Objective To analyze the reasons and influencing factors of ambiguous medical records,and propose targe-ted intervention strategies.Methods The medical records discharged from the Department of Otolaryngology,Head and Neck Surgery of a large Class Ⅲ hospital in Xi'an from January 2023 to December 2023 were retrospectively collected,and the medical records groups and ambiguous medical records were screened through the DRG information platform.The clinical features,defect causes and risk factors of ambiguous cases were analyzed statistically.Results There were 4 124 discharged cases in the year,of which 77 were ambiguous cases,accounting for 1.87％.Compared with the non-ambiguous group,more patients in the ambig-uous group adopted a self-funded approach,experienced a transfer and a longer hospital stay.The proportion of surgery and trau-ma in ambiguous group was higher,but the proportion of tumor patients was lower.Due to DRG grouping rules,64 cases(83.1％)were divided into ambiguous groups,which was the primary direct cause.Binary Logistic regression indicated that self-paid medical treatment(OR=2.2),transfer(OR=4.8),hospitalization ≥7 days(OR=3.1),trauma(OR=15.0),and total cost＞20 000 yuan(OR=1.7)were the risk factors for ambiguous disease cases(P＜0.05),while tumor disease was the pro-tective factor(OR=0.6,P＜0.05).Conclusion The emergence of ambiguous medical records involves many aspects such as clinical diagnosis and treatment,hospital management,health policy and so on,which has an important impact on hospital per-formance and medical insurance fund.Only by strengthening the level of hospital management,improving the quality of informa-tion system,and promoting the coordination and communication between doctors,management and patients,can we effectively re-duce the occurrence of ambiguous medical records.

Objective To analyze the reasons and influencing factors of ambiguous medical records,and propose targe-ted intervention strategies.Methods The medical records discharged from the Department of Otolaryngology,Head and Neck Surgery of a large Class Ⅲ hospital in Xi'an from January 2023 to December 2023 were retrospectively collected,and the medical records groups and ambiguous medical records were screened through the DRG information platform.The clinical features,defect causes and risk factors of ambiguous cases were analyzed statistically.Results There were 4 124 discharged cases in the year,of which 77 were ambiguous cases,accounting for 1.87％.Compared with the non-ambiguous group,more patients in the ambig-uous group adopted a self-funded approach,experienced a transfer and a longer hospital stay.The proportion of surgery and trau-ma in ambiguous group was higher,but the proportion of tumor patients was lower.Due to DRG grouping rules,64 cases(83.1％)were divided into ambiguous groups,which was the primary direct cause.Binary Logistic regression indicated that self-paid medical treatment(OR=2.2),transfer(OR=4.8),hospitalization ≥7 days(OR=3.1),trauma(OR=15.0),and total cost＞20 000 yuan(OR=1.7)were the risk factors for ambiguous disease cases(P＜0.05),while tumor disease was the pro-tective factor(OR=0.6,P＜0.05).Conclusion The emergence of ambiguous medical records involves many aspects such as clinical diagnosis and treatment,hospital management,health policy and so on,which has an important impact on hospital per-formance and medical insurance fund.Only by strengthening the level of hospital management,improving the quality of informa-tion system,and promoting the coordination and communication between doctors,management and patients,can we effectively re-duce the occurrence of ambiguous medical records.

Objective:To describe the application methods, types, and effects of wearable devices in sarcopenia.Methods:Following the guidelines for scoping reviews, a systematic search was conducted in PubMed, Cochrane Library, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data, and VIP for literature related to wearable device applications in sarcopenia. The search period covered publications from database inception to August 1, 2024.Results:A total of 12 studies were included. Wearable devices in sarcopenia were primarily used for monitoring gait speed, surface electromyography, sleep duration, activity time, and supporting exercise interventions. Common types of wearable devices included activity trackers, smart belts, and smart wristbands. Their effectiveness, feasibility, and role in ensuring activity safety have been preliminarily demonstrated.Conclusions:The application of wearable devices in sarcopenia is still in its early stages but shows great potential. Currently, wearable devices are mainly used to monitor one or a few individual indicators. Future research should focus on developing multi-parameter monitoring devices and conducting large-scale, high-quality trials to validate their effectiveness in both monitoring and supporting exercise interventions. This will help promote their broader application and assist healthcare professionals in the early identification, diagnosis, prevention, and treatment of sarcopenia.

Objective:To describe the application methods, types, and effects of wearable devices in sarcopenia.Methods:Following the guidelines for scoping reviews, a systematic search was conducted in PubMed, Cochrane Library, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data, and VIP for literature related to wearable device applications in sarcopenia. The search period covered publications from database inception to August 1, 2024.Results:A total of 12 studies were included. Wearable devices in sarcopenia were primarily used for monitoring gait speed, surface electromyography, sleep duration, activity time, and supporting exercise interventions. Common types of wearable devices included activity trackers, smart belts, and smart wristbands. Their effectiveness, feasibility, and role in ensuring activity safety have been preliminarily demonstrated.Conclusions:The application of wearable devices in sarcopenia is still in its early stages but shows great potential. Currently, wearable devices are mainly used to monitor one or a few individual indicators. Future research should focus on developing multi-parameter monitoring devices and conducting large-scale, high-quality trials to validate their effectiveness in both monitoring and supporting exercise interventions. This will help promote their broader application and assist healthcare professionals in the early identification, diagnosis, prevention, and treatment of sarcopenia.

