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Confronted by the global challenge of ageing and the paucity of elderly care resources in rural areas, a novel smart elderly care ecosystem model integrating digital twins and reinforcement learning is proposed. A digital twin platform for elderly care in rural areas has been constructed, combining multi-source IoT and edge computing nodes. This has enabled the resolution of low-bandwidth data fusion issues. The core innovation of this study lies in the deep integration of backscatter communication and multi-agent deep reinforcement learning (MADDPG) into a cohesive system. This integration empowers the dynamic resource scheduling engine to perform real-time service optimisation and emergency response while simultaneously adapting to hardware energy constraints. Specifically, the backscatter communication module provides energy-efficient data links, the status of which is fed as real-time state inputs to the reinforcement learning agents. In return, the MADDPG algorithm output dynamically guides the adjustment of communication parameters. In the pilot areas of Shandong, a significant reduction in the response time to medical emergencies was observed, from 34.7 s to 8.9 s. Furthermore, the service continuity in disaster situations was recorded at 89.2%, indicating a notable enhancement in the effectiveness of emergency response systems. The device’s backscatter communication technology has been demonstrated to ensure a battery life of 68 h when subjected to ± 15% voltage fluctuations, with a 3.2% loss of communication packets. The community resilience index demonstrated a marked increase from 0.72 to 0.94 over a 12-month period, while user satisfaction levels attained 9.6. The cost-benefit analysis of the platform indicates a 2.8-year investment payback period, attributable to reduced medical expenses and enhanced service efficiency. The present study validates the technical solution’s efficacy in addressing the digital divide in rural elderly care, thereby establishing a replicable “technology - policy - community” model for global ageing governance. Highlights Reduced emergency response time from 34.7s to 8.9s via a Digital Twin-MADDPG dynamic decision-making framework. Achieved 68-hour device operation under ± 15% voltage fluctuations using an adaptive backscatter communication protocol. Increased the community resilience index from 0.72 to 0.94 through a novel quantitative model integrating social, infrastructural, and digital dimensions.

This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. The key innovations of SLF-YOLO include a novel SC_C2f module with a channel gating mechanism to enhance feature representation and regulate information flow, and a newly designed Light-SSF_Neck structure to improve multi-scale feature fusion and morphological feature extraction. Additionally, an improved FIMetal-IoU loss function is introduced to boost generalization performance, particularly for fine-grained and small-target defects. Experimental results demonstrate that SLF-YOLO achieves a mean Average Precision (mAP) of 80.0% on the NEU-DET dataset, outperforming YOLOv8’s 75.9%. On the AL10-DET dataset, SLF-YOLO achieves a mAP of 86.8%, striking an effective balance between detection accuracy and computational efficiency without increasing model complexity. Compared to other mainstream models, SLF-YOLO demonstrates strong detection accuracy while maintaining a lightweight architecture, making it highly suitable for industrial applications in metal surface defect detection. The source code is available at https://github.com/zacianfans/SLF-YOLO .

As scientific outputs continue to surge, research misconduct has garnered global attention. Case-based learning (CBL), an active student-centered learning strategy, possesses many advantages but has not been widely used in China due to resource constraints. This study aimed to address the research gap regarding the impact of CBL and traditional lecture on residents’ knowledge and attitudes towards research misconduct. This controlled before-and-after study was conducted at two tertiary hospitals in southwest China from November 2022 through March 2023. All medical residents at the two hospitals were defined as participants. Residents participating in CBL course at one hospital comprised the experimental group, whereas those engaging in traditional lecture at another hospital constituted the control group. The CBL and control group included 202 and 205 individuals, respectively. A total of 298 subjects were successfully matched after propensity score matching, with 149 individuals in each group. After the courses, the participants’ knowledge on research misconduct, perceived consequences for research misconduct, and their agreement rate regarding research misconduct improved in the CBL and control group ( P < 0.05), but certain aspects of their perceived consequences and agreement rate did not show significant improvement in the control group. The results revealed that there is a marked enhancement in residents’ knowledge about research misconduct, their perception of its consequences, and their overall disapproval of such behavior in the CBL group. This underscores the effectiveness of CBL in fostering a deeper understanding and stronger aversion towards research misconduct among residents.

