Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.

Surface-enhanced Raman Spectroscopy in urinalysis of hypertension patients with kidney disease.

Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.

Antioxidants in Photoaging: From Molecular Insights to Clinical Applications.

Photoaging (PA) is considered a silent disease affecting millions of people globally and is defined as skin damage due to prolonged exposure to ultraviolet radiation (UVR) from the sun. Physiologically, the skin is in a state of renewal and synthesis of components of the extracellular matrix (ECM). However, exposure to UVR affects the production of the ECM, and the functioning and response of skin cells to UVR begins to change, thus expressing clinical and phenotypic characteristics of PA. The primary mechanisms involved in PA are direct damage to the DNA of skin cells, increases in oxidative stress, the activation of cell signaling pathways responsible for the loss of skin integrity, and cytotoxicity. The medical and scientific community has been researching new therapeutic tools that counteract PA, considering that the damage caused by UVR exceeds the antioxidant defense mechanisms of the skin. Thus, in recent years, certain nutraceuticals and phytochemicals have been found to exhibit potential antioxidant and photoprotective effects. Therefore, the main objective of this review is to elucidate the molecular bases of PA and the latest pharmaceutical industry findings on antioxidant treatment against the progression of PA.

Photodynamic Antimicrobial Activity of a Novel 5,10,15,20-Tetrakis (4-Ethylphenyl) Porphyrin against Clinically Important Bacteria.

The growing emergence of microbes resistant to commercially available antibiotic therapies poses a threat to healthcare systems worldwide. Multiple factors have been associated with the increasing incidence of hospital-acquired infections caused by antibiotic-resistant pathogens, including the indiscriminate use of broad-spectrum antibiotics, the massive application of antibiotics in hospitals as a prophylactic measure, self-medication, and nonadherence to pharmacological therapies by patients. In this study, we developed a novel treatment to mitigate the impact of microbial resistance. We synthesized a benzoporphyrin derivative, 5,10,15,20-tetrakis (4-ethylphenyl) porphyrin (TEtPP), with a reaction yield close to 50%. TEtPP exhibited excellent photophysical properties (Φf = 0.12 ± 0.04 and ΦΔ = 0.81 ± 0.23) and was thereby assessed as a potential agent for antibacterial photodynamic therapy. The photophysical properties of the synthesized porphyrin derivative were correlated with the assayed antimicrobial activity. TEtPP showed higher activity against the MRSA strain under irradiation than in the absence of irradiation (minimum inhibitory concentration (MIC) = 69.42 µg/mL vs. MIC = 109.30 µg/mL, p < 0.0001). Similar behavior was observed against P. aeruginosa (irradiated MIC = 54.71 µg/mL vs. nonirradiated MIC = 402.90 µg/mL, p < 0.0001). TEtPP exhibited high activity against S. aureus in both the irradiated and nonirradiated assays (MIC = 67.68 µg/mL vs. MIC = 58.26 µg/mL, p = 0.87).

The dangerous link between coal dust exposure and DNA damage: unraveling the role of some of the chemical agents and oxidative stress.

Exposure to coal mining dust poses a substantial health hazard to individuals due to the complex mixture of components released during the extraction process. This study aimed to assess the oxidative potential of residual coal mining dust on human lymphocyte DNA and telomeres and to perform a chemical characterization of coal dust and urine samples. The study included 150 individuals exposed to coal dust for over ten years, along with 120 control individuals. The results revealed significantly higher levels of DNA damage in the exposed group, as indicated by the standard comet assay, and oxidative damage, as determined by the FPG-modified comet assay. Moreover, the exposed individuals exhibited significantly shorter telomeres compared to the control group, and a significant correlation was found between telomere length and oxidative DNA damage. Using the PIXE method on urine samples, significantly higher concentrations of sodium (Na), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K), iron (Fe), zinc (Zn), and bromine (Br) were observed in the exposed group compared to the control group. Furthermore, men showed shorter telomeres, greater DNA damage, and higher concentrations of nickel (Ni), calcium (Ca), and chromium (Cr) compared to exposed women. Additionally, the study characterized the particles released into the environment through GC-MS analysis, identifying several compounds, including polycyclic aromatic hydrocarbons (PAHs) such as fluoranthene, naphthalene, anthracene, 7H-benzo[c]fluorene, phenanthrene, pyrene, benz[a]anthracene, chrysene, and some alkyl derivatives. These findings underscore the significant health risks associated with exposure to coal mining dust, emphasizing the importance of further research and the implementation of regulatory measures to safeguard the health of individuals in affected populations.

