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PurposePredicting 24-hour peak and average intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma. This study aimed to develop and assess a machine learning model for predicting 24-hour peak and average IOP, leveraging advanced techniques to enhance prediction accuracy. We also aimed to identify relevant features and provide insights into the prediction results to better inform clinical practice.MethodsIn this retrospective study, electronic medical records from January 2014 to May 2024 were analyzed, incorporating 24-hour IOP monitoring data and patient characteristics. Predictive models based on five machine learning algorithms were trained and evaluated. Five time points (10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM) were tested to optimize prediction accuracy using their combinations. The model with the highest performance was selected, and feature importance was assessed using Shapley Additive Explanations.ResultsThis study included data from 517 patients (1,034 eyes). For predicting 24-hour peak IOP, the Random Forest Regression (RFR) model utilizing IOP values at 10:00 AM, 12:00 PM, 2:00 PM, and 4:00 PM achieved optimal performance: MSE 5.248, RMSE 2.291, MAE 1.694, and R2 0.823. For predicting 24-hour average IOP, the RFR model using IOP values at 10:00 AM, 12:00 PM, 4:00 PM, and 6:00 PM performed best: MSE 1.374, RMSE 1.172, MAE 0.869, and R2 0.918.ConclusionThe study developed machine learning models that predict 24-hour peak and average IOP. Specific time point combinations and the RFR algorithm were identified, which improved the accuracy of predicting 24-hour peak and average intraocular pressure. These findings provide the potential for more effective management and treatment strategies for glaucoma patients.

Heavy metals in water bodies pose a significant threat to both ecology and human well-being. Therefore, the development of green and sustainable adsorbents for water remediation is essential. This study presents a crosslinked chitosan composite structure with multiple layers, which was generated by adjusting the ratio of chitosan to wheat straw–derived magnetic biochar in the composite. The adsorption behavior of the composite under different conditions was revealed by varying the Cr(VI) concentration, pH, and temperature. The composite exhibited a maximum adsorption of 121.8 mg g−1 for Cr(VI) in water, which was 11.9 times higher than that of magnetic biochar. It maintained a residual performance of 78.6 % after seven cycles, higher than most chitosan-based composites. The reusability was improved via hydrochloric acid protonation of functional groups and layer-by-layer exposure of the composite structure. This study provides new insights into the application of composites derived from chitosan and wheat straw, an agricultural waste, as sustainable adsorbents.

Triple-negative breast cancer (TNBC) presents significant challenges to female health owing to the lack of therapeutic targets and its poor prognosis. In recent years, in the field of molecular pathology, there has been a growing focus on the role of intra-tumoral microbial communities and metabolic alterations in tumor cells. However, the precise mechanism through which microbiota and their metabolites influence TNBC remains unclear and warrants further investigation. In this study, we analyzed the microbial community composition in various subtypes of breast cancer through 16S rRNA MiSeq sequencing of formalin-fixed, paraffin-embedded (FFPE) tissue samples. Notably, Turicibacter, a microbe associated with cancer response, exhibited a significantly higher abundance in TNBC. Similarly, mass spectrometry-based metabolomic analysis revealed substantial differences in specific metabolites, such as nutriacholic, pregnanetriol, and cortol. Furthermore, we observed significant correlations between the intra-tumoral microbiome, clinicopathological characteristics, and human epidermal growth factor receptor-2 expression(HER2). Three microbial taxa (Cytophagaceae, Conexibacteraceae, and Flavobacteriaceae) were associated with tumor-infiltrating lymphocytes(TILs), which are indicative of antitumor immunity. This study creatively utilized FFPE tissue samples to assess intra-tumoral microbial communities and their related metabolic correlations, presenting avenues for the identification of novel diagnostic biomarkers, the development of therapeutic strategies, and the early clinical diagnosis of TNBC.

