Short term Outcomes of COVID-19 Vaccines Among Lactating Mother and Child Dyads in Bangladesh: A Multi-centre, Cross-sectional Study.

INTRODUCTION: The aims of the study are to: (1) determine the short-term reactogenicity of WHO-approved COVID-19 vaccines (i.e., Pfizer-BioNTech, Moderna, Sinovac, Oxford-AstraZeneca, Johnson and Johnson, Covaxin) amongst lactating women and their children, and 2) evaluate lactation-related outcomes following the same vaccines in Bangladesh. METHODS: This was a multi-centre, self-reported, cross-sectional study of lactating woman-child dyads in Bangladesh. Demographics, past medical history, breastfeeding history and clinical outcomes of lactating woman-child dyads at least 7 days after the last dose of vaccine were determined through a structured questionnaire. RESULTS: There were 750 participants from four centres. The mean age of lactating women and children surveyed were 27.6 (SD ± 4.6) years and 10.3 (SD ± 6.7) months, respectively. Majority (81.2%; 608 of 750) received 2 doses of COVID-19 vaccinations while lactating. Almost all (99.9%; 749 of 750) vaccinated lactating women surveyed reported no change in human milk supply. More than half of the participants (56.9%; 373 of 656) reported no symptoms after both doses of COVID-19 vaccines. There were no serious adverse events such as anaphylaxis or hospital admission. Majority of the lactating women (98.9%; 742 of 750) reported that the children whom they breastfed had no symptoms such as fever or cough. DISCUSSION: This large study of lactating woman-child dyads in Bangladesh, who received a diverse range of WHO-approved COVID-19 vaccines, showed no serious short-term adverse effects.

Improvement of Hand Hygiene Practices among the Healthcare Workers in a Neonatal Intensive Care Unit.

Background: Healthcare workers' (HCWs) hands become progressively colonized with potential pathogens during their patient care and act as a vehicle for transmission of microorganisms to other patients. Hand hygiene is undisputedly one of the most effective infection control measures. The objective of this study was to measure the hand hygiene (HH) compliance among the doctors and nurses before and after intervention. Methodology. This quasi-experimental (before and after) study was conducted from July 2019 to July 2020 in the neonatal intensive care unit in a tertiary hospital in Bangladesh. The doctors and nurses were observed for their compliance to HH before and after the intervention. Several group discussions were arranged, and posters on HH were attached as reminders at the workstations during the intervention period. Binary logistic regression analysis of the predictors for the outcome as HH noncompliance was performed. Result: The overall compliance to HH was significantly increased in both before (from 42.9 to 83.8%, p=<0.0001) and after (28.5 to 95.9%, p=<0.000) patient contact, in both the case of high-risk and low-risk contacts (p=<0.000) following the intervention. A significant reduction in the frequency of inadequate HH (20.2 to 9.7%, p = .000) was documented. In logistic regression analysis, compliance to HH was found more after the intervention (aOR = 13.315, 95% CI: 7.248-24.458). Similarly, being a physician (aOR = 0.012, 95% CI: 0.005-0.030) and moments after patient contact (aOR = 0.114, 95% CI: 0.049-0.261), significant positive predictors for compliance to HH were found. Conclusion: Significant improvements in HH compliance were achieved through a systemic, multidimensional intervention approach among the doctors and nurses in an intensive newborn care setting.

Predicting adverse drug reactions through interpretable deep learning framework.

BACKGROUND: Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs. METHODS: In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori. RESULTS: We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis. CONCLUSIONS: The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.

Proteome Profiling in Lung Injury after Hematopoietic Stem Cell Transplantation.

