Purpose: The aim of this work was to study the demographic profile, clinical diagnostic features, challenges in management, treatment outcomes, and ocular morbidity of microbiological culture-proven Pythium keratitis in a tertiary eye care hospital in South India. Methods: Retrospective analysis of microbiologically proven Pythium keratitis patients was performed at a tertiary eye center from October 2017 to March 2020. Demographic details, risk factors, microbiological investigations, clinical course, and visual outcomes were analyzed. Results: Thirty patients were analyzed. The mean age was 43.1±17.2 years. Most common risk factors were history of injury in 80% and exposure to dirty water in 23.3%. Visual acuity at baseline was 20/30 to perception of light (PL). The most common clinical presentation was stromal infiltrate and hypopyon in 14 (46.6%) patients each. The microbiological confirmation was based on culture on blood agar and vesicles with zoospores formation with incubated leaf carnation method. Seven (23.3%) patients improved with topical 0.2% Linezolid and topical 1% Azithromycin, 19 (63.3%) patients underwent Therapeutic keratoplasty (TPK) and 4 were lost to follow-up. Seven (23.3%) patients had graft reinfection, and 3 (10%) developed endophthalmitis. The final visual acuity was 20/20- 20/200 in 6 (20%) patients, 20/240-20/1200 in 5 (16.6%) patients, hand movement to positive perception of light in 16 patients and no perception of light (Pthisis Bulbi) in 3 (10%) patients. Conclusion: P. insidiosum keratitis is a rapidly progressive infectious keratitis with prolonged and relapsing clinical course. It usually results in irreparable vision loss in majority of the patients. Prompt diagnosis, clinical awareness, and specific treatment options are needed for successfully managing this devastating corneal disease.

Digital eye strain (DES) is an entity encompassing visual and ocular symptoms arising due to the prolonged use of digital electronic devices. It is characterized by dry eyes, itching, foreign body sensation, watering, blurring of vision, and headache. Non-ocular symp-

Summary Data‐driven modeling using measurable battery signals tends to provide robust battery capacity estimation without delving deep into electrochemical phenomenon inside the battery. Nowadays, with the advent of artificial intelligence, deep neural networks are playing crucial role in data modeling and analysis. In this article, models of three different families of network architectures such as feed‐forward neural network (FNN), convolutional neural network (CNN), and long short‐term memory neural network (LSTM) are proposed for battery capacity estimation. Measurements from a set of two rechargeable Li‐ion batteries are considered for the model performance evaluation. The battery capacity estimation by different models has been evaluated by considering the effect of certain parameters such as model complexity, sampling rate of battery measurable signals and type of battery measurable signals. With its ability to process time‐series data efficiently by memorizing long‐term dependencies, LSTM outperforms other model architectures in estimating battery capacity more accurately and flexibly with 4.69% and 19.16% decline in average test root mean square error (RMSE) as compared with FNN and CNN, respectively. Simpler architectures of LSTM and FNN are able to perform well as compared with CNN, which needs architecture with certain hidden layers to interpret the battery aging process. Moreover, investigations reveal that sparsely sampled battery signals help all the proposed models to learn the battery dynamics in a better way as compared to densely sampled battery signals which also entails for less complex model learning process. Further, among all battery measurable signals, battery temperature has relatively less weightage in estimating battery capacity.

Interleukins and cytokines are involved in the pathogenesis of uveitis of heterogeneous origin. Understanding the basics of the ocular immune privilege is a fulcrum to discern their specific role in diverse uveitis to potentially translate as therapeutic targets. This review attempts to cover these elements in uveitis of infectious, noninfectious and masquerade origin. Insights of the molecular targets in novel therapy along with the vision of future research are intriguing.