Objective:To construct a "local-systemic" dual-track prediction model integrating the resistance index (RI) score of bilateral sacroiliac joints and the systemic immune-inflammation index (SII), and to evaluate its predictive efficacy for the active stage of ankylosing spondylitis (AS).Methods:A total of 205 patients with ankylosing spondylitis (AS) from the Second Hospital of Lanzhou University between April 2022 and April 2025 were retrospectively enrolled and categorized into an active group ( n=113) and a remission group ( n=92). Hematological parameters and ultrasound data were collected. The resistance index (RI) of the synovial area in bilateral sacroiliac joints was measured by Doppler ultrasound and scored as follows: RI < 0.5: 3 points; RI 0.5~0.55: 2 points; RI > 0.55: 1 point; undetectable blood flow: 0 points. A total bilateral RI score (range 0 to 6) was calculated. The systemic immune-inflammation index (SII) was derived as (neutrophils× platelets)/lymphocytes. Normality was tested for all continuous variables; normally distributed data were compared using the t-test, while non-normally distributed data were analyzed with the Mann-Whitney U test. Categorical variables were compared using the χ2 test or analysis of variance.Variable selection was performed using Lasso regression, and a multivariate logistic regression model was developed to assess predictive performance. Results:The proportion of patients with a bilateral RI total score≥5 was significantly higher in the active group compared to the remission group (50 of 113, 44.3% vs 2 of 92, 2.2%, χ2=55.63, P<0.001). Multivariate logistic regression analysis, after adjustment for confounding variables, identified the SII [ OR(95% CI)=1.01(1.00, 1.01), P<0.001], bilateral RI total score [ OR(95% CI)=1.67(1.29, 2.26), P<0.001], erythrocyte sedimentation rate [ OR(95% CI)=1.19(1.11, 1.30), P<0.001], and mean corpuscular hemoglobin concentration [ OR(95% CI)=1.09(1.03, 1.17), P<0.001] as independent risk factors for active AS. Conversely, lymphocyte count [ OR(95% CI)=0.42(0.18, 0.92), P=0.030] and globulin [ OR(95% CI)=0.89(0.80, 0.99), P=0.040] were significantly associated with protective effects. The bilateral RI total score demonstrated the strongest predictive effect, with each 1-point increase associated with a 67% elevation in the risk of active disease. ROC curve analysis indicated that the area under the curve (AUC) for predicting whether AS is in the active disease phase was 0.94 for the combined model (SII+bilateral RI total score), compared with 0.93 for the SII-alone model and 0.92 for the bilateral RI total score-alone model, demonstrating superior predictive performance of the combined model (SII+bilateral RI total score). An online prediction tool has been developed based on the combined model. Conclusion:The dual-track prediction model, which integrates local joint hemodynamic characteristics and systemic immune-inflammatory status, facilitates a multidimensional assessment of the risk of active AS and provides an objective basis for early identification.

Objective:To construct a "local-systemic" dual-track prediction model integrating the resistance index (RI) score of bilateral sacroiliac joints and the systemic immune-inflammation index (SII), and to evaluate its predictive efficacy for the active stage of ankylosing spondylitis (AS).Methods:A total of 205 patients with ankylosing spondylitis (AS) from the Second Hospital of Lanzhou University between April 2022 and April 2025 were retrospectively enrolled and categorized into an active group ( n=113) and a remission group ( n=92). Hematological parameters and ultrasound data were collected. The resistance index (RI) of the synovial area in bilateral sacroiliac joints was measured by Doppler ultrasound and scored as follows: RI < 0.5: 3 points; RI 0.5~0.55: 2 points; RI > 0.55: 1 point; undetectable blood flow: 0 points. A total bilateral RI score (range 0 to 6) was calculated. The systemic immune-inflammation index (SII) was derived as (neutrophils× platelets)/lymphocytes. Normality was tested for all continuous variables; normally distributed data were compared using the t-test, while non-normally distributed data were analyzed with the Mann-Whitney U test. Categorical variables were compared using the χ2 test or analysis of variance.Variable selection was performed using Lasso regression, and a multivariate logistic regression model was developed to assess predictive performance. Results:The proportion of patients with a bilateral RI total score≥5 was significantly higher in the active group compared to the remission group (50 of 113, 44.3% vs 2 of 92, 2.2%, χ2=55.63, P<0.001). Multivariate logistic regression analysis, after adjustment for confounding variables, identified the SII [ OR(95% CI)=1.01(1.00, 1.01), P<0.001], bilateral RI total score [ OR(95% CI)=1.67(1.29, 2.26), P<0.001], erythrocyte sedimentation rate [ OR(95% CI)=1.19(1.11, 1.30), P<0.001], and mean corpuscular hemoglobin concentration [ OR(95% CI)=1.09(1.03, 1.17), P<0.001] as independent risk factors for active AS. Conversely, lymphocyte count [ OR(95% CI)=0.42(0.18, 0.92), P=0.030] and globulin [ OR(95% CI)=0.89(0.80, 0.99), P=0.040] were significantly associated with protective effects. The bilateral RI total score demonstrated the strongest predictive effect, with each 1-point increase associated with a 67% elevation in the risk of active disease. ROC curve analysis indicated that the area under the curve (AUC) for predicting whether AS is in the active disease phase was 0.94 for the combined model (SII+bilateral RI total score), compared with 0.93 for the SII-alone model and 0.92 for the bilateral RI total score-alone model, demonstrating superior predictive performance of the combined model (SII+bilateral RI total score). An online prediction tool has been developed based on the combined model. Conclusion:The dual-track prediction model, which integrates local joint hemodynamic characteristics and systemic immune-inflammatory status, facilitates a multidimensional assessment of the risk of active AS and provides an objective basis for early identification.

Idiopathic pulmonary fibrosis (IPF), a chronic interstitial lung disease, is characterized by aberrant wound healing, excessive scarring and the formation of myofibroblastic foci. Although the role of alternative splicing (AS) in the pathogenesis of organ fibrosis has garnered increasing attention, its specific contribution to pulmonary fibrosis remains incompletely understood. In this study, we identified an up-regulation of serine/arginine-rich splicing factor 7 (SRSF7) in lung fibroblasts derived from IPF patients and a bleomycin (BLM)-induced mouse model, and further characterized its functional role in both human fetal lung fibroblasts and mice. We demonstrated that enhanced expression of Srsf7 in mice spontaneously induced alveolar collagen accumulation. Mechanistically, we investigated alternative splicing events and revealed that SRSF7 modulates the alternative splicing of pyruvate kinase (PKM), leading to metabolic dysregulation and fibroblast activation. In vivo studies showed that fibroblast-specific knockout of Srsf7 in conditional knockout mice conferred resistance to bleomycin-induced pulmonary fibrosis. Importantly, through drug screening, we identified lomitapide as a novel modulator of SRSF7, which effectively mitigated experimental pulmonary fibrosis. Collectively, our findings elucidate a molecular pathway by which SRSF7 drives fibroblast metabolic dysregulation and propose a potential therapeutic strategy for pulmonary fibrosis.

Traditional Chinese medicine (TCM) is an ancient medical system distinctive and effective in treating cancer, depression, coronavirus disease 2019 (COVID-19), and other diseases. However, the relatively abstract diagnostic methods of TCM lack objective measurement, and the complex mechanisms of action are difficult to comprehend, which hinders the application and internationalization of TCM. Recently, while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research, the rise of machine learning (ML) has significantly enhanced their integration with TCM. This article introduces representative methodological cases in quality control, mechanism research, diagnosis, and treatment processes of TCM, revealing the potential applications of ML technology in TCM. Furthermore, the challenges faced by ML in TCM applications are summarized, and future directions are discussed.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.

Objective:To evaluate the efficacy of eculizumab in treating hematopoietic stem cell transplantation-associated thrombotic microangiopathy (TA-TMA) .Methods:This retrospective study included 11 patients who developed TA-TMA after allogeneic hematopoietic stem cell transplantation and subsequently received eculizumab treatment at Peking University People′s Hospital between June 2018 and May 2024. The incidence of TA-TMA, treatment details, and clinical outcomes were analyzed.Results:Among the 11 included patients [4 males, 7 females; median age: 29 years (range: 9-56) ], underlying diseases were severe aplastic anemia (SAA) in 5 patients, acute lymphoblastic leukemia (ALL) in 3 patients, and acute myeloid leukemia (AML) in 3 patients. The median time to TA-TMA diagnosis was 48 days post-transplantation (range: 4-213 days), and all patients met the diagnostic criteria for high-risk TA-TMA. The median interval from TA-TMA diagnosis to the initiation of eculizumab treatment was 12 days (range: 1-56 days). Patients received a median of 3 doses of eculizumab (range: 1-14). Ten of the 11 patients were assessed as having no response (NR) to eculizumab at the end of treatment or at death. One patient achieved a partial response (PR) but subsequently died after TA-TMA relapsed due to infection. At the last follow-up, all patients were either lost to follow-up or had died. The median follow-up duration was 88 days (range: 33-326 days), and the median time from TA-TMA diagnosis to the last follow-up was 31 days (range: 21-113 days) .Conclusion:Eculizumab demonstrated poor efficacy in this TA-TMA cohort. This might be attributable to the critical and complex condition of the patients, delayed initiation of eculizumab treatment, and insufficient dosage.

Objective:To investigate the efficacy and safety of allogeneic hematopoietic stem cell transplantation (allo-HSCT) for the treatment of BCR::ABL-negative chronic neutrophilic leukemia (CNL) and MDS/MPN with neutrophilia.Methods:This study retrospectively analyzed 12 cases of CNL and MDS/MPN with neutrophilia that underwent allo-HSCT from March 2017 to June 2024, comprising 7 males and 5 females with a median age of 48 ( IQR: 28, 59) years. The 2-year overall survival (OS), disease-free survival (DFS), cumulative incidence of relapse (CIR), and transplantation-related mortality (TRM) rates were analyzed. Complications were also assessed. Results:Of the 12 patients, 6 received matched sibling HSCT and 6 received haploidentical HSCT. All patients had successful engraftment, and the median times of neutrophil and platelet engraftment were 17 ( IQR: 11, 24) days and 15 ( IQR: 9, 28) days, respectively. Grade Ⅱ–Ⅳ acute graft versus host disease (GVHD) and chronic GVHD occurred in 2 and 4 cases, respectively. The 2-year OS, DFS, CIR, and TRM rates were (65.6 ± 16.4) %, (41.7 ± 16.6) %, (47.2 ±18.2) %, and (11.1 ± 11.4) %, respectively, after a median follow-up time of 637 ( IQR: 330, 943) days. One patient died from treatment-related complications due to respiratory failure caused by coronavirus disease 2019. Two patients died due to relapse. Conclusion:Allo-HSCT can be applied as a safe and effective approach to treat CNL and MDS/MPN with neutrophilia.

Hair follicles are skin appendages with a complex structure and periodical self-renewal ability. The hair follicle cycle includes the anagen phase, catagen phase and telogen phase, and its orderly renewal is important to maintain hair growth. It is currently believed that Wnt, bone morphogenetic protein, and Notch signaling pathways are involved in regulating the hair follicle cycle. As research progresses, various regulatory mechanisms based on these signaling pathways have been gradually revealed, such as growth factors, non-coding RNAs, immune responses, etc. This review elaborates on the above molecular mechanisms and regulatory modalities, aiming to promote better understandingn of regulatory mechanisms underlying the hair follicle cycle and provide a theoretical reference for expanding treatment options for hair loss.