Immune Checkpoint Inhibitors and Vaccination: Assessing Safety, Efficacy, and Synergistic Potential.

Although immune checkpoint inhibitors (ICIs) have become predominant therapies for cancer, the safety and efficacy of combining ICIs with vaccinations remain areas of needed investigation. As ICIs gain broader clinical application, the relevance of current vaccination guidelines for cancer patients-largely developed in the context of cytotoxic therapies-becomes increasingly uncertain. Although data support the safety of combining inactivated influenza and mRNA SARS-CoV-2 vaccines with ICI therapy, comprehensive data on other infectious disease vaccines remain scarce.

Effects of antineoplastic and immunomodulating agents on postvaccination SARS-CoV-2 breakthrough infections, antibody response, and serological cytokine profile.

Background: Despite immunization, patients on antineoplastic and immunomodulating agents have a heightened risk of COVID-19 infection. However, accurately attributing this risk to specific medications remains challenging.

Methods: An observational cohort study from December 11, 2020 to September 22, 2022, within a large healthcare system in San Diego, California, USA was designed to identify medications associated with greatest risk of postimmunization SARS-CoV-2 infection.

Doublecortin-like kinase 1 is a therapeutic target in squamous cell carcinoma.

Doublecortin like kinase 1 (DCLK1) plays a crucial role in several cancers including colon and pancreatic adenocarcinomas. However, its role in squamous cell carcinoma (SCC) remains unknown. To this end, we examined DCLK1 expression in head and neck SCC (HNSCC) and anal SCC (ASCC).

6 month serologic response to the Pfizer-BioNTech COVID-19 vaccine among healthcare workers.

Aim: Healthcare workers (HCWs) were among the first group of people vaccinated with the Pfizer-BioNTech Covid-19 vaccine (BNT162b2). Characterization of the kinetics of antibody response to vaccination is important to devise future vaccination strategies. To better characterize the antibody response to BNT162b2, we analyzed the kinetics of IgG and IgM antibody response to 5 different SARS-CoV-2 epitopes over a period of 6 months.

Machine Learning to Predict Treatment in Oropharyngeal Squamous Cell Carcinoma.

Objectives: This study seeks to (1) demonstrate how machine learning (ML) can be used for prediction modeling by predicting the treatment patients with T1-2, N0-N1 oropharyngeal squamous cell carcinoma (OPSCC) receive and (2) assess the impact patient, socioeconomic, regional, and institutional factors have in the treatment of this population.

Methods: A retrospective cohort of adults diagnosed with T1-2, N0-N1 OPSCC from 2004 to 2013 was obtained using the National Cancer Database. The data was split into 80/20 distribution for training and testing, respectively.

EMR quantity autopopulation removal on hospital discharge prescribing patterns: Implications for opioid stewardship.

What Is Known And Objective: Prescription drug stewardship is critical. Autopopulation(AP) of medication quantities may influence prescriber behaviour. We investigate the impact of AP removal(APR) on opioid prescribing.

The false vital sign: When pain levels are not predictive of discharge opioid prescriptions.

Background: Pain gained recognition as a vital sign in the early 2000s, underscoring the importance of accurate documentation, characterization, and treatment of pain. No prior studies have demonstrated the utility of the 0-10 pain scale with respect to discharge opioid prescriptions, nor characterized the most influential factors in discharge prescriptions.

Methods: Inpatient and emergency department(ED) encounters from July 1, 2012 to April 1, 2018 resulting in a discharge prescription for tablet opioid medications were identified.

Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma.

Importance: Predicting survival of oral squamous cell carcinoma through the use of prediction modeling has been underused, and the development of prediction models would augment clinicians' ability to provide absolute risk estimates for individual patients.

Objectives: To develop a prediction model using machine learning for 5-year overall survival among patients with oral squamous cell carcinoma and compare this model with a prediction model created from the TNM (Tumor, Node, Metastasis) clinical and pathologic stage.

Design, Setting, And Participants: A retrospective cohort study was conducted of 33 065 patients with oral squamous cell carcinoma from the National Cancer Data Base between January 1, 2004, and December 31, 2011.

Pleotropic role of RNA binding protein CELF2 in autophagy induction.

We previously reported that ionizing radiation (IR) mediates cell death through the induction of CUGBP elav-like family member 2 (CELF2), a tumor suppressor. CELF2 is an RNA binding protein that modulates mRNA stability and translation. Since IR induces autophagy, we hypothesized that CELF2 regulates autophagy-mediated colorectal cancer (CRC) cell death.

Autophagy-dependent secretion: mechanism, factors secreted, and disease implications.

Although best understood as a degradative pathway, recent evidence demonstrates pronounced involvement of the macroautophagic/autophagic molecular machinery in cellular secretion. With either overexpression or inhibition of autophagy mediators, dramatic alterations in the cellular secretory profile occur. This affects secretion of a plethora of factors ranging from cytokines, to granule contents, and even viral particles.

Using Machine Learning to Predict Sensorineural Hearing Loss Based on Perilymph Micro RNA Expression Profile.

Hearing loss (HL) is the most common neurodegenerative disease worldwide. Despite its prevalence, clinical testing does not yield a cell or molecular based identification of the underlying etiology of hearing loss making development of pharmacological or molecular treatments challenging. A key to improving the diagnosis of inner ear disorders is the development of reliable biomarkers for different inner ear diseases.

Artificial Intelligence for the Otolaryngologist: A State of the Art Review.

Objective: To provide a state of the art review of artificial intelligence (AI), including its subfields of machine learning and natural language processing, as it applies to otolaryngology and to discuss current applications, future impact, and limitations of these technologies.

Data Sources: PubMed and Medline search engines.

Review Methods: A structured search of the current literature was performed (up to and including September 2018).

Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer.

Objective: To apply a novel methodology with machine learning (ML) to a large national cancer registry to help identify patients who are high risk for delayed adjuvant radiation.

Study Design: Observational cohort study.

Setting: National Cancer Database (NCDB).

Potent Antitumor Effects of a Combination of Three Nutraceutical Compounds.

Head and neck squamous cell carcinoma (HNSCC) is associated with low survival, and the current aggressive therapies result in high morbidity. Nutraceuticals are dietary compounds with few side effects. However, limited antitumor efficacy has restricted their application for cancer therapy.

Cancer-Associated Fibroblasts Drive Glycolysis in a Targetable Signaling Loop Implicated in Head and Neck Squamous Cell Carcinoma Progression.

Despite aggressive therapies, head and neck squamous cell carcinoma (HNSCC) is associated with a less than 50% 5-year survival rate. Late-stage HNSCC frequently consists of up to 80% cancer-associated fibroblasts (CAF). We previously reported that CAF-secreted HGF facilitates HNSCC progression; however, very little is known about the role of CAFs in HNSCC metabolism.

Differential Gene Expression and Pathway Analysis in Juvenile Nasopharyngeal Angiofibroma Using RNA Sequencing.

Juvenile nasopharyngeal angiofibroma (JNA) is a highly vascularized and locally aggressive tumor that typically presents in adolescent males. The molecular biology of this tumor remains understudied. We sought to identify differentially expressed genes in the JNA transcriptome through messenger RNA sequencing of primary fibroblasts from 2 tumor explants and tonsil tissue from tumor-free subjects.

Development and Characterization of an In Vitro Model for Radiation-Induced Fibrosis.

Radiation-induced fibrosis (RIF) is a major side effect of radiotherapy in cancer patients with no effective therapeutic options. RIF involves excess deposition and aberrant remodeling of the extracellular matrix (ECM) leading to stiffness in tissues and organ failure. Development of preclinical models of RIF is crucial to elucidate the molecular mechanisms regulating fibrosis and to develop therapeutic approaches.

Secretory Autophagy in Cancer-Associated Fibroblasts Promotes Head and Neck Cancer Progression and Offers a Novel Therapeutic Target.

Despite therapeutic advancements, there has been little change in the survival of patients with head and neck squamous cell carcinoma (HNSCC). Recent results suggest that cancer-associated fibroblasts (CAF) drive progression of this disease. Here, we report that autophagy is upregulated in HNSCC-associated CAFs, where it is responsible for key pathogenic contributions in this disease.

Inhibition of fibroblast growth factor receptor with AZD4547 mitigates juvenile nasopharyngeal angiofibroma.

Background: Juvenile nasopharyngeal angiofibroma (JNA) is a benign tumor that presents in adolescent males. Although surgical excision is the mainstay of treatment, recurrences complicate treatment. There is a need to develop less invasive approaches for management.

Everolimus downregulates estrogen receptor and induces autophagy in aromatase inhibitor-resistant breast cancer cells.

Background: mTOR inhibition of aromatase inhibitor (AI)-resistant breast cancer is currently under evaluation in the clinic. Everolimus/RAD001 (Afinitor®) has had limited efficacy as a solo agent but is projected to become part of combination therapy for AI-resistant breast cancer. This study was conducted to investigate the anti-proliferative and resistance mechanisms of everolimus in AI-resistant breast cancer cells.

Radiation-induced fibrosis: mechanisms and implications for therapy.

Purpose: Radiation-induced fibrosis (RIF) is a long-term side effect of external beam radiation therapy for the treatment of cancer. It results in a multitude of symptoms that significantly impact quality of life. Understanding the mechanisms of RIF-induced changes is essential to developing effective strategies to prevent long-term disability and discomfort following radiation therapy.