Assessment of prevalence of elevated blood lead levels and risk factors among children and pregnant women in Bihar, India.

BACKGROUND: While modeled estimates and studies in contaminated areas indicate high lead exposure among children in Bihar, India, local data on lead exposure in the child population is limited. OBJECTIVES: To characterize lead exposure, and assess potential sources of lead exposure among a state-representative sample of children and their pregnant mothers residing in Bihar. METHODS: Blood samples were collected from 697 children under five and 55 pregnant women from eight districts in Bihar. Blood lead levels were determined using capillary blood and a portable lead analyzer. Household demographics, home environment, behavior, and nutrition information were collected through computer-assisted personal interviews with primary caregivers. Logistic regression was used to assess associations between potential risk factors and elevated blood lead levels. RESULTS: More than 90% of children and 80% of pregnant women reported blood lead levels ≥5 µg/dL. Living near a lead-related industry and pica behavior of eating soil were significantly associated with increased odds of having elevated blood lead levels. Additional risk factors for having a blood level ≥5 µg/dL included the use of skin lightning cream (aOR = 5.11, 95%CI: 1.62, 16.16) and the use of eyeliners (aOR = 2.81, 95%CI: 1.14, 6.93). Having blood lead levels ≥10 µg/dL was also significantly associated with the household member who had an occupation or hobby involving the use of lead (aOR = 1.75, 95%CI: 1.13, 2.72). DISCUSSION: Elevated blood lead levels were prevalent among children and pregnant women in Bihar, indicating the urgent need for a comprehensive lead poisoning prevention strategy.

Multi-season mobile monitoring campaign of on-road air pollution in Bengaluru, India: High-resolution mapping and estimation of quasi-emission factors.

Mobile monitoring can supplement regulatory measurements, particularly in low-income countries where stationary monitoring is sparse. Here, we report results from a ~ year-long mobile monitoring campaign of on-road concentrations of black carbon (BC), ultrafine particles (UFP), and carbon dioxide (CO2) in Bengaluru, India. The study route included 150 unique kms (average: ~22 repeat measurements per monitored road segment). After cleaning the data for known instrument artifacts and sensitivities, we generated 30 m high-resolution stable 'data only' spatial maps of BC, UFP, and CO2 for the study route. For the urban residential areas, the mean BC levels for residential roads, arterials, and highways were ~ 10, 22, and 56 µg m-3, respectively. A similar pattern (highways being characterized by highest pollution levels) was also observed for UFP and CO2. Using the data from repeat measurements, we carried out a Monte Carlo subsampling analysis to understand the minimum number of repeat measures to generate stable maps of pollution in the city. Leveraging the simultaneous nature of the measurements, we also mapped the quasi-emission factors (QEF) of the pollutants under investigation. The current study is the first multi-season mobile monitoring exercise conducted in a low or middle -income country (LMIC) urban setting that oversampled the study route and investigated the optimum number of repeat rides required to achieve representative pollution spatial patterns characterized with high precision and low bias. Finally, the results are discussed in the context of technical aspects of the campaign, limitations, and their policy relevance for our study location and for other locations. Given the day-to-day variability in the pollution levels, the presence of dynamic and unorganized sources, and active government pollution mitigation policies, multi-year mobile measurement campaigns would help test the long-term representativeness of the current results.

Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India.

Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number of measurements so that assessments cannot be obtained in all locations of interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies of air pollution and images have used static images (e.g., satellite images or Google Street View images). The current study was designed to develop deep learning methodologies to infer on-road pollutant concentrations from videos acquired with dashboard cameras. Fifty hours of on-road measurements of four pollutants (black carbon, particle number concentration, PM2.5 mass concentration, carbon dioxide) in Bengaluru, India, were analyzed. The analysis of each video frame involved identifying objects and determining motion (by segmentation and optical flow). Based on these visual cues, a regression convolutional neural network (CNN) was used to deduce pollution concentrations. The findings showed that the CNN approach outperformed several other machine learning (ML) techniques and more conventional analyses (e.g., linear regression). The CO2 prediction model achieved a normalized root-mean-square error of 10-13.7% for the different train-validation division methods. The results here thus contribute to the literature by using video and the relative motion of on-screen objects rather than static images and by implementing a rapid-analysis approach enabling analysis of the video in real time. These methods can be applied to other mobile-monitoring campaigns since the only additional equipment they require is an inexpensive dashboard camera.

