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Blue-emitting bismuth-doped lanthanum oxide (La2O3: Bi3+) with various concentrations of Bi was synthesized using the sol-gel combustion method and used for visualization of latent fingerprints (LFPs). An X-ray diffraction (XRD) study revealed the hexagonal structure of the phosphors and total incorporation of the bismuth in the La2O3 matrix. Field Emission Scanning Electron Microscopy (FE-SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were used to study the morphology and the relative vibrations of the synthesized samples. Photoluminescence (PL) studies showed strong blue emission around 460 nm due to the 3P1 → 1S0 transition. Clear bright-blue fingerprint images were obtained with the powder dusting method on various surfaces like aluminum, compact discs, glass, wood and marble. A first evaluation of these images indicated a clear visualization of all three levels of details and a very high contrast ranging from 0.41 on marble to 0.90 on aluminum. As a further step, we used an algorithm for extracting fingerprint minutiae with which we succeeded in detecting all three levels of fingerprint details and even the most difficult ones, like open and closed pores. According to these analyses, La2O3: Bi phosphor is demonstrated to be an effective blue fluorescent powder for excellent visualization of latent fingerprints.

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This study investigates the applicability of elemental and Cu isotope compositions in sediments and bivalves from the Korean coast to monitor anthropogenic Cu contamination. Sediments with high Cu (>64.4 mg/kg) and/or moderate enrichment levels (EFCu) exhibit homogenous δ65CuAE647 values (-0.12 to +0.16 ‰), suggesting similar anthropogenic Cu fingerprints along the Korean coast. Sediments with Cu concentrations near natural background levels (< 20.6 mg/kg) display large isotopic variability (Δ65Cumax-mim: ~0.8 ‰), encompassing those from sediments under anthropic influences. We hypothesize that Cu isotopic compositions of Korean geology are heterogeneous, therefore, natural end-members of source mixing models should be established locally at small scales. Cu concentrations in Oysters correlate with sediments, and their isotopic compositions are more suitable for monitoring Cu contamination, while mussel's regulatory mechanisms seem to affect source records. The current Cu isotope data will help to detect shifts attributable to anthropic contamination in future biomonitoring.

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Background: Chemicals may lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost. Methods: In current studies, QSAR models were developed for hepatotoxicity predictions using the ensemble strategy to integrate machine learning (ML) and deep learning (DL) algorithms using various molecular features. A large dataset of 2588 chemicals and drugs was randomly divided into training (80%) and test (20%) sets, followed by the training of individual base models using diverse machine learning or deep learning based on three different kinds of descriptors and fingerprints. Feature selection approaches were employed to proceed with model optimizations based on the model performance. Hybrid ensemble approaches were further utilized to determine the method with the best performance. Results: The voting ensemble classifier emerged as the optimal model, achieving an excellent prediction accuracy of 80.26%, AUC of 82.84%, and recall of over 93% followed by bagging and stacking ensemble classifiers method. The model was further verified by an external test set, internal 10-fold cross-validation, and rigorous benchmark training, exhibiting much better reliability than the published models. Conclusion: The proposed ensemble model offers a dependable assessment with a good performance for the prediction regarding the risk of chemicals and drugs to induce liver damage.

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INTRODUCTION: Schisandrae Chinensis Fructus (SCF), a traditional Chinese medicine, has been used in treating virtual injury and strain since ancient times. The Chinese Pharmacopoeia reveals that SCF includes raw (RSCF) and vinegar-processed (VSCF) decoction pieces. OBJECTIVE: This study developed an effective method combining the electronic eye (e-eye), electronic tongue (e-tongue), and chemometrics to discriminate RSCF and VSCF from the perspective of chemical composition, color, and taste. MATERIAL AND METHODS: First, RSCF were collected and processed into VSCF, and their color parameters, e-tongue sensory properties, high-performance liquid chromatography (HPLC) and ultra-HPLC (UPLC) characteristic fingerprints, and nominal ingredients were determined. Multivariate statistical analyses, including principal component, linear discriminant, similarity, and partial least squares discriminant analyses, were conducted. RESULTS: HPLC and UPLC fingerprints were established, demonstrating a > 0.900 similarity. The content determination indicated increased schisantherin A, schisantherin B, and schisandrin A contents in VSCF. The e-eye data demonstrated a > 1.5 total color difference before and after processing ΔE*ab, indicating the significantly changed sample color and appearance before and after processing. The e-tongue technology was used to quantitatively characterize the taste of RSCF and VSCF. The t-test revealed significantly reduced sourness, aftertaste-bitter, and aftertaste-astringent values of SCF after vinegar processing. Principal component and partial least squares discriminant analyses indicated that e-eye and e-tongue realize the rapid RSCF and VSCF identification. CONCLUSION: The proposed comprehensive strategy of electronic eye and electronic tongue combined with chemometrics demonstrated satisfactory results with high efficiency, accuracy, and reliability. This can be developed into a novel and accurate method for discriminating RSCF and VSCF.

