Visual morbidity and spectrum of ophthalmic changes in pregnancy induced hypertension.

Aim: To determine the prevalence of ocular changes in pregnancy-induced hypertension (PIH) and co-relate the ophthalmic changes and severity of the disease with visual outcome. Methods and Material: This is a retrospective study conducted from a hospital-based cohort of pregnant women, who delivered from June 2018 to December 2020. A total of 153 patients who fulfilled the diagnostic criteria of PIH admitted in the obstetric ward were included in this study. History with regard to age, parity, gravida, gestational age, medical history, and ocular findings were noted from the patient's case records. Anterior segment examination, dilated fundus evaluation, blood pressure (BP) recordings, urine proteinuria were done. All data were analyzed using the satistical package for social science (SPSS) program. Results: Out of 153 patients, 78 (50.98%) were primigravida, 55 (35.95%) were gravida 2, and 20 (13.07%) were multigravida. Gestational age ranged from 23-40 weeks. Ocular changes were seen in 57% of the PIH patients. Hypertensive retinopathy was seen in 23.53% of PIH patients with a mean age of 29.06 ± 4.36 years. Grade 1 hypertensive retinopathy was the most common manifestation in PIH patients (51.16%). The visual loss occurred in 72% of eclampsia and12% of pre-eclampsia which was statistically significant (P = 0.03). Papilledema was seen in 6% and refractive error in 41% of the patients. Conclusions: Ocular examination of PIH patients reveals important objective information concerning the disorder. The presence of retinal change is a marker of the severity of PIH and is the most common ocular feature. Detection of progression of these changes reflects ischemic changes of the placenta. Fundus examination in PIH patients is important to predict adverse fetal outcomes, and risks to mother's life.

Pathogenic bacteria and their antibiotic sensitivity in ophthalmia neonatorum.

CONTEXT: Emergence of coagulase-negative staphylococci as pathogens in ophthalmia neonatorum. AIMS: To analyze the bacteriological spectrum of ophthalmia neonatorum and its associated risk factors. SETTINGS AND DESIGN: Retrospective analysis in a tertiary care hospital in India. SUBJECTS AND METHODS: A retrospective review was performed in a tertiary care center in India on 139 neonates presenting with conjunctivitis over a period of 3 years. All the neonates presenting to the out-patient department, those admitted in the Neonatal Intensive care Unit and in-patient wards were included in our study. The neonates were clinically examined and followed-up by a single experienced ophthalmologist. Details including demographic data, age of the infant, type of delivery, investigations, and treatment outcomes were analyzed. STATISTICAL ANALYSIS USED: Frequency calculation using Microsoft Excel for windows 10. RESULTS: In the 92 samples with growth (66.2%), the most common organisms isolated were coagulase-negative Staphylococci (35.9%), Klebsiella pneumoniae (16.3%), and Acinetobacter species (16.3%). Others were Staphylococcus aureus (14.1%), Pseudomonas aeruginosa (8.7%), and Escherichia coli (8.7%). Ophthalmia neonatorum was significantly higher in preterm infants born out of lower-segment cesarean section and those requiring ventilatory support. CONCLUSIONS: Unlike gonococcus, which is implicated in ophthalmia neonatorum, our study shows varied microbiological spectrum and sensitivity patterns with coagulase-negative staphylococci as the key pathogen. The role of coagulase-negative staphylococci as a disease-causing pathogen becomes increasingly important with an imperative need for prudent use of common antibiotics in treating these pathogenic bacteria.

Diabetic Retinopathy Analysis.

Diabetic retinopathy is one of the common complications of diabetes. Unfortunately, in many cases the patient is not aware of any symptoms until it is too late for effective treatment. Through analysis of evoked potential response of the retina, the optical nerve, and the optical brain center, a way will be paved for early diagnosis of diabetic retinopathy and prognosis during the treatment process. In this paper, we present an artificial-neural-network-based method to classify diabetic retinopathy subjects according to changes in visual evoked potential spectral components and an anatomically realistic computer model of the human eye under normal and retinopathy conditions in a virtual environment using 3D Max Studio and Windows Movie Maker.