Browse Articles

Computer-aided diagnosis system for thoracic computed tomography of rib fractures in older emergency patients: A preliminary study

The peculiarities of older individuals related to osteoporosis and hyperostosis may lead to a higher rate of misdiagnosis of rib fractures on computed tomography (CT) images in older than in middle-aged/young people when using radiologist-only reading. However, none of these studies on rib fracture computer-aided diagnostic (CAD) systems grouped patients by age or evaluated the value of using CAD in older patients. To address these gaps, we divided 1,012 blunt chest trauma emergency patients who underwent chest CT into middle-aged/young and older groups with a cutoff age of 60 years. CT images were read by six radiologists from three institutions (each with 7 years of experience in thoracic CT diagnosis) using two reading methods, radiologist-only and radiologist-CAD reading, to explore the value of a deep learning (DL)-based CAD system for detecting rib fractures in emergency older patients. The final findings of the independent panel consisting of two senior radiologists with or without a thoracic surgeon were set as the reference standard. The sensitivity was calculated by dividing the number of true positives by the overall number of fractures, as confirmed by an expert panel. The false positives per patient (FPPP) was calculated by dividing the number of false positives by the overall number of patients. Sensitivity and FPPP were used to evaluate the diagnostic efficiency. Sensitivity, FPPP, and reading time were compared between the two groups, as well as reading methods. The results showed the following: (1) Sensitivity for detecting fresh fractures using radiologist-only reading was lower in the older than in the middle-aged/young group (86.7% [95% confidence interval (CI): 86.3%, 91.7%] vs. 91.5% [95% CI: 89.9%, 92.9%], p  < 0.05). With the assistance of the CAD system, the sensitivity increased in the older group to the same level as that in the middle-aged/young group using radiologist-only reading (92.5% [95% CI: 90.4%, 94.2%] vs. 91.5% [95% CI: 89.9%, 92.9%], p  > 0.05). (2) The FPPP of fresh fractures with radiologist-only reading was higher in the older than in the middle-aged/young group (0.47 vs. 0.37, p  < 0.05). With the assistance of the CAD system, the FPPP in the older group decreased to the same level as that in the middle-aged/young group when using radiologist-only or radiologist-CAD reading (0.37 vs. 0.37/0.39, p  > 0.05). (3) The reading time of fresh fractures when using radiologist-only reading was longer in the older than in the middle-aged/young group (6.1 vs 5.4 min, p  < 0.05). With the assistance of the CAD system, the reading time in the older group was reduced by approximately 36% ( p  < 0.05). We conclude that the efficiency of intermediate-level radiologists in diagnosing fresh rib fractures by radiologist-only reading in older emergency patients was lower than that in middle-aged/young patients. When a DL-based CAD system assists radiologists, the diagnostic efficiency of identifying fresh fractures in older patients improves to the same level as independent radiologist-only reading in middle-aged/young patients while reducing the reading time.

All‑cause excess mortality in Germany peaked during the late‑2022 influenza period, exceeding peaks during SARS‑CoV‑2 waves (2020–2023)

