Nanocarrier vaccine therapeutics for global infectious and chronic diseases

Immunization has the potential to become a viable weapon for the upcoming pandemic and save millions of lives, while also dramatically lowering the high mortality rate brought on by a number of infectious and chronic illnesses. Despite the success of some vaccinations for infectious illnesses, obstacles remain in avoiding and creating fully protective vaccines. Current COVID-19 pandemic highlights need for vaccination platform improvements. Nanomaterials have been created as a possible nanocarrier to elicit a robust immune response against important global morbidity and mortality drivers by encapsulating targeted antigen and functionalizing nanoparticles with particular molecules. In addition to their application in cancer immunotherapy, nanocarriers are currently being included into the development of vaccines against human immunodeficiency virus (HIV), malaria, TB, and influenza. In order to evaluate conventional and next-generation vaccination platforms, this study focuses on the COVID-19 and cancer vaccine as well as the passage and interaction of nanoparticles with immune cells in the lymph node. It also draws attention to the gaps in current and future HIV, TB, malaria, and influenza vaccinations, as well as nanovaccines. The importance of the dose-dependent vaccine in inducing and maintaining neutralizing antibodies after immunization has been discussed in more detail

Emerging Trends in the Application of Green Synthesized Biocompatible ZnO Nanoparticles for Translational Paradigm in Cancer Therapy

Zinc oxide nanomaterials have been the cynosure of this decade because of their immense potential in different biomedical applications. It includes their usage in the prognosis and treatment of different infectious and cellular diseases, owing to their peculiar physiochemical properties such as variable shape, size, and surface charge etc. Increasing demand and usage of the ZnO nanomaterials raise concerns about their cellular and molecular toxicity and their biocompatibility with human cells. This review comprehensively details their physiochemical properties for usage in biomedical applications. Furthermore, the toxicological concerns of ZnO nanomaterials with different types of cellular systems have been reviewed. Moreover, the biomedical and biocompatible efficacy of ZnO nanomaterials for cancer specific pathways has been discussed. This review offers insights into the current scenario of ZnO nanomaterials usage and signifies their potential future extension usage on different types of biomedical and environmental applications

The translational paradigm of nanobiomaterials : Biological chemistry to modern applications

Recently nanotechnology has evolved as one of the most revolutionary technologies in the world. It has now become a multi-trillion-dollar business that covers the production of physical, chemical, and biological systems at scales ranging from atomic and molecular levels to a wide range of industrial applications, such as electronics, medicine, and cosmetics. Nanobiomaterials synthesis are promising approaches produced from various biological elements be it plants, bacteria, peptides, nucleic acids, etc. Owing to the better biocompatibility and biological approach of synthesis, they have gained immense attention in the biomedical field. Moreover, due to their scaled -down sized property, nanobiomaterials exhibit remarkable features which make them the potential candidate for different domains of tissue engineering, materials science, pharmacology, biosensors, etc. Miscellaneous char-acterization techniques have been utilized for the characterization of nanobiomaterials. Currently, the commer-cial transition of nanotechnology from the research level to the industrial level in the form of nano-scaffolds, implants, and biosensors is stimulating the whole biomedical field starting from bio-mimetic nacres to 3D printing, multiple nanofibers like silk fibers functionalizing as drug delivery systems and in cancer therapy. The contribution of single quantum dot nanoparticles in biological tagging typically in the discipline of genomics and proteomics is noteworthy. This review focuses on the diverse emerging applications of Nanobiomaterials and their mechanistic advancements owing to their physiochemical properties leading to the growth of industries on different biomedical measures. Alongside the implementation of such nanobiomaterials in several drug and gene delivery approaches, optical coding, photodynamic cancer therapy, and vapor sensing have been elaborately discussed in this review. Different parameters based on current challenges and future perspectives are also dis-cussed here

Posterity of nanoscience as lipid nanosystems for Alzheimer's disease regression

Alzheimer's disease (AD) is a type of dementia that affects a vast number of people around the world, causing a great deal of misery and death. Evidence reveals a relationship between the presence of soluble A & beta; peptide aggregates and the severity of dementia in Alzheimer's patients. The BBB (Blood Brain Barrier) is a key problem in Alzheimer's disease because it prevents therapeutics from reaching the desired places. To address the issue, lipid nanosystems have been employed to deliver therapeutic chemicals for anti-AD therapy in a precise and targeted manner. The applicability and clinical significance of lipid nanosystems to deliver therapeutic chemicals (Galantamine, Nicotinamide, Quercetin, Resveratrol, Curcumin, HUPA, Rapamycin, and Ibuprofen) for anti-AD therapy will be discussed in this review. Furthermore, the clinical implications of the aforementioned therapeutic compounds for anti-AD treatment have been examined. Thus, this review will pave the way for researchers to fashion therodiagnostics approaches based on nanomedicine to overcome the problems of delivering therapeutic molecules across the blood brain barrier (BBB)

