Altered functional connectivity of primary visual cortex in adults with strabismus and amblyopia: a resting-state fMRI study.

Functional connectivity of the primary visual cortex was explored with resting functional magnetic resonance imaging among adults with strabismus and amblyopia and healthy controls. We used the two-sample test and receiver operating characteristic curves to investigate the differences in mean functional connectivity values between the groups with strabismus and amblyopia and healthy controls. Compared with healthy controls, functional connectivity values in the left Brodmann areas 17, including bilateral lingual/angular gyri, were reduced in groups with strabismus and amblyopia. Moreover, functional connectivity values in the right Brodmann area 17, including left cuneus, right inferior occipital gyrus, and left inferior parietal lobule, were reduced in adults with strabismus and amblyopia. Our findings indicate that functional connectivity abnormalities exist between the primary visual cortex and other regions. This may be the basis of the pathological mechanism of visual dysfunction and stereovision disorders in adults with strabismus and amblyopia.

Video display terminal use and other risk factors for abnormal blinking in children: gender differences.

OBJECTIVE: To explore the risk factors for abnormal blinking in children and compare these between boys and girls. METHODS: Children attending the Children's Optometry Clinic between June 2019 and June 2020 were recruited for the study. The time they had spent viewing video displays (VDTt) over the past 6 months was recorded. Incomplete blinking (IB) and blinking rate were measured and all participants were allocated to groups based on their blink rate (<20 times/min = normal blinking group, NBG; ≥20 times/min = abnormal blinking group, ABG). Tear film (TF) stability was also evaluated. The corresponding statistical methods are used to analyze the data. RESULTS: A total of 87 boys and 80 girls were enrolled in the study. No significant difference in age was found between the 2 groups. There was a significant difference in TF stability between the two groups (P<0.05). According to binary logistic analysis, VDTt and ocular protection index (OPI) are important risk factors for abnormal blinking, with cut-off values of 1.75 hours and 1.014 respectively in boys; and 1.25 hours and 1.770 respectively in girls. The average of lipid layer thickness was an important protective factor for children using VDT for long periods, with a cut-off value of 58.5 nm in boys and 53.5nm in girls. CONCLUSION: Risk factors for abnormal blinking in both boys and girls include VDTt and OPI.

Abnormal Fractional Amplitude of Low-Frequency Fluctuation Changes in Patients with Monocular Blindness: A Functional Magnetic Resonance Imaging (MRI) Study.

BACKGROUND We used fractional amplitude of low-frequency fluctuation (fALFF) technology to investigate spontaneous cerebral activity in patients with monocular blindness (MB) and in healthy controls (HCs). MATERIAL AND METHODS Thirty MB patient and 15 HCs were included in this study. All subjects were scanned by resting-state functional magnetic resonance imaging (rs-fMRI). The independent sample t test and chi-squared test were applied to analyze demographics of MB patients and HCs. The 2-sample t test and receiver operating characteristic (ROC) curves were applied to identify the difference in average fALFF values between MB patients and HCs. Pearson's correlation analysis was applied to explore the relationship between the average fALFF values of brain areas and clinical behavior in the MB group. RESULTS MB patients had lower fALFF values in the left anterior cingulate and higher fALFF values in the left precuneus and right and left inferior parietal lobes than in HCs. Moreover, the mean fALFF values of MB patients in the left anterior cingulate had negative correlations with the anxiety scale score (r=-0.825, P<0.001) and the depression scale score (r=-0.871, P<0.001). CONCLUSIONS Our study found that MB patients had abnormal spontaneous activities in the visual and vision-related regions. The finding of abnormal neuronal activity helps to reveal the underlying neuropathologic mechanisms of vision loss.

Altered Brain Network Centrality in Patients with Adult Strabismus with Amblyopia: A Resting-State Functional Magnetic Resonance Imaging (fMRI) Study.

