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The gut microbiome has become increasingly recognized for its role in the pathogenesis of Alzheimer’s disease (AD) and is thought to influence AD pathogenesis via metabolic crosstalk with the host. However, mechanistic pathways connecting the gut microbiome to AD pathogenesis remain unknown. To explore potential mechanistic pathways in AD pathogenesis, we created host–microbiome whole-body metabolic models personalized with 16S rRNA microbiome data and predicted emergent metabolic contributions of gut microbiomes. We analyzed 63 metabolites in blood with previously known links with AD. These in silico predictions were then associated with major risk factors for AD in a cohort of 1,065 aging non-AD individuals and subsequently used to inform targeted analyses on serum metabolomics. Our analysis identified increased host-microbial production of L-arginine in older individuals. Lower production of deoxycholate correlated with the neuroprotective APOE E2 allele and it decreased with higher global cognition. Serum metabolomics from the same individuals of cholesterol products and bile acid metabolism corroborated the modeling predictions, suggesting a potential link between the APOE genotype and cognitive health. In conclusion, this study associated metabolic gut microbiome influences on human metabolism with risk factors for AD and identified cholesterol and bile acid metabolism to potentially link with AD pathogenesis.

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 ± 0.12, (77.08, 77.56) and 94.45 ± 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 ± 0.19, (72.20, 73.00) to 90.16 ± 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.

Abstract Neurofilament light chain (NfL) levels in circulation have been established as a sensitive biomarker of neuro-axonal damage across a range of neurodegenerative disorders. Elucidation of the genetic architecture of blood NfL levels could provide new insights into molecular mechanisms underlying neurodegenerative disorders. In this meta-analysis of genome-wide association studies (GWAS) of blood NfL levels from eleven cohorts of European ancestry, we identify two genome-wide significant loci at 16p12 (UMOD) and 17q24 (SLC39A11). We observe association of three loci at 1q43 (FMN2), 12q14, and 12q21 with blood NfL levels in the meta-analysis of African-American ancestry. In the trans-ethnic meta-analysis, we identify three additional genome-wide significant loci at 1p32 (FGGY), 6q14 (TBX18), and 4q21. In the post-GWAS analyses, we observe the association of higher NfL polygenic risk score with increased plasma levels of total-tau, Aβ-40, Aβ-42, and higher incidence of Alzheimer’s disease in the Rotterdam Study. Furthermore, Mendelian randomization analysis results suggest that a lower kidney function could cause higher blood NfL levels. This study uncovers multiple genetic loci of blood NfL levels, highlighting the genes related to molecular mechanism of neurodegeneration.

Background: Graves’ disease (GD) is an autoimmune disorder with genetic predisposition and CD40 plays a pathogenic role in various autoimmune diseases. A single nucleotide polymorphism (SNP) at position –1 of the Kozak sequence of the 5 untranslated region of CD40 gene of exon 1 has been reported to be associated with the development of GD. Objective: The aim of the present study was to investigate whether CD40 gene polymorphism confers susceptibility to GD in Kumaon region. CD40 gene polymorphisms were studied in GD patients (n=50) and healthy control subjects without anti-thyroid autoantibodies or a family history of autoimmune disorders (n=50). Material and Method: CD40 gene polymorphisms were studied in fifty GD patients and fifty healthy control subjects. All samples collected from STG Hospital, Haldwani, Nainital. A C/T polymorphism at position –1 of the CD40 gene was measured using the polymerase chain reaction restriction fragment length polymorphism. Results: There was no significant difference in allele or genotype frequency of the CD40 SNP between GD and control subjects. There was a significant decrease in the TT genotype frequency in the GD patients, who developed GD after 40 years old, then those under 40 years of age. These data suggest that the SNP of CD40 gene is associated with susceptibility to later onset of GD. Conclusion : The CD40 gene was a new susceptibility gene for GD within certain families because it was both linked and associated with GD. Keywords: Autoimmune Diseases, Pathogenesis, Diagnosis, Therapy

During January and February 2022, a huge spike in the cases of COVID-19 was recorded in the Khyber Pakhtunkhwa (KP) province of Pakistan prompting the civil administration to institute new restrictions. To investigate the underlying causes of this surge, the regional Public Health Reference Lab (PHRL) launched a screening effort throughout the Province’s remote and urban areas. Herein, we present our survey results during the peak months of the pandemic. The data indicated the prevalence of highly transmissible delta- and Omicron-like virus variants, which may explain the rise in positive cases. Phylogenetic analysis revealed the genetic association between the sequences from this study and those found in Pakistan as well as from geographically distant countries, suggesting a complex evolutionary history. For screening, we deployed an easy-to-use single primer-based polymerase chain reaction (PCR) assay to identify the circulation of SARS-CoV-2. The assay was validated using clinical swabs of COVID-19 patients and obtained consistent results with Sanger sequencing. Our survey identified a patient simultaneously infected with two variants of concern, indicating the presence of the Deltacron variant, although we cannot discount the possibility of sequencing errors. We recommend that SARS-CoV-2 be continuously monitored to detect single nucleotide polymorphisms (SNPs) that are causally linked to the emergence of new lineages. Our method provides a rapid and cost-effective detection assay for SARS-CoV-2 in a resource-constrained setting.

Abstract Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

Abstract Background Isoprostanes and prostaglandins are biomarkers for oxidative stress and inflammation. Their role in Alzheimer's disease (AD) pathophysiology is yet unknown. In the current study, we aim to identify the association of isoprostanes and prostaglandins with the Amyloid, Tau, Neurodegeneration (ATN) biomarkers (Aβ-42, p-tau, and t-tau) of AD pathophysiology in mild cognitive impairment (MCI) subjects. Methods Targeted metabolomics profiling was performed using liquid chromatography-mass spectrometry (LCMS) in 147 paired plasma-CSF samples from the Ace Alzheimer Center Barcelona and 58 CSF samples of MCI patients from the Mannheim/Heidelberg cohort. Linear regression was used to evaluate the association of metabolites with CSF levels of ATN biomarkers in the overall sample and stratified by Aβ-42 pathology and APOE genotype. We further evaluated the role of metabolites in MCI to AD dementia progression. Results Increased CSF levels of PGF2α, 8,12-iso-iPF2α VI, and 5-iPF2α VI were significantly associated (False discovery rate (FDR)

The handling of the remnants of rice crops in the field is not an easy operation, and farmers prefer burning, which causes air pollution, smog, and disease. This research reports the development of a novel precision crop seeder by handling the remnants of previous crops through mechanization. The precision seeder performed multiple operations in a single path, viz, chop residues, incorporate into soil, make mini trenches, and sow wheat with fertilizer application. The precision seeder has a 2040 mm working width, and specially designed C-type blades are used to shred the crop residue. A multiple-speed gearbox with a gear ratio of 1:0.52 is installed, with a further set of spur gears with 16, 18, and 20 teeth that provide 225, 250, 310, and 350 RPMs to the main rotor. In the middle of the seeder, after the main rotor shaft, 11 V-shaped trencher plates are fixed on the trencher roller for the making of trenches. The trencher roller is powered by star wheels, which showed good results. A zero-tillage-type sharp tip edge novel seeder unit was developed for the precise placement of seed and fertilizer. Seed and fertilizer were placed into the mini trenches through 11 seeder units through a ground wheel calibration system. The field capacity of the precision seeder was 0.408 ha/h and the operational cost was calculated 40.68 USD/ha. The seeder showed good results, with the production of 5028 kg/ha compared to conventional methods. The precision seeder provides a mechanized solution for wheat sowing with minimal operational costs by enhancing organic matter in soil with 13% more yield.

