Predicting Cardiotoxicity of Molecules Using Attention-Based Graph Neural Networks.

In drug discovery, the search for new and effective medications is often hindered by concerns about toxicity. Numerous promising molecules fail to pass the later phases of drug development due to strict toxicity assessments. This challenge significantly increases the cost, time, and human effort needed to discover new therapeutic molecules. Additionally, a considerable number of drugs already on the market have been withdrawn or re-evaluated because of their unwanted side effects. Among the various types of toxicity, drug-induced heart damage is a severe adverse effect commonly associated with several medications, especially those used in cancer treatments. Although a number of computational approaches have been proposed to identify the cardiotoxicity of molecules, the performance and interpretability of the existing approaches are limited. In our study, we proposed a more effective computational framework to predict the cardiotoxicity of molecules using an attention-based graph neural network. Experimental results indicated that the proposed framework outperformed the other methods. The stability of the model was also confirmed by our experiments. To assist researchers in evaluating the cardiotoxicity of molecules, we have developed an easy-to-use online web server that incorporates our model.

iNSP-GCAAP: Identifying nonclassical secreted proteins using global composition of amino acid properties.

Nonclassical secreted proteins (NSPs) refer to a group of proteins released into the extracellular environment under the facilitation of different biological transporting pathways apart from the Sec/Tat system. As experimental determination of NSPs is often costly and requires skilled handling techniques, computational approaches are necessary. In this study, we introduce iNSP-GCAAP, a computational prediction framework, to identify NSPs. We propose using global composition of a customized set of amino acid properties to encode sequence data and use the random forest (RF) algorithm for classification. We used the training dataset introduced by Zhang et al. (Bioinformatics, 36(3), 704-712, 2020) to develop our model and test it with the independent test set in the same study. The area under the receiver operating characteristic curve on that test set was 0.9256, which outperformed other state-of-the-art methods using the same datasets. Our framework is also deployed as a user-friendly web-based application to support the research community to predict NSPs.

Selective and Stable CO2 Electroreduction to CH4 via Electronic Metal-Support Interaction upon Decomposition/Redeposition of MOF.

The CO2 electroreduction to fuels is a feasible approach to provide renewable energy sources. Therefore, it is necessary to conduct experimental and theoretical investigations on various catalyst design strategies, such as electronic metal-support interaction, to improve the catalytic selectivity. Here a solvent-free synthesis method is reported to prepare a copper (Cu)-based metal-organic framework (MOF) as the precursor. Upon electrochemical CO2 reduction in aqueous electrolyte, it undergoes in situ decomposition/redeposition processes to form abundant interfaces between Cu nanoparticles and amorphous carbon supports. This Cu/C catalyst favors the selective and stable production of CH4 with a Faradaic efficiency of ≈55% at -1.4 V versus reversible hydrogen electrode (RHE) for 12.5 h. The density functional theory calculation reveals the crucial role of interfacial sites between Cu and amorphous carbon support in stabilizing the key intermediates for CO2 reduction to CH4 . The adsorption of COOH* and CHO* at the Cu/C interface is up to 0.86 eV stronger than that on Cu(111), thus promoting the formation of CH4 . Therefore, it is envisioned that the strategy of regulating electronic metal-support interaction can improve the selectivity and stability of catalyst toward a specific product upon electrochemical CO2 reduction.

Genetic diversity and declining norovirus prevalence in infants and children during Japan's COVID-19 pandemic: a three-year molecular surveillance.

Noroviruses (NoVs) are a global concern, causing widespread outbreaks and sporadic acute gastroenteritis (AGE) cases across all age groups. Recent research has shed light on the emergence of novel recombinant strains of NoV in various countries. To delve deeper into this phenomenon, we extensively analyzed 1,175 stool samples collected from Japanese infants and children with AGE from six different prefectures in Japan over three years, from July 2018 to June 2021. Our investigation aimed to determine the prevalence and genetic characteristics of NoV associated with sporadic AGE while exploring the possibility of detecting NoV recombination events. Among the analyzed samples, we identified 355 cases positive for NoV, 11 cases attributed to GI genotypes, and 344 associated with GII genotypes. Notably, we discovered four distinct GI genotypes (GI.2, GI.3, GI.4, and GI.6) and seven diverse GII genotypes (GII.2, GII.3, GII.4, GII.6, GII.7, GII.14, and GII.17). The predominant genotypes were GII.4 (56.4%; 194 out of 344), followed by GII.2 and GII.3. Through dual genotyping based on sequencing of the ORF1/ORF2 junction region, we identified a total of 14 different RdRp/capsid genotypes. Of particular interest were the prevalent recombinant genotypes GII.4[P31] and GII.2[P16]. Notably, our study revealed a decrease in the number of children infected with NoV during and after the COVID-19 pandemic. These findings underscore the importance of continuous NoV surveillance efforts.

Morphologic, Morphometric, and Molecular Characterization of Vietnamese Populations of Meloidogyne incognita.

Meloidogyne incognita is considered the most damaging and common root-knot nematode to numerous host plants worldwide. During a survey of nematodes in Vietnam, 1,106 samples from 22 different plant species were collected. M. incognita was recorded on 13 of the 22 host plants. Four populations of M. incognita from four host plants were chosen for comparison and confirmation of their morphologic, morphometric, and molecular characteristics. Genetically based phylogenetic trees were constructed to show relationships among root-knot nematodes. Molecular barcodes of four gene regions, ITS, D2-D3 of 28S rRNA, COI, and Nad5 mtDNA, integrated with morphologic and morphometric data were used as reliable references for molecular identification of M. incognita. Our analyses indicated that tropical root-knot nematodes are very similar in characterization of ITS, D2-D3 of 28S rRNA, and COI regions. However, these gene regions can be used to separate the tropical root-knot nematode group from other groups. On the other hand, the analysis of Nad5 mtDNA and multiplex-PCR with specific primers can be used to distinguish tropical species.

Roles of TGF-ß1 in Viral Infection during Pregnancy: Research Update and Perspectives.

Transforming growth factor-beta 1 (TGF-ß1) is a pleiotropic growth factor playing various roles in the human body including cell growth and development. More functions of TGF-ß1 have been discovered, especially its roles in viral infection. TGF-ß1 is abundant at the maternal-fetal interface during pregnancy and plays an important function in immune tolerance, an essential key factor for pregnancy success. It plays some critical roles in viral infection in pregnancy, such as its effects on the infection and replication of human cytomegalovirus in syncytiotrophoblasts. Interestingly, its role in the enhancement of Zika virus (ZIKV) infection and replication in first-trimester trophoblasts has recently been reported. The above up-to-date findings have opened one of the promising approaches to studying the mechanisms of viral infection during pregnancy with links to corresponding congenital syndromes. In this article, we review our current and recent advances in understanding the roles of TGF-ß1 in viral infection. Our discussion focuses on viral infection during pregnancy, especially in the first trimester. We highlight the mutual roles of viral infection and TGF-ß1 in specific contexts and possible functions of the Smad pathway in viral infection, with a special note on ZIKV infection. In addition, we discuss promising approaches to performing further studies on this topic.

First report of Pratylenchus penetrans (Nematoda: Pratylenchidae) associated with artichokes in Vietnam.

Pratylenchus penetrans is one of the world's most common and destructive root-lesion nematodes and can parasitize more than 400 plant species. P. penetrans has been reported to cause serious damage to artichokes in several countries, such as Greece, Brazil, and France. Until now, there have been no reports of P. penetrans associated with artichokes in Vietnam. In this study, we recorded this species in artichoke fields in Lam Dong province, Vietnam with an average density of 50 nematodes/100g of soil (frequency of appearance at 64.7%). This nematode was associated with symptoms such as yellowing leaves, stunt, and root necrosis of artichokes in Vietnam, indicating its high damaging potential and a need for suitable control strategies. The identification of this species in our study was confirmed by morphology, morphometric data, and molecular characterization of 18S and 28S rRNA regions. Our study also provides the first molecular data of P. penetrans in Vietnam. The inclusion of molecular data for P. penetrans in Vietnam represents a significant contribution to the scientific community and a pivotal advancement in addressing nematode-related challenges in agriculture. This dataset serves as an invaluable reference for various molecular-focused endeavors, including but not limited to molecular identification, pathogenicity studies, and the development of effective management strategies.

