Identification of novel biomarkers and potential molecular targets for uterine cancer using network-based approach.

A better understanding of incidences at the cellular level in uterine cancer is necessary for its effective treatment and favourable prognosis. Till date, it lacks appropriate molecular target-based treatment because of unknown molecular mechanisms that proceed to cancer and no drug has shown the required results of treatment with less severe side effects. Uterine Cancer is one of the top five cancer diagnoses and among the ten most common death-causing cancer in the United States of America. There is no FDA-approved drug for it yet. Therefore, it became necessary to identify the molecular targets for molecular targeted therapy of this widely prevalent cancer type. For this study, we used a network-based approach to the list of the deregulated (both up and down-regulated) genes taking adjacent p-Value ≤ 0.05 as significance cut off for the mRNA data of uterine cancer. We constructed the protein-protein interaction (PPI) network and analyzed the degree, closeness, and betweenness centrality-like topological properties of the PPI network. Then we traced the top 30 genes listed from each topological property to find the key regulators involved in the endometrial cancer (ECa) network. We then detected the communities and sub-communities from the PPI network using the Cytoscape network analyzer and Louvain modularity optimization method. A set of 26 (TOP2A, CENPE, RAD51, BUB1, BUB1B, KIF2C, KIF23, KIF11, KIF20A, ASPM, AURKA, AURKB, PLK1, CDC20, CDKN2A, EZH2, CCNA2, CCNB1, CDK1, FGF2, PRKCA, PGR, CAMK2A, HPGDS, and CDCA8) genes were found to be key genes of ECa regulatory network altered in disease state and might be playing the regulatory role in complex ECa network. Our study suggests that among these genes, KIF11 and H PGDS appeared to be novel key genes identified in our research. We also identified these key genes interactions with miRNAs.

Predictive modeling and web-based tool for cervical cancer risk assessment: A comparative study of machine learning models.

In today's digital era, the rapid growth of databases presents significant challenges in data management. In order to address this, we have developed and designed CHAMP (Cervical Health Assessment using machine learning for Prediction), which is a user interface tool that can effectively and efficiently handle cervical cancer databases to detect patterns for future prediction diagnosis. CHAMP employs various machine learning algorithms which include XGBoost, SVM, Naive Bayes, AdaBoost, Decision Tree, and K-Nearest Neighbors in order to predict cervical cancer accurately. Moreover, this tool also designates to evaluate and optimize processes, to retrieve the significantly augmented algorithm for predicting cervical cancer. Although, the developed user interface tool was implemented in Python 3.9.0 using Flask, which provides a personalized and intuitive platform for pattern detection. The current study approach contributes to the accurate prediction and early detection of cervical cancer by leveraging the power of machine learning algorithms and comprehensive validation tools, which aim to provide learned decision-making.•CHAMP is a user interface tool which is designed for the detection of patterns for future diagnosis and prognosis of cervical cancer.•Various machine learning algorithms are employed for accurate prediction.•This tool provides personalized and intuitive data analysis which enables informed decision-making in healthcare.

Imprinting and Reproductive Health: A Toxicological Perspective.

This overview discusses the role of imprinting in the development of an organism, and how exposure to environmental chemicals during fetal development leads to the physiological and biochemical changes that can have adverse lifelong effects on the health of the offspring. There has been a recent upsurge in the use of chemical products in everyday life. These chemicals include industrial byproducts, pesticides, dietary supplements, and pharmaceutical products. They mimic the natural estrogens and bind to estradiol receptors. Consequently, they reduce the number of receptors available for ligand binding. This leads to a faulty signaling in the neuroendocrine system during the critical developmental process of 'imprinting'. Imprinting causes structural and organizational differentiation in male and female reproductive organs, sexual behavior, bone mineral density, and the metabolism of exogenous and endogenous chemical substances. Several studies conducted on animal models and epidemiological studies provide profound evidence that altered imprinting causes various developmental and reproductive abnormalities and other diseases in humans. Altered metabolism can be measured by various endpoints such as the profile of cytochrome P-450 enzymes (CYP450's), xenobiotic metabolite levels, and DNA adducts. The importance of imprinting in the potentiation or attenuation of toxic chemicals is discussed.

