-
Journal of Arthropod-borne Diseases Dec 2023Current medications especially the pentavalent antimonial compounds have been used as the first line treatment of cutaneous leishmaniasis (CL), but they have limitations...
Anti-Leishmanial Effects of a Novel Biocompatible Non-Invasive Nanofibers Containing Royal Jelly and Propolis against Iranian Strain of (MRHO/IR/75/ER): an In-Vitro Study.
BACKGROUND
Current medications especially the pentavalent antimonial compounds have been used as the first line treatment of cutaneous leishmaniasis (CL), but they have limitations due to serious side effects such as drug resistance, cardio and nephrotoxicity, and high costs. Hence, the demand to find more usable drugs is evident. Synthesis and development of natural, effective, biocompatible, and harmless compounds against is the principal priority of this study.
METHODS
By electrospinning method, a new type of nanofiber were synthesized from royal jelly and propolis with different ratios. Nanofibers were characterized by Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM), Thermogravimetric Analysis (TGA), Contact angle, and Fourier-transform infrared spectroscopy (FTIR). The Half-maximal inhibitory concentration (IC), Half-maximal effective concentration (EC) and the 50% cytotoxic concentration (CC) for different concentrations of nanofibers were determined using quantitative calorimetric methods. Inductively coupled plasma-optical emission spectrometry (ICP-OES) and flow cytometry were performed as complementary tests.
RESULTS
The results showed that the proposed formulas provide a new achievement that, despite the significant killing activity on , has negligible cytotoxicity on the host cells. Royal jelly nanofibers have significantly shown the best 72 hours results (IC= 35 μg/ml and EC=16.4 μg/ml) and the least cytotoxicity.
CONCLUSION
This study presents a great challenge to introduce a new low-cost treatment method for CL, accelerate wound healing, and reduce scarring with minimal side effects and biocompatible materials. Royal jelly and propolis nanofibers significantly inhibit the growth of .
PubMed: 38868671
DOI: 10.18502/jad.v17i4.15294 -
Environmental Pollution (Barking, Essex... Jun 2024Antimony (Sb) is known for its severe and extensive toxicity, and earthworms are considered important indicator organisms in soil ecosystems. Therefore, the present...
Endocrine system, cell growth and death, and energy metabolism induced by Sb(III) exposure in earthworm (Pheretima guillemi) revealed by transcriptome and metabolome analysis.
Antimony (Sb) is known for its severe and extensive toxicity, and earthworms are considered important indicator organisms in soil ecosystems. Therefore, the present study investigated the mechanism of toxicity of the Sb at different concentrations (50, 200 mg/kg) on earthworms using biochemical indicators, pathological sections, as well as metabolomics and transcriptomics analyses. The results showed that as the exposure concentration increased, both the antioxidant system of earthworms, extent of intestinal damage, and their metabolomic characteristics were significantly enhanced. In the 50 and 200 mg/kg Sb treatment group, 30 and 177 significant differentially changed metabolites (DCMs) were identified, respectively, with the most DCMs being down- and up-regulated, respectively. Metabolomics analysis showed that the contents of dl-tryptophan, glutamic acid, glycine, isoleucine, l-methionine, involved in the protein digestion and absorption as well as aminoacyl-tRNA biosynthesis were significantly up-regulated under the 200 mg/kg treatment. At the transcriptional level, Sb mainly affected the immune system, nervous system, amino acid metabolism, endocrine system, and carbohydrate metabolism in earthworms. The integration of transcriptomic and metabolomic data indicated that high doses of Sb regulated the metabolites and genes related to the oxidative phosphorylation pathway in earthworms. Overall, these results revealed global responses beyond the scope of conventional toxicity endpoints and facilitated a more in-depth and comprehensive assessment of the toxic effects of Sb.
PubMed: 38866316
DOI: 10.1016/j.envpol.2024.124357 -
Journal of Integrative Bioinformatics Jun 2024We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the...
We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the Biomodels repository for SBML-based models. MakeSBML is a client-based web application that translates models expressed in human-readable Antimony to the System Biology Markup Language (SBML) and vice-versa. Since MakeSBML is a web-based application it requires no installation on the user's part. Currently, MakeSBML is hosted on a GitHub page where the client-based design makes it trivial to move to other hosts. This model for software deployment also reduces maintenance costs since an active server is not required. The SBML modeling language is often used in systems biology research to describe complex biochemical networks and makes reproducing models much easier. However, SBML is designed to be computer-readable, not human-readable. We therefore employ the human-readable Antimony language to make it easy to create and edit SBML models.
