plants employ to cope with salt stress. The objective of this study was to investigate the impact of GABA on the salt tolerance of eight distinct varieties of bread wheat (Triticum aestivum L.) by examining plant growth rates and physiological and molecular response characteristics. The application of salt stress had a detrimental impact on plant growth markers. Nevertheless, the impact was mitigated by the administration of GABA in comparison to the control treatment. When the cultivars Gemmiza 7, Gemmiza 9, and Gemmiza 12 were exposed to GABA at two distinct salt concentrations, there was a substantial increase in both the leaf chlorophyll content and photosynthetic rate. Both the control wheat cultivars and the plants exposed to salt treatment and GABA treatment showed alterations in stress-related biomarkers and antioxidants. This finding demonstrated that GABA plays a pivotal role in mitigating the impact of salt treatments on wheat cultivars. Among the eight examined kinds of wheat, CV. Gemmiza 7 and CV. Gemmiza 11 exhibited the most significant alterations in the expression of their TaSOS1 genes. CV. Misr 2, CV. Sakha 94, and CV. Sakha 95 exhibited the highest degree of variability in the expression of the NHX1, DHN3, and GR genes, respectively. The application of GABA to wheat plants enhances their ability to cope with salt stress by reducing the presence of reactive oxygen species (ROS) and other stress indicators, regulating stomatal aperture, enhancing photosynthesis, activating antioxidant enzymes, and upregulating genes involved in salt stress tolerance. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. Methods: The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age–chronological age) for every subject. Results: MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p <.001) and BPAD (r = -0.26,p <.001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p =.014) and moderate (p =.040) drinkers. Conclusions: Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health. [ABSTRACT FROM AUTHOR]

Background: Inflammatory bowel disease (IBD) and Parkinson's disease (PD) are chronic disorders that have been suggested to share common pathophysiological processes. LRRK2 has been implicated as playing a role in both diseases. Exploring the genetic basis of the IBD-PD comorbidity through studying high-impact rare genetic variants can facilitate the identification of the novel shared genetic factors underlying this comorbidity. Methods: We analyzed whole exomes from the BioMe BioBank and UK Biobank, and whole genomes from a cohort of 67 European patients diagnosed with both IBD and PD to examine the effects of LRRK2 missense variants on IBD, PD and their co-occurrence (IBD-PD). We performed optimized sequence kernel association test (SKAT-O) and network-based heterogeneity clustering (NHC) analyses using high-impact rare variants in the IBD-PD cohort to identify novel candidate genes, which we further prioritized by biological relatedness approaches. We conducted phenome-wide association studies (PheWAS) employing BioMe BioBank and UK Biobank whole exomes to estimate the genetic relevance of the 14 prioritized genes to IBD-PD. Results: The analysis of LRRK2 missense variants revealed significant associations of the G2019S and N2081D variants with IBD-PD in addition to several other variants as potential contributors to increased or decreased IBD-PD risk. SKAT-O identified two significant genes, LRRK2 and IL10RA, and NHC identified 6 significant gene clusters that are biologically relevant to IBD-PD. We observed prominent overlaps between the enriched pathways in the known IBD, PD, and candidate IBD-PD gene sets. Additionally, we detected significantly enriched pathways unique to the IBD-PD, including MAPK signaling, LPS/IL-1 mediated inhibition of RXR function, and NAD signaling. Fourteen final candidate IBD-PD genes were prioritized by biological relatedness methods. The biological importance scores estimated by protein–protein interaction networks and pathway and ontology enrichment analyses indicated the involvement of genes related to immunity, inflammation, and autophagy in IBD-PD. Additionally, PheWAS provided support for the associations of candidate genes with IBD and PD. Conclusions: Our study confirms and uncovers new LRRK2 associations in IBD-PD. The identification of novel inflammation and autophagy-related genes supports and expands previous findings related to IBD-PD pathogenesis, and underscores the significance of therapeutic interventions for reducing systemic inflammation. [ABSTRACT FROM AUTHOR]

Background: Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future. Methods: A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined. Results: Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425. Conclusions: The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. Clinical revelance: Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection. [ABSTRACT FROM AUTHOR]

Heavy metals (HMs) contamination, owing to their potential links to various chronic diseases, poses a global threat to agriculture, environment, and human health. Nickel (Ni) is an essential element however, at higher concentration, it is highly phytotoxic, and affects major plant functions. Beneficial roles of plant growth regulators (PGRs) and organic amendments in mitigating the adverse impacts of HM on plant growth has gained the attention of scientific community worldwide. Here, we performed a greenhouse study to investigate the effect of indole-3-acetic acid (IAA @ 10− 5 M) and compost (1% w/w) individually and in combination in sustaining cauliflower growth and yield under Ni stress. In our results, combined application proved significantly better than individual applications in alleviating the adverse effects of Ni on cauliflower as it increased various plant attributes such as plant height (49%), root length (76%), curd height and diameter (68 and 134%), leaf area (75%), transpiration rate (36%), stomatal conductance (104%), water use efficiency (143%), flavonoid and phenolic contents (212 and 133%), soluble sugars and protein contents (202 and 199%), SPAD value (78%), chlorophyll 'a and b' (219 and 208%), carotenoid (335%), and NPK uptake (191, 79 and 92%) as compared to the control. Co-application of IAA and compost reduced Ni-induced electrolyte leakage (64%) and improved the antioxidant activities, including APX (55%), CAT (30%), SOD (43%), POD (55%), while reducing MDA and H2O2 contents (77 and 52%) compared to the control. The combined application also reduced Ni uptake in roots, shoots, and curd by 51, 78 and 72% respectively along with an increased relative production index (78%) as compared to the control. Hence, synergistic application of IAA and compost can mitigate Ni induced adverse impacts on cauliflower growth by immobilizing it in the soil. [ABSTRACT FROM AUTHOR]

