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Scientific Reports Jun 2024We previously reported that asthma prevalence was higher in the United States (US) compared to Mexico (MX) (25.8% vs. 8.4%). This investigation assessed differences in...
We previously reported that asthma prevalence was higher in the United States (US) compared to Mexico (MX) (25.8% vs. 8.4%). This investigation assessed differences in microbial dust composition in relation to demographic and housing characteristics on both sides of the US-MX Border. Forty homes were recruited in the US and MX. Home visits collected floor dust and documented occupants' demographics, asthma prevalence, housing structure, and use characteristics. US households were more likely to have inhabitants who reported asthma when compared with MX households (30% vs. 5%) and had significantly different flooring types. The percentage of households on paved roads, with flushing toilets, with piped water and with air conditioning was higher in the US, while dust load was higher in MX. Significant differences exist between countries in the microbial composition of the floor dust. Dust from Mexican homes was enriched with Alishewanella, Paracoccus, Rheinheimera genera and Intrasporangiaceae family. A predictive metagenomics analysis identified 68 significantly differentially abundant functional pathways between US and MX. This study documented multiple structural, environmental, and demographic differences between homes in the US and MX that may contribute to significantly different microbial composition of dust observed in these two countries.
Topics: Dust; Arizona; Humans; Mexico; Housing; Asthma; Bacteria; Female; Family Characteristics; Male; Metagenomics
PubMed: 38834753
DOI: 10.1038/s41598-024-63356-6 -
Environment International Jul 2024Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM sources and determined their contribution to oxidative potential (OP)...
Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM, OP and OP, respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM and OP in complex environments and are thus useful for PM control strategies and actions.
Topics: Particulate Matter; Air Pollutants; Environmental Monitoring; Biomass; Oxidation-Reduction; Vehicle Emissions; Air Pollution
PubMed: 38833875
DOI: 10.1016/j.envint.2024.108787 -
Frontiers in Chemistry 2024IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of... (Review)
Review
IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.
PubMed: 38831915
DOI: 10.3389/fchem.2024.1391409 -
Frontiers in Neuroscience 2024Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to...
INTRODUCTION
Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications.
METHODS
This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering.
RESULTS
The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%.
DISCUSSION
We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.
PubMed: 38831756
DOI: 10.3389/fnins.2024.1304191 -
Heliyon Jun 2024This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar...
This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.
PubMed: 38828356
DOI: 10.1016/j.heliyon.2024.e31774 -
Journal of Global Antimicrobial... May 2024India's projected silica-dust-exposed workers will be 52 million at the end of 2025. Elimination of tuberculosis is also targeted in India by 2025. Scientists in India...
India's projected silica-dust-exposed workers will be 52 million at the end of 2025. Elimination of tuberculosis is also targeted in India by 2025. Scientists in India have already pointed out that unless silicosis is controlled, the said elimination is difficult to achieve. This study evidences an increasing incidence of tuberculosis and multidrug resistant tuberculosis (MDR-TB) with five deaths due to treatment failure among the silica dust-exposed workers compared to their unexposed counterparts. It was also observed that both tuberculosis as well as MDR-TB were directly proportional to the dose and/or duration of silica dust exposure. This means the incidence of MDR-TB is lowest in the unexposed group, moderate in the radiologically negative but silica dust exposed group (subradiological silicosis due to moderate exposure), and highest in the radiologically confirmed silicotic workers (maximally exposed group. Since India has a huge burden of silicosis, they are vulnerable to tuberculosis including multidrug-resistant tuberculosis resulting in the emergence of MDR-TB among the silica dust-exposed workers. This will also lead to a silent epidemic of silicotuberculosis in India shortly. Therefore, it would be important to have tools to quickly detect silicosis cases at an early stage to identify a vulnerable population and adopt an effective intervention measure.
PubMed: 38825150
DOI: 10.1016/j.jgar.2024.05.012 -
Environmental Research May 2024Bracken fern (Pteridium spp.) is a highly problematic plant worldwide due to its toxicity in combination with invasive properties on former farmland, in deforested areas... (Review)
Review
Bracken fern (Pteridium spp.) is a highly problematic plant worldwide due to its toxicity in combination with invasive properties on former farmland, in deforested areas and on disturbed natural habitats. The carcinogenic potential of bracken ferns has caused scientific and public concern for six decades. Its genotoxic effects are linked to illudane-type glycosides (ITGs), their aglycons and derivatives. Ptaquiloside is considered the dominating ITG, but with significant contributions from other ITGs. The present review aims to compile evidence regarding environmental pollution by bracken fern ITGs, in the context of their human and animal health implications. The ITG content in bracken fern exhibits substantial spatial, temporal, and chemotaxonomic variation. Consumption of bracken fern as food is linked to human gastric cancer but also causes urinary bladder cancers in bovines browsing on bracken. Genotoxic metabolites are found in milk and meat from bracken fed animals. ITG exposure may also take place via contaminated water with recent data pointing to concentrations at microgram/L-level following rain events. Airborne ITG-exposure from spores and dust has also been documented. ITGs may synergize with major biological and environmental carcinogens like papillomaviruses and Helicobacter pylori to induce cancer, revealing novel instances of chemical and biological co-carcinogenesis. Thus, the emerging landscape from six decades of bracken research points towards a global environmental problem with increasingly complex health implications.
PubMed: 38821456
DOI: 10.1016/j.envres.2024.119274 -
Cytotherapy May 2024Trained immunity results in long-term immunological memory, provoking a faster and greater immune response when innate immune cells encounter a secondary, often...
BACKGROUND
Trained immunity results in long-term immunological memory, provoking a faster and greater immune response when innate immune cells encounter a secondary, often heterologous, stimulus. We have previously shown that house dust mite (HDM)-induced innate training is amplified in mice expressing the human macrophage migration inhibitory factor (MIF) CATT functional polymorphism.
AIM
This study investigated the ability of mesenchymal stromal cells (MSCs) to modulate MIF-driven trained immunity both in vitro and in vivo.
METHODS
Compared with wild-type mice, in vivo HDM-primed bone marrow-derived macrophages (BMDMs) from CATT mice expressed significantly higher levels of M1-associated genes following lipopolysaccharide stimulation ex vivo. Co-cultures of CATT BMDMs with MSCs suppressed this HDM-primed effect, with tumor necrosis factor alpha (TNF-α) being significantly decreased in a cyclooxygenase 2 (COX-2)-dependent manner. Interestingly, interleukin 6 (IL-6) was suppressed by MSCs independently of COX-2. In an in vitro training assay, MSCs significantly abrogated the enhanced production of pro-inflammatory cytokines by HDM-trained CATT BMDMs when co-cultured at the time of HDM stimulus on day 0, displaying their therapeutic efficacy in modulating an overzealous human MIF-dependent immune response. Utilizing an in vivo model of HDM-induced trained immunity, MSCs administered systemically on day 10 and day 11 suppressed this trained phenomenon by significantly reducing TNF-α and reducing IL-6 and C-C motif chemokine ligand 17 (CCL17) production.
CONCLUSIONS
This novel study elucidates how MSCs can attenuate an MIF-driven, HDM-trained response in CATT mice in a model of allergic airway inflammation.
PubMed: 38819366
DOI: 10.1016/j.jcyt.2024.05.010 -
Heliyon May 2024Dust events in the Canary Islands have been documented since the late 19th century. However, during the past few years, several severe dust episodes have occurred in the...
Dust events in the Canary Islands have been documented since the late 19th century. However, during the past few years, several severe dust episodes have occurred in the Canary Islands, resulting in significant impacts on various sectors, such as aviation, air quality, and health, among others. These recent severe events have drawn the attention of both scientists and the general population, raising questions about whether these episodes are now more frequent and more severe. This study analyzes 483 dust events recorded in the Canary Islands over the last 40 years. Data analysis reveals that the average number of dust event days per year is approximately 24 days, and these events have an average duration of 1.8 days, both of which show a statistically significant decreasing trend over the series. Seasonal examination indicates that events occurring in the first and fourth quarters of the year have twice the duration of those in the other quarters. Furthermore, on an annual basis, events in the first quarter exhibit negative trends in both average and minimum visibilities. This suggests that dust events in the Canary Islands are becoming shorter in duration but more intense in terms of visibility. In this article, the Dust Adversity Index (DAI) is introduced to objectively compare the severity of events. Finally, anomalies in geopotential have been utilized to determine the prevailing synoptic patterns during dust events. It is evident that the dominant synoptic pattern during the first and fourth quarters of the year consists of a low cut-off system located to the west of the Canary Islands and a high-pressure system to the north of the Iberian Peninsula.
PubMed: 38818210
DOI: 10.1016/j.heliyon.2024.e31262 -
European Annals of Allergy and Clinical... May 2024Epistaxis is frequently observed in allergic rhinitis (AR) patients. However, few studies focus on the outcome of epistaxis with treatment of AR patients. This study...
Epistaxis is frequently observed in allergic rhinitis (AR) patients. However, few studies focus on the outcome of epistaxis with treatment of AR patients. This study aimed to retrospectively analyze the efficacy and safety of AR patients with epistaxis treated with sublingual immunotherapy (SLIT). A total of 74 patients aged 4-60 years with house dust mite (HDM)-induced AR accompanied by epistaxis and who completed 1 year of SLIT treatment with standard Dermatophagoides farinae (D. farinae) drops were enrolled in this study. The symptom scores, total medication scores (TMS), combined symptom and medication score (CSMS), visual analog scales (VAS), and bleeding score (BS) were assessed, as well as the nasal endoscopic examinations were performed to observe nasal signs. The levels of symptom scores, TMS, CSMS, VAS, and BS at 0.5 year and 1 year of SLIT treatment were significantly lower than those at the baseline (all p less than 0.01). Also, statistical differences were seen in CSMS (p less than 0.05) and VAS (p less than 0.01) between 0.5 year and 1 year. As expected, BS was positively correlated with CSMS (r = 0.617, 95% CI 0.517-0.699) and VAS (r = 0.777, 95% CI 0.719-0.822) at all three time points. SLIT with D. farinae drops was effective and safe for AR patients with epistaxis, resulting in improving the symptoms of rhinitis while relieving the symptoms of epistaxis.
PubMed: 38813925
DOI: 10.23822/EurAnnACI.1764-1489.342