Browse Articles

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.

Dimensional Isomerism in COFs Enabled by Spirobifluorene Substitution for Enhanced Photocatalytic CO ₂ Reduction

ABSTRACT The concept of dimensional isomerism—covalent organic frameworks (COFs) constructed from identical molecular precursors but forming structures of different dimensions—remains largely unexplored. Here, we report a pair of isomeric COFs derived from the same spirobifluorene core, wherein substitution at either the C2/C7 or C3/C6 positions leads to the formation of a planar two‐dimensional (2D) or a nonplanar three‐dimensional (3D) framework, respectively. This substitution‐controlled strategy enables precise modulation of π‐conjugation and framework dimensionality without altering the molecular identity of the building block. Comparative photocatalytic experiments reveal that the 2D C2/C7‐substituted COF exhibits significantly superior CO 2 reduction activity under visible light, attributed to its extended conjugation and efficient charge transport. These findings underscore the critical role of molecular connectivity in determining the structure–function relationships of COFs and propose a generalizable approach for designing high‐performance photocatalytic materials through topological isomerism.

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.

Tuning Reaction Pathways via Symmetric Fluorination Enables High‐Temperature and High‐Voltage Electrolytes

ABSTRACT Ni‐rich layered oxide cathodes deliver high capacity, but they suffer from severe interfacial instability and thermal safety risks when operated at high voltages and elevated temperatures. Here we propose an electrolyte design strategy based on molecular fluorination symmetry. This approach employs difluoro‐symmetric substitution to precisely steer decomposition pathways towards preferential ring‐opening reactions, thereby effectively suppressing defluorination decomposition and the concomitant formation of acidic byproducts at elevated temperatures. Through rational molecular engineering of synergistic fluorination, we achieve directed interfacial chemistry control. Under harsh operational conditions (4.5 V, 45°C), the modified cells retain 83% of their capacity after 300 cycles, along with significantly reduced gas generation and an elevated thermal runaway onset temperature. Furthermore, 2 Ah graphite||LiNi 0.8 Co 0.1 Mn 0.1 O 2 pouch cells exhibit a capacity retention of 90% after 480 cycles at 45°C and 91% after 200 cycles at 60°C. These results establish molecular fluorination symmetry as a practical design principle for electrolytes that enhance high‐temperature performance and intrinsic safety in Ni‐rich cathodes under demanding operational conditions.

Thermally stable GaN RF HEMTs on proton irradiated GaN substrates for low-noise applications

In this study, proton irradiation is employed to fabricate semi-insulating GaN free-standing wafers with a sheet resistance of 5.9 × 107 Ω/sq. The mechanism for achieving semi-insulating GaN via proton irradiation is associated with the formation of a deep-level trap (EC-0.54 eV), which is tentatively attributed to nitrogen antisite-related defects (NGa). GaN-on-GaN high electron mobility transistors fabricated on the irradiated substrates exhibit stable operation over 300–375 K, with a minimum noise figure as low as 0.25 dB, with suppressed low-frequency noise characteristics. Additionally, the proton irradiation treatment GaN substrate also exhibits high volume production potential. This device is ideal for installation in harsh operational environments, such as satellite communication systems or outdoor base stations.

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.

Outside Back Cover: Tissue Heterogeneity‐Driven Parallel Acquisition for High‐Coverage MS/MS Imaging of Lipids (Angew. Chem. Int. Ed. 31/2026)

