The Central Hospital of Shaoyang

A heated debate has been ongoing regarding the potential overlap in language disorders exhibited by children with developmental language disorder (DLD) and those with autism spectrum disorder plus language impairment (ALI). This study compared the production of Chinese negative structures by these two populations, alongside typically developing age-matched (TDA) children. Nineteen children with DLD, thirty-two children with ALI and twenty-eight TDA children participated in a video task and a game task on two Chinese negative structures (Structure B: V-bu-C; Structure M: mei-V-C). The results reflected a general resemblance between the two groups (DLD vs. ALI) in their most prevalent non-target responses in the two structures. However, children with ALI evinced impairment in both structures, while children with DLD only presented significantly lower-than-TDA performance in Structure M. Moreover, only the ALI group performed better in the game task than in the video task when producing Structure B, and the DLD and ALI groups produced some distinctive non-target responses. The findings indicate that the DLD group’s poor performance may be related to the impairment in the assignment of negative scope and the ALI’s difficulties may be attributed to the deficits in pragmatics. This study supports the idea that while children with DLD and children with ALI share some common symptoms in language, their language difficulties may result from different impairments. Labels should not be attached to children without an in-depth analysis of children’s responses in language assessments.

Accurate and generalizable cancer diagnosis from whole slide images (WSIs) remains challenging due to limited fine-grained annotations, complex tumor architectures, and domain shifts across scanners and institutions1. We introduce StructMIL, a structure-aware and prototype-driven multiple instance learning framework designed for robust and interpretable cancer detection and grading2. StructMIL integrates graph-based topological priors with histological context, employs prototype-enhanced pooling for stable and transparent predictions, and incorporates a unified domain-generalization strategy that combines contrastive alignment, adversarial confusion, and consistency regularization. Evaluated on Camelyon16 for breast cancer metastasis detection and PANDA for prostate cancer Gleason grading, StructMIL achieves state-of-the-art performance. On Camelyon16, StructMIL improves cross-center AUC by +3.2% over standard MIL baselines, reaching an AUC of 0.967. On PANDA, it improves cross-scanner Gleason grading robustness with a +7.4% Cohen’s Kappa gain compared with prior MIL models, demonstrating substantially reduced performance degradation under domain shift. StructMIL further provides interpretable prototype-based attribution maps that highlight biologically meaningful structures more reliably than conventional MIL and graph-free approaches3. By jointly improving accuracy, interpretability, and generalization across scanners and medical centers, StructMIL offers a practical and clinically aligned solution for large-scale deployment in multi-center computational pathology workflows4.

Patients' adherence is important for successful Helicobacter pylori (H. pylori) eradication, and may be improved by WeChat, a popular social networking platform. This study aimed to evaluate patients' adherence, successful H. pylori eradication, and clinical remission under the WeChat-based enhanced guidance. Ninety-nine patients with H. pylori infection, who were treated by a 14-day standard bismuth-containing quadruple therapy (BQT) and received the WeChat-based enhanced guidance, were retrospectively included. Multivariate logistic regression analysis was performed to identify independent predictors of patients' adherence and H. pylori eradication. Odds ratios (ORs) were calculated. The rates of good medication adherence, good re-examination adherence, successful H. pylori eradication, and clinical remission were 97.0%, 83.8%, 89.2%, and 89.5%, respectively. Patients with poor re-examination adherence had significantly lower proportions of individual dining (13.3% versus 50.6%, P=0.008) and no smoking during treatment (66.7% versus 92.8%, P=0.012) than those with good re-examination adherence. Both individual dining (OR=0.182, P=0.035) and no smoking during treatment (OR=0.210, P=0.031) were independently associated with good re-examination adherence. Patients with H. pylori eradication failure had significantly higher proportions of poor medication adherence (22.2% versus 1.4%, P=0.030) and living in rural area (44.4% versus 8.1%, P=0.009) than those with H. pylori eradication success. Both poor medication adherence (OR=20.366, P=0.031) and living in rural area (OR=8.944, P=0.010) were independently associated with H. pylori eradication failure. There is good medication adherence with satisfactory H. pylori eradication and clinical remission under the WeChat-based enhanced guidance. How to improve re-examination adherence should be further explored.

