Browse Articles

Population genomics of Nigerian goat breeds and neighbouring populations in the West Africa–Cameroon transboundary livestock corridor

Indigenous goats in Nigeria and neighbouring countries support livelihoods across forest–savanna–Sahel environments, yet genomic structure, connectivity history, and adaptive signals are rarely investigated in a single corridor-scale transboundary framework. We analysed three Nigerian populations (Sahel, Red Sokoto/Maradi, and West African Dwarf; WAD) and seven neighbouring populations from Burkina Faso, Mali, and Cameroon using 46,431 autosomal markers from 209 unrelated animals (with a South Asian outgroup where needed). We tested whether recurrent vernacular labels map onto shared genomic backgrounds across borders, reconstructed time-layered connectivity, quantified demographic contraction and inbreeding, and prioritised candidate adaptive regions using a structure-aware approach. Model-based ancestry and principal component analysis supported three transboundary genomic backgrounds: (i) a Sahel–Sudan background spanning Nigeria, Burkina Faso, and Mali; (ii) a southern Djallonké/WAD background spanning Nigeria, Burkina Faso, and Mali; and (iii) a distinct Cameroon dwarf lineage, with Guéra representing a drifted subgroup within the Sahel–Sudan background. Admixture-timing analysis, interpreted as approximate dates inferred from linkage-disequilibrium decay, suggested very recent cross-border involving Nigerian Sahel goats (~30–40 years under the assumed generation interval), superimposed on older Sahelian–dwarf exchange (~160–1,000 years). Effective population size declined from ~1,400–2,700 at ~960 generations ago to ~40–111 at 13 generations ago. Runs of homozygosity indicated low-to-moderate genomic inbreeding (0.004–0.040), with long segments (>8 Mb) most pronounced in Guéra and Red Sokoto/Maradi. A multi-statistic composite selection scan identified 53 candidate windows. Enrichment highlighted adhesion and translation quality-control themes in the Djallonké/WAD background background, neuronal/neuroendocrine terms in Guéra, and olfactory transduction in the Sahel–Sudan background. These results define transboundary genomic backgrounds rather than country-bounded “breeds” and provide background-specific hypotheses that can be validated in resilience-oriented breeding under ongoing mobility.

Interpretable machine learning models for predicting the risk of metachronous colorectal liver metastases

Abstract Liver metastasis is a frequent complication in colorectal cancer (CRC), significantly impacting patient prognosis. This study aims to develop a machine learning-based prediction model for metachronous liver metastasis (MLM) in CRC patients, facilitating early diagnosis and intervention to potentially improve treatment outcomes and survival rates. A retrospective analysis was conducted on 620 consecutive patients who underwent radical colorectal cancer resection at the First People’s Hospital of Changzhou during the study period and met the predefined inclusion and exclusion criteria. MLM status was determined according to postoperative follow-up outcomes rather than used as a sampling criterion. Among the final eligible primary cohort, 373 patients had no observed liver metastasis during follow-up, whereas 247 patients were diagnosed with metachronous liver metastasis more than 6 months after radical CRC resection. Patients were split into training (non-MLM = 258, MLM = 176) and internal validation (non-MLM = 258, MLM = 71) cohorts, with an external cohort of 52 non-MLM and 30 MLM patients from the Seventh People’s Hospital of Changzhou. Missing values were imputed using KNN. Based on the features selected by Logistic Regression (LR) and LASSO, five machine learning models (LR, RF, LightGBM, XGBoost, and SVM) were developed. Model performance was comprehensively evaluated using AUROC, DCA, accuracy, sensitivity, specificity, and F1 score, with SHAP illustrating feature influence. The best model was validated internally and externally. Eight independent risk factors (Age, Tumor Embolus, Size, T stage, N stage, Tumor Differentiation, RDW, AST) were included as features in the model. The LR model demonstrated the best performance, with SHAP indicating T stage as the most influential factor. A dynamic online nomogram was constructed to visualize the LR model. ROC analysis showed good discriminative performance of the final LR model, and calibration analysis suggested acceptable agreement between predicted and observed MLM risk. DCA and CIC analyses suggested potential clinical net benefit and clinical impact within the prespecified clinically relevant threshold probability range. In this study, LR model was selected as the final model for predicting the risk of metachronous liver metastasis after radical CRC resection. The dynamic nomogram provides an interpretable and accessible tool for visualizing individualized risk estimates. Given the retrospective design and current validation limitations, the model should be regarded as a supplementary tool to support postoperative risk stratification and follow-up planning rather than as definitive evidence for clinical decision-making. Further prospective and multicenter validation is warranted before routine clinical implementation.

