The endothelial glycocalyx（EG）is a villous polyglycoprotein complex that covers the apical membrane of endothelial cells，situated between the blood vessel wall and the bloodstream.Due to its unique physiological position，the EG plays a crucial role in vascular permeability，mechanical conduction，coagulation，inflammation，and other processes.EG injury can cause endothelial barrier dysfunction，increase vascular permeability，and promote inflammation and thrombosis.Recent studies have revealed that the involvement of EG in inflammatory responses may contribute significantly to the pathogenesis of certain rheumatic and immune diseases as well as multi-system inflammatory syndrome，Kawasaki disease，rheumatoid arthritis，systemic lupus erythematosus and antiphospholipid syndrome，etc.This review paper aims to provide an overview of the structure and function of EG while highlighting its research progress in diagnosing and treating rheumatic and immune diseases.

Adverse drug reactions (ADRs) significantly impact clinical medication safety. The timely identification and prediction of ADRs rely on the efficient analysis of real-world data, such as electronic health records, social media, and spontaneous reporting databases. In recent years, the rapid advancement of artificial intelligence, particularly large language models, in natural language processing, causal reasoning, and complex data mining has provided new technological means for real-time ADRs monitoring and individualized prediction. This paper summarizes the latest research achievements in AI-driven ADRs monitoring. Focusing on diverse data sources, including structured databases and electronic health records, it elaborates on the advantages andchallenges of AI in ADRs event extraction, relationship identification, causal analysis, and risk prediction. The aim is to provide a theoretical reference for constructing more intelligent and efficient ADRs monitoring systems.

Objective:To explore the feasibility and preliminary efficacy of domestic single-port robotic surgical system in the surgical treatment of thyroid cancer. Methods:Thyroid cancer patients who underwent domestic single-port robotic surgery in the Department of Head and Neck Surgery of Sichuan Cancer Hospital from June 2024 to January 2025 were prospectively included. Clinical data, oncological characteristics, and perioperative indicators were systematically collected. Results:A total of 7 patients were included, including 3 males and 4 females, with an age of （34.57±10.26） years. All procedures were successfully completed without conversion to open surgery. Operative time was（180.00±30.41） minutes. Blood loss was（5.00[15.00 ]）mL. Postoperative drainage volume was （167.86±130.95） mL. The postoperative pathological results were all thyroid papillary carcinoma. There were no system failures, no device-related complications and adverse events were observed during the operation and perioperative period. No tumor recurrence or metastasis was observed during the follow-up period. Conclusion:Preliminary data indicate that the domestic single-port robotic surgical system is safe and feasible for the surgical treatment of thyroid cancer, providing a practical basis for subsequent multi-disease, multi-center, and large-sample studies.
Humans ; Thyroid Neoplasms/surgery* ; Robotic Surgical Procedures/instrumentation* ; Male ; Female ; Adult ; Thyroidectomy/methods* ; Operative Time ; Middle Aged ; Prospective Studies

As a major global public health concern,cancer has witnessed a continues rise in both incidence and mortality rates.It pose not only a severe threat to human health but also a heavy burden on socioeconomic systems.Despite remarkable advancements in oncology research,critical challenges such as tumor heterogeneity,drug resistance,and limitations in early screening and diagnostic technologies remain to be addressed.Against this backdrop,artificial intelligence(AI),with its unique advantages in big data analysis,pattern recognition,and predictive modeling,has opened new avenues for cancer research.By integrating multi-modal data,including omics,imaging,and clinical information,AI not only accelerates investigations into fundamental tumor mechanisms but also demonstrates immense potential in areas such as early screening,biomarker discovery,and personalized treatment.These advancements have fostered a deeper integration of precision medicine and oncology.This review provides a comprehensive overview of the most recent progresses in the application of AI in cancer diagnosis and treatment research,with a focus on its practical value across diverse data types and clinical scenarios,as well as future directions for its development.

