Objective To investigate the effects of cinnamic aldehyde(CA)on Bcl-2-associated X protein(Bax)and Bcl-2 homologous antagonist/killer(Bak)in vascular endothelial cells of diabetic ulcer wound tissues,as well as on cell apoptosis.Methods ① Forty-eight healthy SPF-grade male SD rats(5 weeks old,weighing 180～220 g)were randomly assigned to a control group(12 rats)and a diabetes group(36 rats).The diabetic model was established with an intraperitoneal injection of 50 mg/kg STZ-citrate sodium solution and high-fat diet feeding.The diabetes group was further randomly divided into Model group,CA group,and the rb-bFGF group,with 12 animals in each group.Wounds in the Con and Model groups were disinfected and topically treated with normal saline,CA group received topical application of 4 μmol/L CA in PEG 400 gel,and those of the rb-bFGF group were treated with bevacizumab gel.The wound healing rate of each group was calculated at 3,7 and 14 d after intervention.At 14 d after intervention,pathological changes in the wounds were observed with HE staining,and the expression levels of Bax and Bak were detected by Western blotting.② Human umbilical vein endothelial cell line EA.hy926 was treated with 175 mmol/L glucose for 48 h to establish a cell model of high glucose injury.The experimental cells were divided into control group,model group and CA treatment group.Cell scratch test and tube formation test were performed respectively to determine the migration ability and angiogenesis of the cells.The expression levels of Bax and Bak was detected with immunofluorescence assay,and cell apoptosis was detected by TUNEL staining.Results ①The diabetic rats in the Model group exhibited significantly higher blood glucose level(P<0.05),declined wound healing rate at 7 and 14 d after intervention(P<0.05),and enhanced expression levels of Bax and Bak(P<0.05)when compared with the control group.Pathological observation revealed that,at 14 d after intervention,accompanied with inflammatory reactions,dense infiltration of inflammatory cells,fewer new blood vessels,and continuous fluid exudation in the wound were observed in the Model group,but the control group presented complete epithelialization in full-thickness skin.Compared with the conditions in the Model group,both CA and rb-bFGF treatment improved the epithelialization process,with mature granulation tissues,showing good healing condition,promoted wound healing rate(P<0.05),and decreased the expression levels of Bax and Bak(P<0.05).② The results of cell experiments showed that the cells of the model group showed significantly reduced migration ability and tube formation ability(P<0.05),reduced protein levels of Bax and Bak(P<0.05),and lower apoptotic rate(P<0.05)when compared with the cells in the model group.Conclusion CA can inhibit the expression of apoptosis-related proteins Bax and Bak,promote the migration and tube formation of vascular endothelial cells,and inhibit the cell apoptosis under high glucose condition,which may be an important reason for its promoting wound healing in diabetic ulcer rats.

Objective To explore the factors influencing early recovery quality after radical sur-gery for colorectal cancer in elderly patients and establish a prediction model.Methods A total of 182 elderly patients who underwent elective radical surgery for colorectal cancer at the Affiliated Hospi-tal of Yangzhou University between May 2023 and May 2024 were enrolled as study objects.Data such as gender,age,body mass index(BMI),American Society of Anesthesiologists(ASA)classification,albumin,serum creatinine,hemoglobin,and D-dimer levels at admission were collected.Surgical ap-proach,operative time,anesthesia time,length of hospital stay,and whether the patient was transferred to the intensive care unit(ICU)postoperatively were also recorded.Relevant patient information was compiled through the electronic medical record system to calculate the modified frailty index(mFI).The 15-item Quality of Recovery Scale(QoR-15)was used to assess patients'recovery quality three days postoperatively.Results A total of 163 patients had good recovery(QoR-15 score ≥120)and 19 had poor recovery(QoR-15 score＜120).Preoperative mFI(≥0.27)and BMI(≥21.05 kg/m2)were identified as factors influencing early recovery quality after radical surgery for colorectal cancer in elderly patients.The area under the receiver operating characteristic(ROC)curve(AUC)for the prediction model of recovery quality after radical surgery for colorectal cancer in elderly patients was 0.816(95％CI:0.710～0.921),indicating good agreement between the models predicted recovery quality and ac-tual recovery quality,indicating high discrimination and accuracy.Conclusion Preoperative mFI(≥0.27)and BMI(≥21.05 kg/m2)are factors influencing recovery quality after radical surgery for colorectal cancer in elderly patients.Improving perioperative frailty status and appropriately regu-lating BMI levels can help reduce the risk of postoperative complications.