Image-based identification and DEA-based optimization modeling of antibiotic packaging using unsupervised learning techniques

Background Ensuring medication safety requires accurate identification of antibiotic packaging, especially within pharmacy automation and dispensing systems. Advanced imaging and machine learning offer novel avenues for physical package recognition. Objective To investigate visual and textual features of antibiotic packages and evaluate their relationship with identification outcomes using unsupervised learning and efficiency-based analysis. Methods Thirty-six antibiotic formulations from Thailand (2016–2021) were analyzed using binary imaging, entropy metrics, packaging area ratio (PAR), and optical character recognition (OCR). K-means clustering was applied to segment package groups, and data envelopment analysis (DEA) was used to assess relative efficiency without assuming predefined functional relationships between inputs and outputs. Results Nine distinct image clusters were identified. Packages with mid-range entropy (7.1–7.5) and PAR (1.2–1.45) were associated with higher identification consistency. OCR text confidence influenced identification outcomes. DEA identified clusters with relatively efficient input–output configurations. Conclusion Integrating image-derived metrics and OCR-based features supports automated antibiotic package identification. This framework provides a structured approach for evaluating packaging characteristics in pharmacy workflows.

Correction: German soils affect biomass production, elemental profiles, and anti-inflammatory activity of three medicinal plants used in Brazilian traditional medicine: Scoparia dulcis L., Physalis angulata L., and Porophyllum ruderale (Jacq.) Cass.

Mission imputable: Effects of missing data processing on infectious disease detection and prognosis

Background Missing data in medical datasets poses significant challenges for developing effective AI/ML pipelines. Inaccurate imputation can lead to biased results, reduced model performance, and compromised clinical insights. Understanding how different imputation methods affect AI/ML model performance is crucial for ensuring accurate clinical findings. Objective This study systematically investigates the effects of different imputation methods on AI/ML model performance and their clinical implications. Methods We investigate the impact of six different missing data strategies on the performance of common classification algorithms for analyzing medical data. The performance was evaluated based on sensitivity and specificity metrics for the tasks of predicting COVID-19 diagnosis and patient deterioration. We also perform feature analysis to understand the clinical implications that the choice of imputation method has. Results The findings reveal that the effects of imputation depend on the clinical setting. For a general screening cohort (Einstein Data4u), multivariate imputation by chained equations (MICE) yielded the best performance in clinical settings, resulting in a 26% improvement in sensitivity compared to baseline methods and unmasking critical viral coinfections. Conversely, in an intensive care cohort (MIMIC-IV), complete-case analysis initially showed higher raw predictive metrics. However, further analysis demonstrates that this stems from selection bias driven by informative missingness (MNAR), where testing patterns are intrinsically tied to patient severity. Thus, while imputation recovers diagnostic signals in sparse screening data, it serves as a crucial tool for reducing bias in high-acuity settings. Conclusion This study demonstrates the critical impact of missing data imputation on AI/ML model performance and the resulting clinical insights. Our findings underscore the importance of selecting appropriate imputation techniques tailored to the specific characteristics of medical data to ensure accurate and reliable AI/ML predictions. By utilizing a rigorous cross-validation pipeline and a systematic comparison, we provide insights for selecting the most appropriate imputation methods for clinical decision-making applications.

