Heliyon

Robot-assisted hematoma puncture has seen significant development in primary hospitals across the country. Sino Plan software system is the core of the intelligent surgical robot, independently developed by Sinovation.We conducted a comparative study of imaging indicators, such as residual hematoma volume and hematoma clearance rate, as well as prognostic indicators, in patients who underwent hematoma puncture at our hospital over a 9-year period, before and after the introduction of Sino Plan.The results indicated that following the application of Sino Plan, the hematoma clearance rate was significantly enhanced, and the residual hematoma volume was markedly reduced. Regarding patient prognosis, there was no significant difference in GCS scores between the two groups, but the incidence of adverse prognostic events was lower in patients where Sino Plan was utilized.In conclusion, this 9-year retrospective analysis at our hospital reveals that Sino Plan offers distinct advantages. However, its application in certain special cases suggests that further improvements to the software are warranted to better meet the demands of more specific clinical scenarios.

Cancer-related genomic instability (GI) may cause genetic alterations in spermatozoa, implying health issues not only in cancer survivors, but also in their children [1, 2]. We therefore studied Loss of Y chromosome (LOY), considered as hallmark of GI [3-15], in spermatozoa and blood from survivors of childhood and testicular cancer (CC, TC), and controls (CTRL). We found that LOY was statistically significantly more frequent in spermatozoa from cancer survivors than in controls (Odds Ratio [OR]=2.2 for CC vs. CTRL and OR=2.4 for TC vs. CTRL). Furthermore, LOY was about an order of magnitude more prevalent in spermatozoa than in blood among 18-53-year-old males within all cohorts. Our findings suggest that LOY in spermatozoa might be a clinically useful marker of GI, reduced fertility and disease predisposition in males. Introducing LOY in spermatozoa as a biomarker opens a new research avenue into disease prevention and the causes and consequences of LOY.

Early identification of promising drug research topics is challenging yet crucial for the scientific community to accelerate the development of novel therapeutics. In this work, we leverage large-scale public data from the biomedical literature to extract predictive features to identify promising therapeutic research topics at an early stage. We divide the global citation graph of biomedical literature into a time series of research topics and extract topic features based on citation activity, publication content, and measurable flocking of scientists into novel research topics. Based on these features, our machine learning model identifies research topics that in the future yield Food and Drug Administration (FDA)-approved drugs years before approval (F1-score of 0.84). 80% of target drugs are predicted in advance, with 65% predicted 8 or more years before approval. This predates the start of phase 2 clinical trials in the vast majority of positive predictions. These results show this approach can efficiently flag research topics generating approved drugs several years prior to approval using public data that would have been contemporaneous at the time of prediction. Thus, reliable forecasting can be accomplished with a high-level view of the publication and citation behavior of scientists, without depending on clinical trial data that may only be deposited with a significant lag. This demonstrates that it is possible to detect early signals of future FDA approved therapies even without any specialized information about these applied research efforts. TeaserLarge-scale data analysis can use the full set of scientific citations to predict which areas of research will yield new FDA approved drugs, years in advance.

Road traffic accidents represent a global public safety crisis, necessitating advanced computational tools for accurate injury severity prediction and effective decision support. This study evaluates high-performing ensemble machine learning models, including AdaBoost, XGBoost, LightGBM, HistGBRT, CatBoost, Gradient Boosting, NGBoost, and Random Forest, using a comprehensive National Highway Traffic Safety Administration (NHTSA) dataset from 2018 to 2022. While all models demonstrated exceptional predictive accuracy, with HistGBRT achieving the highest overall accuracy of 92.26%, a defining achievement of this work is the perfect classification (100% precision and recall) of fatal injuries across all ensemble architectures. To bridge the gap between predictive performance and actionable intelligence, this research integrates SHapley Additive exPlanations (SHAP) to provide both global insights into dataset-wide risk factors and local, instance-specific rationales for individual crash events. The global analysis identified ethnicity, airbag deployment, and harmful event type as primary drivers of injury severity, while local force and waterfall plots revealed the precise "push and pull" of variables for specific incidents. The results offer a robust, interpretable framework for stakeholders tasked with improving traffic safety and mitigating crash-related harm.

Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but have lagged behind on surgical image-analysis benchmarks. Since surgery requires integrating disparate tasks --- including multimodal data integration, human interaction, and physical effects --- generally-capable AI models could be particularly attractive as a collaborative tool if performance could be improved. On the one hand, the canonical approach of scaling architecture size and training data is attractive, especially since there are millions of hours of surgical video data generated per year. On the other hand, preparing surgical data for AI training requires significantly higher levels of professional expertise, and training on that data requires expensive computational resources. These trade-offs paint an uncertain picture of whether and to-what-extent modern AI could aid surgical practice. In this paper, we explore this question through a case study of surgical tool detection using state-of-the-art AI methods available in 2026. We demonstrate that even with multi-billion parameter models and extensive training, current Vision Language Models fall short in the seemingly simple task of tool detection in neurosurgery. Additionally, we show scaling experiments indicating that increasing model size and training time only leads to diminishing improvements in relevant performance metrics. Thus, our experiments suggest that current models could still face significant obstacles in surgical use cases. Moreover, some obstacles cannot be simply ``scaled away'' with additional compute and persist across diverse model architectures, raising the question of whether data and label availability are the only limiting factors. We discuss the main contributors to these constraints and advance potential solutions.

Introduction: Women aged 45-49 occupy a heterogeneous late-reproductive-life stage, but population research often treats them as a uniform group. This study examined correlates of Demographic and Health Survey (DHS)-defined infecund/menopausal status among Nigerian women aged 45-49. Methods: This cross-sectional secondary analysis used the 2024 Nigeria Demographic and Health Survey Women Recode dataset. Weighted descriptive statistics summarised reproductive exposure status among 3,237 women. Out of these, 3,110 women classified as either fecund or infecund/menopausal were subjected to Survey-adjusted Chi-square tests and Binary Logistic regression at p<0.05, where pregnant and postpartum amenorrhoeic women were excluded. Results: More than half of women were classified as infecund/menopausal (54.1%), while 41.5% were fecund; 3.2% were postpartum amenorrhoeic, and 1.3% were pregnant. Findings indicated that currently married/cohabiting women (AOR=4.87; 95% CI: 2.24-10.56) and formerly married women (AOR=8.30; 95% CI: 3.69-18.66) had higher odds of infecund/menopausal classification than women never in a union. Secondary education, higher education, middle-to-richest wealth quintiles, and five or more children ever born were associated with lower odds, while Northern minority ethnicity was associated with higher odds. Adding the current contraceptive method attenuated several education, wealth and parity associations; modern-method and traditional-method users had markedly lower odds than non-users. Conclusion: Late-reproductive-life exposure status among Nigerian women aged 45-49 is socially patterned, with union status showing the most stable association. DHS-defined infecund/menopausal status is a demographic exposure category rather than clinically confirmed menopause. It is therefore concluded that the cross-sectional associations should not be interpreted causally.

Introduction: Stroke care is time-sensitive, yet patients in low-resource settings may reach tertiary services only after passing through multiple formal and informal care options. This study examined documented care-seeking pathways and time to presentation among stroke cases recorded at the University of Medical Sciences Teaching Hospital (UNIMEDTH), Ondo State, Nigeria. Methods: A retrospective hospital record review was conducted using secondary data from the Stroke Registry, radiology department records, referral notes, and ambulance records at UNIMEDTH. The analysis included 371 stroke cases with documented time from symptom onset to UNIMEDTH presentation and reconstructable care pathways. First-contact routes were classified as hospital/biomedical, self/informal or traditional/faith-based care, and the number of documented steps defined pathway complexity before and including tertiary presentation. Frequencies and percentages described pathway patterns; median presentation times were compared using Mann-Whitney U and Kruskal-Wallis tests. Results: The median time to tertiary presentation was 24 hours (interquartile range [IQR] 9-72), and 317 patients (85.4%) presented after four hours. Only 30 patients (8.1%) presented directly to UNIMEDTH; 44 distinct care-pathway sequences were recorded. Hospital-facility first contact was documented for 81 patients (21.8%). It was associated with a median presentation time of 3 hours (IQR 2-6), compared with 48 hours (IQR 24-72) among patients whose initial contact was outside a hospital facility (U = 699.50, p < 0.001). The median time also differed across grouped first-contact categories and pathway complexity levels (both p < 0.001). Conclusion: Non-hospital or multi-step care-seeking pathways commonly preceded tertiary stroke presentations in this setting. The findings indicate that delayed tertiary arrival is partly embedded in the pathway followed after symptom onset. Interventions should combine public recognition of stroke warning signs with urgent referral linkages involving hospitals, patent medicine vendors, traditional and faith-based providers, and emergency transport systems.

