Search Results (16,044)

Black carbon (BC) from incomplete combustion sources including traffic emissions affects human health due to its physical characteristics and ubiquity in urban environments. We examined the effects of BC on microbial growth in the presence of particulate matter (PM), using Aquadag as a surrogate for BC. Brunauer–Emmett–Teller analysis showed BC had a specific surface area of 123.2 m² g⁻¹, with over 90% of particles smaller than 100 nm, indicating strong surface interaction potential. Pseudomonas aeruginosa PA14 was cultured for 7 days with various BC concentrations and fixed PM. Increasing BC (0–100 ng mL⁻¹) significantly inhibited growth, evidenced by a decline in cellular adenosine triphosphate (cATP) with a slope of −1.296 ± 0.258 cATP ng mL⁻¹/BC ng mL⁻¹. The seven-day mean cATP slope ranged from 77 to 131, with control at 161. The biomass stress index (BSI) increased by 56%, rising from 28.6 ± 8.8% (control) to 44.6 ± 16.1% under high BC. The BSI change was minimal on day 1 (<+0.1% per BC ng mL⁻¹) but greater on days 5 (+0.125 ± 0.052%) and 7 (+0.130 ± 0.075%). BC does not cause immediate microbial death, but prolonged exposure induces cumulative stress, damages synthetic enzymes, inhibits growth, and may lead to cell death, with potential public health implications. Full article

With the rise of environmental protection and health topics in recent years, microbial production of red pigments has gradually become a research hotspot. Red pigment possesses biological properties such as anticancer and antioxidant activities and has a wide range of potential applications in the fields of food and medicine. In this paper, a red pigment-producing strain was screened from rice soil to provide a reserve for obtaining natural and safe red pigments. Methods: The strain LSY1-2 was identified using morphological and 16S rDNA molecular biological identification. The fermentation conditions for red pigment production were optimised to improve pigment yield, and the best conditions were analysed using response surface methodology. Finally, the stabilisation conditions of red pigment were analysed to determine the difficulty of retention. Results: The molecular ecology was identified as the bacterium Arthrobacter sp. of the genus Arthrobacter. The optimal red pigment production medium for the strain was determined by a one-way test with the carbon source beef extract, the nitrogen source peptone, the inoculum size 2%, the temperature 27 °C, the pH value 7, and the rotational speed 160 rpm. Response surface optimisation determined the optimal red pigment production conditions as the incubation temperature of 26.43 °C, the pH value of 6.89, and the rotational speed of 162.77 rpm, which resulted in the yield of red pigment under these optimal conditions as 0.883 U/mL. The stability of red pigment was best under the condition without light, and poorer under conditions of above 50 °C, strong acid, strong alkali, and more than 3% oxidant, and Fe³⁺ had a greater effect on the stability. Conclusions: Strain LSY-1 can produce stable red pigment under the optimised red pigment-producing conditions, which provides a reference for the large-scale production of natural red pigment and subsequent related research. Full article

Persistent bacteria (PB) are a subpopulation of dormant cells that tolerate high antibiotic concentrations and cause chronic, hard-to-treat infections, posing a serious global health threat. In this study, the antibacterial efficacy of six cationic polymers, poly(hexamethylene guanidine) (PHMG), polyethyleneimines of different molecular weights, α-polylysine, ε-polylysine, and polyacrylamide, against persistent bacteria was systematically evaluated. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of these cationic polymers against susceptible and persistent methicillin-susceptible Staphylococcus aureus (MSSA), methicillin-resistant S. aureus (MRSA), and Escherichia coli (E. coli) were determined using a microbroth dilution method, while cytotoxicity to mouse fibroblast (L929) cells was assessed via MTT assay. PHMG demonstrated superior antibacterial activity, with MBC values as low as 2 μg/mL against persistent MSSA, markedly outperforming the other polymers tested. The key novelties of this work are (i) the first establishment of a cationic polymer library with diverse structural parameters for persistent bacteria clearance, offering a potential strategy for treating recalcitrant infections; and (ii) the elucidation of quantitative correlations between polymer charge density and hydrophobic chain segments with antimicrobial efficacy through structure–activity relationship analysis, providing a theoretical basis for the rational design of anti-persistent materials. Full article

