CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder.

Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE.

A shared group of bacterial taxa in the duodenal microbiota of undernourished Pakistani children with environmental enteric dysfunction.

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

Assessment of knowledge about neonatal danger signs among primiparous women at discharge from healthcare facilities.

This study explores the evaluation of knowledge regarding neonatal danger signs (NDS) among first-time mothers in Pakistan during their discharge from healthcare facilities. The investigation aimed to establish connections between their understanding of NDS and factors such as sociodemographic background, prenatal check-ups, and educational measures. Considering the persistently high neonatal mortality rates in low- and middle-income nations, recognizing maternal NDS awareness becomes crucial for promoting early medical attention and reducing neonatal health risks. Existing research highlights the role of maternal knowledge in shaping neonatal well-being; however, gaps in knowledge remain prevalent, especially among first-time mothers. Using a cross-sectional approach, data were gathered through structured questionnaires, revealing significant relationships among maternal NDS awareness, sociodemographic aspects, prenatal care, and educational interventions. It is noteworthy that first-time mothers demonstrated lower NDS knowledge than mothers with multiple childbirth experiences, while those with higher education displayed greater awareness The effects of educational interventions were diverse, and antenatal visits were linked to enhanced knowledge. This study highlights the significance of focused treatments that target knowledge deficiencies, which can empower women and contribute to the reduction of infant health problems and mortality. encouraging further exploration of effective strategies to augment maternal knowledge and proactive healthcare-seeking behaviors.

Cette étude explore l'évaluation des connaissances concernant les signes de danger néonatals (NDS) chez les primipares au Pakistan lors de leur sortie des établissements de santé. L'enquête visait à établir des liens entre leur compréhension du NDS et des facteurs tels que le contexte sociodémographique, les contrôles prénatals et les mesures éducatives. Compte tenu des taux de mortalité néonatale constamment élevés dans les pays à revenu faible ou intermédiaire, il devient crucial de reconnaître la sensibilisation maternelle aux NDS pour promouvoir des soins médicaux précoces et réduire les risques pour la santé néonatale. Les recherches existantes mettent en évidence le rôle des connaissances maternelles dans la formation du bien-être néonatal ; cependant, des lacunes dans les connaissances demeurent répandues, en particulier parmi les primo-mères. En utilisant une approche transversale, les données ont été recueillies au moyen de questionnaires structurés, révélant des relations significatives entre la sensibilisation maternelle au NDS, les aspects sociodémographiques, les soins prénatals et les interventions éducatives. Il convient de noter que les mères primipares ont démontré des connaissances NDS inférieures à celles des mères ayant eu plusieurs accouchements, tandis que celles ayant fait des études supérieures ont montré une plus grande conscience. Les effets des interventions éducatives étaient diversifiés et les visites prénatales étaient liées à une amélioration des connaissances. Cette étude met en évidence l'importance des traitements ciblés ciblant les déficits de connaissances, qui peuvent autonomiser les femmes et contribuer à la réduction des problèmes de santé et de la mortalité infantiles. encourager une exploration plus approfondie de stratégies efficaces pour accroître les connaissances maternelles et les comportements proactifs de recherche de soins de santé.

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.

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

Influence of Nutritional Interventions on Functional Outcomes in Stroke Rehabilitation: A Systematic Review.

Stroke, a major cause of disability worldwide, necessitates comprehensive rehabilitation, with nutrition playing a pivotal role in recovery. Our systematic review assesses the impact of nutritional interventions on stroke survivors' rehabilitation. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched PubMed, Embase, Web of Science, and Scopus, using keywords related to stroke rehabilitation and nutrition. Studies were selected based on criteria emphasizing dietary interventions and their effect on functional recovery in stroke patients. The review involved detailed data extraction and synthesis, covering study design, participant characteristics, interventions, and outcomes. Five studies met our inclusion criteria, encompassing longitudinal and prospective studies, retrospective cohorts, and randomized controlled trials. These studies highlighted the importance of early nutritional assessment, particularly for sarcopenic patients, and the role of energy and protein intake soon after a stroke. Findings indicated high nutritional risk correlated with poorer functional outcomes and increased inflammation. Tailored dietary support appeared beneficial for muscle mass maintenance and overall functional recovery, especially in older patients. Our review emphasizes the critical role of nutritional interventions in stroke rehabilitation. It suggests that personalized nutritional strategies can positively impact functional recovery, notably in older and nutritionally vulnerable stroke survivors. These insights underscore the necessity of integrating dietary assessments and interventions into standard stroke rehabilitation protocols, advocating a holistic approach to patient care.

NTpred: a robust and precise machine learning framework for in silico identification of Tyrosine nitration sites in protein sequences.

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

Diagnosis of ''Helicobacter pylori infection of the gastric biopsy'' by rapid urease test, histopathology and Raman spectroscopy.