Pulmonary complications due to infection and idiopathic pneumonia syndrome (IPS), a noninfectious lung injury in hematopoietic stem cell transplant (HSCT) recipients, are frequent causes of transplantation-related mortality and morbidity. Our objective was to characterize the global bronchoalveolar lavage fluid (BALF) protein expression of IPS to identify proteins and pathways that differentiate IPS from infectious lung injury after HSCT. We studied 30 BALF samples from patients who developed lung injury within 180 days of HSCT or cellular therapy transfusion (natural killer cell transfusion). Adult subjects were classified as having IPS or infectious lung injury by the criteria outlined in the 2011 American Thoracic Society statement. BALF was depleted of hemoglobin and 14 high-abundance proteins, treated with trypsin, and labeled with isobaric tagging for relative and absolute quantification (iTRAQ) 8-plex reagent for two-dimensional capillary liquid chromatography (LC) and data dependent peptide tandem mass spectrometry (MS) on an Orbitrap Velos system in higher-energy collision-induced dissociation activation mode. Protein identification employed a target-decoy strategy using ProteinPilot within Galaxy P. The relative protein abundance was determined with reference to a global internal standard consisting of pooled BALF from patients with respiratory failure and no history of HSCT. A variance weighted t-test controlling for a false discovery rate of ≤5% was used to identify proteins that showed differential expression between IPS and infectious lung injury. The biological relevance of these proteins was determined by using gene ontology enrichment analysis and Ingenuity Pathway Analysis. We characterized 12 IPS and 18 infectious lung injury BALF samples. In the 5 iTRAQ LC-MS/MS experiments 845, 735, 532, 615, and 594 proteins were identified for a total of 1125 unique proteins and 368 common proteins across all 5 LC-MS/MS experiments. When comparing IPS to infectious lung injury, 96 proteins were differentially expressed. Gene ontology enrichment analysis showed that these proteins participate in biological processes involved in the development of lung injury after HSCT. These include acute phase response signaling, complement system, coagulation system, liver X receptor (LXR)/retinoid X receptor (RXR), and farsenoid X receptor (FXR)/RXR modulation. We identified 2 canonical pathways modulated by TNF-α, FXR/RXR activation, and IL2 signaling in macrophages. The proteins also mapped to blood coagulation, fibrinolysis, and wound healing-processes that participate in organ repair. Cell movement was identified as significantly over-represented by proteins with differential expression between IPS and infection. In conclusion, the BALF protein expression in IPS differed significantly from infectious lung injury in HSCT recipients. These differences provide insights into mechanisms that are activated in lung injury in HSCT recipients and suggest potential therapeutic targets to augment lung repair.

Mining Patterns Associated With Mobility Outcomes in Home Healthcare.

BACKGROUND: Mobility is critical for self-management. Understanding factors associated with improvement in mobility during home healthcare can help nurses tailor interventions to improve mobility outcomes and keep patients safely at home. OBJECTIVES: The aims were to (a) identify patient and support system factors associated with mobility improvement during home care, (b) evaluate consistency of factors across groups defined by mobility status at the start of home care, and (c) identify patterns of factors associated with improvement and no improvement in mobility within each group. METHODS: Outcome and Assessment Information Set data extracted from a national convenience sample of 270,634 patient records collected from October 1, 2008 to December 31, 2009 from 581 Medicare-certified, home healthcare agencies were used. Patients were placed into groups based on mobility scores at admission. Odds ratios were used to index associations of factors with improvement at discharge. Discriminative pattern mining was used to discover patterns associated with improvement of mobility. RESULTS: Overall, mobility improved for 49.4% of patients; improvement occurred most frequently (80%) among patients who were able, at admission, to walk only with the supervision or assistance of another person at all times. Numerous factors associated with improvement in mobility outcome were similar across the groups (except for those who were chairfast but were able to wheel themselves independently); however, the number, strength, and direction of associations varied. In most groups, data mining-discovered patterns of factors associated with the mobility outcome were composed of combinations of functional and cognitive status and the type and amount of help required at home. DISCUSSION: This study provides new data mining-based information about how factors associated with improvement in mobility group together and vary by mobility at admission. These approaches have potential to provide new insights for clinicians to tailor interventions for improvement of mobility.

Co-clustering phenome-genome for phenotype classification and disease gene discovery.

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype-gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype-gene association matrix under the prior knowledge from phenotype similarity network and protein-protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype-gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein-protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.

Large-scale screening of clinical assessments to distinguish between states in the Integrated HD Progression Model (IHDPM).

