Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Clindamycin removal from aqueous solution by non-thermal air plasma treatment: performance, degradation pathway and ensuing antimicrobial activity.

The present study set out to investigate clindamycin (CLN) removal from aqueous solution using non-thermal plasma (NTP) under atmospheric air conditions and to address the effects of some variables including pH, initial concentration of CLN, and working voltage on CLN degradation. The result showed that the NTP system exhibited excellent degradation rate and mineralization efficiency on CLN in 15 min under neutral conditions, which exceeded 90 and 45%, respectively, demonstrating its conversion to other organic by-products. Furthermore, CLN degradation was largely dependent upon the initial pH of solution, applied voltage, and reaction time. Specifically, under acidic conditions (pH = 3), working voltage of 24 kV and after 15 min of reaction, almost 100% of CLN was degraded. NTP-initiated CLN degradation products through LC-MS/MS analysis, determined within 10 min of reaction, inferred that the complex structure of CLN has undergone deterioration by active radical species which subsequently generated small molecular organic compounds. Chemical processes involved in CLN degradation were found to be demethylation, desulfonylation, dechlorination, hydroxylation and deamination. Lastly, antimicrobial susceptibility tests revealed that the activity of CLN was reduced following NTP treatment, which is also in good agreement with the minimum inhibitory concentration (MIC) values obtained from microdilution analyses.

Association of SARS-CoV-2 presence in sewage with public adherence to precautionary measures and reported COVID-19 prevalence in Tehran.

Compared to the growing body of literature on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection and quantification in sewage, there are limited studies reporting on correlations between the viral loads in sewage and the prevalence of infected patients. The present work is a part of the regular monitoring effort for SARS-CoV-2 in wastewater influents from seven wastewater treatment plants (WWTPs) in Tehran, Iran, starting from late September 2020 until early April 2021. These facilities cover ~64% of the metropolis serving >5000,000 M individuals. The study set out to track the trends in the prevalence of COVID-19 in the community using wastewater based epidemiology (WBE) and to investigate whether these measurements correlate with officially reported infections in the population. Composite sewage samples collected over 16 h were enriched by polyethylene glycol precipitation and the corresponding threshold cycle (Ct) profiles for CDC 'N' and 'ORF1ab' assays were derived through real time RT-qPCR. Monte Carlo simulation model was employed to provide estimates of the disease prevalence in the study area. RNA from SARS-CoV-2 was detectable in 100% ('N' assay) and 81% ('ORF1ab' assay) of totally 91 sewage samples, with viral loads ranging from 40 to 45,000 gene copies/L. The outbreak of COVID-19 positively correlated (R2 = 0.80) with the measured viral load in sewage samples. Furthermore, sewage SARS-CoV-2 RNA loads preceded infections in the population by 1 to 2 days, which were in line with public adherence with and support for government instructions to contain the pandemic. Given the transient presence of human host-restricted infections such as SARS-CoV-2, these results provide evidence for assessment of the effectiveness of coordinated efforts that specifically address public health responses based on wastewater-based disease surveillance against not only COVID-19 but also for future infectious outbreaks.

Carbamazepine removal from aqueous solution by synthesized reduced graphene oxide-nano zero valent iron (Fe0-rGO) composite: theory, process optimization, and coexisting drugs effects.

Synthesized Fe0-rGO nanocomposite with ratio of 1/1 (w/w) was prepared and has been used as adsorbent for the removal of Carbamazepine (CBZ) from aqueous solution. The adsorbent was characterized by various techniques such as Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and Field Emission Scanning Electron Microscopy (FE-SEM) analyses. Linear experiments were performed to compare the best fitting isotherms and kinetics. The Freundlich isotherm (R2>0.90) and pseudo second order kinetic (R2>0.99) fitted well the experimental data. On the basis of the Langmuir isotherm, the maximum adsorption capacity of Fe0-rGO for CBZ was up to 50 mg g-1 at 30 °C. The pH, adsorbent dose, and initial concentration of CBZ were observed to be the leading parameters that affected the removal of CBZ considering the analysis of variance (ANOVA; p<0.05). The optimum process value of variables obtained by numerical optimization corresponds to pH 3.07, an adsorbent dose of 36.2 mg, an initial CBZ concentration of 5 mg L-1 and at 30.15 °C. The results of optimum conditions reveal that a maximum of 94% removal efficiency can be achieved; whereas, this phenomenon was independent of temperature (p-value>0.05). Moreover, Fe0-rGO can be used to remove diclofenac (DIC) and cetirizine (CTZ) simultaneously. To sum up, the Fe0-rGO is a promising adsorbent not only for the efficient removal of CBZ but also for the reduction of coexisting drugs in aqueous solution.

Sewage Systems Surveillance for SARS-CoV-2: Identification of Knowledge Gaps, Emerging Threats, and Future Research Needs.

The etiological agent for novel coronavirus (COVID-19, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), not only affects the human respiratory system, but also the gastrointestinal tract resulting in gastrointestinal manifestations. The high rate of asymptomatic infected individuals has challenged the estimation of infection spread based on patients' surveillance, and thus alternative approaches such as wastewater-based epidemiology (WBE) have been proposed. Accordingly, the number of publications on this topic has increased substantially. The present systematic review thus aimed at providing state-of-the-knowledge on the occurrence and existing methods for sampling procedures, detection/quantification of SARS-CoV-2 in sewage samples, as well as anticipating challenges and providing future research direction to improve the current scientific knowledge. Articles were collected from three scientific databases. Only studies reporting measurements of virus in stool, urine, and wastewater samples were included. Results showed that improving the scientific community's understanding in these avenues is essential if we are to develop appropriate policy and management tools to address this pandemic pointing particularly towards WBE as a new paradigm in public health. It was also evident that standardized protocols are needed to ensure reproducibility and comparability of outcomes. Areas that require the most improvements are sampling procedures, concentration/enrichment, detection, and quantification of virus in wastewater, as well as positive controls. Results also showed that selecting the most accurate population estimation method for WBE studies is still a challenge. While the number of people infected in an area could be approximately estimated based on quantities of virus found in wastewater, these estimates should be cross-checked by other sources of information to draw a more comprehensive conclusion. Finally, wastewater surveillance can be useful as an early warning tool, a management tool, and/or a way for investigating vaccination efficacy and spread of new variants.

Moore swab performs equal to composite and outperforms grab sampling for SARS-CoV-2 monitoring in wastewater.

Wastewater-based epidemiology (WBE) approaches to detect SARS-CoV-2 in municipal wastewater can provide unique information on the incidence or prevalence of COVID-19 in community. However, there are several technical challenges coupled with sewage sampling for SARS-CoV-2, including intermittent shedding of viruses, sampling time, volume, and frequency. Sampling schemes thus may need to be tailored to reach out highly sensitive, accurate, and reliable results. Herein, we compared the accuracy and threshold cycle (Ct) profiles of SARS-CoV-2 in Moore swabs, composite (16-h), and grab samples taken from sewage manholes (n = 17) at the Middle Eastern city of Tehran, Iran, on two occasions (November 2020 and May 2021). Samples were concentrated by polyethylene glycol precipitation and the corresponding Ct values for CDC 'N' and 'ORF1ab' assays were derived by means of real time RT-qPCR. Overall, the Moore swabs performed equal to samples composited over 16 h for qualitative monitoring, and 34/34 (100%) were positive for SARS-CoV-2. The 'N' assay showed the highest detection frequency as compared to 'ORF1ab'. The mean Moore swab Ct profiles were more consistent with 16 h composite sampling as compared with corresponding grab samples, providing hints as to the best sampling protocol to adopt when planning a sewage monitoring campaign particularly under WBE. Furthermore, our analyses on local differences showed somewhat higher virus copy numbers in the southern areas. The experimental design of this study revealed that the Moore swab and composite samples are more sensitive than grab samples, suggesting that the collection of grab samples may be inappropriate for characterizing total number of viral RNA copies in sewage samples. Given the transiently presence of human host-restricted infections such as SARS-CoV-2 and the simplicity and affordability of Moore swab, the method is well suited for disease surveillance in resource poor regions struggling with limited capacity for clinical testing.

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020).

