Hematite nanomaterial from a tropical freshwater ecosystem: Geological, environmental, and industrial implications.

Synthetic hematite (Fe2O3) nanoparticles are extensively explored for medicine, optics, and environmental remediation. However, natural iron nanoparticles in a freshwater ecosystem have not been well characterized. Here we report the presence of natural iron nanoparticles in a tropical freshwater ecosystem in southern India. These iron nanoparticles that exist as slime in the natural water system were characterized through a multiproxy investigation involving Field-Emission Scanning Electron Microscopy (FE-SEM), X-ray Diffraction (XRD), X-ray Fluorescence (XRF), X-ray Photoelectron Spectroscopy (XPS), and Raman spectroscopy and BET analyses. These nanoparticles exist as amorphous hematite (Fe2O3), with the XRD peaks matching that of the iron arsenate compound. Fe2O3 occurs as mesoporous hollow microspheres with a size range of 14.97 to 61.3 nm and a surface area of 48.45m2/g. Further, the identification of Bacillus cereus in the slime suggests its role in iron sequestration, indicating a biogeochemical origin, which we infer is a particularly common phenomenon in tropical river basins where lateritic soils prevail. This study is the first to describe natural iron nanoparticles in a tropical freshwater ecosystem. It identifies their amorphous hematite structure and biogeochemical origin, offering new insights into their ecological roles and potential applications. This discovery presents an opportunity for utilizing this slime as an important source of hematite nanomaterials, with potential industrial applications.

Late Paleocene paleoclimate recorded in fluid inclusion of halite in Subei basin, East China.

Salt deposits are indicative of relatively extreme climate events. However, due to insufficient independent temperature proxies, paleotemperature records obtained from salt deposition are still lack. The Paleocene evaporite sequence deposited in the Hongze Depression of Subei Basin of eastern China provides an important terrestrial sediment record during this period. In this study we present total of 488 homogenization temperature (Th) data of halite fluid inclusions from drilling core with different stratigraphic depth after detailed petrological observation. The obtained Th ranged from 17.7 °C to 52.3 °C, with the mean Th value of 34.1 °C that in good agreement with the previous studies of climatic proxies. Our study shows that primary fluid inclusions of halite can serve as a robust tool to construct the ancient earth surface temperature.

Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms.

Land subsidence is a worldwide threat. In arid and semiarid lands, groundwater depletion is the main factor that induce the subsidence resulting in environmental damages and socio-economic issues. To foresee and prevent the impact of land subsidence, it is necessary to develop accurate maps of the magnitude and evolution of the subsidences. Land subsidence susceptibility maps (LSSMs) provide one of the effective tools to manage vulnerable areas and to reduce or prevent land subsidence. In this study, we used a new approach to improve decision stump classification (DSC) performance and combine it with machine learning algorithms (MLAs) of naïve Bayes tree (NBTree), J48 decision tree, alternating decision tree (ADTree), logistic model tree (LMT), and support vector machine (SVM) in land subsidence susceptibility mapping (LSSSM). We employ data from 94 subsidence locations, among which 70% were used to train learning hybrid models and the other 30% were used for validation. In addition, the models' performance was assessed by ROC-AUC, accuracy, sensitivity, specificity, odd ratio, root-mean-square error (RMSE), kappa, frequency ratio, and F-score techniques. A comparison of the results obtained from the different models reveals that the new DSC-ADTree hybrid algorithm has the highest accuracy (AUC = 0.983) in preparing LSSSMs as compared to other learning models such as DSC-J48 (AUC = 0.976), DSC-NBTree (AUC = 0.959), DSC-LMT (AUC = 0.948), DSC-SVM (AUC = 0.939), and DSC (AUC = 0.911). The LSSSMs generated through the novel scientific approach presented in our study provide reliable tools for managing and reducing the risk of land subsidence.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India.

The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

Predicting mental and psychomotor delay in very pre-term infants using machine learning.

