Browse Articles

Development and qualification of an enzyme-linked immunosorbent assay to detect human serum immunoglobulin G reactive to multiple lineages of Lassa virus nucleoprotein

Lassa fever is a severe, often fatal febrile illness endemic to West Africa caused by Lassa virus (LASV), with different virus lineages predominating across West African countries. The viral nucleoprotein (NP) is a target antigen for serological assays to identify previous exposure to LASV. To our knowledge, there is no commercially available assay that reliably quantifies anti-LASV-NP IgG antibodies in human serum. We report the development and qualification of an ELISA designed to detect and quantify anti-LASV-NP IgG in human serum samples. Following assay optimization, performance was assessed through assay qualification at clinical trial laboratories within Ghana. Assay positivity criteria, lower limit of detection, upper and lower limits of quantification, inter-assay precision, selectivity and dilutional linearity were determined. A new reference standard prepared from pooled sera from donors in endemic Lassa fever regions was established and calibrated to the first WHO international standard for LASV antibodies. One ELISA assay utilizing lineage IV LASV-NP was applicable for detection of anti-LASV-NP IgG antibodies in serum samples from different West African countries where either LASV lineages I, II, III and IV predominate. The ELISA remained selective in hemolysed serum samples with minimal loss of signal across repeated sample freeze-thaw cycles. Crucially, the developed ELISA was fully concordant with a now discontinued commercially available ELISA kit for quantification of anti-LASV-NP antibodies. Our anti-LASV-NP IgG ELISA was shown to reliably measure anti-LASV-NP IgG levels in human serum. Establishing and conducting this assay within West Africa represents an essential step towards strengthening LASV epidemiology research and supporting urgently needed development of a vaccine to prevent Lassa Fever.

An attention-infused deep convolutional paradigm for multi-label classification of thoracic pathologies in chest radiographs

Wetland conversion to farmland in Bure and Womberma Woredas, Northwestern Ethiopia: Implications for sustainable land use

This study investigates the reasons why wetlands transformed to farmland. The data were garnered using Landsat, questionnaire surveys, and key informant interviews. Descriptive statistics were applied to analyze LULC change and the perceptions of wetland ecosystem services and attitudes towards wetland cultivation. An ordered probit model was applied to examine the influence of household and experts’ attributes on their attitudes towards wetland cultivation. The findings indicated that despite acknowledging the direct and indirect benefits of wetlands, the majority of households and experts/ department heads have preferred transforming wetlands into farmland. Household age, asset holding size (land and livestock) and livelihood strategies (crop farming and non-farm) as well as the institutional perspectives of experts and department heads have significantly influenced the desire to convert wetlands into agricultural land. Driven by the rising young households’ interest and local governments’ goal to create jobs have led to the distribution of wetlands to unemployed graduates and landless households. Consequently, significant (48%) areas of the wetlands have been transformed into farmlands over the past 36 years. It was found that the environmental soundness of wetlands cultivation has not been considered when converting them into farmland. Efforts should be exerted to expand employment opportunities to minimize the heavy reliance of rural people on wetland farming. Besides, it is imperative to execute agro-ecological evaluation prior to converting wetlands into farmland to ensure farming decisions are environmentally responsible.

Low frequency oscillation detection in the presence of renewable energy sources using ambient stochastic subspace identification technique

Assessment of maternal diet inflammatory status and inflammatory markers in human breast milk

Human breast milk is a complex bioactive fluid containing multi-functional components that support many infant physiological functions. Maternal diet has been demonstrated to influence human milk components; however, how maternal diet impacts inflammatory markers in human milk remains unclear. This study investigated the association between maternal dietary inflammatory status, assessed using the Dietary Inflammatory Index (DII), and the profile of inflammatory markers in breast milk from healthy lactating women, quantified using cytometric bead array. Dietary intake of lactating mothers (n = 101) was assessed using a 24-hr food recall and categorised using the DII as either pro-inflammatory (score > 0) or anti-inflammatory (score < 0). Thirteen inflammatory markers were quantified in breast milk by flow cytometric bead array (13-plex panel: IL-4, IL-2, IP-10, IL-1β, TNF-α, MCP-1, IL-17A, IL-6, IL-10, IFN-γ, IL-12p70, IL-8, free active TGF-β1). All participant diets were categorised as anti-inflammatory diets (DII score range −4.83 to −1.22). Participants’ food intake aligned with dietary guidelines (AUSNUT 2023) for lactating women, with most analysed food parameters classified as anti-inflammatory (19/27). Inflammatory marker analysis revealed a chemokine-dominant profile in breast milk with IP-10, MCP-1 and IL-8 present at the highest concentrations and detected in > 96% of participant human milk samples MCP-1 concentration was weakly associated with DII score (p = 0.025, r 2 −0.23, Spearman correlation). This study is the first to investigate the inflammatory index of maternal diets in lactating mothers and characterise inflammatory markers in human milk. Further research is required to fully elucidate the relationship between dietary inflammatory status and inflammatory markers in breast milk and their potential impact on infant health, especially of a more diverse cohort.

Geometric-structural multi-label learning for non-invasive prediction of breast cancer biomarkers

Spatial distribution of uptake of newly introduced vaccines and its associated factors among children aged 12–35 months in Ethiopia: Multi-scale Geographically Weighted Regression Analysis

Background The pneumococcal conjugate vaccine (PCV) and rotavirus vaccine (RVV) have been introduced to Ethiopia’s expanded childhood immunization program in 2011 and 2013, respectively. Methods A cross-sectional study among 2,055 children aged 12–35 months was employed. Data were extracted from the Kids Record file of the 2019 Ethiopian Mini Demographic and Health Survey. Spatial regression models were fitted and compared using corrected Akaike Information Criteria, Bayesian Information Criteria and adjusted R 2 . Spatial predictors were determined to be statistically significant if their p-value was < 0.05. Results Incomplete uptake of recently introduced immunizations was observed in nearly half (48.83%) of children aged 12–35 months. Its distribution throughout Ethiopia’s regions shows significant spatial clustering, with the eastern part of SNNPR and Somali regions having hot spots. A total of 3 significant clusters, located in southern Oromia, east and west Hararge, the entire SNNPR, and the majority of the Somali region, with a high rate of incomplete uptake of newly introduced vaccines, were identified during SaTScan analysis. Not having vaccination cards, household size more than 5 members, home delivery, not having postnatal care, and less than 18 maternal age at first birth are positive significant spatial factors while parents not being head of the household were identified as negative significant spatial factors. Conclusion There is high incomplete uptake and spatial disparities of PCV and RVV. To improve vaccination coverage among children aged 12–35 months, policymakers and health planners should prioritize targeted interventions in hotspot areas, strengthen maternal health service utilization, and enhance vaccination tracking systems. Promoting community awareness and improving access to essential health services will be critical to ensuring equitable immunization uptake across the country.

Research on the impact of green dynamic capability and corporate green transformation–based on the moderating role of ESG management

Prokaryotic Schlafen proteins cleave tRNAs during type III CRISPR immunity

Association of urinary post-translationally modified fetuin-A fragments with diabetic kidney disease risk stratification in Japanese patients with type 2 diabetes

Aims Conventional biomarkers such as estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (uACR) primarily reflect glomerular damage and often fail to detect early tubular injury. Consequently, patients with “non-albuminuric diabetic kidney disease (DKD)” may be overlooked. This study evaluated the independent association between urinary post-translationally modified fetuin-A fragments (uPTM-FetA) and DKD risk stratification in Japanese patients with type 2 diabetes. Methods We conducted a cross-sectional study of 219 outpatients with type 2 diabetes between November 2023 and February 2024 at Edogawa Hospital. First-morning urine samples were analyzed for uPTM-FetA and urinary liver-type fatty acid-binding protein (uL-FABP) using enzyme-linked immunosorbent assays. DKD risk was classified into four categories based on the KDIGO guidelines. The association between uPTM-FetA and higher DKD-risk (categories 2 + 3 + 4) was assessed using multiple logistic regression and restricted cubic spline (RCS) analyses, validated by bootstrapping. Results The optimal cutoff value for uPTM-FetA was determined to be 11.76 ng/mgCr. Multivariable analysis adjusted for potential confounders revealed that high uPTM-FetA levels were significantly and independently associated with DKD-risk categories 2 + 3 + 4 (adjusted odds ratio: 3.88; 95% CI: 2.02–7.45; P < 0.01). RCS analysis indicated a significant non-linear association (P = 0.04). Notably, high uPTM-FetA was detected in 38.8% of patients with normoalbuminuria and 42.0% of those with preserved eGFR. A striking discrepancy was observed compared to uL-FABP: while high uL-FABP was completely absent (0.0%) in patients within the low-to-moderate risk categories (categories 1 and 2), high uPTM-FetA was observed in 34.0% and 60.8% of these patients, respectively. Conclusions uPTM-FetA is independently associated with DKD severity and is elevated in a substantial proportion of patients with early-stage disease where conventional markers remain normal. Unlike uL-FABP, which increases predominantly in advanced stages, uPTM-FetA appears to identify tubular stress earlier. Thus, uPTM-FetA serves as a valuable complementary biomarker to uACR for refining DKD risk stratification.

