Bioaccumulation of Lead and Mercury in Water, Sediment, and Fish Samples of Baraila Lake, Vaishali, Bihar.

In the current study, a protected subtropical wetland in Bihar (India), Baraila Lake, was investigated for heavy metal (Pb and Hg) status. These metals tend to bioaccumulate in fish, posing a concern to human health. This study reported the concentration of lead and mercury in water, sediment, and fish muscles of Baraila Lake in the year 2022. The samples were collected from pre-monsoon and post-monsoon seasons at four sampling locations, i.e., Loma, Dhulwar, Chakaiya, and Kawai Baraila, and were analyzed in triplicates. Lead concentration in water samples of all four sites of Baraila Lake observed during pre-monsoon and post-monsoon season exceeded the permissible limit for drinking water, while the mercury concentration of all sites was under the permissible limit in both seasons as prescribed by WHO. The extent of elemental pollution was evaluated using the Geo-accumulation index (Igeo), contamination factor (CF), contamination degree (Cd), ecological risk factor (Er), and the potential ecological risk index (Ri). Lead concentration in fish muscles of both seasons exceeded the permissible limit, while the concentration of mercury exceeded in Xenentodon cancila (0.55 ± 0.07 µg/g) during the pre-monsoon season. Also, estimated daily intake (EDI), target hazard quotient (THQ), and hazard index (HI) were calculated in different fish muscles to assess potential human health risks. A higher THQ value of 1.303 was observed in carnivore fish during the pre-monsoon season.

Recent advances in microchip electrophoresis for analysis of pathogenic bacteria and viruses.

Life-threatening diseases, such as hepatitis B, pneumonia, tuberculosis, and COVID-19, are widespread due to pathogenic bacteria and viruses. Therefore, the development of highly sensitive, rapid, portable, cost-effective, and selective methods for the analysis of such microorganisms is a great challenge. Microchip electrophoresis (ME) has been widely used in recent years for the analysis of bacterial and viral pathogens in biological and environmental samples owing to its portability, simplicity, cost-effectiveness, and rapid analysis. However, microbial enrichment and purification are critical steps for accurate and sensitive analysis of pathogenic bacteria and viruses in complex matrices. Therefore, we first discussed the advances in the sample preparation technologies associated with the accurate analysis of such microorganisms, especially the on-chip microfluidic-based sample preparations such as dielectrophoresis and microfluidic membrane filtration. Thereafter, we focused on the recent advances in the lab-on-a-chip electrophoretic analysis of pathogenic bacteria and viruses in different complex matrices. As the microbial analysis is mainly based on the analysis of nucleic acid of the microorganism, the integration of nucleic acid-based amplification techniques such as polymerase chain reaction (PCR), quantitative PCR, and multiplex PCR with ME will result in an accurate and sensitive analysis of microbial pathogens. Such analyses are very important for the point-of-care diagnosis of various infectious diseases.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters.

The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.

Efficient photocatalytic degradation of Rhodamine B dye using solar light-driven La-Mn co-doped Fe2O3 nanoparticles.