Abstract Background The gut-derived metabolite Trimethylamine N-oxide (TMAO) and its precursors - betaine, carnitine, choline, and deoxycarnitine – have been associated with an increased risk of cardiovascular disease, but their relation to cognition, neuroimaging markers, and dementia remains uncertain. Methods In the population-based Rotterdam Study, we used multivariable regression models to study the associations between plasma TMAO, its precursors, and cognition in 3,143 participants. Subsequently, we examined their link to structural brain MRI markers in 2,047 participants, with a partial validation in the Leiden Longevity Study (n = 318). Among 2,517 participants, we assessed the risk of incident dementia using multivariable Cox proportional hazard models. Following this, we stratified the longitudinal associations by medication use and sex, after which we conducted a sensitivity analysis for individuals with impaired renal function. Results Overall, plasma TMAO was not associated with cognition, neuroimaging markers or incident dementia. Instead, higher plasma choline was significantly associated with poor cognition (adjusted mean difference: -0.170 [95% confidence interval (CI) -0.297;-0.043]), brain atrophy and more markers of cerebral small vessel disease, such as white matter hyperintensity volume (0.237 [95% CI: 0.076;0.397]). By contrast, higher carnitine concurred with lower white matter hyperintensity volume (-0.177 [95% CI: -0.343;-0.010]). Only among individuals with impaired renal function, TMAO appeared to increase risk of dementia (hazard ratio (HR): 1.73 [95% CI: 1.16;2.60]). No notable differences were observed in stratified analyses. Conclusions Plasma choline, as opposed to TMAO, was found to be associated with cognitive decline, brain atrophy, and markers of cerebral small vessel disease. These findings illustrate the complexity of relationships between TMAO and its precursors, and emphasize the need for concurrent study to elucidate gut-brain mechanisms.

We have synthesized thiadiazole-isatin hybrid analogues (1–20), characterized through different spectroscopic techniques such as 1HNMR, 13CNMR, HREI-MS, and evaluated against α-glucosidase and α-amylase enzymes. All analogues showed potent inhibitory potentials, having an IC50 values ranged from 22.08 ± 0.09 to 54.03 ± 0.07 μM (against α-amylase) and 19.03 ± 0.04 to 50.03 ± 0.02 μM (α-glucosidase) when compared with the standard drug acarbose (IC50 = 22.07 ± 0.02 μM for α-amylase and IC50 = 18.01 ± 0.06 μM for α-glucosidase respectively). Among the series, analogues 13 (IC50 = 19.03 ± 0.04 and 22.08 ± 0.09 μM), 12 (IC50 = 21.03 ± 0.07 and 24.03 ± 0.07 μM), and 5 (IC50 = 22.02 ± 0.03 and 26.02 ± 0.04 μM) were identified as the most active inhibitors. The structure–activity relationship was carried out, mainly associated with the nature, number, position, and electron-donating and electron-withdrawing effects of the substituent (s) on the phenyl ring. With the help of molecular docking, the binding interaction of the most active analogues within the active site of enzymes was confirmed. Almost twelve compounds were also screened for antibacterial potential, and few were found to be effective against Bacillus subtilis. All compounds were verified for cytotoxicity against the 3 T3 mouse fibroblast cell line and detected non-toxic.

Detecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due to complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed in recent research for lung disease classification, focusing on pneumonia analysis using chest X-ray images. We explore conventional machine learning methods, pretrained deep learning models, customized convolutional neural networks (CNNs), and ensemble methods. A comprehensive comparison of different classification approaches is presented, encompassing data acquisition, preprocessing, feature extraction, and classification using machine vision, machine and deep learning, and explainable-AI (XAI). Our analysis highlights the superior performance of transfer learning-based methods using CNNs and ensemble models/features for lung disease classification. In addition, our comprehensive review offers insights for researchers in other medical domains too who utilize radiological images. By providing a thorough overview of various techniques, our work enables the establishment of effective strategies and identification of suitable methods for a wide range of challenges. Currently, beyond traditional evaluation metrics, researchers emphasize the importance of XAI techniques in machine and deep learning models and their applications in classification tasks. This incorporation helps in gaining a deeper understanding of their decision-making processes, leading to improved trust, transparency, and overall clinical decision-making. Our comprehensive review serves as a valuable resource for researchers and practitioners seeking not only to advance the field of lung disease detection using machine learning and XAI but also from other diverse domains.

Estimation of plant community composition, aboveground biomass and carbon stock is crucial for understanding forest ecology, strengthening environmental management, and developing effective tools and policies for forest restoration. This study was conducted in nine different forest reserves in Sri Lanka from 2012 to 2018 to examine the impact of community-based forest restoration (CBFR) on stand structural attributes, aboveground biomass, and carbon stock compared to state-managed forests. In total, 180 plots (90 plots in community-managed restoration blocks (CMRBs) and 90 plots assigned to state-managed restoration blocks (SMRBs)) were sampled at the study site. To conduct an inventory of standing trees, circular plots with a radius of 12.6 m (equivalent to an area of 500 square meters) were established. The Shannon diversity index, Allometric equations and Difference in Differences (DID) estimation were used to assess the data. Our study provides evidence of the positive impact of the CBFR program on enriching trees diversity. Considering stand structural attributes of both blocks showed higher trees density in the smaller diameter at breast height (DBH) category, indicating growth in both CMRBs and SMRBs. The results showed that tree biomass and carbon density were disproportionally distributed across the nine different forest reserves. On average, tree biomass and carbon density were higher in SMRBs (79.97 Mg ha−1, 37.58 Mg C ha−1) compared to CMRBs (33.51 Mg ha−1, 15.74 Mg C ha−1). However, CMRBs in Madigala reserve represent the highest biomass (56.53 and 59.92 Mg ha−1) and carbon density (26.57 and 28.16 Mg C ha−1). The results of biomass and carbon estimates were higher in all SMRBs in the nine different forest reserves compared to CMRBs. The findings suggest that future forest restoration programs in Sri Lanka should enhance participatory approaches to optimize tree species diversity, density and carbon storage, particularly in community-controlled forests. Our findings could assist developing tropical nations in understanding how CBFR impacts forest restoration objectives and improves the provision of ecological services within forests.

