Dry eye disease is a chronic ocular surface disorder of multifactorial origin, characterized by a loss of tear film homeostasis and associated with a range of ocular discomfort symptoms. Growing evidence underscores a significant bidirectional relationship between dry eye and depression: individuals with dry eye disease exhibit a higher prevalence of depressive disorders, and conversely, those diagnosed with depression demonstrate an increased susceptibility to developing dry eye. This interplay is mediated through several pathophysiological pathways, such as chronic inflammation, cerebral functional alterations, gut microbiome dysregulation, and sleep disturbances, which may collectively sustain a vicious cycle. The use of antidepressant therapy introduces further complexity, exerting heterogeneous effects on dry eye—some agents may offer symptomatic relief, whereas others can aggravate ocular surface impairment. The mechanisms responsible for these differential outcomes remain incompletely elucidated and merit further investigation. This review systematically consolidates epidemiological data on the dry eye-depression link, examines potential shared pathological mechanisms, and evaluates current therapeutic options. We propose an integrated management approach that combines conventional dry eye treatments, such as traditional Chinese medicine, electroacupuncture, physical activity and antidepressants—a multimodal strategy that may yield synergistic benefits in alleviating both ocular and affective symptoms, thereby improving overall quality of life. Moving forward, research should focus on deciphering the underlying mechanistic pathways and facilitating the translation of these insights into clinical practice to inform targeted, combined treatment regimens for patients with dry eye and depression.

Objective:To explore baseline clinical and imaging characteristics of children with neonatal arterial ischaemic stroke based on their long-term motor function prognoses.Methods:A retrospective cohort study was conducted. Clinical data, including magnetic resonance imaging (MRI) and electroencephalogram (EEG) findings, were collected from 31 neonates diagnosed with ischemic stroke admitted to the Department of Neonataology, Maternal and Child Health Hospital of Hubei Province between January 2015 and December 2019. Unified follow-up was conducted between May and July 2024. Long-term motor function outcomes were assessed using the modified Rankin scale(mRS) and categorized into two groups: the good motor function group (mRS score 0-1) and the motor impairment group (mRS score≥2).Baseline clinical and imaging data were summarized for all children, and differences between the two groups were analyzed using the Mann-Whitney U test and Fisher′s exact test. Results:A total of 31 neonates (21 males, 10 females) with an admission age of 1-19 days, all diagnosed within 28 days of birth, were included. At follow-up, 4-8 years after disease onset, 26 neonates (84%) showed good motor function, while 5 (16%) had motor impairments. Compared to the good motor function group, the motor impairment group had higher proportions of females (4/5 vs. 23% (6/26)), main middle cerebral artery (MCA) infarction (4/5 vs. 19% (5/26)), basal ganglia involvement (4/5 vs. 27% (7/26)), corticospinal tract involvement (posterior limb of internal capsule (PLIC) 5/5 vs. 38% (10/26) and cerebral peduncles 5/5 vs. 31% (8/26)) shown by MRI images, and meconium-stained amniotic fluid (4/5 vs. 15% (4/26))(all P<0.05).No significant differences were observed in gestational age, birth weight, abnormalities in muscle tone or primitive reflexes at admission or discharge, or abnormal EEG findings (all P>0.05). Conclusions:Neonatal arterial ischemic stroke commonly manifests as seizures, which are generally controllable, with a relatively stable clinical course and a low incidence of long-term motor impairment. Children with long-term motor impairments are more likely to have main MCA infarction, basal ganglia and corticospinal tract involvement (PLIC and cerebral peduncles), as well as meconium-stained amniotic fluid. This condition is also more commonly observed in females.

Objective:To explore baseline clinical and imaging characteristics of children with neonatal arterial ischaemic stroke based on their long-term motor function prognoses.Methods:A retrospective cohort study was conducted. Clinical data, including magnetic resonance imaging (MRI) and electroencephalogram (EEG) findings, were collected from 31 neonates diagnosed with ischemic stroke admitted to the Department of Neonataology, Maternal and Child Health Hospital of Hubei Province between January 2015 and December 2019. Unified follow-up was conducted between May and July 2024. Long-term motor function outcomes were assessed using the modified Rankin scale(mRS) and categorized into two groups: the good motor function group (mRS score 0-1) and the motor impairment group (mRS score≥2).Baseline clinical and imaging data were summarized for all children, and differences between the two groups were analyzed using the Mann-Whitney U test and Fisher′s exact test. Results:A total of 31 neonates (21 males, 10 females) with an admission age of 1-19 days, all diagnosed within 28 days of birth, were included. At follow-up, 4-8 years after disease onset, 26 neonates (84%) showed good motor function, while 5 (16%) had motor impairments. Compared to the good motor function group, the motor impairment group had higher proportions of females (4/5 vs. 23% (6/26)), main middle cerebral artery (MCA) infarction (4/5 vs. 19% (5/26)), basal ganglia involvement (4/5 vs. 27% (7/26)), corticospinal tract involvement (posterior limb of internal capsule (PLIC) 5/5 vs. 38% (10/26) and cerebral peduncles 5/5 vs. 31% (8/26)) shown by MRI images, and meconium-stained amniotic fluid (4/5 vs. 15% (4/26))(all P<0.05).No significant differences were observed in gestational age, birth weight, abnormalities in muscle tone or primitive reflexes at admission or discharge, or abnormal EEG findings (all P>0.05). Conclusions:Neonatal arterial ischemic stroke commonly manifests as seizures, which are generally controllable, with a relatively stable clinical course and a low incidence of long-term motor impairment. Children with long-term motor impairments are more likely to have main MCA infarction, basal ganglia and corticospinal tract involvement (PLIC and cerebral peduncles), as well as meconium-stained amniotic fluid. This condition is also more commonly observed in females.

