Objective·To investigate the performance and psychological mechanisms of inhibitory control in patients with alcohol use disorder(AUD)under different emotional contexts,and to examine the influence of emotion regulation difficulties on inhibitory control.Methods·A total of 28 male AUD inpatients(AUD group)and 28 age-and education-matched healthy controls(HC group)were recruited.The emotional Go/Nogo task(angry/neutral facial expressions)was used to evaluate the subjects'behavioral inhibition,and the hierarchical drift-diffusion model(HDDM)was used to quantify the cognitive parameters(drift rate,decision threshold,and non-decision time).The Difficulties in Emotion Regulation Scale(DERS)and Alcohol Use Disorder Identification Test(AUDIT)were used for clinical evaluation.The moderated mediation effects were tested by bootstrap method.Results·The AUD group scored higher than the HC group on the DERS total score and all sub-dimensions(goal-directed behavior,impulse control,strategy access,and emotional clarity),and the difference was statistically significant(all P<0.05).At the behavioral level,compared with the HC group,the AUD group had elevated commission error rates[F(1,54)=8.62,P=0.005]and omission error rates[F(1,54)=4.28,P=0.043],and the reaction time of angry face stimuli was generally prolonged[F(1,54)=12.26,P=0.001].Cognitive modeling showed that the drift rate of the AUD group was significantly lower than that of the HC group[F(1,54)=15.56,P<0.001],indicating impaired information processing efficiency.The moderated mediation model showed that,under the condition of angry face stimuli,the drift rate partially mediated the relationship between group and commission error rate,and the total indirect effect value was 9.564(95%CI 3.874?16.387).Further analysis showed that the conditional indirect effect increased to 10.133(95%CI 3.963?17.927)at high levels of emotion regulation difficulty(one standard deviation above the mean),and to 9.011(95%CI 3.778?14.921)at low levels(one standard deviation below the mean).Conclusion·The deficits in information processing efficiency of AUD patients partly explains the impairment of inhibitory control,and this effect is associated with individual emotion regulation capacity.It is suggested that abnormal processing of social threat information may be an important factor affecting the impairment of inhibitory control in AUD patients,especially in individuals with weak emotion regulation ability.

Objective·To investigate the performance and psychological mechanisms of inhibitory control in patients with alcohol use disorder(AUD)under different emotional contexts,and to examine the influence of emotion regulation difficulties on inhibitory control.Methods·A total of 28 male AUD inpatients(AUD group)and 28 age-and education-matched healthy controls(HC group)were recruited.The emotional Go/Nogo task(angry/neutral facial expressions)was used to evaluate the subjects'behavioral inhibition,and the hierarchical drift-diffusion model(HDDM)was used to quantify the cognitive parameters(drift rate,decision threshold,and non-decision time).The Difficulties in Emotion Regulation Scale(DERS)and Alcohol Use Disorder Identification Test(AUDIT)were used for clinical evaluation.The moderated mediation effects were tested by bootstrap method.Results·The AUD group scored higher than the HC group on the DERS total score and all sub-dimensions(goal-directed behavior,impulse control,strategy access,and emotional clarity),and the difference was statistically significant(all P<0.05).At the behavioral level,compared with the HC group,the AUD group had elevated commission error rates[F(1,54)=8.62,P=0.005]and omission error rates[F(1,54)=4.28,P=0.043],and the reaction time of angry face stimuli was generally prolonged[F(1,54)=12.26,P=0.001].Cognitive modeling showed that the drift rate of the AUD group was significantly lower than that of the HC group[F(1,54)=15.56,P<0.001],indicating impaired information processing efficiency.The moderated mediation model showed that,under the condition of angry face stimuli,the drift rate partially mediated the relationship between group and commission error rate,and the total indirect effect value was 9.564(95%CI 3.874?16.387).Further analysis showed that the conditional indirect effect increased to 10.133(95%CI 3.963?17.927)at high levels of emotion regulation difficulty(one standard deviation above the mean),and to 9.011(95%CI 3.778?14.921)at low levels(one standard deviation below the mean).Conclusion·The deficits in information processing efficiency of AUD patients partly explains the impairment of inhibitory control,and this effect is associated with individual emotion regulation capacity.It is suggested that abnormal processing of social threat information may be an important factor affecting the impairment of inhibitory control in AUD patients,especially in individuals with weak emotion regulation ability.

