Objective:To construct a thyroid nodule segmentation model using ultrasound dynamic images and explore its potential for assisting in the screening of thyroid nodules.Methods:A total of 126 patients with thyroid nodules（comprising 150 nodules）who were diagnosed and treated at Xuzhou Cancer Hospital from April 2024 to December 2024 were prospectively enrolled. Two-dimensional ultrasound was performed to capture short-axis and long-axis video images of thyroid nodules，forming a dynamic ultrasound image dataset. The dataset was divided into training，validation，and test sets in a ratio of 6∶1∶3. After the training loss curve converged，the model that performed well on the validation set was selected for testing. Three-fold cross-validation was employed for training and testing. All 300 ultrasound videos were divided into three subsets. In each experiment，two subsets were used as the training set，and one subset was used as the test set to evaluate the model's generalization ability. A collaborative spatiotemporal diffusion model was established based on the dynamic trends and tissue texture details of thyroid nodules. Six widely used segmentation metrics were employed to evaluate the model's application capabilities.Results:The study included 126 patients with 150 thyroid nodules，300 dynamic ultrasound images，and video lengths of 3-4 seconds per nodule，resulting in 12 312 segmented images. The size of the thyroid nodules was（10.7 ± 10.6）mm（transverse diameter）×（8.4 ± 6.3）mm（anteroposterior diameter）. Among the nodules，62（41.3%）had clear boundaries，while 88（58.7%）had indistinct boundaries；61（40.7%）exhibited regular shapes，while 89（59.3%）were irregular；66（44.0%）had a taller-than-wide aspect ratio；and 70（46.7%）showed microcalcifications. The collaborative diffusion model based on dynamic ultrasound image segmentation achieved the following scores：a Jaccard score of（69.22 ± 0.03）%，a Dice score of（79.16 ± 0.18）%，a Precision score of（86.70 ± 0.17）%，a Recall score of（77.82 ± 0.04）%，an Sα score of（85.26 ± 0.01）%，and an Eθmn score of（90.58 ± 0.17）%. Compared to other models，this model demonstrated significant improvements across all evaluation metrics，achieving the highest values in each metric with increments of over 8% and 1%，respectively. Conclusions:The collaborative diffusion model with a dynamic controller，constructed based on dynamic ultrasound images of thyroid nodules，demonstrates excellent performance in ultrasound image segmentation. It improves the accuracy of thyroid nodule screening，thereby providing a valuable auxiliary diagnostic tool for clinical practice.

Objective:To propose a deep learning（DL）-based ultrasound imaging auxiliary tool for automatic segmentation and recognition of the brachial plexus（BP），and to enhance the accuracy and safety of clinical procedures.Methods:It was a multicenter study that collected 773 healthy subjects from Peking University Third Hospital and its branch campuses，the Third Medical Center of the Chinese PLA General Hospital，and Shanghai Eighth People's Hospital between August 2024 and February 2025. Brachial plexus（BP）images in the interscalene groove were captured used high-frequency ultrasound by senior sonographers，a dataset comprising 1 289 standardized images were constructed and the improved model（CHA-TransUNet）was trained. The test set was input into 6 different models（CHA-TransUNet，R50-Unet，TransUnet，SegFormer，SwinUnet，MISSFormer）for segmentation. Segmentation accuracy was evaluated using metrics including the Dice similarity coefficient（DSC），95% Hausdorff distance（HD95）and mean intersection over union（mIoU），and was compared with the segmentation results of 3 ultrasound physicians with varying experience levels（junior physicians and senior physicians）to validate the model's segmentation efficacy.Results:The CHA-TransUNet model established based on a dataset of 1 289 standardized images achieved segmentation results for the BP with a DSC of 90.15%，mIoU of 91.02%，and HD95 of 8.08. Its accuracy was higher than other mainstream models（DSC：90.15% vs. 87.60%，87.77%，81.35%，84.78%，84.55%），significantly better than junior physicians（DSC：90.15% vs. 68.73%， Z=-127.76， P<0.001），and approached the level of senior physician（DSC：90.15% vs. 86.15%， Z=-31.33， P=0.549）. The model demonstrated superior boundary recognition in complex anatomical structures（e.g.，C6/C7 nerve roots）compared to ultrasound physicians（junior and senior）（HD95：8.08 vs. 26.34，17.44，56.80）. Conclusions:This study proposes an analysis model for BP ultrasound images，CHA-TransUNet. This model achieves segmentation and recognition of the BP with relatively complex pathways and structures. The model exhibits high accuracy and stability，outperforming current mainstream network models and junior physicians while approaching the performance level of senior physicians. It assists junior physicians or trainees in more accurately identifying and localizing the BP.