BACKGROUND: Very preterm infants are at elevated risk for neurodevelopmental delays. Earlier prediction of delays allows timelier intervention and improved outcomes. Machine learning (ML) was used to predict mental and psychomotor delay at 25 months. METHODS: We applied RandomForest classifier to data from 1109 very preterm infants recruited over 20 years. ML selected key predictors from 52 perinatal and 16 longitudinal variables (1-22 mo assessments). SHapley Additive exPlanations provided model interpretability. RESULTS: Balanced accuracy with perinatal variables was 62%/61% (mental/psychomotor). Top predictors of mental and psychomotor delay overlapped and included: birth year, days in hospital, antenatal MgSO4, days intubated, birth weight, abnormal cranial ultrasound, gestational age, mom's age and education, and intrauterine growth restriction. Highest balanced accuracy was achieved with 19-month follow-up scores and perinatal variables (72%/73%). CONCLUSIONS: Combining perinatal and longitudinal data, ML modeling predicted 24 month mental/psychomotor delay in very preterm infants ½ year early, allowing intervention to start that much sooner. Modeling using only perinatal features fell short of clinical application. Birth year's importance reflected a linear decline in predicting delay as birth year became more recent. IMPACT: Combining perinatal and longitudinal data, ML modeling was able to predict 24 month mental/psychomotor delay in very preterm infants ½ year early (25% of their lives) potentially advancing implementation of intervention services. Although cognitive/verbal and fine/gross motor delays require separate interventions, in very preterm infants there is substantial overlap in the risk factors that can be used to predict these delays. Birth year has an important effect on ML prediction of delay in very preterm infants, with those born more recently (1989-2009) being increasing less likely to be delayed, perhaps reflecting advances in medical practice.

Exploring the potential of sulphur forms in Northeastern Indian coals: Implications in environmental remediation and heavy metal sensing.

The Sampar Coalfield in Northeastern India is a source of plentiful coal reserves, which are burnt for energy production and industrial applications, resulting in the release of pollutants such as sulphur , arsenic, and lead, which are hazardous to the environment and public health. In this work, samples from the Sampar coalfield have been analyzed to understand the origin, distribution, and various forms of sulphur and their ability to detect toxic heavy metals. The total sulphur concentration ranged from 4.31% to 6%, with organic sulphur being the predominant form at 69.21%, followed by pyritic sulphur at 16.49% and sulphate sulphur at 14.28%. With high sulphur content, this coal indicates a marine influence in the peat-forming swamps. The samples have also been examined for petrographic and elemental analysis, which have revealed the presence of vitrinite, liptinite, inertinite, carbon, hydrogen, nitrogen, oxygen, and mineral matter. In addition, the same coal sample has also been used for electrochemical sensing-based detection of toxic heavy metals like arsenic and lead, and the findings indicate an improved efficacy. These results are expected to have significant implications in the development of effective bio-based remediation strategies in the region to mitigate the harmful effects of coal-related pollution.

Serum ferritin level as a prognostic biomarker for predicting dengue disease severity: A systematic review and meta-analysis.

Serum ferritin levels serves as biomarkers in many inflammatory and infectious diseases. This current systematic review and meta-analysis evaluated whether serum ferritin levels are associated with severe dengue and its utility as a biomarker of disease severity. Literature searches were conducted in PubMed, Scopus, ScienceDirect, the Cochrane library, and Google Scholar. A total of 18 studies examining the serum ferritin levels in dengue cases in the context of disease severity (nine studies having dengue classification as non-severe vs. severe dengue cases, and nine studies having dengue classification as dengue without warning signs (DwoWS), dengue with warning signs (DwWS), and severe dengue cases) were included and the quality of the studies was assessed using the Quality in Prognostic Factor Studies tool. The meta-analysis was performed using STATA software to calculate the effect size as a standardized mean difference (SMD) or Hedges 'g' for the continuous outcome. Higher serum ferritin levels were found in severe dengue cases compared to non-severe cases [SMD (Hedges 'g') 4.05 (95% C.I. 2.09-6.00), (I2  = 98.8%)]. In the second group, DwWS cases showed high serum ferritin levels compared to DwoWS [SMD 2.01 (95% C.I. 0.92-3.10), (I2  = 97.89%)], and severe dengue cases showed higher levels of serum ferritin compared to DwWS [SMD 2.66 (95% C.I. 1.72-4.48), (I2  = 98.78%)] and DwoWS cases [SMD 6.65 (95% C.I. 1.72-11.59), (I2  = 99.78%]. Subgroup analysis for the country of study (India vs. others), ferritin testing methods, and ferritin measurement day revealed testing method as a significant contributor to heterogeneity. To conclude, the present study suggests serum ferritin as a prognostic marker for dengue disease severity. Multi-centric studies involving a large number of dengue patients with a uniform case definition accounting for all the confounding variables might help in determining a universal cut-off value to discriminate between non-severe and severe dengue.

Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models.

