Search Results (300)

HgCdSe has recently been proposed as a potential alternative material to HgCdTe for fabricating high-performance infrared detectors. This work presents a study on the growth of high-crystalline-quality HgCdSe materials on GaSb (211)B substrates via molecular beam epitaxy and demonstration of the first prototype HgCdSe-based mid-wave infrared detectors. By optimizing the MBE growth parameters, and especially the thermal cleaning process of the GaSb substrate surface prior to epitaxial growth, high-quality HgCdSe material was achieved with a record XRD full width at half maximum of ~65 arcsec. At a temperature of 77 K, the mid-wave infrared HgCdSe n-type material demonstrated a minority carrier lifetime of ~1.19 µs, background electron concentration of ~2.2 × 10¹⁷ cm⁻³, and electron mobility of ~1.6 × 10⁴ cm²/Vs. The fabricated mid-wave infrared HgCdSe photoconductor presented a cut-off wavelength of 4.2 µm, a peak responsivity of ~40 V/W, and a peak detectivity of ~1.2 × 10⁹ cmHz^1/2/W at 77 K. Due to the relatively high background electron concentration, the detector performance is lower than that of state-of-the-art low-doped HgCdTe counterparts. However, these preliminary results indicate the great potential of HgCdSe materials for achieving next-generation IR detectors on large-area substrates with features of lower cost and larger array format size. Full article

Proper joint function has a significant impact on people’s quality of life. Joints are the point of connection between two or more bones and consist of at least three elements: joint surfaces, the joint capsule, and the joint cavity. Joint diseases are a serious social problem. Risk factors for the development of these diseases include overweight and obesity, gender, and intestinal microbiome disorders. Another factor that is considered to influence joint diseases is trace elements. Under normal conditions, elements such as iron (Fe), copper (Cu), cobalt (Co), iodine (I), manganese (Mn), zinc (Zn), silver (Ag), cadmium (Cd), mercury (Hg), lead (Pb), nickel (Ni) selenium (Se), boron (B), and silicon (Si) are part of enzymes involved in reactions that determine the proper functioning of cells, regulate redox metabolism, and determine the maturation of cells that build joint components. However, when the normal concentration of the above-mentioned elements is disturbed and toxic elements are present, dangerous joint diseases can develop. In this article, we focus on the role of trace elements in joint function. We describe the molecular mechanisms that explain their interaction with chondrocytes, osteocytes, osteoblasts, osteoclasts, and synoviocytes, as well as their proliferation, apoptosis, and extracellular matrix synthesis. We also focus on the role of these trace elements in the pathogenesis of joint diseases: rheumatoid arthritis (RA), osteoarthritis (OA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and systemic lupus erythematosus (SLE). We describe the roles of increased or decreased concentrations of individual elements in the pathogenesis and development of joint diseases and their impact on inflammation and disease progression, referring to molecular mechanisms. We also discuss their potential application in the treatment of joint diseases. Full article

Agricultural soil contaminated with mercury (Hg) poses a serious threat to ecosystems and human health. Although adding an appropriate amount of selenium (Se) can reduce the toxicity and mobility of Hg in soil, Se alone is prone to leaching into groundwater through soil runoff. Therefore, Se-enriched compound fertilizers were developed, and their remediation effect on Hg-contaminated agricultural soil was determined. The Se-enriched compound fertilizers were prepared by combining an organic fertilizer (vinegar residue, biochar, and potassium humate), inorganic fertilizer (urea, KH₂PO₄, ZnSO₄, and Na₂SeO₃), and a binder (attapulgite and bentonite). A material proportioning experiment showed that the optimal granulation rate, organic matter content, and compressive strength were achieved when using 15% attapulgite (Formulation 1) and 10% bentonite (Formulation 2). An analysis of Se-enriched compound fertilizer particles showed that the two Se-enriched compound fertilizers complied with the standard for organic–inorganic compound fertilizers (China GB 18877-2002). Compared with the control, Formulation 1 and Formulation 2 significantly reduced the Hg content in bulk and rhizosphere soil following diethylenetriaminepentaacetic acid (DTPA) extraction by 40.1–47.3% and 53.8–56.0%, respectively. They also significantly reduced the Hg content in maize seedling roots and shoots by 26.4–29.0% and 57.3–58.7%, respectively, effectively limiting Hg uptake, transport, and enrichment. Under the Formulation 1 and Formulation 2 treatments, the total and DTPA-extractable Se contents in soil and maize seedlings were significantly increased. This study demonstrated that Se-enriched compound fertilizer effectively remediates Hg-contaminated agricultural soil and can promote the uptake of Se by maize. The results of this study are expected to positively contribute to the sustainable development of the agro-ecological environment. Full article