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Tuberculosis (TB) stands as the second most fatal infectious disease after COVID-19, the effective treatment of which depends on accurate diagnosis and phenotyping. Metabolomics provides valuable insights into the identification of differential metabolites for disease diagnosis and phenotyping. However, TB diagnosis and phenotyping remain great challenges due to the lack of a satisfactory metabolic approach. Here, a metabolomics-based diagnostic method for rapid TB detection is reported. Serum metabolic fingerprints are examined via an automated nanoparticle-enhanced laser desorption/ionization mass spectrometry platform outstanding by its rapid detection speed (measured in seconds), minimal sample consumption (in nanoliters), and cost-effectiveness (approximately $3). A panel of 14 m z-1 features is identified as biomarkers for TB diagnosis and a panel of 4 m z-1 features for TB phenotyping. Based on the acquired biomarkers, TB metabolic models are constructed through advanced machine learning algorithms. The robust metabolic model yields a 97.8% (95% confidence interval (CI), 0.964-0.986) area under the curve (AUC) in TB diagnosis and an 85.7% (95% CI, 0.806-0.891) AUC in phenotyping. In this study, serum metabolic biomarker panels are revealed and develop an accurate metabolic tool with desirable diagnostic performance for TB diagnosis and phenotyping, which may expedite the effective implementation of the end-TB strategy.

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Forensic chemistry literature has grown exponentially, with many analytical techniques being used to provide valuable information to help solve criminal cases. Among them, matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS), particularly MALDI MS imaging (MALDI MSI), has shown much potential in forensic applications. Due to its high specificity, MALDI MSI can analyze a wide variety of compounds in complex samples without extensive sample preparation, providing chemical profiles and spatial distributions of given analyte(s). This review introduces MALDI MS(I) to forensic scientists with a focus on its basic principles and the applications of MALDI MS(I) to the analysis of fingerprints, drugs of abuse, and their metabolites in hair, medicine samples, animal tissues, and inks in documents.

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The stability and catalytic activity of acidic oxygen evolution reaction (OER) are strongly determined by the coordination states and spatial symmetry among metal sites at catalysts. Herein, an ultrafast oxygen evaporation technology to rapidly soften the intrinsic covalent bonds using ultrahigh electrical pulses is suggested, in which prospective charged excited states at this extreme avalanche condition can generate a strong electron-phonon coupling to rapidly evaporate some coordinated oxygen (O) atoms, finally leading to a controllable half-metallization feature. Simultaneously, the relative metal (M) site arrays can be orderly locked to delineate some intriguing atom-fingerprints at pyrochlore catalysts, where the coexistence of metallic bonds (M─M) and covalent bonds (M─O) at this symmetry-breaking configuration can partially restrain crystal field effect to generate a particular high-spin occupied state. This half-metallization catalyst can effectively optimize the spin-related reaction kinetics in acidic OER, giving rise to 10.3 times (at 188 mV overpotential) reactive activity than pristine pyrochlores. This work provides a new understanding of half-metallization atom-fingerprints at catalyst surfaces to accelerate acidic water oxidation.