BACKGROUND The aim of this study was to explore potential changes in brain function network activity in patients with adult strabismus with amblyopia (SA) using the voxel-wise degree centrality (DC) method. MATERIAL AND METHODS We enrolled 15 patients with SA (6 males, 9 females) and 15 sex-matched healthy controls (HCs). All subjects completed resting functional magnetic resonance imaging scans. Independent-sample t tests and receiver operating characteristic (ROC) curves were used to assess DC value differences between groups, and Pearson correlation analysis was performed to evaluate correlations between DC-changed brain regions and clinical data of patients with SA. RESULTS Compared with the HC group, DC values that were lower in patients with SA included the left middle frontal gyrus and bilateral angular gyri. Increases were observed in the left fusiform gyrus, right lingual gyrus, right middle occipital gyrus, right postcentral gyrus, and left paracentral lobule. However, DC values were not correlated with clinical manifestations. ROC curve analysis showed high accuracy. CONCLUSIONS We found abnormal neural activity in specific brain regions in patients with SA. Specifically, we observed significant changes in DC values compared to HCs. These changes may be useful to identify the specific mechanisms involved in brain dysfunction in SA.

[Protective effect of astragalus saponin extracts on kidneys of diabetic rats].

To study the protective effect of astragalus saponin extracts (AS) on kidneys of diabetic rats. Totally 32 diabetic rats induced by streptozotocin (STZ) were divided into AS high and low dose groups, the positive control group and the model group (DM group) and orally administered with 50 mg x- kg(-1) x d(-1) AS 200, 25 mg x kg(-1) x d(-1) valsartan, 10 mL x kg(-1) x d(1) physiological saline, respectively. Another 8 healthy rats were collected in the normal control group (NC group, physiological saline 10 mL x kg(-1). d(-1)). All rats were treated for consecutively 6 weeks. After the administration, the body weight was measured every week, the concentration of blood glucose was monitored on week 2, 4 and 6. The total urine and total urinary protein (U-TP) in 24 h were measured by the metabolic cage method on week 6; At the end of week 6, blood samples were collected from hearts to detect blood urea nitrogen (BUN), serum creatinine (Scr), uric acid (UA) , total cholesterol (CH) triglyceride (TG) by biochemical methods. Kidneys were collect to calculate the kidney hypertrophy index and observe the pathological sections. The laboratory results show that in the DM group, the blood glucose, metabolic cost in 24 h, kidney hypertrophy index, U-TP, BUN, Scr, UA, TG were significantly higher than that in the NC group (P < 0.01, P < 0.05) , with significant pathological changes; After the intervention with AS, the metabolic value in 24 h, kidney hypertrophy index, U-TP, BUN, Scr, UA, TG were significantly lower in the high dose group (P < 0.01, P < 0.05), and the kidney hypertrophy index, BUN, Scr, UA, TG in the low dose group were also significantly lower (P < 0.05), with slight reduction in renal pathological changes in both groups. In conclusion, Astragalus saponin extracts have a certain protective effect on kidneys of diabetic rats.

Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.

Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.

PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .

Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke.

Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100ß, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100ß, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100ß, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance.

Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study.

BACKGROUND: Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). METHODS: We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM. RESULTS: Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA. CONCLUSION: We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.

Lipidomics Profiles Revealed Alterations in Patients With Meibomian Gland Dysfunction After Exposure to Intense Pulsed Light.

So far, intense pulsed light (IPL) has been widely used in the treatment of meibomian gland dysfunction (MGD), but there was still a lack of research on its specific mechanism. Determining whether there was a correlation between liposome changes and remission of clinical signs in patients with MGD treated with IPL was of great significance in the clinical evaluation of efficacy in patients with MGD. Our study enrolled the 10 healthy subjects and 26 adult patients, who were diagnosed with MGD and had not received any alternative treatments for at least 3 months. Each patient received a series of three treatments at 3-week intervals. The meibum was collected before the first treatment (T0) and the third treatment (T2). The significant changes in ocular surface parameters before and after IPL treatment were analyzed. The results showed that IPL significantly improved the symptoms of MGD, including ocular surface disease index (OSDI), tear breakup time (TBUT), redness of conjunctival (CR), corneal fluorescein staining (CF), the meibomian gland expressibility (MGE), and meibum quality (all p < 0.05). Lipidomics analysis of the meibum characterized the changes in lipid profiles induced by IPL. A total of 323 lipid species compounds were identified in the spectrum. A total of 41 lipid species were significantly different in patients with MGD (T0) vs. healthy controls. Following IPL treatment (T2), 24 lipid species were significantly different compared with T0: TG (10 lipid species), LPC (6 lipid species), OAHFA (4 lipid species), Cer (2 lipid species), SM (1 lipid species), and PE (1 lipid specie). Among these lipids, 4 of the lipids was a high correlation with TBUT, 5 was TH, 6 was CR, and 11 was meibum quality. In a ward, IPL treatment can achieve the therapeutic effect by changing the alternations of tear film lipids in patients with MGD. The changes in lipid expression profiles are potential indexes to evaluate the therapeutic effectiveness of IPL treatment or other treatments on MGD.