This work aims to develop a highly efficient solar light-induced photocatalyst based on La-Mn co-doped Fe2O3 nanoparticles. Pure Fe2O3 and La-Mn co-doped Fe2O3 nanoparticles were fabricated by a simple co-precipitation method. The photocatalysts were analyzed for their morphological, structural, and magnetic characteristics. Scanning electron microscopy analysis demonstrated the formation of semi-spherical nanoparticles along with small aggregations. The size of nanoparticles was measured using a transmission electron microscope and found in the range of 42-49 nm. The crystalline nature and geometry of synthesized nanoparticles were investigated using X-ray diffraction analysis. Due to the incorporation of La-Mn, the saturation magnetization and remanent magnetization of the nanoparticles decreased from 6.17 to 2.89 emu/g and 1.15 to 0.52 emu/g, respectively, while the coercivity was reduced from 756.72 to 756.67 Oe. The surface area of nanoparticles was increased from 77.93 to 87.45 m2/g as a result of La-Mn co-doping. The photocatalytic performance of the Fe2O3, La0.1Mn0.3Fe1.6O3, and La0.2Mn0.2Fe1.6O3 catalysts was assessed by their capability to degrade Rhodamine B (RhB) under solar light illumination. La0.2Mn0.2Fe1.6O3 displayed exceptional degradation performance, degrading RhB to 91.78% in 240 min, in comparison to La0.1Mn0.3Fe1.6O3 (71.09%) and pristine Fe2O3 (58.21%) under specified reaction conditions ((RhB) = 50 ppm; (catalyst) = 40 mg/L; pH = 7; T = 25 °C)). RhB degradation was affected by changing pH, catalytic dosage, dye concentration, and temperature. The degradation of RhB was found to be pseudo-1st order kinetics. The exceptional photocatalytic performance of La0.2Mn0.2Fe1.6O3 catalysts showed that the synthesized nanoparticles could be effectively utilized to remove organic pollutants from industrial wastewater.

Electrochemical Detection of Dopamine using a Phenyl Carboxylic Acid-Functionalized Electrode Made by Electrochemical Reduction of a Diazonium Salt.

A glassy carbon electrode (GCE) has been modified by an in situ electrochemical reduction of an aryldiazonium salt generated from the reaction of 4-aminobenzoic acid and sodium nitrite in acidic ethanolic solution. The as-prepared phenyl carboxylic acid-modified glassy carbon electrode has been, for the first time, used for the electrochemical determination of dopamine. Under optimal experimental parameters, outstanding electrocatalytic activity, high sensitivity at a LOD of 5.6×10-9  m, and broad linearity of 0.1 to 1000 µm were obtained. The crafted electrochemical platform demonstrated excellent stability, specificity, and anti-interference capability towards the sensing of dopamine.

Valuing burden of premature mortality attributable to air pollution in major million-plus non-attainment cities of India.

Accelerating growth due to industrialization and urbanization has improved the Indian economy but simultaneously has deteriorated human health, environment, and ecosystem. In the present study, the associated health risk mortality (age > 25) and welfare loss for the year 2017 due to excess PM2.5 concentration in ambient air for 31 major million-plus non-attainment cities (NACs) in India is assessed. The cities for the assessment are prioritised based on population and are classified as 'X' (> 5 million population) and 'Y' (1-5 million population) class cities. Ground-level PM2.5 concentration retrieved from air quality monitoring stations for the NACs ranged from 33 to 194 µg/m3. Total PM2.5 attributable premature mortality cases estimated using global exposure mortality model was 80,447 [95% CI 70,094-89,581]. Ischemic health disease was the leading cause of death accounting for 47% of total mortality, followed by chronic obstructive pulmonary disease (COPD-17%), stroke (14.7%), lower respiratory infection (LRI-9.9%) and lung cancer (LC-1.9%). 9.3% of total mortality is due to other non-communicable diseases (NCD-others). 7.3-18.4% of total premature mortality for the NACs is attributed to excess PM2.5 exposure. The total economic loss of 90,185.6 [95% CI 88,016.4-92,411] million US$ (as of 2017) was assessed due to PM2.5 mortality using the value of statistical life approach. The highest mortality (economic burden) share of 61.3% (72.7%) and 30.1% (42.7%) was reported for 'X' class cities and North India zone respectively. Compared to the base year 2017, an improvement of 1.01% and 0.7% is observed in premature mortality and economic loss respectively for the year 2024 as a result of policy intervention through National Clean Air Action Programme. The improvement among 31 NACs was found inconsistent, which may be due to a uniform targeted policy, which neglects other socio-economic factors such as population, the standard of living, etc. The study highlights the need for these parameters to be incorporated in the action plans to bring in a tailored solution for each NACs for better applicability and improved results of the programme facilitating solutions for the complex problem of air pollution in India.

Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective.

Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of Δ (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p < 0.05, tstat > tcritical) for Δ, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p < 0.1 and tstat < tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat > tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.

Air pollution aggravating COVID-19 lethality? Exploration in Asian cities using statistical models.

The present work estimates the increased risk of coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 by establishing the linkage between the mortality rate in the infected cases and the air pollution, specifically Particulate Matters (PM) with aerodynamic diameters ≤ 10 µm and ≤ 2.5 µm. Data related to nine Asian cities are analyzed using statistical approaches, including the analysis of variance and regression model. The present work suggests that there exists a positive correlation between the level of air pollution of a region and the lethality related to COVID-19, indicating air pollution to be an elemental and concealed factor in aggravating the global burden of deaths related to COVID-19. Past exposures to high level of PM2.5 over a long period, is found to significantly correlate with present COVID-19 mortality per unit reported cases (p < 0.05) compared to PM10, with non-significant correlation (p = 0.118). The finding of the study can help government agencies, health ministries and policymakers globally to take proactive steps by promoting immunity-boosting supplements and appropriate masks to reduce the risks associated with COVID-19 in highly polluted areas.

BPPO-Based Anion Exchange Membranes for Acid Recovery via Diffusion Dialysis.

To reduce the environmental impact of acids present in various industrial wastes, improved and robust anion exchange membranes (AEMs) are highly desired. Moreover, they should exhibit high retention of salts, fast acid permeation and they should be able to operate with low energy input. In this work, AEMs are prepared using a facile solution-casting from brominated poly-(2,6-dimethyl-1,4-phenylene oxide) (BPPO) and increasing amounts of 2-phenylimidazole (PI). Neither quaternary ammonium salts, nor ionic liquids and silica-containing compounds are involved in the synthesis. The prepared membranes showed an ion exchange capacity of 1.1-1.8 mmol/g, a water uptake of 22%-47%, a linear expansion ratio of 1%-6% and a tensile strength of 0.83-10.20 MPa. These membranes have potential for recovering waste acid via diffusion dialysis, as the acid dialysis coefficient (UH) at room temperature for HCl is in the range of 0.006-0.018 m/h while the separation factor (S) is in the range of 16-28, which are higher than commercial DF-120B membranes (UH = 0.004 m/h, S = 24).

Fabrication of biomembrane-like films on carbon electrodes using alkanethiol and diazonium salt and their application for direct electrochemistry of myoglobin.

Alkanethiols generally form self-assembled monolayers on gold electrodes and the electrochemical reduction of aromatic diazonium salts is a popular method for the covalent modification of carbon. Based on the reaction of alkanethiol with aldehyde groups covalently bound on carbon surface by the electrochemical reduction of aromatic diazonium salts, a new strategy for the modification of carbon electrodes with alkanethiols has been developed. The modification of carbon surface with aldehyde groups is achieved by the electrochemical reduction of aromatic diazonium salts in situ electrogenerated from a nitro precursor, p-nitrophenylaldehyde, in the presence of nitrous acid. By this way, in situ electrogenerated p-aminophenyl aldehyde from p-nitrophenylaldehyde immediately reacts with nitrous acid, effectively minimizing the side reaction of amine groups and aldehyde groups. The as-prepared alkanethiol-modified glassy carbon electrode was further used to make biomembrane-like films by casting didodecyldimethylammonium bromide on its surface. The biomembrane-like films enable the direct electrochemistry of immobilized myoglobin for the detection of hydrogen peroxide. The response is linear over the range of 1-600µM with a detection limit of 0.3µM.

Regulation of Toll signaling and inflammation by ß-arrestin and the SUMO protease Ulp1.

