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Yu-Ching Chen,1 Victor C Kok,1,2 Ching-Hsuan Chien,1 Jorng-Tzong Horng,1,3 Jeffrey J P Tsai11Department of Biomedical Informatics, Asia University, Taichung, 2Department of Internal Medicine, Kuang Tien General Hospital, Taichung, 3Department of Computer Science and Information Engineering, National Central University, Jhongli, TaiwanIntroduction: Accumulating evidence suggests that metformin reduces incident cancer development. Few cohort studies have evaluated the risk of subsequent cancer development in diabetic cohorts receiving antidiabetic monotherapy. We conducted a population-based study in patients with new-onset type 2 diabetes treated with antidiabetic monotherapy.Methods: We identified a cohort of patients with type 2 diabetics aged ≥30 years receiving hypoglycemic monotherapy (n=7,325) from the 1998–2007 Longitudinal Health Insurance Dataset. Patients were grouped according to the antidiabetic therapy they received into metformin (n=2,223), sulfonylurea (n=3,965), glitazone (n=53), meglitinide (n=128), acarbose (n=150), and insulin (n=806) groups. Patients with preexisting cancer were excluded. All patients were followed up until cancer development, dropout, death, or until December 31, 2008. Cox’s model was used to estimate multivariable hazard ratios (HRs) adjusted for age, sex, Charlson comorbidity index, smoking-related comorbidities, alcohol use disorders, morbid obesity, pancreatitis, hypertension, monthly income, and urbanization level. The log-rank test was used to compare cumulative cancer incidence. Two-sided P-values 0.25 of metformin when compared to lower dose will contribute to a reduction of 80% risk.Keywords: type 2 diabetes, antidiabetic drug, monotherapy, metformin, sulfonylureas, cancer risk, NHIRD

Victor C Kok,1,2 Jorng-Tzong Horng,2,3 Hsu-Kai Huang,3 Tsung-Ming Chao,4 Ya-Fang Hong5 1Division of Medical Oncology, Department of Internal Medicine, Kuang Tien General Hospital, Taichung, Taiwan; 2Department of Biomedical Informatics, Asia University Taiwan, Taichung, Taiwan; 3Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan; 4Statistics Unit, Department of Applied Geomatics, Chien Hsin University, Jhongli, Taiwan; 5Institute of Molecular Biology, Academia Sinica, Nankang, Taipei, Taiwan Background: Recent studies have shown that inhaled corticosteroids (ICS) can exert anti-inflammatory effects for chronic airway diseases, and several observational studies suggest that they play a role as cancer chemopreventive agents, particularly against lung cancer. We aimed to examine whether regular ICS use was associated with a reduced risk for future malignancy in patients with newly diagnosed adult-onset asthma. Methods: We used a population-based cohort study between 2001 and 2008 with appropriate person-time analysis. Participants were followed up until the first incident of cancer, death, or to the end of 2008. The Cox model was used to derive an adjusted hazard ratio (aHR) for cancer development. Kaplan–Meier cancer-free survival curves of two groups were compared. Results: The exposed group of 2,117 regular ICS users and the nonexposed group of 17,732 non-ICS users were assembled. After 7,365 (mean, 3.5 years; standard deviation 2.1) and 73,789 (mean, 4.1 years; standard deviation 2.4) person-years of follow-up for the ICS users and the comparator group of non-ICS users, respectively, the aHR for overall cancer was nonsignificantly elevated at 1.33 with 95% confidence interval (CI), 1.00–1.76, P=0.0501. The Kaplan–Meier curves for overall cancer-free proportions of both groups were not significant (log-rank, P=0.065). Synergistic interaction of concurrent presence of regular ICS use was conducted using “ICS-negative and chronic obstructive pulmonary disease (COPD)-negative” as the reference. The aHR for the group of “ICS-positive, COPD-negative” did not reach statistically significant levels with aHR at 1.38 (95% CI, 0.53–3.56). There was a statistically significant synergistic interaction of concurrent presence of regular ICS use and COPD with aHR at 3.78 (95% CI, 2.10–6.81). Conclusion: The protective effect of regular ICS use in the studied East Asian patients with adult-onset asthma was not detectable, contrary to reports of previous studies that ICS might prevent the occurrence of future cancer. Keywords: immortal time bias, NHIRD, population-based study, retrospective cohort study, risk of cancer

