Highly Selective α-Addition and β-Conjugate Addition Products from Peptides Composed of α,β-Unsaturated γ-Amino Acids.

β-Addition products are common in conjugate addition reactions consisting of α,β-unsaturated carbonyl compounds. Here, we are reporting an uncommon α-addition product as a major product in the thioacetic acid conjugate addition reaction on a peptide consisting of ()-α,β-unsaturated γ-amino acids. In addition, we observed highly diastereoselective β-addition products from the thiophenol and thioethanol conjugate addition reaction on peptides.

Metal-directed hierarchical superhelices from hybrid peptide foldamers.

A superhelix is a three-dimensional arrangement of a helix in which the helix is coiled around a common axis. Here, we are reporting a short 12-helix of α,γ-hybrid peptides terminated by metal binding ligands, self-assembled into a right-handed superhelix around a common axis in the presence of Cd(II) ions. Furthermore, these superhelices are assembled into hierarchical superhelical β-sheet-type structural motifs in single crystals.

Present trends, sustainable strategies and energy potentials of crop residue management in India: A review.

India generating huge amount of agricultural waste, especially crop residues. In India, around 141 MT of crop residue is generated each year, in which 92 MT burned due to inadequate sustainable management practices, which results in rise in emissions of particulate matter as well as quality of air pollution. Burning crop residues raises mortality rates and substantially decreases crop production while posing a major risk of threatening the environment, condition of the soil, human health, and air quality.

Exploring intranasal drug delivery via nanocarriers: A promising glioblastoma therapy.

Glioblastoma is one of the most recurring types of glioma, having the highest mortality rate among all other gliomas. Traditionally, the standard course of treatment for glioblastoma involved maximum surgical resection, followed by chemotherapy and radiation therapy. Nanocarriers have recently focused on enhancing the chemotherapeutic administration to the brain to satisfy unmet therapeutic requirements for treating brain-related disorders.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

Background And Purpose: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency.

Materials And Methods: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set.

Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

Background And Purpose: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthesize virtual gadolinium contrast-enhanced T1-weighted MR images from noncontrast multiparametric MR images in patients with primary brain tumors by using deep learning.

Materials And Methods: We trained and validated a deep learning network by using MR images from 335 subjects in the Brain Tumor Segmentation Challenge 2019 training data set.

Codeveloping an Adolescent Health Program in India During the COVID-19 Pandemic: A Case Study of a Community-Partnered Responsive Feedback Approach.

We describe the responsive feedback (RF) approach experience of a nongovernmental organization, Girls Health Champions (now known as Adolescent Health Champions [AHC]), that undertakes peer education interventions in Mumbai, India, schools to improve gender equality and health outcomes for adolescents aged 13-19 years. AHC used the RF approach at the onset of the COVID-19 pandemic in light of uncertainties stemming from school closures and the negative impact of the lockdown on adolescents' physical and mental health. Using an RF approach, AHC was able to: (1) understand pandemic-specific challenges faced by adolescents; (2) overhaul its theory of change; (3) pilot new modes of intervention delivery; (4) design a curriculum for parents/guardians and a COVID-19 module; (5) design an AHC mobile app; (6) develop a new, more gender-inclusive name and visual identity; (7) change the overall structure, adolescent-friendly nature, and agility of the organization; (8) and help clarify future directions taken by the organization.

Proteolytically Stable ααγ-Hybrid Peptides Inhibit the Aggregation and Cytotoxicity of Aβ.

The recent approval of antibody-based therapy for targeting the clearance of amyloid plaques fuels the research in designing small molecules and peptide inhibitors to target the aggregation of Aβ-peptides. Here, we report that the 15-residue ααγ-hybrid peptide not only inhibits the aggregation of soluble Aβ into fibrils but also disintegrates the aggregated Aβ fibrils into smaller assemblies. Further, the hybrid peptide completely rescues neuronal cells from the toxicity of Aβ at equimolar concentrations.

Development and Validation of Stability Indicating RP-HPLC Method for the Estimation of Glycopyrrolate and Neostigmine in Bulk and Injection.

A stability indicating RP-HPLC method is suggested for determination of Glycopyrrolate-Neostigmine (GLY/NEO) in bulk drugs and injection formulation. GLY/NEO were eluted from a Chromolith High Resolution RP-18e (100 mm×4.6 mm) with buffer solution (pH 3.

Cyclization of -Boc-()-α,β-unsaturated γ-amino acid active esters into -Boc-()-α,β-unsaturated γ-lactams through → isomerization.