Chimeric antigen receptor (CAR) T cell therapy utilizes genetically engineered patient-derived T cells to target cancer cells. Despite its clinical successes in multiple cancer types, the underlying molecular mechanisms by which molecules on CAR-T cells and surrounding cells interact with other proteins and collectively determine treatment efficacy remain elusive. Most previous studies have relied on transcriptome profiling, which does not fully reflect protein-level organization and interactions. In this study, we developed an antibody-oligonucleotide conjugate targeting the FMC63 region of CAR and integrated it into molecular pixelation (MPX). This approach enabled profiling of the dynamics of CAR molecules on cell surfaces as well as their colocalization with other proteins at the single-cell level. By applying MPX to longitudinal samples from three patients undergoing CAR-T cell therapy, we characterized the dynamic changes in CAR-associated protein organization in both pre-infusion CAR products and post-infusion peripheral blood. While CAR protein abundance and polarization showed limited variation across clinical courses, remodeling of a CAR-centered co-localization network was observed over time, including different retentions of specific molecular associations between patients with different clinical outcomes. Although derived from a limited cohort, our study identifies insights from this methodological framework beyond those gained by conventional omics analyses and offers results of a systematic investigation to predict and enhance CAR therapeutic outcomes. Key pointsO_LIMolecular pixelation was applied for chimeric antigen receptor (CAR) profiling at single-molecule and single-cell resolutions. C_LIO_LIProtein and transcriptome analyses of the CAR molecule showed dynamic remodeling during CAR-T therapy in patients with non-Hodgkin lymphoma. C_LI

Understanding the immune response against HIV-1 and the natural resistance exhibited by HIV-exposed Seronegative Individuals (HESN) offers the possibility of proposing new control strategies. Several studies suggest an important role of HLA and KIR genes in protecting against HIV-1 infection. Moreover, there is an important gap in the knowledge of these genetic factors in seronegative Latin American men who have sex with men (MSM), a population largely underrepresented in HIV immunogenetic studies. This study aimed to identify HLA and KIR genetic profile associated with potential resistance to HIV-1 acquisition, in a cross-sectional study including a cohort of 60 HIV-1-seronegative Colombian MSM at low and high risk of HIV-1 infection. The high-risk group showed a higher frequency of the HLA-B*18 allele, and a lower frequency of the HLA*B35, which have been previously associated with protection and susceptibility to HIV-1 infection respectively. Likewise, the high-risk group exhibited a low frequency of Bx haplotypes, a higher frequency of one AA haplotype and differences in KIR gene profile, with a low frequency of the inhibitory KIR2DL5 and both activating KIR2DS1, KIR2DS2 and KIR2DS5 genes. These findings suggest that host immunogenetic factors may contribute to resistance to HIV-1 acquisition in highly exposed individuals.