Triglyceride/HDL-cholesterol ratio as a predictor for treatment-related severe hypertriglyceridemia in children with lymphoid malignancies

Modulation of intra-oceanic trench bending and along-trench thermochemical transport by mantle toroidal flow

Toward evidence-based prescription of prosthetic ankle-foot devices: A multisite randomized crossover trial identifying performance-based, patient-reported, and biomechanical parameters sensitive to device type

Prescription of prosthetic ankle-foot devices is constrained by imprecise clinical guidelines and inconsistent scientific evidence, hindering optimal device selection for individuals with lower limb loss. This multisite, prospective, randomized crossover study aimed to identify patient-reported, performance-based, and biomechanical parameters sensitive to ankle-foot device type, providing a foundation for more objective and individualized prescription practices. Ninety-one individuals with unilateral transtibial limb loss completed the crossover trial, and 13 control participants without musculoskeletal impairment were enrolled to provide normative reference data. Participants were fitted with duplicate sockets and randomized to trial three ankle-foot device types: energy storing and returning, articulating, and powered. Participants were heterogeneous in demographic characteristics, including veterans, service members, and civilians. After one week of acclimation per device, participants completed performance-based (6-minute walk, Timed Up and Go, Four Square Step Test, Stair and Hill Assessment Indices, Amputee Mobility Predictor) and patient-reported (Prosthesis Evaluation Questionnaire, 12-Item Short Form Health Survey, Orthotics and Prosthetics Users’ Survey) assessments; a subset (n = 29 completed) underwent full-body gait analysis to capture detailed biomechanical parameters. Biomechanical outcomes demonstrated the greatest sensitivity to device type, with 19 distinct parameters, primarily at the ankle, highlighting ankle mechanics as a key determinant of differences among prosthetic devices. Five Prosthesis Evaluation Questionnaire subscales were sensitive to device type, while performance-based measures showed no significant effects. Results revealed a dichotomy between biomechanical and patient-reported outcomes: Biomechanical parameters were more similar to control values for powered devices, whereas patient-reported outcomes favored non-powered devices. Linear discriminant analysis identified key gait features, including peak plantarflexion during preswing and peak ankle moment, which most strongly contributed to group separation and clinical discrimination. These findings identified distinct biomechanical and patient-reported parameters sensitive to ankle-foot device type and highlight the need for evidence-based, individualized prosthetic prescription to optimize device selection and improve patient outcomes.

Energy transfer and dynamic response of a gasbag-driven confined granular bed

BRG1-mediated suppression of ferroptosis underlies BTK inhibitor resistance

Abstract Resistance to Bruton’s tyrosine kinase inhibitors (BTKi) remains a major therapeutic challenge in B-cell malignancies. Here, we identify chromatin remodeler BRG1-mediated suppression of ferroptosis as a central mechanism of BTKi resistance in mantle cell lymphoma (MCL), in which aberrant BRG1-dependent transcription program protects cells from BTKi-induced ferroptosis by restricting reactive oxygen species (ROS) and labile iron. Mechanistically, BRG1 promotes resistance through regulation of both BTK-dependent survival signaling and a BTK-independent transcriptional program. The latter is mediated by BRG1-driven induction of MEF2B, which upregulates atypical mitochondrial complex I subunit NDUFA4L2. Increased NDUFA4L2 restricts cellular respiration, preemptively limiting mitochondrial ROS generation and activating AMPK signaling, together reducing susceptibility to lipid peroxidation and ferroptosis. Pharmacologic inhibition of BRG1 disrupts these programs, restoring ferroptotic sensitivity and synergizing with BTKi across resistant MCL models. Together, our study establishes BRG1 as a central regulator of BTKi resistance and provides a rationale for co-targeting BRG1 and BTK as a therapeutic strategy for B-cell malignancies.

OncoSeg2D: A deep framework for semantic segmentation of lung cancer in 2D CT scans

