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.

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.

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.

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.

Anti-phasic oscillatory development for speech and noise processing in cochlear implanted toddlers.

Human brain demonstrates amazing readiness for speech and language learning at birth, but the auditory development preceding such readiness remains unknown. Cochlear implanted (CI) children (n = 67; mean age 2.77 year ± 1.31 SD; 28 females) with prelingual deafness provide a unique opportunity to study this stage. Using functional near-infrared spectroscopy, it was revealed that the brain of CI children was irresponsive to sounds at CI hearing onset. With increasing CI experiences up to 32 months, the brain demonstrated function, region and hemisphere specific development. Most strikingly, the left anterior temporal lobe showed an oscillatory trajectory, changing in opposite phases for speech and noise. The study provides the first longitudinal brain imaging evidence for early auditory development preceding speech acquisition.

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 .

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.

Machine learning predictors of risk of death within 7 days in patients with non-traumatic subarachnoid hemorrhage in the intensive care unit: A multicenter retrospective study.

Non-traumatic subarachnoid hemorrhage (SAH) is a critical neurosurgical emergency with a high mortality rate, imposing a significant burden on both society and families. Accurate prediction of the risk of death within 7 days in SAH patients can provide valuable information for clinicians, enabling them to make better-informed medical decisions. In this study, we developed six machine learning models using the MIMIC III database and data collected at our institution. These models include Logistic Regression (LR), AdaBoosting (AB), Multilayer Perceptron (MLP), Bagging (BAG), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGB). The primary objective was to identify predictors of death within 7 days in SAH patients admitted to intensive care units. We employed univariate and multivariate logistic regression as well as Pearson correlation analysis to screen the clinical variables of the patients. The initially screened variables were then incorporated into the machine learning models, and the performance of these models was evaluated. Furthermore, we compared the performance differences among the six models and found that the MLP model exhibited the highest performance with an AUC of 0.913. In this study, we conducted risk factor analysis using Shapley values to identify the factors associated with death within 7 days in patients with SAH. The risk factors we identified include Gcsmotor, bicarbonate, wbc, spo2, heartrate, age, nely, glucose, aniongap, GCS, rbc, sysbp, sodium, and gcseys. To provide clinicians with a useful tool for assessing the risk of death within 7 days in SAH patients, we developed a web calculator based on the MLP machine learning model.

Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study.

Background: Identifying patients with hepatocellular carcinoma (HCC) at high risk of recurrence after hepatectomy can help to implement timely interventional treatment. This study aimed to develop a machine learning (ML) model to predict the recurrence risk of HCC patients after hepatectomy. Methods: We retrospectively collected 315 HCC patients who underwent radical hepatectomy at the Third Affiliated Hospital of Sun Yat-sen University from April 2013 to October 2017, and randomly divided them into the training and validation sets at a ratio of 7:3. According to the postoperative recurrence of HCC patients, the patients were divided into recurrence group and non-recurrence group, and univariate and multivariate logistic regression were performed for the two groups. We applied six machine learning algorithms to construct the prediction models and performed internal validation by 10-fold cross-validation. Shapley additive explanations (SHAP) method was applied to interpret the machine learning model. We also built a web calculator based on the best machine learning model to personalize the assessment of the recurrence risk of HCC patients after hepatectomy. Results: A total of 13 variables were included in the machine learning models. The multilayer perceptron (MLP) machine learning model was proved to achieve optimal predictive value in test set (AUC = 0.680). The SHAP method displayed that γ-glutamyl transpeptidase (GGT), fibrinogen, neutrophil, aspartate aminotransferase (AST) and total bilirubin (TB) were the top 5 important factors for recurrence risk of HCC patients after hepatectomy. In addition, we further demonstrated the reliability of the model by analyzing two patients. Finally, we successfully constructed an online web prediction calculator based on the MLP machine learning model. Conclusion: MLP was an optimal machine learning model for predicting the recurrence risk of HCC patients after hepatectomy. This predictive model can help identify HCC patients at high recurrence risk after hepatectomy to provide early and personalized treatment.

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.

Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults.

BACKGROUND: Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires. METHODS: This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals. RESULTS: Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient. CONCLUSIONS: We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment. TRIAL REGISTRATION: Retrospectively registered.

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.

MLN4924 Promotes Self-Renewal of Limbal Stem Cells and Ocular Surface Restoration.