Cancer is a major global health issue with rising incidence and mortality. Early detection, tumor characterization, and disease surveillance are crucial for timely and effective treatment, ultimately reducing mortality rates. Liquid biopsy (LB) has emerged as a valuable detection tool offering a non-invasive method to determine tumor-derived biomarkers in body fluids with demonstrated translational potential. To increase biomarker sensitivity, high-throughput sequencing platforms deliver massive volumes of data. Artificial Intelligence (AI) is pivotal in enabling huge and complex data integration. This contribution aims to assess the current state of integrative AI-based research in the LB field and provide methodological guidance. First, we conducted a PubMed search and found that the literature is sparse in studies integrating LB features, particularly by applying AI. When adopting the latter approach, defining the study objectives is crucial to guide the subsequent methodological aspects, including study design, patient selection criteria, sample size, nature of the LB features, and metadata to collect. Specifically, we propose strategies and tools for data preprocessing, including normalization and batch correction, as well as handling outliers and missing data. Furthermore, we recommend various Machine/Deep Learning approaches for feature selection techniques to ensure model robustness, and we highlight the importance of undergoing rigorous internal and external validations of the selected models. Assessing clinical utility and interpretability is often overlooked but fundamental for real-world implementation. In conclusion, we provide the LB scientific community with an AI-based methodological guidance to bridge the two fields and enhance the integrative analysis of LB features. Graphical abstractWorkchart for multiomics integrative studies in the liquid biopsy field. Note: CTCs, circulating tumor cells; ctDNA, circulating tumor-DNA; TEPs, tumor-educated platelets; miRNA, microRNA; cfRNAs, cell-free RNAs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/724535v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1f250b2org.highwire.dtl.DTLVardef@18fe36corg.highwire.dtl.DTLVardef@19c02b9org.highwire.dtl.DTLVardef@176f6e0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Abstract: Background: Acute war-related traumatic wounds present significant challenges due to significant soft-tissue damage/loss, risk of contamination, limited access to antimicrobial therapy, need for delayed closure, and limited access to surgical and wound care. Negative Pressure Wound Therapy (NPWT) has been used effectively to reduce the volume of soft-tissue defects, edema, and infection in traumatic wounds, and to promote growth of healthy granulation tissue. However, conventional NPWT devices are costly and electricity-dependent, limiting their utility in conflict settings. Methods: This retrospective cohort study evaluated the use of PragmaVAC, a manually operated, electricity-independent NPWT device, in patients across three hospitals in Gaza with conflict-related wounds that were deemed by the treating surgeon to be unsuitable for primary closure. Secondary analysis was performed of clinical records of patients treated with the PragmaVac NPWT device to assess ability to achieve a primary outcome of wound bed with healthy granulation tissue, time to primary outcome, and rates of adverse effects. Secondary outcome of wound closure and closure method was also assessed. Results: Treatment with PragmaVAC manual NPWT was prescribed to 88 patients. Of those, 27 (31%) had incomplete documentation of their wound healing or were lost to follow up. The remaining 61 (69%) had complete documentation of their wound healing, complications, and final outcome with 59 (67%) successful closure and 2(2%) failure. Conclusion: The use of the PragmaVAC NPWT device provided a safe, effective wound care option to achieve wound closure for large conflict-related traumatic wounds in resource-limited settings. Future studies may further evaluate such use through prospective trials, evalutions of patients' experiences with manual NPWT, and evaluating outcomes beyond primary wound closure to include medium- and long-term complications, cosmesis, and cost of therapy.

Respectful maternity care [RMC] comprises the primary components of high-quality maternal health services. Evidence on RMC levels and determinants in Ethiopia is still inadequate. This study aimed to examine the reception and its determinants among postnatal women in government hospitals in the East Wallaga Zone, West Oromia. An institution-based cross-sectional study was conducted from June to October 2025, within seven days post-delivery. A structured questionnaire based on the WHO RMC tools was used. The total RMC score proved robust reliability [Cronbachs = 0.808] and was organized using the 75th-percentile threshold. Factor analysis revealed basic RMC dimensions, while logistic regression was used to identify predictors of a promising RMC experience. This study presented that only 46.8% of postpartum mothers received adequate RMC, with significant gaps in care. The main deficiencies comprised poor provider self-introduction, failure to call women by name, and infrequent communication and consent practices. Three key RMC dimensions were identified: privacy and consent, explanation and permission, and respectful communication. Using multivariate analysis, interpersonal caring practices were robust predictors of positive RMC experiences. Explaining procedures with possible events, maintaining privacy, obtaining consent, prompt responsiveness, provider self-introduction, and calling mothers by name were significantly associated factors. Sociodemographic and maternal reproductive factors were not significantly associated after adjusting for confounders. Finally, fewer than half [46.6%] of mothers experienced adequate RMC, which indicated major gaps in woman-centered care. Improving respectful interpersonal communication, informed consent, and maintaining privacy should be prioritized to boost the quality of maternal healthcare in the study area.

