Osteoarthritis (OA), a highly prevalent degenerative joint disease worldwide, is defined by articular cartilage degradation, abnormal bone remodeling, and persistent chronic inflammation. It severely compromises patients’ quality of life, and currently, there is no radical cure. Abnormal mechanical stress is widely regarded as a core driver of OA pathogenesis, and the exploration of mechanical signal perception and transduction mechanisms has become crucial for deciphering OA’s pathophysiological processes. Piezo1, a key mechanosensitive cation channel belonging to the Piezo protein family, has recently gained significant attention due to its pivotal role in mediating cellular responses to mechanical stimuli in joint tissues. This review systematically examines Piezo1’s expression patterns, regulatory mechanisms, and pathological functions in OA, with a particular focus on its dual roles in modulating chondrocyte homeostasis and bone metabolism disorders, while also delving into the underlying molecular signaling pathways and potential therapeutic implications. Piezo1, consisting of approximately 2 500 amino acids and forming a unique trimeric propeller-like structure, is widely expressed in chondrocytes, osteocytes, mesenchymal stem cells, and synovial cells. It exhibits permeability to cations such as Ca²⁺, K⁺, and Na⁺, and directly responds to membrane tension changes induced by mechanical stimuli like fluid shear stress and mechanical overload. In OA patients and animal models, Piezo1 expression is significantly upregulated, especially in cartilage regions subjected to abnormal mechanical stress (e.g., human temporomandibular joint cartilage). This overexpression is closely associated with aggravated cartilage degeneration, increased chondrocyte apoptosis, accelerated cellular senescence, and intensified inflammatory responses. Mechanical overload and pro-inflammatory cytokines (e.g., IL-1β) are key inducers of Piezo1 upregulation: IL-1β activates the PI3K/AKT/mTOR signaling pathway to enhance Piezo1 expression, forming a pathogenic positive feedback loop that inhibits chondrocyte autophagy, promotes apoptosis, and further accelerates joint degeneration. Mechanistically, Piezo1 mediates OA progression through multiple interconnected pathways. When activated by mechanical stress, Piezo1 triggers excessive Ca²⁺ influx, leading to endoplasmic reticulum stress (ERS) and mitochondrial dysfunction, which directly induce chondrocyte apoptosis. This process involves the activation of downstream signaling cascades such as cGAS-STING and YAP-MMP13/ADAMTS5. YAP, a transcriptional regulator, upregulates the expression of matrix metalloproteinase 13 (MMP13) and aggrecanase (ADAMTS5), thereby accelerating cartilage matrix degradation. Additionally, Piezo1-driven Ca²⁺ overload promotes the accumulation of reactive oxygen species (ROS) and upregulates senescence markers (p16 and p21), accelerating chondrocyte senescence via the p38MAPK and NF-κB pathways. Senescent chondrocytes secrete senescence-associated secretory phenotype (SASP) factors (e.g., IL-6, IL-1β), further amplifying joint inflammation. In terms of bone metabolism, Piezo1 maintains joint homeostasis by promoting the differentiation of fibrocartilage stem cells into chondrocytes and balancing bone formation and resorption through regulating the FoxC1/YAP axis and RANKL/OPG ratio. Therapeutically, targeting Piezo1 shows promising potential. Preclinical studies have demonstrated that Piezo1 inhibitors (e.g., GsMTx4) can reduce joint damage and alleviate pain in OA mice. Simultaneously, siRNA-mediated co-silencing of Piezo1 and TRPV4 (another mechanosensitive channel) decreases intracellular Ca²⁺ concentration, inhibits chondrocyte apoptosis, and promotes cartilage repair. Conditional knockout of Piezo1 using Gdf5-Cre transgenic mice alleviates cartilage degeneration in post-traumatic OA models by downregulating MMP13 and ADAMTS5 expression. Despite existing challenges, such as off-target effects of inhibitors, inefficient local drug delivery, and interindividual genetic variability, strategies like developing selective Piezo1 antagonists, optimizing targeted nanocarriers, and combining Piezo1-targeted therapy with physical therapy provide viable avenues for clinical translation. The authors propose that Piezo1 serves as a critical therapeutic target for OA, and future research should focus on deciphering its context-dependent regulatory networks, developing tissue-specific intervention strategies, and validating their efficacy and safety in clinical trials to address the unmet medical needs of OA patients.

