Background Monkeypox is a viral zoonotic disease caused by the monkeypox virus, a double‐stranded DNA‐enveloped virus that can be transmitted from animal to human or human to human. Consequently, it emerged as the most important orthopoxvirus for public health. Based on available online literature, this study reviewed the majority of the data representing the outbreak, diagnosis, treatment, and prevention of monkeypox. Methods The literature search was conducted between July 5 and September 15, 2022. In addition to reviewing the databases of World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), Africa CDC, and United Kingdom Health Security Agency monkey pox advice, 43 papers were studied in depth. Results and Discussion Human monkeypox was first identified in 1970 in a child in the Democratic Republic of the Congo. Until May 6, 2022, it was endemic in West and Central African countries and infrequently occurred outside of Africa. However, many cases have been identified in several nonendemic countries since May 13, 2022, with no prior human or animal travel from endemic areas; that was the first time to document the cases and long‐term transmission in countries with no epidemiological ties to endemic African countries. Seven travel‐related human monkeypox cases were recorded outside of Africa from September 2018 to November 2021: one in Israel, one in Singapore, and two in the US Youth are most affected. Monkeypox's unanticipated development in places with no known epidemiological linkages raises concerns about the virus's evolution, which permits undetected transmission for a long period. Conclusion Monkeypox is no longer a rare, self‐limiting disease limited to endemic countries. Its ever‐changing epidemiology and transmission dynamics have increased the possibility of its evolving into a much deadlier pathogen. Therefore, improved surveillance and detailed case and contact investigation are required to comprehend the ever‐changing epidemiology of monkeypox.

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Background and Aims Vitamin C has been predicted to be effective as an antioxidant in treating various ailments, including viral infections such as pervasive coronavirus disease (COVID‐19). With this meta‐analysis, we looked to ascertain the relationship between high‐dose vitamin C administration and mortality, severity, and length of hospitalization of COVID‐19 patients. Methods We collected articles from PubMed, Google Scholar, ScienceDirect, SAGE, and Cochrane databases between January 1, 2020, and May 30, 2022. Odds ratio (ORs) with corresponding 95% confidence interval (CI) and p value were calculated to assess the connection of high‐dose vitamin C in COVID‐19 patients' mortality and severity. The length of hospitalization was calculated and pooled with the mean difference (MD), 95% CI, and p value. Review manager 5.3 was used to carry out this meta‐analysis. Results This meta‐analysis included 15 complete studies involving 2125 COVID‐19 patients. Our study demonstrated a significant correlation between vitamin C consumption and death. Vitamin C consumption significantly reduces mortality risk with COVID‐19 patients (OR = 0.54, 95% CI = 0.42–0.69, p < 0.00001). Furthermore, there was a link between the severity of COVID‐19 and the intake of vitamin C. Patients who consumed vitamin C showed 0.63 times less severity than those who did not take vitamin C (OR = 0.63, 95% CI = 0.43–0.94, p = 0.02). Patients taking vitamin C spent slightly more time in hospital than those who did not take vitamin C (MD = 0.19, 95% CI = −1.57 to 1.96, p = 0.83). Conclusions During COVID‐19, there was a substantial advantage in taking supplementary vitamin C, at least in terms of severity and mortality.

Background and Aims Abnormalities in hematological and biochemical markers are assumed to be associated with the progression of COVID‐19 disease. This meta‐analysis was performed to assess the consequences of abnormalities of biomarkers (D‐dimers, C‐reactive protein [CRP], serum ferritin, lactate dehydrogenase [LDH], random blood sugar [RBS], absolute neutrophil count [ANC], neutrophil to lymphocyte ratio (NLR), serum creatinine, and hemoglobin) in the Bangladeshi COVID‐19 patients. Methods The data of biomarker levels in Bangladeshi COVID‐19 patients were gathered from five databases: PubMed, ScienceDirect, Web of Science, Google Scholar and Bangladesh Journals Online between January 2020 to March 2022. Review Manager 5.4 was used for the meta‐analysis, and Egger's test and Begg‐Mazumdar's rank correlation were used to investigate publication bias. Results This study included 1542 patients with 567 severe and 975 nonsevere statuses. Based on the accumulated data synthesis, there is a strong correlation between disease severity and different biomarkers, including D‐dimer, CRP, ferritin, LDH, RBS, NLR, and serum creatinine (MD = 1.16, p = 0.0004; MD = 22.97, p = 0.003; MD = 419.26, p < 0.00001; MD = 118.37, p = 0.004; MD = 1.96, p = 0.02; MD = 1.26, p = 0.02; and MD = 0.31, p = 0.008, respectively). A significantly decreased correlation was observed for hemoglobin levels in severe COVID‐19 patients (MD = −0.73, p = 0.10). Conclusion The elevated biomarkers level was noticed in severe cases compared to nonsevere patients, revealing that D‐dimer, CRP, ferritin, LDH, RBS, NLR, and serum creatinine are significantly correlated to COVID‐19 severity. Only lower hemoglobin level was found to be associated with COVID‐19 severity.