Background: Understanding the sensitivity and utility of clinical assessments across different HD stages is important for study/trial endpoint selection and clinical assessment development. The Integrated HD Progression Model (IHDPM) characterizes the complex symptom progression of HD and separates the disease into nine ordered disease states. Objective: To generate a temporal map of discriminatory clinical measures across the IHDPM states. Methods: We applied the IHDPM to all HD individuals in an integrated longitudinal HD dataset derived from four observational studies, obtaining disease state assignment for each study visit. Using large-scale screening, we estimated Cohen's effect sizes to rank the discriminative power of 2,472 clinical measures for separating observations in disease state pairs. Individual trajectories through IHDPM states were examined. Discriminative analyses were limited to individuals with observations in both states of the pairs compared (N = 3,790). Results: Discriminative clinical measures were heterogeneous across the HD life course. UHDRS items were frequently identified as the best state pair discriminators, with UHDRS Motor items - most notably TMS - showing the highest discriminatory power between the early-disease states and early post-transition period states. UHDRS functional items emerged as strong discriminators from the transition period and on. Cognitive assessments showed good discriminative power between all state pairs examined, excepting state 1 vs. 2. Several non-UHDRS assessments were also flagged as excellent state discriminators for specific disease phases (e.g., SF-12). For certain state pairs, single assessment items other than total/summary scores were highlighted as having excellent discriminative power. Conclusion: By providing ranked quantitative scores indicating discriminatory ability of thousands of clinical measures between specific pairs of IHDPM states, our results will aid clinical trial designers select the most effective outcome measures tailored to their study cohort. Our observations may also assist in the development of end points targeting specific phases in the disease life course, through providing specific conceptual foci.

Predictors of poor neurodevelopmental outcomes in neonates with clinically observed seizures: A prospective observational study in a tertiary care hospital of Bangladesh.

Neonatal seizures can lead to long-term neurodevelopmental problems. This study aims to identify predictors of poor developmental outcomes in neonates with seizures to aid in early intervention and referral for follow-up and rehabilitation. This observational study was conducted in the Department of Neonatology and Institute of Paediatric Neurodisorder and Autism, Bangabandhu Sheikh Mujib Medical University. Among 75 study cases of neonatal seizure, 23 died, and 46 were followed-up at 6 and 9 months after discharge. EEGs were performed on every patient. A comprehensive neurological examination and developmental evaluation were performed using Bayley Scales of Infant and Toddler Development, Third Edition (Bayley III). Three-fourths of neonates were born at term (76.1 %), and over half were male (56.5 %). The majority were appropriate for gestational age (79.7 %) and had an average birth weight of 2607 ± 696 g (±SD). Over half of the neonates (52.2 %) had adverse neurodevelopmental outcomes, with global developmental delay being the most common. Recurrent seizures, the number of anticonvulsants needed to control seizures, and abnormal Electroencephalograms were identified as independent predictors of adverse neurodevelopmental outcomes. The study highlights the need for early referral for follow-up and rehabilitation of neonates with seizures having abnormal electroencephalograms, recurrent seizures and requiring more anticonvulsants to control seizures.

Vayu bubble continuous positive airway pressure is a promising solution with favorable treatment outcomes for respiratory distress syndrome in newborns: a qualitative study in Bangladesh.

