Objective To investigate the post-discharge exercise behavior and factors influencing moderate to vigorous intensity physical activity (MVPA) in patients undergoing lung surgery. Methods A total of 2874 patients from the large prospective, observational perioperative lung symptom study cohort (CN-PRO-Lung 3) in the Department of Thoracic Surgery at Sichuan Cancer Hospital between April 7, 2021, and January 31, 2024, were selected as the survey subjects. A survey was conducted using the Investigation of Exercise Behavior after Lung Surgery questionnaire and the International Physical Activity Questionnaire-Short Form (IPAQ-SF) among patients who underwent lung surgery. Binary logistic regression was used to analyze the factors influencing patients’ engagement in MVPA. Results A total of 702 patients were surveyed, including 252 males and 450 females, with an average age of (52.4±10.2) years. Patients with lung cancer accounted for 85.9%. Only 36.0% of the patients had regular exercise habits, while 42.3% did not engage in any physical activity. The three main barriers for postoperative exercise were physical discomfort (pain, coughing, shortness of breath, etc, 54.7%), lack of professional guidance (41.7%), and concerns about the surgical wound (28.9%). The proportions of patients engaging in vigorous, moderate, and low-intensity physical activity were 5.7%, 28.2%, and 66.1%, respectively. Multivariate analysis showed that patients with a personal annual income ≥50000 yuan (OR=1.52, 95%CI 1.01-2.29, P=0.044), high school education or above (OR=1.92, 95%CI 1.33-2.76, P<0.001), and lobectomy (OR=1.44, 95%CI 1.02-2.03, P=0.037) engaged in more MVPA. Conclusion Patients undergoing lung surgery have inadequate physical activity after discharge, particularly lacking in MVPA. Patients with higher income, higher educational levels, and lobectomy are more frequently engaged in MVPA. Measures such as symptom control, providing exercise guidance, and enhancing education on wound care may potentially improve the inadequate physical activity in lung surgery patients after discharge.

ObjectiveTo investigate the effects of modified Xiaoyaosan （JJXYS） on behavioral abnormalities and hippocampal mitochondrial quality control （MQC） in the rat model of post-myocardial infarction depression （PMD） and preliminarily explore its potential mechanism. MethodsA rat model of PMD was established by left anterior descending coronary artery ligation combined with chronic unpredictable mild stress （CUMS）. Rats were randomized into a control group， a model group， a fluoxetine （FLX， 10 mg·kg^-1） group， and low-， medium-， and high-dose JJXYS （JJXYS-L/M/H， 1.12， 2.24， 4.48 g·kg^-1， respectively） groups. Depressive-like behaviors were evaluated by body weight monitoring， sucrose preference test， open field test， and forced swimming test. Hematoxylin-eosin staining and Nissl staining were used to observe hippocampal histomorphology and neuronal changes. Enzyme-linked immunosorbent assay was conducted to determine the serum levels of 5-hydroxytryptamine （5-HT）， dopamine （DA）， interleukin-1 beta （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-alpha （TNF-α）. The mRNA levels of MQC-related genes including peroxisome proliferator-activated receptor-gamma coactivator-1 alpha （PGC-1α）， nuclear respiratory factor 1 （Nrf1）， and transcription factor A， mitochondrial （TFAM） in the hippocampal tissue were measured by real-time PCR. The expression of proteins related to the dynamin-related protein 1 （Drp1）/PTEN-induced putative kinase 1 （PINK1）/Parkin signaling pathway was determined by Western blot. ResultsCompared with the control group， the model group showed restricted body weight gain， aggravated depressive-like behaviors， declined serum 5-HT and DA levels， evident hippocampal neuronal damage and reduced Nissl bodies， as well as downregulated expression of MQC-related genes and proteins （P<0.05）. Compared with the model group， both FLX and JJXYS alleviated the above changes to varying degrees. Moreover， the JJXYS-M and JJXYS-H groups showed more pronounced effects， improving behavioral performance， restoring 5-HT and DA levels， alleviating hippocampal pathological injury， and upregulating the expression of PGC-1α/Nrf1/TFAM mRNA and Drp1/PINK1/Parkin signaling pathway-related proteins （P<0.05）. ConclusionJJXYS can significantly alleviate depressive-like behaviors and neurotransmitter imbalance in the rat model of PMD by regulating hippocampal MQC and upregulating the Drp1/PINK1/Parkin-related pathway. This study provides experimental evidence for the intervention of PMD with JJXYS.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