Introduction Numerous studies have examined mortality during the SARS-CoV-2 pandemic in Germany and worldwide. In Germany, excess mortality was highest in 2022 compared to the other pandemic years. In a small-scale analysis in Frankfurt am Main, Germany, the excess mortality 2022 was associated with an influenza wave at the end of the year. The aim of this study was to investigate this for the whole of Germany. Methods We used publicly available data for the number of deaths, for the population data and for notifications of SARS-CoV-2 and influenza. Standardized mortality ratios (SMR) were estimated for Germany for the years 2020–2023, for seven SARS-CoV-2 waves and for the influenza wave at the end of 2022. Expected numbers of deaths were estimated by two methods: in the first, average mortality in the pre-pandemic years 2016–2019 was used; in the latter, an exponential extrapolation was used to consider the increase in life expectancy. Results Relative excess mortality was highest in 2022 (SMR = 1.069 (95% confidence interval: 1.066–1.071) by method 1, SMR = 1.094 (1.092–1.096) by method 2). During the influenza wave from calendar week 47 / 2022 to calendar week 1 / 2023, the SMR was higher than that of any SARS-CoV-2 wave: SMR = 1.252 (95% CI: 1.246–1.258) by method 1, and SMR = 1.374 (95% CI: 1.367–1.380) by method 2. Among all waves considered, the mean number of excess deaths per week was highest during the influenza wave by both methods (5,043, and 6,812, respectively). Age-stratified analyses showed that the excess mortality during the influenza wave at the end of 2022 was highest in individuals aged 70 years and older. Discussion During an influenza wave at the end of 2022, excess mortality was higher than in any SARS-CoV-2 wave in 2020–2022 in Germany. Because this study is based on all‑cause mortality and population‑level surveillance indicators, it cannot establish causation or quantify the proportion of excess deaths directly attributable to influenza infection.

Correction: Global learning opportunities within social innovation in health (GLOWS): A modified Delphi process to identify and pilot core competencies for learning

Modeling diffuse midline glioma through triple-electrode in utero electroporation of the developing mouse pons

The leading cause of brain cancer–related death in children is diffuse midline glioma (DMG). A particularly aggressive DMG subtype is pontine DMG (formerly diffuse intrinsic pontine glioma, DIPG), which is caused by the histone mutation H3.3K27M. Because of its diffuse growth and location in a critical brainstem structure, therapeutic options are limited, and pontine DMG is considered universally fatal. The lack of appropriate animal models has hindered our understanding of the developmental origins and progression of pontine DMG, which in turn has limited the development of effective therapeutics. To address this barrier, several labs have developed mouse in utero electroporation approaches to express canonical DMG mutant oncogenes in the developing pons. In this manuscript and accompanying protocol, we describe a modified in utero electroporation strategy to generate DMG tumors in the developing mouse pons. Our protocol incorporates a single plasmid construct that expresses canonical DMG oncogenes, eliminating the need to co-electroporate multiple plasmids. We also employ a triple-electrode configuration to precisely target neural progenitors lining the fourth ventricle, which give rise to cells in the pons. As the embryos continue to develop in utero and postnatally, they form large, diffuse brainstem tumors with molecular characteristics of pediatric pontine DMG, allowing us to model the formation and progression of this deadly pediatric brain cancer.

Cortical development dynamics across autism spectrum disorder mouse models

Abstract Despite the functional diversity of over 100 causal genes 1–3 , phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogenic mouse models of ASD using single-nucleus multi-omic sequencing across three developmental stages, both sexes and two brain regions. Despite genetic heterogeneity, ASD-linked mutations converged on perturbations of the radial glial cell lineage. These alterations reflect a transient developmental delay rather than lasting lineage misspecification and resolve by postnatal stages. Molecularly, the largest transcriptional differences emerged in neurons at early postnatal stages. These changes included downregulation of synaptic and ion channel-related genes, consistent with homeostatic adaptation or delayed maturation. Network analysis showed molecular convergence across models within each developmental stage, suggesting that diverse mutations linked to ASD impinge on common, stage-specific processes. Convergence becomes less pronounced by postnatal day 14, highlighting the dynamic nature of ASD-associated changes. Cross-genotype heterogeneity is superimposed on stage-specific effects. Electrophysiology corroborated this pattern: mutants generally showed altered neuronal excitability and synaptic properties with model-specific nuances. Our study also highlighted sex-specific gene expression alterations, with female mice often displaying larger effect sizes than male mice. Together, our findings provide a comprehensive view of developmental cellular and molecular dynamics across models of ASD.