Background: According to Bangladesh Demographic and Health Survey (2022), neonatal mortality, comprising 67% of under-5 deaths in Bangladesh, is significantly attributed to prematurity and low birth weight (LBW), accounting for 32% of neonatal deaths. Respiratory distress syndrome (RDS) is a prevalent concern among preterm and LBW infants, leading to substantial mortality. The World Health Organization (WHO) recommends bubble continuous positive airway pressure (bCPAP) therapy, but the affordability and accessibility of conventional bCPAP devices for a large number of patients become major hurdles in Bangladesh due to high costs and resource intensiveness. The Vayu bCPAP, a simple and portable alternative, offers a constant flow of oxygen-enriched, filtered, humidified, and pressurized air. Our study, conducted in five health facilities, explores the useability, acceptability, and perceived treatment outcome of Vayu bCPAP in the local context of Bangladesh. Methods: A qualitative approach was employed in special care newborn units (SCANUs) of selected facilities from January to March 2023. Purposive sampling identified nine key informants, 40 in-depth interviews with service providers, and 10 focus group discussions. Data collection and analysis utilized a thematic framework approach led by trained anthropologists and medical officers. Results: Service providers acknowledged Vayu bCPAP as a lightweight, easily movable, and cost-effective device requiring minimal training. Despite challenges such as consumable shortages and maintenance issues, providers perceived the device as user-friendly, operable with oxygen cylinders, and beneficial during referral transportation. Treatment outcomes indicated effective RDS management, reduced hospital stays, and decreased referrals. Though challenges existed, healthcare providers and facility managers expressed enthusiasm for Vayu bCPAP due to its potential to simplify advanced neonatal care delivery. Conclusions: The Vayu bCPAP device demonstrated useability, acceptability, and favorable treatment outcomes in the care of neonates with RDS. However, sustained quality service necessitates continuous monitoring, mentoring and retention of knowledge and skills. Despite challenges, the enthusiasm among healthcare providers underscores the potential of Vayu bCPAP to save lives and simplify neonatal care delivery. Development of Standard Operating procedure on Vayu bCPAP is required for systematic implementation. Further research is needed to determine how the utilization of Vayu bCPAP devices enhances accessibility to efficient bCPAP therapy for neonates experiencing RDS.

Early and extended erythropoietin monotherapy after hypoxic ischaemic encephalopathy: a multicentre double-blind pilot randomised controlled trial.

OBJECTIVE: To examine the feasibility of early and extended erythropoietin monotherapy after hypoxic ischaemic encephalopathy (HIE). DESIGN: Double-blind pilot randomised controlled trial. SETTING: Eight neonatal units in South Asia. PATIENTS: Neonates (≥36 weeks) with moderate or severe HIE admitted between 31 December 2022 and 3 May 2023. INTERVENTIONS: Erythropoietin (500 U/kg daily) or to the placebo (sham injections using a screen) within 6 hours of birth and continued for 9 days. MRI at 2 weeks of age. MAIN OUTCOMES AND MEASURES: Feasibility of randomisation, drug administration and assessment of brain injury using MRI. RESULTS: Of the 154 neonates screened, 56 were eligible; 6 declined consent and 50 were recruited; 43 (86%) were inborn. Mean (SD) age at first dose was 4.4 (1.2) hours in erythropoietin and 4.1 (1.0) hours in placebo. Overall mortality at hospital discharge occurred in 5 (19%) vs 11 (46%) (p=0.06), and 3 (13%) vs 9 (40.9%) (p=0.04) among those with moderate encephalopathy in the erythropoietin and placebo groups. Moderate or severe injury to basal ganglia, white matter and cortex occurred in 5 (25%) vs 5 (38.5%); 14 (70%) vs 11 (85%); and 6 (30%) vs 2 (15.4%) in the erythropoietin and placebo group, respectively. Sinus venous thrombosis was seen in two (10%) neonates in the erythropoietin group and none in the control group. CONCLUSIONS: Brain injury and mortality after moderate or severe HIE are high in South Asia. Evaluation of erythropoietin monotherapy using MRI to examine treatment effects is feasible in these settings. TRIAL REGISTRATION NUMBER: NCT05395195.

Protein expression profile of rat type two alveolar epithelial cells during hyperoxic stress and recovery.