Neuropathic pain is frequently comorbidity with cognitive deficits. Neuralized1 (Neurl1)-mediated ubiquitination of CPEB3 in the hippocampus is critical in learning and memory. However, the role of Neurl1 in the cognitive impairment in neuropathic pain remains elusive. Herein, we found that lumbar 5 spinal nerve ligation (SNL) in male rat-induced neuropathic pain was followed by learning and memory deficits and LTP impairment in the hippocampus. The Neurl1 expression in the hippocampal CA1 was decreased after SNL. And this decrease paralleled the reduction of ubiquitinated-CPEB3 level and reduced production of GluA1 and GluA2. Overexpression of Neurl1 in the CA1 rescued cognitive deficits and LTP impairment, and reversed the reduction of ubiquitinated-CPEB3 level and the decrease of GluA1 and GluA2 production following SNL. Specific knockdown of Neurl1 or CPEB3 in bilateral hippocampal CA1 in naïve rats resulted in cognitive deficits and impairment of synaptic plasticity. The rescued cognitive function and synaptic plasticity by the treatment of overexpression of Neurl1 before SNL were counteracted by the knockdown of CPEB3 in the CA1. Collectively, the above results suggest that the downregulation of Neurl1 through reducing CPEB3 ubiquitination and, in turn, repressing GluA1 and GluA2 production and mediating synaptic plasticity impairment in hippocampal CA1 leads to the genesis of cognitive deficits in neuropathic pain.
Animals ; Male ; Neuralgia/metabolism* ; Rats ; Down-Regulation/physiology* ; Ubiquitination/physiology* ; Neuronal Plasticity/physiology* ; Rats, Sprague-Dawley ; CA1 Region, Hippocampal/metabolism* ; Cognitive Dysfunction/metabolism* ; RNA-Binding Proteins/metabolism* ; Receptors, AMPA/metabolism*

Objective:To develop and evaluate a medical visual question answering (VQA) model for neonatal lung ultrasound (LUS) images to enhance intelligent auxiliary diagnosis of neonatal pulmonary diseases.Methods:Using data from neonates admitted to Beijing Obstetrics and Gynecology Hospital, Capital Medical University (January 2023 to December 2024), an image-question-answer dataset comprising 251 LUS images was constructed [43 pneumonia (17.1%), 42 neonatal respiratory distress syndrome (16.7%), 83 transient tachypnea (33.1%), and 83 normal (33.1%) images] with a four-tier medical question-answer framework. Building upon the Qwen2.5-VL-7B base model and integrating LoRA fine-tuning with chain-of-thought prompting, we developed the NLUS-VQA model to enhance visual-language semantic alignment and enable stepwise clinical reasoning, achieving efficient small-sample adaptation. Model performance was comprehensively assessed through natural language generation metrics (BLEU-4, ROUGE-1/2/L), qualitative evaluation of characteristic recognition, and clinical consistency analysis.Results:(1) Quantitative evaluation demonstrated that NLUS-VQA achieved scores of 22.38 (BLEU-4), 48.26 (ROUGE-1), 22.40 (ROUGE-2), and 37.20 (ROUGE-L), representing significant improvements over baseline models. (2) Qualitatively, the model exhibited strong performance in identifying lung consolidation, coalescent B-lines, and snowflake signs, with its chain-of-thought strategy enhancing clinical interpretability and answer accuracy. (3) Clinically, NLUS-VQA achieved a Cohen's Kappa coefficient of 0.78 and diagnostic accuracy of 80.8% (21/26), indicating substantial agreement with clinical experts.Conclusion:The NLUS-VQA model demonstrates robust interpretability in recognizing key sonographic patterns (e.g. lung consolidation, confluent B-lines, and snowflake signs), providing a scalable framework for small-sample medical image analysis, though diagnostic performance on complex conditions remains limited by dataset scale and minority class representation.

