ABSTRACT
BACKGROUND: The COVID-19 pandemic has caused overwhelming morbidity, mortality, and hospitalization worldwide, including in the state of California. Vaccination efforts have been an important measure in curtailing the adverse outcomes of COVID-19. METHODS: To quantify the effectiveness of COVID-19 vaccinations in California, we conducted a retrospective cohort study investigating how vaccination has impacted the extent of COVID-19 contraction, hospitalizations, and death totals. We compared outcomes of the Delta Wave, Omicron Wave, and Pre-Delta Period. RESULTS: Vaccinated individuals have far-lower incidence risk ratio (IRR) of and odds of contracting a COVID-19 case (Delta IRR: 0.197) being hospitalized from COVID-19 (Delta IRR: 0.105), and dying from COVID-19 compared with an unvaccinated individual (Delta IRR: 0.0941). The preventive fraction of the unexposed and population-preventive fractions for cases, deaths, and hospitalizations also showed significant proportions. All tests showed P < .001. DISCUSSION: Vaccination was most effective in the Delta Wave, then in the Omicron Wave, and least effective in the Pre-Delta Period. Deaths were the most prevented outcome, followed by hospitalizations, then cases. CONCLUSIONS: This study exposes the massive impact of vaccinations in California in reducing COVID-19 outcomes and the potential for fewer adverse outcomes had there been greater vaccination compliance, demonstrating the need to increase vaccination efforts.
ABSTRACT
This study showcases a novel AI-driven approach to accurately differentiate between stage one and stage two gastric carcinoma based on pathology slide analysis. Gastric carcinoma, a significant contributor to cancer-related mortality globally, necessitates precise staging for optimal treatment planning and patient management. Leveraging a comprehensive dataset of 3540 high-resolution pathology images sourced from Kaggle.com, comprising an equal distribution of stage one and stage two tumors, the developed AI model demonstrates remarkable performance in tumor staging. Through the application of state-of-the-art deep learning techniques on Google's Collaboration platform, the model achieves outstanding accuracy and precision rates of 100%, accompanied by notable sensitivity (97.09%), specificity (100%), and F1-score (98.31%). Additionally, the model exhibits an impressive area under the receiver operating characteristic curve (AUC) of 0.999, indicating superior discriminatory power and robustness. By providing clinicians with an efficient and reliable tool for gastric carcinoma staging, this AI-driven approach has the potential to significantly enhance diagnostic accuracy, inform treatment decisions, and ultimately improve patient outcomes in the management of gastric carcinoma. This research contributes to the ongoing advancement of cancer diagnosis and underscores the transformative potential of artificial intelligence in clinical practice.
ABSTRACT
Gliomas are the most common primary tumors of the nervous system, accounting for approximately 81% of brain tumors. The primary treatment for these primary brain tumors, especially those of high grade, is surgical resection with subsequent therapy such as targeted radiotherapy, chemotherapy, or supportive care. In an effort to devise nuanced ways to treat gliomas, studies have examined various chemical agents to expand therapeutic avenues for gliomas. In this study, we investigate the applications of ethylenediaminetetraacetic acid (EDTA) in the investigation and treatment of gliomas. Searches were conducted on PubMed to find studies about the use of EDTA in the treatment of glioma. We identified 36 studies that had the information needed for analysis. We collected information on the dosage of EDTA, the agent that EDTA was complexed with, the route of administration, the outcome of the EDTA usage, and the type of glioma cells that were involved. In addition, a one-way analysis of variance was performed to identify any relationships between the effect of cell type, study purpose, and year published on dosage. We identified 36 articles that met our inclusion criteria. In-vitro studies utilized EDTA in various complexes to evaluate cellular viability, including proliferation and toxicity, intracellular enzyme kinetics, and intercellular interactions such as chelation and cellular aggregation. In-vivo studies predominantly utilized the versatile nature of EDTA as a tracer for imaging studies involved in diagnostics and identifying recurrent tumor growth and localization in human patients. Our statistical analysis failed to identify any significant relationships between cell type, study purpose, and publication year on EDTA dosage. We identified a variety of uses for EDTA in the investigation hopefully providing physicians with information regarding the context and applications of EDTA to assist in exploring new treatment options for glioma patients.
ABSTRACT
Wernicke's encephalopathy (WE) is a neuropsychiatric condition caused by thiamine deficiency often associated with alcoholism. Other less common causes include prolonged gastroenterology problems or dietary insufficiencies associated with hyperemesis gravidarum, bariatric surgery, and eating disorders. Prolonged WE without proper treatment can lead to the chronic and irreversible condition, Wernicke-Korsakoff syndrome. Despite being known for its classic triad of clinical symptoms (nystagmus/ophthalmoplegia, gait ataxia, and confusion), WE patients more commonly present with non-specific symptoms of altered mental status. Obscure clinical presentations often led to delays in the appropriate of patients with WE. We are presenting a case of WE that is unusual because the underlying cause is schizophrenia and the lack of alcohol use. For a punctual diagnosis, a high index of suspicion is essential to prevent further exacerbation of neuronal death seen in WE. IV thiamine should be administered to any patient with acute encephalopathy or altered mental status, given its low cost and lack of side effects.
