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An automated approach to extracting head and brain circumference from MRI datasets
Head circumference is a fundamental biometric parameter for brain growth in both the clinical pediatric setting and in developmental neuroscience. However, the gold standard for obtaining head circumference by manual tape measurement is notoriously error-prone. Further, while it is known that the growth trajectories of head and brain differ over time, a systematic comparison of these two parameters as a function of age does not yet exist. We developed a new and automated algorithm for obtaining head and brain circumference from MRI data. The algorithm mimics manual head circumference measurement by placing a convex hull around axial slices which must intersect with predefined anatomical landmarks. Several differently-tilted iterations are run and results are combined. In addition to obtaining head circumference, the approach can also be applied to gray matter only, providing “brain circumference” (gray matter hull perimeter) at the same level as head circumference. To assess validity, we used T1-weighted 3D datasets (n = 153) with available, manually measured head circumference values (age range 0–226 months [0–18.8 years]). To assess test-retest reliability, a second dataset (n = 3 with 40 scans each) was used. When compared with the current gold standard (manual measure), high validity was demonstrated for the new approach, with no systematic bias. The algorithm also showed a very high reliability across multiple measurements. Developmental trajectories of both head and brain circumference were generated and compared. In summary, the algorithm represents a valid and reliable method for the automated determination of head as well as brain circumference. It offers an objective way to assess these parameters in retrospect and prospectively, and may shed light on specific clinical situations where they differ, such as in the presence of enlarged subarachnoid spaces.
Intrathecal pump refills at home or at the hospital: Protocol for a randomized controlled crossover trial—The IMPROVE study
Background Intrathecal drug delivery (IDD) offers a therapeutic option for patients suffering from refractory pain or severe spasticity. By allowing targeted and continuous infusion directly into the intrathecal space, IDD bypasses the blood-brain barrier and enhances therapeutic effectiveness of the drug. Following the implantation of an IDD pump, the most commonly performed postoperative maintenance procedure is the pump refill (at regular intervals). This process can be burdensome for patients, affects their comfort, and carries significant risks. The current aim of this study is to evaluate whether intrathecal pump refills performed at home provide a difference in patient comfort compared to refills conducted in the hospital. Methods The IMPROVE study is a monocentric, randomized controlled crossover trial, including 82 patients. For this study, each patient will undergo four intrathecal pump refill procedures (two at home and two in the outpatient clinic) allocated in a randomized order. The primary objective of this study is to determine whether at-home refills provide a difference in patient comfort compared to hospital-based refills. Secondary objectives include assessing differences in quality of life, pain, stress, anxiety, self-efficacy, caregiver burden, patient preferences, safety, and overall cost-effectiveness between the two settings. Patients will be followed over the course of four intrathecal pump refills, which is estimated to span approximately one year. Discussion Within the IMPROVE project, pump refills will be performed through hospital at home. If at-home intrathecal pump refills prove more comfortable for patients and cost-effective for society, this would strengthen the patient-centred care model and support adopting this approach as the new standard treatment for IDD patients. A graphical abstract is provided in the supplementary materials (S1 Fig). Trial registration Details on the study site can be found at ISRCTN with identifier: ISRCTN18031921; [href: https://doi.org/10.1186/ISRCTN18031921 ] https://doi.org/10.1186/ISRCTN18031921 . The trial was registered in the ISRCTN registry on 18 November 2025.
Editorial Note: A risk-averse sustainable perishable food supply chain considering production and delivery times with real-world application
Fetal sex programs immune architecture and cellular differentiation at the maternal–fetal interface in early human pregnancy
Enhancing missense variant classification in predicted intrinsically disordered regions
Classifying disease-causing missense variants in intrinsically disordered regions (IDRs) remains a significant challenge, with over 25% of known deleterious variants occurring in these regions. Existing in silico missense variant predictors that predict variant classification generally perform better in ordered regions of the protein, limiting their effectiveness. To address this, we developed a machine learning methodology that integrates global IDR conformation (gIDRc) features from ALBATROSS, phase separation (PS) features from BioPython, and 1024-dimensional protein embeddings from ProtTransBertBFD generated for both wild-type (WT) and mutant IDR sequences. IDR boundaries were defined using the AlphaFold-RSA predictions, which identifies disordered regions based on AlphaFold2 pLDDT scores and relative solvent accessibility. Using ClinVar variant classifications as ground truth, AlphaMissense, EVE, and ESM1b were the highest scoring unsupervised in silico missense predictors for IDR variants. Our baseline model, using only IDR-specific features achieved competitive performance on the hold-out test set with a PR-AUC of 0.817. Critically, when these IDR features were combined with these methods we saw significant overall improvement. The AlphaMissense-Enhanced model increased its PR-AUC from 0.807 to 0.919. Similarly, ESM1b-Enhanced improved PR-AUC from 0.679 to 0.845 and EVE increased from 0.591 to 0.910. These results demonstrate the effectiveness of our enhancements for classifying missense variants in IDRs and highlight its ability to complement existing in silico missense predictors.