Key trends in laser induced plasma structure revealed by longitudinally resolved optical diffractometry

Potential of Korean forest tree seed extracts as multifunctional bioresources: Evaluation of Antioxidant, anti-inflammatory, whitening, and anticancer activities

Forest tree seeds are mass produced for afforestation and forest restoration programs, but are mostly underutilized beyond propagation. Here, we aimed to evaluate the antioxidant, anti-inflammatory, anticancer, and tyrosinase-inhibitory activities of seed extracts of seven economically important forest tree species in the Republic of Korea to explore their potential as multifunctional natural bioresources. The seed extracts of Alnus japonica , Chamaecyparis obtusa , Cornus kousa , Phellodendron amurense , Pinus densiflora , Prunus sargentii , and Quercus glauca were comparatively assessed using multiple in vitro assays. The results revealed clear species-dependent functional profiles rather than uniform bioactivities across species. Q. glauca exhibited strong antioxidant activity along with significant anti-inflammatory and tyrosinase-inhibitory activities under the present screening conditions, while C. obtusa presented considerable anticancer activity against several cancer cell lines. A. japonica exhibited the highest tyrosinase-inhibitory activity, followed by Q. glauca and C. obtusa ; A. japonica extract also showed a strong antioxidant capacity. Overall, the results revealed clear species-dependent differences in bioactivity profiles among the seven seed extracts under the present screening conditions, providing a comparative baseline for further compound-level and mechanistic studies. By focusing on seed resources generated within existing afforestation systems, we highlight a sustainable approach to valorize forest-derived by-products without additional pressure on natural ecosystems. As all assays were performed at single fixed concentrations using crude extracts, the present findings should be interpreted as a comparative screening; dose–response characterization, selectivity profiling, and identification of active compounds and their mechanisms of action are required next steps.

Machine learning-optimized terahertz biophotonic biosensor for label-free non-invasive glucose monitoring using hybrid two-dimensional materials

Retraction: Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data

Correction: Sex and BMI as predictors of pill residue in dysphagia: a multivariate analysis

Risk factors for decline in estimated glomerular filtration rate amongst Malawian adults living in rural Karonga: Protocol for a prospective cohort study using cystatin C- and creatinine-based eGFR

Background The global burden of chronic kidney disease (CKD) is rising, disproportionately impacting on low- and middle-income countries. In African populations, use of serum creatinine to estimate glomerular filtration rate (GFR) significantly underestimates CKD prevalence, contributing to its under-recognition as a health problem. Serum cystatin C provides more accurate estimates of Iohexol measured GFR than creatinine. Early diagnosis and treatment of CKD is essential to reduce premature morbidity and mortality. However, little is known about risk factors for CKD development and progression in Africa owing to limited longitudinal data. This study aims to determine risk factors for progressive kidney function decline among adults living in rural, northern Malawi. Methods This protocol describes a prospective study being conducted in a general population Health and Demographic Surveillance Site in rural Karonga, Malawi (2024–2025). Eligible participants are adults aged 18 years and over who participated in two population-based surveys of long-term health conditions (2013–2016 and 2021–2025), with availability of baseline measures. New household-level data is being prospectively collected on CKD risk factors, alongside blood and urine samples. Cystatin C and creatinine will be tested on individual-level paired, stored serum samples collected at three longitudinal time points. Urine will undergo dipstick urinalysis, microscopy and testing for albumin and creatinine to quantify proteinuria. The primary outcome will be sustained 25% reduction in estimated GFR (eGFR) from baseline and change in eGFR category, determined using serum cystatin C. Multivariable logistic regression will be used to determine effect size estimates of key risk factors for kidney function decline. Discussion This study will provide important data on risk factors for eGFR decline and CKD progression amongst Malawian adults. The findings will inform future research into important context-specific risk factors, and could directly inform health policies in Malawi for targeting CKD screening, prevention and treatment strategies to high-risk patient groups.

The impact of climate and vegetation on the riverside architecture of the Brazilian Amazon

Abstract This research aimed to investigate the impact of vegetation conservation on the local climate and architectural production of two riverside communities in one of the capitals of the Brazilian Amazon, one close to and the other far from the urban area. Based on the inductive-exploratory method, we collected primary climatological data in the external area and inside the dwellings in both communities (October 2022 to May 2023), developed analyses of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (August 2022 to May 2023), and finally collected architectural information from the dwellings investigated. Our results show that the climatological data from the two communities are statistically different, being more favorable to comfort in the community with greater vegetation conservation. Analysis of remote sensing images showed that vegetation density in this community indicates a strong relationship with vegetative regeneration capacity after the most critical dry months of the period investigated. Regarding thermal comfort inside dwellings, the data showed that, both through statistical analysis and the thermal comfort model adopted, the combined effect of building materials, vegetation cover, and environmental context favors better thermal comfort conditions. The results presented point to the importance of vegetation conservation, which positively influenced the local climate and thermal comfort of riverside dwellings. In addition, our results encourage the perpetuation of traditional knowledge that adapts architecture to the forest.

Implementation and acceptability of high efficiency particulate air filters to reduce respiratory infections in care homes: Process evaluation of the AFRI-c cluster randomised controlled trial

Respiratory infections are easily transmitted within care homes. Within a clinical trial (called AFRI-c), which tested the effectiveness of high-efficiency-particulate-air (HEPA) filters to reduce respiratory infections in care home residents, we conducted a mixed-methods process evaluation. We aimed to understand their acceptability, fidelity, and implementation to aid interpretation of the effectiveness findings. We used qualitative remote and face-to-face interviews with staff (n = 25), residents (n = 20), and relatives (n = 12) from care homes (n = 22) in the AFRI-c trial. We purposively sampled homes for variation in size, nursing or residential provision, and deprivation. We used reflexive thematic analysis, drawing on normalisation process theory to understand implementation. We used staff questionnaires (n ranges from 191 to 351 depending on questionnaire) and resident (n = 1158) questionnaires with descriptive and regression analyses. We used the triangulation protocol to integrate qualitative and quantitative findings. The use of HEPA filters became normalised, although some residents disliked the draught. Self-reported intervention fidelity was high, which is important context for interpreting the trial’s null outcome for infection reduction. HEPA filters made no difference to resident or staff satisfaction with the care home environment. We found no evidence that using HEPA filters changed infection control and prevention practices. While staff felt it was a priority to prevent respiratory infections, residents were more concerned about quality of life and care. Our mixed methods process evaluation of the AFRI-c trial found the use of HEPA filters was acceptable, with high levels of adherence and low levels of contamination, suggesting that the null trial results were not due to poor adherence. Some effort was required to ensure they were kept on. Approaches to data collection may have caused under-reporting of mild infections. We should not assume that infection prevention is always a priority for residents.

The association between best friends or fixed peer groups and academic performance among Korean children and adolescents: a cross-sectional study

Selective GPR17 antagonism enhances structural and functional recovery in animal models of demyelination