Introduction Creativity is important in surgery for problem-solving in the operating room and the development of surgical innovations that improve patient outcomes. However, our limited understanding of what the characteristics and competencies of the highly creative surgeon are has inhibited our ability to develop the tools, programs and interventions necessary for cultivating the creativity of surgeons. We present the protocol for the INSPIRE Study, which aims to identify the factors associated with high creative achievement in surgeons. Methods and Analysis We have designed a sequential mixed-method study, including a cohort study accompanied by qualitative semi-structured interviews. The primary objective of this study will be to identify factors associated with high creative achievement in surgeons, to be assessed through direct involvement in innovation or invention, or a top score (10 out of 10) on any domain in the Inventory of Creative Activities and Achievements questionnaire. We plan to measure 39 different personal, domain-specific, domain-general, and environmental/motivational variables, chosen based on previous literature and on exploratory grounds, to be assessed as possible factors of creative potential. Multivariable logistic regression is planned, with high creative achievement as the dependent variable and all 39 potential factors of creative potential as independent variables. Ethics and Dissemination Ethics approval from the Hamilton Integrated Research Ethics Board has been obtained and no harm is expected due to participation in this study. To facilitate knowledge translation, we plan to publish the feasibility data and results in peer-reviewed journals, and present at international surgical and creativity conferences.

Abstract Background: Mean platelet volume (MPV) is a simple, low-cost biomarker that reflects platelet activation. Its prognostic value in septic shock remains controversial. We aimed to determine whether MPV at intensive care unit (ICU) admission is associated with hospital mortality in patients with septic shock. Methods: Retrospective cohort study of consecutive adults with septic shock (Sepsis-3 criteria) admitted to a single ICU. MPV, severity scores (SOFA, APACHE II, SAPS II), procalcitonin, and clinical data were collected. The primary outcome was in-hospital mortality. Spearman correlation, univariate and multivariate logistic regression (with Firth's correction), ROC curves, and subgroup analyses were performed. Results: Fifty-eight patients were included; mortality was 58.6%. MPV did not differ between non-survivors and survivors (13.09 {+/-} 1.37 vs. 12.66 {+/-} 1.45 fL, p = 0.259). MPV showed a weak correlation with procalcitonin ({rho} = 0.394, p = 0.002) but not with severity scores. In multivariate analysis adjusting for age, sex, SOFA and comorbidity count, MPV was not an independent predictor of mortality (OR 1.075, 95% CI 0.682-1.755, p = 0.749). The area under the ROC curve for MPV was 0.598 (95% CI 0.444-0.752), significantly lower than that of SOFA (0.837) and procalcitonin (0.836). Subgroup analyses showed no significant association between MPV and mortality in any stratum. Conclusions: In this cohort of septic shock patients, MPV at ICU admission was not associated with hospital mortality and had poor discriminative ability. Widely used severity scores and procalcitonin remain superior prognostic markers. MPV should not be used as a prognostic tool in septic shock. Keywords: Septic shock, Mean platelet volume, Mortality, SOFA, Procalcitonin, Biomarker

BackgroundWorking equids are fundamental to the socioeconomic structure of Mexicos small-scale agricultural sector, which accounts for 71.2% of the countrys active Agricultural Production Units (APUs). Despite their critical role in human rural livelihoods, food security, and sustainable development, these animals face systemic "statistical invisibility" within national and international productive frameworks. This study evaluates the long-term population dynamics and geographical distribution of working equids to analyze their current status amidst agricultural modernization. MethodsA retrospective analysis was conducted using national census data from 1970 to 2022 provided by the National Institute of Statistics, Geography, and Informatics (INEGI). Population trends for horses, donkeys, and mules were calculated using the Average Annual Variation Rate (AAVR). The severity of population declines was classified according to an adaptation of the International Union for Conservation of Nature (IUCN) criteria. Finally, national census records from INEGI, Agri-food and Fisheries Information Service (SIAP) and The Ministry of Agriculture and Rural Development (SADER) were contrasted with FAOSTAT database estimates to identify reporting discrepancies. ResultsBetween 1970 and 2022, the total equine population in Mexico decreased by 76.5%, falling from 6.8 to 1.6 million. However, a "paradox of modernization" was identified: while total numbers plummeted, the proportion of equids used specifically for work reached a historical peak of 81% in 2022, effectively having doubled from the 44% recorded in 2007. While donkeys and mules have suffered drastic total reductions (87% and 88%, respectively), working horses experienced a resilient 37% recovery between 2007 and 2022 (+3.71% AAVR). Furthermore, a staggering 710.8% discrepancy was found between national census data and FAOSTAT estimates, representing an overestimation of 11.3 million animals in international records. ConclusionsThe persistence and recent recovery of working equids reflect a "resilience of necessity" for approximately 500,000 APUs that depend exclusively on animal traction and packing due to economic constraints and complex topography. These findings challenge the narrative of total agricultural mechanization and highlight an urgent need for evidence-based public policies that address the statistical invisibility of working equids as indispensable drivers of rural sustainability and food security.

