Browse Articles

Elucidating trends and underlying drivers of neonatal mortality stagnation in Nepal: An analytical perspective on the 2016 and 2022 Demographic and Health Surveys

Background Nepal has made significant progress in reducing the Neonatal Mortality Rate (NMR) over the past two decades. However, since 2016, NMR has stagnated at 21 deaths per 1,000 live births, indicating stalled improvements in neonatal survival. Past studies highlighted the disparities, with socioeconomically disadvantaged groups experiencing a higher rate of neonatal deaths. However, limited evidence exists on NMR trends and determinants in Nepal that examined the factors with the trend. This study analyzed NMR trends and key determinants using data from the two most recent Nepal Demographic and Health Surveys (NDHS). Methods NDHS 2016 and NDHS 2022 data were used to calculate NMR. Both surveys received ethical approval from the Nepal Health Research Council. The study included 106 neonatal deaths out of 5,087 live births in 2016 and 105 out of 5,192 in 2022. Independent variables included household characteristics, parental factors, pregnancy-related factors, maternal and newborn care, women’s empowerment, and health system factors. NMRs were constructed using births within completed months from 1 to 61. A general linear model assessed NMR trends, while logit regression identified key determinants. Results While national NMR remained unchanged since 2016, an increasing trend was observed among disadvantaged groups and mothers who did not utilize maternal/newborn health services. NMR rose from 27.3 to 27.8 per 1,000 live births (p = 0.001) among poor and poorest households. Similarly, women with no education experienced higher NMR at 29.3% in 2022 compared to 25.7% in 2016 (p = 0.002). Maithili-speaking mothers had higher NMR (27.4 in 2022 vs. 23.4 in 2016, p < 0.001). Women lacking decision-making power in healthcare had higher NMRs of 25.9 in 2022 vs. 23.4 in 2016 (p = 0.021). Women who were not assisted by skilled birth attendants (SBA). had significantly higher NMR compared to those, who were assisted by SBA (p = 0.010). Conclusions Targeted health system interventions are needed for disadvantaged groups covering those who had low education, from poor households, low health care decision making and lack access to SBA assisted delivery. While determinants have been explored, further targeted studies are warranted to uncover the causes of neonatal deaths in Nepal.

Desire for childbearing in the view of Iranian parents: A qualitative study

Background and Aims Like many other countries, Iran is facing a declining population growth rate. While previous research has primarily focused on childless individuals or parents of only one child, limited attention has been paid to families who, despite already having two or more children, continue to desire larger families. This study examines childbearing motivations among Iranian parents with two or more planned children. Methods This explorative qualitative study was conducted in 2023 across three provinces in Iran. A total of 46 participants were purposively selected. Data were collected through semi-structured interviews, and thematic analysis was used to identify core themes related to childbearing motivations. Results Participants’ desire to have more children was shaped by six major themes: family altruism, value–attitude motivation, real happiness, resilience development, institutional support, and multifaceted balance. These themes reflect a dynamic interplay of emotional, cultural, and practical considerations in shaping fertility intentions. Conclusion The findings indicate that both personal and societal factors contribute to parents’ motivation for having more children. Institutional support also emerged as an important influence in the decision to pursue larger families. Understanding these motivations can inform policies aimed at encouraging childbearing among younger generations and help guide the development of integrated strategies and educational initiatives. Future quantitative research is recommended to assess the relative importance of each identified theme.

Investigating the Trichosanthis Pericarpium - Trichosanthis Radix herbal pair’s role in alleviating COPD through gut microbiota function, metabolomics analysis and cell validation experiment

The herbal pair of Trichosanthis Pericarpium – Trichosanthis Radix (TP-TR) is derived from the classic prescription Bei-Mu-Gua-Lou-San in Yixue Xinwu, which is commonly used to treat lung heat and cough. Both originate from Trichosanthes kirilowii Maxim, a medicinal plant known for its effects to clear heat, dissolve phlegm, promote salivation, and relieve dryness. However, the compatibility, pharmacological synergy and gut-lung axis regulation mechanisms of TP-TR remain unclear. This study innovatively explores the therapeutic effects and underlying mechanisms of TP-TR in COPD through microbiome and amino acid metabolism. A COPD rat model was established to evaluate the efficacy of TP-TR. The gut microbiota was analyzed with 16S rRNA sequencing, while the metabolites in serum and lung were analyzed by UPLC-MS/MS. Functional prediction of the microbiome and differential metabolite analysis were performed using KEGG/SMP. The LPS, CSE – induced cell model was used to validate the impact of TP-TR and its active components on amino acid metabolism. The results demonstrated that TP-TR significantly improved pulmonary function, alleviated inflammation, modulated gut microbiota composition (e.g., Lactobacillus, g_ Novosphingobium), and regulated metabolic disturbances in COPD rats. Notably, amino acid metabolic pathways were consistently enriched across microbiota function prediction and untargeted metabolomic analyses of serum and lung. Targeted metabolomics further confirmed alterations in amino acid levels. Moreover, TP-TR, along with cucurbitacin B, cynaroside, glutamine, guanine, and apigenin induced alterations in the amino acid content of model cells. These findings reveal a novel mechanism by which TP-TR ameliorates COPD through gut microbiota regulation and amino acid metabolism modulation along the gut-lung axis.

