ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

With the rapid development of competitive sports, the incidence of anterior cruciate ligament (ACL) injury is on the rise. Such injuries may shorten athletes′ career and lead to other long-term adverse consequences. Although athletes generally recover well after ACL reconstruction, many still struggle to return to their pre-injury performance levels. Advances in the understanding of ACL anatomy and injury mechanisms, along with the evolution of surgical techniques and rehabilitation methods, have provided more individualized and tailored options for athletes following ACL injuries. However, there is currently no consensus in China regarding surgical and rehabilitation strategies for competitive athletes aiming to return to sports after ACL injuries. To this end, the Sports Medicine Committee of the Chinese Research Hospital Association and the Editorial Board of the Chinese Journal of Trauma jointly formulated the Expert consensus on surgical treatment and rehabilitation for competitive sports athletes returning to sports after anterior cruciate ligament injury ( version 2025), and presented 14 recommendations covering surgical indications, preoperative rehabilitation, surgical timing, surgical strategies and postoperative rehabilitation strategies, aiming to improve the surgical treatment and rehabilitation system for ACL injuries in competitive athletes and facilitate their return to high-level sports performance after injury.

Objective:To construct a Harvard cancer index-based risk predictive model for perioperative venous thromboembolism (VTE) in elderly patients with femoral neck fracture and assess its predictive effectiveness.Methods:A retrospective cohort study was conducted to analyze the clinical data of 610 elderly patients with femoral neck fracture admitted to Peking Union Medical College Hospital between January 2013 and December 2022, including 193 males and 417 females, aged 60-99 years [(77.3±9.0)years]. The patients were divided into VTE group ( n=125) and non-VTE group ( n=485) according to occurrence of VTE during the perioperative period. The two groups were compared in terms of gender, age, body mass index, smoking status, alcohol consumption, time from fracture to admission, surgical waiting time, comorbidities, perioperative electrolyte disorders, past or present history of malignancy, past history of deep vein thrombosis (DVT) or pulmonary embolism (PE), and preoperative use of oral anticoagulants. Univariate analysis and multivariable stepwise Logistic regression analysis were conducted to evaluate and identify independent risk factors for perioperative VTE in elderly patients with femoral neck fracture. A perioperative VTE risk predictive model for elderly patients with femoral neck fracture was constructed using the Harvard cancer index: (1) assigning a risk score to each variable according to the corresponding conversion criteria of the Harvard cancer index and risk score, based on the magnitude of their ORs; (2) determining the exposure rate of each risk factor based on the population distribution observed in this study; (3) calculating the average population risk score; (4) computing the individual VTE risk score; (5) deriving the ratio (X) of each individual ′s VTE risk score to the population average. Based on the Harvard cancer index classification criteria for disease risk levels, individual VTE risk categories were determined. The predictive performance of the risk stratification was evaluated by comparing the incidence of VTE across different risk levels. The predictive performance of the model was evaluated based on sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) test and internal validation was performed using the bootstrap resampling method with 1000 iterations. Results:Univariate analysis showed that gender, age, time from fracture to admission, surgical waiting time, previous cerebral infarction, stroke within the past month, Alzheimer′s disease, primary Parkinson′s syndrome, hysterectomy with bilateral adnexectomy, perioperative electrolyte disorders, history of DVT or PE, and preoperative use of oral anticoagulant drug were moderately associated with the occurrence of VTE in elderly patients with femoral neck fracture ( P<0.10). Multivariable stepwise logistic regression analysis demonstrated that female gender ( OR=2.26, 95% CI 1.34, 3.80, P<0.01), time from fracture to admission>1 day ( OR=3.70, 95% CI 2.24, 6.12, P<0.01), surgical waiting time>70 hours ( OR=2.06, 95% CI 1.29, 3.30, P<0.01), previous cerebral infarction ( OR=3.78, 95% CI 1.04, 13.76, P<0.05), stroke within the past month ( OR=11.57, 95% CI 1.21, 110.44, P<0.05), Alzheimer′s disease ( OR=3.26, 95% CI 1.12, 9.49, P<0.05), primary Parkinson ′s syndrome ( OR=3.47, 95% CI 1.22, 9.85, P<0.05), previous hysterectomy with bilateral adnexectomy ( OR=4.75, 95% CI 2.09, 10.80, P<0.01), perioperative electrolyte disorders ( OR=2.73, 95% CI 1.39, 5.35, P<0.01), and preoperative oral anticoagulant use ( OR=3.86, 95% CI 1.18, 12.67, P<0.05) were significantly associated with the occurrence of perioperative VTE in elderly patients with femoral neck fracture. Based on the above 10 risk factors, a perioperative VTE risk predictive model for elderly patients with femoral neck fracture was constructed with the Harvard cancer index. The formula was as follows: X=[10×(female gender)+25×(time from fracture to admission>1 day)+10×(surgical waiting time>70 hours)+25×(previous cerebral infarction)+50×(stroke within the past month)+25×(Alzheimer′s disease)+25×(primary Parkinson′s disease)+25×(previous hysterectomy with bilateral adnexectomy)+10×(perioperative electrolyte disorders)+25×(preoperative use of oral anticoagulant drug)]/33. Individualized VTE risk was classified into five levels: very low, low, moderate, high, and very high, with corresponding VTE rates of 4.8%, 11.8%, 14.9%, 32.3%, and 73.5%, respectively ( χ2=87.71, P<0.01). The VTE risk predictive model demonstrated an AUC of 0.74 (95% CI 0.69, 0.79, P<0.01), with a sensitivity of 63.2% and specificity of 74.8%. The H-L goodness-of-fit test indicated satisfactory model calibration ( P>0.05). The internal validation with the bootstrap method confirmed that the AUC remained 0.74. Conclusions:Female gender, time from fracture to admission>1 day, surgical waiting time>70 hours, previous cerebral infarction, stroke within the past month, Alzheimer′s disease, primary Parkinson′s syndrome, hysterectomy with bilateral adnexectomy, perioperative electrolyte disorders, and preoperative use of oral anticoagulant drug are independent risk factors for perioperative VTE in elderly patients with femoral neck fracture. Based on these factors, the perioperative VTE risk predictive model constructed using the Harvard cancer index demonstrates good clinical predictive value. Individualized VTE risk stratification can effectively identify high-, intermediate-, and low-risk populations, providing a valuable reference for tailoring anticoagulant prophylaxis strategies and enhancing postoperative surveillance.

