Keratoconus is an ectatic corneal disorder for which exciting therapeutic and diagnostic technologies are emerging. However, its pathogenesis is still heterogeneous and elusive. We researched overlooked Asian keratoconus data by literature review of databases (PubMed, MEDLINE, Ovid, Google Scholar, Cornea, and Cochrane) using key words "keratoconus, Asia, epidemiology, treatment, risk factors, genes" and names of Asian countries. Articles and their references were analyzed. Studies showed that keratoconus may be more prevalent, have earlier onset, and have greater disease progression in certain Asian and non-Asian ethnicities, particularly Indians, Pakistanis, Middle Easterners, and Polynesians, compared with white populations. Epidemiological risk factors include ethnicity, age (younger than 30 years), gender (male), positive family history, and eye rubbing. Genetic and disease risk factors include atopy, vernal keratoconjunctivitis, Down syndrome, pellucid marginal corneal degeneration, VSX1 (visual system homeobox 1) gene, and Leber congenital amaurosis. Differentiation of heterogeneous keratoconus subsets with detailed genotype-phenotype characterization may advance understanding. Comprehensive multiethnic population studies with valid large-scale data are needed. New effective treatments (deep anterior lamellar keratoplasty, intrastromal corneal ring segments, and corneal collagen cross-linking with riboflavin) are succeeding previous treatments.

Background Randomised controlled trials have demonstrated the benefits of sodium-glucose co-transporter 2 inhibitors (SGLT2is) in people with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). However, real-world data on CKD progression and the development of end-stage kidney disease (ESKD) remains scarce. We aim to study renal outcomes of people with diabetic kidney disease (DKD) using SGLT2is in a highly prevalent DKD population. Methods Between 2016 and 2019, we recruited T2DM patients in the renal or diabetic clinic in a regional hospital in Singapore. Patients prescribed SGLT2is were compared with those on standard anti-diabetic and reno-protective treatment. The outcome measures were CKD progression (a > = 25% drop from baseline and worsening of estimated glomerular filtration rate (eGFR) categories according to KDIGO guidelines) and ESKD (eGFR < 15ml/min/1.73m2). Results We analysed a total of 4446 subjects; 1598 were on SGLT2is. There was a significant reduction in CKD progression (hazard ratio [HR] 0.60; 95% confidence interval [CI] 0.49–0.74) with SGLT2is. The HR for eGFR ≥ 45 ml/min/1.73m2 and eGFR 15–44ml/min/1.73m2 was 0.60 (95% CI 0.47–0.76) and 0.43 (95% CI 0.23–0.66) respectively. There was also a reduction in risk for developing ESKD for the entire cohort (HR 0.33; 95% CI 0.17–0.65) and eGFR 15–44ml/min/1.73m2 (HR 0.24; 95% CI 0.09–0.66). Compared to canagliflozin and dapagliflozin, empagliflozin showed a sustained risk reduction of renal outcomes across stages 1–4 CKD. Conclusions This real-world study demonstrates the benefits of SGLT2is on CKD progression and ESKD. The effect is more pronounced in moderate to advanced CKD patients.

Background Fluid assessment is challenging, and fluid overload poses a significant problem among dialysis patients, with pulmonary oedema being the most serious consequence. Our study aims to develop a simple objective fluid assessment strategy using lung ultrasound (LUS) and artificial intelligence (AI) to assess the fluid status of dialysis patients. Methods This was a single-centre study of 76 hemodialysis and peritoneal dialysis patients carried out between July 2020 to May 2022. The fluid status of dialysis patients was assessed via a simplified 8-point LUS method using a portable handheld ultrasound device (HHUSD), clinical examination and bioimpedance analysis (BIA). The primary outcome was the performance of 8-point LUS using a portable HHUSD in diagnosing fluid overload compared to physical examination and BIA. The secondary outcome was to develop and validate a novel AI software program to quantify B-line count and assess the fluid status of dialysis patients. Results Our study showed a moderate correlation between LUS B-line count and fluid overload assessed by clinical examination (r = 0.475, p < 0.001) and BIA (r = 0.356. p < 0.001). The use of AI to detect B-lines on LUS in our study for dialysis patients was shown to have good agreement with LUS B lines observed by physicians; (r = 0.825, p < 0.001) for the training dataset and (r = 0.844, p < 0.001) for the validation dataset. Conclusion Our study confirms that 8-point LUS using HHUSD, with AI-based detection of B lines, can provide clinically useful information on the assessment of hydration status and diagnosis of fluid overload for dialysis patients in a user-friendly and time-efficient way.

Background Fluid assessment is challenging, and fluid overload poses a significant problem among dialysis patients, with pulmonary oedema being the most serious consequence. Our study aims to develop a simple objective fluid assessment strategy using lung ultrasound (LUS) and artificial intelligence (AI) to assess the fluid status of dialysis patients. Methods This was a single-centre study of 76 hemodialysis and peritoneal dialysis patients. The fluid status of dialysis patients was assessed via a simplified 8-point LUS method using a portable handheld ultrasound device (HHUSD), clinical examination and bioimpedance spectroscopy (BIS). The primary outcome was the performance of 8-point LUS using a portable HHUSD in diagnosing fluid overload compared to physical examination and BIS. The secondary outcome was to develop and validate a novel AI software program to quantify B-line count and assess the fluid status of dialysis patients. Results Our study showed a moderate correlation between LUS B-line count and fluid overload assessed by clinical examination (r=0.475, p<0.001) and BIS (r=0.356. p<0.001). The use of AI to detect B-lines on LUS in our study for dialysis patients was shown to have good agreement with LUS B lines observed by physicians; (r=0.825, p<0.001) for the training dataset and (r=0.844, p<0.001) for the validation dataset. Conclusion Our study confirms that 8-point LUS using HHUSD, with AI-based detection of B lines, can provide clinically useful information on the assessment of hydration status and diagnosis of fluid overload for dialysis patients in a user-friendly and time-efficient way.