Prostate cancer is a common tumor of the male genitourinary system,with its incidence continuously increasing.Early accurate diagnosis is crucial for treatment and prognosis.PSA and MRI are important methods for screening and diagnosis of prostate cancer.However,there is still controversy over whether patients with PSA levels between 4-10 μg/L and PI-RADS score of 3 need to undergo prostate biopsy.Radiomics technology provides a new approach for the early and accurate diagnosis of prostate cancer by min-ing and analyzing medical image information in a high throughput,which helps to reduce unnecessary biopsies.This article reviews the research progress of MRI radiomics in the screening of prostate cancer in the"gray zone"of PSA and PI-RADS score of 3,aiming to improve diagnostic accuracy and provide references for future research in the field of radiomics.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Protein phosphorylation modification is an important mechanism of physiological regulation that is closely related to protein biological functions. In particular, protein kinases are responsible for catalyzing the phosphorylation process of proteins, and phosphatases are responsible for catalyzing the dephosphorylation process of phosphorylation-modified proteins, which together mediate the achievement of dynamic and reversible phosphorylation modifications of proteins. Abnormal phosphorylation levels of proteins contribute to the development of many diseases, such as cancer, neurodegenerative diseases, and chronic diseases. Therefore, rational design of small molecules to regulate protein phosphorylation is an important approach for disease treatment. Based on the mechanism of protein phosphorylation regulation, small molecule drug design strategies can be classified into three types, protein kinase modulators, phosphatase modulators, and bifunctional molecules with proximity-mediated mechanism. This review emphasizes the above three small molecule design strategies for targeting protein phosphorylation regulation, including molecular design ideas, research progress and current challenges, and provides an outlook on small molecule modulators targeting protein phosphorylation modification.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective:To evaluate the effectiveness of oral-nasal oxygen supply mouth guard in painless gastroscopy for snoring patients.Methods:The snoring patients who underwent painless gastroscopy at two Endoscopy Centers of Shanghai East Hospital, Tongji University in July 2022 were randomly divided into the observation group (using oral-nasal oxygen supply mouth guard) and the control group (using ordinary nasal oxygen tube and mouth guard). Parameters such as the wearing time and the removal time of the mouth guard, lowest pulse oxygen saturation (SpO 2), incidence of hypoxemia, and the satisfaction of medical staff were compared between the two groups. Results:The wearing time of mouth guard was 11.63±0.84 seconds and the removal time was 5.33±0.76 seconds in the observation group ( n=40), which were lower than those in the control group ( n=47) (14.91±1.21 seconds, t=-14.463, P<0.001; 10.38±0.80 seconds, t=-30.095, P<0.001). The wearing satisfaction score was 9.80±0.61, the lowest SpO 2 was (96.70±3.42)%, the removal satisfaction score was 9.75±0.67, and the anesthesiologists' satisfaction score was 9.20±1.42 in the observation group, which were higher than those in the control group [7.70±0.93, t=12.209, P<0.001; (94.06±3.72)%, t=3.417, P=0.001; 7.96±0.98, t=9.803, P<0.001; 8.13±1.35, t=3.615, P=0.001] with significant difference. There was no significant difference in the incidence of hypoxemia [10.00% (4/40) VS 14.89% (7/47), χ2=0.130, P=0.718] and endoscopic physician satisfaction score (9.30±0.97 VS 9.02±1.31, t=1.112, P=0.269) between the two groups. Conclusion:The oral-nasal oxygen supply mouth guard is easy to wear and remove, effectively reducing SpO 2 fluctuations during painless gastroscopy for snoring patients. It can enhance medical staff satisfaction with high clinical value.