Many cases of blindness are caused by age-related cataracts (ARCs). N6-methyladenosine (m6A)-modified circRNA widely participates in disease progression. However, the role of m6A modification of circRNA in ARC is unclear. We mined and elucidated the functions and mechanisms of key circRNAs with m6A modification involved in ARC progression. The GSE153722 dataset was used to mine m6A-mediated key circRNA. Loss-of-function assays and rescue assays were used to explore the effect and mechanism of circRNA on ARC cell proliferation and apoptosis. Has_circ_0007905 was a hypermethylated and upregulated expression in the ARC group relative to the control group both in vivo and in vitro. Silencing of has_circ_0007905 promoted proliferation and inhibited the apoptosis of HLE-B3 cells. METTL3 was upregulated in HLE-B3 cells after ARC modeling and had four binding sites with has_circ_0007905 and a mediated m6A modification of has_circ_0007905. Proliferation was significantly inhibited and apoptosis of HLE-B3 cells was facilitated by METTL3 overexpression, whereas these effects were prevented by has_circ_0007905 silencing. Silencing of has_circ_0007905 led to an alteration in the transcriptome landscape. Differentially expressed genes were mainly involved in immune-related processes and pathways. EIF4EBP1 overexpression promoted apoptosis and suppressed proliferation, and also significantly reversed effects of has_circ_0007905 silencing. Moreover, miR-6749-3p significantly decreased the luciferase activities of wild type plasmids with both of has_circ_0007905 and EIF4EBP1. MiR-6749-3p inhibitor blocked elevation in proliferation and reduced EIF4EBP1 expression and apoptosis conferred by has_circ_0007905 silencing. We reveal for the first time that the commitment of ARC progression is guided by METTL3/has_circ_0007905/miR-6749-3p/EIF4EBP1 axis, and the results provide new insights into ARC pathology.

PURPOSE: To evaluate the correlation between changes in tear osmolarity, symptoms, and corneal fluorescein staining in patients with dry eye disease (DED). DESIGN: Retrospective, clinic-based cohort study. METHODS: In this single-institution study, we reviewed the charts of 186 patients with DED from whom we had data on tear osmolarity, symptoms, and corneal fluorescein staining from 2 separate visits. Main outcomes included the correlation of the changes between the 2 visits for tear osmolarity (TearLab system), symptoms (Ocular Surface Disease Index), and corneal fluorescein staining (modified Oxford scheme). For tear osmolarity and corneal fluorescein staining the scores from the eye with highest readings were analyzed. The correlations were repeated on subgroups based on proposed cutoffs for DED severity and on patients' treatment. RESULTS: Wefound amodest, though statistically significant, correlation between changes in corneal fluorescein staining and symptoms of DED (R [ 0.31; P < .001). However, there was no correlation between the recorded change in tear osmolarity and symptoms (R [ L0.091; P[.38) or between changes in tear osmolarity and corneal fluorescein staining (R [ L0.02; P [ .80). This lack of correlation was consistent in all the subgroups studied. A multivariate analysis revealed that changes in corneal fluorescein staining had predictive value on symptom changes, whereas tear osmolarity changes did not. CONCLUSIONS: Changes in tear osmolarity do not correlate significantly with changes in patient symptoms or corneal fluorescein staining in dry eye disease. [ABSTRACT FROM AUTHOR]

Th17 cells, in addition to their proinflammatory functions, have been recognized as potent inducers of angiogenesis in autoimmune diseases and malignancies. In the present study, we demonstrate distinct mechanisms by which IL-17 induces lymphangiogenesis. Using the mouse cornea micropocket and cell culture assays, our data demonstrate that IL-17 directly promotes growth of lymphatic vessels by inducing increased expression of prolymphangiogenic VEGF-D and proliferation of lymphatic endothelial cells. However, IL-17-induced growth of blood vessels is primarily mediated through IL-1β secretion by IL-17-responsive cells. Furthermore, in vivo blockade of IL-17 in a preclinical model of Th17-dominant autoimmune ocular disease demonstrates a significant reduction in the corneal lymphangiogenesis and in the progression of clinical disease. Taken together, our findings demonstrate a novel prolymphangiogenic function for Th17/IL-17, indicating that IL-17 can promote the progression and amplification of immunity in part through its induction of lymphangiogenesis. [ABSTRACT FROM AUTHOR]