Myelination, driven by differentiation of oligodendrocyte precursor cells, is critical for metabolic and structural support and efficient axonal signal transmission in neurons. Loss of myelin is a hallmark of multiple sclerosis and other devastating demyelinating disorders. As demyelination persists, neurons become increasingly vulnerable, leading to neurodegeneration and chronic disability. Restoring myelin through endogenous repair mechanisms offers a promising therapeutic approach to mitigate progressive neuronal loss. One key regulator of myelination is the G protein-coupled receptor 17, GPR17, whose chronic upregulation in oligodendrocyte precursor cells is commonly seen with myelin injury. In line with single-nucleus transcriptomic data showing predominant expression of GPR17 in committed oligodendrocyte precursor cells, our postmortem immunohistochemical analyses of MS patient tissue revealed a significant upregulation of GPR17 + /BCAS1 + oligodendrocyte precursor cells adjacent to and in demyelinated lesions. Importantly, remyelinated lesions lacked GPR17 immunoreactivity, consistent with a model in which sustained GPR17 expression is associated with demyelination and impaired oligodendrocyte precursor cell differentiation. To test the impact of pharmacological GPR17 inhibition on remyelination, we evaluated the effects of a novel, selective GPR17 antagonist in cuprizone-induced murine demyelination models. This toxin-induced approach has been widely used to study mechanisms of de- and remyelination, in the absence of the full inflammatory complexity of demyelinating diseases such as multiple sclerosis. We show that oral treatment results in robust functional recovery consistent with remyelination, as evidenced by improved spatial memory and recovery of visual evoked potential latency delays. GPR17 antagonism also accelerated structural remyelination in the corpus callosum and optic nerve. Together, these findings support a role for pharmacological GPR17 antagonism in promoting remyelination and highlight this G protein-coupled receptor as a promising therapeutic target for demyelinating disorders.

Accelerometer measured physical activity and menstrual distress across menstrual and nonmenstrual days in mild and moderate to severe primary dysmenorrhea

Abstract Dysmenorrhea and menstrual features have a major impact on patterns of physical activity; however, objective assessments of these associations using accelerometry remain limited. This study aimed to compare sitting and physical activity times, measured using a Fibion accelerometer, during the first three days of menstruation and days 8–10 of the follicular phase in age-matched women with mild and moderate-to-severe primary dysmenorrhea (PD). This observational study included 46 women aged 18–45 years whose primary dysmenorrhea severity was categorized based on the Numeric Pain Rating Scale (NPRS). Symptom severity was measured using Working ability, Location, Intensity, Days of pain, Dysmenorrhea (WaLIDD) and Dysmenorrhea Symptom Interference (DSI) scores. Sitting time and light, moderate, and vigorous physical activity were measured using a Fibion triaxial accelerometer worn for three consecutive days in the menstrual phase (days 1–3) and three consecutive days in the follicular phase (days 8–10). Multiple 2 × 2 mixed ANOVA analyses were used to assess differences between the menstruation and follicular phases in both groups for the outcomes of interest. Chi-square tests were performed to compare self-reported menstruation information. Participants demonstrated similar activity patterns during their menstrual and follicular days and spent most of their time sitting (~ 11 h/day), followed by light-intensity activity (~ 3.8–4.0 h/day), with considerably less time in moderate activity (~ 34–36 min/day) and minimal vigorous activity (< 1 min/day). No significant differences in sitting time (p = 0.586), light intensity (p = 0.414), moderate intensity (p = 0.776), or vigorous intensity (p = 0.162) were detected between the two dysmenorrhea groups across both phases. WaLIDD scores were not significantly different for the severity of pain over the last 3 months or the duration of menstrual pain but showed a statistically higher number of pain locations and level of interruption of daily activities in women with moderate-to-severe PD. The DSI differed significantly between the groups in all domains (p < 0.001). While objectively measured physical activity levels did not significantly differ between the menstrual and follicular phases or across dysmenorrhea severity groups, women with moderate-to-severe dysmenorrhea experienced greater symptom interference and disruption of daily activities. According to these results, dysmenorrhea significantly affects self-reported function and the perceived ability to be active during menstruation, even though it may not significantly change overall activity intensity. To clarify the physiological and behavioural mechanisms underlying these relationships, further longitudinal studies are needed.

Dual-input deep learning system for microbial identification from blood agar plates

Objective The morphological classification of microbial cultures and colonies requires specialized knowledge and experience; therefore, automation in this field remains limited. This study examines the feasibility of automating the identification of pathogenic microbial species from colony images of cultured microorganisms. Methods Two distinct image datasets were constructed for 10 clinically relevant species: a colony image dataset, consisting of individual colony images cropped, and a tile image dataset, generated by dividing entire images of the culture plate into tiles. Separate ResNet-50–based models were trained in each dataset, and their outputs were integrated to evaluate the classification performance. Training was conducted using 10,048 colony images and 23,003 tile images collected from 418 strains, and performance was assessed with five-fold cross-validation (K = 5). Results The colony image model achieved a sensitivity of 0.934 and a specificity of 0.993, while the tile image model achieved a sensitivity of 0.918 and a specificity of 0.991. Integration of the two models into an ensemble model further improved performance, yielding a sensitivity of 0.955 and a specificity of 0.995 when tested on 76 independent strains. Conclusion The ensemble model approach provides high accuracy and robustness, suggesting its potential technical contributions to microbial identification in clinical microbiology laboratories.

Integrative multi-omics characterization of HECTD3 across pan-cancer and its functional validation in thyroid cancer

Expression of Concern: Signatures of tumor microenvironment-related genes and long noncoding RNAs predict poor prognosis in osteosarcoma

Effect of massed versus distributed preclinical training on subgingival instrumentation skill across the preclinical-to-clinical transition

Abstract Consolidation of preclinical subgingival instrumentation skills during early clinical exposure is poorly quantified, especially after the new German dental licensing regulation (nGDLR). Within a shared digitised training programme (DTP), we compared in vitro performance after preclinical training and after the first periodontal patient course under distributed (oGDLR; 7th semester, S7) versus massed (nGDLR; 6th semester, S6) schedules. In a non-randomised, sequential two-cohort design (a consequence of a 2020 curriculum change), 102 students (oGDLR, distributed/longitudinal schedule, n = 45; nGDLR, massed/block schedule, n = 57) provided 136 evaluations after preclinical DTP (nGDLR S6, oGDLR S7) and/or after the first periodontal patient course (S8). Because oGDLR training (S7) overlapped with first clinical contacts whereas nGDLR training (S6) preceded intensive patient care by ~ 1.5 semesters, schedule density and clinical proximity co-vary and cannot be fully separated. Only 34 students (oGDLR n = 22; nGDLR n = 12) were evaluated at both time points, so most comparisons are cross-sectional. Each student instrumented six teeth with Gracey curettes (GRA) and a sonic scaler (AIR) on periodontitis manikins. Effectiveness of simulated plaque removal (ESP), treatment time, calculus removal and an 8-item Likert self-assessment were analysed by non-parametric tests and multivariable regression with cluster-robust SE. oGDLR was associated with higher ESP (β=+5.16%, p = 0.001) and twofold higher calculus removal (adjusted OR 2.00, p < 0.001); treatment time was comparable. From post-DTP to S8 both cohorts became faster by ~ 60s/tooth (p < 0.001) while ESP declined (β=−4.26%, p = 0.002) — a speed–accuracy trade-off; the schedule×time interaction was non-significant. In the paired subgroup, the trade-off was confirmed in oGDLR (n = 22; ESP − 7.2%, p < 0.001) but power-limited in nGDLR (n = 12). Excluding magnifying-loupe users (concentrated in nGDLR) widened rather than narrowed the oGDLR ESP advantage (β=+6.02%, p < 0.001). nGDLR rated AIR more favourably (learnability OR 0.40; effectiveness OR 0.48). Pass rates (≥ 55% ESP) exceeded 86%. In this non-randomised cohort, the distributed schedule was associated with higher absolute performance, but the decline occurred similarly under both schedules. Because schedule density could not be separated from proximity to clinical exposure, and because the data are largely cross-sectional and derive from a manikin-based in vitro model, these associations should not be read as causal. The transition to first patient care is vulnerable: cleaning quality may erode as students prioritise speed. Distributed preclinical practice was associated with a higher absolute baseline, but the decline across the transition was similar in both schedules; structured reinforcement during the early clinical phase remains necessary to consolidate competence.