Nucleated red blood cell count as a novel predictive biomarker for acute kidney injury and prognosis in patients with acute pancreatitis: A retrospective cohort study

Objectives Nucleated red blood cells (NRBCs) have been reported to correlate to poor prognosis in critically ill patients. This study aimed to identify the role of NRBC count in early diagnosis of acute kidney injury (AKI) and prediction of poor prognosis in patients with acute pancreatitis (AP). Methods This retrospective cohort study enrolled AP patients who were admitted to our hospital from January 1, 2020 to January 1, 2024. Demographic data, NRBC count, laboratory indicators, and outcomes were recorded. Binary logistic regression analysis was used to identify independent biomarkers for AKI diagnosis and prognosis of AP. Receiver operating characteristic (ROC) curves, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to evaluate the predictive value of NRBC count. Kaplan-Meier curves were generated to compare survival rate between different groups. Results A total of 486 patients with AP were included in this study. Patients with NRBCs in their peripheral blood were classified into the NRBC (+) group (n = 190), and those without NRBCs in their peripheral blood were classified into the NRBC (-) group (n = 296). Patients in the NRBC (+) group had a higher AKI incidence (29.5% vs 3.0%, p < 0.001) and poor prognosis rate (12.7% vs 1.0%, p < 0.001) than patients in the NRBC (-) group. Binary logistic regression analysis showed that NRBC count was significantly associated with AKI incidence (OR = 3601.361, p < 0.05) and poor prognosis (OR = 204.434, p < 0.05) in AP patients. Area under the ROC curve (AUC) was 0.830 for NRBC count predicting AKI (cutoff value: 0/L) and 0.867 for NRBC count predicting poor prognosis in AP patients (cutoff value: 0.015 × 109/L). Kaplan–Meier survival analysis demonstrated that patients with NRBC count > 0.015 × 109/L (χ² = 85.09, p < 0.001) exhibited significantly lower survival rates during the 30-day follow-up period. NRBC count offered predictive performance comparable to procalcitonin (PCT) and outperformed C-reactive protein (CRP). Conclusion NRBC count serves as a valuable predictive biomarker for both AKI incidence and poor prognosis in patients with AP.

Bone mineral density predicts survival in patients with hepatocellular carcinoma and portal vein tumor thrombosis

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality, with portal vein tumor thrombosis (PVTT) being a common complication that significantly worsens prognosis. Recent studies have identified bone mineral density (BMD) as a prognostic factor in patients with HCC; however, its role in patients with PVTT remains unexplored. This retrospective study evaluated the prognostic value of BMD in 462 patients with HCC and PVTT treated between 2005 and 2020. BMD was measured via computed tomography attenuation at the first lumbar vertebra at the time of HCC diagnosis and PVTT onset, using an established threshold of 160 Hounsfield units (HU). Kaplan-Meier analysis assessed overall survival, and multivariate Cox regression adjusted for established prognostic factors. Median BMD was 136 HU (IQR: 113–160) at HCC diagnosis and 134 HU (IQR: 109–159) at PVTT onset. Patients with BMD ≥ 160 HU showed significantly longer overall survival both at HCC diagnosis (10.4 vs. 5.5 months, p < 0.001) and PVTT onset (8.5 vs. 4.7 months, p < 0.001). In multivariate analysis at both time points, BMD remained an independent predictor of survival, alongside tumor growth pattern, therapy, and Albumin-Bilirubin (ALBI) grade (alpha-fetoprotein reached significance only at time of PVTT diagnosis). These findings suggest that BMD independently predicts survival in HCC with PVTT and may enhance prognostic modeling and therapeutic decision-making.