The impact of geo-political socio-economic factors on vaccine dissemination trends: a case-study on COVID-19 vaccination strategies.

BACKGROUND: The world in recent years has seen a pandemic of global scale. To counter the widespread loss of life and severe repercussions, researchers developed vaccinations at a fast pace to immunize the population. While the vaccines were developed and tested through extensive human trials, historically vaccines have been known to evoke mixed sentiments among the generic demographics. In the proposed study, we aim to reveal the impact of political and socio-economic factors on SARS-Cov-2 vaccination trends observed in two hundred and seventeen countries spread across the six continents. METHODS: The study had hypothesized that the citizens who have lower trust in their government would be less inclined towards vaccination programs. To test this hypothesis, vaccination trends of nations under authoritarian rule were compared against democratic nations. Further, the study was synthesized with Cov-2 vaccination data which was sourced from Our World Data repository, which was sampled among 217 countries spread across the 6 continents. The study was analyzed with exploratory data analysis and proposed with relevance and impacting factor that was considered for vaccine dissemination in comparison with the literacy rate of the nations. Another impacting factor the study focused on for the vaccination dissemination trends was the health expenses of different nations. The study has been synthesized on political and socio-economic factors where the features were ardently study in retrospect of varied socio- economic features which may include country wise literacy rate, overall GDP rate, further we substantiated the work to address the political factors which are discussed as the country status of democratic or having other status. RESULTS: The comparison of trends showed that dissemination of SARS-Cov-2 vaccines had been comparable between the two-opposing types of governance. The major impact factor behind the wide acceptance of the SARS-Cov-2 vaccine was the expenditure done by a country on healthcare. These nations used a large number of vaccines to administer to their population and the trends showed positive growth. The overall percentage of vaccine utilized by countries in quantitative terms are Pfizer/BioNTech (17.55%), Sputnik V (7.08%), Sinovac (6.98%), Sinopharm/Beijing (10.04%), Oxford/AstraZeneca (19.56%), CanSino (2.85%), Moderna (12.05%), Covaxin (3.28%), JohnsonandJohnson (10.89%), Sputnik Light (3.07%), Novavax (3.49%). While the nations with the lowest healthcare expenses failed to keep up with the demand and depended on vaccines donated by other countries to protect their population. CONCLUSIONS: The analysis revealed strong indicators that the nations which spend more on healthcare were the ones that had the best SARS-Cov-2 vaccination rollout. To further support decision-making in the future, countries should address the trust and sentiment of their citizens towards vaccination. For this, expenses need to be made to develop and promote vaccines and project them as positive health tools.

GUD-VE visualization tool for physicochemical properties of proteins.

The physicochemical properties of primary sequences of proteins helps in determining both the structure and biological functions. The sequence analysis of the proteins and nucleic acids is most fundamental element of bioinformatics. Without these elements, it is impossible to gain insight deeper molecular and biochemical mechanisms. For this purpose, the computational methods like bioinformatics tools assist experts and novices alike in resolving issues relating to protein analysis. Similarly, this proposed work, for the graphical user interface (GUI) based prediction and visualization through the computations-based method done on Jupyter Notebook with tkinter package which allows the creation of a program on a local host platform and accessed by the programmer.•When it is queried with a protein sequence, it predicts physicochemical parameters of the peptides.•Users can choose to visualize the findings acquired either anonymously or on the user-specified email address and compare the biophysical properties of one protein with other using amino acids (AA) sequences. The aim of this paper is to meet the requirements of experimentalists, not just hardcore bioinformaticians related to biophysical properties prediction and comparison with other proteins. The code for it has been uploaded on GitHub (an online repository of codes) in private mode.

Extracting information and inferences from a large text corpus.