BackgroundSnakebite envenoming is a significant global health crisis that has been long neglected as a global health priority. It is a huge problem for rural communities of low and middle-income countries, India accounts for the largest proportion of snakebite deaths globally. Timely identification of venomous snakebite and its syndromic pattern is essential for effective administration of antivenom and supportive treatment. Expert identification of snake species and syndromes is not always available in peripheral healthcare settings. This leads to delays, unnecessary referrals, or improper treatment choices. Additionally, diverse snake species distribution and venom variations across regions pose challenges. AI-powered image classification methods can help overcome these barriers. We propose a clinically oriented deep learning pipeline for binary classification of venomous and non-venomous snake species of India using real-world imagery data. This pipeline would serve as a baseline step towards aiding snakebite management at peripheral healthcare setups with scarce resources. MethodsThe selected dataset consisted of 20 medically important Indian species. MobileViT-S, ConvNeXt-Tiny, EfficientNet-V2-S and ResNeXt-50 (32x4d) were trained under same conditions for comparison of results. Model interpretability was evaluated using Grad-CAM ++ to ensure that classification was not performed based on background but on features like head shape and stripes present on body. For reliable implementation we connected it to a web interface with human in loop expert verification. Experts can confirm or override predictions in real time. ResultsAmong the evaluated architectures, ResNeXt-50 (32x4d) showed the most reliable and consistent performance in classifying venomous and non-venomous snakes. It achieved the highest test accuracy, sensitivity, specificity, and F1-score. The model also had strong discriminative ability, with a ROC-AUC of 0.9950 and PR-AUC of 0.9959. These results indicate dependable performance in safety-critical screening situations. Grad-CAM++ visualizations confirmed that predictions were based on anatomically relevant features, especially in the head and body contour areas. This supports model interpretability and reduces background bias. ConclusionsAlthough the dataset size and single-institution source limit how widely the results can be applied, the proposed framework shows that its possible to create a clinically oriented, ready-to-use deep learning system for snakebite triage support. This system is intended as a scalable tool to help rural healthcare workers, emergency responders, and telemedicine platforms in areas where snakebites are common. Author SummarySnakebite is a major public health concern that disproportionally affects the rural population. Delays in identifying whether a snake is venomous often lead to delayed treatment, unnecessary use of antivenom, or inappropriate referrals. In many rural settings, access to expert snake identification is limited. To address this gap, authors have developed an artificial intelligence (AI)-based image classification system that distinguishes snakes into two clinically relevant categories: venomous or non-venomous. Unlike many previous studies that focused on ideal, high-quality wildlife images, our model was trained using real-world photographs captured in emergency situations, including images taken by patients and field responders under variable lighting and background conditions. This approach improves the models relevance to practical healthcare settings. The system achieved high accuracy and was further strengthened by visual interpretability tools and expert verification to ensure reliability. By combining AI-assisted classification with human oversight, this work provides a scalable decision-support tool that may improve early triage, rational antivenom use, and surveillance in snakebite-endemic regions