Objective:To develop and evaluate a medical visual question answering (VQA) model for neonatal lung ultrasound (LUS) images to enhance intelligent auxiliary diagnosis of neonatal pulmonary diseases.Methods:Using data from neonates admitted to Beijing Obstetrics and Gynecology Hospital, Capital Medical University (January 2023 to December 2024), an image-question-answer dataset comprising 251 LUS images was constructed [43 pneumonia (17.1%), 42 neonatal respiratory distress syndrome (16.7%), 83 transient tachypnea (33.1%), and 83 normal (33.1%) images] with a four-tier medical question-answer framework. Building upon the Qwen2.5-VL-7B base model and integrating LoRA fine-tuning with chain-of-thought prompting, we developed the NLUS-VQA model to enhance visual-language semantic alignment and enable stepwise clinical reasoning, achieving efficient small-sample adaptation. Model performance was comprehensively assessed through natural language generation metrics (BLEU-4, ROUGE-1/2/L), qualitative evaluation of characteristic recognition, and clinical consistency analysis.Results:(1) Quantitative evaluation demonstrated that NLUS-VQA achieved scores of 22.38 (BLEU-4), 48.26 (ROUGE-1), 22.40 (ROUGE-2), and 37.20 (ROUGE-L), representing significant improvements over baseline models. (2) Qualitatively, the model exhibited strong performance in identifying lung consolidation, coalescent B-lines, and snowflake signs, with its chain-of-thought strategy enhancing clinical interpretability and answer accuracy. (3) Clinically, NLUS-VQA achieved a Cohen's Kappa coefficient of 0.78 and diagnostic accuracy of 80.8% (21/26), indicating substantial agreement with clinical experts.Conclusion:The NLUS-VQA model demonstrates robust interpretability in recognizing key sonographic patterns (e.g. lung consolidation, confluent B-lines, and snowflake signs), providing a scalable framework for small-sample medical image analysis, though diagnostic performance on complex conditions remains limited by dataset scale and minority class representation.

Background::Despite the adverse effects of ambient fine particulate matter (PM 2.5) on type 2 diabetes and the beneficial role of physical activity (PA), the influence of PM 2.5 on the relationship between PA and type 2 diabetes remains unclear. Methods::In this prospective study with 71,689 participants, PA was assessed by a questionnaire and was categorized into quartiles for volume and three groups for intensity. Long-term PM 2.5 exposure was calculated using 1-km resolution satellite-based PM 2.5 estimates. PM 2.5 exposure and PA's effect on type 2 diabetes were assessed by cohort-stratified Cox proportional hazards models, individually and in combination. Results::In 488,166 person-years of follow-up, 5487 incident type 2 diabetes cases were observed. The association between PA and type 2 diabetes was modified by PM 2.5. Compared with the lowest quartile of PA volume, the highest quartile was associated with reduced type 2 diabetes risk in low PM 2.5 stratification (≤65.02 μg/m 3) other than in high PM 2.5 stratification (>65.02 μg/m 3), with the hazard ratio (HR) of 0.75 (95% confidence interval [CI]: 0.66-0.85) and 1.10 (95% CI: 0.99-1.22), respectively. Similar results were observed for PA intensity. High PM 2.5 exposure combined with the highest PA levels increased the risk of type 2 diabetes the most (HR= 1.79, 95% CI: 1.59-2.01 for PA volume; HR = 1.82, 95% CI: 1.64-2.02 for PA intensity). Conclusion::PA could reduce type 2 diabetes risk in low-pollution areas, but high PM 2.5 exposure may weaken or even reverse the protective effects of PA. Safety and health benefits of PA should be thoroughly assessed for long-term polluted residents.

Background::Despite the adverse effects of ambient fine particulate matter (PM 2.5) on type 2 diabetes and the beneficial role of physical activity (PA), the influence of PM 2.5 on the relationship between PA and type 2 diabetes remains unclear. Methods::In this prospective study with 71,689 participants, PA was assessed by a questionnaire and was categorized into quartiles for volume and three groups for intensity. Long-term PM 2.5 exposure was calculated using 1-km resolution satellite-based PM 2.5 estimates. PM 2.5 exposure and PA's effect on type 2 diabetes were assessed by cohort-stratified Cox proportional hazards models, individually and in combination. Results::In 488,166 person-years of follow-up, 5487 incident type 2 diabetes cases were observed. The association between PA and type 2 diabetes was modified by PM 2.5. Compared with the lowest quartile of PA volume, the highest quartile was associated with reduced type 2 diabetes risk in low PM 2.5 stratification (≤65.02 μg/m 3) other than in high PM 2.5 stratification (>65.02 μg/m 3), with the hazard ratio (HR) of 0.75 (95% confidence interval [CI]: 0.66-0.85) and 1.10 (95% CI: 0.99-1.22), respectively. Similar results were observed for PA intensity. High PM 2.5 exposure combined with the highest PA levels increased the risk of type 2 diabetes the most (HR= 1.79, 95% CI: 1.59-2.01 for PA volume; HR = 1.82, 95% CI: 1.64-2.02 for PA intensity). Conclusion::PA could reduce type 2 diabetes risk in low-pollution areas, but high PM 2.5 exposure may weaken or even reverse the protective effects of PA. Safety and health benefits of PA should be thoroughly assessed for long-term polluted residents.

Hepatocellular carcinoma (HCC) is characterized by high postoperative recurrence and mortality rates. In recent years, researchers have identified a significant correlation between microvascular invasion (MVI) and early postoperative recurrence and metastasis of HCC, making it a focal point of HCC research. Accurate preoperative prediction of MVI occurrence and the implementation of relevant interventions (such as expanded resection) could provide substantial benefits to patients. This study analyzes global research over the past decade on MVI predictive indicators based on tumor biological characteristics, genetic measurements, imaging examinations, and tumor markers. The aim is to use these predictive indicators to objectively forecast the occurrence of MVI, thereby aiding in preoperative individual assessments and enhancing treatment plans.