AIM:To investigate the effect of apoptosis stimulation protein 2(ASPP2)on the development of traumatic proliferative vitreoretinopathy(PVR)in a rabbit model.METHODS:A total of 30 New Zealand white rabbits were selected, and the right eyes of all rabbits were inflicted with a scleral penetrating wound of approximately 6 mm. Then rabbits were randomly and evenly divided into experimental and control group. The experimental group received an intravitreal injection of 0.1 mL of ARPE-19 cell suspension transfected with lentivirus-ASPP2, while the control group received an intravitreal injection of 0.1 mL of ARPE-19 cell suspension transfected with negative control lentivirus. At 1, 2, 3, and 4 wk after PVR modeling, a handheld tonometer was used to measure the intraocular pressure. Moreover, fundus photography and ocular ultrasound examination were performed to detect the retinal proliferation. At 4 wk after modeling, hematoxylin-eosin staining was used to observe the morphological retinal changes, and Western blot was used to determine the protein expressions of ASPP2 and the epithelial-mesenchymal transition(EMT)marker Vimentin in the rabbit retinas.RESULTS:At 1, 2, 3, and 4 wk after modeling, there were no significant changes in intraocular pressure within the experimental and control group of rabbit eyes, either before or after PVR modeling, the success rate of PVR modeling in the experimental group was lower than that in the control group(P<0.05), and the retinal proliferation and structural disorder was less severe in the experimental group. At 4 wk after modeling, the retinal protein expression level of ASPP2 in the experimental group was significantly higher than that in the control group(t=3.193, P=0.033), while the Vimentin protein expression level was significantly lower in the experimental group(t=-3.599, P=0.023).CONCLUSION:ASPP2 may be involved in regulating the process of EMT in retinal pigment epithelial cells, thereby delaying the development and progression of traumatic PVR in rabbit eyes.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Small intestinal bacterial overgrowth（SIBO）is a clinically common but poorly recognized disease with clinical symptoms that overlap with those of functional gastrointestinal disorders（FGID）. FGID is a common risk factor for the occurrence of SIBO，and its SIBO incidence rate is significantly higher than that of healthy people，and has a certain correlation. Intestinal microbiota dysbiosis（including SIBO）plays an important role in the pathophysiology of FGID. At present，the diagnosis and treatment of SIBO still face challenges，and the treatment plan of FGID is gradually introduced into the treatment of SIBO. SIBO and FGID are closely related in clinical manifestations，incidence rate，pathophysiology，treatment and other aspects，but there is little research on the relationship between them. Prospective and large-scale clinical research is still needed to improve understanding and diagnostic accuracy.This article reviews the research progress on the relationship between SIBO and FGID.

ObjectiveTo investigate the antidepressant effects and mechanisms of total flavonoids from Cuscutae Semen （TFCC） in the mouse model of chronic unpredictable mild stress （CUMS）. MethodsFifty male 4-week-old ICR mice were randomized into five groups （n=10 per group）： blank control， model， Cuscutae Semen decoction （10.2 g·kg^-1·d^-1）， paroxetine （2.6 mg·kg^-1·d^-1）， and TFCC （173.2 mg·kg^-1·d^-1）. The other groups except the blank control group underwent chronic unpredictable mild stress （CUMS） for 4 weeks. Behavioral assessments were conducted post-modeling. Then， the model group received distilled water （10 mL·kg^-1·d^-1）， while treatment groups were administrated with respective agents via oral gavage （10 mL·kg^-1） for 4 weeks. Depression-like behaviors were evaluated by the sucrose preference test （SPT）， forced swimming test （FST）， and tail suspension test （TST）. Hippocampal neuronal morphology was observed via hematoxylin-eosin staining， and apoptosis in the brain tissue was assessed via terminal- deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling （TUNEL）. Enzyme-linked immunosorbent assay （ELISA） was employed to measure the hippocampal levels of inflammatory cytokines ［interleukin （IL）-1β， IL-6， and TNF-α）］ and neurotransmitters ［5-hydroxytryptamine （5-HT）， dopamine （DA）， and brain-derived neurotrophic factor （BDNF）］， while the reactive oxygen species （ROS） levels were quantified via the DCFH-DA probe. Real-time PCR was performed to measure the mRNA levels of NOD-like receptor protein 3 （NLRP3）， apoptosis-associated Speck-like protein containing a CARD （ASC）， cysteinyl aspartate-specific proteinase-1 （Caspase-1）， IL-1β， and inducible nitric oxide synthase （iNOS）. Western blot was employed to evaluate the protein levels of NLRP3， ASC， Caspase-1， uncoupling protein 2 （UCP2）， and thioredoxin-interacting protein （TXNIP）. ResultsCompared with the blank control group， the model group exhibited weight loss （P<0.01）， reduced sucrose preference （P<0.01）， prolonged immobility time in FST and TST （P<0.01）， neuron disarrangement