From Cell to Symptoms: The Role of SARS-CoV-2 Cytopathic Effects in the Pathogenesis of COVID-19 and Long COVID.

Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection triggers various events from molecular to tissue level, which in turn is given by the intrinsic characteristics of each patient. Given the molecular diversity characteristic of each cellular phenotype, the possible cytopathic, tissue and clinical effects are difficult to predict, which determines the heterogeneity of COVID-19 symptoms. The purpose of this article is to provide a comprehensive review of the cytopathic effects of SARS-CoV-2 on various cell types, focusing on the development of COVID-19, which in turn may lead, in some patients, to a persistence of symptoms after recovery from the disease, a condition known as long COVID. We describe the molecular mechanisms underlying virus-host interactions, including alterations in protein expression, intracellular signaling pathways, and immune responses. In particular, the article highlights the potential impact of these cytopathies on cellular function and clinical outcomes, such as immune dysregulation, neuropsychiatric disorders, and organ damage. The article concludes by discussing future directions for research and implications for the management and treatment of COVID-19 and long COVID.

Analysis of the cytotoxic and genotoxic effects in a population chronically exposed to coal mining residues.

During coal mining activities, many compounds are released into the environment that can negatively impact human health. Particulate matter, polycyclic aromatic hydrocarbons (PAHs), metals, and oxides are part of the complex mixture that can affect nearby populations. Therefore, we designed this study to evaluate the potential cytotoxic and genotoxic effects in individuals chronically exposed to coal residues from peripheral blood lymphocytes and buccal cells. We recruited 150 individuals who lived more than 20 years in La Loma-Colombia and 120 control individuals from the city of Barranquilla without a history of exposure to coal mining. In the cytokinesis-block micronucleus cytome (CBMN-Cyt) assay, significant differences in the frequency of micronucleus (MN), nucleoplasmic bridge (NPB), nuclear bud (NBUD), and apoptotic cells (APOP) were observed between the two groups. In the buccal micronucleus cytome (BM-Cyt) assay, a significant formation of NBUD, karyorrhexis (KRX), karyolysis (KRL), condensed chromatin (CC), and binucleated (BN) cells was observed in the exposed group. Considering the characteristics of the study group, a significant correlation for CBMN-Cyt was found between NBUD and vitamin consumption, between MN or APOP and meat consumption, and between MN and age. Moreover, a significant correlation for BM-Cyt was found between KRL and vitamin consumption or age, and BN versus alcohol consumption. Using Raman spectroscopy, a significant increase in the concentration of DNA/RNA bases, creatinine, polysaccharides, and fatty acids was detected in the urine of individuals exposed to coal mining compared to the control group. These results contribute to the discussion on the effects of coal mining on nearby populations and the development of diseases due to chronic exposure to these residues.

Comparative Analysis of In-House RT-qPCR Detection of SARS-CoV-2 for Resource-Constrained Settings.

We developed and standardized an efficient and cost-effective in-house RT-PCR method to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated sensitivity, specificity, and other statistical parameters by different RT-qPCR methods including triplex, duplex, and simplex assays adapted from the initial World Health Organization- (WHO) recommended protocol. This protocol included the identification of the E envelope gene (E gene; specific to the Sarvecovirus genus), RdRp gene of the RNA-dependent RNA polymerase (specific for SARS-CoV-2), and RNase P gene as endogenous control. The detection limit of the E and the RdRp genes were 3.8 copies and 33.8 copies per 1 µL of RNA, respectively, in both triplex and duplex reactions. The sensitivity for the RdRp gene in the triplex and duplex RT-qPCR tests were 98.3% and 83.1%, respectively. We showed a decrease in sensitivity for the RdRp gene by 60% when the E gene acquired Ct values > 31 in the diagnostic tests. This is associated with the specific detection limit of each gene and possible interferences in the protocol. Hence, developing efficient and cost-effective methodologies that can be adapted to various health emergency scenarios is important, especially in developing countries or settings where resources are limited.