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment's findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer is primarily treated by surgery; however, little is known about the changes in the intraperitoneal immune environment and the prognostic impact of surgery. Surgical stress and cancer-associated inflammation cause immune cells to mobilize into the abdominal cavity via numerous cytokines. One such cytokine, CX3CR1, has various immune-related functions that remain to be fully explained. We characterized the intraperitoneal immune environment by investigating CX3CR1+ cells in intraperitoneal lavage fluid during gastric cancer surgery. Methods: Lavage fluid samples were obtained from a total of 41 patients who underwent gastrectomy. The relative expression of various genes was analyzed using quantitative real-time PCR. The association of each gene expression with clinicopathological features and surgical outcomes was examined. The fraction of CX3CR1+ cells was analyzed by flow cytometry. Cytokine profiles in lavage fluid samples were investigated using a cytometric beads array. Results: CX3CR1high patients exhibited higher levels of perioperative inflammation in blood tests and more recurrences than CX3CR1low patients. CX3CR1high patients tended to exhibit higher pathological T and N stage than CX3CR1low patients. CX3CR1 was primarily expressed on myeloid-derived suppressor cells and tumor-associated macrophages. In particular, polymorphonuclear myeloid-derived suppressor cells were associated with perioperative inflammation, pathological N, and recurrences. These immunosuppressive cells were associated with a trend toward unfavorable prognosis. Moreover, CX3CR1 expression was correlated with programmed death–1 expression. Conclusions: Our results suggest that CX3CR1+ cells are associated with an acute inflammatory response, tumor-promotion, and recurrence. CX3CR1 expression could be taken advantage of as a beneficial therapeutic target for improving immunosuppressive state in the future. In addition, analysis of intra-abdominal CX3CR1+ cells could be useful for characterizing the immune environment after gastric cancer surgery. [ABSTRACT FROM AUTHOR]

Background: Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking. Results: This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis. Conclusions: Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis. [ABSTRACT FROM AUTHOR]

Recent advancements in signal processing and computational power have revolutionized computer vision applications in diverse industries such as agriculture, food processing, biomedical, and the military. These developments are propelling efforts to automate processes and enhance efficiency. Notably, computational techniques are replacing labor-intensive manual methods for assessing the maturity indices of fruits and vegetables during critical growth stages. This review paper focuses on recent advancements in computer vision techniques specifically applied to determine the maturity indices of fruits and vegetables within the food processing sector. It highlights successful applications of Nuclear Magnetic Resonance (NMR), Near-Infrared Spectroscopy (NIR), thermal imaging, and image scanning. By examining these techniques, their underlying principles, and practical feasibility, it offers valuable insights into their effectiveness and potential widespread adoption. Additionally, integrating biosensors and AI techniques further improves accuracy and efficiency in maturity index determination. In summary, this review underscores the significant role of computational techniques in advancing maturity index assessment and provides insights into their principles and effective utilization. Looking ahead, the future of computer vision techniques holds immense potential. Collaborative efforts among experts from various fields will be crucial to address challenges, ensure standardization, and safeguard data privacy. Embracing these advancements can lead to sustainable practices, optimized resource management, and progress across industries. Highlights: 1. Recent advancements in signal processing and computation drive interest in computer vision across industries. 2. The review focuses on non-destructive methods in fruits and vegetables. 3. Computational techniques replace manual methods for maturity index determination. 4. The principles of techniques are highlighted, along with their successful applications. 5. The potential of computation techniques in destructive, non-destructive methods, biosensors, and AI summarized. [ABSTRACT FROM AUTHOR]

Background: Several studies have reported a relationship between retinal thickness and dementia. Therefore, optical coherence tomography (OCT) has been proposed as an early diagnosis method for Alzheimer's disease (AD). In this study, we performed a genome-wide association study (GWAS) aimed at identifying genes associated with retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thickness assessed by OCT and exploring the relationships between the spectrum of cognitive decline (including AD and non-AD cases) and retinal thickness. Methods: RNFL and GCIPL thickness at the macula were determined using two different OCT devices (Triton and Maestro). These determinations were tested for association with common single nucleotide polymorphism (SNPs) using adjusted linear regression models and combined using meta-analysis methods. Polygenic risk scores (PRSs) for retinal thickness and AD were generated. Results: Several genetic loci affecting retinal thickness were identified across the genome in accordance with previous reports. The genetic overlap between retinal thickness and dementia, however, was weak and limited to the GCIPL layer; only those observable with all-type dementia cases were considered. Conclusions: Our study does not support the existence of a genetic link between dementia and retinal thickness. [ABSTRACT FROM AUTHOR]

Background: Acacia species are economically significant as medicinal plants that have been utilized since ancient times. Acacia modesta has been reported to possess potent antibacterial and antioxidant properties, but its growth rate is slow. In this study, we hypothesized that inducing callus in vitro from A. modesta could enhance the production of antibacterial and antioxidant secondary metabolites, thereby circumventing the issues of slow growth and excessive harvesting of the plant. Results: The callus was induced from axillary buds on MS medium supplemented with 1 mg/L of 2,4-D and 1 mg/L of BAP. The secondary metabolites, volatile compounds, antibacterial activity, and antioxidant activity of the callus and parent plant leaf extracts were evaluated. The results revealed that the content of phenolics and flavonoids, the number of volatile compounds, and the antibacterial and antioxidant activities of the callus extract were significantly enhanced (P ≤ 0.05) compared to the leaf extract. The antibacterial and antioxidant effects were strongly correlated with the total phenolic and flavonoid content in the extracts. Conclusions: Our findings suggest that in vitro callus culture increases the production of phenolics, flavonoids, and volatile compounds. This subsequently enhances the antibacterial and antioxidant properties of A. modesta. [ABSTRACT FROM AUTHOR]

Background: Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches. Methodology: To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series). Results: The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts. Conclusion: In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance. [ABSTRACT FROM AUTHOR]