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Photodegradation of plastic consumer products is known to accelerate weathering and facilitate the release of chemicals and plastic particles into the aquatic environment. However, these processes are complex. In our presented pilot study, eight plastic consumer products were leached in distilled water under strong ultraviolet (UV) light simulating eight months of Central European climate and compared to their respective dark controls (DCs). The leachates and formed plastic particles were exploratorily characterized using a range of chemical analytical tools to describe degradation and leaching processes. These techniques covered (a) microplastic analysis, showing substantial liberation of plastic particles further increased under UV exposure, (b) non-targeted mass spectrometric characterization of the leachates, revealing several hundreds of chemical features with typically only minor agreement between the UV exposure and the corresponding DCs, (c) target analysis of 71 organic analytes, of which 15 could be detected in at least one sample, and (d) metal(loid) analysis, which revealed substantial release of toxic metal(loid)s further enhanced under UV exposure. A data comparison with the US-EPA's ToxVal and ToxCast databases showed that the detected metals and organic additives might pose substantial health and environmental concerns, requiring further study and comprehensive impact assessments.

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BACKGROUND: Cannabis sativa is seeing a global resurgence as a food, fiber and medicinal crop for industrial hemp and medicinal Cannabis industries respectively. However, a widespread moratorium on the use and research of C. sativa throughout most of the 20th century has seen the development of improved cultivars for specific end uses lag behind that of conventional crops. While C. sativa research and development has seen significant investments in the recent past, resulting in a suite of publicly available genomic resources and tools, a versatile and cost-effective mid-density genotyping platform for applied purposes in breeding and pre-breeding is lacking. Here we report on a first mid-density fixed-target SNP platform for C. sativa. RESULTS: The High-throughput Amplicon-based SNP-platform for medicinal Cannabis and industrial Hemp (HASCH) was designed using a combination of filtering and Integer Linear Programming on publicly available whole-genome sequencing and RNA sequencing data, supplemented with in-house generated genotyping-by-sequencing (GBS) data. HASCH contains 1,504 genome-wide targets of high call rate (97% mean) and even distribution across the genome, designed to be highly informative (> 0.3 minor allele frequency) across both medicinal cannabis and industrial hemp gene pools. Average numbers of mismatch SNP between any two accessions were 251 for medicinal cannabis (N = 116) and 272 for industrial hemp (N = 87). Comparing HASCH data with corresponding GBS data on a collection of diverse C. sativa accessions demonstrated high concordance and resulted in comparable phylogenies and genetic distance matrices. Using HASCH on a segregating F2 population derived from a cross between a tetrahydrocannabinol (THC)-dominant and a cannabidiol (CBD)-dominant accession resulted in a genetic map consisting of 310 markers, comprising 10 linkage groups and a total size of 582.7 cM. Quantitative Trait Locus (QTL) mapping identified a major QTL for CBD content on chromosome 7, consistent with previous findings. CONCLUSION: HASCH constitutes a versatile, easy to use and cost-effective genotyping solution for the rapidly growing Cannabis research community. It provides consistent genetic fingerprints of 1504 SNPs with wide applicability genetic resource management, quantitative genetics and breeding.

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Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active-while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.

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Quaternary phosphonium salts, a significant category of organophosphorus compounds, have garnered substantial attention from chemists due to their wide range of applications across various research areas. These compounds are utilized in organic synthesis, catalysis, medicinal chemistry, natural materials, and coordination chemistry. Their versatility and effectiveness in these fields make them valuable tools in scientific research. Despite their extensive use in various applications, the potential of quaternary phosphonium compounds as fluorescent agents for revealing latent fingerprints (LFPs) remains largely unexplored, presenting an exciting opportunity for further research and development in forensic science. In this study, we designed molecules that combine the aggregation-induced emission (AIE) chromophore with triphenylphosphine to create a series of novel AIE amphiphiles, namely TPP1, TPP2, and TPP3. Through precise adjustment of the carbon chain length between the phenoxy group and the terminal triphenylphosphine, we were able to finely tune the nanostructures and hydrophobicity of the materials. TPP3 emerged as the optimal candidate, possessing the ideal particle size and hydrophobicity to effectively bind to LFPs, thus enabling efficient fingerprint visualization with enhanced fluorescence upon aggregation. Our findings introduce an innovative approach to fingerprint visualization, offering high selectivity, superior imaging of level 3 structures, and long-term effectiveness (up to 30 days). Additionally, TPP3's outstanding performance in imaging level 3 structures of LFPs is beneficial for analyzing incomplete LFPs and identifying individuals. By significantly improving the detection and analysis of LFPs, this approach ensures more accurate and reliable identification, making it invaluable for forensic investigations and security measures. The adaptability of these compounds to various fingerprint surfaces highlights their potential in diverse practical applications, enhancing their utility in both forensic science and security fields. This versatility allows for precise fingerprint visualization across different scenarios, making them a critical tool for advancing biometric and security technologies.