Atmospheric microplastic and nanoplastic: The toxicological paradigm on the cellular system

The increasing demand for plastic in our daily lives has led to global plastic pollution. The improper disposal of plastic has resulted in a massive amount of atmospheric microplastics (MPs), which has further resulted in the production of atmospheric nanoplastics (NPs). Because of its intimate relationship with the environment and human health, microplastic and nanoplastic contamination is becoming a problem. Because microplastics and nanoplastics are microscopic and light, they may penetrate deep into the human lungs. Despite several studies demonstrating the abundance of microplastics and nanoplastics in the air, the potential risks of atmospheric microplastics and nanoplastics remain unknown. Because of its small size, atmospheric nanoplastic characterization has presented significant challenges. This paper describes sampling and characterization procedures for atmospheric microplastics and nanoplastics. This study also examines the numerous harmful effects of plastic particles on human health and other species. There is a significant void in research on the toxicity of airborne microplastics and nanoplastics upon inhalation, which has significant toxicological potential in the future. Further study is needed to determine the influence of microplastic and nanoplastic on pulmonary diseases

X-RiSAWOZ: High-quality end-to-end multilingual dialogue datasets and few-shot agents

International audienceTask-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost, we apply manual editing to automatically translated data. We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed EnglishHindi language. X-RiSAWOZ has more than 18,000 human-verified dialogue utterances for each language, and unlike most multilingual prior work, is an end-to-end dataset for building fully-functioning agents.The many difficulties we encountered in creating X-RiSAWOZ led us to develop a toolset to accelerate the post-editing of a new language dataset after translation. This toolset improves machine translation with a hybrid entity alignment technique that combines neural with dictionary-based methods, along with many automated and semi-automated validation checks.We establish strong baselines for X-RiSAWOZ by training dialogue agents in the zero- and few-shot settings where limited gold data is available in the target language. Our results suggest that our translation and post-editing methodology and toolset can be used to create new high-quality multilingual dialogue agents cost-effectively. Our dataset, code, and toolkit are released open-source

Estimation of tuberculosis incidence at subnational level using three methods to monitor progress towards ending TB in India, 2015–2020

Objectives We verified subnational (state/union territory (UT)/district) claims of achievements in reducing tuberculosis (TB) incidence in 2020 compared with 2015, in India.Design A community-based survey, analysis of programme data and anti-TB drug sales and utilisation data.Setting National TB Elimination Program and private TB treatment settings in 73 districts that had filed a claim to the Central TB Division of India for progress towards TB-free status.Participants Each district was divided into survey units (SU) and one village/ward was randomly selected from each SU. All household members in the selected village were interviewed. Sputum from participants with a history of anti-TB therapy (ATT), those currently experiencing chest symptoms or on ATT were tested using Xpert/Rif/TrueNat. The survey continued until 30 Mycobacterium tuberculosis cases were identified in a district.Outcome measures We calculated a direct estimate of TB incidence based on incident cases identified in the survey. We calculated an under-reporting factor by matching these cases within the TB notification system. The TB notification adjusted for this factor was the estimate by the indirect method. We also calculated TB incidence from drug sale data in the private sector and drug utilisation data in the public sector. We compared the three estimates of TB incidence in 2020 with TB incidence in 2015.Results The estimated direct incidence ranged from 19 (Purba Medinipur, West Bengal) to 1457 (Jaintia Hills, Meghalaya) per 100 000 population. Indirect estimates of incidence ranged between 19 (Diu, Dadra and Nagar Haveli) and 788 (Dumka, Jharkhand) per 100 000 population. The incidence using drug sale data ranged from 19 per 100 000 population in Diu, Dadra and Nagar Haveli to 651 per 100 000 population in Centenary, Maharashtra.Conclusion TB incidence in 1 state, 2 UTs and 35 districts had declined by at least 20% since 2015. Two districts in India were declared TB free in 2020

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset