Pesticides and environmental injustice in the USA: root causes, current regulatory reinforcement and a path forward.

Many environmental pollutants are known to have disproportionate effects on Black, Indigenous and People of Color (BIPOC) as well as communities of low-income and wealth. The reasons for these disproportionate effects are complex and involve hundreds of years of systematic oppression kept in place through structural racism and classism in the USA. Here we analyze the available literature and existing datasets to determine the extent to which disparities in exposure and harm exist for one of the most widespread pollutants in the world - pesticides. Our objective was to identify and discuss not only the historical injustices that have led to these disparities, but also the current laws, policies and regulatory practices that perpetuate them to this day with the ultimate goal of proposing achievable solutions. Disparities in exposures and harms from pesticides are widespread, impacting BIPOC and low-income communities in both rural and urban settings and occurring throughout the entire lifecycle of the pesticide from production to end-use. These disparities are being perpetuated by current laws and regulations through 1) a pesticide safety double standard, 2) inadequate worker protections, and 3) export of dangerous pesticides to developing countries. Racial, ethnic and income disparities are also maintained through policies and regulatory practices that 4) fail to implement environmental justice Executive Orders, 5) fail to account for unintended pesticide use or provide adequate training and support, 6) fail to effectively monitor and follow-up with vulnerable communities post-approval, and 7) fail to implement essential protections for children. Here we've identified federal laws, regulations, policies, and practices that allow for disparities in pesticide exposure and harm to remain entrenched in everyday life for environmental justice communities. This is not simply a pesticides issue, but a broader public health and civil rights issue. The true fix is to shift the USA to a more just system based on the Precautionary Principle to prevent harmful pollution exposure to everyone, regardless of skin tone or income. However, there are actions that can be taken within our existing framework in the short term to make our unjust regulatory system work better for everyone.

Genetically Encoded Ratiometric RNA-Based Sensors for Quantitative Imaging of Small Molecules in Living Cells.

Precisely determining the intracellular concentrations of metabolites and signaling molecules is critical in studying cell biology. Fluorogenic RNA-based sensors have emerged to detect various targets in living cells. However, it is still challenging to apply these genetically encoded sensors to quantify the cellular concentrations and distributions of targets. Herein, using a pair of orthogonal fluorogenic RNA aptamers, DNB and Broccoli, we engineered a modular sensor system to apply the DNB-to-Broccoli fluorescence ratio to quantify the cell-to-cell variations of target concentrations. These ratiometric sensors can be broadly applied for live-cell imaging and quantification of metabolites, signaling molecules, and other synthetic compounds.

Climate research priorities for policy-makers, practitioners, and scientists in Georgia, USA.

Climate change has far-reaching effects on human and ecological systems, requiring collaboration across sectors and disciplines to determine effective responses. To inform regional responses to climate change, decision-makers need credible and relevant information representing a wide swath of knowledge and perspectives. The southeastern U. S. State of Georgia is a valuable focal area for study because it contains multiple ecological zones that vary greatly in land use and economic activities, and it is vulnerable to diverse climate change impacts. We identified 40 important research questions that, if answered, could lay the groundwork for effective, science-based climate action in Georgia. Top research priorities were identified through a broad solicitation of candidate research questions (180 were received). A group of experts across sectors and disciplines gathered for a workshop to categorize, prioritize, and filter the candidate questions, identify missing topics, and rewrite questions. Participants then collectively chose the 40 most important questions. This cross-sectoral effort ensured the inclusion of a diversity of topics and questions (e.g., coastal hazards, agricultural production, ecosystem functioning, urban infrastructure, and human health) likely to be important to Georgia policy-makers, practitioners, and scientists. Several cross-cutting themes emerged, including the need for long-term data collection and consideration of at-risk Georgia citizens and communities. Workshop participants defined effective responses as those that take economic cost, environmental impacts, and social justice into consideration. Our research highlights the importance of collaborators across disciplines and sectors, and discussing challenges and opportunities that will require transdisciplinary solutions.

Recent advancements in nanomedicine as a revolutionary approach to treating multiple myeloma.

Multiple myeloma, the second most common hematological malignancy, remains incurable with a 5-year survival rate of approximately 50 % and recurrence rates near 100 %, despite significant attempts to develop effective medicines. Therefore, there is a pressing demand in the medical field for innovative and more efficient treatments for MM. Currently, the standard approach for treating MM involves administering high-dose chemotherapy, which frequently correlates with improved results; however, one major limiting factor is the significant side effects of these medications. Furthermore, the strategies used to deliver medications to tumors limit their efficacy, whether by rapid clearance from circulation or an insufficient concentration in cancer cells. Cancer treatment has shifted from cytotoxic, nonspecific chemotherapy regimens to molecularly targeted, rationally developed drugs with improved efficacy and fewer side effects. Nanomedicines may provide an effective alternative way to avoid these limits by delivering drugs into the complicated bone marrow microenvironment and efficiently reaching myeloma cells. Putting drugs into nanoparticles can make their pharmacokinetic and pharmacodynamic profiles much better. This can increase the drug's effectiveness in tumors, extend its time in circulation in the blood, and lower its off-target toxicity. In this review, we introduce several criteria for the rational design of nanomedicine to achieve the best anti-tumoral therapeutic results. Next, we discuss recent advances in nanomedicine for MM therapy.

The social and environmental factors impacting the motivation of adolescents for weight control, why and how? A qualitative study.

INTRODUCTION: Overweight and obesity are common problems among teenagers regardless of ethnicity, race, and socio-economic status. Therefore, this study aims to explore the social and environmental factors impacting adolescents motivation for weight control in Gilan province, Iran. METHODOLOGY: Following a qualitative design, a content analysis approach was used to analyze the data. A total of 79 interviews were conducted with Adolescents (n = 23), Friends and Peers (n = 15), Parents (n = 12), Managers (n = 16), and Health care providers (n = 13), regarding adolescents obesity during 2019. MAXQDA V.10 software was used for our analysis. FINDINGS: The main categories of environmental and social factors affecting adolescents motivation for weight control were external factors (the relative success of weight control intervention programs, the lack of environmental and social support, and the lack of family support for teenagers) that each one had some subcategories, and internal factors (competence, relatedness, and autonomy). CONCLUSION: This study demonstrated the necessity of identifying environmental and social factors that are effective in reducing adolescents' motivation for weight loss. These factors are so influential that teenagers can't overcome them without receiving support from their environment and the government health-related policies. So, it seems that we need integrated multisectoral approaches and we suggest that health policymakers develop practical policies to control adolescents obesity by focusing on factors that have been mentioned in this study.

Quantitative Structure-Property Relationship Study for Prediction of Boiling Point and Enthalpy of Vaporization of Alkenes.

INTRODUCTION: Quantitative structure-property relationships (QSPRs) models have been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been used on 91 alkenes to develop a robust model for the prediction of enthalpy of vaporization under standard condition (ΔH°vap/kJ.mol-1) and at normal temperature of boiling points (T˚bp /K) of alkenes. METHODS: A training set of 81 structurally diverse alkenes was randomly selected and used to construct QSPR models. These models were optimized using backward-multiple linear regression (MLR) analysis. The genetic algorithm and multiple linear regression analysis (GA-MLR) were used to select the suitable descriptors derived from the Dragon software. RESULTS: The multicollinearity properties of the descriptors contributed in the QSPR models were tested and several methods were used for testing the predictive models power such as Leave-One-Out (LOO) cross-validation(Q2 LOO), the five-fold cross-validation techniques, external validation parameters (Q2F1, Q2F2, Q2F3), the concordance correlation coefficient (CCC) and the predictive parameter R2 m. CONCLUSION: The predictive ability of the models was found to be satisfactory, and the five descriptors in three blocks, namely connectivity, edge adjacency indices and 2D matrix-based descriptors could be used to predict the mentioned properties of alkenes.

A label-free electrochemical aptasensor for breast cancer cell detection based on a reduced graphene oxide-chitosan-gold nanoparticle composite.

Regarding the cancer fatal consequences, early detection and progression monitoring are the most vital issues in patients' treatment and mortality reduction. Therefore, there is a great demand for fast, inexpensive, and selective detection methods. Herein, a graphene-based aptasensor was designed for sensitive human breast cancer cell detection. A reduced graphene oxide-chitosan-gold nanoparticles composite was used as a biocompatible substrate for the receptor stabilization. The significant function of the aptamer on this composite is due to the synergistic effects of the components in improving the properties of the composite, including increasing the electrical conductivity and effective surface area. After the aptasensor incubation in MCF-7 cancer cells, the cell membrane proteins interacted specifically with the three dimensional-structure of the AS1411 aptamer, resulting in the cell capture on the aptasensor. The aptasensor fabrication steps were investigated by cyclic voltammetry and electrochemical impedance spectroscopy. The higher cell concentrations concluded to the higher captured cells on the aptasensor which blocked the Ferro/Ferricyanide access to the sensor, causing increases in the charge transfer resistances. This aptasensor shows a linear relationship with the cell concentration logarithm, high selectivity, a wide linear range of 1 × 101-1 × 106 cells/mL, and a low detection limit of 4 cells/mL.

QSPR Models for the Prediction of Some Thermodynamic Properties of Cycloalkanes Using GA-MLR Method.

AIM AND OBJECTIVE: Cycloalkanes have been largely used in the field of medicine, components of food, pharmaceutical drugs, and they are mainly used to produce fuel. In present study the relationship between molecular descriptors and thermodynamic properties such as the standard enthalpies of formation (∆H°f), the standard enthalpies of fusion (∆H°fus), and the standard Gibbs free energy of formation (∆G°f)of the cycloalkanes is represented. MATERIALS AND METHODS: The Genetic Algorithm (GA) and multiple linear regressions (MLR) were successfully used to predict the thermodynamic properties of cycloalkanes. A large number of molecular descriptors were obtained with the Dragon program. The Genetic algorithm and backward method were used to reduce and select suitable descriptors. RESULTS: QSPR models were used to delineate the important descriptors responsible for the properties of the studied cycloalkanes. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF), Pearson Correlation Coefficient (PCC) and the Durbin-Watson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The statistical parameters of the training, and test sets for GA-MLR models were calculated. CONCLUSION: The results of the present study indicate that the predictive ability of the models was satisfactory and molecular descriptors such as: the Functional group counts, Topological indices, GETAWAY descriptors, Constitutional indices, and molecular properties provide a promising route for developing highly correlated QSPR models for prediction the studied properties.

QSPR Models to Predict Thermodynamic Properties of Cycloalkanes Using Molecular Descriptors and GA-MLR Method.

AIMS AND OBJECTIVES: QSPR models establish relationships between different types of structural information to their observed properties. In the present study the relationship between the molecular descriptors and quantum properties of cycloalkanes is represented. MATERIALS AND METHODS: Genetic Algorithm (GA) and Multiple Linear Regressions (MLR) were successfully developed to predict quantum properties of cycloalkanes. A large number of molecular descriptors were calculated with Dragon software and a subset of calculated descriptors was selected with a genetic algorithm as a feature selection technique. The quantum properties consist of the heat capacity (Cv)/ Jmol-1K-1 entropy(S)/ Jmol-1K-1 and thermal energy(Eth)/ kJmol-1 were obtained from quantum-chemistry technique at the Hartree-Fock (HF) level using the ab initio 6-31G* basis sets. RESULTS: The Genetic Algorithm (GA) method was used to select important molecular descriptors and then they were used as inputs for SPSS software package. The predictive powers of the MLR models were discussed using Leave-One-Out (LOO) cross-validation, leave-group (5-fold)-out (LGO) and external prediction series. The statistical parameters of the training and test sets for GA-MLR models were calculated. CONCLUSION: The resulting quantitative GA-MLR models of Cv, S, and Eth were obtained:[r2=0.950, Q2=0.989, r2 ext=0.969, MAE(overall,5-flod)=0.6825 Jmol-1K-1], [r2=0.980, Q2=0.947, r2 ext=0.943, MAE(overall,5-flod)=0.5891Jmol-1K-1], and [r2=0.980, Q2=0.809, r2 ext=0.985, MAE(overall,5-flod)=2.0284 kJmol-1]. The results showed that the predictive ability of the models was satisfactory, and the constitutional, topological indices and ring descriptor could be used to predict the mentioned properties of 103 cycloalkanes.

Structural Relationship Study of Octanol-Water Partition Coefficient of Some Sulfa Drugs Using GA-MLR and GA-ANN Methods.

AIM AND OBJECTIVE: Sulfonamides (sulfa drugs) are compounds with a wide range of biological activities and they are the basis of several groups of drugs. Quantitative Structure-Property Relationship (QSPR) models are derived to predict the logarithm of water/ 1-octanol partition coefficients (logP) of sulfa drugs. MATERIALS AND METHODS: A data set of 43 sulfa drugs was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GAANN) were employed to design the QSPR models. The possible molecular geometries of sulfa drugs were optimized at B3LYP/6-31G* level with Gaussian 98 software. The molecular descriptors derived from the Dragon software were used to build a predictive model for prediction logP of mentioned compounds. The Genetic Algorithm (GA) method was applied to select the most relevant molecular descriptors. RESULTS: The R2 and MSE values of the MLR model were calculated to be 0.312 and 5.074 respectively. R2 coefficients were 0.9869, 0.9944 and 0.9601for the training, test and validation sets of the ANN model, respectively. CONCLUSION: Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method.