Myocardial fibrosis (MF) is a common pathological hallmark of cardiovascular diseases, reflecting shared mechanisms in their progression. However, the lack of reliable MF models that accurately mimic its pathogenesis has hindered drug discovery, highlighting the urgent need for more effective therapeutic agents. Herein, a novel contractile three-dimensional (3D) myocardial tissue model integrating cardiomyocytes, cardiac-fibroblasts, and bone marrow-derived macrophages in collagen hydrogel was developed to simulate the fibrotic changes of cardiovascular disease, and facilitate the screening of anti-MF compounds. The 3D myocardial tissue model exhibited precise, visualizable, and quantifiable contractile characteristics under hypoxia and drug interventions. 76 compounds extracted from the resins of Toxicodendron vernicifluum, a traditional Chinese medicine with clear clinical benefits for fibrotic diseases, were screened for anti-fibrotic activity. Using an in vitro 3D oxygen-glucose deprivation (OGD)-treated myocardial tissue model instead of a two-dimensional transforming growth factor-β treated cardiac-fibroblasts model, two candidates including LQ-40 and SQ-3 exert impressive anti-MF activity, which was further validated in left anterior descending coronary artery ligation-induced MF mouse model. The current results demonstrate the feasibility and advantage of the novel contractile 3D tissue model with multi-cell types in discovering candidates for MF, further stressing the great potential of regulating macrophages in the treatment of MF.

Objective:To construct a thyroid nodule segmentation model using ultrasound dynamic images and explore its potential for assisting in the screening of thyroid nodules.Methods:A total of 126 patients with thyroid nodules（comprising 150 nodules）who were diagnosed and treated at Xuzhou Cancer Hospital from April 2024 to December 2024 were prospectively enrolled. Two-dimensional ultrasound was performed to capture short-axis and long-axis video images of thyroid nodules，forming a dynamic ultrasound image dataset. The dataset was divided into training，validation，and test sets in a ratio of 6∶1∶3. After the training loss curve converged，the model that performed well on the validation set was selected for testing. Three-fold cross-validation was employed for training and testing. All 300 ultrasound videos were divided into three subsets. In each experiment，two subsets were used as the training set，and one subset was used as the test set to evaluate the model's generalization ability. A collaborative spatiotemporal diffusion model was established based on the dynamic trends and tissue texture details of thyroid nodules. Six widely used segmentation metrics were employed to evaluate the model's application capabilities.Results:The study included 126 patients with 150 thyroid nodules，300 dynamic ultrasound images，and video lengths of 3-4 seconds per nodule，resulting in 12 312 segmented images. The size of the thyroid nodules was（10.7 ± 10.6）mm（transverse diameter）×（8.4 ± 6.3）mm（anteroposterior diameter）. Among the nodules，62（41.3%）had clear boundaries，while 88（58.7%）had indistinct boundaries；61（40.7%）exhibited regular shapes，while 89（59.3%）were irregular；66（44.0%）had a taller-than-wide aspect ratio；and 70（46.7%）showed microcalcifications. The collaborative diffusion model based on dynamic ultrasound image segmentation achieved the following scores：a Jaccard score of（69.22 ± 0.03）%，a Dice score of（79.16 ± 0.18）%，a Precision score of（86.70 ± 0.17）%，a Recall score of（77.82 ± 0.04）%，an Sα score of（85.26 ± 0.01）%，and an Eθmn score of（90.58 ± 0.17）%. Compared to other models，this model demonstrated significant improvements across all evaluation metrics，achieving the highest values in each metric with increments of over 8% and 1%，respectively. Conclusions:The collaborative diffusion model with a dynamic controller，constructed based on dynamic ultrasound images of thyroid nodules，demonstrates excellent performance in ultrasound image segmentation. It improves the accuracy of thyroid nodule screening，thereby providing a valuable auxiliary diagnostic tool for clinical practice.