In rodent model systems, the sequential changes in lung morphology resulting from hyperoxic injury are well characterized and are similar to changes in human acute respiratory distress syndrome. In the injured lung, alveolar type two (AT2) epithelial cells play a critical role in restoring the normal alveolar structure. Thus characterizing the changes in AT2 cells will provide insights into the mechanisms underpinning the recovery from lung injury. We applied an unbiased systems-level proteomics approach to elucidate molecular mechanisms contributing to lung repair in a rat hyperoxic lung injury model. AT2 cells were isolated from rat lungs at predetermined intervals during hyperoxic injury and recovery. Protein expression profiles were determined by using iTRAQ with tandem mass spectrometry. Of the 959 distinct proteins identified, 183 significantly changed in abundance during the injury-recovery cycle. Gene ontology enrichment analysis identified cell cycle, cell differentiation, cell metabolism, ion homeostasis, programmed cell death, ubiquitination, and cell migration to be significantly enriched by these proteins. Gene set enrichment analysis of data acquired during lung repair revealed differential expression of gene sets that control multicellular organismal development, systems development, organ development, and chemical homeostasis. More detailed analysis identified activity in two regulatory pathways, JNK and miR 374. A novel short time-series expression miner algorithm identified protein clusters with coherent changes during injury and repair. We concluded that coherent changes occur in the AT2 cell proteome in response to hyperoxic stress. These findings offer guidance regarding the specific molecular mechanisms governing repair of the injured lung.

Assessment of safety and health in the tea industry of Barak valley, Assam: a fuzzy logic approach.

Traditional safety and health system measurement procedures, practiced in various industries produce qualitative results with a degree of uncertainty. This paper presents a fuzzy-logic-based approach to developing a fuzzy model for assessing the safety and health status in the tea industry. For this, the overall safety and health status at a tea estate has been considered as a function of 4 inputs: occupational safety, occupational health, behavioral safety and competency. A set of fuzzy rules based on expert human judgment has been used to correlate different fuzzy inputs and output. Fuzzy set operations are used to calculate the safety and health status of the tea industry. Application of the developed model at a tea estate showed that the safety and health status belongs to the fuzzy class of good with a crisp value of 7.2.

Research prioritisation on prevention and management of preterm birth in low and middle-income countries (LMICs) with a special focus on Bangladesh using the Child Health and Nutrition Research Initiative (CHNRI) method.

Background: Fifteen million babies are born preterm globally each year, with 81% occurring in low- and middle-income countries (LMICs). Preterm birth complications are the leading cause of newborn deaths and significantly impact health, quality of life, and costs of health services. Improving outcomes for newborns and their families requires prioritising research for developing practical, scalable solutions, especially in low-resource settings such as Bangladesh. We aimed to identify research priorities related to preventing and managing preterm birth in LMICs for 2021-2030, with a special focus on Bangladesh. Methods: We adopted the Child Health and Nutrition Research Initiative (CHNRI) method to set research priorities for preventing and managing preterm birth. Seventy-six experts submitted 490 research questions online, which we collated into 95 unique questions and sent for scoring to all experts. A hundred and nine experts scored the questions using five pre-selected criteria: answerability, effectiveness, deliverability, maximum potential for burden reduction, and effect on equity. We calculated weighted and unweighted research priority scores and average expert agreement to generate a list of top-ranked research questions for LMICs and Bangladesh. Results: Health systems and policy research dominated the top 20 identified priorities for LMICs, such as understanding and improving uptake of the facility and community-based Kangaroo Mother Care (KMC), promoting breastfeeding, improving referral and transport networks, evaluating the impact of the use of skilled attendants, quality improvement activities, and exploring barriers to antenatal steroid use. Several of the top 20 questions also focused on screening high-risk women or the general population of women, understanding the causes of preterm birth, or managing preterm babies with illnesses (jaundice, sepsis and retinopathy of prematurity). There was a high overlap between research priorities in LMICs and Bangladesh. Conclusions: This exercise, aimed at identifying priorities for preterm birth prevention and management research in LMICs, especially in Bangladesh, found research on improving the care of preterm babies to be more important in reducing the burden of preterm birth and accelerating the attainment of Sustainable Development Goal 3 target of newborn deaths, by 2030.

Human-centered explainability for life sciences, healthcare, and medical informatics.