The usage of various software applications has grown tremendously due to the onset of Industry 4.0, giving rise to the accumulation of all forms of data. The scientific, biological, and social media text collections demand efficient machine learning methods for data interpretability, which organizations need in decision-making of all sorts. The topic models can be applied in text mining of biomedical articles, scientific articles, Twitter data, and blog posts. This paper analyzes and provides a comparison of the performance of Latent Dirichlet Allocation (LDA), Dynamic Topic Model (DTM), and Embedded Topic Model (ETM) techniques. An incremental topic model with word embedding (ITMWE) is proposed that processes large text data in an incremental environment and extracts latent topics that best describe the document collections. Experiments in both offline and online settings on large real-world document collections such as CORD-19, NIPS papers, and Tweet datasets show that, while LDA and DTM is a good model for discovering word-level topics, ITMWE discovers better document-level topic groups more efficiently in a dynamic environment, which is crucial in text mining applications.

Sarve: synthetic data and local differential privacy for private frequency estimation.

The collection of user attributes by service providers is a double-edged sword. They are instrumental in driving statistical analysis to train more accurate predictive models like recommenders. The analysis of the collected user data includes frequency estimation for categorical attributes. Nonetheless, the users deserve privacy guarantees against inadvertent identity disclosures. Therefore algorithms called frequency oracles were developed to randomize or perturb user attributes and estimate the frequencies of their values. We propose Sarve, a frequency oracle that used Randomized Aggregatable Privacy-Preserving Ordinal Response (RAPPOR) and Hadamard Response (HR) for randomization in combination with fake data. The design of a service-oriented architecture must consider two types of complexities, namely computational and communication. The functions of such systems aim to minimize the two complexities and therefore, the choice of privacy-enhancing methods must be a calculated decision. The variant of RAPPOR we had used was realized through bloom filters. A bloom filter is a memory-efficient data structure that offers time complexity of O(1). On the other hand, HR has been proven to give the best communication costs of the order of log(b) for b-bits communication. Therefore, Sarve is a step towards frequency oracles that exhibit how privacy provisions of existing methods can be combined with those of fake data to achieve statistical results comparable to the original data. Sarve also implemented an adaptive solution enhanced from the work of Arcolezi et al. The use of RAPPOR was found to provide better privacy-utility tradeoffs for specific privacy budgets in both high and general privacy regimes.

ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display.

We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.

Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms.

Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions.

Ayurveda and Allopathic Therapeutic Strategies in Coronavirus Pandemic Treatment 2020.

Purpose of Review: In the last month of 2019, i.e., December, COVID-19 hit Wuhan city in China. Since then, it has infected more than 210 countries and nearly about 33.4 million people with one million deaths globally. It is a viral disease with flu-like symptoms; hence, prevention and management is the best option to be adopted for its cure. Recent Findings: Many healthcare systems, scientists, and researchers are fighting for the cure of this pandemic. Ayurvedic and allopathic treatments have been studied extensively and approached for the cure of COVID-19. In addition to ayurvedic treatments, the Ministry of Ayush, India, has also recommended many remedies to boost up immunity. Allopathic studies involved several antiviral drugs which were used in different combinations for the treatment of COVID-19. Summary: Comparative analysis of Ayurveda and allopathic treatment strategies were carried out in the present study. Depending upon the patient's conditions and symptoms, Ayurveda is useful for the treatment of COVID-19. Allopathic treatments inhibit viral infection by targeting majorly endocytosis, and angiotensin-converting enzyme (Ace) receptor signaling. In this article, we summarize different ayurvedic and allopathic medicines and treatment strategies which have been used for the treatment of COVID-19, a global pandemic.

Disease surveillance during a large religious mass gathering in India: The Prayagraj Kumbh 2019 experience.

