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This AI can improve your peer review — and make it more polite

Correction: Ocimum tenuiflorum extract (HOLIXERTM): Possible effects on hypothalamic–pituitary–adrenal (HPA) axis in modulating stress

Language model-guided anticipation and discovery of mammalian metabolites

Correction: Joint congestion and contention avoidance in a scalable QoS-aware opportunistic routing in wireless ad-hoc networks

Implementation and product- and process evaluation of a co-created gender-informed and culturally-sensitive toolkit to improve symptom recognition and care seeking for ischemic heart disease: RE-AIM framework

Background Tailored health messaging can be used to improve symptom recognition of ischemic heart disease (IHD) and reduce barriers to care. Therefore, we propose that a gender-informed and culturally-sensitive toolkit for health messaging could improve health outcomes by promoting adequate care seeking in the community. Hence, we will develop, implement, and evaluate a toolkit to improve IHD-related symptom recognition and care seeking in an ethnically-diverse population. Methods We developed a toolkit in co-creation with patients, citizens, community leaders, and healthcare professionals. Subsequently, we pilot-implemented the toolkit through organizing information sessions in four different community-based settings. Finally, using a mixed-methods design, we evaluated the Reach, Effectiveness, Adoption, Implementation, and Maintenance of the toolkit (RE-AIM), including its effect on knowledge gain on symptom recognition and care seeking,. Results Approximately 240 people attended the information sessions, of whom 69% women, and the mean age was 63 (standard deviation 10.7) years old. Our results showed a small increase [varying from 53.4–74.4% pre-test to 67.6–85.4% post-test] in knowledge about IHD symptoms. A great willingness to adoption the intervention was observed among the targeted community leaders, and despite several points of improvement, including fidelity to the implementation, attendees and community leaders reflected positively on the toolkit. The main challenge regarding the maintenance of the toolkit was a lack of experience in organizing community events by potential maintenance organizations. Conclusion While the co-created toolkit was well-received, and findings concerning the reach, adoption, and implementation were positive, the (cost)effectiveness should be further evaluated to study the long-term impact of the intervention. Moreover, the need for integration of the intervention in current infrastructure constituted a challenge to the maintenance of the toolkit.

Integrated microwave cavity perturbation sensor for nondestructive estimation of moisture content of grains: A case study on wheat and chickpea

This study presents a low-cost, integrated microwave cavity perturbation sensor operating in TM 010 mode at 2.45 GHz for the non-destructive estimation of moisture content (MC) in wheat and chickpea grains. Unlike conventional methods that rely on expensive vector network analyzers (VNAs), this system uses a custom-designed circuit to derive a density-independent moisture content function, M( Ψ ). Various machine learning models (ML) were trained to predict MC and classify grain types based on these dialectical metrics. The results demonstrate that bagging (BAG), k-nearest neighbors (k-NN), and reduced-error pruning trees (REPTree) models significantly outperform deep learning models. The BAG model achieved the highest predictive performance, yielding correlation coefficients (R) of 0.995 for wheat and 0.989 for chickpea, with root mean square error (RMSE) of 0.207% and 0.302%, respectively. Furthermore, k-NN variants achieved 100% accuracy in classifying the grain types. The proposed system offers a precise, rapid, and cost-effective alternative for real-time grain quality monitoring.

Multidrug resistance patterns and carbapenemase production among Gram-negative bacteria causing healthcare-associated infections in hospitalized patients at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia

Background Klebsiella pneumoniae , Acinetobacter species, and Pseudomonas aeruginosa are priority pathogens identified by the World Health Organization that have emerged as major causes of healthcare-associated infections. Their increasing resistance to multiple antimicrobial agents poses significant challenges to clinical management and infection control efforts. Objective This study aimed to determine the prevalence, associated risk factors, antimicrobial resistance patterns, and carbapenemase production of K. pneumoniae , Acinetobacter spp., and P. aeruginosa among hospitalized patients with suspected bloodstream, urinary tract, and surgical site healthcare-associated infections at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia. Methods A hospital-based cross-sectional study was conducted from August 2024 to June 2025 among 477 patients suspected of bloodstream, urinary tract, or surgical site healthcare-associated infections. Socio-demographic and clinical data were collected using a semi-structured questionnaire. Blood, urine, and wound/pus specimens were aseptically collected and inoculated on MacConkey, blood, and cysteine lactose electrolyte-deficient agar following standard microbiological techniques. Antimicrobial susceptibility testing was performed using the Kirby-Bauer disc diffusion method on Mueller-Hinton agar according to Clinical and Laboratory Standards Institute guidelines. Data were analyzed using SPSS version 27. Bivariate and multivariate logistic regression analyzes were used to identify factors associated with healthcare-associated infections. P value < 0.05 was considered statistically significant. Results Among the 477 patients, 118 (24.7%) developed healthcare-associated infections caused by K. pneumoniae , Acinetobacter spp., and P. aeruginosa , with culture positivity rates of 14.9%, 4.8%, and 5%, respectively. Significant associated factors included age under five (AOR = 13.260, p < 0.001) and 5–17 (AOR = 4.081, p < 0.025), prior hospital admission (AOR = 8.302, p < 0.001), prolonged hospital stay (AOR = 3.213, p < 0.001), and admission to the orthopedic ward (AOR = 6.071, p < 0.003). Multidrug resistance was detected in 94.4% of K. pneumonia e, 69.6% of Acinetobacter spp., and 58.3% of P. aeruginosa isolates. Carbapenemase production occurred in 92%, 77.8%, and 57.1% of these carbapenem-resistant isolates, respectively. Amikacin, meropenem, and ciprofloxacin were the most effective antimicrobials, whereas chloramphenicol was effective only against K. pneumonia e. Conclusion This study showed high prevalence of multidrug resistance and carbapenemase production among K. pneumoniae , Acinetobacter spp., and P. aeruginosa in the study area, highlighting the urgent need to strengthen infection prevention and control measures and to promote antimicrobial stewardship programs.

Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules

Background Artificial intelligence-based radiomic approaches have been shown to accurately evaluate indeterminate pulmonary nodules. With the expansion of lung cancer screening and utilization of computed tomography imaging, indeterminate pulmonary nodules requiring diagnostic evaluation are increasingly common. Accurate non-invasive characterization may reduce time to cancer diagnosis and decrease invasive procedures for benign disease, but the cost-effectiveness of AI-based methods has not been quantified. We sought to evaluate the cost-effectiveness of AI-assisted clinician evaluation compared to clinician evaluation alone for the cancer risk stratification of patients with indeterminate pulmonary nodules. Methods We constructed a decision model assuming guideline-based care from a payer perspective with a lifetime horizon. The base case is a 1.1 cm incidentally discovered IPN in a 60-year-old operative candidate in a clinical population with a 65% malignancy prevalence. Cost per life-year gained (LYG) was the primary outcome. We conducted deterministic sensitivity analyses on all parameters and performed a probabilistic sensitivity analysis. Given clinical variability of malignancy prevalence, we assessed the malignancy prevalence threshold at which utilization of AI would be cost-effective. Results AI-supported clinician risk stratification resulted in an increase of 0.03 life years compared to clinician alone. With a 65% malignancy prevalence, AI was cost-effective with an incremental cost-effectiveness ratio (ICER) of $4,485/LYG. When the malignancy prevalence was < 5%, the ICER for AI support exceeded a standard willingness-to-pay threshold of $100,000/LYG. Conclusions In clinical settings with a pre-test probability of malignancy exceeding 5%, AI-supported IPN risk stratification is cost-effective compared to clinician assessment alone.

