A mini‑review of the validity, quality and efficacy of candidate vaccines in controlling the COVID-19

Few would have thought that in this century, a new coronavirus called SARS-CoV-2 would kill many people around the world, cripple the economy, and leave the medical staff helpless. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a virus that first appeared in Wuhan, China, and spread rapidly around the world, and strict quarantines did not prevent the severe prevalence from spreading worldwide. Antiviral drugs do not work well enough for everyone. The mortality rate in the world is still significant. The only thing that gives hope to the people of the world is the hope of being vaccinated, so by producing vaccines in the shortest possible time, science has once again saved humanity. Thus, from the very beginning, pharmaceutical companies started to produce safe vaccines. Currently, more than 200 types of vaccines around the world are undergoing various stages of production, and about 30 vaccines have entered the clinical trial phase, of which 9 vaccines have entered phase 1 to 3 of clinical trials. DNA and RNA-based vaccines were first developed and were not licensed before coronavirus disease 2019 (COVID-19). Other types of vaccines, including non-replicating viral vectors as well as inactivated vaccines, are undergoing clinical phases. There are currently 9 common vaccines Inovio Pharmaceuticals, Moderna, BioNTech/Pfizer, AstraZeneca, CanSino Biological, Gam-COVID-Vac (Sputnik V), Wuhan Institute of Biological Products/Sinopharm, Beijing Institute of Biological Products/Sinopharm, and Sinovac Institutes in the world. Vaccination with the Pfizer vaccine, which is approved by the World Health Organization (WHO), is underway in many countries. The WHO predicts that by the end of 2021, one billion people worldwide will be vaccinated by the company

Antimicrobial Resistance Pattern and Spectrum of Multiple-drug-resistant Enterobacteriaceae in Iranian Hospitalized Patients with Cancer

Background: Nosocomial infections are one of the most leading causes of morbidity and mortality in patients with cancer. The emergence of multiple-drug-resistant (MDR) strains of Gram-negative bacteria causing nosocomial infection has become a serious concern in cancer patients. Therefore, the present study aimed to determine the spectrum and antibiotic resistance pattern of Gram-negative bacteria related nosocomial infections among Iranian cancer patients. Materials and Methods: This descriptive cross-sectional study was conducted during the 6 months from December 2015 to May 2016 in two tertiary care centers located in Isfahan and Arak Province. Gram-negative bacteria obtained from different clinical specimens from hospitalized patients with cancer and were identified using standard microbiological methods. Antibiotic susceptibility pattern was determined by the disk diffusion method according to the Clinical and Laboratory Standards Institute (CLSI) recommendation. Results: Of totally 259 culture positive cases, Escherichia coli showed the highest isolation rate (60.6%) followed by Klebsiella pneumoniae (26.6%) and Proteus spp (11.2%). The rate of MDR isolates were 91.5% (237/259). Overall, the most frequent source of bacterial isolation was urinary tract infection (65.6%) followed by skin and soft-tissue infection (23.6%). The antibiotic susceptibility results showed meropenem (MEN) and ceftazidime as the most effective antibiotics for E. coli, K. pneumoniae, and Proteus spp. isolates. Moreover, MEN was the most effective antibiotic against MDR isolates. Conclusion: The study findings showed a significant distribution of MDR Gram-negative bacteria which may increase the burden of healthcare-associated infections in cancer patients. Although, carbapenem can be considered as effective agents toward MDR strains for empirical antibiotic therapy in our region

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

<p>Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.</p&gt