Phage mediated growth inhibition and biofilm disruption of the endodontic pathogen Enterococcus faecalis

The failure of endodontic procedures such as root canal therapy is primarily indicated by persistent microbial infections. Root canal therapy involves the removal of decaying dental pulp (internal blood vessels and nerves of teeth), sanitizing the canal, and filling the space with biocompatible materials. Improper cleaning or the breakdown of these materials can lead to secondary infections. These infections, if left untreated, can lead to severe pain, bone and tooth loss, and potentially systemic infection. The bacterium Enterococcus faecalis is one of the most commonly associated organisms with failed root canal therapy, in addition to its prevalence in urinary tract infections, endocarditis, wounds, and sepsis. E. faecalis is known to survive in low nutrient environments and produce extensive biofilms, making it difficult to eradicate. In addition to antibiotic treatment, bacteriophages (phages), which are bacteria-specific viruses that kill their host are an interesting companion or alternative to antibiotics. In this study we isolated and characterized 14 E. faecalis phages from wastewater samples by testing their host range, growth inhibition, and biofilm eradication capabilities against several E. faecalis strains including two that were orally derived. Several phages showed broad host ranges (up to 16 strains), strong bacterial growth inhibition even when applied at low concentrations, and significant eradication of mature biofilms (97% reduction). The phages presented here represent a unique repertoire of antibacterial agents for use in treating endodontic infections and add to the growing library of E. faecalis phages to treat diverse infections.

Construction and validation of an instrument to assess the university dropout intention formation process UDIFP-29

A significant body of research supports the importance of addressing university students’ dropout intention, the process through which university students form the intention to dropout from their studies. However, available measurement tools failed to account for how dropout intention is formed over time. This research aimed to fill that gap by developing and validating a multidimensional self-report instrument to assess how the intention to dropout from the university is formed. Three studies were designed for this purpose. Study 1 used focus groups to explore how the intention to dropout emerges in freshman students enrolled at a university, with the purpose of developing a theoretical model to explain how the intention to dropout emerges. Study 2 focused on the content validation of the preliminary version of the instrument, which was carried out by expert judgment. Study 3 examined the construct validity of the instrument using exploratory and confirmatory factor analysis and analyzing its convergent and divergent validity. The results showed a model with good adjustment and adequate internal consistency indexes. In general, the instrument has adequate psychometric properties, proving to be reliable and valid for measuring the process of forming early university dropout intention. Use of this instrument will allow the design of timely strategies and interventions to reduce dissatisfaction, dropout ideation and dropout.

CHPO coordinates chilling recovery and nitrogen use in rice

A semantic segmentation model to predict subcellular glycogen localization using transmission electron microscopy images

Transmission electron microscopy (TEM) is the gold standard for assessing subcellular glycogen localization in skeletal muscle fibres, but conventional manual analysis is extremely time-consuming and limits large-scale studies. Here, we developed and validated a deep learning–based semantic segmentation approach to automate quantification of glycogen particles across defined subcellular compartments in human skeletal muscle. Skeletal muscle biopsies were obtained from seven healthy men under conditions of normal, depleted, and supercompensated glycogen content. TEM images were acquired from myofibrillar and subsarcolemmal regions and manually annotated to train two complementary attention U-Net models: a region model identifying subcellular structures (intermyofibrillar space, intramyofibrillar regions including A-band, I-band and Z-disc, and mitochondria) and a glycogen model detecting individual glycogen particles. Combining the two models enabled estimation of compartment-specific glycogen areal densities. The model’s outcome was evaluated against manual point-counting. At the fibre level, estimates based on 10–12 images per region achieved biases below 15% and coefficient of variation below 26% for all compartments. Importantly, model-derived total glycogen volume density showed strong concordance with biochemically determined muscle glycogen content across biopsies. In conclusion, this validated semantic segmentation workflow provides an objective and highly time-efficient tool for quantifying subcellular glycogen distribution in skeletal muscle. The model substantially reduces analysis time and enables high-throughput investigations of compartmentalized glycogen metabolism, with model weights and code made openly available.