Engagement is widely recognised as central to learning and academic achievement. Electrodermal activity (EDA) has emerged as an objective physiological indicator of engagement, as it measures sympathetic nervous system activation. However, the high cost of wearable EDA sensors has limited its widespread application. This study answers the call for affordable, high-temporal-resolution engagement measures by validating a video-based quantitative assessment method. Researchers collected 75 minutes of synchronised EDA and video data from 12 upper secondary students (aged 17-18) during regular instruction. Novel software was developed to analyse student movement and sound level for academically relevant content. The OpenPose AI model for pose estimation was also applied. This approach produced six distinct movement variables: two AI-based and four non-AI-based. Six linear models using varying movement variables and sound level were tested to predict tonic EDA levels. All models effectively predicted EDA levels, with non-AI-based movement metrics outperforming AI-based alternatives. The four non-AI-based movement models showed similar performance, indicating that compressed versions reduced computational time without sacrificing predictive power. These findings validate a novel, objective method for comparing engagement across learning activities on short timescales. This method is particularly useful for collaborative learning environments and enables controlling for movement and sound in quantitative classroom analyses.

IntroductionSolitary plasmacytomas (SP) are rare neoplasm of localised proliferation of clonal plasma cells. It can be classified based on site of involvement and bone marrow involvement. It is an indolent disease in the majority of patients. Primary modality of treatment is radiotherapy and surgical excision. Materials and methodsThis was a retrospective audit of SP who were treated and followed up at a tertiary care center in eastern India from January 2012 to December 2025. Patients who has solitary plasma cytoma with more than 10% plasma cells, POEMS syndrome, have been excluded from analysis. ResultsWe identified 46 patients of SP. The median age of the studied population was 53 years (23-75 years). Males were more commonly affected than females (M:F=2.2:1). Most common chief complaints were bony pain (67.4%). SBP was seen in 39 (84.8%) cases whereas SEP was seen in 7 (15.2%) cases. Vertebra was the most common site of involvement (61.4%). Median M band concentration 0.24 g/dL (0.1 to 1.95 gm/dL). IgG was the most common isotype accounting for 60.6% cases. Six cases (13%) had minimal bone marrow involvement. The majority of the patients received local radiotherapy (89.1%). With a median follow up of 5.4 years (95% CI: 1.8 - 9.0), median OS was not reached, median PFS was 9.22 years (95% CI: 5.8-12.6), median time to next treatment (TTNT) was 9.86 years (95% CI: 6.8 - 12.9). ConclusionSolitary plasmacytoma commonly affects young males. Bones are more commonly affected than extramedullary sites. SP has a lower rate of progression and excellent prognosis when treated with local radiotherapy.

IntroductionAnaemia is one of the most prevalent global public health challenges, particularly among women of reproductive age and children. According to the World Health Organization, anaemia is defined as a hemoglobin concentration below 13.0 g/dL in adult men, 12.0 g/dL in non-pregnant women, and 11.0 g/dL in pregnant women. Hemoglobin measurement therefore plays a critical role in diagnosis, classification, and monitoring of anaemia at both clinical and public health levels. Hemoglobin estimation allows early identification and intervention in at-risk populations. MethodologyA cross-sectional study was conducted at Aniniwaa Medical Centre, Kumasi, involving 100 participants who visited the laboratory for a complete blood count. Venous blood samples were collected aseptically into EDTA tubes and analysed first with the fully automated analyser, followed by the two Hb meters. Data were analysed using Chi-square tests, Bland-Altman plots, and descriptive statistics. ResultsResults showed that the prevalence of anaemia varied across methods: 28% by the analyser, 60% by Urit, and 64% by Mission. Both meters demonstrated 100% sensitivity but lower specificities (55.6% for Urit and 50.0% for Mission). Bland-Altman analysis indicated negative biases (Urit = -1.665 g/dL; Mission = -1.55 g/dL), suggesting both underestimated hemoglobin values compared to the reference. ConclusionThe study revealed that while Hb meters offer convenience and portability for field screening, the fully automated analyser remains more accurate and reliable for diagnosing anaemia in clinical settings.

Despite the remarkable progress of artificial intelligence represented by large language models, how AI technologies can contribute to the construction of evidence in evidence-based medicine (EBM) remains an overlooked issue. Now, we need an AI that can be compatible with EBM. In the present paper, we aim to propose an example analysis that may contribute to this approach using variable Vision Transformer.