Acetylcholinesterase (AChE) is one of the most successful targets for the treatment of Alzheimer's disease (AD). Inhibition of AChE can result in preventing AD. In this context, the machine-learning (ML) model, molecular docking, and molecular dynamics calculations were employed to characterize the potential inhibitors for AChE from MedChemExpress (MCE) database. The trained ML model was initially employed for estimating the inhibitory of MCE compounds. Atomistic simulations including molecular docking and molecular dynamics simulations were then used to confirm ML outcomes. In particular, the physical insights into the ligand binding to AChE were clarified over the calculations. Two compounds, PubChem ID of 130467298 and 132020434, were indicated that they can inhibit AChE., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Antimicrobial resistance is one of the most urgent global health threats, especially in the post-pandemic era. Antimicrobial peptides (AMPs) offer a promising alternative to traditional antibiotics, driving growing interest in recent years. dbAMP is a comprehensive database offering extensive annotations on AMPs, including sequence information, functional activity data, physicochemical properties and structural annotations. In this update, dbAMP has curated data from over 5200 publications, encompassing 33,065 AMPs and 2453 antimicrobial proteins from 3534 organisms. Additionally, dbAMP utilizes ESMFold to determine the three-dimensional structures of AMPs, providing over 30,000 structural annotations that facilitate structure-based functional insights for clinical drug development. Furthermore, dbAMP employs molecular docking techniques, providing over 100 docked complexes that contribute useful insights into the potential mechanisms of AMPs. The toxicity and stability of AMPs are critical factors in assessing their potential as clinical drugs. The updated dbAMP introduced an efficient tool for evaluating the hemolytic toxicity and half-life of AMPs, alongside an AMP optimization platform for designing AMPs with high antimicrobial activity, reduced toxicity and increased stability. The updated dbAMP is freely accessible at https://awi.cuhk.edu.cn/dbAMP/. Overall, dbAMP represents a comprehensive and essential resource for AMP analysis and design, poised to advance antimicrobial strategies in the post-pandemic era., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Post-translational modifications (PTMs) are essential for modulating protein function and influencing stability, activity and signaling processes. The dbPTM 2025 update significantly expands the database to include over 2.79 million PTM sites, of which 2.243 million are experimentally validated from 48 databases and over 80 000 research articles. This version integrates proteomic data from 13 cancer types, with a particular focus on phosphoproteomic data and kinase activity profiles, allowing the exploration of personalized phosphorylation patterns in tumor samples. Integrating kinase-substrate phosphorylations with E3 ligase-substrate interactions, dbPTM 2025 provides a detailed map of PTM regulatory networks, offering insights into cancer-specific post-translational regulations. This update also includes advanced search capabilities, enabling users to efficiently query PTM data across species, PTM types and modified residues. The platform's new features-interactive visualization tools and streamlined data downloads-allow researchers to access and analyze PTM data easily. dbPTM 2025 also enhances functional annotations, regulatory networks and disease associations, broadening its application for cancer research and the study of disease-associated PTMs. Through these enhancements, dbPTM 2025 is a comprehensive, user-friendly resource, facilitating the study of PTMs and their roles in cancer research. The database is now freely accessible at https://biomics.lab.nycu.edu.tw/dbPTM/., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The rise of antibiotic resistance necessitates effective alternative therapies. Antimicrobial peptides (AMPs) are promising due to their broad inhibitory effects. This study focuses on predicting the minimum inhibitory concentration (MIC) of AMPs against whom-priority pathogens: Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853. We developed a comprehensive regression model integrating AMP sequence-based and genomic features. Using eight AI-based architectures, including deep learning with protein language model embeddings, we created an ensemble model combining bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN), and multi-branch model (MBM). The ensemble model showed superior performance with Pearson correlation coefficients of 0.756, 0.781, and 0.802 for the bacterial strains, demonstrating its accuracy in predicting MIC values. This work sets a foundation for future studies to enhance model performance and advance AMP applications in combating antibiotic resistance., Competing Interests: The authors have no affiliations with or involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter or materials discussed in this article., (© 2024 The Authors.)

