Estimating the Number of COVID-19 Cases and Impact of New COVID-19 Variants and Vaccination on the Population in Kerman, Iran: A Mathematical Modeling Study.

COVID-19 is spreading all over Iran, and Kerman is one of the most affected cities. We conducted this study to predict COVID-19-related deaths, hospitalization, and infected cases under different scenarios (scenarios A, B, and C) by 31 December 2021 in Kerman. We also aimed to assess the impact of new COVID-19 variants and vaccination on the total number of COVID-19 cases, deaths, and hospitalizations (scenarios D, E, and F) using the modified susceptible-exposed-infected-removed (SEIR) model. We calibrated the model using deaths reported from the start of the epidemic to August 30, 2021. A Monte Carlo Markov Chain (MCMC) uncertainty analysis was used to estimate 95% uncertainty intervals (UI). We also calculated the time-varying reproductive number (R t) following time-dependent methods. Under the worst-case scenario (scenario A; contact rate = 10, self-isolation rate = 30%, and average vaccination shots per day = 5,000), the total number of infections by December 31, 2021, would be 1,625,000 (95% UI: 1,112,000-1,898,000) with 6,700 deaths (95% UI: 5,200-8,700). With the presence of alpha and delta variants without vaccine (scenario D), the total number of infected cases and the death toll were estimated to be 957,000 (95% UI: 208,000-1,463,000) and 4,500 (95% UI: 1,500-7,000), respectively. If 70% of the population were vaccinated when the alpha variant was dominant (scenario E), the total number of infected cases and deaths would be 608,000 (95% UI: 122,000-743,000) and 2,700 (95% UI: 700-4,000), respectively. The R t was ≥1 almost every day during the epidemic. Our results suggest that policymakers should concentrate on improving vaccination and interventions, such as reducing social contacts, stricter limitations for gathering, public education to promote social distancing, incensing case finding and contact tracing, effective isolation, and quarantine to prevent more COVID-19 cases, hospitalizations, and deaths in Kerman.

Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis.

BACKGROUND: The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. RESULTS: Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. CONCLUSIONS: Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

Gene co-expression network approach for predicting prognostic microRNA biomarkers in different subtypes of breast cancer.

New diagnostic miRNA biomarkers for different types of cancer have been studied extensively, particularly for breast cancer (BC), which is a leading cause of death among women and has many different subtypes. In the present study, a systems biology approach was used to find remarkable and novel miRNA biomarkers for five molecular subtypes of BC: luminal A, luminal B, ERBB2, basal-like and normal-like. The mRNA expression data from the five BC subtypes was used to reconstruct co-expression networks. The important mRNA-miRNA interactions were considered when reconstructing the bipartite networks from which the five bipartite sub-networks were reconstructed for further analysis. The novel biomarkers detected for each subtype are as follows: miRNAs 26b-5p and 124-3p for basal-like, 26b-5p, 124-3p and 5011-5p for ERBB2, 26b-5p and 5011-5p for LumA, 124-3p, 26b-5p and 7-5p for LumB and 26b-5p, 124-3p and 193b-3p for normal-like. The roles of the identified miRNAs in the occurrence or development of each subtype of BC remain unclear and should be investigated in future studies. In addition, the target genes of these miRNAs may be critical to the mechanisms underlying each subtype and should be analyzed as therapeutic targets in future studies.

Candidate miRNAs in human breast cancer biomarkers: a systematic review.

BACKGROUND: Breast cancer (BC) is the most prevalent cancer and the main cause of cancer deaths among females around the world. For early diagnosis of BC, there would be an immediate and essential requirement to search for sensitive biomarkers. METHODS: To identify candidate miRNA biomarkers for BC, we performed a general systematic review regarding the published miRNA profiling researches comparing miRNA expression level between BC and normal tissues. A miRNA ranking system was selected, which considered frequency of comparisons in direction and agreement of differential expression. RESULTS: We determined that two miRNAs (mir-21 and miR-210) were upregulated consistently and six miRNAs (miR-145, miR-139-5p, miR-195, miR-99a, miR-497 and miR-205) were downregulated consistently in at least three studies. MiR-21 as the most consistently reported miRNA was upregulated in six profiling studies. CONCLUSIONS: Although these miRNAs require being validated and further investigated, they could be potential candidates for BC miRNA biomarkers and used for early prognosis or diagnosis.

Investigating the Relationship between Personality Traits and Self-Control and Nicotine Dependence Symptoms in Male Prisoners in Kerman, Iran.

BACKGROUND: Smoking is the most common and cheapest addictive substance and has physical, psychological, and social side effects. Personality traits and low self-control have been identified as key factors for substance and tobacco abuse. This study examined the relationship between personality traits and self-control, and symptoms of nicotine dependence in male prisoners. METHODS: This was a descriptive correlational study. The research sample consisted of 384 male prisoners in Kerman, Iran. The participants were selected using simple random sampling method. The data collection tools consisted of the NEO five factor personality inventory (NEO-FFI), self-control Inventory, and the nicotine dependence symptoms inventory. FINDINGS: The mean age of the prisoners was 35.33 ± 9.28 year. The results showed a significant negative relationship between self-control and nicotine dependence. The most important predictors of prisoners' self-control were the personality traits of conscientiousness, neuroticism, openness, and temperamental neuroticism, respectively. The most important predictors of nicotine dependence in prisons were personality traits of adaptability, temperamental neuroticism, extroversion, and openness, respectively. CONCLUSION: Personality traits and self-control have an important role in nicotine dependence; therefore, by training self-control, behaviors such as smoking and consumption of drugs can be reduced.