BACKGROUND: Mass gathering (MG) events are associated with public health risks. During the period January 14 to March 4, 2019, Kumbh Mela in Prayagraj, India was attended by an estimated 120 million visitors. An onsite disease surveillance was established to identify and respond to disease outbreaks. METHODS: A health coordination committee was established for planning. Disease surveillance was prioritized and risk assessment was done to identify diseases/conditions based on epidemic potential, severity of illness, and reporting requirement under the International Health Regulations (IHR) of 2005. A daily indicator and event-based disease surveillance was planned. The indicator-based surveillance (IBS) manually and electronically recorded data from patient hospital visits and collected MG area water testing data to assess trends. The event-based surveillance (EBS) helped identify outbreak signals based on pre-identified event triggers from the media, private health facilities, and the food safety department. Epidemic intelligence was used to analyse the data and events to detect signals, verify alerts, and initiate the response. RESULTS: At Kumbh Mela, disease surveillance was established for 22 acute diseases/syndromes. Sixty-five health facilities reported 156 154 illnesses (21% of a total 738 526 hospital encounters). Among the reported illnesses, 95% (n = 148 834) were communicable diseases such as acute respiratory illness (n = 52 504, 5%), acute fever (n = 41 957, 28%), and skin infections (n = 27 094, 18%). The remaining 5% (n = 7300) were non-communicable diseases (injuries n = 6601, 90%; hypothermia n = 224, 3%; burns n = 210, 3%). Water samples tested inadequate for residual chlorine in 20% of samples (102/521). The incident command centre generated 12 early warning signals from IBS and EBS: acute diarrheal disease (n = 8, 66%), vector-borne disease (n = 2, 16%), vaccine-preventable disease (n = 1, 8%), and thermal event (n = 1, 8%). There were two outbreaks (acute gastroenteritis and chickenpox) that were investigated and controlled. CONCLUSIONS: This onsite disease surveillance imparted a public health legacy by successfully implementing an epidemic intelligence enabled system for early disease detection and response to monitor public health risks. Acute respiratory illnesses emerged as a leading cause of morbidity among visitors. Future MG events should include disease surveillance as part of planning and augment capacity for acute respiratory illness diagnosis and management.

Amelioration of colitis in mouse model by exploring antioxidative potentials of an indigenous probiotic strain of Lactobacillus fermentum Lf1.

Based on the preliminary screening of eight indigenous putative probiotic Lactobacilli, Lactobacillus fermentum Lf1 was selected for assessing its antioxidative efficacy in DSS colitis mouse model based on its ability to enhance the expression of "Nrf2" by 6.43-fold and malondialdehyde (MDA) inhibition by 78.1 ± 0.24% in HT-29 cells under H2O2 stress. The Disease Activity Index and histological scores of Lf1-treated mice were lower than the control group. However, expression of "Nrf2" was not observed in Lf1-treated mice. A significant increase in the expression of antioxidative enzymes such as SOD2 and TrxR-1 was recorded in both of the groups. The expression of SOD2 was significantly downregulated in colitis-induced mice by -100.00-fold relative to control group, and the downregulation was considerably reduced to -37.04-fold in colitis Lf1 treatment group. Almost, a similar trend was recorded in case of "thioredoxin" expression, though "CAT" was refractile to expression. The Lf1-treated group had decreased malondialdehyde level as compared to colitis control (37.92 ± 6.31 versus 91.13 ± 5.76 µM/g). These results point towards Lf1-induced activation of the antioxidant enzyme system in the mouse model and its prospects to be explored as a new strategy for IBD management.

Modulation of anti-inflammatory response in lipopolysaccharide stimulated human THP-1 cell line and mouse model at gene expression level with indigenous putative probiotic lactobacilli.