OBJECTIVE: To study the role of MLN4924 in corneal stem cell maintenance and corneal injury repair. METHODS: In cell experiments, the Sprague-Dawley (SD) rat corneal epithelial cells were co-cultured with mitomycin C-inactivated mouse feeder cells in a supplemental hormonal epithelial medium (SHEM) with or without MLN4924. Cells were photographed using an optical microscope. Furthermore, we performed crystal violet, polymerase chain reaction (PCR), and immunofluorescence staining on limbal stem cells (LSCs). In animal experiments, we scraped the corneal epithelium with a central corneal diameter of 4 mm in SD rats. The area of the corneal epithelial defect was calculated by fluorescein sodium staining. RESULTS: LSCs in the MLN4924 group had significantly proliferated. The MLN4924 treatment evidently enhanced the clone formation rate and clone area of LSCs. The expression levels of Ki67, p63, ABCG2, Bmi1, and C/EBPδ increased in LSCs after MLN4924 treatment, whereas the expression of K12 decreased. At 12 and 24 h after scraping, the corneal epithelium recovery rate in the eyes of the MLN4924-treated rats was accelerated. CONCLUSIONS: MLN4924 can maintain stemness, reduce differentiation, promote the proliferative capacity of rat LSCs, and accelerate corneal epithelial wound healing in SD rats.

TIPE2 Inhibits MGD Inflammation by Regulating Macrophage Polarization.

BACKGROUND: The aim of this study was to decide the role of the polarization of macrophages regulated by tumor necrosis factor-α (TNF-α)-induced protein 8-like 2 (TIPE2) in meibomian gland dysfunction (MGD). METHODS: Firstly, the secretory function of the meibomian gland (MG) in apolipoprotein E knockout (ApoE-/-) MGD mice and normal mice was detected by oil red staining. Then, the expression levels of markers of M1 and M2 macrophages were detected by immunofluorescence staining in MGD, normal mice, and mild and severe MGD corpses to decide the role of M1 and M2 macrophages in MGD inflammation. Meanwhile, the expression levels of TIPE2 in MGD mice and MGD patients were detected by immunofluorescence staining, and the correlations among TIPE2, M1 and M2 macrophages were analyzed by immunofluorescence double staining in MGD mice and MGD patients. Furthermore, lipopolysaccharide (LPS) and interleulkin-4 (IL-4) were used to induce M1 and M2 polarization of macrophages, and the mRNA level of TIPE2 was detected in M1 and M2 macrophages. RESULTS: Oil red staining showed that eyelid fat congestion was more severe in (ApoE-/-) MGD mice than in normal mice, and the M1 macrophage was the primary inflammatory cell infiltrated in (ApoE-/-) MGD mice (p < 0.05). The results of the immunofluorescence staining showed that the infiltration of macrophages in MGD mice was more obvious than that in the normal group, and M1 macrophage was the dominant group (p < 0.05). Similar to the results of the MGD mouse model, more macrophage infiltration was observed in MGD patients' MG tissues, and there were more M1 cells in the severe group than in the mild group (p < 0.05). Moreover, the expression of TIPE2 was positively correlated with the expression of M2 macrophages in MGD patients and mice MG tissues (p < 0.05). The expression of TIPE2 mRNA in LPS-induced M1 macrophages declined, while the expression of TIPE2 mRNA in IL-4-induced M2 macrophages increased (p < 0.05). CONCLUSION: M1 macrophage was the dominant group infiltrated in the MG tissue of MGD, and TIPE2 is a potential anti-inflammatory target for preventing the development of MGD by promoting the M2 polarization of macrophages.

Prediction model of obstructive sleep apnea-related hypertension: Machine learning-based development and interpretation study.

Background: Obstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension. Materials and methods: We retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models. Results: A total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension. Conclusion: We established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society.

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.

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.

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.

Genetic Deficiency of p53 Leads to Structural, Functional, and Synaptic Deficits in Primary Somatosensory Cortical Neurons of Adult Mice.

The tumor suppressor p53 plays a crucial role in embryonic neuron development and neurite growth, and its involvement in neuronal homeostasis has been proposed. To better understand how the lack of the p53 gene function affects neuronal activity, spine development, and plasticity, we examined the electrophysiological and morphological properties of layer 5 (L5) pyramidal neurons in the primary somatosensory cortex barrel field (S1BF) by using in vitro whole-cell patch clamp and in vivo two-photon imaging techniques in p53 knockout (KO) mice. We found that the spiking frequency, excitatory inputs, and sag ratio were decreased in L5 pyramidal neurons of p53KO mice. In addition, both in vitro and in vivo morphological analyses demonstrated that dendritic spine density in the apical tuft is decreased in L5 pyramidal neurons of p53KO mice. Furthermore, chronic imaging showed that p53 deletion decreased dendritic spine turnover in steady-state conditions, and prevented the increase in spine turnover associated with whisker stimulation seen in wildtype mice. In addition, the sensitivity of whisker-dependent texture discrimination was impaired in p53KO mice compared with wildtype controls. Together, these results suggest that p53 plays an important role in regulating synaptic plasticity by reducing neuronal excitability and the number of excitatory synapses in S1BF.