OBJECTIVE: This study aimed to evaluate the relationships of white blood cell (WBC) count, platelet (PLT) count, and PLT-to-WBC ratio (PWR) with muscle mass in Chinese older adults. METHODS: This cross-sectional analysis involved 4,033 Chinese older adults aged ≥ 65 years from the Healthy Ageing and Biomarkers Cohort Study. Muscle mass and total skeletal muscle mass index (TSMI) were measured by bioelectric impedance analysis. WBC, PLT, and PWR were measured using standard methods. Multivariate linear regression was used to examine the associations of WBC count, PLT count, and PWR with TSMI. RESULTS: High WBC count, PLT count, and PWR were associated with low TSMI, with coefficients of -0.0091 (95% confidence interval [ CI]: -0.0142 to -0.0041), -0.0119 (95% CI: -0.0170 to -0.0068), and -0.0051 (95% CI: -0.0102 to -0.0001). The associations between the three inflammatory indices and TSMI were linear. Stratified analyses indicated that the relationship between inflammatory markers and TSMI was more evident in male participants and in individuals aged < 80 years than in their counterparts. CONCLUSION Elevated WBC count, PLT count, and PWR correlated with muscle mass loss. This study highlights the importance of regular monitoring of inflammatory markers as a potential strategy for the screening and management of sarcopenia in older adults.
Humans ; Aged ; Male ; Female ; China ; Leukocyte Count ; Cross-Sectional Studies ; Platelet Count ; Aged, 80 and over ; Muscle, Skeletal/anatomy & histology* ; Independent Living ; Blood Platelets ; Leukocytes ; Sarcopenia

T7 RNA polymerase (T7 RNAP) is one of the simplest known RNA polymerases. Its unique structural features make it a critical model for studying the mechanisms of RNA synthesis. This review systematically examines the static crystal structure of T7 RNAP, beginning with an in-depth examination of its characteristic “thumb”, “palm”, and “finger” domains, which form the classic “right-hand-like” architecture. By detailing these structural elements, this review establishes a foundation for understanding the overall organization of T7 RNAP. This review systematically maps the functional roles of secondary structural elements and their subdomains in transcriptional catalysis, progressively elucidating the fundamental relationships between structure and function. Further, the intrinsic flexibility of T7 RNAP and its applications in research are also discussed. Additionally, the review presents the structural diagrams of the enzyme at different stages of the transcription process, and through these diagrams, it provides a detailed description of the complete transcription process of T7 RNAP. By integrating structural dynamics and kinetics analyses, the review constructs a comprehensive framework that bridges static structure to dynamic processes. Despite its advantages, T7 RNAP has a notable limitation: it generates double-stranded RNA (dsRNA) as a byproduct. The presence of dsRNA not only compromises the purity of mRNA products but also elicits nonspecific immune responses, which pose significant challenges for biotechnological and therapeutic applications. The review provides a detailed exploration of the mechanisms underlying dsRNA formation during T7 RNAP catalysis, reviews current strategies to mitigate this issue, and highlights recent progress in the field. A key focus is the semi-rational design of T7 RNAP mutants engineered to minimize dsRNA generation and enhance catalytic performance. Beyond its role in transcription, T7 RNAP exhibits rapid development and extensive application in fields, including gene editing, biosensing, and mRNA vaccines. This review systematically examines the structure-function relationships of T7 RNAP, elucidates the mechanisms of dsRNA formation, and discusses engineering strategies to optimize its performance. It further explores the engineering optimization and functional expansion of T7 RNAP. Furthermore, this review also addresses the pressing issues that currently need resolution, discusses the major challenges in the practical application of T7 RNAP, and provides an outlook on potential future research directions. In summary, this review provides a comprehensive analysis of T7 RNAP, ranging from its structural architecture to cutting-edge applications. We systematically examine: (1) the characteristic right-hand domains (thumb, palm, fingers) that define its minimalistic structure; (2) the structure-function relationships underlying transcriptional catalysis; and (3) the dynamic transitions during the complete transcription cycle. While highlighting T7 RNAP’s versatility in gene editing, biosensing, and mRNA vaccine production, we critically address its major limitation—dsRNA byproduct formation—and evaluate engineering solutions including semi-rationally designed mutants. By synthesizing current knowledge and identifying key challenges, this work aims to provide novel insights for the development and application of T7 RNAP and to foster further thought and progress in related fields.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