This study aim to investigate the diagnostic potential of Raman spectroscopy in comparison with rapid urease test and histopathology in diagnosis of H. pylori infection. A comparative study was conducted at Pathology Laboratory and a total of 94 samples were collected from patients based on Rome IV criteria. Sensitivity, specificity and accuracy of histopathology, rapid urease test and for Raman spectroscopy were investigated. Rapid urease test showed 23 false negative results of H. pylori as compared to Raman spectroscopy and histopathology. We concluded that Raman spectroscopy showed sensitivity (94.5%), accuracy (94.0%) and specificity of (87.5%) in the diagnosis of H. pylori infection. However rapid urease test showed specificity of 92.5% while low sensitivity 75%, and 78% accuracy as compared to Raman spectroscopy and histopathology . This study illustrates the applicability of Raman spectroscopy as a potent innovative detection tool for the molecular detection of H. pylori infection in gastritis.

Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide.

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

3D Printing Assisted Fabrication of Copper-Silver Mesoporous Bioactive Glass Nanoparticles Reinforced Sodium Alginate/Poly(vinyl alcohol) Based Composite Scaffolds: Designed for Skin Tissue Engineering.

Additive manufacturing (also known as 3D printing) is a promising method for producing patient-specific implants. In the present study, sodium alginate (Na-ALG)/poly(vinyl alcohol) (PVA) polymer blends of varying ratios (1:0, 3:1, 1:1, and 1:3) were used to produce tailored-designed skin scaffolds using a 3D bioprinter. Samples of skin scaffolds were printed at 20 layers with a layer height of 0.15 mm using a needle with an inner diameter of 330 µm while maintaining the extrusion speed, extrusion width, and fill density at 10 mm/s, 0.2 mm, and 85%, respectively. The Na-ALG/PVA blend with a 3:1 ratio showed the best printability due to its good viscosity and minimal nozzle leakage, allowing for the fabrication of skin scaffolds with high fidelity and the desired morphological characteristics. Then, copper-silver doped mesoporous bioactive glass nanoparticles (Cu-Ag MBGNs) were incorporated into the Na-ALG/PVA blend (which had already been prepared by using a Na-ALG:PVA ratio of 3:1) in order to obtain therapeutic (angiogenic and antibacterial) effects. The fabricated Na-ALG/PVA/Cu-Ag MBGNs biocomposite scaffolds with dimensions of 20 mm× 20 × 3 mm3 and pore size of 400 ± 60 µm exhibited a promising fidelity. The presence of chemical bonds attributed to Na-ALG, PVA, and Cu-Ag MBGNs and the uniform distribution of Na, C, and O elements within the microstructure of the scaffolds were confirmed by EDX, SEM, and FTIR analyses. The scaffolds were hydrophilic and exhibited proper swelling and degradation behavior for skin tissue engineering. According to the inhibition halo test, the scaffolds exhibited strong antibacterial activity against Staphylococcus aureus and Escherichia coli. The cytocompatibility to human-derived fibroblast cells was confirmed by the WST-8 assay and in vivo Chorioallantoic Membrane Assay. In addition, Na-ALG/PVA/Cu-Ag MBGNs showed angiogenic potential, exhibiting favorable wound healing properties.

Impact of diarrhoea and acute respiratory infection on environmental enteric dysfunction and growth of malnourished children in Pakistan: a longitudinal cohort study.

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

What is the role of remittance and education for environmental pollution? - Analyzing in the presence of financial inclusion and natural resource extraction.

This study assessed the impact of gross domestic product (GDP), education, natural resources, remittances, and financial inclusion on carbon emissions in G-11 countries from 1990 to 2021. Based on the negative impact of pollution and the need for sustainable development, this study examined factors affecting CO2 emissions in G-11 countries using non-linear panel ARDL model. The study found that a positive GDP shock increases CO2 emissions in the short and long term, while a negative shock decreases emissions in the short term and increases emissions in the long term. Education was found to increase CO2 emissions in the long term but decrease them in the short term, emphasizing the need for education on combating emissions. Natural resources were also found to increase emissions in the long term, highlighting the need for government-defined institutions to minimize extraction effects and enforce transparency and accountability. Positive changes in personal remittances and financial inclusion were found to increase emissions in both the short and long term, suggesting the need for policies that encourage renewable energy sources and energy efficiency improvement. The study concludes that policymakers should prioritize efficient resource allocation, promote renewable energy usage, and enhance environmental awareness to achieve sustainable development goals in G-11 countries. The possible applications of this study include the use of the models to investigate the asymmetric effects on CO2 emissions. This model can be applied in future studies to examine the relationship between GDP, education, natural resources, personal remittances, financial inclusion, and CO2 emissions in other countries.

Translating theory into practice: assessing the privacy implications of concept-based explanations for biomedical AI.

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

Standard RUTF vs. locally-made RUSF for acutely malnourished children: A quasi-experimental comparison of the impact on growth and compliance in a rural community of Pakistan.

