Background: Surgeons face challenges in selecting cost-effective and biologically active agents for rotator cuff healing, given the numerous commercial products available, such as polydeoxyribonucleotide (PDRN) and atelocollagen (ATC). However, the precise efficacy of PDRN and ATC in rotator cuff healing remains debatable, and there is currently a lack of studies directly comparing the effects of the 2 agents on repaired cuff tendons. Therefore, the purpose of this study was to compare the efficacy of PDRN and ATC on bone-to-tendon interface (BTI) healing using a chronic rotator cuff tear (RCT) model in rabbits. Methods: Forty-eight rabbits were randomly divided into 3 groups. To create chronic RCT models, transected tendons were left untreated for 6 weeks, and then were repaired in a transosseous manner with PDRN and ATC injection into the repair site according to group allocation (group A: saline, group B: PDRN, group C: ATC; n = 16 per group). Genetic and immunofluorescence analyses were performed at 4 weeks after surgery. Furthermore, genetic, histologic, and biomechanical analyses were performed at 12 weeks after surgery. Results: At 4 weeks after surgery, ATC-injected shoulders showed the highest mRNA expression levels of collagen type I alpha 1 and aggrecan compared to the other 2 groups (p < 0.001 and p = 0.002, respectively). Meanwhile, there was more preliminary fibrocartilaginous matrix formation in the ATC-injected group. At 12 weeks after surgery, ATC-injected shoulders demonstrated better collagen fiber continuity and orientation, denser collagen fibers, a more mature bone-to-tendon junction, and greater fibrocartilage layer formation compared to the other 2 groups (all p < 0.001). Furthermore, ATC-injected shoulders also demonstrated a significantly higher load-to-failure value (40.4 ± 4.5 N/kg) than the remaining groups (group A, 26.7 ± 3.0 N/kg; group B, 32.8 ± 4.2 N/kg; p < 0.001). Conclusions ATC demonstrated superior efficacy in promoting BTI healing following surgical repair in a chronic RCT model of rabbits.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Objective: To evaluate the causal effects and population heterogeneity of Guangzhou′s Voluntary Blood Donation Privilege Certificate Policy on blood donation behavior, and to provide empirical evidence for optimizing blood management policies. Methods: Using an interrupted time series (ITS) design, we analyzed 30 quarters of blood collection data from Guangzhou Blood Center (from July 2018 to December 2025). Taking the third quarter of 2021 as the intervention node, we constructed ordinary least squares (OLS) regression models incorporating level and trend effects, and conducted stratified analyses by gender, age group, previous donation frequency, and per-donation volume. Results: The tiered incentive policy demonstrated significant heterogeneous effects. Apheresis donations showed sustained growth, with 2 units of component blood exhibiting a significant trend effect (β=304.58, P<0.001); high-frequency donors (11+ times) demonstrated sustained growth trends in apheresis donations. For whole blood, immediate effects were significant for donors with 1-2 previous donations (β=4 537.55, P<0.001) and 3-10 previous donations (β=2 159.69, P<0.05); high-frequency donors (11-30 times) showed sustained growth trends (P<0.01). Stratified by per-donation volume, 400 mL whole blood demonstrated a significant immediate effect (β=4 391.01, P<0.1), while 200 mL whole blood showed a significant declining trend (β=-893.24, P<0.01). Conclusion: The tiered incentive policy effectively enhanced blood donation participation while maintaining the donors’ altruistic motivations. Young and middle-aged adults primarily drove apheresis donations. The policy demonstrated immediate incentive effects on low-frequency whole blood donors and long-term retention effects on high-frequency donors. Per-donation volume showed an "upward migration" trend, optimizing the blood collection structure. Differentiated outreach, conversion and retention strategies are recommended to further strengthen blood supply security.

ObjectiveTo investigate the association of the traditional Chinese medicine （TCM） syndrome types of primary liver cancer （PLC） with clinical features and multimodal quantitative radiological features on computed tomography （CT） and magnetic resonance imaging （MRI）， and to provide a reference for the objectification of TCM syndrome differentiation and precise diagnosis and treatment. MethodsA retrospective analysis was performed for the clinical data of 312 patients who were diagnosed with PLC in The First Affiliated Hospital of Hunan University of Chinese Medicine from March 2020 to June 2025， and according to the TCM syndrome type， they were divided into stagnation of liver Qi group with 40 patients， stagnation of liver Qi and spleen deficiency group with 109 patients， Qi stagnation and blood stasis group with 62 patients， dampness-heat toxin amassment group with 81 patients， and liver-kidney Yin deficiency group with 20 patients. Clinical features and multimodal quantitative radiological features were compared between the patients with different TCM syndrome types. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups； the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between multiple groups， and the Dunn’s multiple test was used for further comparison between two groups； the chi-square test was used for comparison of categorical data between groups， and the Bonferroni method was used for further comparison between two groups. ResultsThere were significant differences between the patients with different TCM syndrome types in China liver cancer staging （CNLC）， Child-Pugh class， alanine aminotransferase， aspartate aminotransferase， albumin， direct bilirubin， total bilirubin， prothrombin time， neutrophil， and albumin-bilirubin score （all P<0.05）. In the stagnation of liver Qi group， the patients with Child-Pugh class A accounted for 75.00%； among the patients with CNLC stage I PLC， the patients with stagnation of liver Qi accounted for 60.00%， and those with Qi stagnation and blood stasis syndrome accounted for 59.68%， while among the patients with CNLC stage IV PLC， the distribution proportion of dampness-heat toxin amassment （27.16%） and liver-kidney Yin deficiency （30.00%） was significantly higher than that of stagnation of liver Qi （2.50%） （all P<0.05）. Radiological examination showed that there were significant differences between the patients with different TCM syndrome types in the number of tumors， ascites， venous tumor thrombus， maximum tumor diameter， intrahepatic metastasis， and lymph node metastasis in the hepatic hilar and retroperitoneal regions （all P<0.05）. Compared with the patients with stagnation of liver Qi， the patients with liver depression and spleen deficiency or liver-kidney Yin deficiency were more likely to develop intrahepatic metastasis； the patients with liver depression and spleen deficiency， dampness-heat toxin amassment， or liver-kidney Yin deficiency were more likely to develop lymph node metastasis in the hepatic hilar and retroperitoneal regions； the patients with liver-kidney Yin deficiency were more likely to experience multiple tumors； the patients with liver depression and spleen deficiency or dampness-heat toxin amassment were more likely to develop ascites （all P<0.05）. Compared with the patients with Qi stagnation and blood stasis syndrome， the patients with liver depression and spleen deficiency had a significantly longer maximum tumor diameter and a significantly higher proportion of patients with venous tumor thrombus （both P<0.05）. Furthermore， among the 184 patients with MRI diffusion-weighted imaging sequences， the patients with dampness-heat toxin amassment or Qi stagnation and blood stasis syndrome had significantly higher ADC values and relative ADC values than those with stagnation of liver Qi （all P<0.05）. ConclusionThere are significant differences in CT/MRI radiological features and clinical features between PLC patients with different TCM syndrome types， among whom the patients with liver depression and spleen deficiency， dampness-heat toxin amassment， and liver-kidney Yin deficiency tend to exhibit progressive radiological features， and those with dampness-heat toxin amassment or Qi stagnation and blood stasis syndrome tend to have higher ADC values. These findings provide an objective basis for TCM syndrome differentiation in PLC.

To summarize the clinical characteristics, antibody spectrum and neuroelectrophysiological features of autoimmune nodopathy(AN), and to explore the phenotypic differences among different antibody-positive subgroups.

Objective To develop and validate a prediction model for in-hospital recurrent intensive care unit (ICU) admission in critically ill patients with ischemic stroke (IS) based on machine learning (ML) algorithms. Methods Clinical data from 2 929 IS patients were included from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to identify predictive factors, and the synthetic minority over-sampling technique (SMOTE) was employed to create a derivation cohort comprising 2 583 patients. These patients were randomly divided into a training set (n=2 066) and a test set (n=517) at an 8:2 ratio. Five ML algorithms, including decision tree, random forest, adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and support vector machine (SVM), were performed to construct prediction models. Five-fold cross-validation was used to evaluate the performance of the model in the training set. The area under the receiver operating characteristic curve (ROC-AUC) and decision curve analysis (DCA) were used to assess and compare the models in the testing set. The best-performing model was interpreted by shapley additive explanations (SHAP). Results Among the 2 929 patients included, 704 (24.0%) experienced in-hospital recurrent ICU admission. Among the five ML models, the random forest model demonstrated the best predictive performance, with an AUC of 0.839 (95%CI 0.801–0.877). Feature importance analysis identified five most significant features affecting model prediction, including APS Ⅲ score, albumin, age, heart rate, and SOFA score. Conclusions ML-based models can effectively predict the risk of in-hospital recurrent ICU admission in critically ill patients with IS. The random forest model showed superior predictive performance, which may have potential applications in early clinical risk stratification and intervention.

ObjectiveTo investigate whether intestinal deficiency of lanosterol synthase （LSS）， a key enzyme in cholesterol synthesis， influences the progression of metabolic dysfunction-associated steatotic liver disease （MASLD） by regulating intestinal cholesterol absorption and immune response. MethodsLSS heterozygous knockout （LSS^+/-） mice and wild-type （WT） controls were generated using CRISPR/Cas9 technology and fed either a high-fat diet （HFD） or regular chow （CHOW）. The model was validated by genotyping. Hepatic steatosis was assessed by HE and oil red O staining. Immunohistochemistry was used to detect the localization and expression of NPC1L1 and CD36 proteins in the intestine. Western blot analysis was performed to measure JNK phosphorylation and TLR4 protein levels in intestinal tissues. Real-time quantitative polymerase chain reaction （qPCR） was employed to examine the mRNA expression of TLR4 and IL-6. ResultsLSS^+/- mice were successfully validated by genotyping and reduced intestinal LSS protein expression. HE and oil red O staining of liver sections showed that， compared with WT mice fed a CHOW diet， WT mice fed a HFD exhibited a marked increase in hepatic lipid vacuoles. In contrast， compared with HFD-fed WT mice， HFD-fed LSS^+/- mice displayed significantly attenuated hepatic lipid deposition and reduced serum ALT levels （P<0.05）. Immunohistochemical analysis revealed that， compared with WT mice， the expression of the cholesterol absorption protein NPC1L1 in the intestinal villi of LSS^+/- mice was downregulated under both CHOW and HFD conditions （P_HFD<0.001）. Conversely， the expression of the fatty acid transporter CD36 was upregulated in the intestines of LSS^+/- mice （P_CHOW<0.05， P_HFD<0.01）. Western blot analysis demonstrated that， compared with WT mice， TLR4 protein expression in the intestines of LSS^+/- mice significantly increased under both CHOW and HFD conditions （both P<0.05）. JNK phosphorylation level was significantly elevated in LSS^+/- mice under CHOW condition （both P<0.05）. Under HFD condition， total JNK protein expression increased， but its phosphorylation level showed no significant change. qPCR analysis showed that， compared with WT mice， the mRNA levels of TLR4 （P_CHOW<0.01， P_HFD<0.000 1） and IL-6 （P_CHOW<0.001， P_HFD<0.01） were significantly upregulated in the intestines of LSS^+/-mice. ConclusionLSS deficiency counteracts hepatic lipid deposition by orchestrating a synergistic reprogramming involving restricted intestinal cholesterol absorption， enhanced fatty acid utilization， and activation of immune pathways， suggesting intestinal LSS as a potential therapeutic target of MASLD.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective: To understand the current status of psychological sub health among secondary vocational school students and explore its related factors, so as to provide a scientific basis for improving their mental health. Methods: From September to December 2024, a convenience sampling method was used to conduct a questionnaire survey among 5 141 secondary vocational school students from 16 schools across 8 provinces and municipalities (Fujian, Chongqing, Guangdong, Guangxi, Hubei, Jiangxi, Shanghai, Zhejiang). A self designed questionnaire was used to collect data on students demographic characteristics and lifestyle behaviors. The Multidimensional Sub health Questionnaire of Adolescents was adopted to assess psychological sub health status. Multivariate binary Logistic stepwise regression analysis was performed to explore the related factors of psychological sub health among secondary vocational school students. Results: The detection rate of psychological sub health among secondary vocational school students was 11.0%, with detection rates of emotional problems, conduct problems, and social adaptation difficulty being 12.3 %, 12.5%, and 9.5%, respectively. Logistic regression analysis revealed that boys and students whose fathers had an educational level of primary school or below, and junior high school, had relatively lower risks of developing psychological sub health or emotional problems, conduct problems, and social adaptation difficulty ( OR =0.55-0.64, all P <0.05). Conversely, students with urban household registration, those with left behind experience, those with poor self rated family economic status, those from other family structures, those classified as overweight and obesity, and those with sleep disorders, frequent consumption of sugar sweetened beverages, frequent consumption of takeout/delivery food, smoking, alcohol consumption, and low to moderate physical activity levels exhibited significantly higher risks across psychological sub health or emotional problems,conduct problems, and social adaptation difficulty ( OR =1.25-4.79, all P <0.05). Conclusions The psychological sub health condition of secondary vocational school students should not be neglected. Psychological sub health of secondary vocational school students is related to multiple factors such as demographic characteristics and lifestyle behaviors. Multi dimensional intervention measures should be implemented to improve their mental health.