with nuclear pyknosis in hippocampal CA3 region， increased apoptosis in the brain tissue， elevated levels of IL-1β， IL-6， and TNF-α （P<0.01）， declined levels of 5-HT， DA， and BDNF （P<0.01）， increased ROS accumulation （P<0.01）， upregulated mRNA levels of NLRP3， ASC， Caspase-1， IL-1β， and iNOS （P<0.01）， down-regulated protein level of UCP2 （P<0.01）， and up-regulated protein levels of NLRP3， ASC， Caspase-1， and TXNIP （P<0.01）. Compared with the model group， the interventions restored sucrose preference （P<0.01）， shortened immobility time （P<0.01）， repaired hippocampal neuronal structure， reduced apoptosis， lowered the levels of inflammatory cytokines （P<0.01）， restored the levels of neurotransmitters （P<0.01）， alleviated ROS accumulation （P<0.01）， downregulated the mRNA levels of NLRP3， ASC， Caspase-1， IL-1β， and iNOS （P<0.01）， upregulated the protein level of UCP2 （P<0.01）， and reduced the protein levels of NLRP3， ASC， Caspase-1， and TXNIP （P<0.01）. Moreover， TFCC outperformed Cuscutae Semen decoction in ameliorating depressive behaviors. TFCC excelled in neuronal repair， neurotransmitter regulation， anti-inflammatory effects， and modulation of the UCP2/TXNIP/NLRP3 pathway （P<0.05）. ConclusionTFCC modulates the hippocampal UCP2/TXNIP/NLRP3 pathway to inhibit inflammasome activation， reduce oxidative stress， restore neurotransmitters， thus suppressing neuronal apoptosis and promoting the rearrangement and morphology recovery of hippocampal cells. It outperforms Cuscutae Semen decoction in the antidepressant efficacy.

ObjectiveTo investigate the antidepressant effects and mechanisms of total flavonoids from Cuscutae Semen （TFCC） in the mouse model of chronic unpredictable mild stress （CUMS）. MethodsFifty male 4-week-old ICR mice were randomized into five groups （n=10 per group）： blank control， model， Cuscutae Semen decoction （10.2 g·kg^-1·d^-1）， paroxetine （2.6 mg·kg^-1·d^-1）， and TFCC （173.2 mg·kg^-1·d^-1）. The other groups except the blank control group underwent chronic unpredictable mild stress （CUMS） for 4 weeks. Behavioral assessments were conducted post-modeling. Then， the model group received distilled water （10 mL·kg^-1·d^-1）， while treatment groups were administrated with respective agents via oral gavage （10 mL·kg^-1） for 4 weeks. Depression-like behaviors were evaluated by the sucrose preference test （SPT）， forced swimming test （FST）， and tail suspension test （TST）. Hippocampal neuronal morphology was observed via hematoxylin-eosin staining， and apoptosis in the brain tissue was assessed via terminal- deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling （TUNEL）. Enzyme-linked immunosorbent assay （ELISA） was employed to measure the hippocampal levels of inflammatory cytokines ［interleukin （IL）-1β， IL-6， and TNF-α）］ and neurotransmitters ［5-hydroxytryptamine （5-HT）， dopamine （DA）， and brain-derived neurotrophic factor （BDNF）］， while the reactive oxygen species （ROS） levels were quantified via the DCFH-DA probe. Real-time PCR was performed to measure the mRNA levels of NOD-like receptor protein 3 （NLRP3）， apoptosis-associated Speck-like protein containing a CARD （ASC）， cysteinyl aspartate-specific proteinase-1 （Caspase-1）， IL-1β， and inducible nitric oxide synthase （iNOS）. Western blot was employed to evaluate the protein levels of NLRP3， ASC， Caspase-1， uncoupling protein 2 （UCP2）， and thioredoxin-interacting protein （TXNIP）. ResultsCompared with the blank control group， the model group exhibited weight loss （P<0.01）， reduced sucrose preference （P<0.01）， prolonged immobility time in FST and TST （P<0.01）， neuron disarrangement with nuclear pyknosis in hippocampal CA3 region， increased apoptosis in the brain tissue， elevated levels of IL-1β， IL-6， and TNF-α （P<0.01）， declined levels of 5-HT， DA， and BDNF （P<0.01）， increased ROS accumulation （P<0.01）， upregulated mRNA levels of NLRP3， ASC， Caspase-1， IL-1β， and iNOS （P<0.01）， down-regulated protein level of UCP2 （P<0.01）， and up-regulated protein levels of NLRP3， ASC， Caspase-1， and TXNIP （P<0.01）. Compared with the model group， the interventions restored sucrose preference （P<0.01）， shortened immobility time （P<0.01）， repaired hippocampal neuronal structure， reduced apoptosis， lowered the levels of inflammatory cytokines （P<0.01）， restored the levels of neurotransmitters （P<0.01）， alleviated ROS accumulation （P<0.01）， downregulated the mRNA levels of NLRP3， ASC， Caspase-1， IL-1β， and iNOS （P<0.01）， upregulated the protein level of UCP2 （P<0.01）， and reduced the protein levels of NLRP3， ASC， Caspase-1， and TXNIP （P<0.01）. Moreover， TFCC outperformed Cuscutae Semen decoction in ameliorating depressive behaviors. TFCC excelled in neuronal repair， neurotransmitter regulation， anti-inflammatory effects， and modulation of the UCP2/TXNIP/NLRP3 pathway （P<0.05）. ConclusionTFCC modulates the hippocampal UCP2/TXNIP/NLRP3 pathway to inhibit inflammasome activation， reduce oxidative stress， restore neurotransmitters， thus suppressing neuronal apoptosis and promoting the rearrangement and morphology recovery of hippocampal cells. It outperforms Cuscutae Semen decoction in the antidepressant efficacy.

