Chiao-Hsin Lan

Topic: To evaluate the efficacy and safety of femtosecond laser-assisted cataract surgery (FLACS) vs conventional phacoemulsification in eyes with shallow anterior chambers or primary angle-closure disease, with endothelial cell loss (ECL) as the primary outcome (PROSPERO: CRD420251274747). Clinical Relevance: Shallow anterior chambers pose unique surgical challenges during phacoemulsification, placing the corneal endothelium at increased risk of damage. This population, disproportionately prevalent in Asia, is expected to grow substantially with aging demographics, underscoring the need for optimized surgical strategies. Methods: MEDLINE, Embase, and Cochrane CENTRAL were searched through January 2026. Randomized controlled trials and observational studies comparing FLACS with conventional phacoemulsification were included. Risk of bias was assessed using Cochrane Risk of Bias 2 tool and Newcastle-Ottawa Scale. Pooled estimates were calculated using random-effects models; certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation framework. Results: 6 studies (505 eyes) were analyzed. FLACS was associated with lower ECL (mean difference [MD] −4.97%; 95% CI −8.27 to −1.67; P = .003; I ² = 98%) and cumulative dissipated energy (MD −2.47; 95% CI −3.64 to −1.30; P < .001; I ² = 14% in sensitivity analysis). FLACS was associated with 60% lower odds of early postoperative complications (odds ratio 0.40; 95% CI 0.23-0.68; P < .001; I ² = 0%). Visual outcomes were comparable. Conclusions: FLACS was associated with fewer early postoperative complications, supported by moderate-certainty evidence, and with lower ECL and ultrasound energy, although the magnitude of these benefits remains uncertain on low-certainty evidence.

Primary glaucoma is a leading global cause of irreversible visual impairment, necessitating management strategies to preserve visual function over a lifetime. This narrative review synthesizes contemporary evidence on risk stratification, target intraocular pressure (IOP), and interventional decision-making for primary open-angle glaucoma (POAG) and the primary angle-closure disease (PACD) spectrum. A risk-stratified approach integrates baseline risk factors, gonioscopic anatomy, structural optical coherence tomography (OCT), standard automated perimetry, rate of progression, life expectancy, ocular surface status, medication adherence, patient preference, and resource setting. Early laser or surgical intervention can reduce medication burden and improve IOP control in selected patients. However, conservative medical management and observation remain appropriate when progression risk is low or procedural risk is disproportionate. Landmark trials support first-line selective laser trabeculoplasty in suitable POAG. Additionally, evidence supports primary trabeculectomy as an option for advanced disease and early lens extraction for selected older patients with primary angle closure or primary angle-closure glaucoma. Artificial intelligence, polygenic risk scores, and sustained-release platforms serve as promising adjuncts. Nevertheless, these technologies require external validation, equitable implementation, and clearly defined clinical action thresholds before routine adoption. Overall, glaucoma care should transition from a uniform sequence prioritizing topical medications toward an individualized, evidence-based escalation strategy. This approach optimally balances visual prognosis, procedural risk, and patient-centered outcomes.

Glaucoma continues to be a primary contributor to permanent vision loss worldwide, frequently advancing even when intraocular pressure management is clinically adequate. Accumulating research emphasizes the metabolic susceptibility of retinal ganglion cells (RGCs), specifically concerning the progressive depletion of nicotinamide adenine dinucleotide (NAD⁺), a pivotal coenzyme fundamental to mitochondrial energy production and cellular survival mechanisms. As a key biosynthetic precursor in NAD⁺ synthesis pathways, nicotinamide (NAM), a form of vitamin B3, has exhibited significant neuroprotective properties across various preclinical experimental models and preliminary clinical investigations, demonstrating enhanced preservation of RGC morphology and physiological function. This comprehensive review systematically examines the current body of evidence supporting NAM’s therapeutic utility in glaucomatous neuroprotection, focusing particularly on underlying metabolic pathways, obstacles in clinical translation, and prospective therapeutic applications. Through systematic integration of data from cellular and molecular research, animal experimental studies, and population-based epidemiological investigations, we establish a conceptual framework for repurposing NAM as an innovative complementary therapeutic strategy in comprehensive glaucoma care, addressing key considerations for future clinical development including optimal dosing strategies, delivery mechanisms, and patient selection criteria for maximizing therapeutic outcomes in this challenging neurodegenerative condition.

Glaucoma is a leading cause of irreversible blindness, with challenges persisting in early diagnosis, disease progression, and surgical outcome prediction. Recent advances in artificial intelligence have enabled significant progress by extracting clinically relevant patterns from structural, functional, and molecular data. This review outlines the current applications of artificial intelligence in glaucoma care, including early detection using fundus photography and OCT and disease progression prediction using deep learning architectures such as convolutional neural networks, recurrent neural networks, transformer models, generative adversarial networks, and autoencoders. Surgical outcome forecasting has been enhanced through multimodal models that integrate electronic health records and imaging data. We also highlight emerging AI applications in omics analysis, including transcriptomics and metabolomics, for biomarker discovery and individualized risk stratification. Despite these advances, key challenges remain in interpretability, integration of heterogeneous data, and the lack of personalized surgical timing guidance. Future work should focus on transparent, generalizable, and multimodal AI models, supported by large, well-curated datasets, to advance precision medicine in glaucoma.