Racial variations in sciatic nerve anatomy: A systematic review and meta-analysis

Background The sciatic nerve (SN), the longest and largest nerve in the body, arises from the L4-S3 nerve roots and exits as a single trunk below the piriformis muscle through the greater sciatic foramen. However, variations in its anatomy are common, believed to originate from embryological development. These variations show significant racial and geographical differences, which have often been overlooked in previous review studies. Therefore, this meta-analysis aims to address this gap by systematically reviewing global data to evaluate the impact of race on sciatic nerve variations. Methods A systematic review and meta-analysis were conducted to assess the pooled prevalence of SN variations among racial subgroups. A comprehensive literature search was performed using PubMed, Google Scholar, Hinari, and additional sources, including major anatomical journals and cross-referenced articles. Subgroup analyses by region and country were also conducted using a random-effects model. Heterogeneity was assessed with the Cochrane Q test and the I² statistic. Results Type A, considered the normal pattern, had the highest pooled prevalence at 86%. The remaining 14% represented variations of the sciatic nerve (SN). Among these, Type B was the most common at 7%, followed by Type C and G each observed in 2% of limbs, while less frequent variations included Type Type D (1%), Type E (0%), and Type F (0% (0–1)). Racial analysis showed that SN variations occurred in 15% of Asians, 12% of Whites, and 13% of Blacks. Regarding continents, the highest prevalence was in Asia with 15%, the second highest prevalence was observed in Europe with 14%, followed by Africa with 13%, and the lowest in America with 11%. No significant differences were found among the races and continents. However, East Asia showed the highest significant prevalence, with China at 35% and Japan at 32%. Conclusion This review revealed only modest and statistically non-significant differences in the prevalence of sciatic nerve variations across broad racial and continental groups. In contrast, substantial variation was observed at the regional level, with particularly high prevalence rates in East Asian countries, specifically China and Japan. These findings suggest that regional factors contribute more to the observed variations than racial factors.

Retraction: Short communication: Upregulation of hypoxia/reoxygenation-induced Shc3 by downregulated miR-455-5p, suppresses trophoblast invasion and is associated with placental inflammation and angiogenesis in preeclampsia

Correction: Streptococcus pneumoniae upregulates Toll2, Toll9, and defensin genes in Bombyx larvae infection model

Land use change and ecological sensitivity in the Qingdao West Coast new area: A 30-year analysis and future scenario simulation

This study aims to reveal the long-term ecological evolution in the Qingdao West Coast New Area (QWCNA) and predict future trends to support its sustainable development. Firstly, it employed GIS-based land use dynamic indices and transfer matrix analyses to assess land use changes from 1990–2020. Secondly, this study assessed ecological sensitivity (1990–2020) using an Analytic Hierarchy Process (AHP) weighted 7-factor system covering the natural environment, land cover, and accessibility. Thirdly, the Patch-Generating Land Use Simulation (PLUS) model predicted 2030 land use under Natural Development (ND), Urban Development (UD), and Ecological Protection (EP) scenarios, which were subsequently used to evaluate future ecological sensitivity patterns. The main results indicate that a drastic land use transformation occurred between 1990 and 2020, marked by a significant expansion of construction land and forestland. This expansion primarily displaced cultivated land, grassland, water bodies, and unused land, driven by rapid urbanization. Furthermore, spatially distinct ecological sensitivity patterns evolved; lower sensitivity areas increased alongside urban expansion, while higher sensitivity zones (High and Extremely High), concentrated around the Xiaozhu, Dazhu, and Cangma–Tiejue Mts, expanded notably. The expansion of these higher sensitivity zones suggests potential environmental improvement attributed to enhanced conservation efforts. Future simulations show that the EP scenario best aligns with sustainability goals, maximizing the extent of High and Extremely High sensitivity areas by 2030 compared to the ND and UD scenarios.

The Nottingham recovery from COVID-19 research platform (NoRCoRP): Functional, clinical and patient-reported outcomes in adults referred to a post-COVID respiratory service

Aims To characterise symptoms, function and patient-reported outcome measures (PROMs), and identify associated factors in adults with persisting respiratory symptoms post-COVID. Methods Cross-sectional analysis of 210 non-hospitalised adults referred to a post-COVID respiratory clinic (December 2020-July 2024) who consented to research. Assessments included demographics, symptoms, lung function, chest CT, and several PROMs: MRC dyspnoea score, Nijmegen Questionnaire score (NQ), Hospital Anxiety and Depression Scale, Chalder Fatigue Scale, Short Physical Performance Battery (SPPB) and Fried Frailty Index. Multivariate logistic regression examined key exposure-outcome associations. Results Among participants (mean age 49.4 years; 68% female; median 13.3 months since COVID-19 diagnosis), 95% reported shortness of breath, 54% had clinically significant breathlessness (MRC ≥ 3), 68% had an NQ score (>23) consistent with dysregulated breathing, 32% had a low SPPB score (<10), and 77% were classed as frail/pre-frail, despite the majority being of working age. Nearly half (47%) of those employed pre-infection had not returned to previous hours. Spirometry and CT abnormalities were not common. Higher body mass index (odds ratio = 1.10, 95% confidence interval = 1.05–1.16, n in model = 190) and depression (2.25, 1.13–4.56, n = 164) were associated with MRC ≥ 3. Dysregulated breathing was associated with female sex (3.63, 1.77–7.60, n = 186), current/ex-smoker (2.56, 1.25–5.47, n = 186), fatigue (8.87, 2.59–37.0, n = 162), anxiety (3.57, 1.70–7.69, n = 162) and depression (5.70, 2.59–13.40, n = 162). A low SPPB score was associated with female sex, current smoking, depression, clinically significant breathlessness, dysregulated breathing, and greater deprivation. Conclusion In non-hospitalised patients with persistent respiratory symptoms post-COVID, dysregulated breathing, deconditioning and psychological distress were key factors linked with symptom burden. These findings suggest a multidisciplinary approach should be considered to optimise recovery.

Are obesity drugs causing a severe complication? What the science says

DNA-based characterization of rays (Elasmobranchii: Batoidea) from Bangladesh using mitochondrial markers: Implications for conservation and management

Rays are an iconic group of chondrichthyan fishes, with many species currently threatened with extinction. Although conservation laws exist in Bangladesh to protect their population, lack of comprehensive law enforcement strategies together with commercial exploitation and habitat destruction resulted in population decline of many species nonetheless. One significant challenge to this conservation effort is a rapid and authentic species identification strategy, as traditional morphological diagnosis is hindered by frequent misidentification, especially when species are morphologically similar or when specimens are damaged or missing key features. The emergence of DNA barcoding technique can overcome this barrier, requiring only a small tissue sample for authentic identification. In the present study, this state-of-the-art technique has been employed for species identification of rays using three different mitochondrial marker gene, namely 16s rRNA, COI, and NADH2. A total of 94 new barcode sequences were generated, including 43 COI, 31 16S rRNA, and 20 NADH2 sequences, representing 23 ray species across 15 genera, 9 families, and 3 orders. Mean genetic distances varied across markers: for COI, 0.23 within species, 7.60 within genera, and 17.34 within families; for 16S rRNA, 0.04, 5.49, and 8.72, respectively; and for NADH2, 0.22, 13.52, and 20.72, respectively. Based on genetic divergence, barcode gap, and phylogenetic resolution, NADH2 proved to be a valuable alternative marker to COI for species-level identification. In contrast, 16s rRNA displayed the lowest divergence limiting its discriminatory power for species-level identification. Approximately 82.61% of our recorded species are categorized as different threatened categories (CR, EN, VU, NT) under the IUCN Global Red List. However, only 4 species are listed in CITES Appendix II for protection, leaving the majority of the ray species vulnerable for exploitation. Furthermore, several Schedule I and II species under Bangladesh Wildlife Act are openly traded in domestic market despite their supposed protection. This study highlights the urgent need to raise awareness among fishing communities and to strengthen measures against this illegal trade of ray species listed under national wildlife protection schedules.

Exploring the effects of IFN-τ on LPS-induced endometritis in cows based on transcriptomics

Endometritis in dairy cows is a common reproductive disease that severely affects reproductive performance and milk production, resulting in significant economic losses. Lipopolysaccharide (LPS) as a PAMP capable of inducing inflammatory responses in the endometrium through the NF-κB pathway. Interferon-tau (IFN-τ) is a type I interferon with significant anti-inflammatory properties. Currently, transcriptomics sequencing technology has gradually become an attractive tool for studying such diseases. This study established an inflammatory model of bovine endometrial cells (BENDs) using LPS induction and employed RNA-seq technology to investigate the expression profiles of mRNAs in BENDs from the Control group (C), the LPS-treated group (L), and the IFN-τ + LPS-treated group (F). The results showed that there were 109, 1109, and 962 Differentially Expressed mRNAs (DEmRNAs) in the C vs L, C vs F, and L vs F. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these DEmRNAs were mainly involved in the regulation of host immune responses (e.g., NOD-like receptor signaling pathway, IL-17 signaling pathway and RIG-I-like receptor signaling pathway, NF-kappa B signaling pathway), signal transduction molecules and interactions (e.g., Cytokine-cytokine receptor interaction; Cell adhesion molecules), metabolic process (e.g., Glycosphingolipid biosynthesis-lacto and neolacto series) and Antigen processing and presentation, Complement and coagulation cascades, Th17 cell differentiation, etc. biological process. This study not only elucidates the molecular mechanisms by which BENDs respond to microbial invasion but also reveals the specific regulatory network through which IFN-τ exerts its anti-inflammatory effects via multiple synergistic pathways. It provides crucial theoretical support for the clinical application of interferon therapy in treating endometritis, demonstrating significant research value and promising applications.

Vision-Controlled autonomous navigation in unstructured environments: Integrating image processing, path planning, and trajectory control in robotic systems

Advancements in artificial intelligence (AI) have driven robotics to the forefront of technological innovation, enhancing productivity and safety across industries. Autonomous navigation, especially in unstructured environments with irregular terrains and dynamic obstacles, remains a key challenge. This paper introduces a vision-controlled autonomous navigation framework that enables robots to traverse complex environments using only vision sensors and image processing. The system integrates visual segmentation, optimized path planning, and advanced trajectory tracking. Key contributions include: (1) Semantic Mapping and Localization – A target detection network generates a global semantic map from local views, enhancing perception without external markers; (2) Improved Path Planning – The RRT-connect algorithm is refined for safer, adaptive navigation in unpredictable terrains; (3) Accurate Trajectory Control–A Soft Actor-Critic (SAC)-based model reduces tracking errors and enhances path-following precision; (4) Empirical Validation – Experiments with a magnetic miniature robot in unstructured environments confirm the system’s robustness and accuracy. The proposed framework addresses existing limitations, paving the way for more autonomous and resilient robotic systems in complex environments.

Experimental investigation of shape-enhanced rotating cylinders with electric heaters and solar panels for augmented pyramid solar still performance

This work investigated the influence of various amendments on a pyramid solar still with rotating cylinders (RCPSS) and rotating corrugated cylinders (RCCPSS). We compared distillate yield from the RCPSS with a baseline design (PSS) to assess the effectiveness of each modification. The study explored incorporating reflectors, silver nanoparticle-infused phase change material (PCM-Ag) composites within the cylinders, and a vapor-withdrawing fan with an external condenser. In addition, three electric heaters were fixed on the basin water to raise its temperature. The energy required to run the heaters was captured from a PV system. Also, the effect of covering corrugated cylinders with wick on the performance of the modified still was also studied. The RCCPSS significantly outperformed the PSS, producing 8500 mL/m² of freshwater daily compared to 3100 mL/m², representing a 174% increase in distillate. Additionally, heaters and PCM-Ag composites further enhanced distillate yield by 244% and 365%, respectively. However, the most optimal configuration involved combining a wick, heaters and fan. This setup yielded the highest distillate production (14950 mL/m²), a 382% increase over PSS, and achieved a thermal efficiency of 75%. Finally, the freshwater production cost was lower for the RCPSS with wick, heaters and fan ($0.01/L) compared to the PSS ($0.02/L). The work demonstrates a strong commitment to advancing the United Nations Sustainable Development Goals (SDGs), particularly SDG 6: Clean Water and Sanitation.

