Assessment of Heavy Metal Contamination and Health Risk Associated with Cultivated Vegetables along Dhaka-Mymensingh Highway, Bangladesh.

The purpose of this research work is to evaluate the degree of eight heavy metals (Fe, Mn, Cu, Zn, Cd, Pb, Cr, and Ni) contamination and health risks of three regularly consumed vegetables (papaya, bottle gourd, and esculent) near one of Bangladesh's busiest roadways, the Dhaka-Mymensingh highway. The heavy metal concentrations in 45 vegetable samples were analyzed using an atomic absorption spectrometer (AAS). These samples were collected from five different sampling sites based on various land use patterns adjacent to the highway. The average concentrations (mg/kg) of Cu, Fe, Mn, Zn, Cr, and Ni were found to be 9.86, 246.8, 16.9, 28.0, 9.02, and 2.02, respectively, for papaya; 14.9, 281.2, 387.6, 49.0, 10.1, and 2.92, respectively, for bottle gourd; and 17.6, 183.4, 107.2, 80.7, 7.98, and 2.34, respectively, for esculent. The mean concentrations of Cr, Zn, and Mn in papaya, bottle gourd, and esculent were higher than the acceptable limit. Correlation analysis revealed a significant positive correlation between Fe-Cu, Zn-Fe, Cu-Fe, and Fe-Zn in papaya; Cu-Zn, Fe-Cr, Zn-Ni, and Cr-Fe in bottle gourd; and Mn-Cr, Mn-Ni, Mn-Fe, and Cr-Ni in esculent, thereby indicating their common anthropogenic sources like agricultural activities, waste from the commercial area, filling station, and vehicular emissions. Health risk assessment through target hazard quotient (THQ) revealed the highest THQ of 9.52 for Cr in bottle gourd, which poses a high non-carcinogenic health risk to the localities upon the intake of these contaminated vegetables. Target cancer risk (TCR) was found to be highest for Cr in papaya (0.013) and bottle gourd (0.014). TCR trends were found for Cr in the following order: bottle gourd > esculent > papaya. This study contributed the greatest concern for both carcinogenic and non-carcinogenic health impacts through ingesting contaminated vegetables.

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches.

Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

Online survey of university students' perception, awareness and adherence to COVID-19 prevention measures.

BACKGROUND: Determining factors correlated with protective measures against COVID-19 is important to improve public health response. This study describes student opinions related to university COVID-19 preventive measures. METHODS: In fall 2020, 643 US university students completed an online survey on perception, awareness, and adherence to COVID-19 preventive measures. Outcomes included protocol effectiveness (self or others), protocol adherence (self or others), consequences of protocol violation, knowledge of violations, and level of concern for COVID-19. Multiple linear regression models determined correlates of outcome variables. Covariates included gender, race, residence, area of study, class, and knowledge of someone with a positive COVID-19 test. RESULTS: Overall, students agreed with protective measures (equivalent to higher scores). In adjusted linear models, females (versus males) had significantly higher scores for protocol effectiveness (self) (p < 0.001), consequences of protocol violation (p = 0.005), and concern about COVID-19 (p < 0.001). Asian/Pacific Islander (versus white) had significantly higher scores for protocol effectiveness (self) (p < 0.001), consequences of protocol violation (p = 0.008), and concern about COVID-19 (p = 0.001). Graduate students (versus freshman) had higher scores for protocol effectiveness (self) (p < 0.001), protocol adherence (self) (p = 0.004) and concern about COVID-19 (p < 0.001). In contrast, participants who had a positive COVID-19 test had significantly lower scores for protocol effectiveness (self) (p = 0.02), protocol adherence (self) (p = 0.004), and consequences of protocol violation (p = 0.008). CONCLUSION: Overall, females, Asian/Pacific Islanders, and graduate students were more likely to agree with or adhere to COVID-19 prevention guidelines but those who tested positive for COVID-19 were less likely to do so. These results may inform future prevention efforts.

Identifying and analyzing sepsis states: A retrospective study on patients with sepsis in ICUs.

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, progression, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis and models disease progression using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Our progression model accurately characterizes the severity level of each pathological trajectory and identifies significant changes in clinical variables and treatment actions during sepsis state transitions. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials, prevention, and therapeutic strategies for sepsis.

Reliability of a computer-based neurobehavioral assessment test battery for Bangladeshi adolescent children.

In developing countries, there is a need for low-cost neurobehavioral (NB) test batteries for vulnerable populations, particularly for children exposed to environmental neurotoxicants. The objective of the current study was to assess the feasibility and test-retest reliability of the Behavioral Assessment and Research System (BARS) in children from a rural community in Bangladesh. Fifty healthy adolescents living in the Health Effects of Arsenic Longitudinal Study (HEALS) area in Araihazar, Bangladesh completed all six tests from the BARS in two test sessions scheduled two weeks apart. The BARS tests evaluated NB functions such as motor coordination, attention, memory, and information processing speed. The reliability assessment, evaluated by test-retest correlations demonstrated moderate to strong correlations (i.e., correlation coefficients ranged from 0.43 to 0.85), which were statistically significant (p < 0.05). Paired t-tests for comparing the test and retest outcomes indicated significant improvement in NB performance, highlighting learning and practice effects. NB performance improved with increasing age in most cases. Adolescent boys performed better than the girls in Finger Tapping, Digit Span, and Simple Reaction Time, whereas the girls performed better in Continuous Performance and Symbol Digit tests. The reliability scores (Pearson's correlations 0.43-0.85) were consistent with other children studies in different cultural settings. The effects of age and sex on NB tests were also consistent with findings reported in other countries. Overall, the findings of the study support the feasibility of using this computer-based test system to assess vulnerability of brain health due to environmental exposures among rural Bangladeshi children.