AIM To establish the near-infrared spectroscopy quantitative models for moisture,23-acetylalismol B and 23-acetylalismol C in Alismatis Rhizoma decoction pieces.METHODS The near-infrared spectroscopy(NIRS)data were collected in 95 batches of decoction pieces,after which drying method was adopted in the content determination of moisture,HPLC was applied to determining the contents of 23-acetylalismol B and 23-acetylalismol C,the quantitative models were established by partial least squares method combined with feature extraction algorithms.RESULTS The model training determination coefficients were 0.952 6,0.958 1 and 0.920 8,along with the prediction determination coefficients of 0.930 0,0.905 2 and 0.906 4,the residual prediction deviations(PRD)of 4.00,3.58 and 3.46,and the root mean square error ratios of prediction values to calibration values(RMSEP/RMSEC)of 1.15,1.11 and 1.06,respectively.CONCLUSION The quantitative models based on NIRS exhibit good prediction effects,which can be used for the rapid quality detection of Alismatis Rhizoma decoction pieces.

Objective:To develop an artificial intelligence-based model for automated recognition of serum indices using machine vision and deep learning.Methods:This study was a cross-sectional study.Serum sample images were collected fromWest China Hospital of Sichuan University from September 21, 2020 to January 20, 2023 using the imaging device of the fully automated sample pre-processing system. A computer random number generator was used to randomly select one whole hour each day, and all serum sample images processed within that hour were included. After excluding samples with unqualified images and missing serum index results, a total of 5, 534 samples were included. These were divided into a training set and a test set in an 8∶2 ratio using Python random shuffle function, and 4, 458 samples were in the training set and 1, 076 samples were in the test set. After manual inspection, the serum regions were annotated using the MATLAB Image Labeler tool and converted into YOLO format, and a YOLO v5-based model was constructed for automatic serum region extraction. The actual values of lipemia index (L-index), hemolysis index (H-index), and icterus index (I-index) were measured using the automatic biochemical analyzerwith matched reagent kits. A serum index regression model was constructed based on the MobileNet v2 network using the PyTorch 1.10.0 framework. The grading performance of the model was evaluated using accuracy, Kappa coefficient, sensitivity, specificity, positive predictive value, and negative predictive value. Regression performance was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and Bland-Altman analysis.Results:The overall accuracy rates for grading L-index, H-index, and I-index were 98.88%, 95.26%, and 92.47%, respectively, with Kappa coefficients of 0.72, 0.72, and 0.59. For L-index, MAE was 5.11, RMSE was 9.77, and R2 was 0.78. For H-index, MAE was 5.18, RMSE was 8.99, and R2 was 0.89. For I-index, MAE was 1.13, RMSE was 3.01, and R2 was 0.71. Bland-Altman analysis showed that 95.5%, 95.1%, and 95.7% of the data points fell within the consistency intervals for L-index, H-index, and I-index, respectively.Conclusion:The study developed an artificial intelligence-based serum index regression modelto estimate serum indices with high efficiency and accuracy. It shows great potential for reducing laboratory costs, improving clinical testing efficiency, and promoting intelligent development in laboratory medicine.