Background: The long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne infectious diseases (ZVBs) remains uncertain. This study sought to examine the changes in ZVBs in China during the COVID-19 pandemic and predict their future trends. Methods: Monthly incidents of seven ZVBs (Hemorrhagic fever with renal syndrome [HFRS], Rabies, Dengue fever [DF], Human brucellosis [HB], Leptospirosis, Malaria, and Schistosomiasis) were gathered from January 2004 to July 2023. An autoregressive fractionally integrated moving average (ARFIMA) by incorporating the COVID-19-associated public health intervention variables was developed to evaluate the long-term effectiveness of interventions and forecast ZVBs epidemics from August 2023 to December 2025. Results: Over the study period, there were 1,599,647 ZVBs incidents. HFRS and rabies exhibited declining trends, HB showed an upward trajectory, while the others remained relatively stable. The ARFIMA, incorporating a pulse pattern, estimated the average monthly number of changes of − 83 (95% confidence interval [CI] − 353–189) cases, − 3 (95% CI − 33–29) cases, − 468 (95% CI − 1531–597) cases, 2191 (95% CI 1056–3326) cases, 7 (95% CI − 24–38) cases, − 84 (95% CI – 222–55) cases, and − 214 (95% CI − 1036–608) cases for HFRS, rabies, DF, HB, leptospirosis, malaria, and schistosomiasis, respectively, although these changes were not statistically significant besides HB. ARFIMA predicted a decrease in HB cases between August 2023 and December 2025, while indicating a relative plateau for the others. Conclusions: China's dynamic zero COVID-19 strategy may have exerted a lasting influence on HFRS, rabies, DF, malaria, and schistosomiasis, beyond immediate consequences, but not affect HB and leptospirosis. ARFIMA emerges as a potent tool for intervention analysis, providing valuable insights into the sustained effectiveness of interventions. Consequently, the application of ARFIMA contributes to informed decision-making, the design of effective interventions, and advancements across various fields. [ABSTRACT FROM AUTHOR]

Key message: Naturally regenerating populations of common beech, pedunculate, and sessile oaks develop strong spatial genetic structures at adult and seedling stages. Significant genetic relationship occurs between individuals growing up to 60 m apart. This indicates the minimum distance separating trees from which seeds used for reforestation should be harvested to avoid the adverse effects of excessive relatedness among offspring. Context: Spatial genetic structure is an inherent characteristic of naturally regenerating plant populations and has practical implications in forests for the management of genetic resources. Aims: We investigated the extent of spatial genetic structure in three broad-leaved forest tree species (common beech—Fagus sylvatica L.; pedunculate oak—Quercus robur L.; and sessile oak—Q. petraea (Matt.) Liebl.) coexisting in the same nature reserve, explored its variation among species and different life stages (adults/offspring), and tested its possible determinants. Methods: We explored patterns of spatial distribution of individuals, and using microsatellites, we estimated parameters of spatial genetic structure based on kinship relationships, considering possible sources of variation. Results: In adults, the strongest spatial genetic structure was found for Q. petraea (Sp = 0.0187), followed by F. sylvatica (Sp = 0.0133), and the weakest in Q. robur (Sp = 0.0080). It was uniform across different age classes in pedunculate oak but decreased with age in sessile oak. No apparent relationship between age and spatial genetic structure was found in beech. Offspring exhibited significant spatial genetic structure (ranging from 0.0122 in beech to 0.0188 in sessile oak). The cohorts of seedlings having both parents present within the study site had stronger spatial genetic structures than cohorts of seedlings with only one local parent. Conclusion: Spatial genetic structure is strong in naturally regenerating populations of heavy-seeded forest trees. Pollen immigration from outside of a local forest stand can significantly decrease the extent of spatial genetic structure in offspring generations. [ABSTRACT FROM AUTHOR]

Background: It is increasingly recognized that conventional food production systems are not able to meet the globally increasing protein needs, resulting in overexploitation and depletion of resources, and environmental degradation. In this context, microbial biomass has emerged as a promising sustainable protein alternative. Nevertheless, often no consideration is given on the fact that the cultivation conditions affect the composition of microbial cells, and hence their quality and nutritional value. Apart from the properties and nutritional quality of the produced microbial food (ingredient), this can also impact its sustainability. To qualitatively assess these aspects, here, we investigated the link between substrate availability, growth rate, cell composition and size of Cupriavidus necator and Komagataella phaffii. Results: Biomass with decreased nucleic acid and increased protein content was produced at low growth rates. Conversely, high rates resulted in larger cells, which could enable more efficient biomass harvesting. The proteome allocation varied across the different growth rates, with more ribosomal proteins at higher rates, which could potentially affect the techno-functional properties of the biomass. Considering the distinct amino acid profiles established for the different cellular components, variations in their abundance impacts the product quality leading to higher cysteine and phenylalanine content at low growth rates. Therefore, we hint that costly external amino acid supplementations that are often required to meet the nutritional needs could be avoided by carefully applying conditions that enable targeted growth rates. Conclusion: In summary, we demonstrate tradeoffs between nutritional quality and production rate, and we discuss the microbial biomass properties that vary according to the growth conditions. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) is an important food and feed crop worldwide and serves as a a vital source of biological trace elements, which are important breeding targets. In this study, 170 maize materials were used to detect QTNs related to the content of Mn, Fe and Mo in maize grains through two GWAS models, namely MLM_Q + K and MLM_PCA + K. The results identified 87 (Mn), 205 (Fe), and 310 (Mo) QTNs using both methods in the three environments. Considering comprehensive factors such as co-location across multiple environments, strict significance threshold, and phenotypic value in multiple environments, 8 QTNs related to Mn, 10 QTNs related to Fe, and 26 QTNs related to Mo were used to identify 44 superior alleles. Consequently, three cross combinations with higher Mn element, two combinations with higher Fe element, six combinations with higher Mo element, and two combinations with multiple element (Mn/Fe/Mo) were predicted to yield offspring with higher numbers of superior alleles, thereby increasing the likelihood of enriching the corresponding elements. Additionally, the candidate genes identified 100 kb downstream and upstream the QTNs featured function and pathways related to maize elemental transport and accumulation. These results are expected to facilitate the screening and development of high-quality maize varieties enriched with trace elements, establish an important theoretical foundation for molecular marker assisted breeding and contribute to a better understanding of the regulatory network governing trace elements in maize. [ABSTRACT FROM AUTHOR]