DNA from hunter-gatherer teeth reveals secrets of ancient plague

REPROGRAM: REsilience PROmotion with GeRoprotectors: AssessMent of biological effect: Rationale and protocol for a trial of biological effect

Background Ageing is associated with reduced resilience to physiological stressors such as infection and surgery. This reduced resilience is believed to be underpinned by the hallmarks of ageing, the key biological mechanisms driving the aged phenotype. Geroprotectors are drugs that are proposed to slow down the ageing process and promote longevity and healthspan. Despite this, mechanistic studies in healthy older adults are lacking. Methods and analysis This trial will test the hypothesis that geroprotectors targeted towards biological mechanisms associated with poor resilience can reverse these pathways within a three-week period. Three geroprotectors with a good safety profile in older adults and evidence of effect on the hallmarks of ageing will be administered to 60 (30 female; 30 male) adults 70 + . Participants will be randomised to one of three arms (Metformin MR 1500 mg, Fisetin 100 mg or Spermidine 15 mg). Participants will be extensively clinically characterised at baseline. Blood, abdominal adipose tissue and stool samples will be taken at baseline and following the three-week intervention. The primary research question will answer whether a three-week course of Metformin, Spermidine, or Fisetin reduce the number of senescent cells as measured by SA-β-GAL in adipose biopsies in healthy older volunteers. Additionally, there will be assessment of the effect of the geroprotectors on other hallmarks of ageing, including autophagy, immunosenescence, chronic inflammation, dysregulated mTOR signalling, epigenetic age, DNA damage, dysregulated metabolism, stem cell exhaustion and microbial composition. Ethics and dissemination Ethical approval is in place (24/LO/0549). The main trial report and any sub-studies will be published in high impact peer-reviewed gerontology journals, presented at academic conferences and through a series of public engagement events. Participants enrolled in the study will be informed of the results by a written summary. Trial registration REPROGRAM was registered with ISRCTN on 10/09/24. ISRCTN47919839. Available at https://www.isrctn.com/search?q=47919839 .

Reimagining machine vision with optical computing

“You never know what’s in front of you”: A mixed methods study of barriers and facilitators to physical activity among blind and low-vision adults with type 2 diabetes

Individuals living with type 2 diabetes and vision loss experience unique, under-researched barriers to physical activity, despite the important role exercise plays in diabetes management. This study examined physical activity participation among blind and low-vision adults with type 2 diabetes ( n  = 30). Participants, ages 44–83, resided in Bexar County, Texas. Closed-ended surveys (conducted in English or Spanish) were administered to participants, followed by in-depth, semi-structured interviews. Interviews were audio-recorded, transcribed verbatim, and double-coded. Participants’ physical activity levels were determined using the Exercise Behaviors Scale. Data were analyzed using a convergent inductive-deductive design. Consistent with the Social-Ecological Model, five main themes and 11 subthemes emerged, depicting how individual/intrapersonal, interpersonal, institutional/organizational, community, and policy-level factors influenced participants’ physical activity participation. These factors can be specifically targeted both at local and broader levels to promote physical activity among individuals with concurrent type 2 diabetes and blindness/vision loss.

A hybrid transformer-BiLSTM model optimized with Firefly Algorithm for network traffic anomaly detection

Network Traffic Anomaly Detection (NTAD) is essential for proactive cyber defense against increasingly sophisticated threats. This paper presents a data-driven framework that integrates adaptive signal decomposition, a hybrid attention-recurrent architecture, and metaheuristic optimization for timely anomaly prediction. Raw traffic sequences are first preprocessed via Empirical Mode Decomposition (EMD) to mitigate non-stationarity and suppress noise, yielding denoised intrinsic mode functions. The refined signal is then modeled by a hybrid deep network that couples a multi-head self-attention mechanism—capturing global, long-range dependencies—with a Bidirectional Long Short-Term Memory (BiLSTM) network that encodes bidirectional temporal dynamics. To circumvent the sensitivity of deep models to hyperparameter selection, the Firefly Algorithm (FA) is employed for automated, population-based optimization. Extensive evaluations on benchmark datasets demonstrate that the proposed EMD-FA-Transformer-BiLSTM model attains state-of-the-art performance, outperforms baseline and state-of-the-art models across all evaluated metrics, with statistically significant improvements in both regression error and classification F 1 -score.