Traditional mass measurement methods are not applicable in microgravity environments,and the main challenge for in-orbit body mass measurement technology based on inertial principles is to address the random errors brought about by the weightless environment.These include additional torques due to shifts in the center of mass,nonlinear accelerations due to non-rigid human bodies,mechanical energy consumption due to organ vibrations,and random vibrations of the measurement device itself.To address the above difficulties,the project proposes a technical scheme based on the principle of linear acceleration,designs and constructs a ground-specific air-floating experimental and simulation platform,studies key data such as motion trajectory,acceleration change,and vibration frequency amplitude during the mass measurement process,and simulates the changes in the center of mass and random vibrations of the human body in a weightless environment.The project has designed an adjustable posture bracket to adapt to changes in the center of mass,enhance body restraint,and greatly reduce shaking;it has also developed an integrated four-bar linkage motion guidance mechanism,high-precision integrated photoelectric distance measurement,and modular motion constant force measurement device to ensure the accurate measurement of acceleration and constant force data.The product has undergone simulation calculations,ground human applicability tests,and in-orbit applicability verification in the space station.Ground test results show that the device achieves a body mass measurement accuracy better than 0.5%,and the dispersion is better than 0.38%;after flight mission verification and evaluation,the in-orbit body mass measurement dispersion is less than 0.4%,which is superior to the SLAMMD,a mass measurement device of the same principle on the International Space Station,and is at the forefront internationally,achieving accurate body mass measurement.

As a comprehensive high technology,robotics has gained extensive attention from numerous researchers.Intelligent soft robots,representing a new direction in robotics research,are shining brightly in fields such as fragile objects manipulating,medical assistance,and environment exploration due to their unique advantages.In the field of specialized medicine,intelligent soft robots can adapt to the complex and extreme space working environment due to their small stiffness,strong variability and high flexibility,and have broad application prospects in specialized medicine like aerospace,navigation and diving.In this paper,the research status of intelligent soft robots is reviewed from four aspects:materials,structure,actuation modalities,modeling and simulation.Furthermore,the application of intelligent soft robots in the field of specialized medicine is summarized and prospected in this paper.