Tweaking Photo CO2 Reduction by Altering Lewis Acidic Sites in Metalated-Porous Organic Polymer for Adjustable H2 /CO Ratio in Syngas Production.

Herein, we have specifically designed two metalated porous organic polymers (Zn-POP and Co-POP) for syngas (CO+H2 ) production from gaseous CO2 . The variable H2 /CO ratio of syngas with the highest efficiency was produced in water medium (without an organic hole scavenger and photosensitizer) by utilizing the basic principle of Lewis acid/base chemistry. Also, we observed the formation of entirely different major products during photocatalytic CO2 reduction and water splitting with the help of the two catalysts, where CO (145.65 µmol g-1 h-1 ) and H2 (434.7 µmol g-1 h-1 ) production were preferentially obtained over Co-POP & Zn-POP, respectively. The higher electron density/better Lewis basic nature of Co-POP was investigated further using XPS, XANES, and NH3 -TPD studies, which considerably improve CO2 activation capacity. Moreover, the structure-activity relationship was confirmed via in situ DRIFTS and DFT studies, which demonstrated the formation of COOH* intermediate along with the thermodynamic feasibility of CO2 reduction over Co-POP while water splitting occurred preferentially over Zn-POP.

iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features.

BACKGROUND: Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genomes significantly contributes to discovering entire structures of genes of interest. Therefore, exploration of promoter regions is one of the most imperative topics in molecular genetics and biology. Besides experimental techniques, computational methods have been developed to predict promoters. In this study, we propose iPromoter-Seqvec - an efficient computational model to predict TATA and non-TATA promoters in human and mouse genomes using bidirectional long short-term memory neural networks in combination with sequence-embedded features extracted from input sequences. The promoter and non-promoter sequences were retrieved from the Eukaryotic Promoter database and then were refined to create four benchmark datasets. RESULTS: The area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR) were used as two key metrics to evaluate model performance. Results on independent test sets showed that iPromoter-Seqvec outperformed other state-of-the-art methods with AUCROC values ranging from 0.85 to 0.99 and AUCPR values ranging from 0.86 to 0.99. Models predicting TATA promoters in both species had slightly higher predictive power compared to those predicting non-TATA promoters. With a novel idea of constructing artificial non-promoter sequences based on promoter sequences, our models were able to learn highly specific characteristics discriminating promoters from non-promoters to improve predictive efficiency. CONCLUSIONS: iPromoter-Seqvec is a stable and robust model for predicting both TATA and non-TATA promoters in human and mouse genomes. Our proposed method was also deployed as an online web server with a user-friendly interface to support research communities. Links to our source codes and web server are available at https://github.com/mldlproject/2022-iPromoter-Seqvec .

iANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation.

Cancer is one of the most deadly diseases that annually kills millions of people worldwide. The investigation on anticancer medicines has never ceased to seek better and more adaptive agents with fewer side effects. Besides chemically synthetic anticancer compounds, natural products are scientifically proved as a highly potential alternative source for anticancer drug discovery. Along with experimental approaches being used to find anticancer drug candidates, computational approaches have been developed to virtually screen for potential anticancer compounds. In this study, we construct an ensemble computational framework, called iANP-EC, using machine learning approaches incorporated with evolutionary computation. Four learning algorithms (k-NN, SVM, RF, and XGB) and four molecular representation schemes are used to build a set of classifiers, among which the top-four best-performing classifiers are selected to form an ensemble classifier. Particle swarm optimization (PSO) is used to optimise the weights used to combined the four top classifiers. The models are developed by a set of curated 997 compounds which are collected from the NPACT and CancerHSP databases. The results show that iANP-EC is a stable, robust, and effective framework that achieves an AUC-ROC value of 0.9193 and an AUC-PR value of 0.8366. The comparative analysis of molecular substructures between natural anticarcinogens and nonanticarcinogens partially unveils several key substructures that drive anticancerous activities. We also deploy the proposed ensemble model as an online web server with a user-friendly interface to support the research community in identifying natural products with anticancer activities.

Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers.

Malaria is a threatening disease that has claimed many lives and has a high prevalence rate annually. Through the past decade, there have been many studies to uncover effective antimalarial compounds to combat this disease. Alongside chemically synthesized chemicals, a number of natural compounds have also been proven to be as effective in their antimalarial properties. Besides experimental approaches to investigate antimalarial activities in natural products, computational methods have been developed with satisfactory outcomes obtained. In this study, we propose a novel molecular encoding scheme based on Bidirectional Encoder Representations from Transformers and used our pretrained encoding model called NPBERT with four machine learning algorithms, including k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), and Random Forest (RF), to develop various prediction models to identify antimalarial natural products. The results show that SVM models are the best-performing classifiers, followed by the XGB, k-NN, and RF models. Additionally, comparative analysis between our proposed molecular encoding scheme and existing state-of-the-art methods indicates that NPBERT is more effective compared to the others. Moreover, the deployment of transformers in constructing molecular encoders is not limited to this study but can be utilized for other biomedical applications.

iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding.

The human cytochrome P450 (CYP) superfamily holds responsibilities for the metabolism of both endogenous and exogenous compounds such as drugs, cellular metabolites, and toxins. The inhibition exerted on the CYP enzymes is closely associated with adverse drug reactions encompassing metabolic failures and induced side effects. In modern drug discovery, identification of potential CYP inhibitors is, therefore, highly essential. Alongside experimental approaches, numerous computational models have been proposed to address this biochemical issue. In this study, we introduce iCYP-MFE, a computational framework for virtual screening on CYP inhibitors toward 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms. iCYP-MFE contains a set of five robust, stable, and effective prediction models developed using multitask learning incorporated with molecular fingerprint-embedded features. The results show that multitask learning can remarkably leverage useful information from related tasks to promote global performance. Comparative analysis indicates that iCYP-MFE achieves three predominant tasks, one equivalent task, and one less effective task compared to state-of-the-art methods. The area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR) were two decisive metrics used for model evaluation. The prediction task for CYP2D6-inhibition achieves the highest AUC-ROC value of 0.93 while the prediction task for CYP1A2-inhibition obtains the highest AUC-PR value of 0.92. The substructural analysis preliminarily explains the nature of the CYP-inhibitory activity of compounds. An online web server for iCYP-MFE with a user-friendly interface was also deployed to support scientific communities in identifying CYP inhibitors.

Adverse events after BNT162b2 mRNA COVID-19 vaccination in health care workers and medical students in Japan.

To control the coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the promotion of vaccination is important. However, adverse reactions following vaccination remain a concern. To investigate adverse events in the vaccinated Japanese population, we conducted a survey-based study among health care workers, including medical doctors and nurses; other medical staff; and medical university faculty, staff, and students in a single medical school and affiliated hospital in Japan. In addition, we analyzed the association of different adverse events with individual factors (e.g., age, sex) by performing network analysis. While young age and female sex are often considered risk factors for more severe adverse events, the regression models showed neither age nor sex was associated with local injection-site reactions after the second dose in this study. In contrast to local reactions, systemic adverse events were associated with young age and female sex. However, myalgia was unique in that it was not associated with younger age even though the network analysis showed that myalgia was consistently related to arthralgia and belonged to the group of systemic events after both the first and second vaccine doses. Further study is needed to ensure safe and effective vaccination to aid in controlling the COVID-19 pandemic.

Emerging norovirus GII.4 Sydney[P31] causing acute gastroenteritis outbreak in children in Japan, during COVID-19, 2021.