Objective: Thyroid diseases are major health problems in our society, which are manifested by alteration in hormone secretion, enlargement of the thyroid gland. FNAC plays a significant role in the diagnosis of thyroid lesions due to its simplicity and low cost and with a diagnostic accuracy rate of 92%. This study was designed to cytomorphological profile of Graves’ disease and its co relation with anti TPO antibody. Materials and Methods: This is a prospective analytical study of fifty patients with clinically enlarged thyroid gland presenting at STG Hospital, Haldwani, Nainital during Oct 2019 to Oct 2022. FNAC smears of these patients were studied and correlated with cytomorphological findings of different types, grades of thyroiditis and hormone level of T4, T3 and T.S.H. Results: Females were mostly affected by thyroid diseases than males. Female patients were 39 (78%) and male patients were 11 (22%). In the present study, patients were in the age group of 13 to 74 years of age. High T3 levels were noted in 80% high T4 levels were found in 90%. Low TSH level was found in only 64%, normal TSH level found in 34% and 2% presented with high TSH level. 34 patients (68%) found high anti-thyroid peroxidase antibody titre >34 U/ml. Cytomorphology revealed moderate to high cellularity in 88% cases, mild to moderate nuclear atypia in 86% and presence of marginal vacuoles in 82%. Lymphocytic infiltrate found in 24% cases and epithelioid cell granuloma in 14% cases. Conclusion: Thyroid cytology proves to be a reliable, simple, and cost-effective first-line diagnostic procedure with high patient acceptance and with rare, usually easily treated and not life-threatening complications. We recommend further studies with a larger population to validate our study. Keywords: Thyroiditis, Graves' Disease, Malignancy Goiter, Histopathology

Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Background: Histopathological examination is subjective and dependent on the skill and experience of the examiner. In recent years, artificial intelligence (AI)-based techniques have emerged as a promising tool in medical imaging, offering the potential to revolutionize tissue phenotyping and improve the accuracy and reliability of the histopathological examination process. The goal of this study was to investigate the use of AI techniques for the detection of various tissue phenotypes in POC after spontaneous abortion and evaluate the accuracy and reliability of these techniques compared to traditional manual methods. Methods: We present a novel publicly available dataset named HistoPoC, which is believed to be the first of its kind, focusing on spontaneous abortion (miscarriage) in early pregnancy. A diverse dataset of 5666 annotated images was prepared from previously diagnosed cases of POC from Atia General Hospital, Karachi, Pakistan, for this purpose. The digital images were prepared at 10× through a camera-connected microscope by a consultant histopathologist. Results: The dataset’s effectiveness was validated using several deep learning-based models, demonstrating its applicability and supporting its use in intelligent diagnostic systems. Conclusions: The insights gained from this study could illuminate the causes of spontaneous abortion and guide the development of novel treatments. Additionally, this study could contribute to advancements in the field of tissue phenotyping and the wider application of deep learning techniques in medical diagnostics and treatment.

Human posture recognition has a wide range of applicability in the detective and preventive healthcare industry. Recognizing posture through frequency-modulated continuous wave (FMCW) radar poses a significant challenge as the human subject is static. Unlike existing radar-based studies, this study proposes a novel framework to extract the postures of two humans in close proximity using FMCW radar point cloud. With radar extracted range, velocity, and angle information, point clouds in the Cartesian domain are retrieved. Afterwards, unsupervised clustering is implemented to segregate the two humans, and finally a deep learning model named DenseNet is applied to classify the postures of both human subjects. Using four base postures (namely, standing, sitting on chair, sitting on floor, and lying down), ten posture combinations for two human scenarios are classified with an average accuracy of 96%. Additionally, using the centroid information of human clusters, an approach to detect and classify overlapping human participants is also introduced. Experiments with five posture combinations of two overlapping humans yielded an accuracy of above 96%. The proposed framework has the potential to offer a privacy-preserving preventive healthcare sensing platform for an elderly couple living alone.

Hybrid molecules based on the 1,3,4-thiadiazole were always the choice of different researchers due to their significant application in medicinal as well as pharmaceutical application. In the present study, twenty analogs of 1,3,4-thiadiazole-bearing thiosemicarbazide moiety (1–20) were synthesized and screened for their anti-diabetic profile. The synthesized compounds were spectroscopically characterized through different spectroscopic techniques such as 1HNMR, 13CNMR, and HREI-MS. Comparing the whole set of afforded compounds to the standard glimepiride drugs (16.01 ± 0.02 μM), the inhibition profiles against α-amylase ranged from 21.02 ± 0.08 to 54.08 ± 0.04 μM. Comparing synthetic analogs to normal glimepiride (IC50 = 14.06 ± 0.05 μM), the range of α-glucosidase activity was likewise variable, ranging from 18.04 ± 0.07 μM to IC50 = 51.05 ± 0.03 μM (against α-glucosidase). Compound 19 demonstrated high potency among the produced analogs since it had both ortho-nitro substitution at the aryl ring. The pattern of substitutions around the aryl ring was used for all analogs to determine the structure–activity relationship. In addition, a molecular docking study was conducted on the potent analogs to examine the interactions between the active residues of the targeted enzymes with the synthesized compound. The synthesized molecule showed different types of interactions with amino acid. The outcome demonstrates that these compounds provide several essential interactions with the active sites of enzymes, hence strengthening their enzymatic activity for the future prediction as drug competitors.

Abstract Trimethylamine N‐oxide (TMAO) is a circulating microbiome‐derived metabolite implicated in the development of atherosclerosis and cardiovascular disease (CVD). We investigated whether plasma levels of TMAO, its precursors (betaine, carnitine, deoxycarnitine, choline), and TMAO‐to‐precursor ratios are associated with clinical outcomes, including CVD and mortality. This was followed by an in‐depth analysis of their genetic, gut microbial, and dietary determinants. The analyses were conducted in five Dutch prospective cohort studies including 7834 individuals. To further investigate association results, Mendelian Randomization (MR) was also explored. We found only plasma choline levels (hazard ratio [HR] 1.17, [95% CI 1.07; 1.28]) and not TMAO to be associated with CVD risk. Our association analyses uncovered 10 genome‐wide significant loci, including novel genomic regions for betaine (6p21.1, 6q25.3), choline (2q34, 5q31.1), and deoxycarnitine (10q21.2, 11p14.2) comprising several metabolic gene associations, for example, CPS1 or PEMT. Furthermore, our analyses uncovered 68 gut microbiota associations, mainly related to TMAO‐to‐precursors ratios and the Ruminococcaceae family, and 16 associations of food groups and metabolites including fish‐TMAO, meat‐carnitine, and plant‐based food‐betaine associations. No significant association was identified by the MR approach. Our analyses provide novel insights into the TMAO pathway, its determinants, and pathophysiological impact on the general population.

Background: The incidence of breast cancer warrants special consideration and understanding of disease demography. The study aimed to determine the clinicopathological characteristics of molecularly classified groups of invasive ductal breast carcinoma in Pakistani women. Methods: Patients (n=83) undergoing modified radical mastectomy with primary microscopically proven invasive ductal carcinoma were recruited from two tertiary care hospitals, Lahore, Pakistan. Grossing, reporting and biomarker testing was performed as per the College of American Pathologists (CAP) protocols. Chi-square was applied to observe associations between variables. A p-value

Abstract Recently, radar sensors have been extensively used for vital sign monitoring in dogs, owing to their noncontact and noninvasive nature. However, a public dataset on dog vital signs has yet to be proposed since capturing data from dogs requires special training and approval. This work presents the first ever ultra wideband radar-based dog vital sign (UWB-DVS) dataset, which was captured in two independent scenarios. In the first scenario, clinical reference sensors are attached to the fainted dogs, and data from UWB radar and reference sensors are captured synchronously. In the second scenario, the dogs can move freely, and video recordings are provided as a reference for movement detection and breathing extraction. For technical validation, a high correlation, above 0.9, is found between the radar and clinical reference sensors for both the heart rate and breathing rate measurements in scenario 1. In scenario 2, the vital signs and movement of the dogs are shown in the form of dashboards, demonstrating the long-term monitoring capability of the radar sensor.

Abstract The return of rural migrant workers to employment is an endogenous force that drives the development of the rural economy and plays a vital role in rural revitalization. Using data from the China Migrants Dynamic Survey (CMDS) conducted in 2017, this study comprehensively examines the influence of social exclusion and the role of identity on migrant workers’ willingness to return home (MWRH). The findings indicate a significant impact of social exclusion on MWRH, and this relationship remains robust even after employing instrumental variables to address endogeneity concerns. Identity emerges as a crucial mediating factor through which social exclusion affects MWRH, demonstrating a partial mediating effect. Notably, the impact of social exclusion on MWRH is particularly prevalent among rural residents in the western and northeastern regions, the older generation, those with lower incomes, and those with limited intra-provincial mobility. Promoting a positive image of migrant workers and encouraging their pursuit of an improved quality of life can help alleviate social exclusion, strengthen their sense of identity, and foster deeper roots in cities. Furthermore, providing supportive platforms for returning migrant workers can enhance their active willingness to return home.

Recently, there has been a remarkable move towards interprofessional collaboration in response to the COVID-19 pandemic and the care of comorbidities. In Germany, there has been a gradual increase in interprofessional learning in medical and pharmacy education, aiming to enhance patient care. To adapt the pharmacy curriculum for collaborative practice between pharmacy and medical students, we developed an immersive interprofessional collaboration course for pharmacy students using adult and pediatric high-fidelity simulators (HFS) to assess and train medication consultation skills. In a randomized controlled trial, we investigated whether interprofessional training between pharmacy and medical students results in differences in pharmacy students’ performance of medication therapy consultation compared to the case of mono-professional training of pharmacy students only. Before and after inter/mono-professional training, each pharmacy student performed an objective structured clinical examination (OSCE) and completed a self-assessment questionnaire. Additionally, an attitude survey towards interprofessional learning was completed by pharmacy and medical students at the end of the training. As expected, interprofessional as well as mono-professional training showed a statistically significant increase in medication consultation skills. Of importance, the performance in the interprofessional training group was significantly better than in the mono-professional group, particularly in drug therapy counselling and consultation behaviors. There was a significant difference between the intervention and control groups in self-assessment scores, and all study participants had positive attitudes toward interprofessional collaboration and training. Therefore, interprofessional training using HFS has been shown to appropriately train pharmacy students for collaborative practice and consultation skills.

Abstract Automatic brain tumour segmentation in MRI scans aims to separate the brain tumour's endoscopic core, edema, non‐enhancing tumour core, peritumoral edema, and enhancing tumour core from three‐dimensional MR voxels. Due to the wide range of brain tumour intensity, shape, location, and size, it is challenging to segment these regions automatically. UNet is the prime three‐dimensional CNN network performance source for medical imaging applications like brain tumour segmentation. This research proposes a context aware 3D ARDUNet (Attentional Residual Dropout UNet) network, a modified version of UNet to take advantage of the ResNet and soft attention. A novel residual dropout block (RDB) is implemented in the analytical encoder path to replace traditional UNet convolutional blocks to extract more contextual information. A unique Attentional Residual Dropout Block (ARDB) in the decoder path utilizes skip connections and attention gates to retrieve local and global contextual information. The attention gate enabled the Network to focus on the relevant part of the input image and suppress irrelevant details. Finally, the proposed Network assessed BRATS2018, BRATS2019, and BRATS2020 to some best‐in‐class segmentation approaches. The proposed Network achieved dice scores of 0.90, 0.92, and 0.93 for the whole tumour. On BRATS2018, BRATS2019, and BRATS2020, tumour core is 0.90, 0.92, 0.93, and enhancing tumour is 0.92, 0.93, 0.94.

Brain tumors, which are uncontrolled growths of brain cells, pose a threat to people worldwide. However, accurately classifying brain tumors through computerized methods has been difficult due to differences in size, shape, and location of the tumors and limitations in the medical field. Improved precision is critical in detecting brain tumors, as small errors in human judgments can result in increased mortality rates. This paper proposes a new method for improving early detection and decision-making in brain tumor severity using learning methodologies. Clinical datasets are used to obtain benchmark images of brain tumors, which undergo pre-processing, data augmentation with a Generative Adversarial Network, and classification with an Adaptive Layer Cascaded ResNet (ALCResNet) optimized with Improved Border Collie Optimization (IBCO). The abnormal images are then segmented using the DeepLabV3 model and fed into the ALCResNet for final classification into Meningioma, Glioma, or Pituitary. The IBCO algorithm-based ALCResNet model outperforms other heuristic classifiers for brain tumor classification and severity estimation, with improvements ranging from 1.3% to 4.4% over COA-ALCResNet, DHOA-ALCResNet, MVO-ALCResNet, and BCO-ALCResNet. The IBCO algorithm-based ALCResNet model also achieves higher accuracy than non-heuristic classifiers such as CNN, DNN, SVM, and ResNet, with improvements ranging from 2.4% to 3.6% for brain tumor classification and 0.9% to 3.8% for severity estimation. The proposed method offers an automated classification and grading system for brain tumors and improves the accuracy of brain tumor classification and severity estimation, promoting more precise decision-making regarding diagnosis and treatment.