Prognostic potential of miR-144 in various cancers: A meta-analysis.

BACKGROUND: MicrorNA-144 (MiR-144) has been shown to be an attractive prognostic tumor biomarker and play a fundamental role in various cancers, However, the conclusion was inconsistency. The aim of this study was to identify the prognostic role of miR-144 in cancers. METHODS: Relevant studies were searched in PubMed, EMBASE and Web of Science up to April 20, 2022. Hazard ratios (HR), odds ratio (OR) and 95% confidence intervals were pooled from the selected studies. RESULTS: A total of 15 articles involving 1846 participants fulfilled the inclusion criteria. The results revealed that low miR-144 expression was significantly associated with favorable overall survival (HR: 0.68, 95% confidence interval [CI]: 0.53-0.88) in various cancers. Low miR-144 expression had better predictive value in patients with urinary system cancer (HR: 0.48, 95% CI: 0.35-0.64). In addition, low miR-144 expression was associated with tumor diameter (big vs small) (OR: 1.69, 95% CI: 1.08-2.75), tumor stage (III-IV vs I-II) (OR: 2.52, 95% CI: 3.76-8.14) and invasion depth (T3 + T4 vs T2 + T1) (OR: 3.24, 95% CI: 1.72-4.89). CONCLUSION: miR-144 may serve as a prognostic biomarker in cancers.

Characteristic of red eye related diseases of Han and Uygur population in Urumchi compared with Shanghai, China.

AIM: To study the characteristics, relative distribution and to compare causes of red eye in ophthalmic clinics in Urumchi and Shanghai, China. METHODS: Data on continuous cases of red-eye patients admitted to the Ophthalmology Center of Xinhua Hospital Affiliated to Shanghai Jiao Tong University and the First Affiliated Hospital of Xinjiang Medical University were collected between November 2018 and September 2019. Demographic data, the incidence of red eye and related disease distribution of all cases were obtained. The independent t-test method was used for age comparison, while the Chi-square test was used to compare classified data information. RESULTS: The information on 335 and 415 patients with red eyes in Shanghai and Urumchi were collected, respectively. The main causes of red eye were conjunctival disease and dry eye. The age of female patients with red eyes was significantly higher than that of males, and the proportion of female patients with dry eyes was also higher. Red-eye-related diseases occurred more frequently in patients over 46 years old than in those under 18, and dry eye was more common with increasing age. The incidence of infectious conjunctivitis in Urumchi was significantly higher than that in Shanghai, and allergic conjunctivitis occurred more frequently in spring, summer, or autumn than in winter (all P<0.05). CONCLUSION: Significant differences exist in the distribution of red-eye-related diseases in Urumchi and Shanghai regions of China, and distribution varies with age and season, the latter being an important feature of allergic conjunctivitis.

Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients.

Breast cancer (BC) was the most common malignant tumor in women, and breast infiltrating ductal carcinoma (IDC) accounted for about 80% of all BC cases. BC patients who had bone metastases (BM) were more likely to have poor prognosis and bad quality of life, and earlier attention to patients at a high risk of BM was important. This study aimed to develop a predictive model based on machine learning to predict risk of BM in patients with IDC. Six different machine learning algorithms, including Logistic regression (LR), Naive Bayes classifiers (NBC), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme gradient boosting (XGB), were used to build prediction models. The XGB model offered the best predictive performance among these 6 models in internal and external validation sets (AUC: 0.888, accuracy: 0.803, sensitivity: 0.801, and specificity: 0.837). Finally, an XGB model-based web predictor was developed to predict risk of BM in IDC patients, which may help physicians make personalized clinical decisions and treatment plans for IDC patients.

