OBJECTIVE: To evaluate the effectiveness of the innervated medial plantar flap for reconstructing soft tissue defects, particularly in the weight-bearing zone, after resection of foot tumors. METHODS: A retrospective analysis was conducted on 12 patients with malignant skin and soft tissue tumors of the foot treated between October 2023 and December 2024. The cohort included 8 males and 4 females, aged 42-67 years (mean, 57.5 years). Tumor types comprised malignant melanoma (5 cases), squamous cell carcinoma (4 cases), arsenical keratosis (2 cases), and tumor-induced osteomalacia (1 case). Soft tissue defects located in the heel weight-bearing region in 10 cases and non-weight-bearing ankle region in 2 cases, with defect sizes ranging from 4.0 cm×3.0 cm to 6.0 cm×4.0 cm. Preoperative photon-counting CT angiography (PC-CTA) was performed to assess the medial plantar artery and its perforators. All patients underwent radical tumor resection with confirmed negative margins. The resulting defects were reconstructed using a innervated medial plantar flap incorporating sensory branches of the medial plantar nerve. The flap donor site was covered with a split-thickness skin graft harvested from the ipsilateral inguinal region. RESULTS: The operation was successfully completed in all 12 patients. All flaps survived completely without vascular compromise, partial necrosis, or total loss. Incisions healed primarily without dehiscence or infection. Minor skin graft necrosis occurred at the donor site in 3 patients, which healed within 2-3 weeks with routine dressing changes. No donor site complication (e.g., tendon or nerve injury) occurred. Patients were followed up 2-16 months (mean, 10.3 months). At last follow-up, there was no tumor recurrence. Flaps exhibited good color and texture match with surrounding tissue, restored sensation, and all feet achieved normal weight-bearing activity. CONCLUSION The innervated medial plantar flap, precisely designed based on PC-CTA localization, provides reliable blood supply and effective sensory restoration. It is an ideal method for reconstructing soft tissue defects after foot tumor resection, especially in the heel weight-bearing region.
Humans ; Male ; Middle Aged ; Female ; Plastic Surgery Procedures/methods* ; Adult ; Aged ; Retrospective Studies ; Soft Tissue Neoplasms/surgery* ; Surgical Flaps/blood supply* ; Foot/surgery* ; Skin Neoplasms/surgery* ; Soft Tissue Injuries/surgery* ; Carcinoma, Squamous Cell/surgery* ; Treatment Outcome ; Skin Transplantation/methods* ; Melanoma/surgery*

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Umbilical cord mesenchymal stem cells (UC-MSCs) have been widely used in regenerative medicine, but there is limited research on the stability of UC-MSCs formulation during production. This study aims to assess the stability of the cell stock solution and intermediate product throughout the production process, as well as the final product following reconstitution, in order to offer guidance for the manufacturing process and serve as a reference for formulation reconstitution methods. Three batches of cell formulation were produced and stored under low temperature (2-8 ℃) and room temperature (20-26 ℃) during cell stock solution and intermediate product stages. The storage time intervals for cell stock solution were 0, 2, 4, and 6 h, while for intermediate products, the intervals were 0, 1, 2, and 3 h. The evaluation items included visual inspection, viable cell concentration, cell viability, cell surface markers, lymphocyte proliferation inhibition rate, and sterility. Additionally, dilution and culture stability studies were performed after reconstitution of the cell product. The reconstitution diluents included 0.9% sodium chloride injection, 0.9% sodium chloride injection + 1% human serum albumin, and 0.9% sodium chloride injection + 2% human serum albumin, with dilution ratios of 10-fold and 40-fold. The storage time intervals after dilution were 0, 1, 2, 3, and 4 h. The reconstitution culture media included DMEM medium, DMEM + 2% platelet lysate, 0.9% sodium chloride injection, and 0.9% sodium chloride injection + 1% human serum albumin, and the culture duration was 24 h. The evaluation items were viable cell concentration and cell viability. The results showed that the cell stock solution remained stable for up to 6 h under both low temperature (2-8 ℃) and room temperature (20-26 ℃) conditions, while the intermediate product remained stable for up to 3 h under the same conditions. After formulation reconstitution, using sodium chloride injection diluted with 1% or 2% human serum albumin maintained a viability of over 80% within 4 h. It was observed that different dilution factors had an impact on cell viability. After formulation reconstitution, cultivation in medium with 2% platelet lysate resulted in a cell viability of over 80% after 24 h. In conclusion, the stability of cell stock solution within 6 h and intermediate product within 3 h meets the requirements. The addition of 1% or 2% human serum albumin in the reconstitution diluent can better protect the post-reconstitution cell viability.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

OBJECTIVE: To examine the association of handgrip strength with aortic stenosis incidence among adults aged 60 years and older. METHODS: We conducted a cohort study using the UK Biobank data to assess the relationship between handgrip strength and incident aortic stenosis in individuals aged 60 years and older. Handgrip strength was measured using a Jamar J00105 hydraulic hand dynamometer. Adjusted Cox proportional hazards regression models were conducted to assess the association between handgrip strength and incident aortic stenosis. RESULTS: We included 157,097 UK Biobank participants (78,151 women and 78,946 men) in our study, with mean age of 64 ± 2.9 years. During a median follow-up of 8.1 (7.4-8.8) years, 1543 (1.0%) participants developed incident aortic stenosis. Compared with those with the lowest handgrip strength (tertile 1), the adjusted hazard ratios (95% confidence interval) of incident aortic stenosis in the middle (tertile 2) and the highest (tertile 3) were 0.86 (0.77-0.97) and 0.76 (0.67-0.87), respectively. CONCLUSIONS Higher handgrip strength was associated with lower risk of developing aortic stenosis in older adults. Future studies warrant preventive strategies for older adults with lower handgrip strength.

Objective:To summarize the clinical diagnosis and treatment process of breast implant associated-anaplastic large cell lymphoma (BIA-ALCL) in symptomatic patients with textured breast implants in Peking Union Medical College Hospital in order to provide reference for the management of such patients in China.Methods:From January 2019 to September 2021, 23 symptomatic patients with textured breast implants were admitted to the Peking Union Medical College Hospital, aged 29-52 years, with an average of 36.2 years. All patients agreed to remove the prosthesis by surgery, and accepted cytology, flow cytometry, and immunohistochemical examination of the fluid around the implants and capsule tissue before or during the operation to complete the BIA-ALCL screening process.Results:All 23 patients presented with unilateral periprosthetic fluid accumulation, with fluid volume ranging from 20 to 130 ml, of which 7 cases were combined with periprosthetic contracture. No tumor cells were found in the pathological findings of effusion and the capsule tissue, and the BIA-ALCL pathologic screening results (CD 30 and ALK) were all negative. No bleeding, infection, wound dehiscence and other complications occurred after the operation.Conclusions:A personalized clinical process should be developed for symptomatic patients with textured breast implants. Attention should be paid to imaging and pathological examinations.