Compositionally Complex Doping Enables High-Voltage Spinel Cathodes with Ultrafast Charging and Chemo-Electro-Mechanical Stability

Abstract Next-generation lithium-ion batteries demand high-voltage cathodes that combine exceptional stability with ultrafast charging capability. Cobalt-free spinel-type oxides, owing to their high operating voltage, energy density, and cost effectiveness, are leading candidates, yet their cycle life is still constrained by intrinsic chemo-electro-mechanical instabilities. Here, by leveraging compositionally complex doping, we reconfigure the reaction thermodynamics of a high-voltage spinel cathode by extending its solid-solution regime to higher states of charge, enabling ultrafast charging while maintaining robust chemo-electro-mechanical stability. Multimodal characterization reveals that the reshaped reaction pathway effectively suppresses high-temperature intragranular cracking, interfacial rock salt phase transformation, and parasitic byproduct accumulation, thereby preserving efficient three-dimensional Li+ diffusion. The cathode delivers unprecedented ultrafast-charging durability, achieving 81.8% after 4000 cycles at 10 C (25 °C) and 82.0% after 1000 cycles at 3 C (60 °C). Our work demonstrates that compositionally complex doping can effectively modulate the thermodynamics of phase transformation and enhance the chemo-electro-mechanical stability of high-voltage spinel cathodes, providing new insights into the design of durable fast-charging cathode materials.

Maternal exposure to oil spill and children’s mental health: The mediating role of resource loss

Objectives To assess the effect of maternal oil spill exposure on children’s mental health and examine whether maternal resource loss mediated this relationship. Methods 445 mother-child pairs from the Women and Their Children’s Health Study in Louisiana (2012–2016) were examined. Maternal oil spill exposure from the 2010 Deepwater Horizon Oil Spill was measured at Wave 1 (2012–2014), while children’s mental health and maternal resource loss were assessed at Wave 2 (2014–2016). Structural equation modeling estimated the direct and indirect effects of maternal oil spill exposure on children’s mental health through maternal resource loss. Results Mothers (mean age 42 years (SD 7.7) and children (mean age 13 years (SD 2.2)) were interviewed. Maternal oil spill exposure was not directly related to children’s mental health (est = −0.08, p = 0.436). However, exposure was associated with increased maternal resource loss (est = 0.45, p < .0001), which was associated with worse children’s mental health (est = 0.27, p < .0001). Maternal oil spill exposure was compatible with an indirect negative effect on children’s mental health (i.e., worse mental health scores) mediated by resource loss (est = 0.12, p < 0.005). Conclusion Findings suggest maternal oil spill exposure indirectly affects children’s mental health through resource loss. Interventions mitigating resource loss are vital for supporting children’s mental well-being after disasters.

Mechanical behavior and constitutive modeling of CO2-foamed carbonated MgO-based solidified slurry under Unconsolidated Undrained (UU) triaxial conditions

Geotechnical construction activities, such as shield tunneling and pile foundation construction, generate large amounts of high-water-content waste slurry, making its disposal and resource utilization challenging. This paper introduces a CO 2 -foamed carbonation solidification technique using a composite binder system made of reactive magnesium oxide (MgO), slag, and calcium carbide slag. The mechanical behavior of the lightweight carbonated slurry was investigated through Unconsolidated Undrained (UU) triaxial compression tests under various confining pressures. Quantitative results indicate that the reactive MgO dosage significantly governs strength evolution; an optimum MgO dosage of 10% yielded the highest peak stress of 190.8 kPa (at 50 kPa confining pressure), while the softening coefficient peaked at 0.46 with a 13% dosage. Regarding the CO 2 foam impact, varying the foam content from 36% to 52% resulted in softening coefficients ranging between 0.20 and 0.50. The 36% foam dosage provided the optimal structural stability with a peak stress of 179 kPa, whereas excessive foam leads to greater pore connectivity, worsening strain-softening. Confining pressure significantly affects the material’s hardening and residual strength. Based on these findings, an improved constitutive model was developed using damage evolution and a modified Duncan–Chang framework, providing accurate descriptions of the material’s elastic, hardening, post-peak softening, and residual characteristics. This study advances our understanding of CO 2 -foamed carbonated MgO-based slurry, offering new insights for both solidification/stabilization and carbon sequestration of high-water-content waste slurry.