A topological index (TI) is a real number that determined the structure of a graph. Topological indices (TIs) serves mathematical descriptors that aid in predicting and understanding the physicochemical properties and structure-activity relationships of chemical compounds, thus facilitating drug design and discovery processes. TIs utilizes to forecast the effectiveness of cancer treatment by providing details on the molecules of the medications and their associated properties. In order to create safe and effective treatments, it is crucial to employ TIs to accurately predict the toxicity and side effects of medications. In this paper, we have considered the physical and biological properties of some blood cancer treatment medications through degree based Banhatti topological indices from the M-polynomial by using derivations. The drugs are Palbociclib, Ivosidenib, Glasdegib, Gilteritinib, and Daunorubicin and the TI's are given in Table 1. Subsequently, a linear regression technique is employed to establish a quantitative structure-property relationship (QSPR) model for predicting various parameters such as molecular weight, complexity, molar volume, boiling point (BP), and flash point (FP). Results indicate that topological indices have potential as a drug discovery and development tool in the fight against cancer.

Abstract The immune response–gut microbiota interaction is implicated in various human diseases, including cancer. Identifying the link between the gut microbiota and systemic inflammatory markers and their association with cancer will be important for our understanding of cancer etiology. The current study was performed on 8090 participants from the population‐based Rotterdam study. We found a significant association (false discovery rate [FDR] ≤0.05) between lymphocytes and three gut microbial taxa, namely the family Streptococcaceae, genus Streptococcus, and order Lactobacillales. In addition, we identified 95 gut microbial taxa that were associated with inflammatory markers (p

Remote sensing technology has been modernized by artificial intelligence, which has made it possible for deep learning algorithms to extract useful information from images. However, overfitting and lack of uncertainty quantification, high-resolution images, information loss in traditional feature extraction, and background information retrieval for detected objects limit the use of deep learning models in various remote sensing applications. This paper proposes a Bayes by backpropagation (BBB)-based system for scene-driven identification and information retrieval in order to overcome the above-mentioned problems. We present the Bayes R-CNN, a two-stage object detection technique to reduce overfitting while also quantifying uncertainty for each object recognized within a given image. To extract features more successfully, we replace the traditional feature extraction model with our novel Multi-Resolution Extraction Network (MRENet) model. We propose the multi-level feature fusion module (MLFFM) in the inner lateral connection and a Bayesian Distributed Lightweight Attention Module (BDLAM) to reduce information loss in the feature pyramid network (FPN). In addition, our system incorporates a Bayesian image super-resolution model which enhances the quality of the image to improve the prediction accuracy of the Bayes R-CNN. Notably, MRENet is used to classify the background of the detected objects to provide detailed interpretation of the object. Our proposed system is comprehensively trained and assessed utilizing the state-of-the-art DIOR and HRSC2016 datasets. The results demonstrate our system’s ability to detect and retrieve information from remote sensing scene images.

Conclusion: Despite that microbial signatures have been established for various subtypes of breast cancer, the crux of healthy microbiome yet needs to be detected. This review represents a modest attempt to compile the body of knowledge regarding the relationship between breast cancer and the human microbiome.

“Cases of SCMR” is a case series on the SCMR website (https://www.scmr.org) for the purpose of education. The cases reflect the clinical presentation, and the use of cardiovascular magnetic resonance (CMR) in the diagnosis and management of cardiovascular disease. The 2022 digital collection of cases are presented in this manuscript.

Background. Generalized morphea is a rare fibrosing skin illness that progresses from erythematous, violet-colored skin patches to sclerotic plaques. Another uncommon immune-mediated connective tissue disease called eosinophilic fasciitis (EF) evolves to cause sclerosis and woody skin induration. The coexistence of the two is extremely rare and has a poorer prognosis. Our case report is one of the first to report burn injuries as a trigger factor for EF and generalized morphea overlap. Case Presentation. A 36-year-old man presented with acute onset of rapidly progressing skin thickening, tender edema, and skin contractures involving all extremities, shortly after enduring burn injuries from a gasoline explosion. Workup was remarkable for peripheral eosinophilia, hypergammaglobulinemia, and elevated C-reactive protein. Skin biopsy demonstrated sclerodermoid changes and sclerotic thickening of subcutaneous fibrous septa associated with stromal mucin, dermal perivascular, diffuse lymphoplasmacytic infiltrate with eosinophils, decreased CD34 expression, and increased factor XIIIa. He was subsequently diagnosed with an overlap of generalized morphea and eosinophilic fasciitis. The patient had only limited improvement with steroids, methotrexate, mycophenolate mofetil, and intralesional triamcinolone acetonide injections. Conclusion. Generalized morphea with concomitant EF indicates some degree of therapeutic resistance and poor prognosis with a low quality of life. Burn injuries can be a trigger factor for this overlap syndrome. Prompt identification of at-risk individuals and initiating aggressive management are necessary.

IntroductionDeveloping efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks.MethodsWe describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation.Results and discussionOverall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

The pertinence and importance of Darcy-Forchheimer flows can be found in large spectrum of engineering, biological and technological mechanisms like solar absorption and filtration, drainage problems, solar cells, ground stream hydrology, solar panels, irrigation and so forth. The major concern of this study is to interpret that how much a Darcy-Forchheimer flow of a micro-structured fluid (e.g., micropolar fluid) within a channel is influenced by the combined effects of static and oscillatory pressure gradients. Numerical treatment is carried out via three steps explicit Runge-Kutta method. Impacts of the preeminent parameters on the flow, mass and heat transport features of the problem are over looked by means of graphical and tabular representations. The numerical consequences evidently point out that the influence of oscillatory component is to elevate the fluid velocity whereas static component of pressure gradient tends to decelerate the velocity inside the channel. It is also determined here that Darcy parameter sufficiently reduces the angular velocity in the flow regime.

Abstract Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F1-score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of research that may prove useful for clinical treatment planning leading to better outcomes.

We show how to implement a deniable encryption method from secret sharing. Unlike the related concept of honey encryption, which employs a preprocessing step in symmetric encryption to re-shape the distribution of a plaintext towards making the real plaintext indistinguishable from a ciphertext for a fake message, we can avoid both, computational intractability assumptions and preprocessing of the data. This accomplishes deniability against an attacker that can force decryptions, and it can brute-force break a ciphertext with sufficient computational power. Following the concept of plausible deniability, we herein have different decryption keys to open up distinct plaintexts from within the same ciphertext. For instance, a plaintext revealed from a ciphertext with a key which was shared by a victim under duress, will convince the attacker that it is real, while the actual secret remains unnoticed. False Bottom Encryption constructs a symmetric scheme (in the sense of using the same key to encrypt and decrypt) that shares the properties of both honey encryption and deniable encryption. We specifically formalize and differentiate “deniable” from “plausibly deniable” as a security feature, showing how plausible deniability falls back to (only) deniability, depending on the plaintext distribution. Our scheme is simple, lightweight to implement and efficient in terms of encryption and decryption, and is based on secret sharing. As such, we do not rely on computational intractability. We corroborate the construction by giving numeric examples and providing implementations of the method as a Jupyter notebook supplementary to this work.

In Pakistan, the Hepatitis E virus (HEV) is prevalent. HEV exposure is deadly during pregnancy, with high infection rates in both the mother and the baby, even in asymptomatic situations. The researcher examined HEV-negative pregnant women with acute hepatitis or increased renal characteristic assessments at 12 medical sites in Khyber Pakhtunkhwa, Pakistan, to identify maternal and fetal disease risks from August to December 2018. Except for one during the entire pregnancy, twenty-five of the 135 females were HEV-free and in the perinatal period. HEV infection was found in 0.19% of newly pregnant women. Also, 11 cases of forcible induction of labor, 7 cases of normal labor, 5 cases of normal labor (full-term), 2 cases of death, and 1 case of intrauterine death were reported before going to the doctor a girl had an abortion. There were eight postpartum deaths: five from infections, four from miscarriages, and two from miscarriages. While 18 mothers who survived voluntarily or intentionally induced abortions and three women who continued to get pregnant but did not deliver died as a result of abortion. The current study found that HEV infection during pregnancy raised the death rate by 14.2%.

Objective: To study the efficacy of per-operative angiogram and Doppler ultrasound as an adjunct to assess the cerebral vessels during intracranial aneurysm clipping surgery. Study Design: Cross-sectional study Place and Duration of Study: Department of Neurosurgery, Combined Military Hospital, Rawalpindi Pakistan, from Jan 2020 to Dec 2021. Methodology: Forty-eight diagnosed cases of subarachnoid haemorrhage secondary to ruptured aneurysm were included. All the patients who were not coiled-able (broad-based, multi-lobed, complex) were operated on (craniotomy and clipping of aneurysm). Intraoperative angiogram and Doppler ultrasound of cerebral vasculature were done in all cases to see the patency of vessels and the position of clips. Results: Mean age of the patients was 43.97±8.23 years. 24(50.00%) patients had anterior communicating artery aneurysms, 18(37.50%) had middle cerebral artery aneurysms, whereas 6(12.50%) were of an anterior cerebral artery. Craniotomy and clipping of aneurysm were done in all the cases; among them, 15(31.25%) were operated on early (between 48-96 hrs), whereas 33(68.75%) were operated on after 10-14 days. Per-operatively, cerebral angiogram and Doppler ultrasound were used to assess the cerebral vasculature and position of the clip. About of 5(10.00%) required clip readjustment, 4(8.33%) had parent vessel occlusion, 10(20.83%) required vasodilator therapy due to vasospasm, and 4(8.33%) had residual aneurysm neck. All these were rectified during the same procedure. Conclusion: Per-operative angiography and Doppler ultrasound are useful adjuncts in aneurysmal brain surgery to prevent complications like cerebral ischemia, recurrence, residual aneurysm and re-bleed.

Objective: To determine the efficacy of mini-craniotomy for evacuation of mix-density subdural hematoma in terms of early clinical outcome. Study Design: Quasi-experimental study. Place and Duration of Study: Neurosurgery Department, Combined Military Hospital, Rawalpindi Pakistan, from Jan to Dec 2020. Methodology: Eighty patients with mixed-density subdural hematoma were included in the study. All were subjected to minicraniotomy under general anaesthesia. Post-operative Glasgow Coma Scale (GCS) was monitored and compared with the preoperative Glasgow Coma Scale (GCS) to see the early clinical outcome of the patients. Results: Out of 80 patients with mixed-density subdural, 38(47.5%) were females, whereas 42(52.5%) were males. The mean age of the patients was 70.9±6.98 years. Improvement in Glasgow Coma Scale (GCS) after 24hrs showed that 68(85%) patients had a favourable outcome, while 12(15%) had the same condition post-operatively. Conclusion: Mini-craniotomy is a suitable surgical procedure for the treatment of mixed-density subdural hematoma.

Objective: To determine the efficacy of pre-operative meningioma embolization in terms of operating time and intraoperative bleeding. Study Design: Prospective longitudinal study. Place and Duration of Study: Department of Neurosurgery, Combined Military Hospital, Rawalpindi Pakistan, from Jan to Dec 2020. Methodology: Twelve diagnosed cases of intracerebral meningioma were included in the study. The patients were diagnosed cases of meningioma as per pre-operative signs and symptoms, radiological confirmation was done with magnetic resonance imaging, and all were subjected to pre-operative embolization of the tumour with catheter advancement in the arterial system and embolizing the target vessels with polyvinyl alcohol particles, followed by craniotomy and excision of the tumour. Results: The mean age of the patients was 47±672. 24 years. The mean operating time (from the incision to closure) was 96.17±10.23 minutes. In contrast, the intraoperative blood loss was 325.33±15.43 ml. Six (50.00%) individuals had parasagittal meningioma, 4(33.33%) were of cerebral convexity, while 2(16.66%) had lesions at sphenoid wing. Conclusion: Meningioma is a quite vascular tumour, and the operative outcome can be improved by pre-operative angioembolization. The reduced vascularity makes the resection easier by decreasing the per-op bleed and operating time. Keywords: Intraoperative bleed, Meningioma, Operating time, Pre-operative embolization.