ObjectiveTo develop a nomogram model for individualized prediction of non-suicidal self-injury (NSSI) risk in adolescent depression patients.MethodsClinical data from 270 adolescent depression patients (August 2022–January 2025) were randomly divided into modeling and validation groups. The modeling group was split into NSSI and non-NSSI subgroups based on NSSI occurrence. Logistic regression identified risk factors. R software was used to construct the nomogram, while ROC and DCA evaluated its discrimination and clinical utility.ResultsA total of 189 patients from our hospital were retrospectively selected, among whom 72 patients (38.10%) were identified as having engaged in NSSI behavior within the past year. Disease duration, depression level, childhood abuse, family dysfunction, school bullying, sleep disorder, and Barratt Impulsiveness were significant risk factors (P<0.05). AUCs were 0.899 (modeling) and 0.954 (validation). H-L tests showed good fit: χ²=7.243 (P=0.721) and χ²=7.010 (P=0.711). The DCA curve indicated high clinical value when probability ranged from 0.05 to 0.97.ConclusionDisease course, severity of depression, childhood abuse, dysfunctional family environment during childhood, experiences of school bullying, sleep disorders, and Barratt Impulsiveness Scale scores were identified as influencing factors for NSSI in adolescents with depression. Based on these factors, a nomogram model was constructed, which showed good predictive consistency and high clinical applicability. This model can assist clinicians in identifying high-risk individuals for early prevention. Although the model may help guide interventions to reduce the incidence of NSSI, further validation through rigorously designed implementation studies is still required.

This study aims to investigate the causal relationship between gut microbiota and Crohn disease (CD) and identify and quantify the role of metabolites as potential mediators. Using summary-level data from a genome-wide association study (GWAS), a two-sample Mendelian randomization (MR) analysis of genetically predicted gut microbiota and CD was performed. Furthermore, we used two-step MR to quantify the proportion of the effect of metabolites-mediated gut microbiota on CD. We identified 15 metabolites (carnitine levels, isovalerate (i5:0) levels, hydroxyoctanoate levels, 6-hydroxyindole sulfate levels, heptenedioate, 4-methylhexanoylglutamine levels, uridine levels, X-13866 levels, X-17354 levels, X-18345 levels, X-22834 levels, X-25519 levels, serine to pyruvate ratio, URIDINE to 2’-deoxyuridine ratio, and histidine to glutamine ratio) with mediating effects in the impact of genus Ruminococcaceae UCG013 on CD. Our study identified a causal relationship between gut microbiota and CD, with a small proportion of the effect mediated by fifteen metabolites.

N6-methyladenosine (m6A) modification serves as a crucial regulator of diverse biological processes and disease progression. A central player in m6A regulation is YT521-B homology domain family member 2 (YTHDF2), a well-characterized m6A-binding protein primarily involved in mRNA stabilization. This study identifies lysine 542 (K542) as a critical acetylation site on YTHDF2, regulated by p300 as the acetyltransferase and SIRT2 as the deacetylase. Notably, while K542 acetylation has minimal effects on YTHDF2's protein stability or subcellular localization, it significantly enhances the protein's binding affinity for m6A-modified mRNAs. This enhanced binding affinity has profound biological implications, as K542 acetylation promotes malignant phenotypes in colorectal cancer (CRC) cells in vitro and in vivo. Supporting its clinical relevance, analysis of CRC tissues reveals elevated levels of YTHDF2 acetylation in primary tumors, strongly associating this modification with CRC pathology. Collectively, these findings highlight K542 acetylation as a key enhancer of YTHDF2's m6A recognition and a critical driver of its oncogenic activity.