PubMed: 38860571
DOI: 10.1515/jib-2024-0002 -
Frontiers in Microbiology 2024Mining activities, even in arctic regions, create waste materials releasing metals and metalloids, which have an impact on the microorganisms inhabiting their...
Assessment of microbial communities from cold mine environments and subsequent enrichment, isolation and characterization of putative antimony- or copper-metabolizing microorganisms.
Mining activities, even in arctic regions, create waste materials releasing metals and metalloids, which have an impact on the microorganisms inhabiting their surroundings. Some species can persist in these areas through tolerance to meta(loid)s via, e.g., metabolic transformations. Due to the interaction between microorganisms and meta(loid)s, interest in the investigation of microbial communities and their possible applications (like bioremediation or biomining) has increased. The main goal of the present study was to identify, isolate, and characterize microorganisms, from subarctic mine sites, tolerant to the metalloid antimony (Sb) and the metal copper (Cu). During both summer and winter, samples were collected from Finnish mine sites (site A and B, tailings, and site C, a water-treatment peatland) and environmental parameters were assessed. Microorganisms tolerant to Sb and Cu were successfully enriched under low temperatures (4°C), creating conditions that promoted the growth of aerobic and fermenting metal(loid) tolerating or anaerobic metal(loid) respiring organism. Microbial communities from the environment and Sb/Cu-enriched microorganisms were studied via 16S rRNA amplicon sequencing. Site C had the highest number of taxa and for all sites, an expected loss of biodiversity occurred when enriching the samples, with genera like or increasing their relative abundances and others like or reducing in relative abundance. From enrichments, 65 putative Sb- and Cu-metabolizing microorganisms were isolated, showing growth at 0.1 mM to 10 mM concentrations and 0°C to 40°C temperatures. 16S rRNA gene sequencing of the isolates indicated that most of the putative anaerobically Sb-respiring tolerators were related to the genus . This study represents the first isolation, to our knowledge, of putative Sb-metabolizing cold-tolerant microorganisms and contributes to the understanding of metal (loid)-tolerant microbial communities in Arctic mine sites.
PubMed: 38855773
DOI: 10.3389/fmicb.2024.1386120 -
Scientific Reports Jun 2024Heavy metal pollution in mining areas is a major cause of groundwater contamination, characterized by high toxicity, difficult degradability, and easy accumulation, and...
Heavy metal pollution in mining areas is a major cause of groundwater contamination, characterized by high toxicity, difficult degradability, and easy accumulation, and the source of pollution is not easily identified. Relying on the results of groundwater quality analysis tests in a typical mining area, this paper uses the SPSS 18.0 statistical analysis model to analyze the statistical characteristics of different indicator factors in the antimony mining area. The conclusions play a crucial role in implementing health and safety measures for the mining area and its surrounding residents. The statistical study results show that Mn, Se, As, and Sb are closely related to human mining activities and are polluted to varying degrees; the principal component analysis model indicates that the upstream monitoring points 26#, 22#, and 25# in the mining area groundwater are less polluted. The five monitoring points with a comprehensive principal component F > 1 are all located within the range of the metal mine cluster, indicating that the groundwater in the mining area is particularly sensitive to the impact of anthropogenic mineral extraction. This research summarizes the hydrogeological and geochemical statistical characteristics of the groundwater in the mining area, providing a reference for groundwater pollution risk diagnosis, ecological restoration, and heavy metal pollution prevention and control in this and similar mining areas.
PubMed: 38844525
DOI: 10.1038/s41598-024-63460-7 -
Scientific Reports Jun 2024Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed...
Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.
Topics: Humans; Machine Learning; Metals, Heavy; Diabetic Retinopathy; Female; Male; Middle Aged; ROC Curve; Adult; Risk Factors; Aged; Environmental Exposure
PubMed: 38844504
DOI: 10.1038/s41598-024-63916-w -
Frontiers in Public Health 2024Heavy metal exposure is an important cause of reduced bone mineral density (BMD). Epidemiological studies focusing on the effects of mixed heavy metal exposure on BMD in...
BACKGROUND
Heavy metal exposure is an important cause of reduced bone mineral density (BMD). Epidemiological studies focusing on the effects of mixed heavy metal exposure on BMD in middle-aged and older people are scarce. In single-metal studies, men and women have shown distinct responses of BMD to environmental metal exposure. This study therefore aimed to elucidate the association between mixed heavy metal exposure and BMD and to investigate whether it is sex-specific.
