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 investigate the correlation between thyroid hormone levels and cognitive function in schizophrenia(SCZ)patients with metabolic syndrome(MetS),as well as the mediating role of thyroid hormones in the relationship between MetS-related indicators and cognitive function.Methods A cross-sectional trial was conducted on 120 SCZ inpatients and outpatients(40 cases of MetS and 80 cases of non-MetS)and 80 healthy controls admitted in the Chongqing Mental Health Center from August 2023 to December 2024.Thyroid function indicators[Thyroid-stimulating hormone(TSH),triiodothyronine(T3),thyroxine(T4),free triiodothyronine(FT3),and free thyroxine(FT4)],MetS-related parameters[blood glucose,triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),and waist circumference],Positive and Negative Syndrome Scale(PANSS)score,and Montreal Cognitive Assessment(MoCA)scores were collected.One-way ANOVA or Kruskal-Wallis rank sum test was applied to analyze the differences among the 3 groups,and LSD test or Bonferroni correction was performed for post hoc analysis.Pearson correlation analysis was conducted to assess the relationship between thyroid hormone levels and metabolic parameters as well as cognitive/clinical scale scores(MoCA,PANSS)in the MetS group.Results The MetS group exhibited significantly lower FT4 level(P<0.05)and MoCA score(P=0.001),but higher TSH level(P<0.05)and PANSS negative symptom score(P<0.001)when compared to the non-MetS group.Correlation analysis indicated that in the MetS group,TSH level was positively correlated with TG(r=0.672,P<0.001)and PANSS negative symptom score(r=0.458,P<0.05),and negatively with HDL-C(r=-0.377,P=0.017)and MoCA score(r=-0.667,P<0.001);FT4 level was positively correlated with MoCA score(r=0.534,P<0.001).In the non-MetS group,TSH level was positively correlated with PANSS negative symptom score(r=0.267,P=0.017)and negatively with HDL-C(r=-0.236,P=0.036),T3 was positively with waist circumference(r=0.268,P=0.017).No correlation was observed in FT4 level with HDL-C(r=-0.207,P=0.067)or MoCA score(r=0.216,P=0.055).Mediation analysis revealed that TSH partially mediated the association between TG and MoCA score,with a mediation effect accounting for 29.91%of the total effect.The mediating effect was not significant in the non-MetS group.Conclusion Abnormal elevation of TSH may serve as a critical link between MetS and cognitive impairment in SCZ patients,which providing novel insights into the mechanisms underlying cognitive dysfunction in the patients.

Umbilical cord inflammatory myofibroblastic tumor is rare. This article reports a case of a male patient admitted to the hospital due to “difficulty in urination for 1 month”. Physical examination revealed a soft abdomen without tenderness or rebound tenderness in the bladder area，and no abnormal masses were palpated. CT imaging showed a cord-like structure connecting to the mass and extending towards the umbilicus，leading to the anatomical localization diagnosis of an umbilical cord origin. The ring-like enhancement of the tumor capsule was its radiological feature. After completing preoperative preparations，the patient underwent laparoscopic umbilical cord tumor resection and transurethral bladder lesion resection under general anesthesia. During the operation，the bladder was filled with fluid，and a localized mucosal protrusion measuring 3.0 cm × 3.0 cm was observed at the bladder dome. The tumor margin was identified with the guidance of the cystoscope light beam. The bladder tissue was incised layer by layer from the outside to the inside under laparoscopy，and the bladder wall was incised 2 cm away from the tumor margin. After the bladder mucosa was exposed，the bladder was emptied，and the tumor was completely resected. This surgical approach ensured negative surgical margins while maximizing the preservation of bladder tissue. The postoperative pathology indicated inflammatory myofibroblastic tumor of the urachus，with no recurrence observed during a 4-month follow-up.

Umbilical cord inflammatory myofibroblastic tumor is rare. This article reports a case of a male patient admitted to the hospital due to “difficulty in urination for 1 month”. Physical examination revealed a soft abdomen without tenderness or rebound tenderness in the bladder area，and no abnormal masses were palpated. CT imaging showed a cord-like structure connecting to the mass and extending towards the umbilicus，leading to the anatomical localization diagnosis of an umbilical cord origin. The ring-like enhancement of the tumor capsule was its radiological feature. After completing preoperative preparations，the patient underwent laparoscopic umbilical cord tumor resection and transurethral bladder lesion resection under general anesthesia. During the operation，the bladder was filled with fluid，and a localized mucosal protrusion measuring 3.0 cm × 3.0 cm was observed at the bladder dome. The tumor margin was identified with the guidance of the cystoscope light beam. The bladder tissue was incised layer by layer from the outside to the inside under laparoscopy，and the bladder wall was incised 2 cm away from the tumor margin. After the bladder mucosa was exposed，the bladder was emptied，and the tumor was completely resected. This surgical approach ensured negative surgical margins while maximizing the preservation of bladder tissue. The postoperative pathology indicated inflammatory myofibroblastic tumor of the urachus，with no recurrence observed during a 4-month follow-up.

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.

We report a case of a female teenage with monogenic diabetes mellitus caused by glucokinase regulator (GCKR) gene mutation who presented with diabetic ketosis and misdiagnosed as type 1 diabetes. The patient was treated with insulin for 3 years since diagnosis. The islet function was well preserved, but polycystic ovary syndrome was developed. Whole-exome gene sequencing revealed a GCKR gene c. 69delG heterozygous mutation. After molecular diagnosis, the insulin dosage was gradually reduced to full cessation, and only metformin sustained-release tablets were taken to control blood glucose. It is necessary to regular evaluate islet function of patient with type 1 diabetes, and genetic test is of significance for accurate diagnosis and treatment.

Type 2 diabetes (T2D), a metabolic disease caused by inadequate insulin secretion or dysfunction, is one of the most common chronic illnesses causing serious health complications in human. The incidence of diabetes in China is high. Increasingly, men in their reproductive years are afflicted with the disease, particularly due to high-fat and high-sugar diets. Male patients with T2D often suffer from reproductive dysfunction, which is characterized by hyposexual desire, penile erectile dysfunction, spermatogenic dysfunction and abnormal ejaculation, etc. In this paper, we review their related mechanisms, in order to provide reference for the clinical solution of these reproductive problems.

Type 2 diabetes (T2D), a metabolic disease caused by inadequate insulin secretion or dysfunction, is one of the most common chronic illnesses causing serious health complications in human. The incidence of diabetes in China is high. Increasingly, men in their reproductive years are afflicted with the disease, particularly due to high-fat and high-sugar diets. Male patients with T2D often suffer from reproductive dysfunction, which is characterized by hyposexual desire, penile erectile dysfunction, spermatogenic dysfunction and abnormal ejaculation, etc. In this paper, we review their related mechanisms, in order to provide reference for the clinical solution of these reproductive problems.