Docking and Molecular Dynamic of Microalgae Compounds as Potential Inhibitors of Beta-Lactamase.

Bacterial resistance is responsible for a wide variety of health problems, both in children and adults. The persistence of symptoms and infections are mainly treated with ß-lactam antibiotics. The increasing resistance to those antibiotics by bacterial pathogens generated the emergence of extended-spectrum ß-lactamases (ESBLs), an actual public health problem. This is due to rapid mutations of bacteria when exposed to antibiotics. In this case, ß-lactamases are enzymes used by bacteria to hydrolyze the beta-lactam rings present in the antibiotics. Therefore, it was necessary to explore novel molecules as potential ß-lactamases inhibitors to find antibacterial compounds against infection caused by ESBLs. A computational methodology based on molecular docking and molecular dynamic simulations was used to find new microalgae metabolites inhibitors of ß-lactamase. Six 3D ß-lactamase proteins were selected, and the molecular docking revealed that the metabolites belonging to the same structural families, such as phenylacridine (4-Ph), quercetin (Qn), and cryptophycin (Cryp), exhibit a better binding score and binding energy than commercial clinical medicine ß-lactamase inhibitors, such as clavulanic acid, sulbactam, and tazobactam. These results indicate that 4-Ph, Qn, and Cryp molecules, homologous from microalgae metabolites, could be used, likely as novel ß-lactamase inhibitors or as structural templates for new in-silico pharmaceutical designs, with the possibility of combatting ß-lactam resistance.

API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis.

The process analytical technology (PAT) initiative proposed by the US Food and Drug Administration (FDA) suggests innovative methods to better understand pharmaceutical processes. The development of analytical methods that quantify active pharmaceutical ingredients (APIs) in powders and tablets is fundamental to monitoring and controlling a drug product's quality. Analytical methods based on vibrational spectroscopy do not require sample preparation and can be implemented during in-line manufacturing to maintain quality at each stage of operations. In this study, a mid-infrared (MIR) quantum cascade laser (QCL) spectroscopy-based protocol was performed to quantify ibuprofen in formulations of powder blends and tablets. Fourteen blends were prepared with varying concentrations from 0.0% to 21.0% (w/w) API. MIR laser spectra were collected in the spectral range of 990 to 1600 cm-1. Partial least squares (PLS) models were developed to correlate the intensities of vibrational signals with API concentrations in powder blends and tablets. PLS models were evaluated based on the following figures of merit: correlation coefficient (R2), root mean square error of calibration, root mean square error of prediction, root mean square error of cross-validation, and relative standard error of prediction. QCL assisted by multivariate analysis was demonstrated to be accurate and robust for analysis of the content and blend uniformity of pharmaceutical compounds.

Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data.

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

Surface Persistence of Trace Level Deposits of Highly Energetic Materials.

In the fields of Security and Defense, explosive traces must be analyzed at the sites of the terrorist events. The persistence on surfaces of these traces depends on the sublimation processes and the interactions with the surfaces. This study presents evidence that the sublimation process of these traces on stainless steel (SS) surfaces is very different than in bulk quantities. The enthalpies of sublimation of traces of four highly energetic materials: triacetone triperoxide (TATP), 2,4-dinitrotoluene (DNT), 2,4,6-trinitrotoluene (TNT), and 1,3,5- trinitrohexahydro-s-triazine (RDX) deposited on SS substrates were determined by optical fiber coupled-grazing angle probe Fourier Transform Infrared (FTIR) Spectroscopy. These were compared with enthalpies of sublimation determined by thermal gravimetric analysis for bulk amounts and differences between them were found. The sublimation enthalpy of RDX was very different for traces than for bulk quantities, attributed to two main factors. First, the beta-RDX phase was present at trace levels, unlike the case of bulk amounts which consisted only of the alpha-RDX phase. Second, an interaction between the RDX and SS was found. This interaction energy was determined using grazing angle FTIR microscopy. In the case of DNT and TNT, bulk and traces enthalpies were statistically similar, but it is evidenced that at the level of traces a metastable phase was observed. Finally, for TATP the enthalpies were statistically identical, but a non-linear behavior and a change of heat capacity values different from zero was found for both trace and bulk phases.