Objective:To propose a deep learning（DL）-based ultrasound imaging auxiliary tool for automatic segmentation and recognition of the brachial plexus（BP），and to enhance the accuracy and safety of clinical procedures.Methods:It was a multicenter study that collected 773 healthy subjects from Peking University Third Hospital and its branch campuses，the Third Medical Center of the Chinese PLA General Hospital，and Shanghai Eighth People's Hospital between August 2024 and February 2025. Brachial plexus（BP）images in the interscalene groove were captured used high-frequency ultrasound by senior sonographers，a dataset comprising 1 289 standardized images were constructed and the improved model（CHA-TransUNet）was trained. The test set was input into 6 different models（CHA-TransUNet，R50-Unet，TransUnet，SegFormer，SwinUnet，MISSFormer）for segmentation. Segmentation accuracy was evaluated using metrics including the Dice similarity coefficient（DSC），95% Hausdorff distance（HD95）and mean intersection over union（mIoU），and was compared with the segmentation results of 3 ultrasound physicians with varying experience levels（junior physicians and senior physicians）to validate the model's segmentation efficacy.Results:The CHA-TransUNet model established based on a dataset of 1 289 standardized images achieved segmentation results for the BP with a DSC of 90.15%，mIoU of 91.02%，and HD95 of 8.08. Its accuracy was higher than other mainstream models（DSC：90.15% vs. 87.60%，87.77%，81.35%，84.78%，84.55%），significantly better than junior physicians（DSC：90.15% vs. 68.73%， Z=-127.76， P<0.001），and approached the level of senior physician（DSC：90.15% vs. 86.15%， Z=-31.33， P=0.549）. The model demonstrated superior boundary recognition in complex anatomical structures（e.g.，C6/C7 nerve roots）compared to ultrasound physicians（junior and senior）（HD95：8.08 vs. 26.34，17.44，56.80）. Conclusions:This study proposes an analysis model for BP ultrasound images，CHA-TransUNet. This model achieves segmentation and recognition of the BP with relatively complex pathways and structures. The model exhibits high accuracy and stability，outperforming current mainstream network models and junior physicians while approaching the performance level of senior physicians. It assists junior physicians or trainees in more accurately identifying and localizing the BP.

Objective To evaluate the protective effect of epigallocatechin gallate (EGCG) on lipopolysaccharide (LPS) -induced acute kidney injury (AKI) in rats and its underlying mechanisms. Methods Sprague-Dawley rats were randomly divided into the Sham group, AKI group, EGCG group and TLR4 group (n = 10 each). To establish the rat model of endotoxemia, serum creatinine (Cr) and urea nitrogen (BUN) levels were detected by biochemical assays; serum interlukin (IL) -6, IL-1β, IL-10, and TNF-α levels were detected by ELISA; kidney histopathology was examined by hematoxylin and eosin (HE) staining method; and expression of TLR4, Myd88 and nuclear factor-kappa B (NF-κB) in rat kidneys at both protein and mRNA levels was detected by Western blotting and qRT-PCR, respectively.Results Kidney injury increased significantly in AKI group compared to the sham group. Serum Cr, BUN, IL-6, IL-1β, and TNF-α levels significantly increased whereas IL-10 levels significantly decreased in AKI group compared to the sham group. Expression levels of TLR4, Myd88, and NF-κB also significantly increased at both protein and mRNA levels in AKI group compared to the sham group. Treatment with EGCG prior to induction of LPS-mediated AKI conferred protection against AKI by significantly reducing the expression of inflammatory markers such as, TLR4, Myd88, and NF-κB. Given TLR4 inhibitor based on this, the protective effect of EGCG on AKI was via inhibition of the TLR4/Myd88/NF-κB pathway. Conclusion EGCG exhibited a protective effect against LPS-induced AKI by inhibiting the activation of TLR4/Myd88/NF-κB pathway.

Objective To investigate the effects of Epigallocatechin gallate (EGCG) on cardiac protection in diabetic rats and the expression of TGF-1/Smad3 signaling pathway. Methods The influence of clean level 40 SD rats, weight 200-220 g, divided into four random groups:control (Sham) group, diabetic cardiomyopathy model (DC) group, EGCG group, and metformin positive control group (Met).Post 8 weeks of high-fat-diet administration, the rats were injected intraperitoneally with STZ to establish the diabetes cardiomyopathy model. Upon successful model establishment, the EGCG group was intraperitoneally injected with EGCG and the cardiac function of the rats was measured after 28 days of drug administration. Then, the pathological results of the myocardial tissue were analyzed. Triglyceride (TG), total cholesterol (TC), glycosylated hemoglobin (HbA1 c), and blood glucose (FBG) concentrations were also measured. Further, the concentrations of superoxide dismutases (SOD), malondialdehyde (MDA), catalase (CAT), and glutathione peroxidase (GPX) in serum were measured by ELISA. The expression of TGF-β1 and Smad3 in kidney tissues of the rats was measured by Western blotting analysis. Results EGCG could reduce the glucose, lipid, and MDA levels in the blood of the diabetic rats, enhance cardiac systolic and diastolic functions, inhibit TGF-β1 and Smad3 protein expression, enhance the activity of SOD, CAT and GPX, and reduce myocardial tissue fibrosis. Conclusion EGCG can protect diabetic rat hearts by improving metabolic disorder, and its mechanism may be related to the oxidative-stress mediated by the TGF-β1/Smad3 signaling pathway.