Understanding the spatial distribution of coronavirus disease 2019 (COVID-19) cases can provide valuable information to anticipate the world outbreaks and in turn improve public health policies. In this study, the cumulative incidence rate (CIR) and cumulative mortality rate (CMR) of all countries affected by the new corona outbreak were calculated at the end of March and April, 2020. Prior to the implementation of hot spot analysis, the spatial autocorrelation results of CIR were obtained. Hot spot analysis and Anselin Local Moran's I indices were then applied to accurately locate high and low-risk clusters of COVID-19 globally. San Marino and Italy revealed the highest CMR by the end of March, though Belgium took the place of Italy as of 30th April. At the end of the research period (by 30th April), the CIR showed obvious spatial clustering. Accordingly, southern, northern and western Europe were detected in the high-high clusters demonstrating an increased risk of COVID-19 in these regions and also the surrounding areas. Countries of northern Africa exhibited a clustering of hot spots, with a confidence level above 95%, even though these areas assigned low CIR values. The hot spots accounted for nearly 70% of CIR. Furthermore, analysis of clusters and outliers demonstrated that these countries are situated in the low-high outlier pattern. Most of the surveyed countries that exhibited clustering of high values (hot spot) with a confidence level of 99% (by 31st March) and 95% (by 30th April) were dedicated higher CIR values. In conclusion, hot spot analysis coupled with Anselin local Moran's I provides a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases shedding light on the high-risk districts.

Neurobehavioral assessment of rats exposed to pristine polystyrene nanoplastics upon oral exposure.

The increasing use of plastics has raised concerns about pollution of freshwater by these polymeric materials. Knowledge about their potential effects on environmental and public health is limited. Recent publications have suggested that the degradation of plastics will result in the release of nano-sized plastic particles to the environment. Therefore, it is of utmost importance to gain knowledge about whether and how nanoplastics affect living organisms. The present study aimed to analyse potential neurobehavioral effects of polystyrene nanoparticles (PS-NPs) after long-term exposure on rat. Potential effects of PS-NPs were investigated using four test dosages (1, 3, 6, and 10 mg PS-NPs/kg of body weight/day) administrated orally with adult Wistar male rats for five weeks. Neurobehavioral tests were chosen to assess a variety of behavioral domains. Particle diameters in test suspensions were determined through dynamic light scattering and showed an average hydrodynamic diameter of approximately 38.92 nm. No statistically significant behavioral effects were observed in all tests performed (p > 0.05). In the elevated plus maze, PS-NPs-exposed rats showed greater number of entries into open arms compared to controls. Also, PS-NPs had no significant influence on body weight of animals. Taking into account the subtle and transient nature of neurobehavioral consequences, however, these results underline the possibility of even pristine plastic nanoparticles to induce behavioral alteration in the rest of the food web, including for marine biota and humans. Indeed even though studied neurobehavioral effects in our study was not statistically significant, the observed subtle effects may be clinically considerable.

Adsorption performance of packed bed column for nitrate removal using PAN-oxime-nano Fe2O3.

A continuous fixed bed study was carried out by using PAN-oxime-nano Fe2O3 as a sorbent for the removal of nitrate from aqueous solution. The effect of factors, such as flow rate (2, 5 and 7 mL/min) and bed depth (5, 10 and 15 cm) were studied. Data confirmed that the breakthrough curves were dependent on flow rate and bed depth. The adsoption capacities observed in diffent conditions of flow rates (2,5 and 7 mL/min) were 11.65, 24.38 and 25.89, respectively. Thomas model was applied to experimental data to predict the breakthrough curves using linear regression and to determine the characteristic parameters of the packed bed column. Bed depth/service time analysis (BDST) model was used to investigate the effect of bed depth on breakthrough curves. The results showed that Thomas model was suitable for the normal description of breakthrough curve at the experimental condition. The data were in good agreement with BDST model with R(2) > 0.98. Statistical analyses were performed on fluoride removal obtained from different flow rates using SPSS16 software by applying Kruskal- Wallis test. These findings suggested that PAN-oxime-nano Fe2O3 in the column structure presents a great potential in removal of nitrate from aqueous solutions.

Detection and identification of Legionella species in hospital water supplies through Polymerase Chain Reaction (16S rRNA).