Background: Respiratory infections have long been recognized as a primary cause of acute exacerbation of chronic obstructive pulmonary disease (AE-COPD). Additionally, the emergence of antimicrobial resistance has led to an urgent and critical situation in developing countries, including Vietnam. This study aimed to investigate the distribution and antimicrobial resistance of bacteria in patients with AE-COPD using both conventional culture and multiplex real-time PCR. Additionally, associations between clinical characteristics and indicators of pneumonia in these patients were examined., Methods: This cross-sectional prospective study included 92 AE-COPD patients with pneumonia and 46 without pneumonia. Sputum specimens were cultured and examined for bacterial identification, and antimicrobial susceptibility was determined for each isolate. Multiplex real-time PCR was also performed to detect ten bacteria and seven viruses., Results: The detection rates of pathogens in AE-COPD patients with pneumonia were 92.39%, compared to 86.96% in those without pneumonia. A total of 26 pathogenic species were identified, showing no significant difference in distribution between the two groups. The predominant bacteria included Klebsiella pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae, followed by Acinetobacter baumannii and Streptococcus mitis. There was a slight difference in antibiotic resistance between bacteria isolated from two groups. The frequency of H. influenzae was notably greater in AE-COPD patients who experienced respiratory failure (21.92%) than in those who did not (9.23%). S. pneumoniae was more common in patients with stage I (44.44%) or IV (36.36%) COPD than in patients with stage II (17.39%) or III (9.72%) disease. ROC curve analysis revealed that C-reactive protein (CRP) levels could distinguish patients with AE-COPD with and without pneumonia (AUC = 0.78)., Conclusion: Gram-negative bacteria still play a key role in the etiology of AE-COPD patients, regardless of the presence of pneumonia. This study provides updated evidence for the epidemiology of AE-COPD pathogens and the appropriate selection of antimicrobial agents in Vietnam., (© 2024. The Author(s).)

Influenza A viruses spread out worldwide, causing several global concerns. Hence, discovering neuraminidase inhibitors to prevent the influenza A virus is of great interest. In this work, a machine learning model was employed to evaluate the ligand-binding affinity of ca. 10 000 compounds from the MedChemExpress (MCE) database for inhibiting neuraminidase. Atomistic simulations, including molecular docking and molecular dynamics simulations, then confirmed the ligand-binding affinity. Furthermore, we clarified the physical insights into the binding process of ligands to neuraminidase. It was found that five compounds, including micronomicin, didesmethyl cariprazine, argatroban, Kgp-IN-1, and AY 9944, are able to inhibit neuraminidase N1 of the influenza A virus. Ten residues, including Glu119, Asp151, Arg152, Trp179, Gln228, Glu277, Glu278, Arg293, Asn295, and Tyr402, may be very important in controlling the ligand-binding process to N1., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

In Indonesia, malaria remains a problem, with 94,610 active cases in 2021 and its current therapy includes chloroquine and artemisinin; however, resistance has been commonly reported. To overcome this problem, studies about potential medicinal plants that can be used as antimalaria, such as moringa ( Moringa oleifera ) started to receive more attention. The aim of this study was to investigate the effects of moringa in parasitemia, monocyte activation, and organomegaly on animal model malaria. This experimental study used male Mus musculus , infected by Plasmodium berghei ANKA, as an animal malaria model. The extract was made by maceration of dry moringa leaves, which were then divided into three concentrations: 25%, 50%, and 75%. Dihydroartemisinin-piperazine was used as a positive control treatment, and distilled water as a negative control treatment. The animals were observed for six days to assess the parasitemia count and the number of monocyte activation. On day 7, the animals were terminated, and the liver, spleen, and kidney were weighed. The results showed that the effective concentrations in reducing parasitemia and inducing monocyte activation were 50% and 25% of moringa leaf extract, respectively. The smallest liver and spleen enlargement was observed among animals within the group treated with a 50% concentration of M. oleifera extract. In contrast, the smallest kidney enlargement was observed in the group treated with 25% of M. oleifera extract. Further analysis is recommended to isolate compounds with antimalarial properties in moringa leaves., Competing Interests: All the authors declare that there are no conflicts of interest., (© 2024 by the authors.)

