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Variation of fruit phenotypic traits of Cinnamomum camphora from 11 provenances in China

This study aimed to characterize the diversity and variation of fruit phenotypic traits in natural provenances of Cinnamomum camphora to facilitate the selection of superior germplasm. According to a provenance-based sampling design, 10 fruit phenotypes were investigated across 170 individuals from 11 provenances from Jiangxi province, China. The results showed that the average phenotypic differentiation coefficients among and within provenances were 73.82% and 26.18%, respectively, indicating that variation among provenances was the main source of phenotypic diversity in C. camphora in Jiangxi province, China. The mean Shannon-Wiener index (H) for the fruit phenotypic traits of C. camphora was 2.62, with the high diversity observed in fruit horizontal diameter/fruit vertical diameter (FHD/FVD, 2.848), fruit thousand-grain weight/fruit volume (FTW/FV, 2.835) and the fruit side diameter/fruit horizontal diameter (FSD/FHD, 2.817). The coefficients of variation of fruit phenotypic traits among different provenances ranged from 2.64% to 25.45%, with the largest value for peel thickness (PT). Furthermore, significant or highly significant correlations existed among fruit phenotypic traits. There was a significant correlation among fruit phenotypic traits of C. camphora and latitude, longitude, climate factors in different provenances of Jiangxi province. Based on the results of principal component analysis and cluster analysis, the cumulative contribution rate reached 78.78%, and the fruit side diameter (FSD), fruit side diameter/fruit vertical diameter (FSD/FVD), fruit side diameter/fruit horizontal diameter (FSD/FHD) could comprehensively reflect the information and ranking of the 10 traits. The fruit phenotypic traits from subgroup B2 were bigger. These selected individuals can be used as basic materials for variety improvement and conservation. The high phenotypic diversity indicates a rich genetic base, suggesting that future studies should employ molecular markers to assess genetic diversity and identify associated genes.

Organophosphate pesticide exerts toxic effect on the optic nerve of glaucoma rats by promoting oxidative stress and inflammation

What Money Cannot Buy: Addressing the Socioeconomic Status Ceiling Effect for Black Patients Across Cancer Types

The association between platelet-to-albumin ratio and diabetic peripheral neuropathy: A cross-sectional study in the Chinese population

Introduction The early prevention and diagnosis of diabetic complications are challenging, particularly for diabetic peripheral neuropathy (DPN), which progresses insidiously and irreversibly. Currently, reliable biomarkers associated with DPN are still limited. This study explored the association between the platelet-to-albumin ratio (PAR) and diabetic peripheral neuropathy in Chinese adults with type 2 diabetes mellitus(T2DM). Methods This cross-sectional study included adult patients with T2DM enrolled in the metabolic management center (MMC) database at the Affiliated Hospital of Southwest Medical University between June 2018 and December 2023. Participants meeting the predefined eligibility criteria were classified into diabetic peripheral neuropathy (DPN) and non-diabetic peripheral neuropathy (non-DPN) groups. The association between PAR and DPN was evaluated using logistic regression models, with subgroup analyses and restricted cubic spline regression used to examine potential effect modification and dose–response relationships. Receiver operating characteristic (ROC) curve analysis was performed to assess the discriminative ability of PAR for identifying DPN. Results Among 1,141 patients with T2DM, including 427 patients with DPN, PAR levels were significantly higher in the DPN group than in the non-DPN group ( P  < 0.001). Univariate analysis showed that higher PAR (OR=1.282, 95% CI: 1.183–1.389, P  < 0.001) significantly increased the prevalence of DPN. After adjusting for confounders, the platelet-to-albumin ratio (PAR) remained independently associated with DPN (OR = 1.158, 95% CI: 1.0511.276, P  = 0.003). In subgroup analysis, no significant interaction was observed (all P for interaction > 0.05). The restricted cubic spline regression confirmed a positive linear association between PAR levels and DPN ( P  = 0.001, P for nonlinearity = 0.986). Additionally, ROC analysis identified an optimal PAR cut-off value of 5.5971 for distinguishing patients with and without DPN. Conclusions In patients with T2DM, higher platelet-to-albumin ratio(PAR) levels were associated with the prevalence of diabetic peripheral neuropathy. This association may be related to underlying inflammatory processes involved in DPN.