Objective:To develop a deep learning-based segmentation model for anterior segment ultrasound biomicroscopy (UBM) images to automatically segment the anterior segment tissues of patients with primary angle-closure glaucoma (PACG).Methods:A single-center retrospective case series was conducted.A small-scale dataset comprised 468 UBM images of the anterior chamber angle closure from 156 patients with PACG who underwent the UBM examination at Tianjin Medical University Eye Hospital between July 12, 2022, and February 20, 2023.The UBM images were randomly split into a training dataset of 228 images and a testing dataset of 152 images using a random seed method in a ratio of 6∶4.The models were trained using the PSPNet model with MobileNet V2 and ResNet50 as backbones, the DeepLab v3+ model with MobileNet V2 and Xception as backbones, and the SegFormer model with MiT-B0 and MiT-B2 as backbones.The testing dataset was used for result prediction and to achieve segmentation of four regions: the cornea and sclera, iris, ciliary body, and anterior lens surface.To evaluate the performance of the models in segmenting the anterior segment structures, multiple metrics were assessed, including the mean intersection over union (mIoU), Dice coefficient, precision, recall, false negative rate, and specificity.A comparative analysis of the test results across the different models was subsequently performed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Tianjin Medical University Eye Hospital (No.2023KY-05).Results:The two models with the best segmentation performance were PSPNet and DeepLab v3+ .The PSPNet model with ResNet50 as the backbone achieved the mIoU of 85.11%, Dice coefficient of 91.38%, precision of 91.83%, recall of 90.94%, false negative rate of 9.06%, and specificity of 98.89%.The DeepLab v3+ model with MobileNet V2 as the backbone achieved an mIoU of 85.84%, Dice coefficient of 92.01%, precision of 92.67%, recall of 91.36%, false negative rate of 8.64%, and specificity of 98.90%.Among the five key metrics, mIoU, Dice coefficient, recall, false negative rate, and specificity, DeepLab v3+ exhibited the best segmentation performance.In addition, the DeepLab v3+ model with Xception as the backbone had the highest precision among all models, reaching 92.77%.Conclusions:The deep learning-based DeepLab v3+ model achieves precise segmentation of anterior segment tissue structures in PACG anterior segment UBM image segmentation, providing auxiliary support for clinical diagnosis.

Objective To explore the influencing factors of urinary dysfunction in patients with rectal cancer after laparoscopic surgery,and to construct and validate a column chart prediction model.Methods A retrospective analysis was conducted on the clinical data of 415 rectal cancer patients in our hospital from January 2021 to April 2024.According to the computer-generated allocation order,they were stochastically grouped into a modeling group of 311 cases and a validation group of 104 cases in a 3∶1 ratio.The modeling group was further separated into a urinary dysfunction group of 55 cases and a non urinary dysfunction group of 256 cases.The patient's sex,diabetes history,tumor diameter and other relevant data were collected;MultivariateLogisticregression analysis was used to screen for risk factors;R software was used to construct a column chart prediction model for predicting urinary dysfunction in patients with colorectal cancer after laparoscopic surgery;The Hosmer-Lemeshow test,ROC curve,calibration curve,and DCA curve were used to validate the predictive performance of the column chart model.Results Male[OR(95％CI)=3.512(1.637～7.533),P=0.001],diabetes[OR(95％CI)=3.684(1.639～8.280),P=0.002],tumor diameter ≥ 5 cm[OR(95％CI)=4.459(1.993～9.979),P=0.000],large intraoperative bleeding[OR(95％CI)=1.018(1.011～1.026),P=0.000],anterior resection of rectum combined with abdominal perineum resection[OR(95％CI)=3.885(1.901～7.940),P=0.000]were Independent risk factors for postoperative urination dysfunction in rectal cancer patients after laparoscopic surgery.In internal and external validations,the Hosmer-Lemeshau test for the column chart model showed x2=0.159,P=0.254＞0.05,and x2=5.991,P=0.648＞0.05.The areas under the receiver operating characteristic curve were 0.846 and 0.828,respectively.The calibration curve indicated that the simulated curve had a similar trend to the actual curve,indicating good discrimination and calibration of the column chart prediction model.Clinical decision curve analysis results showed that when the high-risk threshold probability was between 0.05 and 0.98,the column chart prediction model could produce better clinical benefits.Conclusion The column chart model constructed by integrating independent risk factors for urinary dysfunction in rectal cancer patients after laparoscopic surgery has high predictive value.

