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Purpose To report clinical presentation, in vivo confocal microscopic features, and corneal phenotype in patients with trachomatous keratopathy (TK) and secondary amyloidosis. Methods Histopathological records of all patients undergoing keratoplasty at the Dr. Rajendra Prasad Centre for Ophthalmic Sciences over a 3-year period were scanned retrospectively for a diagnosis of TK and amyloidosis. Demographic profile and details of preoperative comprehensive ophthalmic assessment were extracted. The histopathology was freshly reviewed. Results Fifteen patients (29 eyes) with TK and atypical corneal involvement due to amyloid deposition were identified. Herbert's pits and upper palpebral conjunctival scarring were present in all cases. Central or total diffuse corneal scarring was present involving the anterior stroma in 5 (31%) and the full thickness of the cornea in 11 (69%) of the eyes. Eight (73%) of 11 patients with deep stromal amyloid deposits revealed bilateral, discrete, blue-white opacities at the level of deep stroma and Descemet membrane (DM). Endothelial cells were atrophic and flattened with gutta formation. Confoscans revealed hyperreflective, needle-shaped crystalline deposits of extracellular amyloid at various depths of the corneal stroma up to DM. All host corneal buttons demonstrated Congo red-positive amyloid deposits on histopathological examination. Conclusions We describe a distinct form of TK unlike the usual presentation of dense, leucomatous, vascularized corneal scarring in trachoma. We believe that amyloid deposits in DM and the corneal endothelium have not previously been reported in patients with trachoma.

Kinesin is a microtubule-associated motor protein which works in teams to carry the cellular cargo transport. Lipid rafts on membranous cargos reorganize, causing the motors present in these areas to physically cluster. Unregulated clustering of motors leads to diseases such as Leishmaniasis, Newmann-Pick disease, etc. Various in-vitro and computational studies have reported improved cargo velocity and travel distance of a fluid cargo as compared to a rigid cargo. However, only cargo velocity increases with increase in membrane fluidity of a fluid cargo. Thermal and motor forces acting tangentially on a cargo generate random torque and motor torque respectively, leading to cargo rotation and motor tail sliding on cargo surface. However, it is unknown which of these forces/torques play a crucial role in improving the transport properties. Here, we use computational models that incorporate random torque, motor torque, and combination of both random and motor torques to understand how they influence the clustering of Kinesin motors on cargo surface due to drift and diffusion of their tails. These studies were performed at varying tail diffusivity to understand their effect on clustering of tails in dispersed and clustered arrangement. We find that in dispersed arrangement, random torque does not cause clustering, whereas motor torque is crucial for clustering of tails on cargo surface, and tails sliding due to both random and motor torques have fastest cargo transport and maximum cooperativity. In clustered arrangement, tails slide to form a broad and steady cluster whose size increases with tail diffusivity resulting in decreased cargo runlength, velocity and cooperativity. These findings suggest that increased tail diffusivity negatively impacts the cluster and cargo transport of tails in the clustered arrangement, whereas it aids physical clustering of tails and cargo transport in dispersed arrangement.

Purpose To study the morphology of posterior polar cataracts and posterior lens capsules using modified posterior segment optical coherence tomography (m-OCT). Setting Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India. Design A prospective observational case series. Methods Patients with clinically diagnosed posterior polar cataracts were included. The routine posterior segment OCT was modified by placing an aspheric +20.00 diopter lens along its aperture, enabling it to visualize the anterior segment structures. Cases of posterior polar cataract were assessed preoperatively using this method to study the morphology of posterior lens cortex and the posterior capsule. All m-OCT observations were performed by 2 observers, and findings were recorded. Results A total of 26 eyes of 17 patients were included. The mean age of patients was 53 years. The morphology of the cataract appeared as hyperreflective opacity just anterior to the posterior capsule, subsequently involving it, and with variable hyporeflective spaces. An intact posterior capsule showed a regularly convex contour, whereas a loss in the tracing of posterior capsule at the paracentral region and disturbance in contour with a localized protrusion of lens matter (the conical sign) depicted a possible posterior capsule dehiscence. Four (15.38%) of 26 eyes showed the conical sign, and this posterior capsule defect was confirmed intraoperatively in all cases. Conclusions In posterior polar cataracts, the conical sign could be a valuable clue to predict preexisting posterior capsule dehiscence and could be identified on a m-OCT.