Land subsidence (LS) as a major geological and hydrological hazard poses a major threat to safety and security. The various triggers of LS include intense extraction of aquifer bodies. In this study, we present an LS inventory map of the Daumeghan plain of Iran using 123 LS and 123 non-LS locations which were identified through field survey. Fourteen LS causative factors related to topography, geology, hydrology, and anthropogenic characteristics were selected based on multi-collinearity test. Based on the results, five susceptibility maps were generated employing models and input data. The LS susceptibility models were evaluated and validated using the receiver operating characteristic (ROC) curve and statistical indices. The results indicate that the LS susceptibility maps produced have good accuracy in predicting the spatial distribution of LS in the study area. The result showed that the optimization models BA and GWO were better than the other machine learning algorithm (MLA). In addition, The BA model has 96.6% area under of ROC (AUROC) followed by GWO (95.8%), BART (94.5%), BRT (93.1%), and SVR (92.7%). The LS susceptibility maps formulated in our study can serve as a useful tool for formulating mitigation strategies and for better land-use planning.

Incidence, Subtypes, Risk factors, and Outcome of Delirium: A Prospective Observational Study from Indian Intensive Care Unit.

Background: Delirium is a common, under-recognized, and often fatal condition in critically ill patients, characterized by acute disorder of attention and cognition. The global prevalence varies with a negative impact on outcomes. A paucity of Indian studies exists that have systematically assessed delirium. Objective: A prospective observational study designed to determine the incidence, subtypes, risk factors, complications, and outcome of delirium in Indian intensive care units (ICUs). Patients and methods: Among 1198 adult patients screened during the study period (December 2019-September 2021), 936 patients were included. The confusion assessment method score (CAM-ICU) and Richmond agitation sedation scale (RASS) were used, with additional confirmation of delirium by the psychiatrist/neurophysician. Risk factors and related complications were compared with a control group. Results: Delirium occurred in 22.11% of critically ill patients. The hypoactive subtype was the most common (44.9%). The risk factors recognized were higher age, increased acute physiology and chronic health evaluation (APACHE-II) score, hyperuricemia, raised creatinine, hypoalbuminemia, hyperbilirubinemia, alcoholism, and smoking. Precipitating factors included patients admitted on noncubicle beds, proximity to the nursing station, requiring ventilation, as well as the use of sedatives, steroids, anticonvulsants, and vasopressors. Complications observed in the delirium group were unintentional removal of catheters (35.7%), aspiration (19.8%), need for reintubation (10.6%), decubitus ulcer formation (18.4%), and high mortality (21.3% vs 5%). Conclusion: Delirium is common in Indian ICUs with a potential effect on length of stay and mortality. Identification of incidence, subtype, and risk factors is the first step toward prevention of this important cognitive dysfunction in the ICU. How to cite this article: Tiwari AM, Zirpe KG, Khan AZ, Gurav SK, Deshmukh AM, Suryawanshi PB, et al. Incidence, Subtypes, Risk factors, and Outcome of Delirium: A Prospective Observational Study from Indian Intensive Care Unit. Indian J Crit Care Med 2023;27(2):111-118.

Natural sub-bituminous coal as filler enhances mechanical, insulation and flame retardant properties of coir-polypropylene bio-composites.

Additives provide substantial improvement in the properties of composites. Although bio-based composites are preferred over synthetic polymer and metal-based composites, they do not have the requisite properties to meet specific needs. Hence, organic, inorganic and metallic additives are included to improve the properties of bio-based composites. Coal is a readily available resource with high thermal insulation, flame resistance and other properties. This work demonstrates the addition of 20-30% natural sub-bituminous coal as filler for coir-reinforced polypropylene (PP) composites and exhibits an increased tensile strength by 66% and flexural strength by 55% compared to the composites without any filler. Such composites are intended for insulation applications and as a replacement for gypsum-based false ceiling tiles. Various ratios of coal samples were included in the composites and their effect on mechanical, acoustic, thermal insulation, flame and water resistance have been determined. A substantial improvement in both flexural and tensile properties has been observed due to the addition of coal. However, a marginal improvement has been observed in both thermal conductivity (0.65 W/mK) and flame resistance values due to the presence of coal. Adding coal increases the intensity of noise absorption, particularly at a higher frequency, whereas water sorption of the composites tends to decrease with an increase in the coal content. The addition of coal improves and adds unique properties to composites, allowing coir-coal-PP composites to outperform commercially available gypsum-based insulation panels.

Role of Superconducting Materials in the Endeavor to Stop Climate Change and Reach Sustainable Development.