Co-administration of calcium carbonate or sodium bicarbonate prevents esomeprazole-induced osteoporosis

Diminishing Returns Among Black Patients With Cancer: The Intersection of Race and Neighborhood Socioeconomic Status

PURPOSE Despite narrowing racial gaps, disparities persist across cancer types and socioeconomic levels. The diminishing returns hypothesis suggests that economic advantage yields fewer health benefits for Black individuals but is largely unexplored in the context of cancer. We examined the diminishing returns among Black and White individuals across cancer types using a nationally representative study population. METHODS The study analyzed cancer-specific survival among 5.3 million non-Hispanic Black and White adults diagnosed with primary cancer (2006-2020) using SEER-22. We assessed how race and neighborhood socioeconomic status (SES) jointly affects survival across 21 cancer types. A 10-level race-SES variable was created, using White individuals in the highest-SES group as the reference. The main outcome was cancer-specific death. Diminishing returns were defined quantitively and qualitatively as worse survival for Black individuals even at higher SES. Cox models adjusted for demographics and clinical factors, with multiple imputation for missing data. Social gradients were also evaluated. RESULTS Black women showed strong evidence of diminishing returns overall and for seven cancers, especially uterine and breast cancers. A social gradient was also evident in cancers with diminishing returns, except uterine cancer. For Black men, diminishing returns were not observed across all cancers combined but was present in eight cancers—including prostate and colorectal cancers. Most cancers among men exhibited a strong social gradient. Findings were consistent by time period and upon restricting to localized and regional disease in sensitivity analyses. CONCLUSION Higher SES improves cancer survival for White patients but not Black patients, worsening racial disparities for certain cancers.

Spinal mechanisms and feasibility of Dry Needling versus Botulinum Toxin Type A in post-stroke lower limb spasticity: A proof-of-concept randomized clinical trial protocol (STROKE-POC)

Introduction Stroke often causes spasticity, impacting mobility and quality of life. Botulinum Toxin type A (BTX-A) and Dry Needling (DN) are treatments that reduce spasticity, although Botulinum Toxin type A injections can cause adverse effects. No studies have directly compared their effects at spinal, muscular, functional, quality-of-life, and cost-effectiveness levels. This study aims to determine the spinal mechanisms of BTX-A and DN on post-stroke lower limb spasticity, while also assessing feasibility, safety, and exploratory effects at muscular, functional, quality-of-life, and cost-effectiveness levels. Methods and analysis This is a protocol of a proof-of-concept, feasibility randomized clinical trial including 90 participants from Canada, Belgium, and Spain who experienced a first stroke in the previous 12 months and present plantar flexor spasticity. Time since stroke (0–12 months) will be recorded and explored as a potential modifier of treatment response. Participants will be randomly assigned to receive either one session of BTX-A or 12 weekly sessions of DN. Blinded evaluators will assess outcomes before, during, and after treatment, with a 4-week follow-up. The primary outcome will be spinal mechanisms of spasticity, measured using the Tonic Stretch Reflex Threshold and its velocity sensitivity. Secondary outcomes will assess: a) muscular architecture and echotexture (measured with ultrasound); b) muscle tone/resistance using the Modified Ashworth Scale; c) gait and mobility (instrumented analysis, Timed Up and Go, 10-Meter Walk Test); d) muscle strength with dynamometry; e) quality of life with the EuroQoL questionnaire; and f) cost-effectiveness (analytic model). The findings will provide preliminary data to inform a future definitive trial. Ethics and Dissemination This research project has secured funding from the NEURON ERA-NET 2022 call, supported by the European Union’s Horizon 2020 program (GA 964215) and co-funded by the European Union-Next Generation, and has undergone peer review. Ethical approval has been obtained from Spain, Canada, and Belgium. The study is registered in ClinicalTrials.gov (NCT06296082) and the Clinical Trials Information System (CTIS) under the number 2024-510866-18-00. The study protocol is registered on Zenodo ( https://doi.org/10.5281/zenodo.20034064 ) Clinical Trials Clinical Trials NCT06296082; https://clinicaltrials.gov/study/NCT06296082