Gu-Sui-Bu, the dried rhizome of Davallia mariesii , is a traditional Chinese herbal remedy with a significant history of treating osteoporosis and inflammatory conditions. However, its potential as an anti-influenza agent and its underlying mechanisms of action remain unexplored. To obtain a more potent extract from D. mariesii and gain insights into its mechanism of action against influenza A virus (IAV), we utilized a partitioning process involving organic solvents and water, resulting in the isolation of butanolic subfractions of the D. mariesii extract (DMBE). DMBE exhibited a broad anti-viral spectrum, effectively inhibiting IAV, with an EC 50 of 24.32 ± 6.19 µg/mL and a selectivity index of 6.05. We subsequently conducted a series of in vitro assays to evaluate the antiviral effects of DMBE and to uncover its mechanisms of action. DMBE was found to inhibit IAV during the early stages of infection by hindering the attachment of the virus onto and its penetration into host cells. Importantly, DMBE was observed to hinder IAV-mediated cell-cell fusion. It also inhibited neuraminidase activity, plaque size, and the expression levels of phospho-AKT. In summary, this study provides evidence for the effectiveness of D. mariesii as a complementary and alternative herbal remedy against IAV. Specifically, our data highlight DMBE's capabilities in inhibiting viral entry and the release of virions.

VP39, an essential 2'-O-RNA methyltransferase enzyme discovered in Monkeypox virus (MPXV), plays a vital role in viral RNA replication and transcription. Inhibition of the enzyme may prevent viral replication. In this context, using a combination of molecular docking and molecular dynamics (MDs) simulations, the inhibitory ability of NCI Diversity Set VII natural compounds to VP39 protein was investigated. It should be noted that the computed binding free energy of ligand via molecular docking and linear interaction energy (LIE) approaches are in good agreement with the corresponding experiments with coefficients of R = 0.72 and 0.75, respectively. NSC 319990, NSC 196515 and NSC 376254 compounds were demonstrated that can inhibit MPVX methyltransferase VP39 protein with the similar affinity compared to available inhibitor sinefungin. Moreover, nine residues involving Gln39 , Gly68 , Gly72 , Asp95 , Arg97 , Val116 , Asp138 , Arg140 and Asn156 may be argued that they play an important role in binding process of inhibitors to VP39.Communicated by Ramaswamy H. Sarma.

The challenge of drug-resistant bacteria to global public health has led to increased attention on antimicrobial peptides (AMPs) as a targeted therapeutic alternative with a lower risk of resistance. However, high production costs and limitations in functional class prediction have hindered progress in this field. In this study, we used multi-label classifiers with binary relevance and algorithm adaptation techniques to predict different functions of AMPs across a wide range of pathogen categories, including bacteria, mammalian cells, fungi, viruses, and cancer cells. Our classifiers attained promising AUC scores varying from 0.8492 to 0.9126 on independent testing data. Forward feature selection identified sequence order and charge as critical, with specific amino acids (C and E) as discriminative. These findings provide valuable insights for the design of antimicrobial peptides (AMPs) with multiple functionalities, thus contributing to the broader effort to combat drug-resistant pathogens., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision-recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Dried Iris rhizomes have been used in Chinese and European traditional medicine for the treatment of various diseases such as bacterial infections, cancer, and inflammation, as well as for being astringent, laxative, and diuretic agents. Eighteen phenolic compounds including some rare secondary metabolites, such as irisolidone, kikkalidone, irigenin, irisolone, germanaism B, kaempferol, and xanthone mangiferin, were isolated for the first time from Iris aphylla rhizomes. The hydroethanolic Iris aphylla extract and some of its isolated constituents showed protective effects against influenza H1N1 and enterovirus D68 and anti-inflammatory activity in human neutrophils. The promising anti-influenza effect of apigenin (13: , almost 100% inhibition at 50 µM), kaempferol (14: , 92%), and quercetin (15: , 48%) were further confirmed by neuraminidase inhibitory assay. Irisolidone (1: , almost 100% inhibition at 50 µM), kikkalidone (5: , 93%), and kaempferol (14: , 83%) showed promising anti-enterovirus D68 activity in vitro . The identified compounds were plotted using ChemGPS-NP to correlate the observed activity of the isolated phenolic compounds with the in-house database of anti-influenza and anti-enterovirus agents. Our results indicated that the hydroethanolic Iris aphylla extract and Iris phenolics hold the potential to be developed for the management of seasonal pandemics of influenza and enterovirus infections., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. IMPORTANCE Based on a large amount of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM), were considered for the two-stage model training, and the values of the area under the receiver operating characteristic curve (AUC) were 0.62 for AMC, 0.72 for CAZ, 0.87 for CIP, 0.72 for CRO, and 0.72 for CXM. Further investigations revealed that the informative peak m/z 9714 appeared with some important peaks at m/z 6809, m/z 7650, m/z 10534, and m/z 11783 for CIP and at m/z 6809, m/z 10475, and m/z 8447 for CAZ, CRO, and CXM. This framework has the potential to improve the accuracy by approximately 2.8%, indicating a promising potential for further research., Competing Interests: The authors declare no conflict of interest.

