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Abstract Background The COVID-19 pandemic has significantly impacted public health, with emerging evidence suggesting substantial effects on maternal and neonatal health. This systematic review and meta-analysis aimed to quantify the prevalence and risk of respiratory distress syndrome (RDS) in newborns born to mothers infected with SARS-CoV-2, the virus responsible for COVID-19. Methods We conducted a literature search in Embase, PubMed, and Web of Science up to April 20, without language or date restrictions. Observational studies reporting on the prevalence or risk of RDS among newborns from mothers with confirmed SARS-CoV-2 infection were included. Quality assessment was performed using the JBI tool. Statistical analysis was performed by using R software version 4.3. Results Twenty-two studies met the inclusion criteria. The pooled prevalence of RDS among newborns born to COVID-19-infected mothers was 11.5% (95% CI: 7.4–17.3%), with significant heterogeneity (I² = 93%). Newborns from infected mothers had a significantly higher risk of developing RDS, with a pooled risk ratio (RR) of 2.69 (95% CI: 1.77 to 4.17). Conclusion Newborns born to mothers with COVID-19 have a substantially increased risk of developing RDS. These findings emphasize the need for vigilant monitoring and appropriate management of pregnant women with COVID-19 to mitigate adverse neonatal outcomes.

Abstract Background As e-cigarettes gain popularity as potential tobacco cessation aids, concerns arise about their dual use with traditional cigarettes, especially among pregnant women, potentially subjecting both women and fetuses to heightened risks. This systematic review and meta-analysis aimed to determine the overall prevalence of dual use of tobacco smoking and e-cigarette use in pregnant women. Methods A literature search was conducted across databases including PubMed, Embase, Web of Science, and Cochrane on October 20, 2023. The included studies reported the number of pregnant women and the count of those who were dual users. Quality assessment was undertaken using the JBI tool. The pooled prevalence of dual use was determined via a random-effects model. All statistical analyses were executed using R software, version 4.3. PROSPERO: CRD42023486020. Results Eighteen studies were analyzed, encompassing 5,983,363 pregnant women. The meta-analysis indicated an overall prevalence of 4.6% (95% CI: 2.0-10.3) for dual users with significant heterogeneity (I2 = 100%). Subgroup analysis based on the country showed a prevalence of 4.9% (95% CI: 2.0 to 11.6) for USA and 8.1% (95% CI: 0.00 to 1.00) for UK. Meta-regression revealed reduction of prevalence of dual use from 2019 to 2023. A potential publication bias was indicated by the LFK index and the Doi plot. Conclusion The dual consumption of e-cigarettes and traditional tobacco in pregnant women is a significant health concern, with a notable prevalence. Given the established risks of tobacco smoking during pregnancy and the uncertainties surrounding e-cigarettes, more comprehensive research and public health interventions are urgently needed to address this issue.

Abstract Background People living with HIV (PLWH) are more vulnerable to infectious and non-infectious comorbidities due to chronic inflammation and immune dysfunction. Air pollution is a major global health risk, contributing to millions of deaths annually, primarily from cardiovascular and respiratory diseases. However, the link between air pollution and mortality risk in PLWH is underexplored. This systematic review assesses the association between exposure to pollutants such as particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO) and mortality risk in PLWH. Methods A systematic search of PubMed, Web of Science, and Embase was conducted for studies published up to August 2024. Eligibility criteria included cohort, case-control, and cross-sectional studies assessing air pollution exposure and mortality in PLWH. Nested-Knowledge software was used for screening and data extraction. The Newcastle-Ottawa Scale was applied for quality assessment. A narrative approach and tabular summarization were used for data synthesis and presentation. Results Nine studies, mostly from China, demonstrated a significant association between long-term exposure to PM1, PM2.5, and PM10 and increased risks of AIDS-related and all-cause mortality in PLWH. Hazard ratios for mortality increased by 2.38–5.13% per unit increase in PM concentrations, with older adults (> 60), females, and those with lower CD4 counts (