Legionella spp. are important waterborne pathogens that are normally transmitted through aerosols. The present work was conducted to investigate the presence of Legionella spp. and its common species in hospital water supplies. Considering the limitations of culture method, polymerase chain reaction (PCR) assays were developed to detect the gene 16S rRNA irrespective of the bacterial serotype. Four well-established DNA extraction protocols (freeze & thaw and phenol-chloroform as two manual protocols and two commercial kits) were tested and evaluated to release DNA from bacterial cells. A total of 45 samples were collected from seven distinct hospitals' sites during a period of 10 months. The PCR assay was used to amplify a 654-bp fragment of the 16S rRNA gene. Legionella were detected in 13 samples (28.9%) by all of the methods applied for DNA extraction. Significant differences were noted in the yield of extracted nucleic acids. Legionella were not detected in any of the samples when DNA extraction by freeze & thaw was used. Excluding this method and comparing manual protocol with commercial kits, Kappa coefficient was calculated as 0.619 with p < 0.05. Although no meaningful differences were found between the kits, DNA extraction with Bioneer kit exhibited a higher sensitivity than classical Qiagen. Showerheads and cold-water taps were the most and least contaminated sources with 55.5 and 9 percent positive samples, respectively. Moreover two positive samples were identified for species by DNA sequencing and submitted to the Gene Bank database with accession Nos. FJ480932 and FJ480933. The results obtained showed that despite the advantages of molecular assays in Legionella tracing in environmental sources, the use of optimised DNA extraction methods is critical.

The Efficacy of Residual Chlorine Content on the Control of Legionella Spp. In Hospital Water Systems.

BACKGROUND: Outbreaks of legionellosis may be a side effect of institution-water treatment. However, the long-term outcomes and the predictive factors of Legionella prevalence in such systems have still not been fully studied. This study was therefore conducted to investigate the prevalence of Legionella spp. and to evaluate the role of bacteriological water quality parameters on its prevalence and removal in hospital water systems. METHODS: A total of 45 samples were collected from distinct sites at seven hospitals in Tehran, Iran. The prevalence of this bacterium was assayed through a sensitive and specific technique for DNA detection using PCR. Multivariable stepwise regression analysis was used to explore the independent effects of the baseline factors on the incidence of Legionella. Two positive samples were also identified for species by DNA sequencing. RESULTS: Legionella were detected in 31.1% of samples. Showerheads and cold-water taps were the most and the least contaminated sources with 55.3 and 9 percent positive samples, respectively. Total mean of residual chlorine was 0.38 mg/L, with the peak value of 1.7 mg/L. Legionella detection was proportional to the residual chlorine content of water and the results indicated that residual chlorine content is a critical factor in the incidence and proliferation of Legionella (r=-0.33). The prevalence of Legionella also coincided with the prevalence of HPC and amoeba cysts. CONCLUSION: The high positive rate of Legionella colonization shows that hospital-acquired legionellosis might be under diagnosed in studied hospitals. Further, Legionella colonization is independent of the type of water, system characteristics and of preventive maintenance measures.

Sequential study on reactive blue 29 dye removal from aqueous solution by peroxy acid and single wall carbon nanotubes: experiment and theory.

The majority of anthraquinone dye released to the environment come from antrapogenic sources. Several techniques are available for dyes' removal. In this study removal of reactive blue 29 (RB29) by an advanced oxidation process sequenced with single wall carbon nanotubes was investigated. Advanced oxidation process was optimized over a period of 60 minutes by changing the ratio of acetic acid to hydrogen peroxide, the compounds which form peroxy acid. Reduction of 20.2% -56.4% of reactive blue 29 was observed when the ratio of hydrogen peroxide/acetic acid/dye changed from 344/344/1 to 344/344/0.08 at different times (60, 120 and 180 min). The optimum ratio of acetic acid/hydrogen peroxide/dye was found to be 344/344/0.16 over 60 min. The resultant then was introduced for further removal by single wall carbon nanotubes(SWCNTs) as adsorbent. The adsorption of reactive blue 29 onto SWCNTs was also investigated. Langmuir, Freundlich and BET isotherms were determined and the results revealed that the adsorption of RB29 onto SWCNTs was well explained by BET model and changed to Freundlich isotherm when SWCNTs was used after the application of peroxy acid. Kinetic study showed that the equilibrium time for adsorption of RB 29 on to SWCNT is 4 h. Experiments were carried out to investigate adsorption kinetics, adsorbent capacity and the effect of solution pH on the removal of reactive blue29. The pseudo-second order kinetic equation could best describe the sorption kinetics. The most efficient pH for color removal (amongst pH=3, 5 and 8) was pH= 5. Further studies are needed to identify the peroxy acid degradation intermediates and to investigate their effects on SWCNTs.