Objective:To develop a deep learning-based segmentation model for anterior segment ultrasound biomicroscopy (UBM) images to automatically segment the anterior segment tissues of patients with primary angle-closure glaucoma (PACG).Methods:A single-center retrospective case series was conducted.A small-scale dataset comprised 468 UBM images of the anterior chamber angle closure from 156 patients with PACG who underwent the UBM examination at Tianjin Medical University Eye Hospital between July 12, 2022, and February 20, 2023.The UBM images were randomly split into a training dataset of 228 images and a testing dataset of 152 images using a random seed method in a ratio of 6∶4.The models were trained using the PSPNet model with MobileNet V2 and ResNet50 as backbones, the DeepLab v3+ model with MobileNet V2 and Xception as backbones, and the SegFormer model with MiT-B0 and MiT-B2 as backbones.The testing dataset was used for result prediction and to achieve segmentation of four regions: the cornea and sclera, iris, ciliary body, and anterior lens surface.To evaluate the performance of the models in segmenting the anterior segment structures, multiple metrics were assessed, including the mean intersection over union (mIoU), Dice coefficient, precision, recall, false negative rate, and specificity.A comparative analysis of the test results across the different models was subsequently performed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Tianjin Medical University Eye Hospital (No.2023KY-05).Results:The two models with the best segmentation performance were PSPNet and DeepLab v3+ .The PSPNet model with ResNet50 as the backbone achieved the mIoU of 85.11%, Dice coefficient of 91.38%, precision of 91.83%, recall of 90.94%, false negative rate of 9.06%, and specificity of 98.89%.The DeepLab v3+ model with MobileNet V2 as the backbone achieved an mIoU of 85.84%, Dice coefficient of 92.01%, precision of 92.67%, recall of 91.36%, false negative rate of 8.64%, and specificity of 98.90%.Among the five key metrics, mIoU, Dice coefficient, recall, false negative rate, and specificity, DeepLab v3+ exhibited the best segmentation performance.In addition, the DeepLab v3+ model with Xception as the backbone had the highest precision among all models, reaching 92.77%.Conclusions:The deep learning-based DeepLab v3+ model achieves precise segmentation of anterior segment tissue structures in PACG anterior segment UBM image segmentation, providing auxiliary support for clinical diagnosis.

Objective To explore the influencing factors of urinary dysfunction in patients with rectal cancer after laparoscopic surgery,and to construct and validate a column chart prediction model.Methods A retrospective analysis was conducted on the clinical data of 415 rectal cancer patients in our hospital from January 2021 to April 2024.According to the computer-generated allocation order,they were stochastically grouped into a modeling group of 311 cases and a validation group of 104 cases in a 3∶1 ratio.The modeling group was further separated into a urinary dysfunction group of 55 cases and a non urinary dysfunction group of 256 cases.The patient's sex,diabetes history,tumor diameter and other relevant data were collected;MultivariateLogisticregression analysis was used to screen for risk factors;R software was used to construct a column chart prediction model for predicting urinary dysfunction in patients with colorectal cancer after laparoscopic surgery;The Hosmer-Lemeshow test,ROC curve,calibration curve,and DCA curve were used to validate the predictive performance of the column chart model.Results Male[OR(95％CI)=3.512(1.637～7.533),P=0.001],diabetes[OR(95％CI)=3.684(1.639～8.280),P=0.002],tumor diameter ≥ 5 cm[OR(95％CI)=4.459(1.993～9.979),P=0.000],large intraoperative bleeding[OR(95％CI)=1.018(1.011～1.026),P=0.000],anterior resection of rectum combined with abdominal perineum resection[OR(95％CI)=3.885(1.901～7.940),P=0.000]were Independent risk factors for postoperative urination dysfunction in rectal cancer patients after laparoscopic surgery.In internal and external validations,the Hosmer-Lemeshau test for the column chart model showed x2=0.159,P=0.254＞0.05,and x2=5.991,P=0.648＞0.05.The areas under the receiver operating characteristic curve were 0.846 and 0.828,respectively.The calibration curve indicated that the simulated curve had a similar trend to the actual curve,indicating good discrimination and calibration of the column chart prediction model.Clinical decision curve analysis results showed that when the high-risk threshold probability was between 0.05 and 0.98,the column chart prediction model could produce better clinical benefits.Conclusion The column chart model constructed by integrating independent risk factors for urinary dysfunction in rectal cancer patients after laparoscopic surgery has high predictive value.