Background: Cancer-secreted exovesicles are important for cell-to-cell communication by altering cancer-related signalling pathways. Exovesicles-derived miRNAs (exomiRNAs)-target genes can be useful for diagnostic and prognostic purposes. Methods: ExomiRNA from prostate cancer (PCa) cells (PC-3 and LNCaP) were quantified by qRT-PCR and compared to the healthy cell line RWPE-1 by using miRNome PCR 752 miRNAs Panel. MiRNet database was used to predict exomiRNA-target genes. ExomiRNA-target genes pathway functional enrichment was performed by using Reactome database and Enrichr platform. Protein–protein interaction analysis was carried out by using the STRING database. RNA target-gene sequencing data from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) database was screened out in 465 PCa patients for candidate gene expression in prostate tumour (PT) tissue and non-pathologic prostate (N-PP) tissue. Signature gene candidates were statistically analysed for diagnosis and prognosis usefulness. Results: A total of 36 exomiRNAs were found downregulated when comparing PCa cells vs a healthy cell line; and when comparing PC-3 vs LNCaP, 14 miRNAs were found downregulated and 52 upregulated. Reactome pathway database revealed altered pathways and genes related to miRNA biosynthesis, miRNA-mediated gene silencing (TNRC6B and AGO1), and cell proliferation (CDK6), among others. Results showed that TNRC6B gene expression was up-regulated in PT tissue compared to N-PP (n = 52 paired samples) and could be useful for diagnostic purposes. Likewise, gene expression levels of CDK6, TNRC6B, and AGO1 were down-regulated in high-risk PT (n = 293) compared to low-risk PCa tissue counterparts (n = 172). When gene expression levels of CDK6, TNRC6B, and AGO1 were tested as a prognostic panel, the results showed that these improve the prognostic power of classical biomarkers. Conclusion: ExomiRNAs-targets genes, TNRC6B, CDK6, and AGO1, showed a deregulated expression profile in PCa tissue and could be useful for PCa diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

Background: Biological networks connect genes, gene products to one another. A network of co-regulated genes may form gene clusters that can encode proteins and take part in common biological processes. A gene coexpression network describes inter-relationships among genes. Existing techniques generally depend on proximity measures based on global similarity to draw the relationship between genes. It has been observed that expression profiles are sharing local similarity rather than global similarity. We propose an expression pattern based method called GeCON to extract Gene CO-expression Network from microarray data. Pair-wise supports are computed for each pair of genes based on changing tendencies and regulation patterns of the gene expression. Gene pairs showing negative or positive co-regulation under a given number of conditions are used to construct such gene co-expression network. We construct co-expression network with signed edges to reflect up- and down-regulation between pairs of genes. Most existing techniques do not emphasize computational efficiency. We exploit a fast correlogram matrix based technique for capturing the support of each gene pair to construct the network. Results: We apply GeCON to both real and synthetic gene expression data. We compare our results using the DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge data with three well known algorithms, viz., ARACNE, CLR and MRNET. Our method outperforms other algorithms based on in silico regulatory network reconstruction. Experimental results show that GeCON can extract functionally enriched network modules from real expression data. Conclusions: In view of the results over several in-silico and real expression datasets, the proposed GeCON shows satisfactory performance in predicting co-expression network in a computationally inexpensive way. We further establish that a simple expression pattern matching is helpful in finding biologically relevant gene network. In future, we aim to introduce an enhanced GeCON to identify Protein-Protein interaction network complexes by incorporating variable density concept. [ABSTRACT FROM AUTHOR]

The composition and diversity of animal-associated microbial communities are shaped by multiple ecological and evolutionary processes acting at different spatial and temporal scales. Skin microbiomes are thought to be strongly influenced by the environment due to the direct interaction of the host's skin with the external media. As expected, the diversity of amphibian skin microbiomes is shaped by climate and host sampling habitats, whereas phylogenetic effects appear to be weak. However, the relative strength of phylogenetic and environmental effects on salamander skin microbiomes remains poorly understood. Here, we analysed sequence data from 1164 adult salamanders of 44 species to characterise and compare the diversity and composition of skin bacteria. We assessed the relative contribution of climate, host sampling habitat, and host phylogeny to the observed patterns of bacterial diversity. We found that bacterial alpha diversity was mainly associated with host sampling habitat and climate, but that bacterial beta diversity was more strongly associated with host taxonomy and phylogeny. This phylogenetic effect predominantly occurred at intermediate levels of host divergence (0–50 Mya). Our results support the importance of environmental factors shaping the diversity of salamander skin microbiota, but also support host phylogenetic history as a major factor shaping these bacterial communities. [ABSTRACT FROM AUTHOR]

Background: Concomitant diseases often occur in cancer patients and are important in decision-making regarding treatments. However, information regarding the prognostic relevance of comorbidities for mortality risk is still limited among Chinese gastric cancer (GC) patients. This study aimed to investigate the association between comorbidities and 3-year mortality risk. Methods: This retrospective study enrolled 376 GC patients undergoing radical gastrectomy at the Affiliated Zhongshan Hospital of Dalian University from January 2011 to December 2019. Demographic and clinicopathological information and treatment outcomes were collected. Patients were divided into low-, moderate- and high-risk comorbidity groups based on their Charlson Comorbidity Index (CCI) and age-adjusted CCI (ACCI) scores. Kaplan-Meier survival and Cox regression analyses were used to examine 3-year overall survival (OS) and mortality risk for each group. Results: The median follow-up time was 43.5 months, and 40.2% (151/376) of GC patients had died at the last follow-up. There were significant differences in OS rates between ACCI-based comorbidity groups (76.56; 64.51; 54.55%, log-rank P = 0.011) but not between CCI-based comorbidity groups (log-rank P = 0.16). The high-risk comorbidity group based on the ACCI remained a significant prognostic factor for 3-year OS in multivariate analysis, with an increased mortality risk (hazard ratio [HR], 1.99; 95% CI, 1.15–3.44). Subgroup analysis revealed that this pattern only held for male GC patients but not for female patients. Conclusion: The present study suggested that high-risk comorbidities were significantly associated with a higher mortality risk, particularly in Chinese male GC patients. Moreover, the ACCI score was an independent prognostic factor of long-term mortality. [ABSTRACT FROM AUTHOR]