Objective: To study the efficacy of topical application of alpha-linolenic acid (ALA) and linoleic acid (LA) for dry eye treatment. Methods: Formulations containing ALA, LA, combined ALA and LA, or vehicle alone, were applied to dry eyes induced in mice. Corneal fluorescein staining and the number and maturation of corneal CD11b+ cells were determined by a masked observer in the different treatment groups. Real-time polymerase chain reaction was used to quantify expression of inflammatory cytokines in the cornea and conjunctiva. Results: Dry eye induction significantly increased corneal fluorescein staining; CD11b+ cell number and major histocompatibility complex Class II expression; corneal IL-1α and tumor necrosis factor α(TNF-α) expression; and conjunctival IL-1α, TNF-α, interferon γ, IL-2, IL-6, and IL-10 expression. Treatment with ALA significantly decreased corneal fluorescein staining compared with both vehicle and untreated controls. Additionally, ALA treatment was associated with a significant decrease in CD11b+ cell number, expression of corneal IL-1α and TNF-α, and conjunctival TNF-α. Conclusions: Topical ALA treatment led to a significant decrease in dry eye signs and inflammatory changes at both cellular and molecular levels. Clinical Relevance: Topical application of ALA omega-3 fatty acid may be a novel therapy to treat the clinical signs and inflammatory changes accompanying dry eye syndrome. [ABSTRACT FROM AUTHOR]

Transparency of the cornea, the window of the eye, is a prerequisite for vision. Angiogenesis into the normally avascular cornea is incompatible with good vision and, therefore, the cornea is one of the few tissues in the human body where avascularity is actively maintained. Here, we provide evidence for a critical mechanism contributing to corneal avascularity. VEGF receptor 3, normally present on lymphatic and proliferating blood vascular endothelium, is strongly constitutively expressed by corneal epithelium and is mechanistically responsible for suppressing inflammatory corneal angiogenesis. [ABSTRACT FROM AUTHOR]

BACKGROUND Predicting 24-hour intraocular pressure (IOP) fluctuations is crucial for enhancing glaucoma management. Traditional methods of measuring 24-hour IOP fluctuations are complex and present certain limitations. The present study leverages machine learning techniques to forecast 24-hour IOP fluctuations based on daytime IOP measurements. MATERIAL AND METHODS A binary method was used to classify 24-hour IOP fluctuations as either >8 mmHg or £8 mmHg. Data were collected from 24-hour IOP monitoring, including 22 different features. Feature selection involved the chi-square test and point-biserial correlation, leading to the establishment of 4 subsets with significance levels of P<1, P<0.1, P<0.05, and P<0.025. Five binary classification machine learning algorithms were used to construct the model. Model performance was assessed by comparing accuracy, specificity, 10-fold cross-validation, precision, sensitivity, F1 score, area under the curve (AUC), and Area Under the Precision-Recall Curve (AUCPR). The model with the highest performance was selected, and feature importance was assessed using Shapley additive explanations.   RESULTS In the subset of features where P<0.05, all models performed better than those in the other subsets, with XGBoost standing out the most. The XGBoost algorithm achieved an accuracy of 0.886, a specificity of 0.972, a 10-fold cross-validation of 0.872, a precision of 0.857, a sensitivity of 0.585, and an F1 score of 0.696. Additionally, it obtained an AUC of 0.890 and an AUCPR of 0.794. CONCLUSIONS Our study illustrates the predictive capabilities of machine learning algorithms in forecasting 24-hour IOP fluctuations. The exceptional performance of the XGBoost algorithm in predicting IOP fluctuations underscores its significance for future research and clinical applications.