Search

Novel treatments are needed as neurological issues become more frequent worldwide. According to the report, plants, oceans, microorganisms, and animals contain interesting drug discovery compounds. Alzheimer's, Parkinson's, and stroke reviews emphasize neurological disorders' complexity and natural substances' safety. Learn about marine-derived and herbal substances' neuroprotective characteristics and applications. Molecular pathways show these substances' neurological healing effects. This article discusses clinical usage of Bryostatin-1, Fucoidan, Icariin, Salvianolic acid, Curcumin, Resveratrol, etc. Their potential benefits for asthma and Alzheimer's disease are complex. Although limited, the study promotes rigorous scientific research and collaboration between traditional and alternative medical practitioners. Unexplored natural compounds, quality control, well-structured clinical trials, and interdisciplinary collaboration should guide future study. Developing and employing natural chemicals to treat neurological illnesses requires ethical sourcing, sustainability, and public awareness. This detailed analysis covers natural chemicals' current state, challenges, and opportunities in neurological disorder treatment. See also the graphical abstract(Fig. 1)., Competing Interests: The authors declare that they have no conflict of interest., (Copyright © 2024 Rushendran et al.)

The world has recently experienced one of the biggest and most severe public health disasters with severe acute respiratory syndrome coronavirus (SARS-CoV-2). SARS-CoV-2 is responsible for the coronavirus disease of 2019 (COVID-19) which is one of the most widespread and powerful infections affecting human lungs. Current figures show that the epidemic had reached 216 nations, where it had killed about 6,438,926 individuals and infected 590,405,710. WHO proclaimed the outbreak of the Ebola virus disease (EVD), in 2014 that killed hundreds of people in West Africa. The development of vaccines for SARS-CoV-2 becomes more difficult due to the viral mutation in its non-structural proteins (NSPs) especially NSP2 and NSP3, S protein, and RNA-dependent RNA polymerase (RdRp). Continuous monitoring of SARS-CoV-2, dynamics of the genomic sequence, and spike protein mutations are very important for the successful development of vaccines with good efficacy. Hence, the vaccine development for SARS-CoV-2 faces specific challenges starting from viral mutation. The requirement of long-term immunity development, safety, efficacy, stability, vaccine allocation, distribution, and finally, its cost is discussed in detail. Currently, 169 vaccines are in the clinical development stage, while 198 vaccines are in the preclinical development stage. The majority of these vaccines belong to the Ps-Protein subunit type which has 54, and the minor BacAg-SPV (Bacterial antigen-spore expression vector) type, at least 1 vaccination. The use of computational methods and models for vaccine development has revolutionized the traditional methods of vaccine development. Further, this updated review highlights the upcoming vaccine development strategies in response to the current pandemic and post-pandemic era, in the field of vaccine development., (© 2023 John Wiley & Sons Ltd.)

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.

Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.

Uncontrolled proliferation is a hallmark of cancer and can be assessed by labelling breast tissue using immunohistochemistry for Ki67, a protein associated with cell proliferation. Accurate measurement of Ki67-positive tumour nuclei is of critical importance, but requires annotation of the tumour regions by a pathologist. This manual annotation process is highly subjective, time-consuming and subject to inter- and intra-annotator experience. To address this challenge, we have developed Proliferation Tumour Marker Network (PTM-NET), a deep learning model that objectively annotates the tumour regions in Ki67-labelled breast cancer digital pathology images using a convolution neural network. Our custom designed deep learning model was trained on 45 immunohistochemical Ki67-labelled whole slide images to classify tumour and non-tumour regions and was validated on 45 whole slide images from two different sources that were stained using different protocols. Our results show a Dice coefficient of 0.74, positive predictive value of 70% and negative predictive value of 88.3% against the manual ground truth annotation for the combined dataset. There were minimal differences between the images from different sources and the model was further tested in oestrogen receptor and progesterone receptor-labelled images. Finally, using an extension of the model, we could identify possible hotspot regions of high proliferation within the tumour. In the future, this approach could be useful in identifying tumour regions in biopsy samples and tissue microarray images.

Background: Consistent localization of cerebellar cortex in a standard coordinate system is important for functional studies and detection of anatomical alterations in studies of morphometry. To date, no pediatric cerebellar atlas is available., New Method: The probabilistic Cape Town Pediatric Cerebellar Atlas (CAPCA18) was constructed in the age-appropriate National Institute of Health Pediatric Database asymmetric template space using manual tracings of 16 cerebellar compartments in 18 healthy children (9-13 years) from Cape Town, South Africa. The individual atlases of the training subjects were also used to implement multi atlas label fusion using multi atlas majority voting (MAMV) and multi atlas generative model (MAGM) approaches. Segmentation accuracy in 14 test subjects was compared for each method to 'gold standard' manual tracings., Results: Spatial overlap between manual tracings and CAPCA18 automated segmentation was 73% or higher for all lobules in both hemispheres, except VIIb and X. Automated segmentation using MAGM yielded the best segmentation accuracy over all lobules (mean Dice Similarity Coefficient 0.76; range 0.55-0.91; mean Hausdorff distance 0.9 mm; range 0.8-2.7 mm)., Comparison With Existing Methods: In all lobules, spatial overlap of CAPCA18 segmentations with manual tracings was similar or higher than those obtained with SUIT (spatially unbiased infra-tentorial template), providing additional evidence of the benefits of an age appropriate atlas. MAGM segmentation accuracy was comparable to values reported recently by Park et al. (Neuroimage 2014;95(1):217) in adults (across all lobules mean DSC=0.73, range 0.40-0.89)., Conclusions: CAPCA18 and the associated multi-subject atlases of the training subjects yield improved segmentation of cerebellar structures in children., (Copyright © 2016 Elsevier B.V. All rights reserved.)