Background. In the current era, Twitter is an increasingly popular tool for the dissemination of information as a social media voice. Social media is a valid, but underutilized, education tool at medical education institutions. Social media technologies provide opportunities for the presentation of information in alternative and multiple formats to enhance engagement, content creation, and motivation for individual and collaborative learning. Objective. This study examined the type of social structure and sub-clusters do exist regarding “MedEd” on the Twitter network. Additionally, it determined the top opinion leaders in these networks and which type of topics generates users’ interest regarding “MedEd”. Methods. This study applied NodeXL to analyze the results and retrieved Twitter data on November 1, 2022 by using the keywords “MedEd”. The data were saved and interpreted in the “vertices” and “edges” on the NodeXL worksheets. Results. We found that the top opinion leader (vertex) “Cryptovitas” had the highest in- betweenness and out-degree centrality. “Innov_medicine” had the in-degree centrality for networks. “In-Degree” and “Out-Degree” are the count of Tweets an opinion leader gets and forwards messages out, correspondingly. The study found that although “Cryptovitas” had the highest in-betweenness centrality, “taylorswift13” had the maximum number of followers (91,523,045) with in-betweenness centrality of 0.0. This indicates that the vertex having maximum influence with the largest number of in-betweenness centrality has not linked with several followers. Conclusions. Using Twitter embodies a potential prospect to engage the medical education community. The content of top networks’ tweets was around the number of “MedEd” innovations with the potential to significantly improve medical education delivery and innovative technologies in healthcare services. There is no link between the number of followers and in-betweenness centrality to influence the strength of social media voice. Although clinical and social tweets were there, not much was discussed regarding the curriculum reforms, continued professional development, technical issues in MedEd, and assessments. This triggers the insistence for rapid and innovative adaptations to the new learning environments and remarkable revolutions in medical education, including the encouragement of evidence-based education. The Twitter discussions promoted a research network circulating a wide range of informative innovations and collaborations.

In order to increase vaccination rates, the Government of Germany introduced vaccination against influenza and COVID-19 into the regular care administered by pharmacists. However, vaccination training is yet not integrated into the German pharmacy curriculum. Therefore, the Institute for Clinical Pharmacy and Pharmacotherapy in Duesseldorf had developed an innovative vaccination course using high-fidelity simulation for students. To investigate the acceptance further, the course was carried out at three different German universities (Bonn, Duesseldorf, Greifswald). Students were asked to give their self-assessment before and after and satisfaction only after the training course. Responses from 33 participants from the University of Bonn, 42 from the University of Duesseldorf and 49 from the University of Greifswald were analyzed. Every participant at the respective universities showed a significant increase in their self-assessment and indicated a high level of satisfaction with the course. The results also did not differ significantly between the respective universities. Consequently, the results lead to the hypothesis that the satisfaction of pharmacy students with this kind of training using high-fidelity simulation is very high and attractive, and can be recommended for other German universities. The integration of such vaccination training into the German pharmacy curriculum might be a future step.

A meta-analysis of genome-wide association studies of circulating total-tau levels identifies loci specific to European or African American ancestries, providing further insight to the genetic etiology of neurological diseases.

Abstract Background Genetic variants within the APOE locus may modulate Alzheimer’s disease (AD) risk independently or in conjunction with APOE*2/3/4 genotypes. Identifying such variants and mechanisms would importantly advance our understanding of APOE pathophysiology and provide critical guidance for AD therapies aimed at APOE. The APOE locus however remains relatively poorly understood in AD, owing to multiple challenges that include its complex linkage structure and uncertainty in APOE*2/3/4 genotype quality. Here, we present a novel APOE*2/3/4 filtering approach and showcase its relevance on AD risk association analyses for the rs439401 variant, which is located 1801 base pairs downstream of APOE and has been associated with a potential regulatory effect on APOE. Methods We used thirty-two AD-related cohorts, with genetic data from various high-density single-nucleotide polymorphism microarrays, whole-genome sequencing, and whole-exome sequencing. Study participants were filtered to be ages 60 and older, non-Hispanic, of European ancestry, and diagnosed as cognitively normal or AD (n = 65,701). Primary analyses investigated AD risk in APOE*4/4 carriers. Additional supporting analyses were performed in APOE*3/4 and 3/3 strata. Outcomes were compared under two different APOE*2/3/4 filtering approaches. Results Using more conventional APOE*2/3/4 filtering criteria (approach 1), we showed that, when in-phase with APOE*4, rs439401 was variably associated with protective effects on AD case-control status. However, when applying a novel filter that increases the certainty of the APOE*2/3/4 genotypes by applying more stringent criteria for concordance between the provided APOE genotype and imputed APOE genotype (approach 2), we observed that all significant effects were lost. Conclusions We showed that careful consideration of APOE genotype and appropriate sample filtering were crucial to robustly interrogate the role of the APOE locus on AD risk. Our study presents a novel APOE filtering approach and provides important guidelines for research into the APOE locus, as well as for elucidating genetic interaction effects with APOE*2/3/4.

Objective: To assess the outcome in patients with diabetic foot osteomyelitis being treated with antibiotics alone with those being treated with antibiotics coupled with conservative surgery. Study Design: Quasi-experimental study. Place and Duration of Study: Combined Military Hospital, Rawalpindi from Feb to Aug 2016. Methodology: Sixty patients with diabetic foot osteomyelitis, fulfilling inclusion criteria, were included in this study. They were divided into two groups of 30 each. The "Antibiotic-Group” received only antibiotics while, the "Surgical-Group" underwent surgery in addition to antibiotics. Results: There were 37 males (61.66%) and 23 females (38.33%). The average age of patients was 59.72±9.79 years. 20 patients (66.67%) had full recovery in the Surgical-Group, and in the Antibiotics-Group, 18 patients (60%) had healed wounds (pvalue=0.599) Conclusion: The outcome of antibiotics only and conservative surgery plus antibiotics is similar.

Introduction: Pregnant females are considered as a high-risk group for the prevention and control of various communicable diseases. Therefore, this research was undertaken to study the clinic-epidemiological profile and biochemical parameters of COVID-19-positive pregnant females in a tertiary care hospital. Methods: This hospital-based retrospective study was done on COVID-19-positive pregnant females admitted during April 2020–March 2021. A total of 139 patients were included in the study. Clinical, epidemiological, hematological, and biochemical profiles were described using frequency, percentages, mean, standard deviation using Microsoft Excel software. Results: The mean age of study participants was 25.36 ± 3.79 years, mean duration of pregnancy was 37.53 ± 3.31 weeks. The majority of the patients were asymptomatic (89.2%). Abdominal pain was the most common symptom (66.7%) among symptomatic. All patients were managed conservatively. Conclusion: The study showed the mild nature of COVID-19 among pregnant females as the majority of them pregnant were asymptomatic and few presented with mild symptoms.

Abstract Cisplatin (CP) is a well-known anticancer drug used to effectively treat various kinds of solid tumors. CP causes acute kidney injury (AKI) and unfortunately, there is no therapeutic approach in hand to prevent AKI. Several signaling pathways are responsible for inducing AKI which leads to inflammation in proximal convoluted tubule cells in the kidney. Furthermore, the nucleotide-binding oligomerization domain (NOD)-like receptor containing pyrin domain 3 (NLRP3) inflammasome is involved in the CP-induced AKI. In this study, we investigated therapeutic effects of rosmarinic acid (RA) against inflammation-induced AKI. RA was orally administered at the dose of 100 mg/kg for two consecutive days after 24 h of a single injection of CP at the dose of 20 mg/kg administered intraperitoneally in Swiss albino male mice. Treatment of RA inhibited the activation of NLRP3 signaling pathway by blocking the activated caspase-1 and downstream signal molecules such as IL-1β and IL18. CP activated HMGB1-TLR4/MyD88 axis was also found to be downregulated with the RA treatment. Activation of nuclear factor-κB and elevated protein expression of cyclooxygenase-2 (COX-2) were also found to be downregulated in RA-treated animals. Alteration of early tubular injury biomarker, kidney injury molecule-1 (KIM-1), was found to be subsided in RA-treated mice. RA has been earlier reported for antioxidant and anti-inflammatory properties. Our findings show that blocking a critical step of inflammasome signaling pathway by RA treatment can be a novel and beneficial approach to prevent the CP-induced AKI.