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This study addresses the challenges posed by rainfall variability, leading to water deficits during critical stages of crop growth, resulting in a drastic reduction of cotton yield. In a comprehensive evaluation, thirty cotton genotypes, including five Gossypium arboreum (wild) and twenty-five Gossypium hirsutum (cultivated), were grown under rainfed and irrigated conditions. Drought tolerance indices (DTI) were evaluated, categorizing genotypes based on their resilience. Further, in-vitro screening at the seedling stage (20 days) under PEG-induced drought identified tolerant genotypes exhibiting elevated levels of free proline (19.07±5.31 mg.g-100fr.wt.), amino acids (34.59±6.51 mg.g-100fr.wt.), soluble proteins (13.73±2.65 mg.g-1fr.wt.), and glycine betaine (10.95±3.62 mg.g-100fr.wt.), in their leaves, positively correlating (p<0.001) with relative water content (87.70±3.45 %). Molecular analysis using drought-specific simple sequence repeats (SSR) markers revealed significant genetic variability in a cotton genotypes, with lower observed and higher expected heterozygosity. F statistics exposed a higher level of gene flow corresponding to low differentiation among populations. Among the genotypes group, wild GAM-67 and cultivated Deviraj emerged as the most potent, exhibiting the higher DTI and diverse gene pools. Study exhibited higher total gene diversity in drought-tolerant wild GAM-67 (0.8501) and greater expected heterozygosity (0.626) and gene flow (0.6731) in cultivated Deviraj, underlining their robust genetic adaptability to drought conditions. The integrated approach of field evaluations, in-vitro screening, and molecular analyses explained substantial genetic diversity, with the identification of promising genotypes displaying higher drought tolerance indices, elevated levels of stress-related biochemical osmolytes, and pronounced genetic adaptability, thereby contributing to the advancement of breeding initiatives for enhanced drought resilience in cotton.

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Fluorescence sensing of latent fingerprints (LFPs) has gained extensive attention due to its high sensitivity, non-destructive testing, low biotoxicity, ease of operation, and the potential for in situ visualization. However, the realization of in situ visualization of LFPs especially with green emission and rapid speed is still a challenge. Herein, we synthesized an amphibious green-emission AIE-gen TPE-NI-AOH (PLQY = 62%) for instant in situ LFP detecting, which integrates the excellent fluorescence properties of naphthalimide (NI) with a hydrophilic head and the AIE character as well as the donating property of tetraphenylethene (TPE). TPE-NI-AOH in ethanol/water binary solvent was used as an environmentally friendly LFP developer and achieved in situ green-fluorescence visualization of LFPs. The fluorescence signal achieves its 60% saturated intensity in 0.37 s and nearly 100% in 2.50 s, which is an instant process for the naked eye. Moreover, level 3 details and super-resolution images of LFPs could be observed clearly. Besides, the TPE-NI-AOH developer could be stored for at least 6 months, suitable for long-term storage. This instant in situ highlighting method does not require post-processing operations, providing a more convenient, rapid, and efficient detection method of LFPs. This work would inspire the further advancement of fluorescent sensors for fingerprint imaging.