Background: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a cerebral small vessel disease that carries mutations in NOTCH3. The clinical manifestations are influenced by genetic and environmental factors that may include gut microbiome. Results: We investigated the fecal metagenome, fecal metabolome, serum metabolome, neurotransmitters, and cytokines in a cohort of 24 CADASIL patients with 28 healthy household controls. The integrated-omics study showed CADASIL patients harbored an altered microbiota composition and functions. The abundance of bacterial coenzyme A, thiamin, and flavin-synthesizing pathways was depleted in patients. Neurotransmitter balance, represented by the glutamate/GABA (4-aminobutanoate) ratio, was disrupted in patients, which was consistent with the increased abundance of two major GABA-consuming bacteria, Megasphaera elsdenii and Eubacterium siraeum. Essential inflammatory cytokines were significantly elevated in patients, accompanied by an increased abundance of bacterial virulence gene homologs. The abundance of patient-enriched Fusobacterium varium positively correlated with the levels of IL-1β and IL-6. Random forest classification based on gut microbial species, serum cytokines, and neurotransmitters showed high predictivity for CADASIL with AUC = 0.89. Targeted culturomics and mechanisms study further showed that patient-derived F. varium infection caused systemic inflammation and behavior disorder in Notch3R170C/+ mice potentially via induction of caspase-8-dependent noncanonical inflammasome activation in macrophages. Conclusion: These findings suggested the potential linkage among the brain-gut-microbe axis in CADASIL. 99hFA7addb1qytHAFkqi-M Video Abstract [ABSTRACT FROM AUTHOR]

Background: Soil and phyllosphere (leaves and fruit) microbes play critical roles in the productivity and health of crops. However, microbial community dynamics are currently understudied in orchards, with a limited number incorporating temporal monitoring. We used 16S rRNA gene amplicon sequencing to investigate bacterial community temporal dynamics and community assembly processes on the leaves and fruit, and in the soil of 12 kiwifruit orchards across a cropping season in New Zealand. Results: Community composition significantly differed (P < 0.001) among the three sample types. However, the communities in the phyllosphere substrates more closely resembled each other, relative to the communities in the soil. There was more temporal stability in the soil bacterial community composition, relative to the communities residing on the leaves and fruit, and low similarity between the belowground and aboveground communities. Bacteria in the soil were more influenced by deterministic processes, while stochastic processes were more important for community assembly in the phyllosphere. Conclusions: The higher temporal variability and the stochastic nature of the community assembly processes observed in the phyllosphere communities highlights why predicting the responsiveness of phyllosphere communities to environmental change, or the likelihood of pathogen invasion, can be challenging. The relative temporal stability and the influence of deterministic selection on soil microbial communities suggests a greater potential for their prediction and reliable manipulation. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) pathology and amyloid-beta (Aβ) plaque deposition progress slowly in the cerebellum compared to other brain regions, while the entorhinal cortex (EC) is one of the most vulnerable regions. Using a knock-in AD mouse model (App KI), we show that within the cerebellum, the deep cerebellar nuclei (DCN) has particularly low accumulation of Aβ plaques. To identify factors that might underlie differences in the progression of AD-associated neuropathology across regions, we profiled gene expression in single nuclei (snRNAseq) across all cell types in the DCN and EC of wild-type (WT) and App KI male mice at age 7 months. We found differences in expression of genes associated with inflammatory activation, PI3K–AKT signalling, and neuron support functions between both regions and genotypes. In WT mice, the expression of interferon-response genes in microglia is higher in the DCN than the EC and this enrichment is confirmed by RNA in situ hybridisation, and measurement of inflammatory cytokines by protein array. Our analyses also revealed that multiple glial populations are responsible for establishing this cytokine-enriched niche. Furthermore, homogenates derived from the DCN induced inflammatory gene expression in BV2 microglia. We also assessed the relationship between the DCN microenvironment and Aβ pathology by depleting microglia using a CSF1R inhibitor PLX5622 and saw that, surprisingly, the expression of a subset of inflammatory cytokines was increased while plaque abundance in the DCN was further reduced. Overall, our study revealed the presence of a cytokine-enriched microenvironment unique to the DCN that when modulated, can alter plaque deposition. [ABSTRACT FROM AUTHOR]

Background: Lumpy skin disease (LSD) is an infectious viral disease of cattle caused by a virus of the genus Capripoxvirus. LSD was reported for the first time in Ethiopia in 1981 and subsequently became endemic. This time series study was undertaken with the aims of identifying the spatial and temporal distribution of LSD outbreaks and to forecast the future pattern of LSD outbreaks in Ethiopia. Results: A total of 3811 LSD outbreaks were reported in Ethiopia between 2000 and 2015. In this period, LSD was reported at least once in 82% of the districts (n = 683), 88% of the administrative zones (n = 77), and all of the regional states or city administrations (n = 9 and n = 2) in the country. The average incidence of LSD outbreaks at district level was 5.58 per 16 years (0.35 year-1). The incidence differed between areas, being the lowest in hot dry lowlands and highest in warm moist highland. The occurrence of LSD outbreaks was found to be seasonal. LSD outbreaks generally have a peak in October and a low in May. The trend of LSD outbreaks indicates a slight, but statistically significant increase over the study period. The monthly precipitation pattern is the reverse of LSD outbreak pattern and they are negatively but non-significantly correlated at lag 0 (r = -0.05, p = 0.49, Spearman rank correlation) but the correlation becomes positive and significant when the series are lagged by 1 to 6 months, being the highest at lag 3 (r = 0.55, p < 0.001). The forecast for the period 2016-2018 revealed that the highest number of LSD outbreaks will occur in October for all the 3 years and the lowest in April for the year 2016 and in May for 2017 and 2018. Conclusion: LSD occurred in all major parts of the country. Outbreaks were high at the end of the long rainy season. Understanding temporal and spatial patterns of LSD and forecasting future occurrences are useful for indicating periods when particular attention should be paid to prevent and control the disease. [ABSTRACT FROM AUTHOR]

Polymerase I and transcript release factor (PTRF) plays a role in the regulation of gene expression and the release of RNA transcripts during transcription, which have been associated with various human diseases. However, the role of PTRF in glioma remains unclear. In this study, RNA sequencing (RNA-seq) data (n = 1022 cases) and whole-exome sequencing (WES) data (n = 286 cases) were used to characterize the PTRF expression features. Gene ontology (GO) functional enrichment analysis was used to assess the biological implication of changes in PTRF expression. As a result, the expression of PTRF was associated with malignant progression in gliomas. Meanwhile, somatic mutational profiles and copy number variations (CNV) revealed the glioma subtypes classified by PTRF expression showed distinct genomic alteration. Furthermore, GO functional enrichment analysis suggested that PTRF expression was associated with cell migration and angiogenesis, particularly during an immune response. Survival analysis confirmed that a high expression of PTRF is associated with a poor prognosis. In summary, PTRF may be a valuable factor for the diagnosis and treatment target of glioma. [ABSTRACT FROM AUTHOR]

