OsLRR-RLP2 Gene Regulates Immunity to Magnaporthe oryzae in Japonica Rice.

Rice is an important cereal crop worldwide, the growth of which is affected by rice blast disease, caused by the fungal pathogen Magnaporthe oryzae. As climate change increases the diversity of pathogens, the disease resistance genes (R genes) in plants must be identified. The major blast-resistance genes have been identified in indica rice varieties; therefore, japonica rice varieties with R genes now need to be identified. Because leucine-rich repeat (LRR) domain proteins possess R-gene properties, we used bioinformatics analysis to identify the rice candidate LRR domain receptor-like proteins (OsLRR-RLPs). OsLRR-RLP2, which contains six LRR domains, showed differences in the DNA sequence, containing 43 single-nucleotide polymorphisms (SNPs) in indica and japonica subpopulations. The results of the M. oryzae inoculation analysis indicated that indica varieties with partial deletion of OsLRR-RLP2 showed susceptibility, whereas japonica varieties with intact OsLRR-RLP2 showed resistance. The oslrr-rlp2 mutant, generated using clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9), showed increased pathogen susceptibility, whereas plants overexpressing this gene showed pathogen resistance. These results indicate that OsLRR-RLP2 confers resistance to rice, and OsLRR-RLP2 may be useful for breeding resistant cultivars.

Hexavalent Chromium Inhibited Zebrafish Embryo Development by Altering Apoptosis- and Antioxidant-Related Genes.

This study aimed to assess the effects of hexavalent chromium on zebrafish (Danio rerio) embryo development. The zebrafish embryos were treated with solutions containing chromium at different concentrations (0.1, 1, 3.125, 6.25, 12.5, 50, and 100 µg/mL). The development of zebrafish embryos was estimated by the determination of survival rate, heart rate, and the measurement of larvae body length. Real time RT-PCR and Western blot were performed to assess the expression of apoptosis- and antioxidant-related genes. The results showed that the reduced survival rate of zebrafish embryos and larvae was associated with an increase in chromium concentration. The exposure of higher concentrations resulted in a decrease in body length of zebrafish larvae. In addition, a marked increase in heart rate was observed in the zebrafish larvae under chromium treatment, especially at high concentrations. The real-time RT-PCR analysis showed that the transcript expressions for cell-cycle-related genes (cdk4 and cdk6) and antioxidant-related genes (sod1 and sod2) were downregulated in the zebrafish embryos treated with chromium. Western blot analysis revealed the upregulation of Caspase 3 and Bax, while a downregulation was observed in Bcl2. These results indicated that hexavalent chromium induced changes in zebrafish embryo development by altering apoptosis- and antioxidant-related genes.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption.

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives.

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction.

Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.

AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks.

Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained significant attention, driven by the need for efficient and cost-effective toxicity assessments. In this paper, we proposed AMPred-CNN, an innovative Ames mutagenicity prediction model based on Convolutional Neural Networks (CNNs), uniquely employing molecular structures as images to leverage CNNs' powerful feature extraction capabilities. The study employs the widely used benchmark mutagenicity dataset from Hansen et al. for model development and evaluation. Comparative analyses with traditional ML models on different molecular features reveal substantial performance enhancements. AMPred-CNN outshines these models, demonstrating superior accuracy, AUC, F1 score, MCC, sensitivity, and specificity on the test set. Notably, AMPred-CNN is further benchmarked against seven recent ML and DL models, consistently showcasing superior performance with an impressive AUC of 0.954. Our study highlights the effectiveness of CNNs in advancing mutagenicity prediction, paving the way for broader applications in toxicology and drug development.

Clicked BODIPY-Fullerene-Peptide Assemblies: Studies of Electron Transfer Processes in Self-Assembled Monolayers on Gold Surfaces.