INTRODUCTION: Norovirus (NoV) is the most common agent causing outbreaks and sporadic cases of acute gastroenteritis among all ages, especially children under 5 years old. During the coronavirus disease 2019 (COVID-19) pandemic, NoV infection has decreased drastically in Japan due to school closures and no outbreak related to NoV infection had been reported. METHOD: In mid-September 2021, NoV outbreak occurred in kindergarten and nursery schools in Maizuru, Kyoto prefecture, Japan. Twenty-six stool samples collected from patients who were diagnosed of NoV gastroenteritis from the outbreak by an immunochromatographic (IC) kit at a pediatric outpatient clinic in Maizuru city during 3 weeks from September 13 to October 8, 2021 were examined for the presence of NoV GII by reverse transcriptase-polymerase chain reaction (RT-PCR), genome sequencing, and phylogenetic analysis. RESULT: All 26 samples were confirmed positive to NoV GII and their genotypes were identified as GII.4 Sydney[P31]. The amino acid substitutions in open reading frame1 (ORF1) and ORF2 genes were found when compared with previously detected sporadic NoV GII.4 Sydney[P31] strains isolated in Japan. The clinical characterization of infected children was described. Most of the children were mild cases and vomiting was the most frequent clinical symptom. CONCLUSION: This study reported a recent emergence of NoV GII.4 Sydney[P31] causing acute gastroenteritis outbreak in children in Japan during the COVID-19 pandemic and suggests a need for further monitoring of NoV GII.4 variants.

The association of three doses of the BNT162b2 mRNA vaccine with abnormal bleeding and an irregular menstrual cycle among premenopausal females: A single institute observation study.

BACKGROUND: The COVID-19 vaccine is effective in preventing severe cases of COVID-19. For women, gynecological adverse events, such as menstrual irregularities and irregular bleeding, could be a concern after COVID-19 vaccination. In this study, we investigated gynecological adverse events in the vaccinated Japanese female population. METHODS: We conducted a survey-based study with health-care workers, including medical doctors and nurses, medical coworkers, and medical university faculty, staff, and students, at a single medical school and affiliated hospital in Japan. We used McNemar's test and network analysis. RESULTS: Overall, we obtained 819 responses, and 424 were from females. After the exclusion of contradictory answers, 309 surveys were finally considered appropriate for the analysis. The frequencies of abnormal bleeding were 0.6%, 1.0%, and 3.0% for the first, second, and third doses, respectively. An irregular menstrual cycle was more common than abnormal bleeding: 1.9%, 4.9%, and 6.6% for the first, second, and third doses, respectively. Network analysis revealed that abnormal bleeding and an irregular menstrual cycle were not associated with other adverse reactions. CONCLUSION: The present study showed that the effects of COVID-19 vaccination on menstruation seem limited.

SERS Chemical Enhancement of 2,4,5-Trichlorophenoxyacetic Acid Adsorbed on Silver Substrate.

Surface-enhanced Raman spectroscopy (SERS) was employed to gain an understanding of the chemical enhancement mechanism of 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), an Agent Orange, adsorbed on a silver substrate surface. Experimental measurements were performed using a micro-Raman spectrophotometer with an excitation wavelength of 532 nm and successfully detected 2,4,5-T at a relatively low concentration of 0.4 nM. Density functional theory (DFT) calculations on the interactions of the 2,4,5-T molecule with some small silver clusters, Agn with n = 4, 8, and 20, as well as with extended Ag surfaces, demonstrate that the most stable adsorption configuration is formed via coordination of Cl9 sites and carbonyl C═O group on the 2,4,5-T ligand to the Ag atoms on surfaces. Analyses of charge transfer mechanism and frontier orbitals distributions show an electron transfer from 2,4,5-T to the cluster in the ground state, and an inversed trend occurs for the excited singlet state process, consequently leading to a chemical enhancement of SERS signals. The obtained results are of importance for subsequent work in guiding the design of mobile sensors specifically used for services of rapid screening and detection of these toxic compounds present in the environment, as well as agricultural and food products. Extensive computations pointed out that small silver clusters, in particular of Ag20 size, can be used as appropriate models for a metal nanoparticle surface.

First report and new molecular and morphological characterizations of root-knot nematode, Meloidogyne javanica, infecting ginger and long coriander in Vietnam.

