Histopathological spectrum of ocular surface squamous neoplasia: A retrospective study of 776 lesions

Background: Ocular surface squamous neoplasia (OSSN) comprises neoplasm arising from the ocular surface, which includes conjunctiva, cornea, and limbus and ranges from mild dysplasia to invasive squamous cell carcinoma. Purpose: The aim of this work was to study the spectrum of OSSN based on histopathological analysis. Materials and Methods: This was a retrospective cross-sectional study comprising 776 histopathologically diagnosed cases of OSSN from January 2004 to December 2014. Results: The mean age of presentation of OSSN was 45 years (median, 45 years; 2 to 87 years) with male preponderance (74%). The most common age group of presentation was 41–60 years (n = 299; 39%). The most common type of OSSN was invasive squamous cell carcinoma seen in 50% (n = 383) eyes followed by severe dysplasia/carcinoma in situ in 31% (n = 250) eyes. Tumor infiltration at base was seen in 16% (n = 124), positive margins in 32% (n = 248), scleral infiltration in 14% (n = 109), intraocular extension in 3% (n = 23), and orbital extension in 4% (n = 26) eyes. OSSN was associated with actinic keratosis in 21% (n = 165) cases. Conclusion: Based on histopathology, invasive squamous cell carcinoma is the most common form of OSSN in the Asian Indian population.

Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy – Artificial intelligence versus clinician for screening

Purpose: Deep learning is a newer and advanced subfield in artificial intelligence (AI). The aim of our study is to validate a machine-based algorithm developed based on deep convolutional neural networks as a tool for screening to detect referable diabetic retinopathy (DR). Methods: An AI algorithm to detect DR was validated at our hospital using an internal dataset consisting of 1,533 macula-centered fundus images collected retrospectively and an external validation set using Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (MESSIDOR) dataset. Images were graded by two retina specialists as any DR, prompt referral (moderate nonproliferative diabetic retinopathy (NPDR) or above or presence of macular edema) and sight-threatening DR/STDR (severe NPDR or above) and compared with AI results. Sensitivity, specificity, and area under curve (AUC) for both internal and external validation sets for any DR detection, prompt referral, and STDR were calculated. Interobserver agreement using kappa value was calculated for both the sets and two out of three agreements for DR grading was considered as ground truth to compare with AI results. Results: In the internal validation set, the overall sensitivity and specificity was 99.7% and 98.5% for Any DR detection and 98.9% and 94.84%for Prompt referral respectively. The AUC was 0.991 and 0.969 for any DR detection and prompt referral respectively. The agreement between two observers was 99.5% and 99.2% for any DR detection and prompt referral with a kappa value of 0.94 and 0.96, respectively. In the external validation set (MESSIDOR 1), the overall sensitivity and specificity was 90.4% and 91.0% for any DR detection and 94.7% and 97.4% for prompt referral, respectively. The AUC was. 907 and. 960 for any DR detection and prompt referral, respectively. The agreement between two observers was 98.5% and 97.8% for any DR detection and prompt referral with a kappa value of 0.971 and 0.980, respectively. Conclusion: With increasing diabetic population and growing demand supply gap in trained resources, AI is the future for early identification of DR and reducing blindness. This can revolutionize telescreening in ophthalmology, especially where people do not have access to specialized health care.

Exserohilum keratitis: Clinical profile of nine patients and comparison of morphology versus ITS-Based DNA sequencing for species identification of the fungal isolates

Purpose: The objective of this study was to describe the microbiological and clinical features of nine cases of Exserohilum keratitis. Patients and Methods: Fungal isolates from corneal scrapings were identified based on macroscopic and microscopic characteristics of the colonies and DNA sequencing of ITS1-5.8S-ITS2 region in the rRNA gene. All patients were treated with topical and if required systemic antifungals. Therapeutic penetrating keratoplasty (TPK) was done in case of failed medical therapy. Results: Morphologically, all fungal isolates were Exserohilum rostratum except one Exserohilum mcginnisii. Based on the BLAST analysis, 6 isolates showed 100% similarity to Setosphaeria rostrata (CBS 112815) and E. mcginnisii (CBS 20308). The other three isolates were Setosphaeria holmii (CBS 128053), not reported earlier in fungal keratitis. Three patients were lost to follow-up and response to medical therapy was good (Healed scar � 4 patients). Two out of nine patients were advised TPK. Conclusions: Diagnosis and clinical features of Exserohilum keratitis are akin to other dematiaceous keratitis. The two morphological species of E. mcginnisii and E. rostratum are indistinguishable from Setosphaeria rostratum at DNA sequence level, which justifies the retention of the latter nomenclature.

Dendritic cell engineering for selective targeting of female reproductive tract cancers

Female reproductive tract cancers (FRCs) are considered as one of the most frequently occurring malignancies and a foremost cause of death among women. The late-stage diagnosis and limited clinical effectiveness of currently available mainstay therapies, primarily due to the developed drug resistance properties of tumour cells, further increase disease severity. In the past decade, dendritic cell (DC)-based immunotherapy has shown remarkable success and appeared as a feasible therapeutic alternative to treat several malignancies, including FRCs. Importantly, the clinical efficacy of this therapy is shown to be restricted by the established immunosuppressive tumour microenvironment. However, combining nanoengineered approaches can significantly assist DCs to overcome this tumour-induced immune tolerance. The prolonged release of nanoencapsulated tumour antigens helps improve the ability of DC-based therapeutics to selectively target and remove residual tumour cells. Incorporation of surface ligands and co-adjuvants may further aid DC targeting (in vivo) to overcome the issues associated with the short DC lifespan, immunosuppression and imprecise uptake. We herein briefly discuss the necessity and progress of DC-based therapeutics in FRCs. The review also sheds lights on the future challenges to design and develop clinically effective nanoparticles-DC combinations that can induce efficient anti-tumour immune responses and prolong patients' survival.