Lithium‐Germanium Alloy Interfaces for Efficient Low‐Pressure Ammonia Synthesis

ABSTRACT The lithium‐mediated nitrogen reduction reaction (Li‐NRR) offers a sustainable pathway for ammonia synthesis, providing a viable alternative to the energy‐intensive Haber‐Bosch process. While most prior studies focused on solution‐phase optimization, we introduce a transformative electrode engineering strategy via the rational design of triethylammonium germanate‐modified copper (Cu/TEG) electrodes. Multi‐technique characterization reveals the formation of the Li 15 Ge 4 alloy during the Li‐NRR. Electrochemical measurements combined with DFT calculations demonstrate that the Li 15 Ge 4 alloy simultaneously facilitates lithium deposition and shifts the N 2 adsorption energy more exothermic. Most importantly, this interfacial engineering enables a Faradaic efficiency (FE) of up to 92.5% at an unprecedented N 2 pressure as low as 4 bar and with minimal overpotential relative to the onset of lithium deposition. This synergy minimizes energy losses, achieving the highest reported pseudo‐energy efficiency value (17.6%) in a batch cell to the best of our knowledge. Overall, this work establishes dual‐function interfacial engineering via lithium‐germanium alloy formation as a breakthrough strategy, moving beyond conventional solution‐phase optimization to provide a new approach for efficient electrochemical nitrogen fixation.

Rice straw-derived 3D porous carbon-based electrochemical immunosensor for ultrasensitive detection of alpha-fetoprotein

To detect the core tumor marker of alpha-fetoprotein (AFP) for the early diagnosis and prognostic evaluation, N/O co-doped 3D porous carbons (SDNOC3D PCs) were synthesized using rice straw as the green precursor to construct the sensitive electrochemical immunosensor. The SDNOC3D PCs possess the hierarchically interconnected 3D porous network with the specific surface area of ca. 1333 m2 g−1, accompanied by uniformly distributed N/O heteroatomic functional groups and abundant defect sites. The SDNOC3D PCs electrochemical immunosensor exhibits a linear response range of 0.001–1000 pg ml−1 for AFP, with the limit of detection of ca. 0.93 fg ml−1. The excellent electrochemical detection performance may be due to the unique structure of SDNOC3D PCs, which enables the efficient immobilization of AFP antibodies by the synergy of physical adsorption and electrostatic interactions, accelerating the electron transfer kinetics at the electrode interface. This work provides a novel green paradigm for the high-value utilization of agricultural wastes for the rapid clinical detection of tumor markers.

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

Oxidative PCET‐Triggered Benzylic C─H Iminolactonization via 1,5‐HAT/Cyclization Enabled by Cage‐Confined Photocatalysis

ABSTRACT We present a visible‐light‐driven photocatalytic platform based on a photosensitive metal–organic cage (PMOC), which enables selective recognition and activation of amidyl N─H bonds while suppressing competing homocoupling pathways. This system promotes a controlled intramolecular 1,5‐HAT/cyclization cascade via an oxidative proton‐coupled electron transfer (PCET) process, using commercially available BPO as oxidant and the in situ generated benzoate anion as the base. Mechanistic studies reveal that the cage architecture serves a dual role—facilitating oxidative PCET and enforcing product selectivity through spatial confinement—which steers the reaction through a unimolecular pathway, effectively inhibiting N─N homodimerization typical of bulk‐phase systems. This work establishes cage‐confined photocatalysis as a powerful strategy for directing nitrogen‐centered radical (NCR) reactivity toward the synthesis of value‐added heterocyclic compounds.

Kinetic study of <b> <i>κ</i> </b> ( <b> <i>ε</i> </b> ) to <b> <i>β</i> </b> phase transformation in Ga2O3 thin films via <i>in situ</i> high-temperature XRD

The kinetics of the κ(ε) to β-Ga2O3 phase transformation were investigated in five batches of nominally phase-pure κ(ε)-Ga2O3 thin films heteroepitaxially grown on c-plane sapphire, with film thickness ranging from 700 to 1100 nm, using in situ high-temperature x-ray diffraction. Phase fractions were quantitatively extracted through modified Rietveld refinement that accounts for preferred orientation, and the transformation kinetics were analyzed using the Johnson–Mehl–Avrami–Kolmogorov (JMAK) model. The applicability of the JMAK model to thin-film materials was evaluated, and its lower and upper bounds for thin films and bulk materials were established. Based on this analysis, a model specifically suited for thin-film kinetic studies was developed and yielded reproducible and robust results across all five sample batches. The results indicate that the κ(ε) to β phase transformation in ∼700–1100 nm films is best described as a diffusionless transformation comprising local atomic rearrangements with effectively two-dimensional (2D) growth, as evidenced by an Avrami exponent near 2 throughout the transformation interval.

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.