Background/Objectives: Acute ischemic stroke is a leading cause of mortality and long-term disability globally, with limited reliable early predictors of functional outcomes and survival. This study aimed to assess the prognostic value of two novel predictors: the hypoperfusion intensity ratio calculated from mean transit time and time-to-drain maps (HIR-MTT–TTD), derived from computed tomography perfusion (CTP) imaging parameters, and the Inflammation–Coagulation Index (ICI), which integrates systemic inflammatory (C-reactive protein and white blood cell count) and hemostatic (D-dimer) markers. Methods: This prospective, single-center observational study included 60 patients with acute ischemic stroke treated with intravenous thrombolysis and underwent pre-treatment CTP imaging. HIR-MTT–TTD evaluated collateral status and perfusion deficit severity, while ICI integrated C-reactive protein (CRP), white blood cell (WBC) count, and D-dimer levels. Functional outcomes were assessed using the National Institutes of Health Stroke Scale (NIHSS), Barthel Index, and modified Rankin Scale (mRS) at 24 h, 3 months, and 1 year. Results: Of 60 patients, 53.3% achieved functional independence (mRS 0–2) at 1 year. Unadjusted Cox models showed HIR-MTT–TTD (HR = 6.25, 95% CI: 1.48–26.30, p = 0.013) and ICI (HR = 1.08, 95% CI: 1.00–1.17, p = 0.052) were associated with higher 12-month mortality, worse mRS, and lower Barthel scores. After adjustment for age, BMI, smoking status, and sex, these associations became non-significant (HIR-MTT–TTD: HR = 2.83, 95% CI: 0.37–21.37, p = 0.314; ICI: HR = 1.07, 95% CI: 0.96–1.19, p = 0.211). Receiver operating characteristic (ROC) analysis indicated moderate predictive value, with ICI (AUC = 0.756, 95% CI: 0.600–0.867) outperforming HIR-MTT–TTD (AUC = 0.67, 95% CI: 0.48–0.83) for mortality prediction. Conclusions: The study introduces promising prognostic tools for functional outcomes. Elevated HIR-MTT–TTD and ICI values were independently associated with greater initial stroke severity, poorer functional recovery, and increased 1-year mortality. These findings underscore the prognostic significance of hypoperfusion intensity and systemic thrombo-inflammation in acute ischemic stroke. Combining the use of the presented indices may enhance early risk stratification and guide individualized treatment strategies. Full article

Fine particulate matter (PM_2.5) has been associated with mortality at low concentrations, with higher per-unit risk at lower exposure levels, and no threshold of effect. We examined characteristics of Medicare decedents living in zip codes at the lowest end of the U.S. PM_2.5 exposure distribution to determine whether there is a demographic, health or exposure profile of beneficiaries for whom even low PM_2.5 exposure is associated with increased mortality. The study included 2,773,647 decedent cases and 27,736,470 non-decedent controls, matched on decile of long-term PM_2.5 exposure from among 36 million Medicare fee-for-service beneficiaries enrolled 2015–2016. Outcomes of the study included all-cause and cause-specific mortality, stratified by decile and beneficiary characteristics. Increased PM_2.5-related mortality within the lowest exposure decile was found only among Native American beneficiaries, with odds ratios of 1.11 (95% CI, 1.01–1.21) and 1.21 (95% CI, 1.11–1.32) per 1 µg/m³ increase in PM_2.5, for those eligible and ineligible for Medicaid, respectively, and was driven by significant increases in selected kidney and cardiovascular outcomes, diabetes, and chronic obstructive pulmonary disease. These results may reflect particular sensitivity to PM_2.5; factors varying with PM_2.5 at the zip code level, including constituent exposures or social determinants of health; or inaccuracies in exposure estimates. Full article

Water quality in natural mineral springs is essential for sustainable use and conservation in the Amazon region. This study presents a hydrogeochemical characterization of 21 springs in the Peruvian Tropical Highlands, expanding on previous records of only six sources. The springs, which are thermal, saline, and sulfurous, are located between 384 and 3147 m a. s. l., mainly in mountainous areas with structural slopes and permeable sedimentary formations, such as the Pulluicana Group (composed mainly of sandstones and shales) and the Sarayaquillo Formation (characterized by reddish sandstones and siltstones). Physicochemical analysis showed temperatures ranging from 15.1 to 38.2 °C, pH from 5.20 to 8.72, conductivity between 0.05 and 253 mS/cm, and total dissolved solids from 0.02 to 162.50 g/L. High levels of arsenic and aluminum, likely originating from the natural weathering of rocks rich in these elements, exceeded national limits. Microbiological analysis detected fecal coliforms and Escherichia coli, indicating potential health risks. The results highlight the importance of regular monitoring and proper management to ensure safe use and explore its therapeutic and biotechnological applications, such as microbial bioremediation or development of extremophile-based enzymes. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global public health issue, affecting approximately 25% of the population and currently offering limited treatment options. Trimetazidine (TMZ) serves as a metabolic modulator that shifts cellular energy metabolism from fatty acid oxidation to glucose oxidation, thereby providing a novel therapeutic strategy aimed at addressing the underlying metabolic dysfunctions that contribute to the pathogenesis of MASLD. Our study aims to assess the efficacy of trimetazidine in improving hepatic steatosis, inflammation, and various metabolic parameters. Methods: In this double-masked, randomized controlled trial, 60 patients with confirmed MASLD diagnoses were randomly assigned in a 1:1 ratio to receive either trimetazidine 20 mg three times daily or a placebo, alongside lifestyle modifications, for 24 weeks. The trial was conducted in accordance with the Declaration of Helsinki and approved by the ethics committees of both participating institutions. We measured changes in hepatic steatosis, non-invasive fibrosis scores, inflammatory markers (including interleukin-6, tumor necrosis factor-alpha, and highly sensitive C-reactive protein), liver enzymes, and lipid profiles at baseline and at the end of the 24 weeks. Results: Hepatic steatosis decreased significantly, with controlled attenuation parameter scores from 352.5 to 302 dB/m in the TMZ group compared to the control (p < 0.001). TNF-α was reduced significantly in the TMZ group compared to the control group (p = 0.001). Fibrosis to AST score decreased from 0.49 to 0.25 in the TMZ group (p < 0.001). Aspartate aminotransferase decreased significantly compared to the control group (p 0.032). Notably, TMZ also imparted cardioprotective benefits, reducing total cholesterol by 14%, LDL by 17% (both p < 0.05), and triglycerides by 16% (p = 0.176). Conclusions: This groundbreaking trial supports the potential of trimetazidine as a promising therapeutic agent for MASLD, indicating substantial improvements in hepatic steatosis, inflammation, and metabolic disturbances. These findings underscore the urgency and importance of further multicenter trials to validate trimetazidine’s efficacy as a disease-modifying therapy for MASLD. Full article

Individual cow identification is a prerequisite for automatically monitoring behavior patterns, health status, and growth data of each cow, and can provide the assistance in selecting excellent cow individuals for breeding. Despite high recognition accuracy, traditional implantable electronic devices such as RFID (i.e., Radio Frequency Identification) can cause some degree of harm or stress reactions to cows. Image-based methods are widely used due to their non-invasive advantages, but these methods have poor adaptability to different environments and target size, and low detection accuracy in complex scenes. To solve these issues, this study designs a Dy_Conv (i.e., dynamic convolution) module and innovatively constructs a Dynamic_Bottleneck module based on the Dy_Conv and S2Attention (Sparse-shift Attention) mechanism. On this basis, we replaces the first and fourth bottleneck layers of Resnet50 with the Dynamic_Bottleneck to achieve accurate extraction of local features and global information of cows. Furthermore, the QAConv (i.e., query adaptive convolution) module is introduced into the front end of the backbone network, and can adjust the parameters and sizes of convolution kernels to adapt to the scale changes in cow targets and input images. At the same time, NAM (i.e., normalization-based attention module) attention is embedded into the backend of the network to achieve the feature fusion in the channels and spatial dimensions, which contributes to better distinguish visually similar individual cows. The experiments are conducted on the public datasets collected from different cowsheds. The experimental results showed that the Rank-1, Rank-5, and mAP metrics reached 96.8%, 98.9%, and 95.3%, respectively. Therefore, the proposed model can effectively capture and integrate multi-scale features of cow body appearance, enhancing the accuracy of individual cow identification in complex scenes. Full article