Purpose: To describe the surgical results of concomitantly performed optical penetrating keratoplasty (PKP) with glued intrascleral haptic fixation (ISHF). Methods: Retrospective review of 18 patients (15–72 years) with best-corrected visual acuity (BCVA) of ≤1/60 subjected to unilateral concomitant optical PKP with ISHF and followed up for 13.11 ± 5.83 months (6–26 months) was undertaken. Results: The most common diagnoses were failed PKP (9/18, 50%) followed by aphakic bullous keratopathy (5/18, 27%). Preoperative glaucoma, peripheral anterior synechiae (PAS), and deep vascularization were present in 7/18 (38.88%), 12/18 (61.11%), and 5/18 (27.77%) patients, respectively. Intraoperatively, concomitant procedures such as pupilloplasty and intraocular lens explant were undertaken in 5/18 (27.277%) patients and 1/18 patients (5.55%) experienced suprachoroidal hemorrhage. At final follow-up, BCVA was ≥6/60 in 50% patients (mean astigmatism: 4.79 ± 1.68D), and 55.55% cases experienced graft failure (90% failed within one year of surgery). The most common causes of graft failure were glaucoma (50%), glaucoma with rejection (20%), rejection (10%), retinal detachment (10%), and suprachoroidal hemorrhage (10%). The ODDS ratio (OR) of having graft failure with the following factors was postoperative secondary interventions (OR: 6), postoperative complications (OR: 2.25), prior failed graft (OR: 1.8), preoperative PAS (OR: 1.75), intraoperative concomitant procedures (OR: 1.5), preoperative glaucoma (OR: 1.33), previous surgeries (OR: 1.24), and deep corneal vessels (OR: 0.66). Conclusion: All patients underlying PKP combined with glued ISHF must be counseled about suboptimal surgical outcomes. Emphasis is laid on appropriate case selection and stringent follow-up during the first year after surgery. Secondary interventions should be undertaken cautiously and judiciously in these patients.

Objective: To evaluate the adjunctive role of conjunctival autofluorescence in the management of ocular surface squamous neoplasia (OSSN). Materials and Methods: Seventeen patients with clinically diagnosed OSSN were included. Morphological characteristics, type of OSSN, and autofluorescence photographs of the lesion were captured. Presence and area of conjunctival ultraviolet autofluorescence (CUVAF) were the main outcome measures. Results: Overall, 17 patients with 15 (88%) primary and 2 (12%) recurrent OSSN were included. Common locations were temporal (n = 10), nasal (n = 5), and diffuse variety (n = 2). Morphologically, there were 4 (22.2%) nodular, 4 (22.2%) leucoplakic, 3 (16.7%) gelatinous, and 1 (5.5%) each of papillary, nodulo-ulcerative, and diffuse variety. Mixed morphology was present in 4 eyes (22.2%). Sixteen of 18 eyes (88.9%) with OSSN displayed autofluorescence on CUVAF images. The mean area of CUVAF was 15.82 mm2 (10.77–19.59 mm2). Autofluorescence was reported in 8 eyes (44.4%) which had negative reports on impression cytology. Conclusions: Conjunctival autofluorescence was seen in the majority of cases with OSSN, in spite of negative cytology reports. Our study demonstrates that CUVAF may serve as an effective ancillary, non-invasive, and resource-friendly tool for supplementing the clinical diagnosis of OSSN, especially in diffuse and recurrent lesions that are not amenable to surgical intervention.

Fungi are a part of normal ocular flora and usually do not cause clinical infection in the absence of predisposing factors. We report a 7-year-old healthy boy from a rural area of India, who presented with a gradually increasing mass in the left eye. Excisional biopsy of the mass was performed, and pathological examination revealed multiseptate hyphae with acute-angle branching consistent with aspergillosis.

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.

An assistive device is proposed for visually impaired people to provide them automatic navigation and guidance. The device can impart navigation free of obstacles and perform real-time image processing to acknowledge the obstacle. The method is intended to perform all tasks in real-time to get all the information about obstacles in the path with optimised information flow. The proposed navigation stick is low cost obstacle detector, obstacle identifier and object detector. The whole setup for rehabilitation for visually impaired people consists of the heterogeneous set of sensors and computation components such as - ultrasonic sensors, camera, single-board DSP processor, a wet floor sensor, and a battery. The machine learning model is used for object recognition to make the user familiar with the environments. To provide the best navigation guidance, proposed device can be used to detect obstacles, up-stairs, down-stairs, edges, potholes, speed breaker, narrowing passages, pothole, wet floors, etc. The output is provided in the form of the audio prompt instead of the vibration, to make user comfortable and user-friendly.

Keratoconus (KC) is a bilateral corneal dystrophy and a multifactorial, multigenic disorder with an etiology involving a strong environmental component and complex inheritance patterns. The underlying pathophysiology of KC is poorly understood because of potential crosstalk between genetic-epigenetic variants possibly triggered by the environmental factors. Here, we decode the etiopathological basis of KC using genomic, transcriptomic, proteomic and metabolic approaches. The lack of relevant models that accurately imitate this condition has been particularly limiting in terms of the effective management of KC. Tissue-engineered in vitro models of KC could address this need and generate valuable insights into its etiopathology for the establishment of disease models to accelerate drug discovery.

Intraocular foreign bodies (FBs) are common ocular injuries reporting to the emergency services all over the world. The authors highlight the findings and surgical management of a case of intralenticular metallic FB following an injury while using chisel and hammer. The ocular path of the FB (2 mm) could be traced from a self-sealed corneal perforation, extending through the anterior capsule rupture, terminating at the posterior capsule, forming a posterior capsule tent with a part embedded in clear lens. Preoperative ultrasound biomicroscopy gave clues on posterior capsule integrity and the exact site of FB, and helped prognosticate and plan the surgical management of the case. The ‘locked-in’ FB was extracted after clear lens aspiration and posterior capsulorrhexis. The posterior capsule acted as a natural barrier between anterior and posterior segment, where the FB was found embedded.

It is easy for a normal human being to perceive and recognize any banknote effortlessly but it is really much difficult for any visually impaired or blind person to perform the same task. As money has an important role in daily lives for any business transaction, real-time detection and recognition of banknotes become a necessity for a person especially who is blind or visually impaired. For that purpose, the YOLO-v3 CNN model based banknote detection and recognition system is proposed which is fast and accurate. Images of different denominations and in different conditions were are collected initially and then, these images are augmented with different geometric and image transformations on images, to make the system robust. These augmented images are then annotated manually, from which training sets and validation image sets are prepared. Later, the performance of the trained model has evaluated on a real-time scene as well as a test dataset. The test result shows that the proposed YOLO-v3 model based method has detection and recognition accuracy of 95.71% and 100%, respectively. The whole system is standalone and works in real-time.

A 62-year-old male patient was planned for left eye phacoemulsification with premium intraocular lens (IOL) implantation under topical anaesthesia. His preoperative best corrected visual acuity (BCVA) was 6/24 in left eye and there was no evidence of any other abnormality apart from cataract.

Currently, a lot of methods have been implemented for prediction analysis. In the proposed study, clustering and classification of the input information for heart disease forecasting is executed with the help of k-means clustering algorithm and SVM (support vector machine) classification model on the basis of prediction analysis methods. The back propagation algorithm along with k-means clustering algorithm is applied for the clustering of information. These algorithms support to enhance the precision of prediction analysis. A heart disease data suite obtained from the UCI repository is used for judging the performance of proposed algorithm. This data suite comprises total 76 features. But, whole tests require a subset of 14 features. Particularly, Cleveland database particularly is utilized by the machine learning researchers throughout the tests. A comparison between proposed study and earlier method (using arithmetic mean) is performed in terms of certain parameters such as accuracy, error recognition rate and execution time.

The prediction analysis is the approach which predicts future possibilities from the previous data. The student performance prediction technique has the three phases which are pre-processing, feature extraction and classification. In the previous technique approach of SVM classifier is applied for the student performance prediction. The approach of naive bayes is applied in the proposed work for the student performance prediction analysis. The proposed and existing algorithm is implemented in python. The results of proposed model are compared with existing model in terms of accuracy and execution time.