Rapid advances in artificial intelligence (AI) and availability of biological, medical, and healthcare data have enabled the development of a wide variety of models. Significant success has been achieved in a wide range of fields, such as genomics, protein folding, disease diagnosis, imaging, and clinical tasks. Although widely used, the inherent opacity of deep AI models has brought criticism from the research field and little adoption in clinical practice. Concurrently, there has been a significant amount of research focused on making such methods more interpretable, reviewed here, but inherent critiques of such explainability in AI (XAI), its requirements, and concerns with fairness/robustness have hampered their real-world adoption. We here discuss how user-driven XAI can be made more useful for different healthcare stakeholders through the definition of three key personas-data scientists, clinical researchers, and clinicians-and present an overview of how different XAI approaches can address their needs. For illustration, we also walk through several research and clinical examples that take advantage of XAI open-source tools, including those that help enhance the explanation of the results through visualization. This perspective thus aims to provide a guidance tool for developing explainability solutions for healthcare by empowering both subject matter experts, providing them with a survey of available tools, and explainability developers, by providing examples of how such methods can influence in practice adoption of solutions.

Exchange Transfusion for Hyperbilirubinemia among Term and Near Term in NICU of a Tertiary Care Hospital of Bangladesh: Findings from a Prospective Study.

BACKGROUND: Exchange transfusion in newborns is recommended as emergency management of hyperbilirubinemia to prevent bilirubin encephalopathy and kernicterus. AIM: This study aimed to determine the frequency and document common side effects of exchange transfusion and outcomes of newborns requiring exchange transfusion. MATERIALS AND METHODS: This prospective study was done in the Neonatal Intensive Care Unit (NICU) of Bangabandhu Sheikh Mujib Medical University (BSMMU), Bangladesh, from January 2016 to December 2019. Information was obtained regarding maternal details, newborn demographics, and clinical status. Blood grouping and Rh typing were done for both mothers and newborns. In all newborns, pre-exchange complete blood count, peripheral blood film, Coombs test, reticulocyte count, serum bilirubin and post-exchange serum bilirubin, hemoglobin, random blood sugar, serum electrolyte, and calcium were done. G6PD level was done wherever suspected. Frequency, maternal and neonatal factors, indications, and outcomes were analyzed. RESULTS: Among 839 admitted cases of unconjugated hyperbilirubinemia, 41 patients (4.9%) required exchange transfusion. Most of the babies were inborn (90.2%). Ninety-five percent of mothers received regular antenatal care; among them, 76.3% had bad obstetric history. Only 36.6% of mothers received anti-D in previous pregnancy. None had sonographic findings of hydrops. The commonest indication was Rh incompatibility (80.5%). Coombs test was positive in 58.5% of cases. Mean pre-exchange TSB was 9.44 ± 6.4, and post-exchange TSB was 4.41 ± 2.59. The commonest adverse events noted were hyperglycemia (51.2%), sepsis (19.5%), anemia requiring top-up transfusion (17.1%), and hypocalcemia (14.6%). There were no catheter-related complications. Bilirubin encephalopathy was present in 4.9% of cases. There was one mortality but not due to the procedure. CONCLUSION: Exchange transfusion was required among 4.9% of the admitted newborns with unconjugated hyperbilirubinemia. The common adverse effects were hyperglycemia and sepsis. The commonest indication was Rh incompatibility (80.5%). Overall outcome after exchange transfusion was favorable. HOW TO CITE THIS ARTICLE: Dey SK, Jahan S, Jahan I, et al. Exchange Transfusion for Hyperbilirubinemia among Term and Near Term in NICU of a Tertiary Care Hospital of Bangladesh: Findings from a Prospective Study. Euroasian J Hepato-Gastroenterol 2021;11(1):21-26.

Impact of Clinical and Genomic Factors on COVID-19 Disease Severity.

To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we address the problem of understanding the host genome's influence, in concert with clinical variables, on the severity of COVID-19 manifestation in the patient. Leveraging positive-unlabeled machine learning algorithms coupled with RubricOE, a state-of-the-art genomic analysis framework, on UK BioBank data we extract novel insights on the complex interplay. The algorithm is also sensitive enough to detect the changing influence of the emergent B.1.1.7 SARS-CoV-2 (alpha) variant on disease severity, and, changing treatment protocols. The genomic component also implicates biological pathways that can help in understanding the disease etiology. Our work demonstrates that it is possible to build a robust and sensitive model despite significant bias, noise and incompleteness in both clinical and genomic data by a careful interleaving of clinical and genomic methodologies.

Finding Causal Mechanistic Drug-Drug Interactions from Observational Data.

Many adverse drug reactions (ADRs) are caused by drug-drug interactions (DDIs), meaning they arise from concurrent use of multiple medications. Detecting DDIs using observational data has at least three major challenges: (1) The number of potential DDIs is astronomical; (2) Associations between drugs and ADRs may not be causal due to observed or unobserved confounding; and (3) Frequently co-prescribed drug pairs that each independently cause an ADR do not necessarily causally interact, where causal interaction means that at least some patients would only experience the ADR if they take both drugs. We address (1) through data mining algorithms pre-filtering potential interactions, and (2) and (3) by fitting causal interaction models adjusting for observed confounders and conducting sensitivity analyses for unobserved confounding. We rank candidate DDIs robust to unobserved confounding more likely to be real. Our rigorous approach produces far fewer false positives than past applications that ignored (2) and (3).

Newborn signal functions in Bangladesh: identification through expert consultation and assessment of readiness among public health facilities-study protocol using Delphi technique.

INTRODUCTION: There is a set of globally accepted and nationally adapted signal functions for categorising health facilities for maternal services. Newborn resuscitation is the only newborn intervention which is included in the WHO recommended list of emergency obstetric care signal functions. This is not enough to comprehensively assess the readiness of a health facility for providing newborn services. In order to address the major causes of newborn death, the Government of Bangladesh has prioritised a set of newborn interventions for national scale-up, the majority of which are facility-based. Effective delivery of these interventions depends on a core set of functions (skills and services). However, there is no standardised and approved set of newborn signal functions (NSFs) based on which the service availability and readiness of a health facility can be assessed for providing newborn services. Thus, this study will be the first of its kind to identify such NSFs. These NSFs can categorise health facilities and assist policymakers and health managers to appropriately plan and adequately monitor the progress and performance of health facilities delivering newborn healthcare. METHODS AND ANALYSIS: We will adopt the Delphi technique of consensus building for identification of NSFs and 1-2 indicator for each function while employing expert consultation from relevant experts in Bangladesh. Based on the identified NSFs and signal function indicators, the existing health facility assessment (HFA) tools will be updated, and an HFA survey will be conducted to assess service availability and readiness of public health facilities in relation to the new NSFs. Descriptive statistics (proportion) with a 95% CI will be used to report the level of service availability and readiness of public facilities regarding NSFs. ETHICS AND DISSEMINATION: Ethical approval was obtained from Research Review and Ethical Review Committee of icddr, b (PR-17089). Results will be disseminated through meetings, seminars, conference presentations and international peer-review journal articles.

Accuracy of transcutaneous bilirubinometry in the preterm infants: a comprehensive meta-analysis.

BACKGROUND: Transcutaneous bilirubin (TcB) measurement is widely used in term babies. But its effectiveness till debated in preterm infants. So, our objective was to pool data to see the accuracy of transcutaneous bilirubinometry in preterm infants. METHOD: MEDLINE, Embase, Cochrane Library database were searched from 2000 to July 2017. The included studies had compared TcB with total serum bilirubin (TSB) in preterm infants before phototherapy and data were presented as correlation coefficients. Data were extracted by two reviewers and checked for accuracy by the third reviewer. The risk bias assessments were done by an assessment quality assessment of diagnostic accuracy studies tool. Pooled correlation coefficient assed after Fisher's z transformation and then converted to r. RESULTS: We included 28 studies; all those studies reported results as correlation coefficients. In combination of both sternal and forehead site measurement, our pooled estimates of r = 0.82 (95% CI: 0.78-0.85) in random effect and r = 0.803 (95% CI: 0.78-0.81) in fixed effect model. For separate sites of measurement of TcB pooled r for forehead and sternum were comparable, r = 0.82 (95% CI: 0.78-0.85), and pooled correlation coefficient for the two devices JM103 and Bilicheck the estimated pooled r were also comparable (Pooled r = 0.83). CONCLUSION: Our study found that TcB measurement is well related with TSB values and can represent a reliable method for evaluating preterm infants with possible hyperbilirubinemia. Our findings support the use of investigated devices at both forehead and sternum sites in preterm infants.