Lung cancer lesion segmentation in two-dimensional computed tomography (2D CT) images remains challenging due to blurred boundaries, heterogeneous morphologies, and annotation uncertainty, leading to unreliable delineations and reduced clinical usability. To address this research gap, we propose a novel 2D CT lung cancer semantic segmentation framework, OncoSeg2D, which explicitly tackles boundary ambiguity and morphological distortion through two complementary modules. Specifically, an Uncertainty-aware Boundary Modeling (UBM) module probabilistically represents tumor edges via learnable mean–variance estimation and gradient-weighted sampling, while a Morphology-Preserving Regularization (MPR) module constrains the segmentation with curvature, compactness, and convexity priors to maintain global shape consistency. The framework integrates these designs with multi-scale feature extraction from a SegFormer backbone and requires no additional annotations or three-dimensional (3D) reconstruction. Experiments conducted on the Medical Segmentation Decathlon Challenge dataset and the lung cancer segmentation dataset demonstrate that OncoSeg2D achieves IoU scores of 0.865 and 0.788, mIoU scores of 0.881 and 0.799, and Dice Similarity Coefficients (DSC) of 0.923 and 0.816, consistently outperforming conventional CNN-based models and mainstream Transformer-based methods. Compared with the SegFormer baseline, the proposed method improves mIoU by 3.8% and 4.0% on the two datasets, respectively, while reducing the Hausdorff distance from 5.61 to 3.41 and from 7.26 to 5.28, indicating superior boundary refinement and stronger global shape consistency. These results verify that explicitly integrating uncertainty modeling and morphological priors yields both higher accuracy and better interpretability. Overall, the proposed framework not only enhances segmentation accuracy but also improves clinical interpretability and reliability, offering a promising solution for lung cancer diagnosis assistance and therapeutic outcome monitoring.

Correlation of dielectrophoretic crossover frequency with optical density and plate counts for assessing Staphylococcus aureus concentration analysis

Sulfur-passivated Pt cluster edges on CeO2 for selective CO2-to-CO conversion

Spontaneous passage of common bile duct stones: predictive factors and impact on post-ERCP complications

Background Spontaneous passage of common bile duct stones (CBDSs) may render endoscopic retrograde cholangiopancreatography (ERCP) unnecessary. Although predictors of passage have been described, most prior studies were limited by a small number of events, and the impact of spontaneous passage on post-ERCP complications remains under-investigated. This study aimed to identify clinical predictors of spontaneous passage and evaluate its association with post-ERCP complications. Methods We conducted a retrospective cohort study of patients diagnosed with CBDSs who underwent endoscopic ultrasonography (EUS) or ERCP at a tertiary referral center. Spontaneous passage was defined as the absence of stones confirmed during the procedure. Multivariable risk regression was used to identify predictors of passage and to assess the association between spontaneous passage and post-ERCP complications. Results Spontaneous passage was observed in 113 of 404 patients (28%). Independent predictors of spontaneous passage included younger age (RR 0.88 per 10 years; 95% CI 0.81–0.96), smaller CBDS size (RR 0.78 per 1 mm; 95% CI 0.71–0.85), and single CBDS (RR 1.64; 95% CI 1.04–2.61). Regarding complications, post-ERCP pancreatitis (PEP) occurred more frequently in patients with spontaneous passage compared to those without (16.5% vs 7.6%, P = 0.01). After adjusting for relevant confounders, including procedural factors, spontaneous passage remained an independent risk factor for PEP (RR 2.48, 95% CI 1.25–4.92). Conclusions Spontaneous passage of CBDSs is an independent risk factor for PEP. Younger age, smaller stone size, and a single stone are significant predictors of passage. These findings suggest that pre-procedural risk stratification and non-invasive confirmation of ductal clearance may be beneficial in selecting appropriate candidates, potentially reducing unnecessary ERCP and associated complications.

An intelligent embedded fuzzy genetic classifier based network intrusion detection system for secured communication in internet of things

Abstract Internet of things (IoT) is a distributed connection of smart objects which collects the data or information from the deployed environment and communicates the data to other devices with Internet as a backbone. Due to its unfriendly deployment nature and openness in communication via Internet, IoT is vulnerable to various types of attacks during data transmission. Intrusion Detection System (IDS) is an effective method to provide strong and efficient security to IoT devices. IDS is a software that tracks the network traffic and identifies the anomalies and abnormal activities. An Improved Ant Colony Optimization (IACO) algorithm with mutual information is proposed which effectively identifies the features in the given dataset and ranks. Moreover, for classification a Hybrid Fuzzy Genetic Algorithm (HFGA) is proposed to identify various types of attacks in the network. The proposed classifier comprises two layers namely external and internal. The external layer generates fuzzy sets and internal layer generates fuzzy rules. In the proposed system during training phase the External Fuzzy Genetic Algorithm (EFGA) helps Internal Fuzzy Genetic Algorithm (IFGA) and the finest individual from EFGA is associated with the fragile individual from IFGA to produce a new output which enhances the estimation of mutated attacks. The performance of the proposed system is evaluated using NSL-KDD dataset. Most of the existing IDS in IoT suffers from lower Intrusion detection accuracy, false alarm rate and has high computational and communication overhead. From the result, the proposed system has achieved better intrusion accuracy and reduced the false alarm rate in the network. Moreover, the proposed system identifies both known attacks and unknown attacks in the network.