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After claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures. We process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making. Population density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance. States forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.

Cardiac glycosides (CGs) have a long history of treating cardiac diseases. However, recent reports have suggested that CGs also possess anticancer and antiviral activities. The primary mechanism of action of these anticancer agents is by suppressing the Na+/k+-ATPase by decreasing the intracellular K+ and increasing the Na+ and Ca2+. Additionally, CGs were known to act as inhibitors of IL8 production, DNA topoisomerase I and II, anoikis prevention and suppression of several target genes responsible for the inhibition of cancer cell proliferation. Moreover, CGs were reported to be effective against several DNA and RNA viral species such as influenza, human cytomegalovirus, herpes simplex virus, coronavirus, tick-borne encephalitis (TBE) virus and Ebola virus. CGs were reported to suppress the HIV-1 gene expression, viral protein translation and alters viral pre-mRNA splicing to inhibit the viral replication. To date, four CGs (Anvirzel, UNBS1450, PBI05204 and digoxin) were in clinical trials for their anticancer activity. This review encapsulates the current knowledge about CGs as anticancer and antiviral drugs in isolation and in combination with some other drugs to enhance their efficiency. Further studies of this class of biomolecules are necessary to determine their possible inhibitory role in cancer and viral diseases.

Cardiac glycosides (CGs) are a diverse family of naturally derived compounds having a steroid and glycone moiety in their structures. CG molecules inhibit the α-subunit of ubiquitous transmembrane protein Na+/K+-ATPase and are clinically approved for the treatment of cardiovascular diseases. Recently, the CGs were found to exhibit selective cytotoxic effects against cancer cells, raising interest in their use as anti-cancer molecules. In this current study, we explored the underlying mechanism responsible for the anti-cancer activity of Lanatoside C against breast (MCF-7), lung (A549), and liver (HepG2) cancer cell lines. Using Real-time PCR, western blot, and immunofluorescence studies, we observed that (i) Lanatoside C inhibited cell proliferation and induced apoptosis in cell-specific and dose-dependent manner only in cancer cell lines; (ii) Lanatoside C exerts its anti-cancer activity by arresting the G2/M phase of cell cycle by blocking MAPK/Wnt/PAM signaling pathways; (iii) it induces apoptosis by inducing DNA damage and inhibiting PI3K/AKT/mTOR signaling pathways; and finally, (iv) molecular docking analysis shows significant evidence on the binding sites of Lanatoside C with various key signaling proteins ranging from cell survival to cell death. Our studies provide a novel molecular insight of anti-cancer activities of Lanatoside C in human cancer cells.

During the COVID-19 pandemic, the prevalence of asymptomatic cases challenged the reliability of epidemiological statistics in policymaking. To address this, we introduced contagion potential (CP) as a continuous metric derived from sociodemographic and epidemiological data to quantify the infection risk posed by the asymptomatic within a region. However, CP estimation is hindered by incomplete or biased incidence data, where underreporting and testing constraints make direct estimation infeasible. To overcome this limitation, we employ a hypothesis-testing approach to infer CP from sampled data, allowing for robust estimation despite missing information. Even within the sample collected from spatial contact data, individuals possess partial knowledge of their neighborhoods, as their awareness is restricted to interactions captured by available tracking data. We introduce an adjustment factor that calibrates the sample CPs so that the sample is a reasonable estimate of the population CP. Further complicating estimation, biases in epidemiological and mobility data arise from heterogeneous reporting rates and sampling inconsistencies, which we address through inverse probability weighting to enhance reliability. Using a spatial model for infection spread through social mixing and an optimization framework based on the SIRS epidemic model, we analyze real infection datasets from Italy, Germany, and Austria. Our findings demonstrate that statistical methods can achieve high-confidence CP estimates while accounting for variations in sample size, confidence level, mobility models, and viral strains. By assessing the effects of bias, social mixing, and sampling frequency, we propose statistical corrections to improve CP prediction accuracy. Finally, we discuss how reliable CP estimates can inform outbreak mitigation strategies despite the inherent uncertainties in epidemiological data.

COVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection during future outbreaks of the same or variants of the virus. The reliance on existing prediction and contact tracing approaches on prior knowledge of inter- or intra-zone mobility renders them impracticable. We present a spatiotemporal approach that employs a network inference approach with sliding time windows solely on the date and number of daily infection numbers of zones within a geographical region to generate temporal networks capturing the influence of each zone on another. It helps analyze the spatial interaction among the hotspot or spreader zones and highly affected zones based on the flow of network contagion traffic. We apply the proposed approach to the daily infection counts of New York State as well as the states of USA to show that it effectively measures the phase shifts in the pandemic timeline. It identifies the spreaders and affected zones at different time points and helps infer the trajectory of the pandemic spread across the country. A small set of zones periodically exhibit a very high outflow of contagion traffic over time, suggesting that they act as the key spreaders of infection. Moreover, the strong influence between the majority of non-neighbor regions suggests that the overall spread of infection is a result of the unavoidable long-distance trips by a large number of people as opposed to the shorter trips at a county level, thereby informing future mitigation measures and public policies.

