Relooking the monkeypox virus during this present outbreak: epidemiology to therapeutics and vaccines.

OBJECTIVE: The recent monkeypox disease outbreak is another significant threat during the ongoing COVID-19 pandemic. This viral disease is zoonotic and contagious. The viral disease outbreak is considered the substantial infection possessed by the Orthopoxvirus family species after the smallpox virus' obliteration, a representative of the same family. It has potentially threatened the Republic of Congo's regions and certain African subcontinent zones. Although repeated outbreaks have been reported in several parts of the world, as conferred from the epidemiological data, very little is explored about the disease landscape. Thus, here we have reviewed the current status of the monkeypox virus along with therapeutic options available to humanity. MATERIALS AND METHODS: We have accessed and reviewed the available literature on the monkeypox virus to highlight its epidemiology, pathogenicity, virulence, and therapeutic options available. For the review, we have searched different literature and database such as PubMed, PubMed Central, Google Scholar, Web of Science, Scopus, etc., using different keywords such as "monkeypox", "Orthopox", "smallpox", "recent monkeypox outbreak", "therapeutic strategies", "monkeypox vaccines", etc. This review has included most of the significant references from 1983 to 2022. RESULTS: It has been reported that the monkeypox virus shows a remarkable similarity with smallpox during the ongoing outbreak. Sometimes, it creates considerable confusion due to misdiagnosis and similarity with smallpox. The misdiagnosis of the disease should be immediately corrected by rendering some cutting-edge techniques especially intended to isolate the monkeypox virus. The pathophysiology and the histopathological data imply the immediate need to design effective therapeutics to confer resistance against the monkeypox virus. Most importantly, the potential implications of the disease are not given importance due to the lack of awareness programs. Moreover, specific evolutionary evidence is crucial for designing effective therapeutic strategies that confer high resistance, particularly against this species. CONCLUSIONS: The review focuses on a brief overview of the recent monkeypox virus outbreak, infection biology, epidemiology, transmission, clinical symptoms, and therapeutic aspects. Such an attempt will support researchers, policymakers, and healthcare professionals for better treatment and containment of the infection caused by the monkeypox virus.

COVID-19 vaccines and vaccination program for aging adults.

OBJECTIVE: COVID-19 vaccines have developed quickly, and vaccination programs have started in most countries to fight the pandemic. The aging population is vulnerable to different diseases, also including the COVID-19. A high death rate of COVID-19 was noted from the vulnerable aging population. A present scenario regarding COVID-19 vaccines and vaccination program foraging adults had been discussed. MATERIALS AND METHODS: This paper reviews the current status and future projections till 2050 of the aging population worldwide. It also discusses the immunosenescence and inflammaging issues facing elderly adults and how it affects the vaccinations such as influenza, pneumococcal, and herpes zoster. RESULTS: This paper recommends clinical trials for all approved COVID-19 vaccines targeting the elderly adult population and to project a plan to develop a next-generation COVID-19 vaccine. CONCLUSIONS: The review has mapped the COVID-19 vaccination status from the developed and developing countries for the elderly population. Finally, strategies to vaccinate all elderly adults globally against COVID-19 to enhance longevity has been suggested.

Evaluation of molecular interaction, physicochemical parameters and conserved pattern of SARS-CoV-2 Spike RBD and hACE2: in silico and molecular dynamics approach.

OBJECTIVE: Recent pandemic virus SARS-CoV-2 is a global warning for the healthcare system. The spike protein of virus SARS-CoV-2 is significant because of two reasons. Firstly, the spike protein of this virus binds with the human ACE2 (hACE2) receptor. Secondly, it has several antigenic regions that might be targeted for vaccine development. However, the structural analytical data for the spike protein of this virus is not available. MATERIALS AND METHODS: Here, we performed an analysis to understand the structural two subunits of S glycoprotein (S gp) of SARS-CoV-2. Further, an analysis of secondary structure components and the tertiary structure analysis of RBD was carried out. We also performed molecular interaction analysis between S gp of this virus and hACE2 as well as between SARS-CoV S gp and hACE2 to compare the binding properties of these two viruses. RESULTS: We noted that the molecular interaction of SARS-CoV-2 S gp and hACE2 form eleven hydrogen bonds, while the molecular interaction of SARS-CoV S gp and hACE2 receptor form seven hydrogen bonds, indicating that the molecular interaction of SARS-CoV-2 S gp and hACE2 receptor is more stable than SARS-CoV S gp and hACE2 receptor. The pairwise sequence alignment of S gp SARS-CoV and SARS-CoV-2 shows several conserved residues of these two proteins. Besides, conserved pattern analysis of SARS-CoV-2 S gp and hACE2 revealed the presence of several highly conserved regions for these two proteins. The molecular dynamics simulation shows a stable interplay between SARS-CoV-2 S gp with the hACE2 receptor. CONCLUSIONS: The present study might help determine the SARS-CoV-2 virus entrance mechanism into the human cell. Moreover, the understanding of the conserved regions may help in the process of therapeutic development from the infection of the deadly virus.

Diabetes and COVID-19: a major challenge in pandemic period?

OBJECTIVE: Diabetes is a lifestyle disease and it has become an epidemic worldwide in recent decades. In the ongoing COVID-19 pandemic situation, diabetes has become a serious health concern since large numbers of patients are vulnerable to die from the virus. Thus, diabetic patients affected by COVID-19 cause a major health crisis now. Reports show that large occurrence of diabetes makes it a serious comorbidity in COVID-19 patients. MATERIALS AND METHODS: It is crucial to understand how COVID-19 affects diabetes patients. This paper has reviewed published literature extensively to understand the pattern, importance, care, and medication. RESULTS: This review summarizes the association between COVID-19 and diabetes in terms of susceptibility for pneumonia and other diseases. It also discusses the harshness of COVID-19 with diabetes populations and immunological impacts. It further adds the ACE2 receptor role in diabetes with COVID-19 patients. CONCLUSIONS: Finally, this paper illustrates different types of diabetes management techniques, such as blood glucose management, self-management, mental health management, and therapeutic management. It also summarizes the current knowledge about diabetic patients with COVID-19 to fight this pandemic.

SARS-CoV-2 causing pneumonia-associated respiratory disorder (COVID-19): diagnostic and proposed therapeutic options.

SARS-CoV-2 is responsible for the outbreak of severe respiratory illness (COVID-19) in Wuhan City, China and is now spreading rapidly throughout the world. The prompt outbreak of COVID-19 and its quick spread without any controllable measure defines the severity of the situation. In this crisis, a collective pool of knowledge about the advancement of clinical diagnostic and management for COVID-19 is a prerequisite. Here, we summarize all the available updates on the multidisciplinary approaches for the advancement of diagnosis and proposed therapeutic strategies for COVID-19. Moreover, the review discusses different aspects of the COVID-19, including its epidemiology; incubation period; the general clinical features of patients; the clinical features of intensive care unit (ICU) patients; SARS-CoV-2 infection in the presence of co-morbid diseases and the clinical features of pediatric patients infected with the SARS-CoV-2. Advances in various diagnostic approaches, such as the use of real-time polymerase chain reaction (RT-PCR), chest radiography, and computed tomography (CT) imaging; and other modern diagnostic methods, for this infection have been highlighted. However, due to the unavailability of adequate evidence, presently there are no officially approved drugs or vaccines available against SARS-CoV-2. Additionally, we have discussed various therapeutic strategies for COVID-19 under different categories, like the possible treatment plans with drug (antiviral drugs and anti-cytokines) therapy for disease prevention. Lastly, potentials candidates for the vaccines against SARS-CoV-2 infection have been described. Collectively, the review provides an overview of the SARS-CoV-2 infection outbreak along with the recent advancements and strategies for diagnosis and therapy of COVID-19.

Author Correction: Room-temperature valley coherence in a polaritonic system.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Room-temperature valley coherence in a polaritonic system.

The emerging field of valleytronics aims to coherently manipulate an electron and/or hole's valley pseudospin as an information bearing degree of freedom (DOF). Monolayer transition metal dichalcogenides, due to their strongly bound excitons, their degenerate valleys and their seamless interfacing with photons are a promising candidate for room temperature valleytronics. Although the exciton binding energy suggests room temperature valley coherence should be possible, it has been elusive to-date. A potential solution involves the formation of half-light, half-matter cavity polaritons based on 2D material excitons. It has recently been discovered that cavity polaritons can inherit the valley DOF. Here, we demonstrate the room temperature valley coherence of valley-polaritons by embedding a monolayer of tungsten diselenide in a monolithic dielectric cavity. The extra decay path introduced by the exciton-cavity coupling, which is free from decoherence, is the key to room temperature valley coherence preservation. These observations paves the way for practical valleytronic devices.

Textural pattern classification for oral squamous cell carcinoma.

Despite being an area of cancer with highest worldwide incidence, oral cancer yet remains to be widely researched. Studies on computer-aided analysis of pathological slides of oral cancer contribute a lot to the diagnosis and treatment of the disease. Some researches in this direction have been carried out on oral submucous fibrosis. In this work an approach for analysing abnormality based on textural features present in squamous cell carcinoma histological slides have been considered. Histogram and grey-level co-occurrence matrix approaches for extraction of textural features from biopsy images with normal and malignant cells are used here. Further, we have used linear support vector machine classifier for automated diagnosis of the oral cancer, which gives 100% accuracy.