In contrast to mercury, an extremely toxic element, selenium is an essential micronutrient, which by complexing with mercury can mitigate its toxicity. In this regard, we quantified mercury and selenium concentrations in samples (n = 309) of fish tissues and analyzed the Se:Hg molar ratio and HBVSe as toxicological risk biomarkers. The data indicated that mercury levels in planktivorous fish (0.630 ± 0.202 mg kg⁻¹) and carnivorous fish (1.196 ± 0.513 mg kg⁻¹) were above the Brazilian limits considered safe for daily consumption. The highest selenium concentrations were observed in planktivores (0.272 ± 0.093 mg kg⁻¹) and the lowest in herbivores (0.099 ± 0.092 mg kg⁻¹). Molar ratios greater than one and positive HBVSe values were found in 42% of the fish samples (n = 131). As a result, we found that (i) the trophic level influences the risk of mercury exposure through the intake of fish in the diet; (ii) the approach presented in our study (model II) involves greater rigor concerning intake and exposure via fish consumption, since it considers the antagonistic Se:Hg ratio; and (iii) selenium can attenuate mercury toxicity, but safe thresholds vary depending on the species. Full article

Volcanic eruptions release gases and particulates that may adversely affect human health. The Tajogaite eruption on La Palma provided a unique opportunity to evaluate inorganic pollutant exposure in a directly affected population. As part of the ISVOLCAN study, blood samples from 393 adults residing in the island’s western region were analyzed for 43 inorganic elements using Inductively Coupled Plasma Mass Spectrometry (ICP-MS), including 20 toxic elements identified by the Agency for Toxic Substances and Disease Registry (ATSDR). The median age of participants was 51 years, and 56.7% were female. Higher levels of Hg and Mn were associated with long-term occupational exposure, while smoking was linked to elevated Cd, Pb, and Sr levels. Participants living within 6.5 km of the volcano had significantly higher concentrations of Al and Ti. Ash cleanup activities were associated with increased levels of Ni and Cu, and those spending over five hours outdoors daily showed elevated Se and Pb. This is the first biomonitoring study to assess blood concentrations of inorganic pollutants in a population exposed to volcanic emissions. The findings highlight key exposure factors and underscore the need for continued research to assess long-term health effects and inform public health measures. Full article