Salivary microbiome in peritoneal dialysis patients with and without sarcopenia: A pilot study

Purpose To investigate salivary microbiota composition in end-stage kidney disease (ESKD) patients with sarcopenia (SESKD), ESKD patients without sarcopenia (NSESKD), and individuals without chronic kidney disease (control group). Materials and Methods Thirty-three participants were enrolled: 10 SESKD patients, 12 NSESKD patients, and 11 controls. Demographic data, oral examinations, and unstimulated saliva samples were collected. Salivary bacterial microbiomes were analyzed using high-throughput sequencing targeting the V3-V4 region of the bacterial 16S rRNA gene. Results The overall bacterial abundance and distribution were significantly higher in the SESKD and NSESKD groups compared to the control group (p < 0.05), with no significant differences between the SESKD and NSESKD groups. ESKD, educational level, and muscle strength were significantly associated with variations in the salivary microbiome composition. Analysis of bacterial abundance revealed a shift in trends as the disease combined from control to NSESKD and SESKD group, respectively, across 7 genera: Actinobacillus, TM7x, Capnocytophaga, Neisseria, and Leptotrichia increased in abundance, while Actinomyces and Atopobium decreased. Linear discriminant analysis effect size (LEfSe) identified Leptotrichia as a potential biomarker for ESKD (both with and without sarcopenia). Conclusion ESKD condition impacted microbial composition, with minimal influence from sarcopenia. Specifically, Leptotrichia was notably higher in the ESKD group.

An adaptive sliding mode controller with free-will arbitrary time convergence for three-phase rectifiers in autonomous agricultural vehicles

This study describes a novel adaptive free-will arbitrary time sliding mode controller (AFWATSMC) designed to improve the performance of a three-phase rectifier in an autonomous oil palm grabber vehicle (Robot Autonomous Mechanical Buffalo Grabber (MBG)). The graber, initially powered by a diesel engine with an uncontrolled rectifier, was upgraded to support intelligent systems that require stable DC voltage management. To address the limitations of conventional rectifiers, the suggested AFWATSMC integrates adaptive factors to improve the performance of the original free-will arbitrary time algorithm. The key innovation of this work lies in combining the adaptive sliding-mode control structure with the free-will arbitrary convergence time algorithm, permitting user-defined system settling time nevertheless of dynamic uncertainties (system parameters and initial conditions), a capability not demonstrated in prior rectifier control strategies to the best of the current knowledge. An optimized control laws using genetic algorithm (GA) and particle swarm optimization (PSO) is suggested to tune the parameters using MATLAB Simulink and coding. A smooth waveform with reduced ripple factor was achieved for the DC output of the alternator with a total improvement of 75.47%; the AC output alternator current exhibited an enhanced sinusoidal shape, a reduction of the total harmonic distortion (THD) with a 46.69% improvement, and an achievement of unity power factor of 0.20% improvement was obtained compared to another adaptive SMC.

Bias in the estimated reporting fraction due to vaccination in the time-series SIR model

The time-series Susceptible-Infectious-Recovered (TSIR) model has been a standard tool for studying the non-linear dynamics of acute, immunizing infectious diseases. The standard assumption of the TSIR model, that vaccination is equivalent to a reduction in the recruitment of susceptible individuals, or the birth rate, can lead to a bias in the estimate of the reporting fraction and of the total incidence. We show that this bias increases with the level of vaccination due to a double counting of individuals who are infected prior to the age of vaccination. We present a simple correction for this bias by discounting the observed number of cases by the product of the number that occur prior to the average age of vaccination and the vaccination coverage during the initial susceptible reconstruction step of the TSIR model fitting. We generate a time series of measles cases using an age-structured SIR transmission model with vaccination after birth (at 9 months of age) and illustrate the bias with the standard TSIR fitting method. We then illustrate that our proposed correction eliminates the bias in the estimated reporting fraction and total incidence. We note further that this bias does not impact the estimates of the seasonality of transmission.

A novel and robust feature selection method with FDR control for omics-wide association analysis

Omics-wide association analysis is a very important tool for medicine and human health study. However, the modern omics data sets collected often exhibit the high-dimensionality, unknown distribution response, unknown distribution features and unknown complex association relationships between the response and its explanatory features. Reliable association analysis results depend on an accurate modeling for such data sets. Most of the existing association analysis methods rely on the specific model assumptions and lack effective false discovery rate (FDR) control. To address these limitations, the paper firstly applies a single index model for omics data. The model shows robust performance in allowing the relationships between the response variable and linear combination of covariates to be connected by any unknown monotonic link function, and both the random error and the covariates can follow any unknown distribution. Then based on this model, the paper combines rank-based approach and symmetrized data aggregation approach to develop a novel and robust feature selection method for achieving fine-mapping of risk features while controlling the false positive rate of selection. The theoretical results support the proposed method and the analysis results of simulated data show the new method possesses effective and robust performance for all the scenarios. The new method is also used to analyze the two real datasets and identifies some risk features unreported by the existing finds.