AutoIHC-scoring: a machine learning framework for automated Allred scoring of molecular expression in ER- and PR-stained breast cancer tissue.

In prognostic evaluation of breast cancer Immunohistochemical (IHC) markers namely, oestrogen receptor (ER) and progesterone receptor (PR) are widely used. The expert pathologist investigates qualitatively the stained tissue slide under microscope to provide the Allred score; which is clinically used for therapeutic decision making. Such qualitative judgment is time-consuming, tedious and more often suffers from interobserver variability. As a result, it leads to imprecise IHC score for ER and PR. To overcome this, there is an urgent need of developing a reliable and efficient IHC quantifier for high throughput decision making. In view of this, our study aims at developing an automated IHC profiler for quantitative assessment of ER and PR molecular expression from stained tissue images. We propose here to use CMYK colour space for positively and negatively stained cell extraction for proportion score. Also colour features are used for quantitative assessment of intensity scoring among the positively stained cells. Five different machine learning models namely artificial neural network, Naïve Bayes, K-nearest neighbours, decision tree and random forest are considered for learning the colour features using average red, green and blue pixel values of positively stained cell patches. Fifty cases of ER- and PR-stained tissues have been evaluated for validation with the expert pathologist's score. All five models perform adequately where random forest shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.9192). In the proposed approach the average variation of diaminobenzidine (DAB) to nuclear area from the expert's score is found to be 7.58%, as compared to 27.83% for state-of-the-art ImmunoRatio software.

Imprint cytology-based breast malignancy screening: an efficient nuclei segmentation technique.

Imprint cytology (IC) refers to one of the most reliable, rapid and affordable techniques for breast malignancy screening; where shape variation of H&E stained nucleus is examined by the pathologists. This work aims at developing an automated and efficient segmentation algorithm by integrating Lagrange's interpolation and superpixels in order to delineate overlapped nuclei of breast cells (normal and malignant). Subsequently, a computer assisted IC tool has been designed for breast cancer (BC) screening. The proposed methodology consists of mainly three subsections: gamma correction for preprocessing, single nuclei segmentation and segmentation of overlapping nuclei. Single nuclei segmentation combines histogram-based thresholding and morphological operations; where segmentation of overlapping nuclei includes concave point detection, Lagrange's interpolation for overlapping arc area detection and the fine segmentation of overlapped arc area by superpixels. Total 16 significant features (p < 0.05) quantifying shape and texture of nucleus were extracted, and random forest (RF) classifier was skilled for automated screening. The proposed methodology has been tested on 120 IC images (approximately 12 000 nuclei); where 98% segmentation accuracy and 99% classification accuracy were achieved. Besides, performance evaluation was studied by using Jaccard's index (= 94%), correlation coefficient (= 95%), Dice similarity coefficient (= 97%) and Hausdorff distance (= 43%). The proposed approach could offer benefit to the pathologists for confirmatory BC screening with improved accuracy and could potentially lead to a better shape understanding of malignant nuclei.

MRF-ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images.

Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells. The proposed segmentation methodology is found to have F-measure 0.95. Artificial neural network is subsequently used to obtain intensity-based score for ER cells, from pixel colour intensity features. Simultaneously, proportion score - percentage of ER positive cells is computed via cell counting. The final ER score is computed by adding intensity and proportion scores - a standard Allred scoring system followed by pathologists. The classification accuracy for classification of cells by classifier in terms of F-measure is 0.9626. The problem of subjective interobserver ability is addressed by quantifying ER score from two expert pathologist and proposed methodology. The intraclass correlation achieved is greater than 0.90. The study has potential advantage of assisting pathologist in decision making over manual procedure and could evolve as a part of automated decision support system with other receptor scoring/analysis procedure.

Computational microscopic imaging for malaria parasite detection: a systematic review.

Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection.

A novel segmentation approach for noisy medical images using intuitionistic fuzzy divergence with neighbourhood-based membership function.

Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuzzy divergence-based thresholding. A neighbourhood-based membership function is defined here. The intuitionistic fuzzy divergence-based image thresholding technique using the neighbourhood-based membership functions yield lesser degradation of segmentation performance in noisy environment. Its ability in handling noisy images has been validated. The algorithm is independent of any parameter selection. Moreover, it provides robustness to both additive and multiplicative noise. The proposed scheme has been applied on three types of medical image datasets in order to establish its novelty and generality. The performance of the proposed algorithm has been compared with other standard algorithms viz. Otsu's method, fuzzy C-means clustering, and fuzzy divergence-based thresholding with respect to (1) noise-free images and (2) ground truth images labelled by experts/clinicians. Experiments show that the proposed methodology is effective, more accurate and efficient for segmenting noisy images.

Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.