OBJECTIVE: To observe the clinical efficacy of acupoint thread-embedding therapy of regulating governor vessel, dispersing lung, and suppressing reflux for gastroesophageal reflux cough (GERC). METHODS: A total of 120 GERC patients were randomly assigned to an observation group (60 cases, 1 case dropped out) and a control group (60 cases, 1 case was eliminated). The observation group received acupoint thread-embedding treatment at positive response points of governor vessel. If no such points were detected, the following acupoints were used: Dazhui (GV14), Fenghu (Extra), Shendao (GV11), Lingtai (GV10), and Zhiyang (GV9). Treatment was administered once every two weeks. The control group received oral rabeprazole enteric capsules at 20 mg twice daily. All the treatment was given for 6 weeks. Clinical outcomes were assessed using cough symptom score, reflux disease questionnaire (RDQ) score, and Leicester cough questionnaire (LCQ) score before and after treatment in the two groups. Clinical efficacy was also compared between the two groups. RESULTS: After treatment, both groups showed decreased cough symptom scores and the each item scores and total scores of RDQ (P<0.001), and increased LCQ scores (P<0.001) compare with those before treatment. The observation group exhibited lower cough symptom score and chest pain, reflux and total score of RDQ, and higher LCQ score compared to those in the control group (P<0.05). The total effective rate in the observation group was 94.9% (56/59), which was higher than 84.7% (50/59) in the control group (P<0.05). CONCLUSION Acupoint thread-embedding therapy of regulating governor vessel, dispersing lung, and suppressing reflux could effectively alleviate cough and reflux symptoms in patients with GERC and improve their quality of life.
Humans ; Acupuncture Points ; Gastroesophageal Reflux/physiopathology* ; Male ; Female ; Cough/physiopathology* ; Middle Aged ; Aged ; Acupuncture Therapy ; Adult ; Treatment Outcome ; Lung/physiopathology* ; Meridians

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.

Glycosylation plays an important role in tumorigenesis and development. Mucoprotein-1 (MUC1), a transmembrane glycoprotein, is overexpressed in a variety of malignant tumors. The biochemical characteristics of tumor-specific short chain O-glycosylation (such as Tn/sialyl Tn (STn) antigens) are significantly different from those of normal tissue MUC1, which provides a molecular basis for precision diagnosis and treatment of tumors. This article systematically reviews the molecular design, clinical transformation progress and technical bottleneck of three types of probes targeting MUC1 glycosylation epitopes in tumors, including aptamers, peptides and monoclonal antibodies, focusing on their tumor uptake efficiency, targeting specificity and the potential of diagnosis and treatment integration, so as to provide theoretical basis for optimizing the clinical transformation path of MUC1 targeting probes.

Objective To investigate the diagnostic value of magnetic resonance imaging(MRI)-based intratumoral and peritumoral radiomics of prostate cancer(PCa)for bone metastases.Methods A total of 211 patients diagnosed with PCa by biopsy pathology at Gansu Provincial People's Hospital from January 2018 to January 2023 were retrospectively analyzed.These patients were randomly divided into a training set(n=147)and a validation set(n=64)in a 7:3 ratio.Regions of interest(ROIs)were delineated from the patients'T2-weighted imaging(T2WI),diffusion-weighted imaging(DWI),and apparent diffusion coefficient imaging(ADC)sequences to extract radiomic features.Z-score(normalization)and the LASSO algorithm were used for feature dimensionality reduction,selection,and construction.A predictive model was then built using a logistic regression(LR)machine learning classifier.The receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to assess the model's performance.Calibration curves and decision curves(DCA)were plotted to evaluate the model's fit and clinical net benefit.Results Radiomic features were extracted from the tumor and peritumoral regions in each patient's T2WI,DWI,and ADC images,with a total of 312 features from each region.The LASSO regression model ultimately identified 10 intratumoral radiomic features closely related to bone metastasis,including 2 T2 sequence features,7 DWI features,and 1 ADC sequence feature;and 9 peritumoral radiomic features,including 4 T2 sequence features,3 DWI features,and 2 ADC sequence features.The predictive model based on intratumoral radiomic features achieved an AUC of 0.845(95%CI 0.747-0.943),while the predictive model based on peritumoral radiomic features had an AUC of 0.818(95%CI 0.716-0.919).A combined nomogram model incorporating intratumoral features,peritumoral radiomic features,and clinical features(including Gleason score,total prostate specific antigen,and body mass index)yielded an AUC of 0.936(95%CI 0.902-0.970).Calibration curves indicated that the combined model had good fit,and DCA demonstrated that the combined model provided better clinical net benefit.Conclusions Peritumoral radiomics has excellent predictive value for bone metastasis in newly diagnosed PCa.Combining with intratumoral radiomics features and clinical features,it significantly enhances the predictive capability of the model.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