Potentially toxic elements such as cadmium (Cd) in agricultural soils represent a global concern due to their toxicity and potential accumulation in the food chain. However, our understanding of cadmium’s complex sources and the mechanisms controlling its spatial distribution across diverse edaphic and geological contexts remains limited, particularly in underexplored agricultural regions. Our study aimed to assess the total accumulated Cd content in soils under avocado cultivation and its association with edaphic, geochemical, and geomorphological variables. To this end, we considered the total concentrations of other metals and explored their associations to gain a better understanding of Cd’s spatial distribution. We analyzed 26 physicochemical properties, the total concentrations of 22 elements (including heavy and trace metals such as As, Ba, Cr, Cu, Hg, Ni, Pb, Sb, Se, Sr, Tl, V, and Zn and major elements such as Al, Ca, Fe, K, Mg, and Na), and six geospatial variables in 410 soil samples collected from various avocado-growing regions in Peru in order to identity potential associations that could help explain the spatial patterns of Cd. For data analysis, we applied (1) univariate statistics (skewness, kurtosis); (2) multivariate methods such as Spearman correlations and principal component analysis (PCA); (3) spatial modeling using the Geodetector tool; and (4) non-parametric testing (Kruskal–Wallis test with Dunn’s post hoc test). Our results indicated (1) the presence of hotspots with Cd concentrations exceeding 3 mg·kg⁻¹, displaying a leptokurtic distribution (skewness = 7.3); (2) dominant accumulation mechanisms involving co-adsorption and cation competition (Na⁺, Ca²⁺), as well as geogenic co-accumulation with Zn and Pb; and (3) significantly higher Cd concentrations in Leptosols derived from Cretaceous intermediate igneous rocks (diorites/tonalites), averaging 1.33 mg kg⁻¹ compared to 0.20 mg·kg⁻¹ in alluvial soils (p < 0.0001). The factors with the greatest explanatory power (q > 15%, Geodetector) were the Zn content, parent material, geological age, and soil taxonomic classification. These findings provide edaphogenetic insights that can inform soil cadmium (Cd) management strategies, including recommendations to avoid establishing new plantations in areas with a high risk of Cd accumulation. Such approaches can enhance the efficiency of mitigation programs and reduce the risks to export markets. Full article

Deep learning has demonstrated significant success in hyperspectral image (HSI) classification by effectively leveraging spatial–spectral feature learning. However, current approaches encounter three challenges: (1) high spectral redundancy and the presence of noisy bands, which impair the extraction of discriminative features; (2) limited spatial receptive fields inherent in convolutional operations; and (3) unidirectional context modeling that inadequately captures bidirectional dependencies in non-causal HSI data. To address these challenges, this paper proposes HG-Mamba, a novel hybrid geometry-aware bidirectional Mamba network for HSI classification. The proposed HG-Mamba synergistically integrates convolutional operations, geometry-aware filtering, and bidirectional state-space models (SSMs) to achieve robust spectral–spatial representation learning. The proposed framework comprises two stages. The first stage, termed spectral compression and discrimination enhancement, employs multi-scale spectral convolutions alongside a spectral bidirectional Mamba (SeBM) module to suppress redundant bands while modeling long-range spectral dependencies. The second stage, designated spatial structure perception and context modeling, incorporates a Gaussian Distance Decay (GDD) mechanism to adaptively reweight spatial neighbors based on geometric distances, coupled with a spatial bidirectional Mamba (SaBM) module for comprehensive global context modeling. The GDD mechanism facilitates boundary-aware feature extraction by prioritizing spatially proximate pixels, while the bidirectional SSMs mitigate unidirectional bias through parallel forward–backward state transitions. Extensiveexperiments on the Indian Pines, Houston2013, and WHU-Hi-LongKou datasets demonstrate the superior performance of HG-Mamba, achieving overall accuracies of 94.91%, 98.41%, and 98.67%, respectively. Full article