Two BODIPY-C60-peptide assemblies were synthesized by CuAAC reactions of BODIPY-C60 dyads and a helical peptide functionalized with a terminal alkyne group and an azide group, respectively. The helical peptide within these assemblies was functionalized at its other end by a disulfide group, allowing formation of self-assembled monolayers (SAMs) on gold surfaces. Characterizations of these SAMs, as well as those of reference molecules (BODIPY-C60-alkyl, C60-peptide and BODIPY-peptide), were carried out by PM-IRRAS and cyclic voltammetry. BODIPY-C60-peptide SAMs are more densely packed than BODIPY-C60-alkyl and BODIPY-peptide based SAMs. These findings were attributed to the rigid peptide helical conformation along with peptide-peptide and C60-C60 interactions within the monolayers. However, less dense monolayers were obtained with the target assemblies compared to the C60-peptide, as the BODIPY entity likely disrupts organization within the monolayers. Finally, electron transfer kinetics measurements by ultra-fast electrochemistry experiments demonstrated that the helical peptide is a better electron mediator in comparison to alkyl chains. This property was exploited along with those of the BODIPY-C60 dyads in a photo-current generation experiment by converting the resulting excited and/or charge separated states from photo-illumination of the dyad into electrical energy.

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives.

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

Comparison Between Magnetic Resonance Imaging and Computed Tomography in the Detection and Volumetric Assessment of Lung Nodules: A Prospective Study.

Rationale and Objectives: Computed tomography (CT) lung nodule assessment is routinely performed and appears very promising for lung cancer screening. However, the radiation exposure through time remains a concern. With the overall goal of an optimal management of indeterminate lung nodules, the objective of this prospective study was therefore to evaluate the potential of optimized ultra-short echo time (UTE) MRI for lung nodule detection and volumetric assessment. Materials and Methods: Eight (54.9 ± 13.2 years) patients with at least 1 non-calcified nodule ≥4 mm were included. UTE under high-frequency non-invasive ventilation (UTE-HF-NIV) and in free-breathing at tidal volume (UTE-FB) were investigated along with volumetric interpolated breath-hold examination at full inspiration (VIBE-BH). Three experienced readers assessed the detection rate of nodules ≥4 mm and ≥6 mm, and reported their location, 2D-measurements and solid/subsolid nature. Volumes were measured by two experienced readers. Subsequently, two readers assessed the detection and volume measurements of lung nodules ≥4mm in gold-standard CT images with soft and lung kernel reconstructions. Volumetry was performed with lesion management software (Carestream, Rochester, New York, USA). Results: UTE-HF-NIV provided the highest detection rate for nodules ≥4 mm (n = 66) and ≥6 mm (n = 32) (35 and 50%, respectively). No dependencies were found between nodule detection and their location in the lung with UTE-HF-NIV (p > 0.4), such a dependency was observed for two readers with VIBE-BH (p = 0.002 and 0.03). Dependencies between the nodule's detection and their size were noticed among readers and techniques (p < 0.02). When comparing nodule volume measurements, an excellent concordance was observed between CT and UTE-HF-NIV, with an overestimation of 13.2% by UTE-HF-NIV, <25%-threshold used for nodule's growth, conversely to VIBE-BH that overestimated the nodule volume by 28.8%. Conclusion: UTE-HF-NIV is not ready to replace low-dose CT for lung nodule detection, but could be used for follow-up studies, alternating with CT, based on its volumetric accuracy.

Surface and Electrochemical Properties of Polymer Brush-Based Redox Poly(Ionic Liquid).

Redox-active poly(ionic liquid) poly(3-(2-methacryloyloxy ethyl)-1-(N-(ferrocenylmethyl) imidazolium bis(trifluoromethylsulfonyl)imide deposited onto electrode surfaces has been prepared using surface-initiated atom transfer radical polymerization SI-ATRP. The process starts by electrochemical immobilization of initiator layer, and then methacrylate monomer carrying ferrocene and imidazolium units is polymerized in ionic liquid media via SI-ATRP process. The surfaces analyses of the polymer exhibit a well-defined polymer brushlike structure and confirm the presence of ferrocene and ionic moieties within the film. Furthermore, the electrochemical investigations of poly(redox-active ionic liquid) in different media demonstrate that the electron transfer is not restricted by the rate of counterion migration into/out of the polymer. The attractive electrochemical performance of these materials is further demonstrated by performing electrochemical measurement, of poly(ferrocene ionic liquid), in solvent-free electrolyte. The facile synthesis of such highly ordered electroactive materials based ionic liquid could be useful for the fabrication of nanostructured electrode suitable for performing electrochemistry in solvent free electrolyte. We also demonstrate possible applications of the poly(FcIL) as electrochemically reversible surface wettability system and as electrochemical sensor for the catalytic activity toward the oxidation of tyrosine.