Objective:To compare the ability of deep convolutional neural network-crop (DCNN-C) and deep convolutional neural network-whole (DCNN-W), 2 artificial intelligence systems based on different training methods to dignose early gastric cancer (EGC) diagnosis under magnifying image-enhanced endoscopy (M-IEE).Methods:The images and video clips of EGC and non-cancerous lesions under M-IEE under narrow band imaging or blue laser imaging mode were retrospectively collected in the Endoscopy Center of Renmin Hospital of Wuhan University, for the training set and test set for DCNN-C and DCNN-W. The ability of DCNN-C and DCNN-W in EGC identity in image test set were compared. The ability of DCNN-C, DCNN-W and 3 senior endoscopists (average performance) in EGC identity in video test set were also compared. Paired Chi-squared test and Chi-squared test were used for statistical analysis. Inter-observer agreement was expressed as Cohen′s Kappa statistical coefficient (Kappa value).Results:In the image test set, the accuracy, sensitivity, specificity and positive predictive value of DCNN-C in EGC diagnosis were 94.97%(1 133/1 193), 97.12% (202/208), 94.52% (931/985), and 78.91%(202/256), respectively, which were higher than those of DCNN-W(86.84%, 1 036/1 193; 92.79%, 193/208; 85.58%, 843/985 and 57.61%, 193/335), and the differences were statistically significant ( χ2=4.82, 4.63, 61.04 and 29.69, P=0.028, =0.035, <0.001 and <0.001). In the video test set, the accuracy, specificity and positive predictive value of senior endoscopists in EGC diagnosis were 67.67%, 60.42%, and 53.37%, respectively, which were lower than those of DCNN-C (93.00%, 92.19% and 87.18%), and the differences were statistically significant ( χ2=20.83, 16.41 and 11.61, P<0.001, <0.001 and =0.001). The accuracy, specificity and positive predictive value of DCNN-C in EGC diagnosis were higher than those of DCNN-W (79.00%, 70.31% and 64.15%, respectively), and the differences were statistically significant ( χ2=7.04, 8.45 and 6.18, P=0.007, 0.003 and 0.013). There were no significant differences in accuracy, specificity and positive predictive value between senior endoscopists and DCNN-W in EGC diagnosis (all P>0.05). The sensitivity of senior endoscopists, DCNN-W and DCNN-C in EGC diagnosis were 80.56%, 94.44%, and 94.44%, respectively, and the differences were not statistically significant (all P>0.05). The results of the agreement analysis showed that the agreement between senior endoscopists and the gold standard was fair to moderate (Kappa=0.259, 0.532, 0.329), the agreement between DCNN-W and the gold standard was moderate (Kappa=0.587), and the agreement between DCNN-C and the gold standard was very high (Kappa=0.851). Conclusion:When the training set is the same, the ability of DCNN-C in EGC diagnosis is better than that of DCNN-W and senior endoscopists, and the diagnostic level of DCNN-W is equivalent to that of senior endoscopists.