Background: Recent evidence suggests increased glucose variability (GV) causes endothelial dysfunction, a central pathology of hypertensive disorders of pregnancy (HDP). We aimed to investigate the association between GV in early pregnancy and subsequent HDP development among non-diabetes mellitus (DM) pregnancies. Methods: This multicenter retrospective study used data from singleton pregnancies between 2009 and 2019. Among individuals who had 75 g-OGTT before 20 weeks of gestation, we evaluated GV by 75 g-OGTT parameters and examined its relationship with HDP development, defining an initial-increase from fasting-plasma glucose (PG) to 1-h-PG and subsequent-decrease from 1-h-PG to 2-h-PG. Results: Approximately 3.0% pregnancies (802/26,995) had 75 g-OGTT before 20 weeks of gestation, and they had a higher prevalence of HDP (14.3% vs. 7.5%). The initial-increase was significantly associated with overall HDP (aOR 1.20, 95% CI 1.02–1.42), and the subsequent-decrease was associated with decreased and increased development of early-onset (EoHDP: aOR 0.56, 95% CI 0.38–0.82) and late-onset HDP (LoHDP: aOR 1.38, 95% CI 1.11–1.73), respectively. Conclusions: A pattern of marked initial-increase and minor subsequent-decrease (i.e., sustained hyperglycemia) was associated with EoHDP. Contrarily, the pattern of marked initial-increase and subsequent-decrease (i.e., increased GV) was associated with LoHDP. This provides a new perspective for future study strategies. [ABSTRACT FROM AUTHOR]

Background: In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions. Methods: Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD. Results: The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided. Conclusions: This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses. [ABSTRACT FROM AUTHOR]

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogenous disease characterized by unexplained persistent fatigue and other features including cognitive impairment, myalgias, post-exertional malaise, and immune system dysfunction. Cytokines are present in plasma and encapsulated in extracellular vesicles (EVs), but there have been only a few reports of EV characteristics and cargo in ME/CFS. Several small studies have previously described plasma proteins or protein pathways that are associated with ME/CFS. Methods: We prepared extracellular vesicles (EVs) from frozen plasma samples from a cohort of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) cases and controls with prior published plasma cytokine and plasma proteomics data. The cytokine content of the plasma-derived extracellular vesicles was determined by a multiplex assay and differences between patients and controls were assessed. We then performed multi-omic statistical analyses that considered not only this new data, but extensive clinical data describing the health of the subjects. Results: ME/CFS cases exhibited greater size and concentration of EVs in plasma. Assays of cytokine content in EVs revealed IL2 was significantly higher in cases. We observed numerous correlations among EV cytokines, among plasma cytokines, and among plasma proteins from mass spectrometry proteomics. Significant correlations between clinical data and protein levels suggest roles of particular proteins and pathways in the disease. For example, higher levels of the pro-inflammatory cytokines Granulocyte-Monocyte Colony-Stimulating Factor (CSF2) and Tumor Necrosis Factor (TNFα) were correlated with greater physical and fatigue symptoms in ME/CFS cases. Higher serine protease SERPINA5, which is involved in hemostasis, was correlated with higher SF-36 general health scores in ME/CFS. Machine learning classifiers were able to identify a list of 20 proteins that could discriminate between cases and controls, with XGBoost providing the best classification with 86.1% accuracy and a cross-validated AUROC value of 0.947. Random Forest distinguished cases from controls with 79.1% accuracy and an AUROC value of 0.891 using only 7 proteins. Conclusions: These findings add to the substantial number of objective differences in biomolecules that have been identified in individuals with ME/CFS. The observed correlations of proteins important in immune responses and hemostasis with clinical data further implicates a disturbance of these functions in ME/CFS. [ABSTRACT FROM AUTHOR]

Background: Wheat is a major cereal that can narrow the gap between the increasing human population and food production. In this connection, assessing genetic diversity and conserving wheat genetic resources for future exploitation is very important for breeding new cultivars that may withstand the expected climate change. The current study evaluates the genetic diversity in selected wheat cultivars using ISSR and SCoT markers, the rbcL and matK chloroplast DNA barcoding, and grain surface sculpture characteristics. We anticipate that these objectives may prioritize using the selected cultivars to improve wheat production. The selected collection of cultivars may lead to the identification of cultivars adapted to a broad spectrum of climatic environments. Results: Multivariate clustering analyses of the ISSR and SCoT DNA fingerprinting polymorphism grouped three Egyptian cultivars with cultivar El-Nielain from Sudan, cultivar Aguilal from Morocco, and cultivar Attila from Mexico. In the other group, cultivar Cook from Australia and cultivar Chinese-166 were differentiated from four other cultivars: cultivar Cham-10 from Syria, cultivar Seri-82 from Mexico, cultivar Inqalab-91 from Pakistan, and cultivar Sonalika from India. In the PCA analysis, the Egyptian cultivars were distinct from the other studied cultivars. The rbcL and matK sequence variation analysis indicated similarities between Egyptian cultivars and cultivar Cham-10 from Syria and cultivar Inqalab-91 from Pakistan, whereas cultivar Attila from Mexico was distinguished from all other cultivars. Combining the data of ISSR and SCoT with the rbcL and matK results retained the close resemblance among the two Egyptian cultivars EGY1: Gemmeiza-9 and EGY3: Sakha-93, and the Moroccan cultivar Aguilal, and the Sudanese cultivar El-Nielain and between Seri-82, Inqalab-91, and Sonalika cultivars. The analysis of all data distinguished cultivar Cham-10 from Syria from all other cultivars, and the analysis of grain traits indicated a close resemblance between cv. Cham-10 from and the two Egyptian cultivars Gemmeiza-9 and Sakha-93. Conclusions: The analysis of rbcL and matK chloroplast DNA barcoding agrees with the ISSR and the SCoT markers in supporting the close resemblance between the Egyptian cultivars, particularly Gemmeiza-9 and Sakha-93. The ISSR and SCoT data analyses significantly expressed high differentiation levels among the examined cultivars. Cultivars with closer resemblance may be recommended for breeding new wheat cultivars adapted to various climatic environments. [ABSTRACT FROM AUTHOR]