Kinesiophobia is prevalent among school-aged children with asthma,and severe kinesiophobia can hinder their participation in physical activities and disease control,adversely affecting their physical and mental health.This paper reviews the concept,development,assessment tools,influencing factors,and intervention strategies of kinesiophobia in school-aged children with asthma,analyzes existing challenges,and proposes directions for future research.It aims to provide insights for developing scientific and personalized intervention plans to enhance children's exercise levels and improve their quality of life.

Objective To explore the mechanism of the thioredoxin-interacting protein(TXNIP)/thioredoxin-1(Trx-1)pathway in regulating the polarization of retinal microglia in rats after retinal ischemia-reperfusion(RIR)in rats,and to provide new ideas for the prevention and treatment of retinal ischemia reperfusion injury(RIRI).Methods For-ty-two healthy adult male Sprague-Dawley rats were randomly divided into a Sham group,a RIRI group and a TXNIP siRNA group.The right eye of the rats was experimented.For RIRI and TXNIP siRNA groups,RIRI models were established using the anterior chamber high intraocular pressure method.Rats in the TXNIP siRNA group were given the intravitreal injection of TXNIP siRNA 3 d before modeling.Hematoxylin-eosin(HE)staining was used to analyze retinal histopathologic changes of rats in all groups 24 h after modeling.Immunohistochemical staining of brain-specific homeobox/POU domain proteins 3A(Brn-3a)was made to count the number of retinal ganglion cells(RGCs).The dynamical changes in the number of TXNIP+cells 6 h,24 h,72 h and 7 d after modelling were analyzed through immunohistochemical staining in the RIRI group.The retinal microglia polarization and changes in the expression of TXNIP and Trx-1 proteins in each group were de-tected by double immunofluorescence staining and Western blot 24 h after modeling.Results HE staining results showed that 24 h after modelling,the retinal cells were disordered and the inner retinal layer was thickened and swelled in RIRI and TXNIP siRNA groups,compared with those in the Sham group(all P＜0.05).Immunohistochemical staining results of Brn-3a showed that 24 h after modeling,the number of Brn-3a+cells in RIRI and TXNIP siRNA groups significantly decreased,compared with that in the Sham group(both P＜0.05).The number of Brn-3a+cells in the TXNIP siRNA group was signifi-cantly higher than that in the RIRI group(P＜0.05).Immunohistochemical staining results of TXNIP at different time points after modeling showed that the expression of TXNIP+proteins started to increase 6 h after modeling.The TXNIP+protein level reached a peak at 24 h and then decreased gradually.Western blot results revealed that 24 h after modeling,RIRI and TXNIP siRNA groups had significantly higher TXNIP levels and significantly lower Trx-1 levels than the Sham group(all P＜0.05).Compared with those in the RIRI group,the expression of TXNIP proteins was significantly lower and the expression of Trx-1 proteins was significantly higher in the TXNIP siRNA group(both P＜0.05).Double immunofluores-cence staining showed that 24 h after modeling,Iba1+/CD206+cells were significantly more and Iba1+/CD16+cells were significantly less in the TXNIP siRNA group than those in the RIRI group(both P＜0.05).RIRI and TXNIP siRNA groups had significantly more Ibal+/TXNIP+cells and significantly less Iba1+/Trx-1+cells than the Sham group(both P＜0.05).The number of Iba1+/TXNIP+cells was significantly lower and the number of Iba1+/Trx-1+cells was significantly higher in the TXNIP siRNA group than those in the RIRI group(both P＜0.05).Conclusion RIR activates the TXNIP/Trx-1 path-way to induce the activation of retinal microglia and regulate the polarization of microglia,thereby resulting in RIRI in rats.