Heavy metal contamination in food has become a significant global food safety concern. This study assessed the concentrations of As, Ca, Cd, Co, Cr, Cu, Fe, Hg, Mn, K, Mg, Na, Ni, Se, Pb, and Zn in 13 locally grown vegetables using microwave-assisted acid digestion and ICP-MS. The potential human health risks associated with their consumption were also evaluated. Vegetable samples were collected from the local farmer’s market in Grand Forks, North Dakota. The mean levels (μg/g) of Na, Mg, K, Ca, Fe, Se, Mn, Cu, Zn, Co, Hg, Cr, Ni, As, Cd, and Pb were 1001, 2935, 30474, 686.0, 52.90, 0.171, 37.63, 4.936, 21.33, 0.069, 0.0030, 0.049, 0.736, 0.083, 0.298, and 0.019, respectively, having the following decreasing trend: K > Mg > Na > Ca > Fe > Mn > Zn > Cu > Ni > Cd > Se > As > Co > Cr > Pb > Hg. The highest total metals level was found in spinach, with the following decreasing order: spinach > tomato > sugar beet > white eggplant > cucumber ~ kale > green chili > green bean > dill ~ potato > capsicum > onion > corn. Spinach exhibited the highest concentrations of Cd, Cr, Pb, and Hg, which suggests a higher risk of metal exposure from its consumption. Toxic metals except Cd were found to be lower than the maximum allowable concentrations set by international agencies among the analyzed vegetables, while Cd levels were higher than maximum allowable levels in most of the vegetables. Health risks associated with metal intake by vegetable consumption were evaluated in terms of estimated daily intake (EDI), non-carcinogenic risks were evaluated by the target hazard quotient (THQ) and Hazard Index (HI), and carcinogenic risks were evaluated by target cancer risk (TCR). The EDI values of all the metals were found to be below the maximum tolerable daily intake (MTDI). The highest EDI value for Mn, Zn, Hg, Cr, Cd, and Pb was noted in spinach. THQ values for Cd, Co, and As were higher than 1 in most of the vegetable species analyzed, indicating non-carcinogenic health effects to consumers. HI results also posed a non-carcinogenic health risk associated with the intake of these vegetables. Mean TCR values of Cr, Ni, As, and Cd indicated carcinogenic risk for consumers. This study showed that there are potential health risks with consumption of these vegetables. Lastly, regular monitoring of metal levels in vegetables is suggested/recommended to minimize health risks and support pollution control efforts. Full article

Uncomplicated hypertension (UH) during pregnancy represents a common condition, worsening maternal and fetal prognosis. However, no single biomarker has proven optimal for determining the risk of UH. We developed an early risk multivariate model for UH, integrating hemodynamics with biochemistry, focusing on the relationship between blood pressure (BP) indices, uric acid (UA), and angiogenesis-related factors (AF). We collected and analyzed data on 24 h ambulatory BP monitoring, demographic, epidemiological, clinical, and laboratory variables from 132 pregnancies. The main predictors were BP indices and serum UA and AF levels. Uncomplicated hypertension, defined as the presence of gestational hypertension or worsening of essential hypertension beyond the 20th week, was the main outcome. The combined second-degree polynomial transformation of UA and the AF (sFlt-1/PIGF) ratio, called the UA-AF Index, consistently showed a positive association with UH. The models incorporating nighttime BP indices combined with the UA-AF Index outperformed the others, with the best-performing model based on the nocturnal systolic BP (SBP). Specifically, in the best-fitting model (nighttime SBP + UA-AF Index as predictors), each 1 mmHg increase in nocturnal SBP was associated with a 10% higher risk of UH, while each one-unit increase in the UA-AF Index raised the likelihood of UH by more than twofold (accuracy: 0.830, AUC 0. 874, SE 0.032, p-value < 0.001, 95%CI 0.811–0.938). The combination of nighttime blood pressure indices, serum uric acid, and angiogenesis-related factors may provide added value in the assessment of uncomplicated hypertension during pregnancy. Full article

In 2019, an unusual mortality event (UME) affected bottlenose dolphins (Tursiops truncatus) in the Mississippi Sound (MSS) following an extended dual opening of the Bonnet Carré Spillway (BCS), which prevents flooding in New Orleans. This resulted in low salinity, skin lesions, and electrolyte imbalances in dolphins. Additionally, the influx likely altered the MSS’s environmental chemical composition, including levels of heavy metals and metalloids; thus, we quantified heavy metals, metalloids, and essential elements in the tissues of dolphins that stranded in the MSS before and after the 2019 UME. We hypothesized that levels of heavy metals and metalloids (such as mercury (Hg), arsenic (As), lead (Pb), and cadmium (Cd)) would not show significant changes post-UME. Indeed, we found no major changes associated with the UME in most metals; sodium (Na) and magnesium (Mg) levels were lower in several tissues after 2019, which correlated with the average yearly salinity measured from the MSS. Toxic metals and metalloids were detectable with some changes over time; however, the selenium (Se):Hg molar ratio increased in some tissues post-2019. Additionally, we confirmed that Hg can bioaccumulate, with positive correlations between Hg levels and dolphin size as assessed by straight length. Overall, our findings indicate that freshwater incursions into the MSS can alter dolphin exposure to essential and toxic elements. Full article