Background: Coronary artery calcium (CAC) improves cardiovascular event prediction. Visceral adipose tissue (VAT) is a cardiometabolic risk factor that may directly or through its related comorbidities determine the obesity-related risk. A clinical VAT estimator could allow an efficient evaluation of obesity-related risk. We aimed to analyze the effect of VAT and its related cardiometabolic risk factors on CAC progression. Methods: CAC was quantified at baseline and after 5 years by computed tomography (CT), determining its progression. VAT and pericardial fat were measured by CT and estimated by a clinical surrogate (METS-VF). Considered cardiometabolic risk factors were: peripheral insulin resistance (IR), HOMA-IR, adipose tissue IR (ADIPO-IR), and adiponectin. Factors independently associated to CAC progression were analyzed by adjusted Cox proportional hazard models, including statin use and ASCVD risk score as covariates. We performed interaction and mediation models to propose possible pathways for CAC progression. Results: The study included 862 adults (53 ± 9 years, 53% women), incidence CAC progression rate: 30.2 (95% CI 25.3–35.8)/1000 person-years. VAT (HR: 1.004, 95% CI 1.001–1.007, p < 0.01) and METS-VF (HR: 1.001, 95% CI 1.0–1.001, p < 0.05) independently predicted CAC progression. VAT-associated CAC progression risk was evident among low-risk ASCVD subjects, and attenuated among medium–high-risk subjects, suggesting that traditional risk factors overcome adiposity in the latter. VAT mediates 51.8% (95% CI 44.5–58.8%) of the effect attributable to IR together with adipose tissue dysfunction on CAC progression. Conclusions: This study supports the hypothesis that VAT is a mediator of the risk conferred by subcutaneous adipose tissue dysfunction. METS-VF is an efficient clinical surrogate that could facilitate the identification of at-risk adiposity subjects in daily clinical practice. [ABSTRACT FROM AUTHOR]

Background: Methicillin-Resistant Staphylococcus aureus (MRSA) causes life-threatening infections, with narrow therapeutic options including: vancomycin and linezolid. Accordingly, this study aimed to characterize phenotypically and genotypically, the most relevant means of linezolid resistance among some MRSA clinical isolates. Methods: A total of 159 methicillin-resistant clinical isolates were collected, of which 146 were indentified microscopically and biochemically as MRSA. Both biofilm formation and efflux pump activity were assessed for linezolid-resistant MRSA (LR-MRSA) using the microtiter plate and carbonyl cyanide 3-chlorophenylhydrazone (CCCP) methods, respectively. Linezolid resistance was further characterized by polymerase chain reaction (PCR) amplification and sequencing of domain V of 23 S rRNA; rplC; rplD;and rplV genes. Meanwhile, some resistance genes were investigated: cfr; cfr(B); optrA; msrA;mecA; and vanA genes. To combat LR-MRSA, the effect of combining linezolid with each of 6 different antimicrobials was investigated using the checkerboard assay. Results: Out of the collected MRSA isolates (n = 146), 5.48% (n = 8) were LR-MRSA and 18.49% (n = 27) were vancomycin-resistant (VRSA). It is worth noting that all LR-MRSA isolates were also vancomycin-resistant. All LR-MRSA isolates were biofilm producers (r = 0.915, p = 0.001), while efflux pumps upregulation showed no significant contribution to development of resistance (t = 1.374, p = 0.212). Both mecA and vanA genes were detected in 92.45% (n = 147) and 6.92% (n = 11) of methicillin-resistant isolates, respectively. In LR-MRSA isolates, some 23 S rRNA domain V mutations were observed: A2338T and C2610G (in 5 isolates); T2504C and G2528C (in 2 isolates); and G2576T (in 1 isolate). Amino acids substitutions were detected: in L3 protein (rplC gene) of (3 isolates) and in L4 protein (rplD gene) of (4 isolates). In addition, cfr(B) gene was detected (in 3 isolates). In 5 isolates, synergism was recorded when linezolid was combined with chloramphenicol, erythromycin, or ciprofloxacin. Reversal of linezolid resistance was observed in some LR-MRSA isolates when linezolid was combined with gentamicin or vancomycin. Conclusions: LR-MRSA biofilm producers' phenotypes evolved in the clinical settings in Egypt. Various antibiotic combinations with linezolid were evaluated in vitro and showed synergistic effects. [ABSTRACT FROM AUTHOR]