A reproducible benchmark of QRS detection algorithms across diverse ECG datasets and noise conditions

Abstract Accurate R-peak detection in electrocardiograms is critical for heart rate monitoring, heart rate variability analysis, and cardiac condition diagnosis. However, reliable detection remains challenging in real-world scenarios due to noise, artifacts, and signal variability. A key limitation in current research is the lack of reproducibility and comparability, as algorithms are often tested on varying datasets, hindering direct performance comparisons. To address this, we benchmark 17 R-peak detection algorithms, encompassing traditional signal processing, machine learning, and deep learning approaches, within a unified evaluation framework using five open-access ECG datasets from the PhysioNet platform. These databases represent diverse conditions, including long-term monitoring, arrhythmias, and noisy environments, enabling a standardized evaluation. Our results reveal that under a strict cross-dataset generalization setting, in which ML and DL models were trained on a single dataset without any target-domain adaptation, traditional signal processing methods provided more consistent overall performance. This highlights a trade-off between peak performance on familiar data and generalizable performance under distribution shift, whose extent for data-driven methods may depend substantially on training diversity. To support reproducibility and future benchmarking, we provide a fully open evaluation framework including all implementations, dataset references, and evaluation pipelines. These findings offer guidance for researchers and clinicians selecting R-peak detection algorithms for diverse clinical and practical scenarios.

Reply to: Immune Checkpoint Blockade in Deficient Mismatch Repair/Microsatellite Instability–High Gastric Cancer: Solid Evidence or Premature Extrapolation?

Computational models for the classification of antibody specificity using heavy chain features

Background Antibodies play a critical role in immune defense, with their antigen specificity primarily governed by the unique sequences of their heavy chains, rendering them invaluable tools in research and diagnostics. High-throughput sequencing technologies have facilitated comprehensive profiling of the immune repertoire, generating vast antibody sequence datasets that necessitate advanced analytical methods. Methods In this study, we utilized curated antibody sequences from NCBI databases to develop computational classification models for categorizing antibodies into predefined antigen classes. We extracted multifaceted features from the heavy chain sequences, encompassing physicochemical properties, structural composition, sequence order, and evolutionary information. These features were input into machine-learning classifiers to predict antigen specificity across five classes of antibodies: anti-dengue virus, anti-influenza virus, anti-tetanus bacillus, anti-SARS-CoV-2, and anti-Mycobacterium tuberculosis. Results Five tree-based machine-learning models were employed, with CatBoost achieving the highest accuracy of 0.7713. To further enhance predictive performance, we developed a stacking model leveraging multiple algorithms, resulting in an improved accuracy of 0.7803. Additionally, a Feature-Based Transformer deep-learning architecture was implemented, yielding an accuracy of 0.7399 and an F1-score of 0.6761. To elucidate the key determinants of antibody-antigen interactions, we applied the SHAP framework to assess feature importance. Among the top 30 contributing features, those representing sequence order and evolutionary information predominated, with amino acids such as cysteine (C), isoleucine (I), histidine (H), and phenylalanine (F) exhibiting notable SHAP values. Notably, cysteine (Cys) emerged as the most influential feature, underscoring its critical role in antibody structure and function. Specific antibodies contributed variably to these key features; for instance, the anti-tuberculosis antibody accounted for approximately 11% of a sequence order feature associated with alanine (A), while the anti-SARS-CoV-2 antibody contributed about 9.26% to a feature associated with isoleucine (I). Conclusions Our study demonstrates the efficacy of machine-learning and deep-learning approaches in classifying antibodies into specific antigen categories, providing sequence-based insights into features associated with antibody specificity. These findings have significant implications for the mechanistic understanding, isolation, and development of potential therapeutic antibodies.

Retraction Note: Fusion of transfer learning models for detection of alzheimer’s disease using bidirectional long short-term memory with equilibrium optimization algorithm

Is It Time to Move Beyond Graft-Versus-Host Disease-Free, Relapse-Free Survival as a Primary End Point in Clinical Trials for Hematopoietic Cell Transplantation?