Progress in the mass production of newly developed bulk (Gd0.33Y0.13Er0.53)Ba2Cu3Oy "(Gd,Y,Er)123" and MgB2 systems is presented. Two batches of (Gd,Y,Er)123 pellets of 20 mm diameter and 7 mm thick were prepared in air by an infiltration growth "IG" process. Trapped field distribution profiles of fully grown bulk samples clearly showed that all samples were single-grain and the trapped field values were more than 0.5 T at 77 K, 1.3 mm above top surface. The best bulk exhibited the trapped field value of 0.63 T at 77 K. Ultra-sonication technique was employed for refining precursors of both (Gd,Y,Er)211 and boron. TEM studies revealed that boron powder subjected to ultrasonication was refined up to nanoscale. The micron-sized particles were reduced to nanoscale, which led to improvement of critical current by up to 36% in bulk MgB2 at 20 K and self-field. This progress in fabrication of high-performance LREBa2Cu3Oy and MgB2 superconducting bulks further promotes commercialization of superconductors' production as a mode of sustainable technology.

Optimizing machine learning algorithms for spatial prediction of gully erosion susceptibility with four training scenarios.

Gully erosion causes high soil erosion rates and is an environmental concern posing major risk to the sustainability of cultivated areas of the world. Gullies modify the land, shape new landforms, and damage agricultural fields. Gully erosion mapping is essential to understand the mechanism, development, and evolution of gullies. In this work, a new modeling approach was employed for gully erosion susceptibility mapping (GESM) in the Golestan Dam basin of Iran. The measurements of 14 gully erosion (GE) factors at 1042 GE locations were compiled in a spatial database. Four training datasets comprised of 100%, 75%, 50%, and 25% of the entire database were used for modeling and validation (for each data set in the common 70:30 ratio). Four machine learning models-maximum entropy (MaxEnt), general linear model (GLM), support vector machine (SVM), and artificial neural network (ANN)- were employed to check the usefulness of the four training scenarios. The results of random forest (RF) analysis indicated that the most important GE effective factors were distance from the stream, elevation, distance from the road, and vertical distance of the channel network (VDCN). The receiver operating characteristic (ROC) was used to validate the results. Our study showed that the sample size influenced the performance of the four machine learning algorithms. However, the ANN had a lower sensitivity to the reduction of sample size. In addition, validation results revealed that ANN (AUROC = 0.85.7-0.90.4%) had the best performance based on all four sample data sets. The results of this research can be useful and valuable guidelines for choosing machine learning methods when a complete gully inventory is not available in a region.

Pharmacokinetics, pharmacodynamics, safety, and efficacy of crizanlizumab in patients with sickle cell disease.

Crizanlizumab is an anti-P-selectin monoclonal antibody indicated to reduce the frequency/prevent recurrence of vaso-occlusive crises (VOCs) in patients with sickle cell disease (SCD) aged ≥16 years. This analysis of an ongoing phase 2, nonrandomized, open-label study reports the pharmacokinetics (PK), pharmacodynamics (PD), safety, and efficacy of crizanlizumab 5.0 mg/kg (N = 45) and 7.5 mg/kg (N = 12) in patients with SCD with a history of VOCs. The median treatment duration was 104.7 and 85.7 weeks in the 5.0 and 7.5 mg/kg groups, respectively. For both doses, serum crizanlizumab concentrations rose to near maximum levels shortly after infusion, and near complete and sustained ex vivo P-selectin inhibition was observed. Grade ≥3 adverse events (AEs) occurred in 48.9% and 33.3% of patients in the 5.0 and 7.5 mg/kg groups, respectively; only 1 event was deemed treatment-related (7.5 mg/kg group). No treatment-related serious AEs occurred. One infusion-related reaction was recorded (5.0 mg/kg, grade 2 "pain during infusion"), which resolved without treatment withdrawal. Infections occurred in 57.8% and 41.7% of patients in the 5.0 and 7.5 mg/kg groups, respectively; none were drug-related. No treatment-related bleeding events were reported. No patients developed immunogenicity. The median (range) absolute reduction from baseline in the annualized rate of VOCs leading to a health care visit was -0.88 (-14.7 to 13.3) and -0.93 (-2.0 to 0.4) in the 5.0 and 7.5 mg/kg groups, respectively. Results here demonstrate the PK/PD properties of crizanlizumab in patients with SCD and the potential sustained efficacy and long-term safety of the drug after >12 months' treatment. This trial was registered at www.clinicaltrials.gov as #NCT03264989.

Geochemical, mineralogical and toxicological characteristics of coal fly ash and its environmental impacts.

