Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study.

Background: Breast cancer (BC) is caused by the uncontrolled proliferation of breast epithelial cells followed by malignant transformation, and it has the highest incidence among female malignant tumors. The metastasis of BC occurs through direct and lymphatic spread. Although ocular metastasis is relatively rare, it is a good indicator of a worse prognosis.

Drug-associated glaucoma: A real-world study based on the Food and Drug Administration adverse event reporting system database.

Background: This study aims to assess the risk of drug-associated glaucoma and track its epidemiological characteristics using real-world data.

Methods: Adverse event reports from the Food and Drug Administration Adverse Event Reporting System (FAERS) from January 2004 to December 2023 were analysed. Disproportionality analysis and the Bayesian Confidence Propagation Neural Network algorithm were used.

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.

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.

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.

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.

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.

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.

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

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. This is a retrospective cohort study.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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

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. 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.

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

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 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.

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.