In this paper, we propose a comprehensive image characterization cum classification framework for malaria-infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three-image segmentation algorithms (namely, rule-based, Chan-Vese-based and marker-controlled watershed methods), marker-controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F-statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features' subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria-infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F-statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria-infected stage classification.

Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia.

Anaemia is one of the most common diseases in the world population. Primarily anaemia is identified based on haemoglobin level; and then microscopically examination of peripheral blood smear is required for characterizing and confirmation of anaemic stages. In conventional approach, experts visually characterize abnormality present in the erythrocytes under light microscope, and this evaluation process is subjective in nature and error prone. In this study, we have proposed a methodology using machine learning techniques for characterizing erythrocytes in anaemia associated with anaemia using microscopic images of peripheral blood smears. First, peripheral blood smear images are preprocessed based on grey world assumption technique and geometric mean filter for reducing unevenness of background illumination and noise reduction. Then erythrocyte cells are segmented using marker-controlled watershed segmentation technique. The erythrocytes in anaemia, such as, tear drop, echinocyte, acanthocyte, elliptocyte, sickle cells and normal erythrocytes cells have been characterized and classified based on their morphological changes. Optimal subset of features, ranked by information gain measure provides highest classification performance using logistic regression classifier in comparison with other standard classifiers.

Automated diagnosis of oral cancer using higher order spectra features and local binary pattern: a comparative study.

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The objective of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, the approach introduced is used to grade the histopathological tissue sections into normal, OSF without dysplasia (OSFWD) and OSF with dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The main objective of this work is to evaluate the use of Higher Order Spectra (HOS) features and Local Binary Pattern (LBP) features extracted from the epithelial layer in classifying normal, OSFWD and OSFD. For this purpose, we extracted twenty three HOS features and nine LBP features and fed them to a Support Vector Machine (SVM) for automated diagnosis. One hundred and fifty eight images (90 normal, 42 OSFWD and 26 OSFD images) were used for analysis. LBP features provide a good sensitivity of 82.85% and specificity of 87.84%, and the HOS features provide higher values of sensitivity (94.07%) and specificity (93.33%) using SVM classifier. The proposed system, can be used as an adjunct tool by the onco-pathologists to cross-check their diagnosis.

Potentialities of induced pluripotent stem (iPS) cells for treatment of diseases.

Induced pluripotent stem (iPS) cell research has been growing a new height throughout the world due to its potentialities in medical applications. We can explore several therapeutic applications through the iPS cell research. In this review, we have first discussed the development of iPS cells, reprogramming factors, and effectiveness of iPS cells. Then we have emphasized the potential applications of iPS cells in pharmaceutical and medical sectors, such as, study of cellular mechanisms for spectrum of disease entities, disease-specific iPS cell lines for drugs discovery and development, toxicological studies of drugs development, personalized medicine, and regenerative medicine.

Decorin-mediated inhibition of proliferation and migration of the human trophoblast via different tyrosine kinase receptors.

Decorin (DCN), a decidua-derived TGFbeta-binding proteoglycan, negatively regulates proliferation, migration, and invasiveness of human extravillous trophoblast (EVT) cells in a TGFbeta-independent manner. The present study examined underlying mechanisms, in particular possible roles of epidermal growth factor receptor (EGFR), IGF receptor (IGFR)-I, and vascular endothelial growth factor receptor (VEGFR)-2. EVT cell sprouting from first-trimester chorionic villus explants in the presence or absence of TGFbeta-neutralizing antibody was inhibited with DCN, suggesting its negative regulatory role in situ. Inhibition of migration of the human EVT cell line HTR-8/SVneo in transwells undercoated with fibronectin was stronger when cells were briefly preincubated with DCN at 4 C (known to retard dissociation of receptor-ligand complex) than at 37 C, suggesting possible DCN action by cell membrane binding. Pretreatment of cells with an IGFR-I blocking agent, but not two EGFR blocking agents or a VEGFR blocking agent, significantly abrogated migration inhibitory effects of DCN, suggesting the involvement of IGFR-I but not EGFR or VEGFR in migration inhibition by DCN. On the other hand, pretreatment with either of the EGFR blocking agents, or the VEGFR blocking agent but not the IGFR-I blocking agent, blocked proliferation inhibitory effects of DCN, indicating the roles of EGFR and VEGFR, but not IGFR-I in antiproliferative action of DCN. EVT cells expressed EGFR, IGFR-I, and VEGFR-2. IGFR-I and VEGF-R2 were phosphorylated in the presence of their natural ligands as well as DCN, and these events were blocked by pretreatment with respective receptor blocking agents indicating DCN-mediated activation of these receptors. In conclusion, DCN effects on EVT cells are mediated selectively by multiple tyrosine kinase receptors.