Natural course of non-center-involving diabetic macular edema progression in patients under initial observation.

PURPOSE: We aim to report the natural course of non-center involving diabetic macular edema (NCIDME) progression to center involving diabetic macular edema (CIDME) and associated risk factors. METHODS: This is a multicenter retrospective comparative study. Data was collected from electronic medical records from 8 centers in India covering. We included patients with type 2 diabetes above 18 years of age with treatment-naïve NCIDME on OCT and best-corrected visual acuity at baseline of 6/12 or better who were under observation for NCIDME and had 2 years follow-up data. RESULTS: Out of 72 patients with NCIDME, 26.38% patients progressed to CI DME by 2 years, and the visit wise proportion was 11.11% at 6 months, 7% at 1st year and 8.3% at 2 years. The change in CST was statistically significant at 2 years in patients who developed CIDME, the mean difference was 137.73 ± 48.56 microns p = 0.045. Duration of diabetes mellitus > 10 years was the only risk factor for conversion to CIDME. CONCLUSION: A quarter of eyes with NCIDME developed CIDME and 15% progressed from NPDR to PDR by 2 years, highlighting the disease burden in these patients with NCIDME.

Discovery of pharmacological agents for triple-negative breast cancer (TNBC): molecular docking and molecular dynamic simulation studies on 5-lipoxygenase (5-LOX) and nuclear factor kappa B (NF-κB).

Global burden of breast cancer is expected to cross 26 million new cases by 2030. The term 'triple negative breast cancer' (TNBC) refers to lack of expression of hormone receptors (ER, PR and HER2). 5-Lipoxygenase (5-LOX) inhibition promotes breast cancer apoptosis, ferroptosis and inhibits metastases. Nuclear factor kappa B (NF-κB) activation induces cell survival in breast cancer through stimulation of angiogenesis. Therefore, inhibiting NF-B signalling can stop the growth of tumours. In light of these facts, an attempt is made to investigate binding characteristics of LOX inhibitors against 5-LOX (PDB-IDs 3V99 and 6N2W) and NF-κB (PDB-IDs 4KIK and 3DO7) through molecular docking, MM-GBSA calculation, molecular dynamic simulations (MDSs) and drug-likeness analysis. The eight lead molecules A169, A156, A162, A154, A102, A240, A86 and A58 were identified. The higher NF-B inhibiting potential of A169 was discovered through the sequential HTVS, SP docking and XP docking study. The hydrophobic interaction of Leu607, Phe610, Gln557 and Asn554 with 3V99 and Cys99, Glu97 and Arg20 of 4KIK is crucial for the inhibition. The LE, LLE and FQ values of A169 suggest their optimal binding with the target. This study strongly suggests the LOX and NF-κB inhibitory potential of A169, further lead optimisation and biological validation requires for the confirmations.Communicated by Ramaswamy H. Sarma.

Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma.

Purpose: To analyze the efficacy of a deep learning (DL)-based artificial intelligence (AI)-based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore whether the use of this algorithm can reduce the cross-referral in three clinical settings: a diabetologist clinic, retina clinic, and glaucoma clinic. Methods: This is a prospective observational study. Patients between 35 and 65 years of age were recruited from glaucoma and retina clinics at a tertiary eye care hospital and a physician's clinic. Non-mydriatic fundus photography was performed according to the disease-specific protocols. These images were graded by the AI system and specialist graders and comparatively analyzed. Results: Out of 1085 patients, 362 were seen at glaucoma clinics, 341 were seen at retina clinics, and 382 were seen at physician clinics. The kappa agreement between AI and the glaucoma grader was 85% [95% confidence interval (CI): 77.55-92.45%], and retina grading had 91.90% (95% CI: 87.78-96.02%). The retina grader from the glaucoma clinic had 85% agreement, and the glaucoma grader from the retina clinic had 73% agreement. The sensitivity and specificity of AI glaucoma grading were 79.37% (95% CI: 67.30-88.53%) and 99.45 (95% CI: 98.03-99.93), respectively; DR grading had 83.33% (95 CI: 51.59-97.91) and 98.86 (95% CI: 97.35-99.63). The cross-referral accuracy of DR and glaucoma was 89.57% and 95.43%, respectively. Conclusion: DL-based AI systems showed high sensitivity and specificity in both patients with DR and glaucoma; also, there was a good agreement between the specialist graders and the AI system.