Background/Objectives: Recent evidence suggests that gut microbiota plays an important role in anxiety and stress-related disorders through interactions along the gut–brain axis. Our aim was to determine the microbiological diversity of intestinal microorganisms in individuals with acute and remission phases of PD when compared to healthy individuals. Another aim was also to analyze the differences in the metabolic pathways occurring in the intestinal microbiota of individuals from the three analyzed groups. Methods: A diagnosis was established using the Mini-International Neuropsychiatric Interview (M.I.N.I). The gut’s microbiota composition was analyzed through bacterial 16S rRNA gene sequencing (V1–V2 regions). The clinical evaluations included a BMI measurement, Short Form-36 Health Survey (SF-36), Hamilton Anxiety Scale (HAM-A), Montgomery–Åsberg Depression Rating Scale (MADRS), Columbia-Suicide Severity Rating Scale (C-SSRS), and State-Trait Anxiety Inventory (STAI). Results: We recruited 62 participants (31 PD and 31 controls). After conducting quality control filtering, data from 54 participants were analyzed (25 PD, 11 acute, 14 remission, and 29 controls). Observed richness was lower in the acute PD (63) group than in the control (74) and remission (66) (p = 0.038) groups, whereas the Shannon and Simpson indices and beta diversity (PERMANOVA) were not significantly different. The Ruminococcus gnavus group was enriched in acute PD; no other deconfounded differences in microbial composition were detected. Predicted functional differences were detected by edgeR only and included the pathways that are related to steroid biosynthesis and innate immune signaling. Conclusions: Distinct gut microbial signatures were associated with PD, implicating both the metabolic and inflammatory pathways in disease pathophysiology. Full article

The emergence and dissemination of extended-spectrum beta-lactamase (ESBL)-producing bacteria pose a major public health threat, necessitating a One Health approach to addressing this threat. Thus, the diversity, ESBL production, and potential public health implications of Gram-negative bacteria recovered from man-made lakes and surrounding lettuce in Yamoussoukro, Côte d’Ivoire were assessed in this study. Also, the lakes’ physicochemical parameters were assessed and correlated with bacteria community using Pearson correlation. A total of 68 Gram-negative bacterial isolates were recovered from the samples and identified via 16S rDNA gene sequencing. Phylogenetic analysis suggested multiple genus-/species-level variations within the isolates. Escherichia coli was the most prevalent in lake water (39.5%), while Acinetobacter was the dominant genus in lettuce (30%). E. coli isolates showed high resistance to ampicillin (90.9%), cefepime (72.7%), cefotaxime (68.2%), and aztreonam (63.6%). Moreover, ESBL production was confirmed in E. coli isolates (22.05%), predominantly mediated by the bla_CTX-M gene. Multidrug-resistant phenotypes were widespread, yielding similar multiple antibiotic resistance index (MARI) values in water (0.27–0.63) and lettuce (0.27–0.81). These data indicate high environmental contamination, which unfortunately is not being taken into account by lettuce producers according to an interview. Statistical analyses showed a significant relationship between bacterial diversity and lakes’ physicochemical parameters, including dissolved oxygen, pH, and turbidity. The basic education level of farmers, the prevalence of ESBL-producing E. coli, and the high prevalence of MDR Gram-negative bacteria in both environmental and crop sources in Yamoussoukro underscore the need for both integrated surveillance and management strategies to mitigate potential microbial public health risks within a One Health framework. Full article

Antimicrobial resistance (AMR) in environmental reservoirs is an emerging global health concern, particularly in urban settings with inadequate wastewater management. This study aimed to investigate the prevalence and resistance profiles of Salmonella spp. in canal water in Bangkok and assess the distribution of key antibiotic resistance genes (ARGs). Between 2016 and 2019, a total of 1381 water samples were collected from 29 canals. Salmonella spp. were isolated using standard microbiological methods and tested for susceptibility to 13 antibiotics. Polymerase chain reaction (PCR) was used to detect extended-spectrum β-lactamase (ESBL) genes and class 1 integron. Salmonella was found in 89.7% of samples. Among these, 62.1% showed resistance to at least one antimicrobial, and 54.8% were multidrug-resistant (MDR). The highest resistance was observed against streptomycin (41.4%). ESBL genes, predominantly blaCTX-M, were detected in 72.2% of tested isolates, while class 1 integrons were found in 67.8%, indicating a strong potential for gene dissemination. The results highlight urban canals as critical environment reservoirs of AMR Salmonella serovars, posing significant public health risks, particularly where canal water is used for agriculture, household, or recreational purposes. Strengthened environmental surveillance and effective wastewater regulation are urgently needed to mitigate AMR bacteria transmission at the human–environment–animal interface. Full article

Individuals are routinely exposed to traffic-related air pollution on their commutes, which has significant health impacts. Mitigating exposure to traffic-related pollution is a key urban sustainability concern. In Denver, Colorado, low-income Americans are more likely to rely on buses and spend time waiting at bus stops. Evaluating the contribution of traffic emissions at bus stops can provide important information on risks experienced by these populations. We measured PM_2.5 constituents at eight bus stops and one background reference site in Denver, in the summer of 2023. Source profiles, including gasoline emissions from traffic, were estimated using Positive Matrix Factorization (PMF) analysis of PM_2.5 constituents collected at a Chemical Speciation Network site in our study region. The contributions of the different sources at each bus stop were estimated by regressing the vector of species concentrations at each site (dependent variable) on the source-profile matrix from the PMF analysis (independent variables). Traffic-related emissions (~2.5–6.6 μg/m³) and secondary organics (~3–5 μg/m³) contributed to PM_2.5 at the bus stops in our dataset. The highest traffic-related emissions-derived PM_2.5 concentrations were observed at bus stops near local sources: a gas station and a car wash. The contribution of traffic-related emissions was lower at the background site (~1 μg/m³). Full article

This study investigated the effects of dietary supplementation with varying doses of Clostridium butyricum (C. butyricum) on growth performance, diarrhea incidence, serum biochemical parameters, fecal short-chain fatty acids (SCFAs), and fecal microbiota in preweaning Holstein calves. Forty healthy newborn Holstein heifer calves with comparable birth weights were randomly assigned to four groups (control, 1 g/d supplementation, 3 g/d supplementation, and 5 g/d supplementation) for a 60-day trial. Growth parameters, diarrhea incidence, serum immunoglobulins (IgA, IgG, IgM), cytokines (IL-1β, TNF-α), antioxidant indicators (T-AOC, MDA), fecal short-chain fatty acids (SCFAs), and microbial composition were measured to evaluate the effects of C. butyricum. The results indicated that calves in the 5 g/d group exhibited a significantly higher average daily gain (ADG) compared with the control group (804.67 vs. 701.67 g/d, p < 0.05), with no significant differences in feed intake observed among groups (p > 0.05). During Days 22–42, the diarrhea incidence in the 5 g/d group was 7.74% lower than that in the control group (p < 0.05). This group exhibited significantly elevated serum IgM levels (Day 42, p < 0.05) and reduced IL-1β concentrations (Day 42, p < 0.05). Additionally, total antioxidant capacity (T-AOC) was significantly enhanced (Days 42 and 60, p < 0.05), while malondialdehyde (MDA) content was significantly decreased (Days 21 and 42, p < 0.05). At Day 42, fecal propionate and butyrate concentrations were significantly elevated in the 5 g/d group (p < 0.05), while the relative abundances of Bacteroides, Acidaminococcus, Bifidobacterium, Olsenella, Faecalitalea, and Ruminococcus were significantly increased (p < 0.05). The increase in these short-chain fatty acids and beneficial bacteria contributes to improved intestinal health and thus helps alleviate diarrhea. These findings indicate that supplementing preweaning calves’ milk with 5 g/d of C. butyricum significantly enhances growth performance and intestinal health. This provides evidence for the use of C. butyricum as a natural alternative to antibiotics in calf rearing. Full article

