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.

Modulation and study of photoblinking behavior in dye doped silver-silica core-shell nanoparticles for localization super-resolution microscopy

Blinking of fluorescent nanoparticles is a compelling phenomenon with widely debated mechanisms. The ability to inhibit or control blinking is important for applications in the field of optical, semiconductor and fluorescent imaging. Self-blinking nanomaterials are also attractive labels for localization-based super-resolution microscopy. In this work, we have synthesized silver core silica nanoparticles (Ag@SiO2) doped with Rhodamine 110 and studied the parameters that affect blinking. We found that under nitrogen rich conditions the nanoparticles shifted towards higher duty cycles. Also, it was found that hydrated nanoparticles showed a less drastic response to nitrogen rich conditions as compared to dried nanoparticles, indicating that surrounding matrix played a role in the response of nanoparticles to molecular oxygen. Further, the blinking is not a multi-body phenomena, super-resolution localization combined with intensity histogram analysis confirmed that single particles are emitting.

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.

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.

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.

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.

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.

COX-2-mediated stimulation of the lymphangiogenic factor VEGF-C in human breast cancer

Increased expression of COX-2 or VEGF-C has been correlated with progressive disease in certain cancers. Present study utilized several human breast cancer cell lines (MCF-7, T-47D, Hs578T and MDA-MB-231, varying in COX-2 expression) as well as 10 human breast cancer specimens to examine the roles of COX-2 and prostaglandin E (EP) receptors in VEGF-C expression or secretion, and the relationship of COX-2 or VEGF-C expression to lymphangiogenesis. We found a strong correlation between COX-2 mRNA expression and VEGF-C expression or secretion levels in breast cancer cell lines and VEGF-C expression in breast cancer tissues. Expression of LYVE-1, a selective marker for lymphatic endothelium, was also positively correlated with COX-2 or VEGF-C expression in breast cancer tissues. Inhibition of VEGF-C expression and secretion in the presence of COX-1/2 or COX-2 inhibitors or following downregulation of COX-2 with COX-2 siRNA established a stimulatory role COX-2 in VEGF-C synthesis by breast cancer cells. EP1 as well as EP4 receptor antagonists inhibited VEGF-C production indicating the roles of EP1 and EP4 in VEGF-C upregulation by endogenous PGE₂. Finally, VEGF-C secretion by MDA-MB-231 cells was inhibited in the presence of kinase inhibitors for Her-2/neu, Src and p38 MAPK, indicating a requirement of these kinases for VEGF-C synthesis. These results, for the first time, demonstrate a regulatory role of COX-2 in VEGF-C synthesis (and thereby lymphangiogenesis) in human breast cancer, which is mediated at least in part by EP1/EP4 receptors.

RNA interference: potential therapeutic targets

One of the most exciting findings in recent years has been the discovery of RNA interference (RNAi). RNAi methodologies hold the promise to selectively inhibit gene expression in mammals. RNAi is an innate cellular process activated when a double-stranded RNA (dsRNA) molecule of greater than 19 duplex nucleotides enters the cell, causing the degradation of not only the invading dsRNA molecule, but also single-stranded (ssRNAs) RNAs of identical sequences, including endogenous mRNAs. The use of RNAi for genetic-based therapies has been widely studied, especially in viral infections, cancers, and inherited genetic disorders. As such, RNAi technology is a potentially useful method to develop highly specific dsRNA-based gene-silencing therapeutics.

Factors regulating trophoblast migration and invasiveness: possible derangements contributing to pre-eclampsia and fetal injury

Impaired trophoblast invasiveness and spiral arterial remodelling, which results in poor placental perfusion during early pregnancy, is believed to cause fetal injury and growth retardation, and also endothelial cell activation/dysfunction in a susceptible mother, leading to clinical manifestations of pre-eclampsia. This article briefly reviews the regulatory roles of certain locally active factors in trophoblast migration and invasiveness. This background is then used to discuss and debate whether derangements or dysfunction of some of these factors can manifest as early serum markers predictive of the disease, as opposed to the intermediate and late stage markers which may reflect manifestations and consequences of the disease. Of particular significance are the observed derangements in uPA/uPAR/PAI system, IGFBP-1, HGF, HB-EGF and TGFbeta, factors which are known to regulate trophoblast migration and invasiveness in situ. An emphasis is placed on the need for longitudinal studies in order to identify predictive serum markers which may help strategies for prevention or amelioration of fetal injury and pre-eclampsia.

Human placental trophoblast as an in vitro model for tumor progression

The human placenta is a highly invasive tumor-like structure in which a subpopulation of placental trophoblast cells known as the "extravillous trophoblast" (EVT) invades the uterine decidua and its vasculature to establish adequate fetal-maternal exchange of molecules. By utilizing in vitro-propagated short-lived EVT cell lines we found that molecular mechanisms responsible for their invasiveness are identical to those of cancer cells; however, unlike cancer cells, their proliferation, migration, and invasiveness in situ are stringently controlled by decidua-derived transforming growth factor (TGF)-beta. By SV40T antigen transfection of normal EVT cells followed by a forced crisis regimen in culture we produced an immortalized premalignant derivative that is hyperproliferative, hyperinvasive, and deficient in gap-junctional intercellular communication. Both premalignant and malignant EVT (JAR and JEG-3 choriocarcinoma) cell lines were found to be TGF-beta-resistant. Using these cell lines, we investigated genetic changes responsible for transition of the normal EVT cells to premalignant and malignant phenotype. Hyperinvasiveness in both cases resulted from a downregulation of tissue inhibitor of metalloprotease (TIMP)-1 and plasminogen activator inhibitor (PAI)-1 genes. In contrast to normal EVT cells, both cell types failed to upregulate these genes in response to TGF-beta. Loss of TGF-beta response in malignant EVT cells was explained by the loss of expression of Smad3 gene. Differential mRNA display of normal and premalignant EVT cells identified up- and down-regulation of numerous known or novel genes in premalignant EVT cells, with potential oncogenic and (or) tumor-suppressor functions, e.g., loss of fibronectin and insulin-like growth factor binding protein (IGFBP-5). Premalignant EVT cells also lost IGF receptor type 2 (IGFR-II). IGFBP-5 was shown to be a negative regulator of IGF-1-induced proliferation of premalignant EVT cells, so that loss of IGFBP-5 as well as IGFR-II permitted their unrestricted proliferation in an IGF-I-rich microenvironment of the fetal-maternal interface. The present model may be a good prototype for identifying genetic changes underlying epithelial tumor progression.