Towards autonomous medical artificial intelligence agents

Abstract Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather than systems integrated into clinical workflows 1,2 . However, building physician copilots will require models that operate within the electronic health record (EHR), with governed access to patient data and the ability to initiate permitted EHR actions within defined safety constraints. Yet it remains unproven whether such a system can manage patient cases with physician-level performance. Here we show that MIRA (Medical Intelligence for Reasoning and Action), an autonomous artificial intelligence agent operating in a sandboxed EHR environment, can navigate a large clinical action space to obtain patient histories; order and interpret laboratory, imaging and microbiology tests; generate differential diagnoses; and formulate treatment plans such as prescribing medications, scheduling surgical procedures and planning admissions. In simulations on real patient cases spanning multiple diagnoses, MIRA outperformed physicians in diagnostic accuracy and made guideline-concordant, medication-safe and appropriate admission decisions. Compared with previous LLM applications that addressed isolated subtasks or provided free-text advice, these results suggest that an EHR-integrated artificial intelligence agent can turn clinical intent into structured, actionable EHR operations, possibly making it a more effective decision-support partner for physicians. Further work is needed to establish generalization, safety and governance through prospective, real-world studies.

Siderophore screening in marine sponge extracts using LC-HRMS and an R-based metabolomics workflow

Siderophores are pivotal ‌‌iron-acquisition biomolecules integral to microbial survival, pathogenicity, and ecology. Elucidating these compounds offers critical insights into the microbial dynamics of marine holobionts and potential therapeutic applications. In this study, we present a culture-independent, data-centric strategy to annotate siderophores from the body mass of three marine sponge species: Dragmacidon reticulatum , Aplysina fulva , and Amphimedon viridis . Utilizing Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) coupled with a custom R-based analytical workflow (XCMS and MetaboAnnotation), we putatively annotated 59 siderophores. We employed a validation pipeline, utilizing iron-adduct calculations [M-2H + Fe] + , [M-H + Fe] 2+ , [2M-2H + Fe] + , mass accuracy thresholds (<3 ppm), retention time deviation (Coefficient of variation < 2%), and chromatograph peak analysis. According to the Metabolomics Standards Initiative (MSI), these annotations correspond to Level 2 (putatively annotated compounds) because they are based on accurate mass matching without chemical standard confirmation. Notably, iron supplementation during extraction did not significantly alter siderophore detection, suggesting constitutive production or environmental saturation. This workflow bypasses the limitations of traditional cultivation, revealing a diverse landscape of iron-chelating metabolites, including Ferricrocin, Aeruginic acid, and Madurastatin directly within the sponge’s body.

“The impact of COVID-19 on employee productivity and the future of remote work”

Purpose This paper explores the impact of the COVID-19 pandemic on employee productivity and the future of remote work beyond the pandemic years. Using data collected from 646 remote workers, we examined the influence of interpersonal relations, time management, stress, and skill development on productivity. Design/methodology/approach We validated our model using structural equation modelling (SEM), and the results revealed that the sudden shift to remote work had no significant negative impact on productivity. Findings The results of this study may encourage HR managers to expand remote work opportunities, thereby enhancing talent attraction and retention in a highly competitive job market. Originality/value Despite prevalent concerns about COVID-19 and the evolving nature of workplaces, employees perceived remote work as offering greater flexibility in task organization and the opportunity to create a healthier work-life balance. Furthermore, our findings suggest that HR managers should consider allowing remote work post-pandemic as a strategic aspect of workplace evolution.