Objective:To understand the current status of human resources in Operating Room nursing in China, so as to provide reference for nursing management, human resource allocation, nursing education and training in Operating Rooms.Methods:Using the stratified sampling method, a self-made Operating Room nursing human resource survey questionnaire of Chinese Nursing Society was used as a research tool in July 2021 to investigate the general situation, surgical workload, human resource allocation, Operating Room management, Operating Room information construction, nursing education and training of 2 201 hospitals in 31 provinces, autonomous regions and municipalities of China.Results:Among the 2 201 hospitals, there were 1 021 tertiary hospitals (46.39%), 1 177 secondary hospitals (50.75%), and 63 primary and below hospitals (2.86%). There were 2 056 hospitals with less than 30 Operating Rooms, accounting for 93.41%. There were 1 991 hospitals with an annual number of surgical cases less than 20 000, accounting for 90.46%, the educational background of Operating Room nurses was mainly undergraduate (66.93%, 43 359/64 780), with a total of 67.99% (44 045/64 780) having a bachelor's degree or above. Nurses were the main professional titles (42.66%, 27 632/64 780). Number of Operating Rooms: the number of Operating Room nurses (median) was 1: 2.43 and 78.96% (1 738/2 201) of hospital operating theatres were managed by Nursing Departments or hospitals. A total of 1 479 hospitals (67.20%) established anesthesia recovery rooms in their Operating Rooms, which was higher than 59.34% (1 210 hospitals) surveyed in 2016, and the difference was statistically significant (χ 2=226.701, P<0.01). 74.69% (1 644/2 201) and 87.87% (1 934/2 201) of hospitals carried out post management and capacity classification management in Operating Rooms, respectively. Day surgery and robotic surgery were performed in 47.80% (1 052/201) and 7.68% (169/2 201) hospitals, respectively. 36.98% (814/2 201) of the hospitals passed the information evaluation system certification and 64.61% (1 422/2 201) of the hospitals used the Operating Room information management system. In the Operating Room information system of the hospital, 2.54% (56/2 201) had intelligent functions. And 77.24% (1 700/2 201) of hospitals participated in the qualification training of Operating Room specialist nurses. Conclusions:By July 2021, the number of Operating Rooms in most hospitals in China is less than 30, and the annual number of operating cases is less than 20 000. The educational background and professional title of Operating Room nurses are mainly undergraduate and nurse. More than 60% of hospitals have set up anesthesia recovery rooms and have information management systems for Operating Rooms. At the same time, Operating Rooms in Chinese hospitals have widely implemented diversified nursing management models such as post management and ability grading management.

To develop a rapid differential detection method for porcine epidemic diarrhea virus(PEDV)and transmissible gastroenteritis virus(PEDV),M gene sequences of PEDV and TGEV were compared,the inner and outer primer pairs and probes were designed according to the highly conserved region.PEDV-Probe was labeled with FAM at5'end and BHQ1 at 3'end.TGEV-Probe was labeled with CY5.5 at the 5'end and BHQ2 at the 3'end.After optimizing the reaction condi-tions and system,a dual fluorescent RT-LAMP assay for PEDV and TGEV rapid identification was established.The amplification could be completed within 60 min in a 63 ℃ water bath and then stopped at 85 ℃ for 10 min.Then the tubes were placed in a multicolor imaging system,and the re-sults could be observed under 520 nm and 690 nm dual channels.There was no cross-reaction when other common swine viral pathogens were detected by this method.The sensitivity of the assay was evaluated with a 10-fold diluted recombinant plasmid as templates.The lowest detection limit was 102 copies/&mu;L recombinant plasmid,which was 10 times more sensitive than the conventional RT-PCR method.Seventy-two PEDV-positive samples,49 TGEV-positive samples,and 40 PEDV and TGEV co-infected samples were detected from 175 anal swab samples of diarrheic piglets by the established method,which were all higher than the detection rates of the conventional RT-PCR method.The dual fluorescent RT-LAMP method established in this study can be used to amplify the target gene in an ordinary water bath without gel electrophoresis,which provides technical sup-port for rapid and convenient differential diagnosis of PED and TGE and simultaneous detection of PEDV and TGEV co-infection.

Objective To develop the'E-Bone',a comprehensive one-stop preoperative planning system for reverse total shoulder arthroplasty with improved accuracy and efficiency.Methods The nnU-net deep neural network was utilized for scapula segmentation to obtain precise scapula segmentation results.Based on the 3 key factors,namely bone density,upward and downward angle and nail length,the base was automatically positioned.The quantitative parameters required for surgical planning were calculated.A personalized guide plate was generated by combining glenoid morphology and base positioning information.The system interface was developed to modularize various functions for easy use,providing interactive operation and real-time display.Results Compared with the Mimics system,the'E-bone'preoperative planning system reduced complex manual adjustments during the planning process.The average planned nail length was longer than that of the Mimics system,and the planning time was reduced by 86%.The scapula segmentation accuracy of this system reached 99.93%,better than that of Mimics to achieve a higher precision.Conclusion The"E-bone"system provides a one-stop,efficient,and accurate preoperative planning system for reverse shoulder replacement and potentially broader clinical applications.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.