Recently, the non-high-density to high-density lipoprotein cholesterol ratio (NHHR) has gained growing attention as an indicator for predicting diseases associated with lipid metabolism. Hepatic steatosis and fibrosis are tightly associated lipid metabolism. Our study aims to analyze the correlations among NHHR, hepatic steatosis, and fibrosis. This study analysed data from 14,578 adults in the US National Health and Nutrition Examination Survey (2005–2018). The degree of hepatic steatosis was measured through the Fatty Liver Index (FLI), while liver fibrosis severity was evaluated with the Fibrosis-4 (FIB-4) index. Multivariate linear regression assessed the association between NHHR and the FLI and FIB-4 score. Smooth curve describing the relationship between NHHR and FLI or FIB-4. Additionally, a two-part linear regression model adopted in order to more accurately account for the nonlinear relationship, with threshold effects estimated through its two components. To confirm the robustness of the findings, interaction tests and subgroup analyses were conducted. The multivariate logistic regression analysis demonstrated a significantly positive correlation of lnNHHR with FLI across all three models. In Model 3, the association was (β = 11.14, 95%CI:10.38,11.90). Curve fitting indicated a nonlinear relationship. The positive correlation between lnNHHR and FLI persists across gender, BMI, and physical activity groups. Nevertheless, a notable negative correlation between lnNHHR and FIB-4 was observed in all three models. In Model 3, the relationship between lnNHHR and FIB-4 was as follows: (β = -0.20; 95% CI: -0.22, -0.17). Curve fitting revealed a V-shaped relationship, with threshold effect analysis identifying a breakpoint at 1.51. Above this threshold, the relationship was found to be statistically insignificant (p-value = 0.424). Receiver operating characteristic (ROC) curve analysis demonstrated that NHHR exhibited better predictive performance for MASLD compared to non-HDL-C, HDL-C, and LDL-C/HDL-C. The current study’s findings suggest that elevated levels of NHHR correlate with a greater risk of hepatic steatosis among adults in the U.S. Our findings imply that NHHR may be a valuable tool in improving MASLD prevention strategies in the general population.

Cardiac hypertrophy is closely correlated with diverse cardiovascular diseases, augmenting the risk of heart failure and sudden death. Long non-coding RNAs (lncRNAs) have been studied in cardiac hypertrophy for their regulatory function. LncRNA MEG3 has been reported in human cancers. Whereas, it is unknown whether MEG3 regulates the growth of cardiac hypertrophy. Therefore, this study aims to investigate the specific role of MEG3 in the progression of cardiac hypertrophy. Here, we found that MEG3 contributed to the pathogenesis of cardiac hypertrophy. MEG3 expression was remarkably strengthened in the mice heart which undergone the transverse aortic constriction (TAC). Moreover, qRT-PCR analysis revealed that MEG3 was upregulated in the cardiomyocytes which were treated with Ang-II. Silenced MEG3 inhibited the increasing size of hypertrophic cardiomyocytes and reversed other hypertrophic responses. Mechanically, MEG3 could affect cardiac hypertrophy by regulating gene expression. Mechanically, we found that MEG3 could be upregulated by the transcription factor STAT3 and could regulate miR-361-5p and HDAC9 by acting as a ceRNA. Finally, rescue assays were made to do further confirmation. All our findings revealed that STAT3-inducetd upregulation of lncRNA MEG3 controls cardiac hypertrophy by regulating miR-362-5p/HDAC9 axis.

Heteroplasmy occurs when wild-type and mutant mitochondrial DNA (mtDNA) molecules co-exist in single cells 1 . Heteroplasmy levels change dynamically in development, disease and ageing 2 , 3 , but it is unclear whether these shifts are caused by selection or drift, and whether they occur at the level of cells or intracellularly. Here we investigate heteroplasmy dynamics in dividing cells by combining precise mtDNA base editing (DdCBE) 4 with a new method, SCI-LITE (single-cell combinatorial indexing leveraged to interrogate targeted expression), which tracks single-cell heteroplasmy with ultra-high throughput. We engineered cells to have synonymous or nonsynonymous complex I mtDNA mutations and found that cell populations in standard culture conditions purge nonsynonymous mtDNA variants, whereas synonymous variants are maintained. This suggests that selection dominates over simple drift in shaping population heteroplasmy. We simultaneously tracked single-cell mtDNA heteroplasmy and ancestry, and found that, although the population heteroplasmy shifts, the heteroplasmy of individual cell lineages remains stable, arguing that selection acts at the level of cell fitness in dividing cells. Using these insights, we show that we can force cells to accumulate high levels of truncating complex I mtDNA heteroplasmy by placing them in environments where loss of biochemical complex I activity has been reported to benefit cell fitness. We conclude that in dividing cells, a given nonsynonymous mtDNA heteroplasmy can be harmful, neutral or even beneficial to cell fitness, but that the ‘sign’ of the effect is wholly dependent on the environment. A new method for tracking single-cell heteroplasmy, called SCI-LITE, is combined with mitochondrial DNA base editing to reveal principles of heteroplasmy dynamics in dividing cells.