Applying machine learning to predict stunting in children under 5 years old based on water, sanitation and hygiene behaviors and infrastructure

Objective Child stunting continues to pose a substantial global health challenge, requiring multifaceted strategies that combine conventional epidemiological approaches with advanced analytic methods. The aim of this study was to determine the most effective machine learning model for predicting stunting based on water, sanitation, and hygiene behaviors and infrastructure, with the goal of identifying high-risk children who would benefit most from targeted interventions. Methods This study was a secondary analysis of data from a matched cohort study assessing the effectiveness of combined on-premise piped water and improved sanitation for improved health outcomes in rural Odisha, India. Data for the parent study were collected from 2,398 households with a child under five years of age across 90 villages, and complete data were available for 1,196 children. Feature engineering techniques were employed to identify the most relevant predictors and utilized structural equation modeling, forward selection, backward elimination, and least absolute shrinkage and selection operator techniques. Five machine learning algorithms commonly used for binary classification tasks were compared: logistic regression, classification tree, support vector machine, neural network, and extreme gradient boosting. Results Among 1,196 children analyzed, the extreme gradient boosting model with forward selection feature engineering best predicted stunting based on water, sanitation, and hygiene (WaSH) factors. It correctly identified 81% of stunted children and 92% of non-stunted children, with an overall accuracy of 88%. The model’s area under the receiver operating characteristic curve (AUROC) was 0.959 (95% CI: 0.949–0.968), indicating that WaSH factors strongly predict child stunting when analyzed using this advanced machine learning technique. Four WaSH factors were identified as having the strongest power to predict stunting in our sample: improved sanitation coverage, presence of a handwashing station, piped water coverage, and availability of preferred drinking water source. Conclusions The results demonstrate the efficacy of machine learning algorithms, especially extreme gradient boosting to potentially inform targeted WaSH interventions for reducing childhood stunting in resource-limited settings. However, these findings require external validation in other populations, and the complete-case analysis approach (excluding 35% of children with missing data) may limit generalizability to settings with less systematic data collection.

Measurement of urban vitality and the influence mechanism of the built environment on it based on multi-source data: A case study of Yantai City

Under a human-centered approach, accurately identifying the spatial patterns of urban vitality and revealing the mechanisms through which the built environment affects it can scientifically guide the organic cultivation of urban vitality. In light of this, the main urban area of Yantai City is taken as a case study, utilizing multi-source geographic big data to conduct both theoretical and empirical research. An index system for the urban built environment is established based on four dimensions: human perception, functional, accessibility, and building form. Advanced methods, including Deep Fully Convolutional Neural Networks (SegNet), Random Forest Regression (RFR), and Spatial Lag Regression (SLR), are employed to explore the impact of the built environment on urban vitality. The research findings indicate that: (1) Urban vitality presents a composite spatial structure that embodies both “multi-center” and “clustered” characteristics, exhibiting two primary types of local spatial autocorrelation: “high-high” clustering and “low-low” clustering. (2) The disparities in urban vitality reflect an imbalance in the distribution of functional, accessibility, building form, and human perception, with functional playing a more critical role in nighttime and daytime urban vitality than other dimensions. (3) The effects of the built environment on daytime and nighttime urban vitality show varying degrees of heterogeneity regarding significance and direction. Factors such as BPOI(Commercial Points of Interest), integration, accessibility, and vibrancy have a substantial positive impact on vitality clustering, while human perception becomes increasingly important for enhancing nighttime vitality. These results provide refined technical support for urban micro-renewal, enhancing the relevance and effectiveness of response strategies.