Bias in PM2.5 measurements using collocated reference-grade and optical instruments.

Optical PM2.5 measurements are sensitive to aerosol properties that can vary with space and time. Here, we compared PM2.5 measurements from collocated reference-grade (beta attenuation monitors, BAMs) and optical instruments (two DustTrak II and two DustTrak DRX) over 6 months. We performed inter-model (two different models), intra-model (two units of the same model), and inter-type (two different device types: optical vs. reference-grade) comparisons under ambient conditions. Averaged over our study period, PM2.5 measured concentrations were 46.0 and 45.5 µg m-3 for the two DustTrak II units, 29.8 and 38.4 µg m-3 for DRX units, and 18.3 and 19.0 µg m-3 for BAMs. The normalized root square difference (NRMSD; compares PM2.5 measurements from paired instruments of the same type) was ~ 5% (DustTrak II), ~ 27% (DRX), and ~ 15% (BAM). The normalized root mean square error (NRMSE; compares PM2.5 measurements from optical instruments against a reference instrument) was ~ 165% for DustTrak II, ~ 74% after applying literature-based humidity correction and ~ 27% after applying both the humidity and BAM corrections. Although optical instruments are highly precise in their PM2.5 measurements, they tend to be strongly biased relative to reference-grade devices. We also explored two different methods to compensate for relative humidity bias and found that the results differed by ~ 50% between the two methods. This study highlights the limitations of adopting a literature-derived calibration equation and the need for conducting local model-specific calibration. Moreover, this is one of the few studies to perform an intra-model comparison of collocated reference-grade devices.

Impact of COVID-19 lockdown on the fine particulate matter concentration levels: Results from Bengaluru megacity, India.

Leveraging the COVID-19 India-wide lockdown situation, the present study attempts to quantify the reduction in the ambient fine particulate matter concentrations during the lockdown (compared with that of the pre-lockdown period), owing to the highly reduced specific anthropogenic activities and thereby pollutant emissions. The study was conducted over Bengaluru (India), using PM2.5 (mass concentration of particulate matter having size less than or equal to 2.5 µm) and Black Carbon mass concentration (BC) data. Open-access datasets from pollution control board (PCB) were also utilised to understand the spatial variability and region-specific reduction in PM2.5 across the city. The highest percentage reduction was observed in BCff (black carbon attributable to fossil fuel combustion), followed by total BC and PM2.5. No decrease in BCbb (black carbon attributable to wood/biomass burning) was observed, suggesting unaltered wood-based cooking activities and biomass-burning (local/regional) throughout the study period. Results support the general understanding of multi-source (natural and anthropogenic) nature of PM2.5 in contrast to limited-source (combustion based) nature of BC. The diurnal amplitudes in BC and BCff were reduced, while they remained almost the same for PM2.5 and BCbb. Analysis of PCB data reveal the highest reduction in PM2.5 in an industrial cluster area. The current lockdown situation acted as a natural model to understand the role of a few major anthropogenic activities (viz., traffic, construction, industries related to non-essential goods, etc.) in enhancing the background fine particulate matter levels. Contemporary studies reporting reduction in surface fine particulate matter and satellite retrieved columnar Aerosol Optical Depth (AOD) during COVID-19 lockdown period are discussed.

Tree growth rate regulate the influence of elevated CO2 on soil biochemical responses under tropical condition.