Body measurement plays a crucial role in cattle breeding selection. Traditional manual measurement of cattle body size is both time-consuming and labor-intensive. Current automatic body measurement methods require expensive equipment, involve complex operations, and impose high computational costs, which hinder efficient measurement and broad application. To overcome these limitations, this study proposes an efficient automatic method for cattle body measurement. Lateral and dorsal image datasets were constructed by capturing cattle keypoints characterized by symmetry and relatively fixed positions. A lightweight SCW-YOLO keypoint detection model was designed to identify keypoints in both lateral and dorsal cattle images. Building on the detected keypoints, 11 body measurements—including body height, chest depth, abdominal depth, chest width, abdominal width, sacral height, croup length, diagonal body length, cannon circumference, chest girth, and abdominal girth—were computed automatically using established formulas. Experiments were performed on lateral and dorsal datasets from 61 cattle. The results demonstrated that the proposed method achieved an average relative error of 4.7%. Compared with the original model, the parameter count decreased by 58.2%, compute cost dropped by 68.8%, and model size was reduced by 57%, thus significantly improving lightweight efficiency while preserving acceptable accuracy.

To evaluate the feasibility of an AI system for identifying active tuberculosis (ATB) in TB-specialized hospitals in high-prevalence settings. An AI system designed to identify ATB was retrospectively validated using a multi-center dataset of 1741 CT images from three TB-specialized hospitals. The dataset included ATB, pneumonia, pulmonary nodules and normal cases. The system’s utility and generalizability were assessed across four application scenarios, and pairwise comparisons of the system’s performance were conducted among the three hospitals. The system demonstrated good generalizability across three settings. It achieved an AUC over 0.9 for distinguishing between abnormal and normal, over 0.95 for distinguishing between ATB and normal, over 0.8 for distinguishing between ATB and non-ATB, and an AUC ranging from 0.762 to 0.906 for distinguishing between ATB and other abnormalities (pneumonia and pulmonary nodules). For all evaluation matrices, at least one pairwise comparison showed no significant difference in performance among the three hospitals across different scenarios. Using an AI system to identify ATB in CT images is feasible in TB-specialized hospitals. This evaluation provides valuable insights for those looking to implement AI to support clinical decision-making and optimize resource utilization in hospitals overwhelmed by TB cases.

Background Numerous studies have demonstrated restless legs syndrome (RLS) might worsen motor and non-motor symptoms in patients with Parkinson’s Disease (PD). However, research into the effects of concurrent RLS on the function of the autonomic nervous system remains limited. Our study particularly focused on its effects on the autonomic nervous system. Method From October 2022 to February 2025, 392 patients with PD were continuously included in our study. PD patients were categorized into those with RLS and those without RLS, based on the criteria established by the International Restless Legs Syndrome Study Group (IRLSSG). A variety of questionnaires were utilized to evaluate the severity of symptoms in PD patients, including the King’s Parkinson’s Disease Pain Scale (KPPS), Parkinson’s Disease Sleep Scale (PDSS), and the Scales for Outcomes in Parkinson’s Disease for Autonomic Dysfunction (SCOPA-AUT), among others. Result Our research included 98 patients (25.0%) who met the IRLSSG diagnostic criteria for RLS. The concurrent RLS in PD patients was significantly related to KPPS scores KPPS scores [OR = 1.049, 95%CI:1.007–1.093, P  = 0.021], thermoregulatory subscores [OR = 1.275, 95%CI:1.007–1.615, P  = 0.044] and PDSS scores [OR = 0.978, 95%CI:0.963–0.993, P  = 0.004]. Moreover, the Restless Leg Syndrome Rating Scale (RLSRS) scores in PD with RLS group were positively associated with Pittsburgh Sleep Quality Index (PSQI) scores [β = 0.312, 95%CI:0.031–0.683, P  = 0.032]. Conclusion 1.PD patients experiencing more severe pain symptoms, more severe sleep disorders, and more severe dysfunction of the thermoregulatory system were at a higher risk of developing RLS. Among these factors, pain score was the most effective predictor of concurrent RLS. 2. PD patients with RLS who had poorer sleep quality tended to have a more severe RLS.