Kritika Srinivasan Rajsri

Background/Objectives: Oral potentially malignant disorders (OPMDs) require accurate risk stratification to identify patients at the highest risk for severe oral epithelial dysplasia (OED) or oral squamous cell carcinoma (OSCC). We developed and internally validated the oral cancer numerical index (OCNI), a quantitative risk score derived from clinical features and deep learning-based brush cytology measurements. Methods: This retrospective model development and internal validation study was conducted using data from the multicenter Grand Opportunity study. Prospectively recruited subjects with OPMD with complete data were divided at the subject level into a training set (n = 384) and a holdout test set (n = 164) using a 70:30 diagnosis-stratified split. The primary endpoint was severe OED or OSCC versus benign diagnoses, and mild and moderate OED. Predictors included age, sex, tobacco history, lesion color, lesion size, multiple lesions, ulcerative morphology, and the percentages of differentiated squamous epithelial and small round cells derived from deep learning-based cytology. Prespecified rule-out and rule-in thresholds were selected in the training set to target 90% sensitivity and 90% specificity, respectively, and then applied to the holdout test set. Results: At the prespecified rule-out threshold (OCNI ≤ 37.6), sensitivity was 92% and negative predictive value was 97%. At the rule-in threshold (OCNI > 60.0), specificity was 89% and positive predictive value was 67%. Calibration was good in the holdout set (intercept, −0.07; slope, 1.13; Hosmer–Lemeshow p = 0.36), and OCNI increased significantly with worsening histopathologic severity. Conclusions: OCNI provided an objective, clinically interpretable estimate of risk for severe OED or OSCC, with strong rule-out and rule-in performance and good calibration. These findings support further external validation of OCNI as an adjunctive tool for oral lesion risk stratification.

Human papillomavirus (HPV), a DNA virus, causes one the most common sexually transmitted infections (STI), spreading through direct sexual contact to the anogenital and oropharyngeal regions. Although most infected individuals may be asymptomatic and naturally undergo viral clearance, in some individuals HPV causes carcinogenesis of the infected epithelia. HPV now accounts for over 70% of oropharyngeal carcinomas (OPC). More than 90% of these OPC are associated with HPV 16, one of the 14 high-risk subtypes. In recent years, HPV positive (HPV+) OPC has been one of the most rapidly rising cancers, by new cancer incidence rates, especially among men. The HPV driven OPCs present some unique infection driven cellular and molecular features, constituting distinct preventive, therapeutic and prognostic strategies, in contrast to HPV negative (HPV-) OPC. In this manuscript, we drive through the role of HPV, from a sexually transmitted infection to malignancy transformation landscape, while parsing through the significant immune response environment. In this comprehensive review we collate key literature around transmission and epidemiology of HPV, while addressing the current state of clinical management, diagnosis, prevention and screening strategies, that can help shed light on this critical area.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant of concern has been the dominant cause of worldwide COVID-19 cases since 2022. All Omicron sub-lineage viruses have demonstrated high transmissibility and an ability to escape vaccine-induced immunity. While first-generation vaccines, including monovalent vaccines, continue to provide protection against severe disease, hospitalization, and mortality, their efficacy against Omicron subvariants remains sparse. These vaccines have also been associated with rapidly waning protection against primary COVID-19 and COVID-19 reinfections conferred by evolving Omicron sub-lineages. This led to the development and deployment of updated vaccines and the introduction of the bivalent booster. Through this review, we highlight the brief journey of the variants of concern leading to the dominance of Omicron and the effectiveness of the key vaccines against these variants, including the updated (bivalent) boosters.

As COVID-19 pandemic public health measures are easing globally, the emergence of new SARS-CoV-2 strains continue to present high risk for vulnerable populations. The antibody-mediated protection acquired from vaccination and/or infection is seen to wane over time and the immunocompromised populations can no longer expect benefit from monoclonal antibody prophylaxis. Hence, there is a need to monitor new variants and its effect on vaccine performance. In this context, surveillance of new SARS-CoV-2 infections and serology testing are gaining consensus for use as screening methods, especially for at-risk groups. Here, we described an improved COVID-19 screening strategy, comprising predictive algorithms and concurrent, rapid, accurate, and quantitative SARS-CoV-2 antigen and host antibody testing strategy, at point of care (POC). We conducted a retrospective analysis of 2553 pre- and asymptomatic patients who were tested for SARS-CoV-2 by RT-PCR. The pre-screening model had an AUC (CI) of 0.76 (0.73–0.78). Despite being the default method for screening, body temperature had lower AUC (0.52 [0.49–0.55]) compared to case incidence rate (0.65 [0.62–0.68]). POC assays for SARS-CoV-2 nucleocapsid protein (NP) and spike (S) receptor binding domain (RBD) IgG antibody showed promising preliminary results, demonstrating a convenient, rapid (<20 min), quantitative, and sensitive (ng/mL) antigen/antibody assay. This integrated pre-screening model and simultaneous antigen/antibody approach may significantly improve accuracy of COVID-19 infection and host immunity screening, helping address unmet needs for monitoring vaccine effectiveness and severe disease surveillance.

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.

The severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant of concern has been the dominant cause of worldwide COVID-19 cases since 2022. All the Omicron sublineage viruses have demonstrated high transmissibility and an ability to escape vaccine-induced immunity. While first-generation vaccines, including monovalent vaccines, continue to provide protection against severe disease, hospitalization and mortality, their efficacy against Omicron sub variants remains sparse. These vaccines have also been associated with rapidly waning protection against primary COVID-19 and COVID-19 reinfections conferred by evolving Omicron sublineages.. This led to the development and deployment of updated vaccines and the introduction of the bivalent booster. Through this review, we highlight the brief journey of the variants of concern leading to the dominance of Omicron and the effectiveness of the key vaccines against these variants, including the updated (bivalent) boosters.

Poxviridae have been successful pathogens throughout recorded history, infecting humans among a variety of other hosts. Although eradication of the notorious smallpox has been a globally successful healthcare phenomenon, the recent emergence of Monkeypox virus, also belonging to the Orthopoxvirus genus and causing human disease, albeit milder than smallpox, is a cause of significant public health concern. The ongoing outbreak of monkeypox, demonstrating human–human transmission, in previously nonendemic countries, calls for critical need into further research in the areas of viral biology, ecology, and epidemiology to better understand, prevent and treat human infections. In the wake of these recent events, it becomes important to revisit poxviral infections, their pathogenesis and ability to cause infection across multiple nonhuman hosts and leap to a human host. The poxviruses that cause human diseases include Monkeypox virus, Molluscum contagiosum virus, and Orf virus. In this review, we summarize the current understanding of various poxviruses causing human diseases, provide insights into their replication and pathogenicity, disease progression and symptoms, preventive and treatment options, and their importance in shaping modern medicine through application in gene therapy, oncolytic viral therapies for human cancers, or as poxvirus vectors for vaccines.

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.

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.