Coal and coal-based products (by-products), along with other fossil fuels should be used with caution because of their impact on human health and the global climate. In the light of the environmental impact these fossil fuels cause, it's essential to understand the elemental configuration of coal-derived samples and their impact on the ecosystem. Some reports in past have described, geochemical and mineralogical physiognomies of fly-ash and their impact on the environment. However, a comprehensive investigation of various aspects of fly ash like geochemistry, mineralogy, morphology, and toxicological effects has been very sparse and the present study reports the above aspects. The ICP-OES studies confirm the presence of various elements (Al, Ca, Fe, Mg, Na, P, S, Si, and Ti) in the samples. The XRD analysis exposed the presence of minerals like Quartz, H-Hematite, Anatase, Muscovite, and Rutile, in addition to the various phases such as amorphous and crystalline in the fly-ash. Specific samples also possessed Ilmenite which is uncommon in many other samples. Chromium and lead, the well-known heavy metals to cause soil and water pollution in the neighbourhood were found to be existing in higher concentrations in the fly-ash samples, whereas cadmium was found to be the least among the toxic elements found in the samples. The samples were subjected to FE-SEM analysis, which reveals the presence of irregularly shaped minerals and unburnt carbon known to reduce the burning efficiency of coal, especially in power plants. Toxicology studies reported in the work suggested that fly-ash is toxic to the environment at higher concentrations than at lower concentrations.

Microplastic atmospheric dustfall pollution in urban environment: Evidence from the types, distribution, and probable sources in Beijing, China.

Airborne microplastics (MPs) pollution is an environmental problem of increasing concern, due to the ubiquity, persistence and potential toxicity of plastics in the atmosphere. In recent years, most studies on MPs have focused on aquatic and sedimentary environments, but little research has been done on MPs in the urban atmosphere. In this study, a total of ten dustfall samples were collected in a transect from north to south across urban Beijing. The compositions, morphologies, and sizes of the MPs in these dustfall samples were determined by means of Laser Direct Infrared (LDIR) imaging and Field Emission Scanning Electron Microscopy (FESEM). The number concentrations of MPs in the Beijing dustfall samples show an average of 123.6 items/g. The MPs concentrations show different patterns in the central, southern, and northern zones of Beijing. The number concentration of MPs was the highest in the central zone (224.76 items/g), as compared with the southern zone (170.55 items/g), and the northern zone (24.42 items/g). The LDIR analysis revealed nine compositional types of MPs, including Polypropylene (PP), Polyamide (PA), Polystyrene (PS), Polyethylene (PE), Polyethylene Terephthalate (PET), Silicone, Polycarbonate (PC), Polyurethane (PU) and Polyvinylchloride (PVC), among which PP was overall dominant. The PP dominates the MPs in the central zone (76.3%), and the PA dominates the MPs in the southern zone (55.86%), while the northern zone had a diverse combination of MPs types. The morphological types of the individual MPs particle include fragments, pellets, and fibers, among which fragments are dominant (70.9%). FESEM images show the presence of aged MPs in the Beijing atmosphere, which could pose a yet unquantified health risk to Beijing's residents. The average size of the MPs in the Beijing samples is 66.62 µm. Our study revealed that the numbers of fibrous MPs increase with the decrease in size. This pollution therefore needs to be carefully monitored, and methods of decreasing the sources and mitigations developed.

An atypical bilateral trifurcation of recurrent laryngeal nerve.

BACKGROUND: Thyroidectomy is a frequently performed surgery for benign and malignant conditions. Nevertheless, one of the most critical complications of thyroidectomy is recurrent laryngeal nerve (RLN) injury leading to vocal cord paralysis. A thorough knowledge of the anatomical variations of RLN and ligation of the related vessels close to their distal branches is critical to avoid injury. CASE PRESENTATION: Here, we report the first case of bilateral trifurcation of recurrent laryngeal nerve (RLN) in a 40-year old woman with multinodular goitre. Total thyroidectomy was performed and RLN was preserved bilaterally. Followed by a precise dissection, fine branches were traced penetrating the larynx. We did not observe any further post-operative complications and patient was discharged with desired outcomes. CONCLUSIONS: Anatomical variations of the RLN include-bifurcations, trifurcations, relation of RLN with inferior thyroid artery (ITA) and presence of non-recurrent laryngeal nerve. Only RLN dividing at a distance greater than 5 mm (branching point distance) before its entry into the larynx beneath the cricothyroid are said to bifurcate or trifurcate. Approximately 25% of nerves show branching [71%-unilateral and 18%-bilateral bifurcation]. Incidence of unilateral trifurcations have been noted be 0.9% and the rates of bilateral trifurcation and the divisions of the branches is yet to be ascertained. This is the first report of a bilateral trifurcation of RLN, detected in patient with multinodular goitre and hence warrants a precise analysis of variations of the RLN in patients undergoing thyroidectomy, which is critical to prevent RLN injury.