Tree growth rate can complicate our understandings of plant belowground responses to elevated CO2 (eCO2) in tropical ecosystems. We studied the effects of eCO2 on plant growth parameters, and rhizospheric soil properties including soil organic carbon (SOC), glomalin related soil protein (GRSP), microbial biomass C (Cmic), CO2 efflux (Cefflux), and microbial extracellular enzyme activities under two tropical tree saplings of fast-growing Tectona grandis (Teak) and slow-growing Butea monosperma (Butea). We exposed these saplings to eCO2 (∼550 ppm) and ambient CO2 (aCO2; ∼395 ppm) in the Indo-Gangetic plain region, and further (after 10 and 46 months) measured the changes in their rhizospheric soil properties. With respect to aCO2 treatment, eCO2 significantly increased plant height, stem and shoot weight, and total plant biomass of Teak. However, these plant traits did not considerably differed between eCO2 and aCO2 treatments of Butea. The eCO2 induced greater extent of increase in rhizospheric soil properties including SOC fractions (particulate OC, non-particulate OC and total OC), GRSP fractions (easily extractable- GRSP, difficulty extractable- GRSP and total- GRSP), Cmic, Cefflux and extracellular enzyme activities (phosphatase, dehydrogenase, ß-glucosidase and fluorescein diacetate) were observed under Teak compared with Butea. Compared with aCO2 treatment, eCO2 slightly reduced soil available N and P under the Teak, but no changes were apparent between eCO2 and aCO2 treatments of the Butea. The greater extent of responses from soil variables observed after longer period (46 months) of CO2 exposure. The multivariate analysis confirmed that eCO2 treatment with Teak is more responsive compared with other treatments of Teak and Butea. This contrasting rhizospheric soil feedback to eCO2 between two tropical trees, suggesting fast-growing species will be more responsive to future climate. Such species will have a competitive advantage over coexisting less responsive species (e.g. Butea) under future eCO2 climate.

Differentiation of human mesenchymal stem cells on niobium-doped fluorapatite glass-ceramics.

OBJECTIVE: Our goal was to characterize the response of human mesenchymal stem cells (hMSCs) to a niobium-doped fluorapatite-based glass-ceramic (FAp). METHODS: The glass was prepared by twice melting at 1525 °C for 3 h, and cast into cylindrical ingots later sectioned into discs and heat-treated to promote crystallization of fluorapatite submicrometer crystals. Tissue culture polystyrene (TCP) was used as control. The surface of the FAp discs was either left as-heat treated, ground or etched. Initial cell attachment was assessed at 3 h. Proliferation and alkaline phosphatase (ALP) expression data were collected at days 1, 4, and 8. Cell morphology was examined using SEM, at days 2 and 4. Mineralization was evaluated by Alizarin Red staining and SEM. RESULTS: Initial cell attachment on as heat-treated, etched, or ground surfaces was similar to that of the positive control group (p>0.05). The percentage of area covered by living cells increased significantly on as heat-treated, etched, or ground surfaces between days 1 and 8 (p<0.05). There was no significant difference among groups in cell coverage at day 8, compared to TCP control. SEM revealed well spread polygonal cells with numerous filopodia, either attached to the ceramic surface or connected to neighboring cells. ALP expression at day 8 was significantly higher in osteogenic media compared to growth media on both FAp and control. FAp discs stained positively with Alizarin Red and calcium-rich mineralized granules associated with fibrils were observed by SEM at day 35. SIGNIFICANCE: hMSCs displayed excellent attachment, proliferation, and differentiation on niobium-doped FAp glass-ceramic.

A hybrid biomimetic nanomatrix composed of electrospun polycaprolactone and bioactive peptide amphiphiles for cardiovascular implants.