Classical Least Squares-Assisted Mid-Infrared (MIR) Laser Spectroscopy Detection of High Explosives on Fabrics.

Mid-infrared (MIR) laser spectroscopy was used to detect the presence of residues of high explosives (HEs) on fabrics. The discrimination of the vibrational signals of HEs from a highly MIR-absorbing substrate was achieved by a simple and fast spectral evaluation without preparation of standards using the classical least squares (CLS) algorithm. Classical least squares focuses on minimizing the differences between the spectral features of the actual spectra acquired using MIR spectroscopy and the spectral features of calculated spectra modeled from linear combinations of the spectra of neat components: HEs, fabrics, and bias. Samples in several combinations of cotton fabrics/HEs were used to validate the methodology. Several experiments were performed focusing on binary, ternary, and quaternary mixtures of TNT, RDX, PETN, and fabrics. The parameters obtained from linear combinations of the calculated spectra were used to perform discrimination analyses and to determine the sensitivity and selectivity of HEs with respect to the substrates and to each other. However, discrimination analysis was not necessary to achieve successful detection of HEs on cotton fabric substrates. The RDX signals ( mRDX > 0.02 mg) on cotton were used to calculate the limit of detection (LOD). The signal-to-noise ratios (S/N) calculated from the spectra of cotton dosed with decreasing masses of RDX until S/N ≈ 3 resulted in a LOD of 15-33 µg, depending on the vibrational band used. Linear fits generated by comparing the mass dosed RDX with the fraction predicted were also used to calculate the LOD based on the uncertainty of the blank and the slope. This procedure resulted in a LOD of 58 µg. Probably the most representative value of the method LOD was calculated using an interpolation of a threshold determined using the predicted average value for the blank plus 3.28 times the standard deviations ( p-value threshold) for low surface dosages of RDX (LOD = 40 µg). The contribution demonstrates that to achieve HE detection on fabrics using the proposed algorithm, i.e., determining the presence/absence of HEs on the substrates, the library must contain the spectra of HEs, substrates, and potential interferents or that these spectra be added to the models in the field. If the model does not contain the spectra of the fabric components, there is a high probability of finding false positives for clean samples (no HEs) and a low probability for failed detection in samples with HEs. More work will be required to demonstrate that these new approaches to HE detection work on real-world samples and when contaminating materials are present in the samples.

Optical Properties of ß-RDX Thin Films Deposited on Gold and Stainless Steel Substrates Calculated from Reflection-Absorption Infrared Spectra.

The optical properties for crystalline films of the highly energetic material (HEM) hexahydro-1,3,5-trinitro-s-triazine, which is also known as RDX, deposited on gold (Au) and stainless steel (SS) substrates are presented. RDX has two important stable conformational polymorphs at room temperature: α-RDX and ß-RDX. The optical properties obtained in the present work correspond to thin film samples of predominantly ß-RDX polymorph. The infrared spectroscopic intensities measured showed significant differences in the ß-RDX crystalline films deposited on the two substrates with respect to the calculated real part of refractive index. The ß-RDX/Au crystalline films have a high dynamic response, which is characterized by the asymmetric stretching mode of the axial nitro groups, whereas for the ß-RDX/SS crystalline films, the dynamic response was mediated by the -N-NO2 symmetric stretch mode. This result provides an idea of how the electric field vector propagates through the ß-RDX crystalline films deposited on the two substrates.

Applications of Quantum Cascade Laser Spectroscopy in the Analysis of Pharmaceutical Formulations.