Background: ISARIC mortality score is a risk stratification tool that helps predict the in-hospital mortality of COVID-19 patients. However, this tool was developed and validated in a British population, and thus, the external validation of this tool in local populations is important. Objectives: External validation of the ISARIC mortality score in COVID-19 patients from a large Saudi Arabian intensive care unit (ICU). Methods: This is a retrospective study that included all adult patients with COVID-19 admitted to the ICU of King Saud Medical City, Riyadh, Saudi Arabia, from March 2020 to June 2021. Patients who were pregnant or had pulmonary tuberculosis/human immunodeficiency virus were excluded along with patients with missing variables. Data were collected to calculate the ISARIC mortality score and then fitting receiver operator characteristic curve against patients' outcome. Results: A total of 1493 critically ill COVID-19 patients were included. The mortality was 38%, the area under the curve of the score was 0.81 (95% confidence interval [CI]: 0.79–0.83, P < 0.001) and the cutoff value correctly classified 72.7% of the cohort. The cutoff value of >9 had sensitivity of 70.5% (95% CI: 66.6–74.3); specificity, 73.97% (95% CI: 71–76.8); positive predictive value, 62.4% (95% CI: 59.5–65.2) and negative predictive value, 80.2% (95% CI: 78.2–82.4). Conclusion: The ISARIC score was found to have excellent predictive ability for mortality in critically ill COVID-19 patients in our Saudi Arabian cohort. A cutoff score of >9 was the optimal criterion.

The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor to earthquakes. Several studies have been conducted across the globe to confirm the correlation of radon emission dynamics and earthquakes, and concluded that the soil radon gas is the witness of anomalous behaviour before the occurrences of several earthquakes. This anomalous behavior can help to construct a better prediction model for earthquake forecasting. This paper aims at employing different ensemble and individual machine learning methods on real time radon time series data with different scenarios to predict anomalies in data caused by the seismic activities.The ensemble methods include boosted tree, bagged cart and boosted linear model while standalone machine learning methods include support vector machine with linear and radial kernels and k-nearest neighbors ( ${K}$ -NN). We tested the methods on a dataset recorded on the fault line located in Muzaffarabad. Time series data was collected over a period ranging from March 1, 2017 to May 11, 2018 including nine(09) earthquakes. The methods are tested in four different settings with 10 times 10 folds cross validation procedure over the time window of 1 to 4. The repeated 10 fold cross validation is performed to reduce the noise in the model performance estimation by replicating the 10 fold cross validation procedure 10 times. Statistical performance evaluation measures viz. root mean square error (RMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), percentage bias (PB), and mean squared error (MSE) have been calculated for the assessment of performance. In setting 1, the support vector machine with radial kernel performs better with the minimum RMSE score of 1381.023 when compared to other prediction models. In setting 3, it can be observed through different performance metrics such as RMSE, the value in the range [1262.864, 1409.616] which is minimum when other prediction models for predicting soil radon gas concentration dataset. For setting 4, the boosted tree model yielded the minimum RMSE and MAPE scores of 1573.174 and 0.056 respectively. Findings of the study shows that boosted tree and support vector machine with radial kernel proved to be better regression models for the prediction of anomalies in soil radon gas concentration during seismic activities. An important finding of this study suggests that by employing boosted tree ensemble method make us able to accurately predict soil radon gas concentration automatically from environmental parameters.

The gunshot event localization and classification have numerous real-time applications. The study is also useful for steering the video camera and guns in the directed direction. This paper proposes a framework that can be used for a surveillance system to accurately localize and classify the type of gunshots impregnated with wind noise. The main contribution of this paper is the localization of the gunshot for the very first time using Hadamard product with wavelet de-noising in windy conditions. We have evaluated our framework on airborne gunshots acoustic dataset, and a derived (simulated) sound dataset, as an offline scenario, using four microphones’ geometry. For localization, the proposed system outperformed with an accuracy of 99.95%. The other contribution is a sensitivity-based comprehensive examination of gunshot sound signals, with normal to strong wind noise of varying SNRs, for machine learning and deep learning classifiers to categorize the type of gunshots. For classification, it has been found, not known before for the gunshots dataset, that ELM is robust for original, normal, and strong windy environments with an accuracy of 93.01%, 91.61%, and 88.11% respectively with the threshold SNR. A comprehensive comparison of recent techniques with the proposed approach has also been added.

The similarity measure is used to tackle many issues that include indistinct as well as blurred information excluding is not in a position to deal with the general fuzziness along with obscurity of the problems that have various information. The main purpose of this research is to propose a multipolar interval-valued neutrosophic soft set (mPIVNSS) with operations and basic properties. We also develop Hamming distance and Euclidean distance by using mPIVNSS and numerical examples and use the developed distances to introduce similarity measures. By using the developed similarity measures a decision-making approach is presented for mPIVNSS. Finally, we used the developed decision-making approach for medical diagnosis.

Topological characterization of 3D molecular structures is an emerging study area in theoretical and computational chemistry. These structural descriptors are used in a variety of domains, including chemical graph theory, drug delivery, and nanomaterial characterization. Quantitative structural descriptors can be used to characterize the chemical and physical properties of a given compound. Topological indices of molecular graphs are numerical quantities that allow us to collect information about the chemical structure and reveal its hidden qualities without performing experiments. Due to the low cost of implementation, zeolite networks are considered popular chemical networks. Zeolites are widely used networks with applications in chemistry, medicine, and commercial production owing to their excellent chemical features. The sodalite network is composed of a very unique type of zeolite framework called sodalite. It is a three-dimensional network of interconnected cages and tunnels that provide an ideal environment for a wide range of chemical and physical processes. This paper deals with the sodalite material network’s degree-based and reverse degree-based irregularity indices. These indices provide a quantitative measure of the irregular behaviour of the sodalite material network. It can be used to identify areas of the network where irregular behaviour is occurring and to compare different networks to determine which is more irregular. Additionally, these indices can be used to monitor changes in irregularity over time, allowing us to measure the impact of any interventions that are implemented.