This study aims to explore the scientific connotation of sinking Rehmanniae Radix has the best quality and compare the quality between floating and sinking fresh Rehmanniae Radix samples. Ultra-performance liquid chromatography tandem quadrupole electrostatic field Orbitrap high-resolution mass spectrometry(UHPLC-Q-Orbitrap HRMS) was employed to detect the chemical components in floating and sinking fresh Rehmanniae Radix samples. The fingerprint of fresh Rehmanniae Radix was established by high performance liquid chromatography(HPLC), and four index components were determined simultaneously. The cluster analysis, principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) were conducted to compare the quality of floating and sinking fresh Rehmanniae Radix samples. An evaporative light-scattering detector was used to compare the content of five sugars. The extract yield and drying rate were determined, and the quality connotation of sinking Rehmanniae Radix has the best quality was explained by multiple indicators. A total of 41 components were preliminarily identified from fresh Rehmanniae Radix by UHPLC-Q-Orbitrap HRMS, including 7 iridoid glycosides, 9 phenylethanol glycosides, 6 amino acids, 4 sugars, 3 phenolic acids, 5 nucleosides, 3 organic acids, 1 ionone, 1 furan, 1 coumarin, and 1 phenylpropanoid. The results showed that the main chemical components were consistent between floating and sinking fresh Rehmanniae Radix. Nine common peaks were identified in the fingerprints of 15 batches of floating and sinking fresh Rehmanniae Radix samples, and the similarity of fingerprints was greater than 0.9. The cluster analysis, PCA, and OPLS-DA classified floating and sinking fresh Rehmanniae Radix sasmples into two categories, indicating differences in the quality between them. The total content of catalpol, rehmannioside D, ajugol, and verbascoside in sinking fresh Rehmanniae Radix samples was higher than that in floating samples of the same batch and specification, and the main differential component was catalpol. The total content of fructose, glucose, sucrose, raffinose, and stachyose in sinking fresh Rehmanniae Radix samples was higher than that in floating samples of the same batch and specification, and the main differential component was stachyose. The extract yield and drying rate of the sinking samples were higher than those of floating samples. This study preliminarily showed that floating and sinking fresh Rehmanniae Radix samples had the same components but great differences in the content of medicinal substance basis. The total content of four glycosides and five sugars, extract yield, and drying rate of sinking fresh Rehmanniae Radix samples is higher than that of floating samples of the same batch and specification. These findings, to a certain extent, explains the scientificity of sinking Rehmanniae Radix has the best quality recorded in ancient books and provide a reference for the quality control and clinical application of fresh Rehmanniae Radix.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Rehmannia/chemistry* ; Chemometrics ; Mass Spectrometry/methods* ; Quality Control ; Principal Component Analysis ; Plant Extracts

OpenSim is an open source, free motion simulation and gait analysis software, which can be used to dynamically simulate and analyze the complex motion of the human body, and is widely used in human biomechanical research. Since OpenSim can analyze multi-dimensional motion data such as muscle strength, joint torque, and muscle synergistic activation during human movement, it can be used to study the biomechanical mechanism of musculoskeletal imbalance diseases and various treatment methods in TCM orthopedics, and has a broad application prospect in the field of TCM orthopedics. By the analysis of the basic characteristics, elements, analysis process, and application prospects of OpenSim, it is concluded that OpenSim musculoskeletal model has a large application space in the field of traditional Chinese medicine orthopedic, which is helpful to explain the pathogenesis and mechanism of diseases, and promote the precision diagnosis and treatment of orthopedics diseases;the application of OpenSim musculoskeletal model can solve the problem that the previous research paid attention to the bone malalignment and not enough attention to the tendon, and provide a new method for the research of orthopedic diseases. At present, there are still problems in the promotion and application of OpenSim, such as large equipment requirements and high operation threshold. Therefore, multidisciplinary cooperation, clinical research, and data sharing are the basic research strategies in this field.
Humans ; Biomechanical Phenomena ; Orthopedics ; Traumatology ; Software ; Medicine, Chinese Traditional ; Musculoskeletal System ; Models, Biological