Current cardiovascular therapies are limited by the loss of endothelium, restenosis and thrombosis. The goal of this study was to develop a biomimetic hybrid nanomatrix that combined the unique properties of electrospun polycaprolactone (ePCL) nanofibers with self-assembled peptide amphiphiles (PAs). ePCL nanofibers have interconnected nanoporous structures, but are hampered by a lack of surface bioactivity to control cellular behavior. It has been hypothesized that PAs could self-assemble onto the surface of ePCL nanofibers and endow them with the characteristic properties of native endothelium. The PAs, which comprised hydrophobic alkyl tails attached to functional hydrophilic peptide sequences, contained enzyme-mediated degradable sites coupled to either endothelial cell-adhesive ligands (YIGSR) or polylysine (KKKKK) nitric oxide (NO) donors. Two different PAs (PA-YIGSR and PA-KKKKK) were successfully synthesized and mixed in a 90:10 (YK) ratio to obtain PA-YK. PA-YK was reacted with pure NO to develop PA-YK-NO, which was then self-assembled onto ePCL nanofibers to generate a hybrid nanomatrix, ePCL-PA-YK-NO. Uniform coating of self-assembled PA nanofibers on ePCL was confirmed by transmission electron microscopy. Successful NO release from ePCL-PA-YK-NO was observed. ePCL-YK and ePCL-PA-YK-NO showed significantly increased adhesion of human umbilical vein endothelial cells (HUVECs). ePCL-PA-YK-NO also showed significantly increased proliferation of HUVECs and reduced smooth muscle cell proliferation. ePCL-PA-YK-NO also displayed significantly reduced platelet adhesion compared with ePCL, ePCL-PA-YK and a collagen control. These results indicate that this hybrid nanomatrix has great potential application in cardiovascular implants.

A nitric oxide releasing, self assembled peptide amphiphile matrix that mimics native endothelium for coating implantable cardiovascular devices.

Cardiovascular disease is the number one cause of death in the United States. Deployment of stents and vascular grafts has been a major therapeutic method for treatment. However, restenosis, incomplete endothelialization, and thrombosis hamper the long term clinical success. As a solution to meet these current challenges, we have developed a native endothelial ECM mimicking self-assembled nanofibrous matrix to serve as a new treatment model. The nanofibrous matrix is formed by self-assembly of peptide amphiphiles (PAs), which contain nitric oxide (NO) donating residues, endothelial cell adhesive ligands composed of YIGSR peptide sequence, and enzyme-mediated degradable sites. NO was successfully released from the nanofibrous matrix rapidly within 48 h, followed by sustained release over period of 30 days. The NO releasing nanofibrous matrix demonstrated a significantly enhanced proliferation of endothelial cells (51+/-3% to 67+/-2%) but reduced proliferation of smooth muscle cells (35+/-2% to 16+/-3%) after 48 h of incubation. There was also a 150-fold decrease in platelet attachment on the NO releasing nanofibrous matrix (470+/-220 platelets/cm(2)) compared to the collagen-I (73+/-22 x 10(3)platelets/cm(2)) coated surface. The nanofibrous matrix has the potential to be applied to various cardiovascular implants as a self-assembled coating, thereby providing a native endothelial extracellular matrix (ECM) mimicking environment.

Effect of endothelium mimicking self-assembled nanomatrices on cell adhesion and spreading of human endothelial cells and smooth muscle cells.

The goal of this study is to develop unique native endothelium mimicking nanomatrices and evaluate their effects on adhesion and spreading of human umbilical vein endothelial cells (HUVECs) and aortic smooth muscle cells (AoSMCs). These nanomatrices were developed by self-assembly of peptide amphiphiles (PAs) through a solvent evaporation technique. Three PAs, one containing the Tyr-Ile-Gly-Ser-Arg (YIGSR) ligand, the second containing the Val-Ala-Pro-Gly (VAPG) ligand, and a third without cell adhesive ligands, were developed. Cell adhesion and spreading were evaluated by a PicoGreen-DNA assay and live/dead assay, respectively. Our results show that PA-YIGSR significantly enhances HUVEC adhesion (26,704 +/- 2708), spreading (84 +/- 8%), and proliferation (50 +/- 2%) compared with that of other PAs. PA-VAPG and PA-YIGSR showed significantly greater AoSMC adhesion compared with that of PA-S. PA-VAPG also showed significantly greater spreading of AoSMCs (63 +/- 11%) compared with that of other PAs. Also, all the PAs showed significantly reduced platelet adhesion compared with that of collagen I (control). These findings would facilitate the development of novel vascular grafts, heart valves, and cell-based therapies for cardiovascular diseases. FROM THE CLINICAL EDITOR: The goal of this study was to develop unique native endothelium mimicking nanomatrices and evaluate their effects on adhesion and spreading of human umbilical vein endothelial cells (HUVECs) and aortic smooth muscle cells (AoSMCs). These nanomatrices were developed by self-assembly of peptide amphiphiles through a solvent evaporation technique. The findings are expected to facilitate the development of novel vascular grafts, heart valves, and cell based therapies for cardiovascular diseases.

Osteogenic differentiation of human mesenchymal stem cells directed by extracellular matrix-mimicking ligands in a biomimetic self-assembled peptide amphiphile nanomatrix.

This study investigated the ability of nanoscale, biomimetic peptide amphiphile (PA) scaffolds inscribed with specific cellular adhesive ligands to direct the osteogenic differentiation of human mesenchymal stem cells (hMSCs) without osteogenic supplements. PA sequences were synthesized to mimic the native bone extracellular matrix (ECM), expressing different isolated ligands (i.e., RGDS, DGEA, KRSR). All PAs were presented as self-assembled two-dimensional coatings for the seeded hMSCs. Initial attachment results demonstrated that the different PAs could be individually recognized based on the incorporated adhesive ligands. Long-term studies assessed osteogenic differentiation up to 35 days. The RGDS-containing PA nanomatrix expressed significantly greater alkaline phosphatase activity, indicating the early promotion of osteogenic differentiation. A progressive shift toward osteogenic morphology and positive staining for mineral deposition provided further confirmation of the RGDS-containing PA nanomatrix. Overall, the PA nanomatrix clearly has great promise for directing the osteogenic differentiation of hMSCs without the aid of supplements by mimicking the native ECM, providing an adaptable environment that allows for different adhesive ligands to control cellular behaviors. This research model establishes the beginnings of a new versatile approach to regenerate bone tissues by closely following the principles of natural tissue formation.

A hybrid biomimetic scaffold composed of electrospun polycaprolactone nanofibers and self-assembled peptide amphiphile nanofibers.

Nanofibrous electrospun poly (epsilon-caprolactone) (ePCL) scaffolds have inherent structural advantages, but lack of bioactivity has limited their usefulness in biomedical applications. Thus, here we report the development of a hybrid, nanostructured, extracellular matrix (ECM) mimicking scaffold by a combination of ePCL nanofibers and self-assembled peptide amphiphile (PA) nanofibers. The PAs have ECM mimicking characteristics including a cell adhesive ligand (RGDS) and matrix metalloproteinase-2 (MMP-2) mediated degradable sites. Transmission electron microscope imaging verified successful PA self-assembly into nanofibers (diameters of 8-10 nm) using a solvent evaporation method. This evaporation method was then used to successfully coat PAs onto ePCL nanofibers (diameters of 300-400 nm), to develop hybrid, bioactive scaffolds. Scanning electron microscope characterization showed that the PA coatings did not interfere with the porous ePCL nanofiber network. Human mesenchymal stem cells (hMSCs) were seeded onto the hybrid scaffolds to evaluate their bioactivity. Significantly greater attachment and spreading of hMSCs were observed on ePCL nanofibers coated with PA-RGDS as compared to ePCL nanofibers coated with PA-S (no cell adhesive ligand) and uncoated ePCL nanofibers. Overall, this novel strategy presents a new solution to overcome the current bioactivity challenges of electrospun scaffolds and combines the unique characteristics of ePCL nanofibers and self-assembled PA nanofibers to provide an ECM mimicking environment. This has great potential to be applied to many different electrospun scaffolds for various biomedical applications.