Ginger (Zingiber officinale Roscoe) and long coriander (Eryngium foetidum L.) are commonly grown and used as important spices and medicinal plants in Vietnam. Our study recovered for the first time one of the most damaging tropical root-knot nematodes, Meloidogyne javanica, associated with these plants in the Western Highlands of Vietnam. In this study, M. javanica was characterized based on morphology and molecular characterization of D2-D3 fragment of 28S rRNA, ITS, and Nad5 mtDNA regions. The identification of this species was done based on a combination of morphology, multiplex-PCR with specific primer, network haplotype analysis, and PPNID program.

Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia.

In acute myeloid leukaemia (AML), the cell of origin, nature and biological consequences of initiating lesions, and order of subsequent mutations remain poorly understood, as AML is typically diagnosed without observation of a pre-leukaemic phase. Here, highly purified haematopoietic stem cells (HSCs), progenitor and mature cell fractions from the blood of AML patients were found to contain recurrent DNMT3A mutations (DNMT3A(mut)) at high allele frequency, but without coincident NPM1 mutations (NPM1c) present in AML blasts. DNMT3A(mut)-bearing HSCs showed a multilineage repopulation advantage over non-mutated HSCs in xenografts, establishing their identity as pre-leukaemic HSCs. Pre-leukaemic HSCs were found in remission samples, indicating that they survive chemotherapy. Therefore DNMT3A(mut) arises early in AML evolution, probably in HSCs, leading to a clonally expanded pool of pre-leukaemic HSCs from which AML evolves. Our findings provide a paradigm for the detection and treatment of pre-leukaemic clones before the acquisition of additional genetic lesions engenders greater therapeutic resistance.

First report of Rotylenchulus reniformis infecting turmeric in Vietnam and consequent damage.

Turmeric (Curcuma longa L.) is one of the common medicinal crops of high economical value in Vietnam. A survey in the Central Highlands of Vietnam revealed a turmeric growing area showing serious disease symptoms, including stunting of the plant, yellowing or darkening of the leaf margins and tips, and underdeveloped dry and rotten rhizomes. An inspection for plant pathogens in soil samples from this area revealed a high density and frequency of Rotylenchus reniformis, with a significant relationship between the density of R. reniformis, rhizome weight, and level of plant damage. This study provides the first report of R. reniformis found in parasitic association with turmeric in Vietnam with the support of molecular data and examines its resulting damage.Turmeric (Curcuma longa L.) is one of the common medicinal crops of high economical value in Vietnam. A survey in the Central Highlands of Vietnam revealed a turmeric growing area showing serious disease symptoms, including stunting of the plant, yellowing or darkening of the leaf margins and tips, and underdeveloped dry and rotten rhizomes. An inspection for plant pathogens in soil samples from this area revealed a high density and frequency of Rotylenchus reniformis, with a significant relationship between the density of R. reniformis, rhizome weight, and level of plant damage. This study provides the first report of R. reniformis found in parasitic association with turmeric in Vietnam with the support of molecular data and examines its resulting damage.

First report of Hemicriconemoides litchi associated with Piper sarmentosum and revision of the genus Hemicriconemoides in Vietnam.

The taxonomic status of Hemicriconemoides litchi, H. mangiferae, and H. strictathecatus has been in debates for many years. In this study, a population of H. litchi collected from the rhizosphere of Piper sarmentosum is characterized using a combination of morphology and molecular data. Our results are in agreement with other authors to maintain the validity of H. litchi and provide sequences of H. litchi with correct names on GenBank. A revision on the genus Hemicriconemoides in Vietnam is also discussed.The taxonomic status of Hemicriconemoides litchi, H. mangiferae, and H. strictathecatus has been in debates for many years. In this study, a population of H. litchi collected from the rhizosphere of Piper sarmentosum is characterized using a combination of morphology and molecular data. Our results are in agreement with other authors to maintain the validity of H. litchi and provide sequences of H. litchi with correct names on GenBank. A revision on the genus Hemicriconemoides in Vietnam is also discussed.