Optical Coherence Tomography-Based Prevalence of Diabetic Macular Edema and its Associated Risk Factors in Urban South India: A Population-Based Study.

BACKGROUND: To estimate the prevalence of optical coherence tomography (OCT)-defined diabetic macular oedema (DME) in urban South Indian population and to elucidate their associated risk factors. METHODS: Of 911 participants from the Sankara Nethralaya Diabetic Retinopathy and Molecular Genetics Study-II (SN-DREAMS-), 759 who underwent OCT were analysed. The participants underwent a comprehensive examination and retinal photography following a standard protocol for diabetic retinopathy (DR) grading. The subjects were categorized into centre-involving DME (CI-DME), non-centre involving DME (NCI-DME), and No-DME based on the mean retinal thickness at the central 1 mm, inner and outer ETDRS subfields. RESULTS: The prevalence of CI-DME and NCI-DME in the Chennai population was 3.03% (95% CI: 3.01-3.05) and 10.80% (95% CI: 10.7-11.02). NCI-DME was found to be higher by 9.5% (95% CI: 0.07-0.11) in the early stages of DR. A greater number of subjects with CI DME were aged >60 years and had diabetes mellitus (DM) for >10 years. The significant risk factors for NCI-DME are diastolic blood pressure, serum total cholesterol, serum triglyceride, insulin use and neuropathy (OR (95% CI): 0.97 (0.94-100), 1.00 (1.00-1.01), 0.99 (0.98-0.99), 2.32 (1.15-4.68) and 4.24 (1.22-14.69), respectively) and for CI DME are duration of diabetes, anaemia, neuropathy and insulin use (OR (95% CI): 2.49 (0.96-6.40), 3.41 (1.34-8.65), 10.58 (1.68-66.56) and 3.51 (1.12-10.95), respectively). CONCLUSIONS: The prevalence of NCI-DME was found to be higher than that of CI-DME in patients with DR.

Novel Benzothiazole-Based Highly Selective Ratiometric Fluorescent Turn-On Sensors for Zn2+ and Colorimetric Chemosensors for Zn2+, Cu2+, and Ni2+ Ions.

Metal ions play a very important role in environmental as well as biological fields. The detection of specific metal ions at a minute level caught much attention, and hence, several probes are available in the literature. Even though benzothiazole-based molecules have a special place in the medicinal field, only very few chemosensors are reported based on this moiety. The current work describes the design and synthesis of the benzothiazole-based chemosensor for a highly selective and sensitive detection of biologically important metal ions such as Zn2+, Cu2+, and Ni2+. The sensing studies of compound-1 showed a ratiometric as well as colorimetric response toward Zn2+, Cu2+, and Ni2+ ions and color changes from colorless to yellow and is found to be insensitive toward various metal ions (Cd2+, Cr3+, Mn2+, Pb2+, Ba2+, Al3+, Ca2+, Fe2+, Fe3+, Mg2+, K+, and Na+). Further, compound-1 exhibited ratiometric as well as turn-on-enhanced fluorescence response toward Zn2+ ions and turn off response for Cu2+ and Ni2+ ions. The Job plots revealed that the binding stoichiometry of compound-1 and metal ions is 2:1. The detection limits were found to be 0.25 ppm for Zn2+, while it was 0.30 ppm and 0.34 ppm for Ni2+ and Cu2+, respectively. In addition, density functional theory results strongly support the colorimetric response of metals, and the reversibility studies suggested that compound-1 can be used as a powerful chemosensor for the detection of Zn2+, Cu2+, and Ni2+ ions. The bioimaging data illustrated that compound-1 is a very effective ratiometric sensor for Zn2+ ions in live cells.