Metal exposure is a potential risk factor for amyotrophic lateral sclerosis (ALS). Increasing evidence suggests that elevated levels of DNA damage are present in both familial (fALS) and sporadic (sALS) forms of ALS, characterized by the selective loss of motor neurons in the brain, brainstem, and spinal cord. However, identifying and differentiating initial biomarkers of DNA damage response (DDR) in both forms of ALS remains unclear. The toxicological profiles from the Agency for Toxic Substances and Disease Registry (ATSDR) and our previous studies have demonstrated the influence of metal exposure-induced genotoxicity and neurodegeneration. A comprehensive overview of the ATSDR’s toxicological profiles and the available literature identified 15 metals (aluminum (Al), arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), mercury (Hg), nickel (Ni), selenium (Se), uranium (U), vanadium (V), and zinc (Zn)) showing exposure-induced genotoxicity indicators associated with ALS pathogenesis. Genetic factors including mutations seen in ALS types and with concomitant metal exposure were distinguished, showing that heavy metal exposure can exacerbate the downstream effect of existing genetic mutations in fALS and may contribute to motor neuron degeneration in sALS. Substantial evidence associates heavy metal exposure to genotoxic endpoints in both forms of ALS; however, a data gap has been observed for several of these endpoints. This review aims to (1) provide a comprehensive overview of metal exposure-induced genotoxicity in ALS patients and experimental models, and its potential role in disease risk, (2) summarize the evidence for DNA damage and associated biomarkers in ALS pathogenesis, (3) discuss possible mechanisms for metal exposure-induced genotoxic contributions to ALS pathogenesis, and (4) explore the potential distinction of genotoxic biomarkers in both forms of ALS. Our findings support the association between metal exposure and ALS, highlighting under or unexplored genotoxic endpoints, signaling key data gaps. Given the high prevalence of sALS and studies showing associations with environmental exposures, understanding the mechanisms and identifying early biomarkers is vital for developing preventative therapies and early interventions. Limitations include variability in exposure assessment and the complexity of gene–environment interactions. Studies focusing on longitudinal exposure assessments, mechanistic studies, and biomarker identification to inform preventative and therapeutic strategies for ALS is warranted. Full article

Osteoporosis is a major public health concern, particularly among postmenopausal women. Environmental exposure to metals has been proposed as a potential contributor to osteoporosis, but human data remain limited and inconsistent. This study investigated changes in urinary concentrations of 20 metal(loid)s in patients with osteoporosis, as well as the association of these elements with bone mineral density (BMD), in a cohort of 380 postmenopausal women aged 50–70 years from Cascavel, Paraná, Brazil. Demographic, lifestyle, and clinical data were collected, and urinary concentrations of aluminum (Al), barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), copper (Cu), mercury (Hg), lithium (Li), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), rubidium (Rb), antimony (Sb), selenium (Se), tin (Sn), strontium (Sr), thallium (Tl), uranium (U), and zinc (Zn) were measured by inductively coupled plasma mass spectrometry. BMD was assessed at the lumbar spine, femoral neck, and total hip using dual-energy X-ray absorptiometry. Osteoporosis was diagnosed in 73 participants (19.2%). Osteoporotic women had significantly higher urinary concentrations of Cd, Mn, Pb, Sb, Sn, and Zn (p < 0.05). Statistically significant negative correlations were observed between BMD and urinary concentrations of Al, Cd, Hg, Mn, Sb, and U. After adjustment for confounders, elevated urinary concentrations of Cd, Mn, Pb, and Sb remained independently and significantly associated with higher odds of osteoporosis, with Cd (aOR = 1.495; p = 0.026) and Sb (aOR = 2.059; p = 0.030) showing the strongest associations. In addition, women with urinary concentrations above the 90th percentile for both Cd and Sb had a significantly higher prevalence of osteoporosis compared to those with lower levels (44.4% vs. 18.0%; p = 0.011). Longitudinal studies are needed to confirm causality and inform prevention strategies. Full article