Phylogeny of Dengue virus type 2 isolated in the Central Highlands, Vietnam / Filogenia del virus del Dengue tipo2 identificado en el Altiplano Central de Vietnam

AbstractDengue fever is perhaps the most important viral re-emergent disease especially in tropical and sub-tropical countries, affecting about 50 million people around the world every year. In the Central Highlands regions of Vietnam, dengue fever still remains as a major public health issue. Although four viral serotypes have been currently identified, dengue virus type 2 (DENV-2) was involved in the most important outbreaks during 2010-2012, especially, 2010 when the fatality rate highly increased. Detection of genotype of DENV2 provided information on origin, distribution and genotype of the virus. In this study, DEN-2 isolated from dengue patients during the 2010-2012 epidemics was amplified and sequenced with E gene. The consensus sequences were aligned with reference E gene sequences of globally available Genbank. Phylogenetic analysis was performed using Neighbor-joining and Kimura 2-parameter model to construct phylogenetic tree. A total of 15 isolates (seven from 2010; one from 2011 and seven from 2012) were obtained from human serum samples. Phylogenetic analysis revealed that Asian genotype 1 is currently circulating locally in Central Highlands region. Isolates of this genotype were closely related to viruses from Thailand, Laos, and Cambodia. It indicated that these epidemics maybe imported into the Central Highlands region from South-East Asia neighbor countries. The study results would help in planning for prevention and control of dengue virus in Vietnam. Continuous monitoring of DENV genotypes is necessary to confirm the current findings and detect possible genotype shifts within the dengue viruses in the future.

ResumenPosiblemente, la fiebre del dengue es la enfermedad viral recurrente más importante en los países tropicales y subtropicales que afecta cerca de 50 millones de personas cada año en todo el mundo. En las regiones del Altiplano Central de Vietman, la fiebre del dengue aun se considera como una gran preocupación de salud pública. Aunque los cuatro serotipos virales han sido identificados, el virus del dengue tipo 2 (DENV-2) estuvo involucrado en el brote más importante durante el 2010-2012, especialmente en el 2010 cuando los índices de mortalidad aumentaron considerablemente. El descubrimiento del genotipo DENV-2 proporcionó información del origen, distribución y genotipo del virus. En este estudio, el serotipo DENV-2 identificado de pacientes con dengue durante las epidemias de 2010-2012 se amplificaron y secuenciaron con E gene. Las secuencias consenso se alinearon con secuencias de referencia mundiales de E gene disponibles en GenBank. El análisis filogenético se llevó a cabo utilizando el modelo Neighbor-joining y Kimura 2-parámetros para construir el árbol filogenético. Un total de 15 cepas (siete de 2010, una de 2011 y 7 de 2012) se obtuvieron de las muestras de suero humano. El análisis filogenético reveló que el genotipo asiático 1 circula localmente en la región del Altiplano Central. Las cepas de este genotipo estan muy relacionadas con los virus de Tailandia, Laos y Camboya. El análisis también indicó que estas epidemias pudieron migrar a la región del Altiplano Central desde los países vecinos del sureste asiático. Los resultados de este estudio pueden ayudar en la planificación de la prevención y el control del virus del dengue en Vietnam. Un monitoreo contante de los genotipos DENV es necesario para confirmar los hallazgos recientes y detectar los posibles cambios del genotipo de los virus del dengue en el futuro.