The Toll signaling pathway has a highly conserved function in innate immunity and is regulated by multiple factors that fine tune its activity. One such factor is ß-arrestin Kurtz (Krz), which we previously implicated in the inhibition of developmental Toll signaling in the Drosophila melanogaster embryo. Another level of controlling Toll activity and immune system homeostasis is by protein sumoylation. In this study, we have uncovered a link between these two modes of regulation and show that Krz affects sumoylation via a conserved protein interaction with a SUMO protease, Ulp1. Loss of function of krz or Ulp1 in Drosophila larvae results in a similar inflammatory phenotype, which is manifested as increased lamellocyte production; melanotic mass formation; nuclear accumulation of Toll pathway transcriptional effectors, Dorsal and Dif; and expression of immunity genes, such as Drosomycin. Moreover, mutations in krz and Ulp1 show dosage-sensitive synergistic genetic interactions, suggesting that these two proteins are involved in the same pathway. Using Dorsal sumoylation as a readout, we found that altering Krz levels can affect the efficiency of SUMO deconjugation mediated by Ulp1. Our results demonstrate that ß-arrestin controls Toll signaling and systemic inflammation at the level of sumoylation.

Substrate envelope-designed potent HIV-1 protease inhibitors to avoid drug resistance.

The rapid evolution of HIV under selective drug pressure has led to multidrug resistant (MDR) strains that evade standard therapies. We designed highly potent HIV-1 protease inhibitors (PIs) using the substrate envelope model, which confines inhibitors within the consensus volume of natural substrates, providing inhibitors less susceptible to resistance because a mutation affecting such inhibitors will simultaneously affect viral substrate processing. The designed PIs share a common chemical scaffold but utilize various moieties that optimally fill the substrate envelope, as confirmed by crystal structures. The designed PIs retain robust binding to MDR protease variants and display exceptional antiviral potencies against different clades of HIV as well as a panel of 12 drug-resistant viral strains. The substrate envelope model proves to be a powerful strategy to develop potent and robust inhibitors that avoid drug resistance.

Fluorescent silica nanoparticle-based probe for the detection of ozone via fluorescence resonance energy transfer.

A fluorescence resonance energy transfer (FRET) platform for the detection of ozone was developed by combining the overlap of the fluorescence spectrum of Ru(bpy)3(2+)-doped silica nanoparticles with the absorption spectrum of indigo carmine at around 600 nm. This FRET system can be used to detect ozone simply within 10 min. Simple qualitative ozone detection methods using cotton swabs or paper were also developed.

Synthesis and electrocatalytic properties of tetrahexahedral, polyhedral, and branched Pd@Au core-shell nanocrystals.

Tetrahexahedral, polyhedral, and branched Pd@Au core-shell nanostructures were selectively produced by manipulating their growth thermodynamics and kinetics. The current density in the electrochemical oxidation of H2O2 and the electrochemiluminescence density of luminol-H2O2 systems at the tetrahexahedron modified electrode are 43.5 and 16.4 times that at the bulk Au electrode, respectively.

Visual and surface plasmon resonance sensor for zirconium based on zirconium-induced aggregation of adenosine triphosphate-stabilized gold nanoparticles.

Owing to its high affinity with phosphate, Zr(IV) can induce the aggregation of adenosine 5'-triphosphate (ATP)-stabilized AuNPs, leading to the change of surface plasmon resonance (SPR) absorption spectra and color of ATP-stabilized AuNP solutions. Based on these phenomena, visual and SPR sensors for Zr(IV) have been developed for the first time. The A(660 nm)/A(518 nm) values of ATP-stabilized AuNPs in SPR absorption spectra increase linearly with the concentrations of Zr(IV) from 0.5 µM to 100 µM (r=0.9971) with a detection limit of 95 nM. A visual Zr(IV) detection is achieved with a detection limit of 30 µM. The sensor shows excellent selectivity against other metal ions, such as Cu(2+), Fe(3+), Cd(2+), and Pb(2+). The recoveries for the detection of 5 µM, 10 µM, 25 µM and 75 µM Zr(IV) in lake water samples are 96.0%, 97.0%, 95.6% and 102.4%, respectively. The recoveries of the proposed SPR method are comparable with those of ICP-OES method.

Fluorescence detection of glutathione reductase activity based on deoxyribonucleic acid-templated silver nanoclusters.