As a major global public health concern,cancer has witnessed a continues rise in both incidence and mortality rates.It pose not only a severe threat to human health but also a heavy burden on socioeconomic systems.Despite remarkable advancements in oncology research,critical challenges such as tumor heterogeneity,drug resistance,and limitations in early screening and diagnostic technologies remain to be addressed.Against this backdrop,artificial intelligence(AI),with its unique advantages in big data analysis,pattern recognition,and predictive modeling,has opened new avenues for cancer research.By integrating multi-modal data,including omics,imaging,and clinical information,AI not only accelerates investigations into fundamental tumor mechanisms but also demonstrates immense potential in areas such as early screening,biomarker discovery,and personalized treatment.These advancements have fostered a deeper integration of precision medicine and oncology.This review provides a comprehensive overview of the most recent progresses in the application of AI in cancer diagnosis and treatment research,with a focus on its practical value across diverse data types and clinical scenarios,as well as future directions for its development.

Studies have suggested that the nucleus accumbens (NAc) is implicated in the pathophysiology of major depression; however, the regulatory strategy that targets the NAc to achieve an exclusive and outstanding anti-depression benefit has not been elucidated. Here, we identified a specific reduction of cyclic adenosine monophosphate (cAMP) in the subset of dopamine D1 receptor medium spiny neurons (D1-MSNs) in the NAc that promoted stress susceptibility, while the stimulation of cAMP production in NAc D1-MSNs efficiently rescued depression-like behaviors. Ketamine treatment enhanced cAMP both in D1-MSNs and dopamine D2 receptor medium spiny neurons (D2-MSNs) of depressed mice, however, the rapid antidepressant effect of ketamine solely depended on elevating cAMP in NAc D1-MSNs. We discovered that a higher dose of crocin markedly increased cAMP in the NAc and consistently relieved depression 24 h after oral administration, but not a lower dose. The fast onset property of crocin was verified through multicenter studies. Moreover, crocin specifically targeted at D1-MSN cAMP signaling in the NAc to relieve depression and had no effect on D2-MSN. These findings characterize a new strategy to achieve an exclusive and outstanding anti-depression benefit by elevating cAMP in D1-MSNs in the NAc, and provide a potential rapid antidepressant drug candidate, crocin.

Systemic juvenile idiopathic arthritis is one of the common rheumatic and immune diseases in children. It has a sudden onset, obvious systemic symptoms, and lung involvement. However, systemic juvenile idiopathic arthritis with an early manifestation of pulmonary ground-glass opacities combined with macrophage activation syndrome is rare. The clinical data of a child with systemic juvenile idiopathic arthritis with pulmonary ground-glass shadow and macrophage activation syndrome who was admitted to Hubei Maternal and Child Health Care Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology in December 2021 were analyzed retrospectively in order to improve the understanding of rheumatic diseases and pulmonary lesions. The child was admitted to the hospital for 10 days due to rash and fever. Thoracic CT showed scattered ground glass like shadows in both lungs due to the prevention and control screening of COVID-19 pneumonia epidemic situation. After admission, the child was still repeatedly flaccid with high fever, accompanied by dysfunction of both lower limbs. The knee joint MRI found that there was synovitis in the knee joint, and various laboratory indicators suggested macrophage activation syndrome. After that, systemic juvenile idiopathic arthritis was diagnosed. After being treated with methylprednisolone, cyclosporine and topzumab, the clinical remission and the ground-glass shadow of the lung basically disappeared. Through the analysis of this case, it is suggested that clinicians should not ignore other diseases that cause ground glass shadow in the lung during the current epidemic of COVID-19.

Objective:To improve clinicians' understanding of Multisystem Inflammatory Syndrome in Children (MIS-C) and to reduce missed and misdiagnosis.Methods:The clinical data of a patient with Multisystem inflammatory syndrome in children admitted to Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology in January 2023 were analyzed retrospectively, together with literature review.Results:A 10-year-old male patient was admitted to hospital with a fever for 5 days. This patient had a history of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection 1 month prior to admission; after admission, he continued to have recurring high fevers, accompanied by abdominal pain and Kawasaki disease like manifestations, shock, multiple organ function impairment, and a significant increase in inflammatory indicators. Finally, the patient was diagnosed with MIS-C, treated with intravenous immunoglobulin, glucocorticoids and aspirin, and discharged with clinical improvement.Conclusion:MIS-C was a novel syndrome with Kawasaki-like manifestations.Currently, the overall prognosis for MIS-C is good, but its pathogenesis is unclear and the therapeutic consensus remains limited. More studies are needed to clarify the best diagnosis and treatment methods and true prognosis.

Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: -0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
Algorithms ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices