BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

Objective To investigate the post-discharge exercise behavior and factors influencing moderate to vigorous intensity physical activity (MVPA) in patients undergoing lung surgery. Methods A total of 2874 patients from the large prospective, observational perioperative lung symptom study cohort (CN-PRO-Lung 3) in the Department of Thoracic Surgery at Sichuan Cancer Hospital between April 7, 2021, and January 31, 2024, were selected as the survey subjects. A survey was conducted using the Investigation of Exercise Behavior after Lung Surgery questionnaire and the International Physical Activity Questionnaire-Short Form (IPAQ-SF) among patients who underwent lung surgery. Binary logistic regression was used to analyze the factors influencing patients’ engagement in MVPA. Results A total of 702 patients were surveyed, including 252 males and 450 females, with an average age of (52.4±10.2) years. Patients with lung cancer accounted for 85.9%. Only 36.0% of the patients had regular exercise habits, while 42.3% did not engage in any physical activity. The three main barriers for postoperative exercise were physical discomfort (pain, coughing, shortness of breath, etc, 54.7%), lack of professional guidance (41.7%), and concerns about the surgical wound (28.9%). The proportions of patients engaging in vigorous, moderate, and low-intensity physical activity were 5.7%, 28.2%, and 66.1%, respectively. Multivariate analysis showed that patients with a personal annual income ≥50000 yuan (OR=1.52, 95%CI 1.01-2.29, P=0.044), high school education or above (OR=1.92, 95%CI 1.33-2.76, P<0.001), and lobectomy (OR=1.44, 95%CI 1.02-2.03, P=0.037) engaged in more MVPA. Conclusion Patients undergoing lung surgery have inadequate physical activity after discharge, particularly lacking in MVPA. Patients with higher income, higher educational levels, and lobectomy are more frequently engaged in MVPA. Measures such as symptom control, providing exercise guidance, and enhancing education on wound care may potentially improve the inadequate physical activity in lung surgery patients after discharge.

Objective To investigate the prevalence of thyroid nodules in the physical examination population in Mianyang region and analyze its association with metabolic syndrome. Methods A retrospective study was conducted on 9 978 individuals who underwent health examinations at our hospital from January 2024 to May 2025. Thyroid examinations were performed using color Doppler ultrasound to analyze the prevalence of thyroid nodules in this population. Clinical data of all subjects were collected, and logistic regression analysis was employed to assess the association between metabolic syndrome and the risk of thyroid nodule development. Results The prevalence of thyroid nodules in the physical examination population of Mianyang region was 17.98% (1 794/9 978). The logistic regression results showed that after adjusting for gender, age, BMI, occupation, consumption of non-iodized salt, staying up late, daily sleep duration, anxiety, and depression, metabolic syndrome (OR=6.593, 95% CI: 3.961-10.975) was associated with thyroid nodules (P<0.05). Conclusion The prevalence of thyroid nodules among the physical examination population in the Mianyang area is 17.98%, and metabolic syndrome remains associated with the risk of thyroid nodules after effectively controlling for confounding factors.

ObjectiveTo clarify whether METTL14 mediates the core role of acupuncture at Neiguan (PC6) in promoting myelination and improving behavior in young autistic rats through gene intervention technology. MethodsThe ASD model was established by intraperitoneal injection of valproic acid (VPA) in pregnant rats. Male offspring were intracerebroventricularly injected with adenovirus-packaged METTL14 shRNA (sh-METTL14) or its control (sh-NC) on postnatal day 1, with a model group set as well. Subsequently, the juvenile rats were divided into model group, acupuncture group, acupuncture+sh-NC group, and acupuncture+sh-METTL14 group. The acupuncture group received acupuncture at Neiguan (PC6) from postnatal day 7, once daily for 21 consecutive days. Neurobehavioral changes were evaluated by behavioral tests; METTL14 knockdown efficiency and the expression of METTL14, METTL3, and PTEN were detected by quantitative real-time PCR (qRT-PCR) and Western blot (WB); PTEN m⁶A levels were measured by RNA immunoprecipitation-qPCR (RIP-qPCR); myelin ultrastructure, expression of myelin basic protein (MBP) and neurofascin 155 (NF155), and dendritic spine density were observed using transmission electron microscopy (TEM), enzyme-linked immunosorbent assay (ELISA), immunofluorescence, qRT-PCR, and primary neuron culture. ResultsBehaviorally, knockdown of METTL14 significantly counteracted the beneficial effects of acupuncture in improving self-grooming, open field exploration, three-chamber social interaction, and Morris water maze learning and memory (P<0.05, P<0.01). Compared with the acupuncture+sh-NC group, the acupuncture+sh-METTL14 group showed significantly decreased mRNA and protein expression of hippocampal METTL14 (P<0.01), and the upregulating effects of acupuncture on METTL3 and PTEN expression were reversed (P<0.01). Meanwhile, knockdown of METTL14 significantly inhibited the acupuncture-induced increase in PTEN m⁶A levels (P<0.01). Morphologically, knockdown of METTL14 attenuated the improvement of myelin structure by acupuncture, reversed the downregulation of MBP and upregulation of NF155 induced by acupuncture, and blocked the increase in dendritic spine density (P<0.05, P<0.01). ConclusionMETTL14 is a key molecule mediating the therapeutic effect of acupuncture at Neiguan. Acupuncture at Neiguan upregulates METTL14, thereby enhancing m⁶A methylation modification of PTEN mRNA to stabilize its expression, ultimately promoting myelin development and improving behavioral symptoms in ASD juvenile rats. This preliminarily reveals the modern biological connotation of “opening Xuanfu and dredging myelin”.

ObjectiveTo clarify whether METTL14 mediates the core role of acupuncture at Neiguan (PC6) in promoting myelination and improving behavior in young autistic rats through gene intervention technology. MethodsThe ASD model was established by intraperitoneal injection of valproic acid (VPA) in pregnant rats. Male offspring were intracerebroventricularly injected with adenovirus-packaged METTL14 shRNA (sh-METTL14) or its control (sh-NC) on postnatal day 1, with a model group set as well. Subsequently, the juvenile rats were divided into model group, acupuncture group, acupuncture+sh-NC group, and acupuncture+sh-METTL14 group. The acupuncture group received acupuncture at Neiguan (PC6) from postnatal day 7, once daily for 21 consecutive days. Neurobehavioral changes were evaluated by behavioral tests; METTL14 knockdown efficiency and the expression of METTL14, METTL3, and PTEN were detected by quantitative real-time PCR (qRT-PCR) and Western blot (WB); PTEN m⁶A levels were measured by RNA immunoprecipitation-qPCR (RIP-qPCR); myelin ultrastructure, expression of myelin basic protein (MBP) and neurofascin 155 (NF155), and dendritic spine density were observed using transmission electron microscopy (TEM), enzyme-linked immunosorbent assay (ELISA), immunofluorescence, qRT-PCR, and primary neuron culture. ResultsBehaviorally, knockdown of METTL14 significantly counteracted the beneficial effects of acupuncture in improving self-grooming, open field exploration, three-chamber social interaction, and Morris water maze learning and memory (P<0.05, P<0.01). Compared with the acupuncture+sh-NC group, the acupuncture+sh-METTL14 group showed significantly decreased mRNA and protein expression of hippocampal METTL14 (P<0.01), and the upregulating effects of acupuncture on METTL3 and PTEN expression were reversed (P<0.01). Meanwhile, knockdown of METTL14 significantly inhibited the acupuncture-induced increase in PTEN m⁶A levels (P<0.01). Morphologically, knockdown of METTL14 attenuated the improvement of myelin structure by acupuncture, reversed the downregulation of MBP and upregulation of NF155 induced by acupuncture, and blocked the increase in dendritic spine density (P<0.05, P<0.01). ConclusionMETTL14 is a key molecule mediating the therapeutic effect of acupuncture at Neiguan. Acupuncture at Neiguan upregulates METTL14, thereby enhancing m⁶A methylation modification of PTEN mRNA to stabilize its expression, ultimately promoting myelin development and improving behavioral symptoms in ASD juvenile rats. This preliminarily reveals the modern biological connotation of “opening Xuanfu and dredging myelin”.