Beta-cyfluthrin is an insecticide that belongs to the category of pyrethroid pesticide which is considered a “highly hazardous” substance and is often misused in different countries, leading to self-poisoning. Forensic toxicology laboratories frequently encounter pesticide poisoning due to its severe effects. The current research presents an affordable analytical method that combines Vortex-Assisted Dispersive Liquid-Liquid Microextraction to Thin Layer Chromatography Imaging System(DLLME-TLC-IS) that, is fast, efficient, safe, and green(er) to the environment. In this research, 20 µL of the extract obtained by DLLME-TLC-IS was applied to a thin layer chromatography (TLC) plate by using a developing solvent ethyl acetate: hexane (1:1). The TLC plate was then analysed using the free software ImageJ after being exposed to ultraviolet light at 254 nm. Various extraction parameters were tested, including pH, ionic strength, type and volume of extraction and disperser solvent., and vortex speed and time. After optimizing these conditions, the limit of detection (LOD) was observed to be 0.45 µg/spot in blood samples and 0.94 µg/spot in urine samples. At the same time, the limit of quantification (LOQ) was 0.29 µg/spot in blood and 0.9 µg/spot in urine samples respectively. The method demonstrated linearity in the range of 1–10 µg/spot, with a correlation coefficient of determination of 0.9912 for blood and 0.9931 for urine. Beta-cyfluthrin recoveries were 96 % in blood and 99 % in urine samples, respectively. Overall, this developed method for detecting beta-cyfluthrin in human blood and urine samples is efficient, cost-effective, and adheres to the principles of green analytical chemistry(GAC). This approach offers advantages over previously established methods. The green characteristics of the developed method were assessed using complexGAPI and AGREE tools of greenness.

Objectives: Integrating digital technology into daily life has made video games a primary form of entertainment for adolescents worldwide. Despite their benefits, excessive gaming has emerged as a significant public health issue, recognized as a gaming disorder by the World Health Organization in the ICD-11. This study aims to assess the prevalence of gaming disorders among adolescents through a systematic review and meta-analysis. Study design: Systematic review and meta-analysis. Methods: A search was conducted across multiple databases until February 15, 2024. Observational studies that assessed the prevalence of gaming disorder were included. Nested Knowledge software was used for screening and data extraction. The quality assessment was performed using the Joanna Briggs Institute tool. Meta-analysis using a random effect model was used to synthesize prevalence rates. Statistical analyses were performed in R software version 4.3. Results: The meta-analysis included 84 studies covering a diverse geographical scope totaling 641,763 individuals. The pooled prevalence of gaming disorder was 8.6 % (95 % CI: 6.9 %–10.8 %), (I2 = 100 %). Subgroup analysis revealed varying prevalence rates by country, with China reporting the highest rate at 11.7 % (95 % CI: 8.6 %–15.7 %). Meta-regression analysis highlighted an increasing trend in the prevalence of gaming disorder over the years, underscoring the growing impact of digital technologies. Conclusion: A significant prevalence of gaming disorder among adolescents is observed. With an increasing trend, fostering healthy gaming habits, enhancing awareness, and implementing effective intervention programs are crucial. This emphasizes the importance of global efforts in combating the growing challenge of gaming disorder among adolescents.

Purpose: This innovative technique determines the symmetry of anterior teeth arrangement discrepancies and ascertain the extension of the smile during the procedure of jaw relation procedure performed by prosthodontist. Methods: Smiling images of 20 patients were captured and analyzed using PicsArt© software. Facial midline, canine line, smile line, and other conventional factors were overlaid onto the photographs. Pre-designed smiles were extracted from previous designs and superimposed onto each patient’s photograph. The resulting images were systematically evaluated to enhance aesthetic outcomes, promoting attractiveness, encouraging patient satisfaction during jaw relation. Results: Patients were highly motivated on the day of jaw relation appointment. Prosthodontists were able to evaluate asymmetric smile, midline shift, extension of canines, and other discrepancies. Conclusion: Edentulous patients deserve a superior aesthetic and beautiful smile. Prosthodontist can now will be able to design smiles more easily and in less time before the appointment of try-in. At the same time, dentist will also be able to take approval from the patient regarding the appearance of the smile.

Socioeconomic inequalities are known to negatively impact anthropometric outcomes among children, particularly in developing countries. This study, therefore, assesses the gap in anthropometric outcomes of children 6-59 months along the ethnicity-based social groups in India using the National Family Heath Survey 2015-16 and 2019-21. The paper utilizes logistic regression models, the exogenous switching treatment effect regression (ESTER) model, and the Blinder-Oaxaca Model to disentangle the role of ethnicity (referred to as caste in India) in influencing child anthropometric outcomes while accounting for socio/economic factors. Approximately 35% of children in the sample were stunted and 20% wasted. Result indicates that despite the progress made in reducing child undernutrition between the two survey periods, there is a higher risk of chronic growth faltering (stunting) and underweight in socially disadvantageous groups, and these ethnicity-based disparities exist independent of education and household economic status. To improve children's nutritional status, India needs to develop new nutrition strategies prioritizing double-duty action due to the persistence of undernutrition and rising overweight/obesity among children. The study suggests a need for a distinguished understanding of the underlying causes of chronic and acute forms of malnourishment, and separate interventions are required to reduce the disparities among disadvantaged groups, particularly in tribal communities.