BACKGROUND: The reduction in severe and moderate acute malnutrition (SAM and MAM) rates in Pakistan has been sub-optimal compared to other low-and middle-income countries (LMICs). Specially-formulated products have been designed globally to manage SAM and MAM, such as ready-to-use therapeutic food (RUTF) and ready-to-use supplementary food (RUSF), with variable efficacies. RUTF is primarily produced and patented in industrialized countries, raising supply challenges in resource-constrained regions with a high burden of acute malnutrition. RUSF minimizes costs by using locally-available ingredients while providing similar nutritional value. In this study, we compared the efficacy, side effects, and compliance of two months of supplementation with either RUTF or RUSF. METHODS: Children aged nine months in the rural district of Matiari, Pakistan, with a weight-for-height z-score (WHZ) <-2 received either RUTF (500 kcal sachet) for two months in 2015 or RUSF (520 kcal sachet) for two months in 2018. RESULTS: The RUSF group had a higher height gain and mid-upper arm circumferences (MUAC) score. Higher compliance was noted with lower side effects in the RUSF group. A higher compliance rate did correlate with the growth parameters in respective groups. CONCLUSION: Our study found that both RUTF and RUSF partially improve the anthropometric status of acutely malnourished children, with neither being superior to the other.

Co-carriage of Staphylococcus aureus and Streptococcus pneumoniae among children younger than 2 years of age in a rural population in Pakistan.

Objectives: Staphylococcus aureus and Streptococcus pneumoniae are common colonizers of the human nasopharynx. In this study, we describe S. aureus nasopharyngeal carriage and evaluate its association with S. pneumoniae carriage post-10-valent pneumococcal conjugate vaccine (PCV10) introduction in Pakistan. Methods: A serial cross-sectional study was undertaken from 2014 to 2018, children <2 years were randomly selected, and nasopharyngeal swabs were collected using standard WHO guidelines. S. aureus and S. pneumoniae isolates were identified using standard methods and tested for antimicrobial susceptibility by the standard Kirby-Bauer disk-diffusion method as per Clinical & Laboratory Standards Institute (CLSI) recommendations. Regression analysis was used to determine predictors associated with S. aureus carriage. Results: We enrolled 3140 children. S. aureus carriage prevalence was 5.6% (176/3140), and 50.1% (81/176) of the isolates were methicillin-resistant S. aureus (MRSA). S. aureus carriage was higher in the absence of pneumococcus compared to isolates in which pneumococcus was present (7.5% vs 5.0%). S. aureus carriage was negatively associated with pneumococcal carriage, being in 3rd and 4th year of enrollment, and vaccination with two and three PCV10 doses, in addition, fast breathing, ≥2 outpatients visits, and rainy season were positively associated. The following resistance rates were observed: 98.9% for penicillin, 74.4% for fusidic acid, and 23.3% for gentamicin, 10.2% for erythromycin, and 8.5% for cotrimoxazole. All isolates were susceptible to amikacin. Conclusions: Overall S. aureus carriage prevalence was low, PCV10 vaccine was protective against the carriage. The proportion of MRSA carriage and antimicrobial resistance was high in this community warranting continuous monitoring for invasive infections.

Essential Fatty Acid Deficiency Associates with Growth Faltering and Environmental Enteric Dysfunction in Children.

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

Differential coverage for vaccines in the expanded program on immunization (EPI) among children in rural Pakistan.

BACKGROUND: Pakistan has a well-established Expanded Program on Immunization (EPI) however vaccine-preventable diseases still account for high infant and child mortality rates. This study describes the differential vaccine coverage and determinants of vaccine uptake in rural Pakistan. METHODS: From October 2014 to September 2018, we enrolled children younger than 2 years of age from the Matiari Demographic Surveillance System in Sindh, Pakistan. Socio-demographic and vaccination history were collected from all participants. Vaccine coverage rates and timeliness were reported. Socio-demographic variables for missed and untimely vaccination were studied in multivariable logistic regression. RESULTS: Of the 3140 enrolled children, 48.4 % received all EPI recommended vaccines. Only 21.2 % of these were age appropriate. Around 45.4 % of the children were partially vaccinated, and 6.2 % were unvaccinated. Highest coverage was seen for the first dose of pentavalent (72.8 %), 10-valent Pneumococcal Conjugate Vaccine (PCV10) (70.4 %) and Oral Polio Vaccine (OPV) (69.2 %) and the lowest coverage was for measles (29.3 %) and rotavirus (1.8 %) vaccines. Primary caretakers and wage earners with a higher level of education were protective against missed and untimely vaccination. Enrollment in the 2nd, 3rd and 4th study year was negatively associated with being unvaccinated whereas distance from a major road was positively associated with non-adherence to schedule. CONCLUSION: Vaccine coverage was low among children in Matiari, Pakistan, and majority received delayed doses. Parents' education status and year of study enrollment was protective against vaccine dropout and delayed vaccination whereas geographical distance from a major road was a predictor. Vaccine promotion and outreach efforts may have had a beneficial impact on vaccine coverage and timeliness.

Quantitative Morphometry and Machine Learning Model to Explore Duodenal and Rectal Mucosal Tissue of Children with Environmental Enteric Dysfunction.

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

DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method.

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

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.

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

Community case management of fast-breathing pneumonia with 3 days oral amoxicillin vs 5 days cotrimoxazole in children 2-59 months of age in rural Pakistan: A cluster randomized trial.

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