ObjectiveTo investigate the therapeutic effects and mechanism of decoctions from four kinds of processed products of Aconiti Radix Lateralis Praeparata（ARLP） in deficiency-cold syndrome. MethodsA total of 36 SD rats were randomly divided into the control group， model group， Shengfupian（SFP） group， Paofuzi（PFZ） group， Heishunpian（HSP） group and Paofupian（PFP） group with 6 rats in each group. Except for the control group， rats in other groups were administered hydrocortisone sodium succinate via intramuscular injection to induce a cold deficiency syndrome model. After 14 consecutive days， each ARLP decoction pieces was administered via continuous gastric lavage at a dose of 12 g·kg^-1·d^-1 for 7 d， while the control and model groups received an equivalent volume of physiological saline. After the end of administration， body weight， spleen weight and thymus weight were measured for calculating the spleen and thymus indexes. Hematoxylin-eosin（HE） staining was used to observe the pathological morphology of adrenal tissue. The fully automatic biochemistry analyzer was used to measure the total cholesterol（TC）， triglyceride（TG）， lactic dehydrogenase（LDH） and lactate（LAC） levels in serum. Enzyme-linked immunosorbent assay（ELISA） was employed to measure the contents of 17-hydroxycorticosteroid（17-OHCS）， cortisol（CORT）， triiodothyronine（T₃）， thyroxine（T₄）， thyrotropin（TSH）， immunoglobulin（Ig） M， IgG， cyclic adenosine monophosphate（cAMP） and cyclic guanosine monophosphate（cGMP）. Western blot was used to measure the protein expression levels of protein kinase A（PKA）， cAMP response element-binding protein（CREB）， silent information regulator 1（Sirt1） and peroxisome proliferator-activated receptor γ coactivator-1α（PGC-1α）. And high performance liquid chromatography（HPLC） was used to determine the content of major alkaloids， followed by Pearson correlation analysis with pharmacodynamic indicators. ResultsAfter modeling， compare with the control group， the model rats exhibited symptoms such as lethargy and loose stools， mild abnormalities were observed in adrenal tissue structure， and both spleen and thymus indices were significantly reduced（P<0.01）. Thyroid， adrenal and immune system functions were suppressed， with decreased serum cAMP level and significantly elevated cGMP level（P<0.01）. Compared with the model group， the adrenal injury by hydrocortisone sodium succinate were repaired and the spleen index were increased significantly in all four ARLP groups（P<0.05， P<0.01）. The thymus index in SFP and PFZ groups were increased significantly（P<0.05）. The contents of T₃， TSH， 17-OHCS and CORT were increased significantly in SFP and PFZ groups（P<0.05）. In addition， the content of IgG in SFP， PFZ and PFP groups were increased significantly（P<0.01）， while the content of IgM in PFZ and HSP groups were also increased significantly（P<0.05）. Regarding the cyclic nucleotide system， PFZ significantly elevated cAMP level while reducing cGMP level（P<0.05）， exhibiting the most pronounced effect among the four decoction pieces. For energy metabolism indicators， PFZ significantly improved abnormal markers including TC， TG， LDH， and LAC（P<0.05）. HSP showed marked improvement effects on TG， LDH， and LAC（P<0.05）. Both PFZ and SFP significantly elevated the expression levels of PKA， CREB， Sirt1， and PGC-1α proteins（P<0.01）. Additionally， the diester alkaloids in ARLP showed a strong positive correlation with TG， IgG， and CORT， a strong negative correlation with LAC， a moderate positive correlation with T₄， and moderate negative correlations with cAMP and spleen index. Monomeric alkaloids showed strong positive correlations with TG and IgG， strong negative correlations with LAC， moderate positive correlations with CORT and T₄， and moderate negative correlations with cAMP and spleen index. However， the content of water-soluble alkaloids showed strong positive correlations with TC， LDH， 17-OHCS， T₃， TSH， and thymus index， moderate positive correlations with cAMP， CORT， T₄， and spleen index， and moderate negative correlation with cGMP. ConclusionAmong different processed ARLP decoction pieces， PFZ processed according to ZHANG Zhongjing's method exhibits the most potent warming and cold-dispelling effects. Its pharmacological actions are mediated through regulating the thyroid， adrenal， immune， cyclic nucleotide systems， and material-energy metabolism pathways. Among these， water-soluble alkaloids show strong or moderate correlations with more indicators of deficiency-cold syndrome and exhibit the highest content in PFZ. Therefore， PFZ processed according to ZHANG Zhongjing's method may exert its warming and cold-dispelling effects through water-soluble alkaloids.

Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

OBJECTIVE To evaluate the cost-effectiveness of rezivertinib versus gefitinib as first-line treatment for epidermal growth factor receptor （EGFR） mutation-positive advanced non-small cell lung cancer （NSCLC） from the perspective of the Chinese healthcare system. METHODS A Markov model was constructed based on the REZOR trial data， with a cycle length of 3 weeks and a study duration of 5 years. Both costs and health outcomes were discounted at an annual rate of 5%. A cost-utility analysis was conducted using 3 times China’s 2024 per capita gross domestic product as the willingness-to-pay （WTP） threshold. The economic differences between the rezivertinib regimen versus the gefitinib regimen were evaluated using the incremental cost- effectiveness ratio （ICER） and incremental net monetary benefit （INMB）. Sensitivity and scenario analyses were performed to verify the robustness of the model. RESULTS Compared to the gefitinib regimen， the rezivertinib regimen saved 225 310.47 yuan and gained an additional 0.57 quality- adjusted life years （QALYs）， resulting in an ICER of －395 562.80 yuan/QALY， which was much lower than the WTP threshold of this study， indicating that rezivertinib had an absolute economic advantage. The INMB analysis （389 041.26 yuan） further validated this conclusion. One-way and probabilistic sensitivity analyses confirmed the robustness of the model. Scenario analysis， incorporating a 15% reduction in drug prices and adjustments to the utility values for progression free survival and progression disease， yielded consistent results with the base case analysis. CONCLUSIONS Compared to gefitinib， rezivertinib as a first-line treatment for EGFR mutation-positive advanced NSCLC has an absolute economic advantage.

Obesity is a multi-factorial disease involving genetics, individual behavior, socio-economic status, and environmental factors, and has become a global public health issue. The food environment, as an external factor amenable to direct intervention, affects the development of obesity by shaping individual food acquisition and consumption behaviors. The food environment refers to the physical and social environment where food is accessible, and can be assessed from dimensions such as availability, accessibility, and affordability through geographic information system spatial analysis, field surveys, commercial databases, and questionnaires. Studies indicate that the food environment can influence obesity through the spatial shaping effects of dietary structure and sociobehavioral pathways. A healthy food environment is negatively correlated with the risk of obesity, whereas an unhealthy food environment is positively correlated with the risk of obesity. This paper reviews studies related to the correlation between the food environment and obesity, covering the prevalence of obesity, the definition and assessment methods of the food environment, and the mechanisms by which the food environment affects obesity. It summarizes food environment intervention strategies centered on urban planning, policies and regulations, and community education to provide a reference for obesity prevention and control.