The anti-inflammatory potential of eight indigenous probiotic Lactobacillus isolates was evaluated in vitro in terms of modulating the expression of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) in human acute monocytic leukemia (THP-1) cells under inflammatory conditions. Amongst these, Lactobacillus plantarum Lp91 was the most potent anti-inflammatory strain as it evoked a significant (P < 0.001) down-regulation of TNF-α by -1.45-fold relative to the control in THP-1 cells. However, in terms of IL-6 expression, all the strains could up-regulate its expression considerably at different levels. Hence, based on in vitro expression of TNF-α, Lp91 was selected for in vivo study in lipopolysaccharide (LPS)-induced mouse model to look at the expression of TNF-α, IL-6, monocyte chemotactic protein-1 (MCP-1), vascular cell adhesion molecule-1 (VCAM-1), intercellular adhesion molecule (ICAM-1) and E-selectin in mouse aorta. In LPS challenged (2 h) mice group fed with Lp91 for 10 days, TNF-α, IL-6, MCP-1, VCAM-1, ICAM-1 and E-selectin expressions were significantly down-regulated by 3.10-, 10.02-, 4.22-, -3.14-, 2.28- and 5.71-fold relative to control conditions. In conclusion, Lp91 could serve as a candidate probiotic strain to explore it as a possible biotherapeutic anti-inflammatory agent against inflammatory diseases including cardiovascular disease.

Inclusion complex of colchicine in hydroxypropyl-ß-cyclodextrin tenders better solubility and improved pharmacokinetics.

CONTEXT: Colchicine (CLC) causes cell death by destabilizing the tubulin unit. However, it ionizes at physiological pH resultant low bioavailability and therapeutic efficacy. OBJECTIVES: We have attempted to augment the bioavailability of CLC by fabricating the inclusion complex with hydroxypropyl-ß-cyclodextrin (HP-ß-CD). MATERIALS AND METHODS: CLC-HP-ß-CD inclusion complex was prepared and evaluated with Fourier-transform infrared spectroscopy, differential scanning calorimetry, powder X-ray diffractometry, scanning electron microscopy, (1)H nuclear magnetic resonance ((1)H NMR) spectroscopy and rotating frame overhauser enhancement spectroscopy (ROESY). Oral bioavailability of CLC-HP-ß-CD inclusion complex was analyzed using high performance liquid chromatography method. RESULTS AND DISCUSSION: Our phase-solubility data indicated the formation of a stable complex with K(c) ~0.31 mM(-1) at pH 7.4. (1)H NMR ascertains that NHCOCH(3) moiety of CLC enters in the HP-ß-CD cavity and deshielded the H-3 and H-5 protons. ROESY also correlates the H(f) and H(g) of CLC with H-3 and H-5 protons of HP-ß-CD and indicates that H(f) and H(g) protons of CLC are present either as cis and/or trans form in CLC-HP-ß-CD inclusion complex. Pharmacokinetic studies showed a 1.82-fold increase in absolute bioavailability of CLC upon complexation. CONCLUSION: CLC-HP-ß-CD inclusion complex may potentially be used as a viable formulation of CLC.

Moving from rabies research to rabies control: lessons from India.

BACKGROUND: Despite the availability of effective interventions and public recognition of the severity of the problem, rabies continues to suffer neglect by programme planners in India and other low and middle income countries. We investigate whether this state of 'policy impasse' is due to, at least in part, the research community not catering to the information needs of the policy makers. METHODS #ENTITYSTARTX00026; FINDINGS: Our objective was to review the research output on rabies from India and examine its alignment with national policy priorities. A systematic literature review of all rabies research articles published from India between 2001 and 2011 was conducted. The distribution of conducted research was compared to the findings of an earlier research prioritization exercise. It was found that a total of 93 research articles were published from India since 2001, out of which 61% consisted of laboratory based studies focussing on rabies virus. Animals were the least studied group, comprising only 8% of the research output. One third of the articles were published in three journals focussing on vaccines and infectious disease epidemiology and the top 4 institutions (2 each from the animal and human health sectors) collectively produced 49% of the national research output. Biomedical research related to development of new interventions dominated the total output as opposed to the identified priority domains of socio-politic-economic research, basic epidemiological research and research to improve existing interventions. CONCLUSION: The paper highlights the gaps between rabies research and policy needs, and makes the case for developing a strategic research agenda that focusses on rabies control as an expected outcome.