Speech coding plays a significant role in voice communication and improving network bandwidth efficiency for applications that require long-distance communication or storage space utilization. Non-uniform sampling (NUS) is a technique for the same, which performs data reduction by sampling at irregular intervals. In the literature, researchers use the structural property of the speech waveform for studying various NUS methods, such as LCSS, MMD, IPD, and zero-crossing point. However, in this paper, we consider the speech signal’s statistical properties to propose an optimal NUS approach. The proposed technique statistically analyzes the speech signal to sample the abrupt changes over a time frame and approximates the signal with minimal reconstruction error using cost and linear penalty functions for avoiding the over-fitting problem. The proposed technique further performs the optimization using the branch-and-bound. To evaluate the proposed NUS, we design a speech waveform encoder called Block Adaptive Amplitude Sampling (BAAS). A BAAS encoder can directly perform statistical analysis on the speech waveform to select data samples corresponding to the most significant changes in the signal. The decoder approximates the eliminated values using linear interpolation. We experimentally study the proposed technique using various matrices and measures such as POLQA and MUSHRA test. The evaluation shows that the proposed NUS technique retains only 25% of data samples to get an acceptable quality signal regeneration. In addition, comparative studies with MMD and IPD show that the proposed algorithm performs 1.6% better with 30% lower MSE scores.

Recent research on the SARS-CoV-2 pandemic has exploded around the furin-cleavable polybasic insert PRRAR↓S, found within the spike protein. The insert and the receptor-binding domain, (RBD), are vital clues in the Sherlock Holmes-like investigation into the origin of the virus and in its zoonotic crossover. Based on comparative analysis of the whole genome and the sequence features of the insert and the RBD domain, the bat and the pangolin have been proposed as very likely intermediary hosts. In this study, using the various databases, in-house developed tools, sequence comparisons, structure-guided docking, and molecular dynamics simulations, we cautiously present a fresh, theoretical perspective on the SARS-CoV-2 virus activation and its intermediary host. They are a) the SARS-CoV-2 has not yet acquired a fully optimal furin binding site or this seemingly less optimal sequence, PRRARS, has been selected for survival; b) in structural models of furin complexed with peptides, PRRAR↓S binds less well and with distinct differences as compared to the all basic RRKRR↓S; c) these differences may be exploited for the design of virus-specific inhibitors; d) the novel polybasic insert of SARS-CoV-2 may be promiscuous enough to be cleaved by multiple enzymes of the human airway epithelium and tissues which may explain its unexpected broad tropism; e) the RBD domain of the feline coronavirus spike protein carries residues that are responsible for high-affinity binding of the SARS-CoV-2 to the ACE 2 receptor; f) en route zoonotic transfer, the virus may have passed through the domestic cat whose very human-like ACE2 receptor and furin may have played some role in optimizing the traits required for zoonotic transfer.

Counting the number of speakers in an audio sample can lead to innovative applications, such as a real-time ranking system. Researchers have studied advanced machine learning approaches for solving the speaker count problem. However, these solutions are not efficient in real-time environments, as it requires pre-processing of a finite set of data samples. Another approach for solving the problem is via unsupervised learning or by using audio processing techniques. The research in this category is limited and does not consider the large-scale open set environment. In this paper, we propose a distributed clustering approach to address the speaker count problem. The separability of the speaker is computed using statistical pitch parameters. The proposed solution uses multiple microphones available in smartphones in a large geographical area to capture and extract statistical pitch features from the audio samples. These features are shared between the nodes to estimate the number of speakers in the neighborhood. One of the major challenges is to reduce the error count that arises due to the proximity of the users and multiple microphones. We evaluate the algorithm’s performance using real smartphones in a multi-group arrangement by capturing parallel conversations between the users in both indoor and outdoor scenarios. The average error count distance is 1.667 in a multi-group scenario. The average error count distances in indoor environments are 16% which is better than in the outdoor environment.