Drug toxicity is closely related to both clinical drug safety and new drug development. Therefore, it is vital to understand the mechanisms of drug toxicity fully and to use appropriate research models with advanced technologies. Zebrafish has become an important vertebrate animal model for high-throughput drug screening and toxicity assessment. At the same time, zebrafish has an intact biological complexity, reflecting the whole organism's toxicity, which gives it an advantage over other high-throughput models in toxicity studies. Despite the gradual increase in toxicity studies utilizing zebrafish, a comprehensive and systematic review of the underlying mechanisms and new techniques is still lacking. This review aims to analyze common toxicity mechanisms in zebrafish models, such as oxidative stress, endoplasmic reticulum stress, inflammation, and apoptosis, and macroscopic changes in biological processes like lipid metabolism disorders and neurotransmitter expression abnormalities. It also introduces new technologies applied in toxicity assessment, such as gene editing, novel fluorescence imaging technology, 3D imaging technology, and novel automated technology for high-throughput screening, such as fish capsules. In addition, it also summarizes the advantages and disadvantages of the model. By doing so, it will provide new suggestions for the development and improvement of the model, make it better serve the toxicity study of clinical drugs and provide a more comprehensive perspective for drug toxicity study, thus promoting the development of the field of drug toxicity study.

Paclitaxel， a highly effective natural antitumor drug， has been demonstrated to be efficacious in the treatment of a variety of cancers， including breast cancer， ovarian cancer， and lung cancer. The traditional paclitaxel injections have been observed to present certain issues， including overt adverse reactions and a decline in the quality of life of patients following treatment. This ultimately leads to an inability to meet the comprehensive needs of patients， thereby limiting the clinical applications of the drugs. Compared with injectable administration， the oral administration can avoid the risk of infection present in the invasive route， is conducive to improving patient compliance and quality of life， and reduces healthcare costs， and has a good application prospect. However， paclitaxel has low solubility， poor permeability， and is susceptible to the exocytosis of P-glycoprotein， which presents a significant challenge in the development of its oral preparations. Novel drug delivery technologies can enhance the solubility of paclitaxel and facilitate its controlled release， which is beneficial for the oral absorption and efficacy. The paper reviews the development history of oral preparations of paclitaxel， and summarizes the delivery technologies such as polymer micelles， nanoparticles， nanoemulsions and nanocrystals， and discusses the application mechanisms， advantages and limitations of these technologies and their adaptability in different cancer treatments. Finally， the challenges faced in the development of oral preparations of paclitaxel are summarized， and future research directions are proposed in order to provide new ideas for the development of oral delivery of paclitaxel.

Paclitaxel， a highly effective natural antitumor drug， has been demonstrated to be efficacious in the treatment of a variety of cancers， including breast cancer， ovarian cancer， and lung cancer. The traditional paclitaxel injections have been observed to present certain issues， including overt adverse reactions and a decline in the quality of life of patients following treatment. This ultimately leads to an inability to meet the comprehensive needs of patients， thereby limiting the clinical applications of the drugs. Compared with injectable administration， the oral administration can avoid the risk of infection present in the invasive route， is conducive to improving patient compliance and quality of life， and reduces healthcare costs， and has a good application prospect. However， paclitaxel has low solubility， poor permeability， and is susceptible to the exocytosis of P-glycoprotein， which presents a significant challenge in the development of its oral preparations. Novel drug delivery technologies can enhance the solubility of paclitaxel and facilitate its controlled release， which is beneficial for the oral absorption and efficacy. The paper reviews the development history of oral preparations of paclitaxel， and summarizes the delivery technologies such as polymer micelles， nanoparticles， nanoemulsions and nanocrystals， and discusses the application mechanisms， advantages and limitations of these technologies and their adaptability in different cancer treatments. Finally， the challenges faced in the development of oral preparations of paclitaxel are summarized， and future research directions are proposed in order to provide new ideas for the development of oral delivery of paclitaxel.