Fluorescent silver nanoclusters stabilized by DNA (DNA-AgNCs) exhibit distinct response rates to thiol and disulfide. Glutathione reductase can catalyze the reduction of the oxidized glutathione (GSSG) quickly to reduced glutathione (GSH) in the presence of ß-nicotinamide adenine dinucleotide 2'-phosphate reduced tetrasodium salt hydrate (NADPH). Consequently, DNA-AgNCs can serve as a new fluorescent platform for assaying the glutathione reductase (GR) activity. This newly proposed assay has a high sensitivity and a good selectivity toward GR. The GR activity can be detected in the range of 0.2-2.0 mU mL(-1) with a minimum detectable concentration of 0.2 mU mL(-1). Pepsin, lysozyme, trypsin, avidin, thrombin, myoglobin, and BSA have little effect on the fluorescence intensity of DNA-AgNCs. The GR activity assay is successfully used to monitor the inhibition of GR activity by a typical inhibitor 1,3-bis(2-chloroethyl)-1-nitrosourea.

Electrochemical cholesterol sensor based on carbon nanotube@molecularly imprinted polymer modified ceramic carbon electrode.

A monolithic molecular imprinting sensor based on ceramic carbon electrode (CCE) has been reported. The sensor can be renewed simply by smoothing. It was fabricated by thoroughly mixing multiwalled carbon nanotube@molecularly imprinted polymer (MWCNT@MIP), graphite powder, and silicon alkoxide, and then packing the resulting complex mixture of components firmly into the electrode cavity of a Teflon sleeve. The incorporated MWCNT@MIP in CCEs functioned as a recognition element for cholesterol determination. The MWCNT@MIP-CCEs were tested in the presence or absence of cholesterol by cyclic voltammetry and linear sweep voltammetry. The cholesterol sensor has excellent sensitivity with a linear range of 10-300nM and a detection limit of 1nM (S/N=3). The monolithic molecular imprinting sensor exhibits good stability, high sensitivity, and user-friendly reusability for cholesterol determination. This study shows that CCE is a promising matrix for MIP sensors.

Structure-based design, synthesis, and structure-activity relationship studies of HIV-1 protease inhibitors incorporating phenyloxazolidinones.

A series of new HIV-1 protease inhibitors with the hydroxyethylamine core and different phenyloxazolidinone P2 ligands were designed and synthesized. Variation of phenyl substitutions at the P2 and P2' moieties significantly affected the binding affinity and antiviral potency of the inhibitors. In general, compounds with 2- and 4-substituted phenyloxazolidinones at P2 exhibited lower binding affinities than 3-substituted analogues. Crystal structure analyses of ligand-enzyme complexes revealed different binding modes for 2- and 3-substituted P2 moieties in the protease S2 binding pocket, which may explain their different binding affinities. Several compounds with 3-substituted P2 moieties demonstrated picomolar binding affinity and low nanomolar antiviral potency against patient-derived viruses from HIV-1 clades A, B, and C, and most retained potency against drug-resistant viruses. Further optimization of these compounds using structure-based design may lead to the development of novel protease inhibitors with improved activity against drug-resistant strains of HIV-1.

Additivity in the analysis and design of HIV protease inhibitors.

We explore the applicability of an additive treatment of substituent effects to the analysis and design of HIV protease inhibitors. Affinity data for a set of inhibitors with a common chemical framework were analyzed to provide estimates of the free energy contribution of each chemical substituent. These estimates were then used to design new inhibitors whose high affinities were confirmed by synthesis and experimental testing. Derivations of additive models by least-squares and ridge-regression methods were found to yield statistically similar results. The additivity approach was also compared with standard molecular descriptor-based QSAR; the latter was not found to provide superior predictions. Crystallographic studies of HIV protease-inhibitor complexes help explain the perhaps surprisingly high degree of substituent additivity in this system, and allow some of the additivity coefficients to be rationalized on a structural basis.