Prolonged Shedding of SARS-CoV-2 in Feces of COVID-19 Positive Patients: Trends in Genomic Variation in First and Second Wave.

The main route of the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are through respiratory pathways and close contact of human-to-human. While information about other modes of transmission is comparatively less, some published literature supporting the likelihood of a fecal-oral mode of transmission has been accumulating. The diagnosis of SARS-COV-2 infected cases is based on the real-time reverse transcription-PCR (RT-PCR). The fecal excretion of SARS-COV-2 has been reported frequently, however, the role of fecal viral load with the severity of disease is not yet clear. Our study focused on the investigation of SARS-CoV-2 shedding in the fecal samples of patients with coronavirus disease 2019 (COVID-19). A total of 280 RT-PCR-positive patients were enrolled, among them 15.4% had gastrointestinal (GI) symptoms. It was shown that 62% of the patients were positive for SARS-CoV-2 RNA in fecal specimens. This positivity was not related to the presence of GI symptoms and the severity of disease. The next generation sequencing [NGS] of SARS-CoV-2 from fecal samples of patients was performed to analyze mutational variations. Findings from this study not only emphasized the potential presence of SARS-CoV-2 in feces, but also its continuing mutational changes and its possible role in fecal-oral transmission.

Visible light photodegradation of 2,4-dichlorophenol using nanostructured NaBiS2: Kinetics, cytotoxicity, antimicrobial and electrochemical studies of the photocatalyst.

Removal of the hazardous and endocrine-disrupting 2,4-dichlorophenol (2,4-DCP) from water bodies is crucial to maintain the sanctity of the ecosystem. As a low bandgap material (1.37 eV), NaBiS2 was hydrothermally prepared and used as a potential photocatalyst to degrade 2,4-DCP under visible light irradiation. NaBiS2 appeared to be highly stable and remained structurally undeterred despite thermal variations. With a surface area of 6.69 m2/g, NaBiS2 has enough surface-active sites to adsorb the reactive molecules and exhibit a significant photocatalytic activity. In alkaline pH, the adsorption of 2,4-DCP on NaBiS2 appeared to decrease whereas, the acidic and neutral environments favoured the degradation. An increase in the photocatalyst dosage enhanced the degradation efficiency from 81 to 86 %, because of higher vacant adsorbent sites and the electrostatic attraction between NaBiS2 and 2,4-DCP. The dominant scavengers degraded 2,4-DCP by forming a coordination bond between chlorine's lone pair of electrons and the vacant orbitals of bismuth, following the order hole> OH > singlet oxygen. Being non-toxic to both natural and aquatic systems, NaBiS2 exhibits antifungal properties at higher concentrations. Finally, the electron-rich NaBiS2 is an excellent electrocatalyst that effectively degrades organic pollutants and is a promising material for industrial and environmental applications.

COVID-19 mortality and exposure to airborne PM2.5: A lag time correlation.

COVID-19 has escalated into one of the most serious crises in the 21st Century. Given the rapid spread of SARS-CoV-2 and its high mortality rate, here we investigate the impact and relationship of airborne PM2.5 to COVID-19 mortality. Previous studies have indicated that PM2.5 has a positive relationship with the spread of COVID-19. To gain insights into the delayed effect of PM2.5 concentration (µgm-3) on mortality, we focused on the role of PM2.5 in Wuhan City in China and COVID-19 during the period December 27, 2019 to April 7, 2020. We also considered the possible impact of various meteorological factors such as temperature, precipitation, wind speed, atmospheric pressure and precipitation on pollutant levels. The results from the Pearson's correlation coefficient analyses reveal that the population exposed to higher levels of PM2.5 pollution are susceptible to COVID-19 mortality with a lag time of >18 days. By establishing a generalized additive model, the delayed effect of PM2.5 on the death toll of COVID-19 was verified. A negative correction was identified between temperature and number of COVID-19 deaths, whereas atmospheric pressure exhibits a positive correlation with deaths, both with a significant lag effect. The results from our study suggest that these epidemiological relationships may contribute to the understanding of the COVID-19 pandemic and provide insights for public health strategies.