METHODS
Data from the 2017-2020 National Health and Nutrition Examination Survey were selected for this cross-sectional study. The study used three statistical methods, i.e., linear regression, Bayesian kernel machine regression (BKMR) modeling, and weighted quartiles (WQS) regression, to explore the association between the urinary concentrations of 11 metals (barium, cadmium, cobalt, cesium, manganese, molybdenum, lead, antimony, tin, thallium, and Tungsten), either individually or as a mixture, and total femoral BMD.
RESULTS
A total of 1,031 participants were included in this study. Femoral BMD was found to be higher in men than women. A significant negative correlation between the urinary concentrations of the 10 metals and femoral BMD was found in the overall cohort. Further gender sub-stratified analyses showed that in men, urinary metal concentrations were negatively correlated with femoral BMD, with cobalt and barium playing a significant and non-linear role in this effect. In women, although urinary metal concentrations negatively modulated femoral BMD, none of the correlations was statistically significant. Antimony showed sex-specific differences in its effect.
CONCLUSION
The urinary concentrations of 10 mixed heavy metals were negatively correlated with femoral BMD in middle-aged and older participants, and this effect showed gender differences. These findings emphasize the differing role of mixed metal exposure in the process of BMD reduction between the sexes but require further validation by prospective studies.
Topics: Humans; Female; Male; Bone Density; Nutrition Surveys; Cross-Sectional Studies; Aged; Metals, Heavy; Middle Aged; Femur; Sex Factors; Environmental Exposure; Bayes Theorem; Aged, 80 and over
PubMed: 38827609
DOI: 10.3389/fpubh.2024.1363362 -
ArXiv May 2024Antimony is a high-level, human-readable text-based language designed for defining and sharing models in the systems biology community. It enables scientists to describe...
Antimony is a high-level, human-readable text-based language designed for defining and sharing models in the systems biology community. It enables scientists to describe biochemical networks and systems using a simple and intuitive syntax. It allows users to easily create, modify, and distribute reproducible computational models. By allowing the concise representation of complex biological processes, Antimony enhances collaborative efforts, improves reproducibility, and accelerates the iterative development of models in systems biology. This paper provides an update to the Antimony language since it was introduced in 2009. In particular, we highlight new annotation features, support for flux balance analysis, a new rateOf method, support for probability distributions and uncertainty, named stochiometries, and algebraic rules. Antimony is also now distributed as a C/C++ library, together with python and Julia bindings, as well as a JavaScript version for use within a web browser. Availability: https://github.com/sys-bio/antimony.
PubMed: 38827452
DOI: No ID Found -
ACS Omega May 2024Modulation of intramolecular charge transfer (ICT) has been tested in two antimony(V) porphyrins, SbT(DMP)P(OMe)·PF and SbT(DMP)P(OTFE)·PF, where the -positions are...
Modulation of intramolecular charge transfer (ICT) has been tested in two antimony(V) porphyrins, SbT(DMP)P(OMe)·PF and SbT(DMP)P(OTFE)·PF, where the -positions are occupied by 3,5-dimethoxyphenyl (DMP), and the axial positions are linked with either methoxy (OMe) or trifluoroethoxy (OTFE) units, respectively. The presence of the Sb(+5) ion makes the porphyrin center electron poor. Under this situation, placing electron-rich units in the -position creates a condition for push-pull type ICT in the SbT(DMP)P(OMe)·PF. Remarkably, it is shown that the ICT character can be further enhanced in SbT(DMP)P(OTFE)·PF with the help of electron-withdrawing TFE units in the axial position, which makes the porphyrin center even more electron scarce. The steady-state and transient studies as well as solvatochromism studies establish the ICT in SbT(DMP)P(OMe)·PF and SbT(DMP)P(OTFE)·PF, and the strength of the ICT can be modulated by exploiting the structural properties of antimony(V) porphyrin. The existence of ICT is further supported by density functional theory calculations. The transient studies show that upon excitation of these porphyrin, their charge-transfer states convert to a full charger-separated states with appreciable lifetimes.
PubMed: 38826543
DOI: 10.1021/acsomega.4c01773 -
Unlocking the power of AI models: exploring protein folding prediction through comparative analysis.Journal of Integrative Bioinformatics May 2024Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However,...
Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated. These proteins contain four domains of unknown function and are present in spp. ARM refers to an antimony resistance marker. The study's main objective is to assess the accuracy of the model's predictions, thereby providing insights into the complexities and supporting metrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with and , leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.
PubMed: 38797876
DOI: 10.1515/jib-2023-0041