Design principles for social-ecological research at the landscape scale applied to western Rwanda

Place-based social-ecological systems research provides major opportunities to advance sustainability and often involves large, interdisciplinary groups. Researchers adopt various methodologies when studying landscapes, gathering a wide array of data such as socioeconomic information from households, ecological data from specific areas, and qualitative insights from interviews. To integrate these varied methods, we propose identifying social-ecological research units as shared anchor points for data collection across teams. We outline four design principles: (i) spatial scale of social-ecological units, (ii) key social-ecological gradients in the study area, (iii) accessibility of stratification data, and (iv) flexibility in response to logistical challenges. We applied these principles to design a social-ecological study on ecosystem restoration in western Rwanda. We identified five distinct and spatially homogenous clusters, from which we sampled a total of 152 villages (~9.5% of all villages in our study area), which will be visited by different researchers within our study consortium, hence enabling to identify cross-sectional similarities and differences. Through our stratification according to these principles, we created a framework to guide interdisciplinary collaboration. This structured approach supports integration of diverse research efforts and offers insights for advancing place-based social-ecological systems research globally. Sharing our stratification data and methodology, we highlight its potential applicability to other landscapes and sustainability challenges.

Disseminated intravascular coagulation, associated factors and clinical outcomes among critically Ill septic adults admitted to a tertiary hospital in Ethiopia: A prospective longitudinal study

Background Despite the established link between sepsis and disseminated intravascular coagulation (DIC), data on the prevalence of DIC, associated factors and patient outcomes in sepsis patients are inadequate in resource-limited settings. Therefore, the present study aimed to determine the magnitude of DIC and associated factors and mortality and predictors in septic adults admitted to intensive care units (ICUs). Methods A prospective longitudinal study involving adults admitted to intensive care units was conducted. A structured checklist and questionnaire were used to collect patient demographic and clinical data. Blood samples were collected on days 1, 3, and 5 of admission for all laboratory analyses. A DIC diagnosis was made on the basis of the Japanese Association for Acute Medicine (JAAM) score. Descriptive statistics, multivariable logistic regression analysis, receiver operating characteristic (ROC) curve analysis and Kaplan‒Meier survival analysis were employed in this study. Results The overall prevalence of DIC in sepsis patients was 38 (25.7%). There were 24 (16.2%) patients who developed DIC on day 1 of admission, while 20 (19.4%) and 9 (12.9%) patients developed DIC on day 3 and day 5 of admission, respectively. Increased aspartate transaminase (AST) (AOR: 4.39; 95% CI: 1.75–11.01), thrombocytopenia (AOR: 6.04; 95% CI: 2.41–15.12), and prolonged prothrombin time (PT) (AOR: 3.40; 95% CI: 1.36–8.51) were independent predictors of DIC in sepsis patients. There was no statistically significant difference in survival between patients with and without DIC (p < 0.328). The JAAM score at ICU admission predicted ICU mortality (AUC: 0.787; 95% CI: 0.624–0.950). Conclusion A quarter of ICU-admitted septic adults developed DIC. The incidence was notably greater by the third day after admission, highlighting the importance of closely monitoring these patients for DIC progression. Elevated AST liver enzyme levels, thrombocytopenia, and prolonged PT are linked to the development of DIC. Changes in these variables could prompt further examination for DIC. The mortality rate did not significantly differ between septic patients with and without DIC. The JAAM score used to diagnose DIC in sepsis patients can serve as a predictor of ICU mortality in sepsis patients with DIC.

Enhanced CNN for induction motor fault diagnosis via multi-source data fusion

This study addresses the challenge of low recognition precision in single fault signal isolation for induction motors by proposing a novel fault diagnosis strategy that integrates multi-source information and an enhanced Convolutional Neural Network (CNN). This approach aims to mitigate the effects of strong nonlinear correlations inherent in fault characteristics. Initially, vibration and stator current signals are preprocessed using denoising autoencoders to improve signal quality. Subsequently, multi-source homogeneous data are fused at the data layer using a correlation variance contribution rate method, effectively integrating information from disparate sources. The fused signals are then transformed into two-dimensional images, serving as input for a refined CNN architecture designed to handle heterogeneous data integration and feature extraction. Finally, the proposed adaptive CNN fault diagnostic model is evaluated using induction motor test datasets. Empirical results demonstrate the method’s ability to effectively utilize both redundant and complementary information from multiple sources and to model the nonlinear dynamics of feature datasets. Specifically, the proposed method achieves an average diagnostic accuracy of 99.0% at 1800 r/min and 94.8% at 2400 r/min, significantly outperforming traditional methods under the same conditions. Furthermore, when compared to other advanced multi-source fusion techniques, the proposed method demonstrates superior performance. These results highlight highlights its potential as a robust tool for induction motor fault diagnosis.