Brain Activity Changes in Slow 5 and Slow 4 Frequencies in Patients With Optic Neuritis: A Resting State Functional MRI Study.

Objective: We used the amplitude of low-frequency fluctuation (ALFF) method to investigate spontaneous brain activity in patients with optic neuritis (ON) in specific frequency bands. Data and Methods: A sample of 21 patients with ON (13 female and eight male) and 21 healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) scans in the resting state. We analyzed the ALFF values at different frequencies (slow-4 band: 0.027-0.073 Hz; slow-5 band: 0.01-0.027 Hz) in ON patients and HCs. Results: In the slow-4 frequency range, compared with HCs, ON patients had apparently lower ALFF in the insula and the whack precuneus. In the slow-5 frequency range, ON patients showed significantly increased ALFF in the left parietal inferior and the left postcentral. Conclusion: Our results suggest that ON may be involved in abnormal brain function and can provide a basis for clinical research.

Altered regional homogeneity of spontaneous brain activity in patients with toothache: A resting-state functional magnetic resonance imaging study.

Toothache (TA) is a common and severe pain, but its effects on the brain are somewhat unclear. In this study, functional magnetic resonance imaging (fMRI) was used to compare regional homogeneity (ReHo) between TA patients and a normal control group and to explore the brain activity changes during TA, establishing the theoretical basis for the mechanism of neuropathic pain. In total, 20 TA patients and 20 healthy controls (HCs) were recruited and underwent assessment of pain, and then resting-state fMRI (rs-fMRI). The ReHo method was used to analyze the original whole-brain images. Pearson's correlation analysis was used to assess the relationship between mean ReHo values in each brain region and clinical symptoms, and the receiver operating characteristic (ROC) curve was used to conduct correlation analysis on the brain regions studied. The ReHo values of the right lingual gyrus (RLG), right superior occipital gyrus (RSOG), left middle occipital gyrus (LMOG) and right postcentral gyrus (RPG) in the TA group were significantly higher than in HCs. The mean ReHo values in the RLG were positively correlated with the anxiety score (AS) (r = 0.723, p < 0.001), depression score (DS) (r = 0.850, p < 0.001) and visual analogue score (VAS) (r = 0.837, p < 0.001). The mean ReHo values of RSOG were also positively correlated with AS (r = 0.687, p = 0.001), DS (r = 0.661, p = 0.002) and VAS (r = 0.712, p < 0.001). The areas under the ROC curve of specific brain area ReHo values were as follows: RLG, 0.975; RSOG, 0.959; LMOG, 0.975; RPG, 1.000. Various degrees of brain activity changes reflected by ReHo values in different areas of the brain indicate the impact of TA on brain function. These findings may reveal related neural mechanisms underlying TA.

Potential Factors of Corneal Endothelial Cells for Progression in Children with Uveitis.

OBJECTIVE: To observe the morphological changes and abnormal structure of corneal endothelial cells in children with uveitis, to analyze the related factors affecting the morphological changes of corneal endothelial cells, and to explore the clinical application of a corneal endothelial microscope in children with uveitis. METHODS: The corneal endothelial cells of 70 patients with uveitis were photographed with the Topcon SP-3000 noncontact corneal endothelial microscope, and the corneal endothelial cell density (CD), average cell area (AVE), coefficient of variation of the cell area (CV), and percentage of hexagonal cells (PHC) were measured with the IMAGEnet system. Twenty-eight patients (56 eyes) with monocular uveitis were selected, with the affected eyes (28 eyes) as the experimental group and the contralateral healthy eyes (28 eyes) as the control group. The corneal endothelial cell parameters between the two groups were statistically analyzed. The parameters of corneal endothelial cells in 70 children with uveitis were compared, and the effects of the course of the disease, inflammatory cells in the anterior chamber, and posterior corneal deposition (KP) on the parameters of corneal endothelial cells were analyzed. RESULTS: There are four abnormal forms of the corneal endothelium in children with uveitis: enlarged cell area gap, irregular cell shape, blurred intercellular space, and cell loss. KP showed irregular high reflective white spots in the corneal endothelial microscope images, surrounded by dark areas, and existed in all the eyes with dusty KP found in slit lamp examination and a small number of eyes without obvious KP. Comparing the corneal endothelial cell parameters between the experimental group and the control group, it was found that the corneal endothelial CD and PHC of the former were lower than those of the latter, and the difference was statistically significant (P < 0.001 and P = 0.018, respectively). The AVE and CA of the former were higher than those of the latter (P = 0.013 and P = 0.046, respectively). The corneal endothelial cell density of the eyes with a course of the disease of more than 1 year was lower than that of the eyes with a course of the disease less than 1 year, the coefficient of variation of the corneal endothelial cell area of the eyes with KP was higher than that of the eyes without KP, and the difference was statistically significant (P = 0.003 and P = 0.030, respectively). CONCLUSION: Corneal endothelial microscopy is one of the important methods for the detection of uveitis with high sensitivity. The change of morphological parameters of corneal endothelial cells is one of the important indexes to assist in the diagnosis of uveitis and can be further promoted in ophthalmological examination.

Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy.

Background: In recent years, deep learning has been widely used in a variety of ophthalmic diseases. As a common ophthalmic disease, meibomian gland dysfunction (MGD) has a unique phenotype in in-vivo laser confocal microscope imaging (VLCMI). The purpose of our study was to investigate a deep learning algorithm to differentiate and classify obstructive MGD (OMGD), atrophic MGD (AMGD) and normal groups. Methods: In this study, a multi-layer deep convolution neural network (CNN) was trained using VLCMI from OMGD, AMGD and healthy subjects as verified by medical experts. The automatic differential diagnosis of OMGD, AMGD and healthy people was tested by comparing its image-based identification of each group with the medical expert diagnosis. The CNN was trained and validated with 4,985 and 1,663 VLCMI images, respectively. By using established enhancement techniques, 1,663 untrained VLCMI images were tested. Results: In this study, we included 2,766 healthy control VLCMIs, 2,744 from OMGD and 2,801 from AMGD. Of the three models, differential diagnostic accuracy of the DenseNet169 CNN was highest at over 97%. The sensitivity and specificity of the DenseNet169 model for OMGD were 88.8 and 95.4%, respectively; and for AMGD 89.4 and 98.4%, respectively. Conclusion: This study described a deep learning algorithm to automatically check and classify VLCMI images of MGD. By optimizing the algorithm, the classifier model displayed excellent accuracy. With further development, this model may become an effective tool for the differential diagnosis of MGD.

Mean Tear-Film Lipid Layer Thickness and Video Display Terminal Time as Risk Factors for Abnormal Blinking in Children.

Objective: To explore the risk factors for abnormal blinking in children and the role of the tear-film lipid layer thickness (LLT) as a function of duration of video display terminal (VDT) use in children. Methods: Children attending the Optometry Clinic of Xinhua Hospital affiliated with Shanghai Jiao Tong University were recruited for the study between June 2019 and June 2020. Time spent viewing a VDT (VDTt) over the previous 6 months was recorded. Incomplete blinking (IB) and blinking rate were measured over a 10 s period using the Lipiview® interferometer (Tear Science, Morrisville, NC, USA), and participants were allocated into groups with normal blinking (NBG, blink rate < 20 blinks/min) and abnormal blinking (ABG, blink rate ≥ 20 blinks/min). T-test, chi-square test and Mann-Whitney U-test were used to compare the differences in tear film (TF) stability indexes and meibomian gland function indexes between the two groups. Binary logistic analysis was used to analyze the risk factors for abnormal blinking and protective factors related to children's use of VDT, and receiver operating characteristic (ROC) curve analysis was also conducted. Results: A total of 167 children were included, with no statistically significant differences in age or sex between the two groups. According to the t-test, VDTt was significantly higher in ABG than NBG, while TF stability indices including tear break up time, LLT and the height of the tear meniscus, were significantly higher in NBG than ABG (P < 0.001). The results also showed better meibomian gland function in NBG than ABG (P < 0.05). Binary logistic analysis showed that VDTt is an important risk factor for abnormal blinking, and the average of LLT (AVG) was found to be an important protective factor for children using a VDT for long periods, with a cut-off value of 1.5 h and 57.5 nm, respectively. ROC curve analysis showed that the area under the curve value of VDTt and AVG was 0.833 and 0.969, respectively (P < 0.001). Conclusion: In children, VDTt is an important risk factor for abnormal blinking, and the AVG is an important protective factor for children using VDT for long periods.

Fractional amplitude of low-frequency fluctuation in patients with neovascular glaucoma: a resting-state functional magnetic resonance imaging study.

BACKGROUND: Neovascular glaucoma (NVG) is a secondary refractory disease with a poor prognosis, and there are few advanced studies on its pathogenesis and treatment. In this research, the fractional amplitude of low-frequency fluctuation (fALFF) technology was used in resting-state functional magnetic resonance imaging (rsfMRI) to investigate intrinsic neuron activity in the patient's brain with NVG. METHODS: Sixteen patients with NVG (eight males and eight females) and 16 healthy controls (HCs) of similar age and sex were included. All patients and controls received rsfMRI scans, and the differences between the two groups in fALFF values were compared by independent sample t-test. Receiver operating characteristic (ROC) curves were used to compare fALFF values in the brain regions of NVG patients and HCs and assess accuracy. Finally, Pearson linear correlation analysis assessed the correlation between fALFF signals in brain regions and the clinical evaluation indicators of patients with NVG. RESULTS: In patients with NVG, fALFF signal values in the right Rolandic operculum, left anterior cingulate and paracingulate gyri, and right caudate were significantly decreased. In contrast, fALFF signal values in the left precuneus were significantly higher than those recorded in the HCs. Analysis of the ROC curve for each brain region showed that the area under the ROC curve of NVG patients was large (close to 1), and the accuracy was good. In the NVG group, the hospital anxiety and depression scale (r=-0.952, P<0.001) and left best-corrected visual acuity (r=-0.802, P<0.001) had a negative linear correlation with the fALFF signal value of the right Rolandic operculum. The hospital anxiety and depression scale had a negative linear correlation with the fALFF signal value of the right caudate (r=-0.948, P<0.001). CONCLUSIONS: NVG patients showed dysfunction in several brain regions. These findings may assist in revealing the underlying neural mechanism of brain activity associated with NVG.

Alternation of brain intrinsic activity in patients with hypertensive retinopathy: a resting-state fMRI study.

OBJECTIVE: To investigate the changes of amplitude of low-frequency fluctuation (ALFF) in brain regions of patients with hypertensive retinopathy by using resting-state functional magnetic resonance imaging (rs-fMRI) and change in the relationship of ALFF value with potential emotional and psychological changes. METHODS: Thirty-one patients with hypertensive retinopathy (HR) (16 men and 15 women) and 31 healthy controls (HCs; 16 men and 15 women) matched for age, sex, and weight were enrolled in the research. The changes in mean ALFF values could reflect brain activity between HR patients and HCs. We used the independent samples t-test to evaluate different demographic and general information between the two groups. Two-sample t-test was used to detect differences of mean ALFF values in the brain region between the two groups using the same software. RESULTS: The ALFF values in the brain areas of HR and HCs were different. HR patients had lower ALFF value in the left medial superior frontal gyrus and left middle frontal gyrus than the HCs. The higher ALFF values were found in the cerebellum (left inferior and right superior lobes, vermis) and left inferior temporal gyrus of the HR patients than the controls. CONCLUSION: Our findings showed fluctuations in ALFF values in the HR patients' brain regions. ALFF values reflect over or reduced activity in brain regions. Abnormal ALFF values in these brain areas can predict early HR development, preventing the malignant transformation of hypertensive microangiopathy.