Effectiveness of liquorice gel as an adjunct to non-surgical periodontal therapy in chronic periodontitis: a randomized controlled, clinical and microbiological trial
Identifying cluster profiles based on barriers and facilitators to physical activity during COVID-19 confinement: A cross-sectional study using machine learning analysis
Social restrictions, such as confinement periods, tend to reduce physical activity (PA) levels. However, sociodemographic factors may influence specific barriers and facilitators to PA during such periods. This study aimed to identify cluster profiles of individuals based on barriers and facilitators to physical activity (PA) during COVID-19 confinement. Brazilian adults participated in a cross-sectional online survey. The questionnaire collected demographic data, PA levels, sedentary behavior (SB), and perceived barriers and facilitators for PA. During data preprocessing, correlated barriers and facilitators related to a similar topic were aggregated. Using machine learning analysis, the K-modes evaluated by the Silhouette Score were used for barriers and the ROCK evaluated by the Silhouette Score was used for facilitators. The barriers model produced well-defined profiles, whereas the facilitators model did not. The facilitator model generated clusters with multiple negative silhouette coefficients and exhibited a significantly less cohesive cluster structure. Therefore, only the barriers-based model was used for further analysis. The best model generated eight clusters, each named according to the most frequent barriers in the group, such as “Inactive depressive women”, “Active depressive women” and “Super active”. The depressive clusters presented more barriers to PA, three barriers each one. Significant differences in PA and SB were observed across clusters. This work highlights the novelty of using unsupervised machine learning to uncover latent subgroups based on multiple concurrent barriers. In conclusion, tailored home-based and outdoor strategies should be developed, particularly targeting individuals with depressive symptoms and those facing significant time constraints.
Experimental study on crack initiation mechanism of loess containing non-penetrating fissures
Age-stratified prognostic performance of hematologic inflammatory indices for 30-day mortality in emergency department patients with PCR-confirmed COVID-19: A cohort study from the pre-vaccination pandemic era
Background During the pandemic era, rapid and accessible prognostic tools were essential to support clinical decision-making for emergency department (ED) patients presenting with acute infectious symptoms. Hematologic inflammatory indices derived from complete blood count (CBC) parameters, such as the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation value (PIV), have been increasingly investigated for risk stratification. This retrospective cohort study evaluated the age-stratified prognostic performance of these indices for 30-day mortality in ED patients during the pandemic period. Methods This retrospective cohort study included adults presenting to a tertiary-care ED between March 1 and May 31, 2020. All included patients were retrospectively confirmed to have SARS-CoV-2 infection by RT-PCR. CBC-derived inflammatory markers (SII, SIRI, and PIV) were calculated at admission. The primary outcome was 30-day mortality; the secondary outcome was ICU admission. Age-stratified analyses (<65 and ≥65 years) were performed. Receiver operating characteristic (ROC) analyses, area under the curve (AUC) values, optimal cut-offs, and negative predictive values (NPVs) were determined; logistic regression models assessed independent associations with mortality. Results A total of 2,778 PCR-confirmed patients were included (mean age 47.8 ± 16.2; 58.7% male). Thirty-day mortality was 6.2%. In the overall cohort, SII, SIRI, and PIV demonstrated modest prognostic performance for mortality (AUCs: 0.663, 0.659, and 0.649, respectively). In patients <65 years, performance improved particularly for SII (AUC 0.727), with SIRI and PIV yielding AUCs of 0.676 and 0.677, respectively. Among patients ≥65 years, discrimination was lower (SII: 0.570; SIRI: 0.604; PIV: 0.588). Formal DeLong testing confirmed statistically significant age-related attenuation for SII (ΔAUC = 0.159; P = 0.0055), with non-significant trends for SIRI and PIV. As an exploratory secondary outcome, direct ED-to-ICU admission occurred in 2.9% of patients; this endpoint primarily reflects the institutional pandemic-era pathway of low-threshold ward admission with subsequent ICU escalation upon clinical deterioration. All indices demonstrated high negative predictive values, particularly in younger patients, indicating potential utility for identifying lower-risk individuals during high-volume pandemic ED operations. Conclusions Hematologic inflammatory indices obtained at ED presentation demonstrated age-dependent prognostic performance for 30-day mortality, with SII showing good discrimination and high negative predictive value (98.9%) in patients younger than 65 years and reduced discriminatory performance in elderly patients. These readily available and cost-effective parameters may support rule-out decisions for younger adults in emergency settings, while in elderly patients clinical assessment and comorbidity profiling should be prioritized over inflammatory marker interpretation.
Integrated value iteration and simple games enable calibrated strategic coalition formation in social networks
Understanding support needs of African and African-Caribbean people living with dementia, their care partners and families, and impacts of delayed support: Identifying inclusive strategies to facilitate social care support: A study protocol
African and African-Caribbean (AAC) people living with dementia (PLWD) are a population at high risk of inequitable access to health and social care services, and have poor health and wellbeing outcomes. Research suggests they are likely to experience failings in care, be recognised by services late and at points of crisis, and are at increased risk of institutionalisation. This research aims to examine the experiences of AAC PLWD, their care partners and families of accessing social care services and support. Employing an intersectionality theoretical framework, this study will use a multi-method, flexible exploratory sequential design. An evidence synthesis using the JBI meta-aggregation approach, co-produced with AAC PLWD, their care partners, and families, will be conducted to synthesise existing evidence on the experiences of AAC PLWD, their care partners’ and families’ of support seeking in the UK. Narrative interviews will involve 3 sequential interviews conducted with 40 AAC PLWD (n = 10 per site), with opportunities for dyadic interviews with care partners and families. Ethnographic fieldwork will be conducted across 4 Local Authority sites within Adult Social Care teams (n = 30 days per site) to provide insight into institutional and organisational processes and cultures, and staff practices and engagement with PLWD. Artistic art workshops will be conducted to facilitate a diverse range of participant voices and support meaningful engagement for AAC PLWD at increased risk of isolation. Ethical approval has been obtained. Dissemination will include peer reviewed publications, conference presentations and free publicly available resources for health and social care professionals, third-sector partners, PLWD and their carer partners.
Long-term viability and stable germline transmission of prion-free cattle over multiple generations
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.