Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.

Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis. Therefore, the information on UM patients from the SEER database (2000-2020) was split into a 7:3 ratio training set and an internal test set based on distant metastasis presence. Univariate and multivariate logistic regression analyses assessed distant metastasis risk factors. Six machine learning methods constructed a predictive model post-feature variable selection. The model evaluation identified the multilayer perceptron (MLP) as optimal. Shapley additive explanations (SHAP) interpreted the chosen model. A web-based calculator personalized risk probabilities for UM patients. The results show that nine feature variables contributed to the machine learning model. The MLP model demonstrated superior predictive accuracy (Precision = 0.788; ROC AUC = 0.876; PR AUC = 0.788). Grade recode, age, primary site, time from diagnosis to treatment initiation, and total number of malignant tumors were identified as distant metastasis risk factors. Diagnostic method, laterality, rural-urban continuum code, and radiation recode emerged as protective factors. The developed web calculator utilizes the MLP model for personalized risk assessments. In conclusion, the MLP machine learning model emerges as the optimal tool for predicting distant metastasis in UM patients. This model facilitates personalized risk assessments, empowering early and tailored treatment strategies.

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.

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.

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.

[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.

The impact of vitamin E, vitamin B6, and niacin intake on cataract incidence based on NHANES 2005-2008 data.

Objective: This investigation aims to elucidate the correlations between dietary intakes of vitamin E, B6, and niacin and the incidence of cataracts, utilizing the comprehensive NHANES 2005-2008 dataset to affirm the prophylactic roles of these nutrients against cataract formation. Methods: Using data from the NHANES 2005-2008 cycles, this analysis concentrated on 7,247 subjects after exclusion based on incomplete dietary or cataract data. The identification of cataracts was determined through participants' self-reported ophthalmic surgical history. Nutritional intake was gauged using the automated multiple pass method, and the data were analyzed using logistic and quantile regression analyses to investigate the relationship between vitamin consumption and cataract prevalence. Results: Our analysis identified significant inverse associations between the intake of vitamins E, B6, and niacin and the risk of cataract development. Specifically, higher intakes of vitamin B6 (OR = 0.85, 95% CI = 0.76-0.96, p = 0.0073) and niacin (OR = 0.98, 95% CI = 0.97-1.00, p = 0.0067) in the top quartile were significantly associated with a reduced likelihood of cataract occurrence. Vitamin E intake showed a consistent reduction in cataract risk across different intake levels (OR = 0.96, 95% CI = 0.94-0.99, p = 0.0087), demonstrating a nonlinear inverse correlation. Conclusion: The outcomes indicate that elevated consumption of vitamin B6 and niacin, in conjunction with regular vitamin E intake, may have the potential to delay or prevent cataract genesis. These results suggest a novel nutritional strategy for cataract prevention and management, advocating that focused nutrient supplementation could be instrumental in preserving eye health and reducing the risk of cataracts. Further research is recommended to validate these findings and establish optimal dosages for maximum benefit.

Drug-Related Keratitis: A Real-World FDA Adverse Event Reporting System Database Study.

Purpose: This study aimed to assess the drug risk of drug-related keratitis and track the epidemiological characteristics of drug-related keratitis. Methods: This study analyzed data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database from January 2004 to December 2023. A disproportionality analysis was conducted to assess drug-related keratitis with positive signals, and drugs were classified and assessed with regard to their drug-induced timing and risk of drug-related keratitis. Results: A total of 1606 drugs were reported to pose a risk of drug-related keratitis in the FAERS database, and, after disproportionality analysis and screening, 17 drugs were found to significantly increase the risk of drug-related keratitis. Among them, seven were ophthalmic medications, including dorzolamide (reporting odds ratio [ROR] = 3695.82), travoprost (ROR = 2287.27), and brimonidine (ROR = 2118.52), and 10 were non-ophthalmic medications, including tralokinumab (ROR = 2609.12), trazodone (ROR = 2377.07), and belantamab mafodotin (ROR = 680.28). The top three drugs having the highest risk of drug-related keratitis were dorzolamide (Bayesian confidence propagation neural network [BCPNN] = 11.71), trazodone (BCPNN = 11.11), and tralokinumab (BCPNN = 11.08). The drug-induced times for non-ophthalmic medications were significantly shorter than those for ophthalmic medications (mean days, 141.02 vs. 321.96, respectively; P < 0.001). The incidence of drug-related keratitis reached its peak in 2023. Conclusions: Prevention of drug-related keratitis is more important than treatment. Identifying the specific risks and timing of drug-induced keratitis can support the development of preventive measures. Translational Relevance: Identifying the specific drugs related to medication-related keratitis is of significant importance for drug vigilance in the occurrence of drug-related keratitis.

Real-world large sample assessment of drug-related dry eye risk: Based on the FDA adverse event reporting system database.

PURPOSE AND DESIGN: This study aimed to evaluate the risk of drug-related dry eye using real-world data, underscoring the significance of tracing pharmacological etiology for distinct clinical types of dry eye. METHODS: Analyzing adverse event reports in the Food and Drug Administration Adverse Event Reporting System (FAERS) from January 2004 to September 2023, we employed disproportionality analysis and the Bayesian confidence propagation neural network algorithm. The analysis involved categorizing drugs causing dry eye, assessing risk levels, and conducting segmental assessments based on the time of onset of drug-related dry eye adverse reactions. RESULTS: In the FAERS database, adverse reactions related to dry eye were linked to 1160 drugs. Disproportionality analysis identified 33 drugs with significant risk, notably in ophthalmic (brimonidine, bimatoprost), oncology (tisotumab vedotin, erdafitinib), and other medications (isotretinoin, oxymetazoline). The top three drugs with the highest risk of drug-related dry eye are isotretinoin (Bayesian confidence propagation neural network (BCPNN) = 6.88), tisotumab vedotin (BCPNN = 6.88), and brimonidine (BCPNN = 6.77). Among different categories of drugs, respiratory medications have the shortest mean onset time for drug-related dry eye, averaging 50.99 days. The prevalence skewed towards females (69.9 %), particularly in menopausal and elderly individuals (45-70 years old, mean age 54.7 ± 18.2). Reports of drug-related dry eye adverse reactions showed an annual increase. CONCLUSION: Informed clinical decision-making is crucial for preventing drug-related dry eye. Assessing the risk of dry eyes associated with both local and systemic medications helps optimize treatment and provide necessary cautionary information.

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. RESULTS: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. CONCLUSION: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.

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.

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.

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.

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.

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.

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.