Objective To explore the effects of different glucose concentrations on the synthesis and secretion of bone collagen in osteoblasts and the impact of diabetes on bone quality in mice.Methods(1)Primary osteoblasts were extracted from the skulls of neonatal mice via collagenase digestion and cultured in four groups under different glucose concentrations:normal glucose(5.5 mmol/L),moderate glucose(11.5 mmol/L),moderate-high glucose(16.5 mmol/L),and high glucose(25 mmol/L).EdU staining was performed to evaluate cell proliferation,while the Transwell assay was used to assess cell migration.Immunofluorescence and Western blotting were performed to detect and quantitatively analyze the content of type Ⅰ collagen(Col-1).Alizarin red S(ARS)staining and alkaline phosphatase(ALP)staining were applied to assess the effects of different glucose concentrations on osteogenic differentiation.(2)Six-week-old male C57BL/6 mice were randomly divided into control group and model group(5 in each group).The model group was fed a high-fat diet for 4 weeks followed by streptozotocin(STZ)injection to establish a diabetic mouse model.The osteogenic differentiation capacity of primary osteoblasts from both groups was assessed.(3)Micro-computed tomography(Micro-CT)was employed to analyze femoral bone mineral density(BMD),bone volume/tissue volume(BV/TV),trabecular number(Tb.N),and trabecular separation(Tb.Sp).Three-point bending test was conducted to evaluate mechanical parameters including maximum load,Young's modulus,fracture energy,and stiffness.RT-qPCR was employed to assess the expression of osteogenic differentiation genes(Alp,Opn,Col1a1,and Lox).Masson staining and Mallory staining were used to evaluate Col-1 content in trabecular bone.Results(1)EdU and Transwell assay results demonstrated that with the gradual increase in glucose concentration,the proliferation and migration abilities of osteoblasts were significantly decreased(P<0.001),and the protein expression levels of Col-1 and lysyl oxidase(LOX)were significantly reduced(P<0.01 or P<0.001).ARS and ALP staining revealed that calcium salt deposition and ALP activity in osteoblasts were significantly decreased with increasing glucose concentration(P<0.05 or P<0.001).(2)Compared with control group,mice in model group exhibited typical"three polies and one weight loss"symptoms(polyuria,polydipsia,polyphagia,and weight loss)of diabetes,and ARS and ALP staining showed a significant reduction in osteoblasts(P<0.001).(3)Micro-CT and three-point bending test results indicated that,compared with control group,mice in model group showed microarchitectural deterioration of bone,decreased Tb.N,increased Tb.Sp,and significantly reduced maximum load,Young's modulus,fracture energy,and stiffness(P<0.05).RT-qPCR results showed that the relative mRNA expression levels of osteogenic differentiation genes(Alp,Opn,Col1a1,and Lox)were significantly decreased in model group compared with control group(P<0.01 or P<0.001).Masson and Mallory staining indicated a significant reduction in collagen content in model group compared with control group(P<0.01).Conclusions High-glucose environment inhibits osteoblast proliferation,differentiation,and migration.Diabetic mice exhibit reduced bone quality and increased bone fragility,potentially mediated by decreased lysyl oxidase and collagen levels.

Objective This study aimed to provide an effective scientific basis for prevention and control of cockroaches on aircrafts by identifying cockroach-carried pathogens,and assess the insecticidal efficacy of gel bait mediated cockroach control on aircrafts,to provide technical guidance for aircraft disinsection.Methods Cassette-trapping was used to trap cockroaches,and the carried pathogens were detected using bacterial cultivation techniques.The gel bait mediated killing rate was calculated after 1,7,and 30 d by field application of gel bait.Results A total of 411 cockroaches were captured,and all were identified as Blattella germanica.26 strains of pathogenic bacteria were isolated from the trapped cockroaches.The killing rates of cockroaches were 58.8％-96.3％with 1-30 day application of gel bait.Statistically significant differences were observed in cockroach killing rates on different days(χ2=58.95,P<0.01).Conclusions B.germanica carry a large variety of pathogenic bacteria and opportunistic pathogens and are thus important infectious disease carriers.Gel bait agents have proven to be very effective against cockroaches on aircrafts.