A Review of Systemic Biologics and Local Immunosuppressive Medications in Uveitis

Uveitis is one of the most common causes of vision loss and blindness worldwide. Local and/or systemic immunosuppression is often required to treat ocular inflammation in noninfectious uveitis. An understanding of safety and efficacy of these medications is required to individualize treatment to each patient to ensure compliance and achieve the best outcome. In this article, we reviewed the effectiveness of systemic biologic response modifiers and local treatments commonly used in the management of patients with noninfectious uveitis

Medication-induced Uveitis: An Update

Drug-induced uveitis is an uncommon but important cause of ocular inflammation. Uveitis can be seen in association with various systemic, topical, and intraocular medications. In this article, we review common medications associated with uveitis. Most cases of drug-induced uveitis resolve with termination of the suspected medication with or without administration of topical or systemic steroids. It is important for clinicians to readily identify medications that may cause uveitis in order to provide rapid treatment, avoid consequences of longstanding inflammation, and prevent costly and excessive laboratory testing

Brain abscess as a manifestation of spinal dermal sinus

Dermal sinuses have been associated with a wide spectrum of clinical manifestations ranging from asymptomatic to drainage of purulent material from the sinus tract, inclusion tumors, meningitis, and spinal abscess. To date, there has been no documented report of brain abscess as a complication of spinal dermal sinus. Here, we report an 8-month-old girl who was presented initially with a brain abscess at early infancy but lumbar dermal sinus and associated spinal abscess were discovered afterwards. The probable mechanisms of this rare association have been discussed

Gamma-Irradiation Reduces the Allogenicity of Donor Corneas

Purpose. To evaluate the utility and allogenicity of gamma-irradiated corneal allografts. Methods. Corneal buttons were harvested from C57BL/6 mice and decellularized with gamma irradiation. Cell viability was assessed using TUNEL and viability/cytotoxicity assays. Orthotopic penetrating keratoplasty was performed using irradiated or nonirradiated (freshly excised) C57BL/6 donor grafts and BALB/c or C57BL/6 recipients. Graft opacity was assessed over an 8-week period and graft survival was evaluated using Kaplan-Meier survival curves. Mixed-lymphocyte reactions and delayed-type hypersensitivity assays were performed to evaluate T-cell alloreactivity. Real-time PCR was used to investigate the corneal expression of potentially pathogenic T-helper 1, 2, and 17 cell-associated cytokines. Results. Corneal cells were devitalized by gamma irradiation as evidenced by widespread cellular apoptosis and plasma membrane disruption. Nonirradiated allograft and isograft rates of survival were superior to irradiated allograft and isograft rates of survival (P < 0.001). Mixed lymphocyte reactions demonstrated that T-cells from irradiated allograft recipients did not exhibit a secondary alloimmune response (P < 0.001). Delayed-type hypersensitivity assays demonstrated that irradiated allografts did not elicit an alloreactive delayed-type hypersensitivity response in graft recipients (P ≤ 0.01). The corneal expression of T-helper 1, 2, and 17 cell-associated cytokines was significantly lower in failed irradiated allografts than rejected nonirradiated allografts (P ≤ 0.001). Conclusions. Gamma-irradiated corneas failed to remain optically clear following murine penetrating keratoplasty; however, gamma irradiation reduced the allogenicity of these corneas, potentially supporting their use in procedures such as anterior lamellar keratoplasty or keratoprosthesis implantation

Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy

PURPOSE: Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in developing models predicting the success of pneumatic retinopexy (PR) in treatment of rhegmatogenous retinal detachment (RRD). These models were then compared with custom models created by machine learning (ML) experts. DESIGN: Retrospective multicenter study. PARTICIPANTS: Five hundred and thirty nine consecutive patients with primary RRD that underwent PR by a vitreoretinal fellow at 6 training hospitals between 2002 and 2022. METHODS: We used 2 AutoML platforms: MATLAB Classification Learner and Google Cloud AutoML. Additional models were developed by computer scientists. We included patient demographics and baseline characteristics, including lens and macula status, RRD size, number and location of breaks, presence of vitreous hemorrhage and lattice degeneration, and physicians\u27 experience. The dataset was split into a training (n = 483) and test set (n = 56). The training set, with a 2:1 success-to-failure ratio, was used to train the MATLAB models. Because Google Cloud AutoML requires a minimum of 1000 samples, the training set was tripled to create a new set with 1449 datapoints. Additionally, balanced datasets with a 1:1 success-to-failure ratio were created using Python. MAIN OUTCOME MEASURES: Single-procedure anatomic success rate, as predicted by the ML models. F2 scores and area under the receiver operating curve (AUROC) were used as primary metrics to compare models. RESULTS: The best performing AutoML model (F2 score: 0.85; AUROC: 0.90; MATLAB), showed comparable performance to the custom model (0.92, 0.86) when trained on the balanced datasets. However, training the AutoML model with imbalanced data yielded misleadingly high AUROC (0.81) despite low F2-score (0.2) and sensitivity (0.17). CONCLUSIONS: We demonstrated the feasibility of using AutoML as an accessible tool for medical professionals to develop models from clinical data. Such models can ultimately aid in the clinical decision-making, contributing to better patient outcomes. However, outcomes can be misleading or unreliable if used naively. Limitations exist, particularly if datasets contain missing variables or are highly imbalanced. Proper model selection and data preprocessing can improve the reliability of AutoML tools. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article

A Review of Systemic Biologics and Local Immunosuppressive Medications in Uveitis.

Uveitis is one of the most common causes of vision loss and blindness worldwide. Local and/or systemic immunosuppression is often required to treat ocular inflammation in noninfectious uveitis. An understanding of safety and efficacy of these medications is required to individualize treatment to each patient to ensure compliance and achieve the best outcome. In this article, we reviewed the effectiveness of systemic biologic response modifiers and local treatments commonly used in the management of patients with noninfectious uveitis

The Ocular Redness Index: A Novel Automated Method for Measuring Ocular Injection

Purpose. To develop and validate a novel automated system to assess ocular redness (OR) in clinical images. Methods. We developed a novel software that quantifies OR in digital images based on a mathematic algorithm using a centesimal continuous scoring scale. Subsequently, we conducted a study to validate the scores obtained with this system by correlating them with those obtained by two physicians using two image-based comparative subjective scales, the Efron and the Validated Bulbar Redness (VBR) grading scales. Additionally, we evaluated the level of clinical agreement between the Ocular Redness Index (ORI) score and the two image-based methods by means of the Bland-Altman analysis. Main outcome measures included correlation and level of agreement between the ORI score, Efron score, and the VBR score. Results. One hundred and two clinical photographs of eyes with OR were evaluated. The ORI scores significantly correlated with the scores obtained by the two clinicians using the Efron (Observer 1, R = 0.925, P < 0.001; Observer 2, R = 0.857, P < 0.001), and VBR (Observer 1, R = 0.830, P < 0.001; Observer 2, R = 0.821, P < 0.001) scales. The Bland-Altman analysis revealed levels of disagreement of up to 30 and 27 units for the ORI–Efron and ORI–VBR score comparisons, respectively. Conclusions. The ORI provides an objective and continuous scale for evaluating ocular injection in an automated manner, and without need for a trained physician for scoring. The ORI may be used as a new alternative for objective OR evaluation in clinics and in clinical trials