Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India.

Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is the fifth leading cause of blindness worldwide. Given the critical nature of the disease, it is paramount that individuals with diabetes undergo annual screening for early and timely detection of DR, facilitating prompt ophthalmic assessment and intervention. However, screening for DR, which involves assessing visual acuity and retinal examination through ophthalmoscopy or retinal photography, presents a significant global challenge due to the massive volume of individuals requiring annual reviews.

Synthesis of -glycoside analogues of isopropyl β-D-1-thiogalactopyranoside (IPTG) and 1-β-D-galactopyranosyl-2-methylpropane. Conformational analysis and evaluation as inhibitors of the lac repressor in and as galactosidase inhibitors.

Isopropyl 1-thio-β-D-galactopyranoside (IPTG, 1) is used widely as an inducer of protein expression in and 1-β-D-galactopyranosyl-2-methylpropane (2), a -glycoside analogue of 1, has also been identified as an inducer. Here, synthesis and study of mimetics of 1 and 2, 1-β-D-galactopyranosyl-2-methylpropan-1-ols and two cyclic acetals derivatives, that constrain the presentation of the iPr group in various geometries is described. Conformational analysis of -glycosides in protic solvent is performed using (i) Desmond metadynamics simulations (OPLS4) and (ii) use of values obtained by H-NMR spectroscopy.

Small lectin ligands as a basis for applications in glycoscience and glycomedicine.

Glycan recognition by lectins mediates important biological events. This Tutorial Review aims to introduce lectin-ligand interactions and show how these molecular recognition events inspire innovations such as: (i) glycomimetic ligands; (ii) multivalent ligand agonists/antagonists; (iii) ligands for precision delivery of therapies to cells, where therapies include vaccines, siRNA and LYTACs (iv) development of diagnostics. A small number of case studies are selected to demonstrate principles for development of new ligands for applications inspired by knowledge of natural glycan ligand structure and function.

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring.

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs.

Multi-Level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA Toward Diagnosis of Cerebrovascular Disorders.

Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation.

Feasibility of Coacervate-Like Nanostructure for Instant Drug Nanoformulation.

Despite the enormous advancements in nanomedicine research, a limited number of nanoformulations are available on the market, and few have been translated to clinics. An easily scalable, sustainable, and cost-effective manufacturing strategy and long-term stability for storage are crucial for successful translation. Here, we report a system and method to instantly formulate NF achieved with a nanoscale polyelectrolyte coacervate-like system, consisting of anionic pseudopeptide poly(l-lysine isophthalamide) derivatives, polyethylenimine, and doxorubicin (Dox) via simple "mix-and-go" addition of precursor solutions in seconds.

Protecting group principles suited to late stage functionalization and global deprotection in oligosaccharide synthesis.

Chemical synthesis is a powerful tool to access homogeneous complex glycans, which relies on protecting group (PG) chemistry. However, the overall efficiency of chemical glycan assembly is still low when compared to oligonucleotide or oligopeptide synthesis. There have been many contributions giving rise to collective improvement in carbohydrate synthesis that includes PG manipulation and stereoselective glycoside formation and some of this chemistry has been transferred to the solid phase or adapted for programmable one pot synthesis approaches.

Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives.

Diabetic retinopathy is a pathological change of the retina that occurs for long-term diabetes. The patients become symptomatic in advanced stages of diabetic retinopathy resulting in severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy stages. There is a need of an automated screening tool for the early detection and treatment of patients with diabetic retinopathy.

Cyclisations and Strategies for Stereoselective Synthesis of Piperidine Iminosugars.

This personal account focuses on synthesis of polyhydroxylated piperidines, a subset of compounds within the iminosugar family. Cyclisations to form the piperidine ring include reductive amination, substitution via amines, iminium ions and cyclic nitrones, transamidification (N-acyl transfer), addition to alkenes, ring contraction and expansion, photoinduced electron transfer, multicomponent Ugi reaction and ring closing metathesis. Enantiomerically pure piperidines are obtained from chiral pool precursors (e.

Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification.

Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient.

Dissecting multi drug resistance in head and neck cancer cells using multicellular tumor spheroids.

One of the hallmarks of cancers is their ability to develop resistance against therapeutic agents. Therefore, developing effective in vitro strategies to identify drug resistance remains of paramount importance for successful treatment. One of the ways cancer cells achieve drug resistance is through the expression of efflux pumps that actively pump drugs out of the cells.

Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval.

Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation.

A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.

Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user.

Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images.

Purpose: Boundary roughness of a pulmonary nodule is an important indication of its malignancy. The irregularity of the shape of a nodule is represented in terms of a few diagnostic characteristics such as spiculation, lobulation, and sphericity. Quantitative characterization of these diagnostic characteristics is essential for designing a content-based image retrieval system and computer-aided system for diagnosis of lung cancer.