SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89–0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

We are beginning a new era of Smart Diagnostics—integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.

Monkeypox, once a rare zoonotic disease, has been endemic to some African countries since its original identification among humans in 1970. Since then, cases in non-endemic regions have been linked to returning travelers or those who had contact with transported animals. The causative agent, Monkeypox virus, belongs to Orthopoxviruses, the same family as Variola—the causative organism for smallpox. Although most monkeypox outbreaks until recently were linked to zoonotic transmission, secondary human–human transmission in smallpox-unvaccinated individuals was observed in a small proportion of overall cases. Smallpox was declared to be eradicated in 1980, and since its eradication, Monkeypox virus has been the most significant poxvirus to cause human disease. The 2022 monkeypox outbreak marks a significant paradigm shift in the human and poxvirus association, with new modes of transmission and concerns of viral evolution and entrenchment as a sexually transmitted disease. Monkeypox clinically resembles smallpox but is far milder. At this time, there are no approved therapies for monkeypox, and antiviral agents effective against smallpox are being utilized. Additionally, preventive strategies being utilized include smallpox vaccinations such as JYNNEOS and ACAM2000. In this narrative review, we discuss the virology, epidemiology, transmission, clinical manifestations, diagnosis, management, and prevention strategies associated with monkeypox.

The coronavirus disease of 2019 (COVID-19) has caused significant morbidity and mortality among infected individuals across the world. High transmissibility rate of the causative virus – Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) – has led to immense strain and bottlenecking of the health care system. While noteworthy advances in vaccine development have been made amid the current global pandemic, most therapeutic agents are repurposed from use in other viral infections and are being evaluated for efficacy in COVID-19. Favipiravir, an orally administered drug originally developed in Japan against emerging influenza viral strains, has been shown to have widespread application and safety across multiple ribonucleic acid (RNA) viral infections. With a strong affinity toward the viral RNA-dependent RNA polymerase (RdRp), favipiravir could be a promising therapy against SARS-CoV-2, by targeting downstream viral RNA replication. Initial trials for usage in COVID-19 have suggested that favipiravir administration during initial infection stages, in individuals with mild to moderate infection, has a strong potential to improve clinical outcomes. However, additional well-designed clinical trials are required to closely examine ideal timing of drug administration, dosage, and duration, to assess the role of favipiravir in COVID-19 therapy. This review provides evidence-based insights and throws light on the current clinical trials examining the efficacy of favipiravir in tackling COVID-19, including its mechanism, pharmacodynamics, and pharmacokinetics.

As of 8 August 2022, SARS-CoV-2, the causative agent of COVID-19, has infected over 585 million people and resulted in more than 6.42 million deaths worldwide. While approved SARS-CoV-2 spike (S) protein-based vaccines induce robust seroconversion in most individuals, dramatically reducing disease severity and the risk of hospitalization, poorer responses are observed in aged, immunocompromised individuals and patients with certain pre-existing health conditions. Further, it is difficult to predict the protection conferred through vaccination or previous infection against new viral variants of concern (VoC) as they emerge. In this context, a rapid quantitative point-of-care (POC) serological assay able to quantify circulating anti-SARS-CoV-2 antibodies would allow clinicians to make informed decisions on the timing of booster shots, permit researchers to measure the level of cross-reactive antibody against new VoC in a previously immunized and/or infected individual, and help assess appropriate convalescent plasma donors, among other applications. Utilizing a lab-on-a-chip ecosystem, we present proof of concept, optimization, and validation of a POC strategy to quantitate COVID-19 humoral protection. This platform covers the entire diagnostic timeline of the disease, seroconversion, and vaccination response spanning multiple doses of immunization in a single POC test. Our results demonstrate that this platform is rapid (~15 min) and quantitative for SARS-CoV-2-specific IgG detection.

Acute myeloid leukemia (AML) is a hematological malignancy characterized by an abundance of incompletely matured or immature clonally derived hematopoietic precursors called leukemic blasts. Rare leukemia stem cells (LSCs) that can self-renew as well as give rise to leukemic progenitors comprising the bulk of leukemic blasts are considered the cellular reservoir of disease initiation and maintenance. LSCs are widely thought to be relatively resistant as well as adaptive to chemotherapy and can cause disease relapse. Therefore, it is imperative to understand the molecular bases of LSC forms and functions during different stages of disease progression, so we can more accurately identify these cells and design therapies to target them. Irrespective of the morphological, cytogenetic, and cellular heterogeneity of AML, the uniform, singularly important and independently significant prognosticator of disease response to therapy and patient outcome is measurable or minimal residual disease (MRD) detection, defined by residual disease detection below the morphology-based 5% blast threshold. The importance of LSC identification and frequency estimation during MRD detection, in order to make MRD more effective in predicting disease relapse and modifying therapeutic regimen is becoming increasingly apparent. This review focuses on summarizing functional and cellular composition-based LSC identification and linking those studies to current techniques of MRD detection to suggest LSC-inclusive MRD detection as well as outline outstanding questions that need to be addressed to improve the future of AML clinical management and treatment outcomes.