Social connectedness and healthcare engagement among adults with disabilities: The role of living arrangement

People with disabilities face significant healthcare engagement barriers, leading to increased risks of preventable conditions and mortality. This is particularly concerning for the quarter who live alone. Despite recognition of healthcare engagement as a social process, empirical understanding remains limited. This study examined how social connectedness predicts healthcare engagement, testing whether disability status and living arrangements moderate these associations. We analyzed data from 335 U.S. adults (ages 21–83) via an online survey. Healthcare engagement was assessed through three indicators: having a usual source of care, a personal doctor, and routine checkups. Social connectedness included intimate network size and informal health information network size. Using logistic regression, we tested main effects of social connectedness after controlling for covariates and three-way interactions (social connectedness × disability × living arrangements). Informal health information network size significantly predicted having a usual source of care and a personal doctor while intimate networks showed no main effects. A three-way interaction revealed that larger informal health information networks improved personal doctor access among adults without disabilities (regardless of living arrangements) and adults with disabilities living with others, but not among those with disabilities living alone. For routine checkups, larger intimate networks reduced utilization among adults with disabilities living alone. Social connectedness plays a conditional rather than universal role in healthcare engagement. Adults with disabilities living alone represent a particularly vulnerable population who do not benefit from social connectedness in accessing healthcare, highlighting the need for targeted interventions addressing their unique barriers.

Microwave drying effects on modeling thin-layer drying kinetics, energy efficiency analysis, and physical properties of garlic

Endurance from Structural Distortion and Conservation of Adsorption Ability of Moisture-sensitive Metal–Organic Frameworks (CPM-33x)

Quality of Chemoradiotherapy in Esophageal Adenocarcinoma Matters

Linguistic complexity of EFL writing with different levels of English proficiency: A stratified study of an application-oriented university

English writing competence is a significant manifestation of students’ second language proficiency. However, comprehensive synchronic empirical research remains scarce regarding how students at application-oriented universities perform in terms of linguistic complexity in their compositions. To bridge this gap, this study analyzes 66 students’ compositions hierarchically at the level of lexis, syntax, and text. Results demonstrate that: (1) Lexical and syntactic complexity indicators exhibit different sensitivities to writing proficiency. (2) Both lexical and syntactic competence display non-linear developmental features. (3) Students’ text coherence is consistently correlated with the writing quality. Future research should prioritize students’ metalinguistic cognition towards different linguistic dimensions. EFL instruction should be tailored to students’ varying language proficiency levels. This study highlights the need for curricula that evolve alongside students’ English linguistic complexity development, offering new insights into English writing education at Chinese application-oriented universities.

Advanced smartwatch for noninvasive sweat biomarker monitoring using a wearable FET biosensor array with Ag nanowire cross-linked MoS2 and MXene

Circulating Tumor DNA for Minimal Residual Disease in Colon Cancer: Ready for Prime Time?

Sound-evoked pupil dilation quantifies misophonic symptoms

Misophonia is a debilitating disorder where seemingly innocuous sounds (often human made, such as chewing or throat clearing) evoke intense negative cognitive, emotional and physical “fight-or-flight” responses. Recent studies reported alarmingly high prevalence across different countries and population characteristics, revealing an urgent need for a better understanding of this condition as well as improved measurement and diagnostic tools. First, current misophonia symptom measurements rely on (subjective) self-reports, and different studies employ various diagnostic approaches and cut-off scores. There is an urgent need for a complementary (objective) psychophysiological measurement tool. Second, the role of “mild” or “moderate” symptoms is currently topic of debate: are they still manifestations of the misophonic disorder? Here, we employ pupillometry to map out arousal responses to misophonia trigger sounds. We show that (i) pupil dilation can reliably differentiate misophonic responses from responses to generally unpleasant sounds (such as nails on chalk board), (ii) the “milder cases” of misophonia still show arousal responses characteristic of misophonia, and (iii) pupillometry can even be used to aid diagnosis in an individual; based on only pupil dilation, misophonic symptom severity could be reliably predicted at the level of a single individual. We conclude that even mild misophonic responses can reliably, objectively and cost-effectively be indexed by pupil-linked arousal.