Background and Aims The dengue virus is widespread throughout Bangladesh and significantly contributes to morbidity and mortality. One effective method for preventing further dengue epidemics is to reduce mosquito breeding at the most opportune period each year. This study aims to determine dengue prevalence in 2022 by comparing previous years' data and estimating the period of this disease's most significant incidence. Methods From the beginning of 2008–December 15, 2022, we looked at the monthly reports of cases made to the Bangladesh Institute of Epidemiology, Disease Control, and Research. Results According to our findings, there were 61089 confirmed dengue cases in 2022, with 269 fatalities ‐ the highest annual death toll for this disease since 2000. Almost one‐third (32.14%) of all dengue deaths in Bangladesh occurred in 2022 (1 January–15 December), highlighting the severity of the threat posed by this disease in the coming year. Furthermore, we observe that the months in the second half of any year in Bangladesh are the most at risk for dengue transmission. In 2022, Dhaka city and Chittagong are hit the hardest (incidence: 63.07% vs. 14.42%; morality: 63.34% vs. 24.16%), showing the relevance of population density in spreading this fatal disease. Conclusion Statistics show an increase in dengue cases every day, and the year 2022 will be marked as the peak of the disease's death prevalence. Both the individuals and the government of Bangladesh need to take action to reduce the dissemination of this epidemic. If not, the country will soon be in great peril.

Objectives: The TNFAIP8 gene family and TNFAIP2 gene are inextricably linked to an elevated risk of cancer development. This systemic review and meta-analysis seeks to establish the relationship between TNFAIP8 (rs11064, rs1045241, rs1045242, and rs3813308), TNFAIP8L1 (rs1060555), and TNFAIP2 (rs710100 and rs8126) polymorphisms with the risk of cancer. Methods and Materials: A systematic search of multiple databases from January 2022 to April 2022 was used to identify relevant studies. Odds ratios (ORs) with corresponding 95% CI and p-value were calculated to assess the association. Bonferroni correction was performed to correct p-values. Trial sequential analysis (TSA) and in-silico messenger RNA expression were also performed. Review Manager 5.4 software was used for performing this meta-analysis. Results: This study comprised 6909 cancer patients and 7087 healthy participants from 14 studies. Four genetic models of rs11064 (codominant 2 [COD2]: OR = 2.30, p = 7.83 × 10−5; codominant 3 [COD3]: OR = 2.10, p = .0006; recessive model [RM]: OR = 2.24, p = .0001; AC: OR = 1.47, p = .037), two genetic models of rs1045241 (codominant 1 [COD1]: OR = 1.27, p = .009; overdominant model [ODM]: OR = 1.24, p = .018), four genetic models of rs1045242 (COD1: OR = 1.52, p = .005; dominant model (DM): OR = 1.56, p = .002; OD: OR = 1.48, p = .008; AC: OR = 1.48, p = .002), and three genetic models of rs8126 (COD2: OR = 1.41, p = .0005; COD3: OR = 1.44, p = .0002; RM: OR = 1.43, p = .0001) were statistically linked to cancer risk. Only one genetic model of rs1060555 polymorphism showed a significant protective association with cancer (COD2: OR = 0.80, p = .048). The outcomes of TSA also validated the findings of the meta-analysis. Conclusion: This study summarizes that rs11064, rs1045241, and rs1045242 polymorphisms of TNFAIP8 gene and rs8126 polymorphism of TNFAIP2 gene are significantly linked with the risk of cancer development. This meta-analysis was registered at INPLASY (registration number: INPLASY202270073).

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