(1) Objective: Volatile organic compounds (VOCs) monitoring in industrial parks is crucial for environmental regulation and public health protection. However, current techniques face challenges related to cost and real-time performance. This study aims to develop a dynamic calibration framework for accurate real-time conversion of VOCs volume fractions (nmol mol⁻¹) to mass concentrations (μg m⁻³) in industrial environments, addressing the limitations of conventional monitoring methods such as high costs and delayed response times. (2) Methods: By innovatively integrating photoionization detector (PID) with machine learning, we developed a robust conversion model utilizing PID signals, meteorological data, and a random forest’s (RF) algorithm. The system’s performance was rigorously evaluated against standard gas chromatography-flame ionization detectors (GC-FID) measurements. (3) Results: The proposed framework demonstrated superior performance, achieving a coefficient of determination (R²) of 0.81, root mean squared error (RMSE) of 48.23 μg m⁻³, symmetric mean absolute percentage error (SMAPE) of 62.47%, and a normalized RMSE (RMSE_norm) of 2.07%, outperforming conventional methods. This framework not only achieved minute-level response times but also reduced costs to just 10% of those associated with GC-FID methods. Additionally, the model exhibited strong cross-site robustness with R² values ranging from 0.68 to 0.69, although its accuracy was somewhat reduced for high-concentration samples (>1500 μg m⁻³), where the mean absolute percentage error (MAPE) was 17.8%. The inclusion of SMAPE and RMSE_norm provides a more nuanced understanding of the model’s performance, particularly in the context of skewed or heteroscedastic data distributions, thereby offering a more comprehensive assessment of the framework’s effectiveness. (4) Conclusions: The framework’s innovative combination of PID’s real-time capability and RF’s nonlinear modeling achieves accurate mass concentration conversion (R² = 0.81) while maintaining a 95% faster response and 90% cost reduction compared to GC-FID systems. Compared with traditional single-coefficient PID calibration, this framework significantly improves accuracy and adaptability under dynamic industrial conditions. Future work will apply transfer learning to improve high-concentration detection for pollution tracing and environmental governance in industrial parks. Full article

Crop health assessment is essential for the early detection of nutrient deficiencies, diseases, and pests, allowing for timely interventions that optimize yield, reduce losses, and support sustainable agricultural practices. While traditional methods and satellite-based remote sensing offer broad scale monitoring, they often suffer from coarse spatial resolution, and insufficient precision at the plant level. These limitations hinder accurate and dynamic assessment of crop health, particularly for high-resolution applications such as nutrient diagnosis during different crop growth stages. This study addresses these gaps by leveraging high-resolution UAV (Unmanned Aerial Vehicle) imagery to monitor the health of paddy crops across multiple temporal stages. A novel methodology was implemented to assess the crop health condition from the predicted Above-Ground Biomass (AGB) and essential macro-nutrients (N, P, K) using vegetation indices derived from UAV imagery. Four machine learning models were used to predict these parameters based on field observed data, with Random Forest (RF) and XGBoost outperforming other algorithms, achieving high regression scores (AGB > 0.92, N > 0.96, P > 0.92, K > 0.97) and low prediction errors (AGB < 80 gm/m², N < 0.11%, P < 0.007%, K < 0.08%). A significant contribution of this study lies in the development of decision-making rules based on threshold values of AGB and specific nutrient critical, optimum, and toxic levels for the paddy crop. These rules were used to derive crop health maps from the predicted AGB and NPK values. The resulting spatial health maps, generated using RF and XGBoost models with high classification accuracy (Kappa coefficient > 0.64), visualize intra-field variability, allowing for site-specific interventions. This research contributes significantly to precision agriculture by offering a robust, plant-level monitoring approach that supports timely, site-specific nutrient management and enhances sustainable crop production practices. Full article