Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine

Addressing the challenges of high fall risk among the elderly, untimely post-fall assessment, and lack of targeted rehabilitation plans, this paper proposes a method for post-fall micro-expression perception and rehabilitation prescription recommendation based on lightweight multimodal deep learning and Traditional Chinese Medicine knowledge graph. Firstly, the YOLOv9-tiny pose estimation technique is employed to achieve lightweight real-time human fall detection. Secondly, using Mini-Xception as the foundational model for facial micro-expression detection after falls, an SE-Mini-Xception model is constructed by embedding the Squeeze-and-Excitation (SE) attention mechanism to enhance facial micro-expression detection performance, while establishing a facial expression pain scale based on the Facial Action Coding System (FACS). Finally, based on the expression pain scale, Neo4j is utilized to construct a Traditional Chinese Medicine rehabilitation prescription recommendation system, with frontend-backend interaction implemented through Flask and Vue. The results demonstrate that YOLOv9-tiny achieves a precision (P) of 89.7%, recall (R) of 88.4%, and mAP@0.5 of 92.4% in human fall detection, with a parameter count of 2.62M. The improved SE-Mini-Xception model shows 8.78% and 9.04% enhancements in accuracy and recall rates respectively for facial micro-expression detection after falls compared to the original Mini-Xception. The constructed knowledge graph system for TCM classical prescriptions enables rapid matching and recommendation of corresponding rehabilitation prescriptions. In summary, the proposed method demonstrates substantial effectiveness in real-time fall detection and rehabilitation for the elderly, while being more suitable for deployment on lightweight mobile terminals, providing a novel solution for real-time fall detection and rehabilitation in practical scenarios for the elderly.

<title>Abstract</title> <bold>Objective</bold> To analyze risk factors for hemothorax in patients with multiple rib fractures and establish a nomogram prediction model. <bold>Methods</bold> A modeling cohort of 280 patients with multiple rib fractures treated at our hospital from June 2022 to June 2024 was selected. Patients were divided into a hemothorax group ( <italic>n</italic> =70) and a non-hemothorax group ( <italic>n</italic> =210). Additionally, 120 patients treated at our hospital during the same period were collected as the validation group, also divided into a hemothorax group ( <italic>n</italic> =33) and a non-hemothorax group ( <italic>n</italic> =87) based on hemothorax occurrence. General clinical data were collected. Independent risk factors were identified using a multivariate logistic regression model, and a nomogram prediction model was constructed using the “rms” package in R software. <bold>Results</bold> In the modeling cohort, patients in the hemothorax group exhibited statistically significant differences compared to the non-hemothorax group in terms of history of hypertension, number of fractures, bilateral fractures, chest injury severity score, concomitant pulmonary contusion, flail chest, white blood cell count, hemoglobin, and D-dimer ( <italic>P</italic> &lt; 0.05). In the validation cohort, patients with multiple rib fractures and complicated hemothorax showed statistically significant differences compared to the non-complicated group in terms of history of hypertension, number of fractures, bilateral fractures, chest injury severity score, concomitant pulmonary contusion, flail chest, white blood cell count, hemoglobin, and D-dimer ( <italic>P</italic> &lt; 0.05). Bilateral fractures, flail chest, white blood cell count, hemoglobin, and D-dimer were independent risk factors for hemothorax in patients with multiple rib fractures (P &lt; 0.05). A nomogram prediction model was constructed based on these factors. The <italic>Hosmer-Lemeshow</italic> goodness-of-fit test revealed <italic>χ²</italic> =13.303, <italic>P</italic> =0.102 for the modeling group and <italic>χ²</italic> =8.526, <italic>P</italic> =0.384 for the validation group, indicating good consistency and model fit. The decision curve analysis revealed that the predictive model provided additional clinical net benefit when the risk threshold was set between 0.08 and 0.96 in the modeling group and between 0.08 and 0.95 in the validation group. The receiver operating characteristic curve revealed an area under the curve (AUC) of 0.854 (95% <italic>CI</italic> : 0.794–0.914) for the modeling group and 0.835 (95% <italic>CI</italic> : 0.744–0.926) for the validation group, indicating good predictive performance. <bold>Conclusion</bold> The nomogram model constructed based on bilateral fractures, flail chest, white blood cell count, hemoglobin, and D-dimer provides important strategic guidance for the predictive assessment and clinical care intervention of hemothorax in patients with multiple rib fractures.

The coronavirus 3C-like protease (3CLpro) is essential for SARS-CoV-2 replication, making it a key target for antiviral drug development. Natural products represent a valuable source of bioactive compounds. This study aimed to identify novel 3CLpro inhibitors from natural compounds through a combination of virtual screening and experimental validation. Recombinant 3CLpro was expressed, purified, and evaluated for enzymatic activity using Fluorescence Resonance Energy Transfer (FRET) assays under optimized conditions. Out of 583 virtually screened compounds, 30 were selected for experimental validation. Epitheaflagallin 3-O-gallate (ETFGg) was further analyzed for binding interactions using Molecular Dynamics (MD) simulations. ETFGg exhibited strong binding affinity (-66.90 kcal/mol) and inhibitory activity (IC50 = 8.73 ± 2.30 μM) against 3CLpro. MD simulations revealed stable interactions with key residues (HIE163, THR190, GLN192) in the 3CLpro active site. Our findings demonstrate that ETFGg is a potent and stable inhibitor of SARS-CoV-2 3CLpro, with a binding profile distinct from known inhibitors such as ebselen. The substrate inhibition kinetics observed suggest a novel allosteric mechanism, which may provide a new strategy for targeting 3CLpro. This study supports the value of natural product libraries combined with computational and FRET-based screening for discovering antiviral leads. ETFGg was identified as a promising 3CLpro inhibitor with high binding stability, highlighting its potential as a lead compound for the development of anti-- COVID-19 drugs.