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications.

Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends insight into the critical nodes, key pathways, and potential points of failure that may impact the connectivity and operational integrity of the underlying system. In this work, we investigate the topological properties or indicators , such as shortest path length, modularity, efficiency, graph density, diameter, assortativity, and clustering coefficient, that determine the vulnerability to (or robustness against) diverse attack scenarios. Specifically, we examine how node- and link-based network growth or depletion based on specific attack criteria affect their robustness gauged in terms of the largest connected component (LCC) size and diameter. We employ partial least squares discriminant analysis to quantify the individual contribution of the indicators on LCC preservation while accounting for the collinearity stemming from the possible correlation between indicators. Our analysis of 14 complex network datasets and 5 attack models invariably reveals high modularity and disassortativity to be prime indicators of vulnerability, corroborating prior works that report disassortative modular networks to be particularly susceptible to targeted attacks. We conclude with a discussion as well as an illustrative example of the application of this work in fending off strategic attacks on critical infrastructures through models that adaptively and distributively achieve network robustness.

Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant prior information. This work discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINNs) in particular are highlighted. Additionally, we explore recent works toward utilizing neural operator learning to intuit relationships in physics systems traditionally modeled by sets of complex governing equations and solved with expensive differentiation techniques. Finally, expansive applications of traditional physics-informed machine learning and potential limitations are discussed. In addition to summarizing recent work, we propose a novel taxonomic structure to catalog physics-informed machine learning based on how the physics-information is derived and injected into the machine learning process. The taxonomy assumes the explicit objectives of facilitating interdisciplinary collaboration in methodology, thereby promoting a wider characterization of what types of physics problems are served by the physics-informed learning machines and assisting in identifying suitable targets for future work. To summarize, the major twofold goal of this work is to summarize recent advancements and introduce a taxonomic catalog for applications of physics-informed machine learning.

Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational costs of detailed mechanistic models while leveraging the predictive power of machine learning. This study applies this fusion to the biomedical challenge of A$$\\beta$$fibril aggregation, a key factor in Alzheimer’s disease. Central to the research is the introduction of an automatic reaction order model reduction framework, designed to optimize reduced-order kinetic models. This framework represents a shift in model construction, automatically determining the appropriate level of detail for reaction network modeling. The proposed approach significantly improves simulation efficiency and accuracy, particularly in systems like A$$\\beta$$aggregation, where precise modeling of nucleation and growth kinetics can reveal potential therapeutic targets. Additionally, the automatic model reduction technique has the potential to generalize to other network models. The methodology offers a scalable and adaptable tool for applications beyond biomedical research. Its ability to dynamically adjust model complexity based on system-specific needs ensures that models remain both computationally feasible and scientifically relevant, accommodating new data and evolving understandings of complex phenomena.

The COVID-19 pandemic has infected millions worldwide, leaving a global burden for long-term care of COVID-19 survivors. It is thus imperative to study post-COVID (i.e., short-term) and long-COVID (i.e., long-term) effects, specifically as local and systemic pathophysiological outcomes of other coronavirus-related diseases (such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS)) were well-cataloged. We conducted a comprehensive review of adverse post-COVID health outcomes and potential long-COVID effects. We observed that such adverse outcomes were not localized. Rather, they affected different human systems, including: (i) immune system (e.g., Guillain–Barré syndrome, rheumatoid arthritis, pediatric inflammatory multisystem syndromes such as Kawasaki disease), (ii) hematological system (vascular hemostasis, blood coagulation), (iii) pulmonary system (respiratory failure, pulmonary thromboembolism, pulmonary embolism, pneumonia, pulmonary vascular damage, pulmonary fibrosis), (iv) cardiovascular system (myocardial hypertrophy, coronary artery atherosclerosis, focal myocardial fibrosis, acute myocardial infarction, cardiac hypertrophy), (v) gastrointestinal, hepatic, and renal systems (diarrhea, nausea/vomiting, abdominal pain, anorexia, acid reflux, gastrointestinal hemorrhage, lack of appetite/constipation), (vi) skeletomuscular system (immune-mediated skin diseases, psoriasis, lupus), (vii) nervous system (loss of taste/smell/hearing, headaches, spasms, convulsions, confusion, visual impairment, nerve pain, dizziness, impaired consciousness, nausea/vomiting, hemiplegia, ataxia, stroke, cerebral hemorrhage), (viii) mental health (stress, depression and anxiety). We additionally hypothesized mechanisms of action by investigating possible molecular mechanisms associated with these disease outcomes/symptoms. Overall, the COVID-19 pathology is still characterized by cytokine storm that results to endothelial inflammation, microvascular thrombosis, and multiple organ failures.