Paper-based fluorogenic RNA aptamer sensors for label-free detection of small molecules.

Sensors based on fluorogenic RNA aptamers have emerged in recent years. These sensors have been used for in vitro and intracellular detection of a broad range of biological and medical targets. However, the potential application of fluorogenic RNA-based sensors for point-of-care testing is still little studied. Here, we report a paper substrate-based portable fluorogenic RNA sensor system. Target detection can be simply performed by rehydration of RNA sensor-embedded filter papers. This affordable sensor system can be used for the selective, sensitive, and rapid detection of different target analytes, such as antibiotics and cellular signaling molecules. We believe that these paper-based fluorogenic RNA sensors show great potential for point-of-care testing of a wide range of targets from small molecules, nucleic acids, proteins, to various pathogens.

QSAR Models for Predicting Aquatic Toxicity of Esters Using Genetic Algorithm-Multiple Linear Regression Methods.

AIM AND OBJECTIVE: Esters are of great importance in industry, medicine, and space studies. Therefore, studying the toxicity of esters is very important. In this research, a Quantitative Structure-Activity Relationship (QSAR) model was proposed for the prediction of aquatic toxicity (log 1/IGC50) of aliphatic esters towards Tetrahymena pyriformis using molecular descriptors. MATERIALS AND METHODS: A data set of 48 aliphatic esters was separated into a training set of 34 compounds and a test set of 14 compounds. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods were used to select the suitable descriptors and to generate the correlation models that relate the chemical structural features to the biological activities. RESULTS: The predictive powers of the MLR models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The best QSAR model is obtained with R2 value of 0.899, Q2 LOO =0.928, F=137.73, RMSE=0.263. CONCLUSION: The predictive ability of the GA-MLR model with two selected molecular descriptors is satisfactory and it can be used for designing similar group and predicting of toxicity (log 1/IGC50) of ester derivatives.

Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors.

AIM AND OBJECTIVE: A Quantitative Structure-Activity Relationship (QSAR) has been widely developed to derive a correlation between chemical structures of molecules to their known activities. In the present investigation, QSAR models have been carried out on 76 Camptothecin (CPT) derivatives as anticancer drugs to develop a robust model for the prediction of physicochemical properties. MATERIALS AND METHODS: A training set of 60 structurally diverse CPT derivatives was used to construct QSAR models for the prediction of physiochemical parameters such as Van der Waals surface area (SvdW), Van der Waals Volume (VvdW), Molar Refractivity (MR) and Polarizability (α). The QSAR models were optimized using Multiple Linear Regression (MLR) analysis. A test set of 16 compounds was evaluated using the defined models. The Genetic Algorithm And Multiple Linear Regression Analysis (GA-MLR) were used to select the descriptors derived from the Dragon software to generate the correlation models that relate the structural features to the studied properties. RESULTS: QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF) and the Durbin-Watson (DW) statistics. CONCLUSION: The predictive ability of the models was found to be satisfactory. Thus, QSAR models derived from this study may be helpful for modeling and designing some new CPT derivatives and for predicting their activity.

A quantitative assessment of the dynamic modification of lipid-DNA probes on live cell membranes.

Synthetic lipid-DNA probes have recently attracted much attention for cell membrane analysis, transmembrane signal transduction, and regulating intercellular networks. These lipid-DNA probes can spontaneously insert onto plasma membranes simply after incubation. The highly precise and controllable DNA interactions have further allowed the programmable manipulation of these membrane-anchored functional probes. However, we still have quite limited understanding of how these lipid-DNA probes interact with cell membranes and also what parameters determine this process. In this study, we have systematically studied the dynamic process of cell membrane modification with a group of lipid-DNA probes. Our results indicated that the hydrophobicity of the lipid-DNA probes is strongly correlated with their membrane insertion and departure rates. Most cell membrane insertion stems from the monomeric form of probes, rather than the aggregates. Lipid-DNA probes can be removed from cell membranes through either endocytosis or direct outflow into the solution. As a result, long-term probe modifications on cell membranes can be realized in the presence of excess probes in the solution and/or endocytosis inhibitors. For the first time, we have successfully improved the membrane persistence of lipid-DNA probes to more than 24 h. Our quantitative data have dramatically improved our understanding of how lipid-DNA probes dynamically interact with cell membranes. These results can be further used to allow a broad range of applications of lipid-DNA probes for cell membrane analysis and regulation.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives.

AIM AND OBJECTIVE: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. MATERIALS AND METHODS: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree-Fock level of theory and 3-21G basis sets by Gaussian 09. RESULTS: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. CONCLUSION: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.