Here, we are reporting the spontaneous transformation of the active esters of -Boc protected -α,β-unsaturated γ-amino acids into the corresponding -α,β-unsaturated γ-lactams with concomitant → isomerization in the presence of a weak base. No cyclization was observed in the absence of the base. Analysis revealed that amide γ-NH is crucial for both lactamization and → isomerization.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g.

Stereoselective synthesis of backbone extended π-conjugated amino esters.

Utilization of the Wittig reaction to synthesize conjugative multiple double bonds is rare. We examined the utility of the Wittig reaction to construct conjugative two and three carbon-carbon double bonds on the N-protected amino acid backbone. The ethyl esters of -Boc amino acids with multiple carbon-carbon double bonds in the backbone were isolated in excellent yields with exceptional -selectivity of the double bonds.

A cationic amphiphilic peptide chaperone rescues Aβ aggregation and cytotoxicity.

Directing Aβ to adopt a conformation that is free from aggregation and cell toxicity is an attractive and viable strategy to design therapeutics for Alzheimer's disease. Over the years, extensive efforts have been made to disrupt the aggregation of Aβ using various types of inhibitors but with limited success. Herein, we report the inhibition of aggregation of Aβ and disintegration of matured fibrils of Aβ into smaller assemblies by a 15-mer cationic amphiphilic peptide.

Corrigendum to: A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas.

[This corrects the article DOI: 10.1093/noajnl/vdaa066.].

Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

: Deep learning has shown promise for predicting the molecular profiles of gliomas using MR images. Prior to clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. The purpose of this study is to perform a preliminary evaluation on the effects of simulated motion artifact on glioma marker classifier performance and determine if motion correction can restore classification accuracies.

Selectively targeting myeloid-derived suppressor cells through TRAIL receptor 2 to enhance the efficacy of CAR T cell therapy for treatment of breast cancer.

Background: Successful targeting of solid tumors such as breast cancer (BC) using chimeric antigen receptor (CAR) T cells has proven challenging, largely attributed to the immunosuppressive tumor microenvironment (TME). Myeloid-derived suppressor cells (MDSCs) inhibit CAR T cell function and persistence within the breast TME. To overcome this challenge, we have developed CAR T cells targeting tumor-associated mucin 1 (MUC1) with a novel chimeric costimulatory receptor that targets tumor necrosis factor-related apoptosis-inducing ligand receptor 2 (TR2) expressed on MDSCs.

A clinical comparative study of dexmedetomidine as an adjuvant to 2% plain lignocaine and 2% lignocaine with 1:200,000 adrenaline as local anesthetic agents for surgical removal of impacted mandibular third molars.

Introduction: Dexmedetomidine is a selective alpha-2 adrenoceptor agonist. It is conventionally used as a sedative in the intensive care unit. However, recently, the application of dexmedetomidine as an adjuvant to a local anesthetic agent has been studied.

MRI-Based Deep-Learning Method for Determining Glioma Promoter Methylation Status.

Background And Purpose: () promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining promoter methylation status using T2 weighted Images (T2WI) only.

Materials And Methods: Brain MR imaging and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas.

BrainNET: Inference of Brain Network Topology Using Machine Learning.

To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. BrainNET is based on extremely randomized trees to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open-source simulated fMRI data of 50 subjects in 28 different simulations under various confounding conditions with known ground truth were used to validate the method.

A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas.

Background: One of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted (T2w) MRI only.

A Fully Automated Deep Learning Network for Brain Tumor Segmentation.

We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance images from the BraTS2018 data set were used. We designed 3 separate 3D-Dense-UNets to simplify the complex multiclass segmentation problem into individual binary-segmentation problems for each subcomponent. We implemented a 3-fold cross-validation to generalize the network's performance.

Piceatannol: A natural stilbene for the prevention and treatment of cancer.

The World Health Organization (WHO) has documented that cancer is the second foremost reason for death worldwide. Various factors are responsible for cancer, for instance, exposure to different physical, chemical and biological carcinogens, infections, hereditary, poor dietary habits and lifestyle etc. Cancer is a preventable disease if detected at an early stage; however, most of the cases of cancer are diagnosed at an incurable advanced or metastatic stage.

TNFR2 limits proinflammatory astrocyte functions during EAE induced by pathogenic DR2b-restricted T cells.

Multiple sclerosis (MS) is an autoimmune neuroinflammatory disease where the underlying mechanisms driving disease progression have remained unresolved. HLA-DR2b (DRB1*15:01) is the most common genetic risk factor for MS. Additionally, TNF and its receptors TNFR1 and TNFR2 play key roles in MS and its preclinical animal model, experimental autoimmune encephalomyelitis (EAE).