BACKGROUND: Accurate serum total thyroxine (TT4) measurement is important for thyroid disorder diagnosis and management. We compared the performance of six automated immunoassays with that of isotope-diluted liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS) as the reference method. We also evaluated the correlation of thyroid stimulating hormone (TSH) with TT4 measured by ID-LC-MS/MS and immunoassays. METHODS: Serum was collected from 156 patients between October 2015 and January 2016. TT4 was measured by immunoassays from Abbott (Architect), Siemens (ADVIA Centaur XP), Roche (E601), Beckman-Coulter (Dxi800), Autobio (Autolumo A2000), and Mindray (CL-1000i), and by ID-LC-MS/MS. Results were analyzed using Passing-Bablok regression and Bland-Altman plots. Minimum requirements based on biological variation were as follows: a mean bias of ≤4.5% and total imprecision (CV) of ≤3.7%. RESULTS: All immunoassays showed a correlation >0.945 with ID-LC-MS/MS; however, the slope of the Passing-Bablok regression line varied from 0.886 (Mindray) to 1.23 (Siemens) and the intercept from −12.8 (Siemens) to 4.61 (Mindray). Only Autobio, Beckman-Coulter, and Roche included the value of one in the 95% confidence interval for slope. The mean bias ranged from −10.8% (Abbott) to 9.0% (Siemens), with the lowest value noted for Roche (3.5%) and the highest for Abbott (−10.8%). Only Abbott and Roche showed within-run and total CV ≤3.7%. CONCLUSIONS: Though all immunoassays correlated strongly with ID-LC-MS/MS, most did not meet the minimum clinical requirement. Laboratories and immunoassay manufacturers must be aware of these limitations.
Bias (Epidemiology) ; Diagnosis ; Humans ; Immunoassay ; Mass Spectrometry ; Methods ; Thyroid Gland ; Thyrotropin ; Thyroxine

The default mode network (DMN) is associated with the occurrence of mind-wandering or task-unrelated thought. In contrast, the frontal-parietal network (FPN) and visual network (VS) are involved in tasks with external stimuli. However, it is not clear how these functional network interactions support these two different processes - mind-wandering and on-task - especially with regard to individual variation in the mind-wandering experience. In this study, we investigated the functional connectivity and modular structure among the DMN, FPN, and VS. Our results showed that, compared to the on-task period, mind-wandering was associated with increased DMN activity and increased DMN-VS connectivity. Moreover, mind-wandering was accompanied by a large number of transitional nodes, which expressed a diversity of brain regions. Intriguingly, the functional connectivity of the FPN and VS was strongly correlated with individual behavioral performance. Our findings highlight the individual variation of mind-wandering, which implies the importance of other complementary large-scale brain networks.
Adult ; Attention ; physiology ; Brain ; diagnostic imaging ; physiology ; Brain Mapping ; Female ; Humans ; Image Processing, Computer-Assisted ; Intention ; Magnetic Resonance Imaging ; Male ; Models, Neurological ; Oxygen ; blood ; Young Adult

Objective This paper aims at establishing a inductively coupled plasma mass spectrometry ( ICP-MS) method for quantification and evaluation of iodine in human urine and serum in routine clinical laboratory .Methods This study was methodology validation research on iodine evaluation using ICP-MS.Ammonia, isopropanol and ultrapure water were mixed at certain ratio to dilute samples in the ratio of 1:10, and then the diluted samples were analyzed by ICP -MS.Re was used as the internal standard.And linearity, lower limit of detection, recovery, precision, accuracy, carryover and stability was evaluated thoroughly .Results of iodine of pregnant women who required iodine tests were retrospectively analyzed to evaluate the status of iodine .Results The method only needs 30s for analysis of one sample .It was sensitive with a lower limit detection of 0.87μg/L, the correlation coefficient was higher than 0.999 9 in ten measurements.The recovery in both serum and urine was approximately 100% (95.3% -109.9%). Based on the NIST standard reference material 3668 comparison, the bias was less than 4%( -0.9% -3.9%).The inter-coefficient variation (CV) for serum iodine and urine iodine was 1.2%-3.0%, 2. 0%-2.9%, respectively;and total CV for serum iodine and urine iodine were 3.0%-3.8%, 4.1%-4.9%, respectively.The mean carryover of this method was 0.03% and iodine was stable for at least one month at -20℃ and 4℃.The urine and serum iodine for pregnant women was (154.8 ±89.7) μg/L (mean ±SD),(75.8 ±21.4) μg/L, respectively.The correlation between urine and serum iodine was 0.21. Conclusion Establishe a rapid and simple ICP -MS method for urine and serum iodine measurement with high accurate and precise in routine clinical laboratory .