Quantum cascade laser spectroscopy was used to quantify active pharmaceutical ingredient content in a model formulation. The analyses were conducted in non-contact mode by mid-infrared diffuse reflectance. Measurements were carried out at a distance of 15 cm, covering the spectral range 1000-1600 cm(-1) Calibrations were generated by applying multivariate analysis using partial least squares models. Among the figures of merit of the proposed methodology are the high analytical sensitivity equivalent to 0.05% active pharmaceutical ingredient in the formulation, high repeatability (2.7%), high reproducibility (5.4%), and low limit of detection (1%). The relatively high power of the quantum-cascade-laser-based spectroscopic system resulted in the design of detection and quantification methodologies for pharmaceutical applications with high accuracy and precision that are comparable to those of methodologies based on near-infrared spectroscopy, attenuated total reflection mid-infrared Fourier transform infrared spectroscopy, and Raman spectroscopy.

Standoff detection of highly energetic materials using laser-induced thermal excitation of infrared emission.

A laser-mediated methodology for standoff infrared detection of threat chemicals is described in this article. Laser-induced thermal emissions (LITE) from vibrationally excited residue of highly energetic material (HEM) deposited on substrates were detected remotely. Telescope-based Fourier transform infrared (FT-IR) spectroscopy measurements were carried out on substrates containing small amounts of HEM at surface concentrations of 5-200 µg/cm(2). Target substrates of various thicknesses were heated remotely using a carbon dioxide laser, and their mid-infrared (mid-IR), thermally stimulated emission spectra were recorded after heating. The telescope was configured from reflective optical elements to minimize emission losses in the mid-IR frequencies. Spectral replicas were acquired at distances from 4 to 64 m using an FT-IR interferometer at 4 cm(-1) resolution. The laser power, laser exposure times, and acquisition time of the FT-IR interferometer were adjusted to improve the detection and identification of samples. The advantages of increasing the thermal emission were easily observed in the results. The signal intensities were proportional to the thickness of the coated surface (a function of the surface concentration) as well as the laser power and laser exposure time. The limits of detection obtained for the HEM studied were 140-21 µg/cm(2) at 4 m. Detection was achieved at 64 m for a surface concentration of 200 µg/cm(2).

Mechanism for the uncatalyzed cyclic acetone-peroxide formation reaction: an experimental and computational study.

In this study, a mechanism for the uncatalyzed reaction between acetone and hydrogen peroxide is postulated. The reaction leads to the formation of the important homemade explosives collectively known as cyclic acetone peroxides (CAP). The proposed mechanistic scheme is based on Raman, GC-MS, and nuclear magnetic resonance measurements, and it is supported by ab initio density functional theory (DFT) calculations. The results demonstrate that the proposed mechanism for the uncatalyzed formation reaction of CAP occurs in three steps: monomer formation, polymerization of the 2-hydroperoxipropan-2-ol monomer, and cyclization. The temporal decay of the intensities of important assigned-bands is in excellent agreement with the proposed mechanism. Previous reports also confirm that the polymerization step is favored in comparison to other possible pathways.

FT-IR standoff detection of thermally excited emissions of trinitrotoluene (TNT) deposited on aluminum substrates.

A standoff detection system was assembled by coupling a reflecting telescope to a Fourier transform infrared spectrometer equipped with a cryo-cooled mercury cadmium telluride detector and used for detection of solid-phase samples deposited on substrates. Samples of highly energetic materials were deposited on aluminum substrates and detected at several collector-target distances by performing passive-mode, remote, infrared detection measurements on the heated analytes. Aluminum plates were used as support material, and 2,4,6-Trinitrotoluene (TNT) was used as the target. For standoff detection experiments, the samples were placed at different distances (4 to 55 m). Several target surface temperatures were investigated. Partial least squares regression analysis was applied to the analysis of the intensities of the spectra obtained. Overall, standoff detection in passive mode was useful for quantifying TNT deposited on the aluminum plates with high confidence up to target-collector distances of 55 m.

TNT removal from culture media by three commonly available wild plants growing in the Caribbean.

Plants growing in the Caribbean, Rubia tinctorum, Lippia dulcis and Spermacoce remota, were used in vitro to remove TNT from culture media. Plants were found to be resistant to high TNT levels. S. remota was able to remove TNT in less than 48 h. Part of the TNT was physically removed from the culture media by evaporation.