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Powder dusting method is the most widely used approach due to its low cost, simplicity, minimal instrument dependence, and extensive applicability for developing latent fingerprints (LFPs). Herein, a novel optical and electrochemical dual-mode method for high-resolution LFP enhancement has been explored based on size-tunable polydopamine (PDA) nanoparticles (NPs) and scanning electrochemical microscopy (SECM). Dark PDAs rich in functional groups and negative charges can combine with the residues of LFPs on various surfaces with high sensitivity and selectivity to realize high-resolution visual fingerprint physical patterns on various porous and nonporous substrates with light color. However, optical visualization is not feasible for LFPs on dark or multicolored surfaces. Fortunately, based on the differences in electrochemical reactivity between ridges and furrows caused by the conductivity and reducibility of PDA powders, SECM can serve as a powerful supplement to optical methods to effectively overcome background color interference and distinctly display fingerprint patterns. Intriguingly, it is noteworthy that the binding amount and particle size of PDA powder significantly affected the optical and electrochemical visualization of LFPs: more powder binding amounts provided darker ridges in optical, and more surface reaction sites (larger powder binding mass at the same particle size or smaller particle size at the same mass) provided higher currents of ridges in electrochemical imaging. It demonstrates that the PDA powder as a dual-mode developer for LFPs offers a promising method for individual identification in forensics.

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Histone deacetylase 6 (HDAC6) is a promising drug target for the treatment of human diseases such as cancer, neurodegenerative diseases (in particular, Alzheimer's disease), and multiple sclerosis. Considerable attention is paid to the development of selective non-toxic HDAC6 inhibitors. To this end, we successfully form a set of 3854 compounds and proposed adequate regression QSAR models for HDAC6 inhibitors. The models have been developed using the PubChem, Klekota-Roth, 2D atom pair fingerprints, and RDkit descriptors and the gradient boosting, support vector machines, neural network, and k-nearest neighbours methods. The models are integrated into the developed HT_PREDICT application, which is freely available at https://htpredict.streamlit.app/. In vitro studies have confirmed the predictive ability of the proposed QSAR models integrated into the HT_PREDICT web application. In addition, the virtual screening performed with the HT_PREDICT web application allowed us to propose two promising inhibitors for further investigations.

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Breast cancer, a complex disease with a significant prevalence to form metastases, necessitates novel therapeutic strategies to improve treatment outcomes. Here, we present the results of a comparative molecular study of primary breast tumours, their metastases, and the corresponding primary cell lines using Desorption Electrospray Ionisation (DESI) and Laser-Assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) imaging. Our results show that ambient ionisation mass spectrometry technology is suitable for rapid characterisation of samples, providing a lipid- and metabolite-rich spectrum within seconds. Our study demonstrates that the lipidomic fingerprint of the primary tumour is not significantly distinguishable from that of its metastasis, in parallel with the similarity observed between their respective primary cell lines. While significant differences were observed between tumours and the corresponding cell lines, distinct lipidomic signatures and several phospholipids such as PA(36:2), PE(36:1), and PE(P-38:4)/PE(O-38:5) for LA-REIMS imaging and PE(P-38:4)/PE(O-38:5), PS(36:1), and PI(38:4) for DESI-MSI were identified in both tumours and cells. We show that the tumours' characteristics can be found in the corresponding primary cell lines, offering a promising avenue for assessing tumour responsiveness to therapeutic interventions. A comparative analysis by DESI-MSI and LA-REIMS imaging revealed complementary information, demonstrating the utility of LA-REIMS in the molecular imaging of cancer.

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Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds.

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The baru (Dipteryx alata Vog.), a fruit native to the Cerrado biome, is well-known for its almonds, which are extensively exploited and exported. Unfortunately, the remaining parts of this fruit are often discarded. This study investigates the fixed chemical constituents of the baru, including the bark, pulp, endocarp, and almonds, using the PS-MS technique in positive and negative ionization modes. Notably, this research presents the first chemical profile of baru almonds in both their raw and roasted states. The analysis identified 57 compounds reported for the first time in a baru and 24 common compounds. The majority of these compounds are classified as flavonoids. In both ionization modes, the peel exhibited a higher proportion of phenolic compounds, although the chemical compounds varied among the peel, pulp, almond, and endocarp. These findings highlight the perspective of bioeconomy and biotechnology. By staggering baru fruit production alongside extractivists, we can optimize the utilization of all parts of the fruit. Furthermore, given the knowledge of the biological properties of flavonoids and the baru composition, we recommend additional studies to analyze their potential in preventing chronic non-communicable diseases.

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Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

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The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow-from sample collection to data analysis-should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice.

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