Background: Soil microorganisms are in constant interaction with plants, and these interactions shape the composition of soil bacterial communities by modifying their environment. However, little is known about the relationship between microorganisms and native plants present in extreme environments that are not affected by human intervention. Using high-throughput sequencing in combination with random forest and co-occurrence network analyses, we compared soil bacterial communities inhabiting the rhizosphere surrounding soil (RSS) and the corresponding bulk soil (BS) of 21 native plant species organized into three vegetation belts along the altitudinal gradient (2400–4500 m a.s.l.) of the Talabre–Lejía transect (TLT) in the slopes of the Andes in the Atacama Desert. We assessed how each plant community influenced the taxa, potential functions, and ecological interactions of the soil bacterial communities in this extreme natural ecosystem. We tested the ability of the stress gradient hypothesis, which predicts that positive species interactions become increasingly important as stressful conditions increase, to explain the interactions among members of TLT soil microbial communities. Results: Our comparison of RSS and BS compartments along the TLT provided evidence of plant-specific microbial community composition in the RSS and showed that bacterial communities modify their ecological interactions, in particular, their positive:negative connection ratios in the presence of plant roots at each vegetation belt. We also identified the taxa driving the transition of the BS to the RSS, which appear to be indicators of key host-microbial relationships in the rhizosphere of plants in response to different abiotic conditions. Finally, the potential functions of the bacterial communities also diverge between the BS and the RSS compartments, particularly in the extreme and harshest belts of the TLT. Conclusions: In this study, we identified taxa of bacterial communities that establish species-specific relationships with native plants and showed that over a gradient of changing abiotic conditions, these relationships may also be plant community specific. These findings also reveal that the interactions among members of the soil microbial communities do not support the stress gradient hypothesis. However, through the RSS compartment, each plant community appears to moderate the abiotic stress gradient and increase the efficiency of the soil microbial community, suggesting that positive interactions may be context dependent. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has impacted the world negatively with huge health and socioeconomic consequences. This study estimated the seasonality, trajectory, and projection of COVID-19 cases to understand the dynamics of the disease spread and inform response interventions. Method: Descriptive analysis of daily confirmed COVID-19 cases from January 2020 to 12th March 2022 was conducted in four purposefully selected sub-Saharan African countries (Nigeria, Democratic Republic of Congo (DRC), Senegal, and Uganda). We extrapolated the COVID-19 data from (2020 to 2022) to 2023 using a trigonometric time series model. A decomposition time series method was used to examine the seasonality in the data. Results: Nigeria had the highest rate of spread (β) of COVID-19 (β = 381.2) while DRC had the least rate (β = 119.4). DRC, Uganda, and Senegal had a similar pattern of COVID-19 spread from the onset through December 2020. The average doubling time in COVID-19 case count was highest in Uganda (148 days) and least in Nigeria (83 days). A seasonal variation was found in the COVID-19 data for all four countries but the timing of the cases showed some variations across countries. More cases are expected in the 1st (January-March) and 3rd (July–September) quarters of the year in Nigeria and Senegal, and in the 2nd (April-June) and 3rd (October-December) quarters in DRC and Uganda. Conclusion: Our findings show a seasonality that may warrant consideration for COVID-19 periodic interventions in the peak seasons in the preparedness and response strategies. [ABSTRACT FROM AUTHOR]

Background: Compared to 2017, India achieved a significant reduction in malaria cases in 2020. Madhya Pradesh (MP) is a tribal dominated state of India with history of high malaria burden in some districts. District Mandla of MP state showed a considerable decline in malaria cases between 2000 and 2013, except in 2007. Subsequently, a resurgence of malaria cases was observed during 2014 and 2015. The Malaria Elimination Demonstration Project (MEDP) was launched in 2017 in Mandla with the goal to achieve zero indigenous malaria cases. This project used: (1) active surveillance and case management using T4 (Track fever, Test fever, Treat patient, and Track patient); (2) vector control using indoor residual sprays and long-lasting insecticidal nets; (3) information education communication and behaviour change communication; and (4) regular monitoring and evaluation with an emphasis on operational and management accountability. This study has investigated malaria prevalence trends from 2008 to 2020, and has predicted trends for the next 5 years for Mandla and its bordering districts. Methods: The malaria prevalence data of the district Mandla for the period of January 2008 to August 2017 was obtained from District Malaria Office (DMO) Mandla and data for the period of September 2017 to December 2020 was taken from MEDP data repository. Further, the malaria prevalence data for the period of January 2008 to December 2020 was collected from DMOs of the neighbouring districts of Mandla. A univariate time series and forecast analysis was performed using seasonal autoregressive integrated moving average model. Findings: Malaria prevalence in Mandla showed a sharp decline [− 87% (95% CI − 90%, − 84%)] from 2017 to 2020. The malaria forecast for Mandla predicts zero cases in the next 5 years (2021–2025), provided current interventions are sustained. By contrast, the model has forecasted a risk of resurgence of malaria in other districts in MP (Balaghat, Dindori, Jabalpur, Seoni, and Kawardha) that were not the part of MEDP. Conclusion: The interventions deployed as part of MEDP have resulted in a sustainable zero indigenous malaria cases in Mandla. Use of similar strategies in neighbouring and other malaria-endemic districts in India could achieve similar results. However, without adding extra cost to the existing intervention, sincere efforts are needed to sustain these interventions and their impact using accountability framework, data transparency, and programme ownership from state to district level. [ABSTRACT FROM AUTHOR]

Background: Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking., Results: This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis., Conclusions: Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis., (© 2024. The Author(s).)

Background: Considerable number of people still use opium worldwide and many believe in opium's health benefits. However, several studies proved the detrimental effects of opium on the body, especially the cardiovascular system. Herein, we aimed to provide the first evidence regarding the effects of opium use on one-year major adverse cardiovascular events (MACE) in the patients with ST-elevation MI (STEMI) who underwent primary PCI. Methods: We performed a propensity score matching of 2:1 (controls: opium users) that yielded 518 opium users and 1036 controls. Then, we performed conventional statistical and machine learning analyses on these matched cohorts. Regarding the conventional analysis, we performed multivariate analysis for hazard ratio (HR) of different variables and MACE and plotted Kaplan Meier curves. In the machine learning section, we used two tree-based ensemble algorithms, Survival Random Forest and XGboost for survival analysis. Variable importance (VIMP), tree minimal depth, and variable hunting were used to identify the importance of opium among other variables. Results: Opium users experienced more one-year MACE than their counterparts, although it did not reach statistical significance (Opium: 72/518 (13.9%), Control: 112/1036 (10.8%), HR: 1.27 (95% CI: 0.94–1.71), adjusted p-value = 0.136). Survival random forest algorithm ranked opium use as 13th, 13th, and 12th among 26 variables, in variable importance, minimal depth, and variable hunting, respectively. XGboost revealed opium use as the 12th important variable. Partial dependence plot demonstrated that opium users had more one-year MACE compared to non-opium-users. Conclusions: Opium had no protective effects on one-year MACE after primary PCI on patients with STEMI. Machine learning and one-year MACE analysis revealed some evidence of its possible detrimental effects, although the evidence was not strong and significant. As we observed no strong evidence on protective or detrimental effects of opium, future STEMI guidelines may provide similar strategies for opium and non-opium users, pending the results of forthcoming studies. Governments should increase the public awareness regarding the evidence for non-beneficial or detrimental effects of opium on various diseases, including the outcomes of primary PCI, to dissuade many users from relying on false beliefs about opium's benefits to continue its consumption. [ABSTRACT FROM AUTHOR]