Abstract Background: The concurrent usage of herbal medicines with conventional therapies is an important concern in cancer treatment which can lead to unexpected consequences like herb-drug interactions. The aim of this study was to determine the prevalence of potential herb-drug interactions and to predict factors associated with herb-drug interactions for cancer patients. Methods: This cross-sectional study was conducted among a convenience sample of 315 cancer patients referring to the oncology clinics of Kerman in 2018. Data were collected via comprehensive face-to-face interviews and medical chart reviews. A drug interaction checker was used to determine herb-drug interactions. The information of patients was compared based on herb-drug interactions by using bivariable logistic regression models and predictors were determined by the multivariable logistic regression model. All analyses were performed by Stata software version 16. Results: Of 262 patients who used herbal medicines, 209 patients [79.8% (95% CI: 75.2 – 85.1)] had potential herb-drug interactions. Chamomile was the most popular herbal medicine (n=163, 78%), and minor and moderate herb-drug interactions were caused by green tea (n=34, 16.3%) and peppermint (n=78, 37.5%). The number of chemotherapeutic agents (OR: 1.92, 95% CI: 1.43–2.58; P-value<0.0001) and the experience of pain during chemotherapy courses (OR=2.22, 95%CI:1.00–4.94; P-value=0.04) were some of the predictors of herb-drug interactions among cancer patients. Conclusion: The majority of cancer patients used herbal medicines during chemotherapy courses and physicians could reduce the odds of herb-drug interactions with proper education, monitor the side effects of chemotherapy, and prevent patients from self-medication with herbal medicines.

Background In this study, the time and path of transmission of H1N1 serotype influenza A viruses in Iran and neighboring countries have been investigated by using Bayesian phylogeography analysis on the sequences extracted from the gene bank. Methods We obtained all hemagglutinin (HA) and neuraminidase (NA) nucleotide sequences of influenza H1N1 available up to December 25, 2020, from Iran and its neighboring countries (i.e., Pakistan, Afghanistan, Turkmenistan, Armenia, Azerbaijan, Turkey, and Iraq). We also performed a Bayesian Markov chain Monte Carlo method to infer the evolutionary dynamic and the most recent common ancestor for the HA and NA sequences. Results Based on the extracted sequences, the age of emergence of H1N1 influenza virus serotype was older in Iran compared to neighboring countries, and Tehran had a key role and epicenter of transmission to other cities within Iran. The mean time of the most recent common ancestor of H1N1 viruses was 1989 (95% HPD: 1980-1994) for HA and NA as well. Conclusions Along with ordinary measures like resource management, diagnostic approaches, and preparedness to fight against viruses that were in place, continuous monitoring, and screening of H1N1 serotype influenza virus in the country, especially by implementation of feasible, effective, and innovative measures at border line should be initiated and identified gaps and shortage that should be a priority for virus control. It is also important for countries to have a regional monitoring program in addition to internal monitoring programs, as well as to start a virus molecular care program.

Background. This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves.  Methods. EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold.Results. Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online.Interpretation. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.