Objective: To explore the safety of liver transplantation recipients with Rh blood group mismatchming. Methods: From May 2005 to December 2018, 1 546 cases of liver transplantation in our hospital were retrospectively analyzed. Among these cases, 5 cases of Rh blood group mismatched were Rh(-) recipients receiving Rh(+ ) donor liver. For each Rh blood group mismatched liver transplantation, 5 patients received the same Rh blood group liver allograft were matched according to a certain principle and were defined as Rh-mismatch group and Rh-match group respectively. The serum alanine aminotransferase (ALT), aspartate aminotransferase(AST)and creatinine(SCr)were compared between two groups at Days 7 & 14 post-operation. Serum total bilirubin(TB), gamma-glutamyl transpeptidase(GGT)were compared between two groups at Month 1, 6 & 12 post-operation. Hemoglobin (Hb)were compared between two groups Month 1, 3 & 6 post-operation. The rates of infection, vascular complications and acute rejection was also compared. Indirect antiglobulin test (IAT)was used for detecting the production of anti-RhD antibody in patients in Rh-mismatch group at Month 1, 6 & 12 post-operation. Results: At the mentioned time, no significant inter-group difference existed in serum ALT, AST, SCr, TB, GGT and blood Hb levels(all P>0.05); Also, no significant difference existed in the incidence of infection, vascular complications or acute rejection(all P>0.05). In Rhmismatch group, 4 recipients received Rh(+ )RBC transfusion during perioperative period and no hemolytic anemia occurred after operation. Rh(D) antibody was negative at all timepoints. Conclusions Taking into account the rarity of Rh-negative blood group in Chinese, it is safe and feasible to carry out Rh blood group mismatched liver transplantation when donor or recipient with the same Rh blood group is not available.

Objective To summarize our experience in the diagnosis and treatment of hepatic epithelioid angiomyolipoma (HEAML),with the aim to reduce the future misdiagnosis rate.Methods The PubMed,Medline,China Science Periodical Database (CSPD),and VIP Databases were searched from January 2000 to March 2018 on all reports on HEAML.Results There were 409 cases of HEAML in 97 reports.The ratio of men to women was 1∶4.84.The age ranged from 12 to 80 years and the median age was 44 years.61.9％ of patients (205/331) were asymptomatic,while 34.7％ (115/331) had upper or right upper quadrant abdominal discomfort.Some patients presented with abdominal mass,gastrointestinal reaction,low grade fever or weight loss.The clinical symptoms in 78 patients were not mentioned in the reports.The misdiagnostic rate of HEAML was as high as 40.3％ (165/409).The imaging findings of HEAML were nonspecific.Ultrasound,CT and MRI scan usually showed contrast enhancement in the arterial phase.Most lesions were accompanied by central vessels with early drainage veins.The enhanced scans showed varied characteristics.The ratios of fast wash-in and fast wash-out,to fast wash-in and slow wash-out,and to delayed enhancement were roughly 4∶ 5∶ 1.A definitive diagnosis of HEAML is based on the pathological findings of epithelioid cells in the lesions and the expressions of HMB45,SMA,Melan-A and Actin on immunohistochemical staining.HEAML had a relatively low malignant rate of 3.9％.Surgical resection was the main treatment for HEAML.Conclusion HEAML was a rare and easily misdiagnosed disease.,which could be diagnosed by taking into account the clinical course,imaging,pathological and immunohistochemical findings.HEAML.

Objective To understand the life quality status of freshmen in higher medical colleges,and discuss the factors influencing the quality of life,in order to improve their life quality,and put forward the countermeasures and suggestions.Methods We randomly selected a higher medical colleges and universities in Yunnan,the grade one students were investigated with SF-36 scale investigation,the data were analyzed by t test and multiple linear regression analysis.Results The quality of life scores (PCS,MCS) of freshmen in this medical college are lower than the national norm,the segmentation in the field of eight only GH is higher than the national norm,and the RP,BP,VT,RE are lower than the national norm.There are six factors into the regression equation of the quality of life:health,insomnia,life pressure,communicating with people,life rule.Conclusions The QOL of freshmen in higher medical colleges and universities is low,relevant departments should be caused to take seriously.To improve the QOL,the government,society,school,personal must make joint efforts in many ways,and take targeted measures.