Research on the trophic transfer of trace elements in food chains, particularly toxic elements like mercury (Hg) and essential elements like selenium (Se), is crucial for understanding their impact on human health. In this work, we assessed the transfer of Hg and Se in the blue shark (Prionace glauca), a top predator with economic importance. Muscle samples from sharks, as well as their main prey (squid, red shrimp, sardine, and mackerel), were analyzed for Hg and Se concentrations. The Hg levels of sharks were below the recommended legal limit for seafood consumption in Mexico (1 µg·g⁻¹ ww), while Se levels were significantly lower than previously reported for the species. Biomagnification was evaluated in this species by calculating biomagnification factors (BMF) for Hg and Se based on predator-prey element concentrations. Hg showed a BMF of 2.8, indicating biomagnification, while Se had a BMF of 0.2, suggesting biodilution. Trophic transfer factor models supported these findings, showing a positive correlation of Hg concentration with trophic level and a negative correlation with Se. However, while a hazard quotient under one does not pose a risk for consumption, a Se:Hg molar ratio under one estimated in the muscle tissue indicates that Hg levels along this food web should be approached with caution. Full article

Traditional deep learning algorithms struggle to effectively utilize local spectral info in forest HS images and adequately capture subtle feature differences, often causing model confusion and misclassification. To tackle these issues, we present SF-UNet, a novel pixel-level classification network for forest HS images. It integrates the strengths of SpectralFormer and U-Net. First, the HGSE module generates semicomponent spectral nesting, strengthening local info element connections via spectral embedding. Next, the CAM within SpectralFormer serves as an auxiliary U-Net encoder. This allows cross-level jump connections and cascading through interlayer soft residuals, enhancing feature representation via cross-regional adaptive learning. Finally, the U-Net decoder is used for pixel-level classification. Experiments on forest Sentinel-2 data show that SF-UNet outperforms mainstream frameworks. While Vision Transformer has an 88.29% classification accuracy, SF-UNet achieves 95.28%, a significant 6.99% improvement. Moreover, SF-UNet excels in land cover change analysis using multi-temporal Sentinel-2 images. It can accurately capture subtle land use changes and maintain classification consistency across seasons and years. These results highlight SF-UNet’s effectiveness in forest remote sensing image classification and its potential application value in deep learning-based forest HS remote sensing image classification research. Full article

Chronic kidney disease (CKD) is a noteworthy global health issue affecting 10% of the world’s populace. It is increasingly linked to environmental exposures; however, the interplay of toxic metals, per- and polyfluoroalkyl substances (PFAS), and essential elements has not been fully elucidated. This cross-sectional study analyzed 5800 out of the 9245 participants from the 2017–2018 NHANES dataset to evaluate the combined effect of PFAS, essential elements, and toxic metals on CKD using logistic regression and advanced environmental mixture models, namely, Bayesian Kernel Machine Regression (BKMR), quantile g-computation (qgcomp), and Weighted Quantile Sum (WQS) regression. Our results showed cadmium (Cd) emerging as a significant contributor to CKD (OR = 2.16, p = 0.023) from the logistic regression analysis. Mercury (Hg) demonstrated the highest contribution in mixtures (posterior inclusion probability = 0.908) from our BKMR analysis, with a non-linear U-shaped dose–response relationship. Essential elements like selenium (Se) and manganese (Mn) exhibited protective correlations but complex non-linear interactions, moderating toxic metal effects from our qgcomp and WQS regression. Notably, antagonistic interactions between essential elements and some pollutants reduced the overall mixture impact on CKD, showing an overall decreasing joint effect of the combined PFAS, toxic metals, and essential elements on CKD, from the 25th to the 75th quantile. This study highlights the role of environmental co-exposures in CKD risk and highlights the need for advanced statistical and machine learning approaches in studying complex environmental mixture interactions on human health. Full article