Daily briefing: Primates might have evolved in the cold climate of North America

Glitch cop

In silico screening and experimental validation identify riboflavin as an RNA-targeted antiviral against SARS-CoV-2

Abstract This study explored drug repurposing strategies against conserved RNA structures in the SARS-CoV-2 genome to address viral mutation challenges. Conserved RNA elements were computationally identified by aligning 283 SARS-CoV-2 genomes from Korean patients. RNA secondary structures were predicted using RNAfold and RNAstructure, followed by virtual screening of 11 compounds using the RNALigands database (binding energy threshold: -6.0 kcal/mol). The antiviral activity and cytotoxicity of riboflavin were experimentally validated in vitro using Vero E6 cells infected with SARS-CoV-2 (MOI 0.01). Riboflavin exhibited selective antiviral activity against SARS-CoV-2 (IC50 = 59.41 µM), showing no cytotoxicity at concentrations < 100 µM. Riboflavin treatment during viral inoculation significantly reduced viral replication, whereas riboflavin treatment pre- or post-inoculation had no effect. The other screened compounds lacked antiviral efficacy. In terms of antiviral activity, riboflavin was less potent than remdesivir (IC50 = 25.81 µM). Riboflavin is a potential RNA-targeted therapeutic agent against SARS-CoV-2. This study established a framework for integrating computational and experimental methods to identify conserved RNA targets, thus offering a strategy applicable to other RNA viruses. This result indicates the potential of riboflavin as a broad-spectrum antiviral agent against SARS-CoV-2 and highlights the importance of considering nutritional factors in the context of viral infections.

Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires

Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records

Micro-variations in timing and loudness affect music-evoked mental imagery

Abstract Music can shape the vividness, sentiment, and content of directed mental imagery. Yet, the role of specific musical features in these effects remains elusive. One important aspect of human musical performances is the presence of micro-variations—small deviations in timbre, pitch, and timing, driven by motor and attentional processes. These variations enhance perceived “naturalness” compared to mechanical playing without such variations. Here, we investigated whether random micro-variation, as opposed to mechanical playing, affects mental imagery characteristics. One hundred participants performed a directed mental imagery task where they imagined a journey, accompanied either by drumming with micro-variation, drumming without micro-variation, or silence. Participants rated the vividness, distance and time travelled of their imagined content, alongside free-format content responses. Bayesian multilevel regression model showed that repetitive quasi-isochronous drumming enhanced mental imagery vividness, with a stronger effect observed when the drumming contained random micro-variation. Drumming with random micro-variation also increased imagined distance and time travelled compared with silence. Furthermore, individual traits in absorption, ability to imagine vividly, and level of musical training interacted with auditory conditions to further shape mental imagery characteristics. The findings have implications for the use of music to support imagery in creative, recreational, and therapeutic settings.

A nomogram for predicting non-curative resection in patients with early gastric cancer based on white light imaging

A novel wide input range transformerless PV microinverter with natural power decoupling

Abstract In this paper, a novel wide range microinverter circuit that can interface with a single-phase grid and operates without a transformer is presented. The proposed topology uses six switches: two of those switches function at line frequency every half cycle while the other switches function at high switching frequency. In the proposed topology, the circuit functions in discontinuous conduction mode (DCM) across all possible operating conditions, ensuring high gain and minimal switching losses. A common connection between the PV panel and the grid exists, ensuring no common mode current. The proposed topology naturally decouples the power between the DC and AC sides without using an active power decoupling circuit. Passive power decoupling techniques implemented using a large electrolytic capacitor which is very well known to have low reliability is also not needed. Thus, the microinverter’s reliability is increased by using thin film capacitors. The analysis and verification of the proposed system are presented in this paper. Additionally, a standalone version of the presented circuit is verified experimentally through a fabricated prototype. In addition to the benefits of the presented circuit, both simulation and experimental data demonstrate that the circuit can operate without requiring a duty cycle constraint, offering significantly greater flexibility and a wider operating range.