Rationale:Minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is widely applied for lumbar spine disorders due to reduced surgical trauma and faster recovery. However, postoperative interbody fusion cage migration remains a challenging complication. This report introduces a contralateral intervertebral foramen revision strategy as an alternative solution to correct cage migration without removing the original cage.Patient concerns:A 55-year-old Asian female presented with progressive lower back pain, radiating leg pain, and limited mobility 5 weeks after MIS-TLIF. Symptoms significantly affected daily activities and were consistent with neurological compression.Diagnoses:Postoperative cage migration with associated nerve compression was confirmed by physical examination and imaging studies, including X-ray, computed tomography, and magnetic resonance imaging.Interventions:A revision MIS-TLIF procedure was performed via the contralateral intervertebral foramen. The migrated cage was repositioned using the original cage handle and Kirschner wire, and an additional interbody cage was implanted to enhance segmental stability. This approach avoided removing the original cage and minimized anatomical disruption.Outcomes:Neurological symptoms improved immediately after surgery, and postoperative imaging confirmed satisfactory cage correction and stabilization. At the 1-year follow-up, the patient reported no recurrent discomfort, and computed tomography imaging demonstrated stable cage position and interbody fusion.Lessons:Contralateral intervertebral foramen access in MIS-TLIF revision surgery offers a minimally invasive alternative for cage migration management, avoiding the need for cage removal and reducing operative trauma. This technique may provide a viable option for similar revision scenarios requiring neural decompression and cage repositioning.

Menstrual irregularity is a common reproductive endocrine disorder among women of reproductive age, serving both as an early indicator of various gynecological diseases and a potential marker of underlying metabolic disturbances. Low-density lipoprotein cholesterol (LDL-C), a key indicator of lipid metabolism, lacks robust epidemiological evidence regarding its association with menstrual irregularity. This study utilized data from the 2013 to 2014 cycle of the National Health and Nutrition Examination Survey, which included (n = 623) women aged 15 to 49 years, to train a logistic regression model for assessing the association between LDL-C levels and menstrual irregularity. The model was externally validated using data from the 2009 to 2010 cycle (n = 799). Model performance was evaluated using the area under the receiver operating characteristic curve (area under the curve [AUC]), calibration plots, and confusion matrices. A nomogram was constructed to enhance clinical interpretability. The analysis indicated that LDL-C levels and age were significant predictors of menstrual irregularity. The AUCs of the model were 0.754 in the training set and 0.765 in the validation set, demonstrating good predictive performance and robustness. Residual analysis and collinearity diagnostics further supported the good fit of the model. The nomogram based on the final model enables visualized assessment of individual risk, aiding clinical risk stratification and decision-making. Elevated LDL-C levels are significantly associated with menstrual irregularity in women of reproductive age, suggesting its potential role as a metabolic biomarker of reproductive health. Longitudinal studies are recommended to clarify the causal relationship and to explore the potential of lipid-lowering interventions in menstrual regulation.

Bibliometric and visual analysis of end-tidal carbon dioxide (EtCO2) literature was conducted to clarify the current research hotpots and development trends, and provide new ideas for basic research and clinical diagnosis and treatment. Without any time limit, Science Citation Index Expanded, Social Sciences Citation Index, and Conference Proceedings Citation Index - Science databases from the Web of Science core collection were used as data sources, and bibliometric and VOSviewer software were used to visualize and analyze the literature in terms of authors, journals, countries, institutions, and their collaborative networks, as well as the keyword networks. The final inclusion of 4442 EtCO2-related publications, the number of publications is generally on the rise; the top 3 countries in terms of publications are the United States, the United Kingdom, and Canada; the top 3 institutions are the University of Toronto, the University of British Columbia, and Harvard University; the journal with the largest number of publications is the Journal of Applied Physiology. Currently, research in this field mainly focuses on the definition of end-expiratory CO2 partial pressure, monitoring methods, monitoring indexes, and application disciplines; the influencing factors related to the application of EtCO2, its application in various diseases, and its relationship with obstructive sleep apnea are the hot spots of research, and perhaps a major trend in the future. An increasing number of publications indicate that researchers are showing interest in the field of EtCO2, and ongoing research maintains a relatively mature level. The international community has established a good foundation for cooperation, and it is necessary to increase cooperation among researchers, institutions, and countries. At the same time, it is necessary to explore in-depth and strengthen personnel cooperation, expand the coverage of fund support, and further improve the quality of literature.

Heart failure (HF) is a complex clinical syndrome of progressive cardiac dysfunction, posing a major global burden on public health. The coupling between cardiomyocyte electrophysiology and energy metabolism is critical for sustaining cardiac electromechanical function. This review aims to elucidate the role of disrupted metabolic-electrophysiological coupling in disease progression and summarize emerging therapeutic targets. Currently, drugs that correct electrical or metabolic issues are proving effective in patients, while theoretical research and preliminary experiments have corroborated electro-metabolic coupling and its regulatory crosstalk among metabolic signaling, electrical activity, and energy utilization. These foundational studies have established a viable basis for multi-target interventions in HF. Restoring the disrupted integration between metabolism and electrophysiology emerges as a promising strategic direction in HF therapy. Interventions targeting this integration, such as metabolic modulators and mitochondrially targeted agents, may suppress arrhythmias, reverse adverse remodeling, and improve contractile function. This integrated approach may ultimately lead to more effective treatments that improve clinical outcomes for HF patients.