Since December 2019, the Coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread rapidly around the world, overburdening healthcare systems and creating significant global health concerns. Rapid detection of infected individuals via early diagnostic tests and administration of effective therapy remains vital in pandemic control, and recent advances in the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated proteins (Cas) system may support the development of novel diagnostic and therapeutic approaches. Cas-based SARS-CoV-2 detection methods (FnCAS9 Editor Linked Uniform Detection Assay (FELUDA), DNA endonuclease-targeted CRISPR trans reporter (DETECTR), and Specific High-sensitivity Enzymatic Reporter Unlocking (SHERLOCK)) have been developed for easier handling compared to quantitative polymerase chain reaction (qPCR) assays, with good rapidity, high specificity, and reduced need for complex instrumentation. Cas-CRISPR-derived RNA (Cas-crRNA) complexes have been shown to reduce viral loads in the lungs of infected hamsters, by degrading virus genomes and limiting viral replication in host cells. Viral-host interaction screening platforms have been developed using the CRISPR-based system to identify essential cellular factors involved in pathogenesis, and CRISPR knockout (CRISPRKO) and activation screening results have revealed vital pathways in the life cycle of coronaviruses, including host cell entry receptors (ACE2, DPP4, and ANPEP), proteases involved in spike activation and membrane fusion (cathepsin L (CTSL) and transmembrane protease serine 2 (TMPRSS2)), intracellular traffic control routes for virus uncoating and budding, and membrane recruitment for viral replication. Several novel genes (SWI/SNF Related, Matrix Associated, Actin Dependent Regulator of Chromatin, subfamily A, member 4 (SMARCA4), ARIDIA, and KDM6A) have also been identified via systematic data mining analysis as pathogenic factors for severe CoV infection. This review highlights how CRISPR-based systems can be applied to investigate the viral life cycle, detect viral genomes, and develop therapies against SARS-CoV-2 infection., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii , Acinetobacter nosocomialis , Enterococcus faecium , and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% ( A. baumannii ), 90.96% ( A. nosocomialis ), 78.54% ( E. faecium ), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.

Background: The purpose of this study was to examine the in vivo activity of rosmarinic acid (RA) - a phytochemical with antioxidant, anti-inflammatory, and antiviral properties - against influenza virus (IAV). An antibody-based kinase array and different in vitro functional assays were also applied to identify the mechanistic underpinnings by which RA may exert its anti-IAV activity., Methods: We initially examined the potential efficacy of RA using an in vivo mouse model. A time-of-addition assay and an antibody-based kinase array were subsequently applied to investigate mechanism-of-action targets for RA. The hemagglutination inhibition assay, neuraminidase inhibition assay, and cellular entry assay were also performed., Results: RA increased survival and prevented body weight loss in IAV-infected mice. In vitro experiments revealed that RA inhibited different IAV viruses - including oseltamivir-resistant strains. From a mechanistic point of view, RA downregulated the GSK3β and Akt signaling pathways - which are known to facilitate IAV entry and replication into host cells., Conclusions: RA has promising preclinical efficacy against IAV, primarily by interfering with the GSK3β and Akt signaling pathways., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2022. Published by Elsevier B.V.)

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species and predict antibiotic-resistant strains. However, the conventional approach using few single peaks would result in insufficient prediction power without using complete information of whole MS spectra. In the past few years, machine learning algorithms have been successfully applied to analyze the MALDI-TOF MS peaks pattern for rapid strain typing. In this study, we developed a convolutional neural network (CNN) method to deal with the complete information of MALDI-TOF MS spectra for detecting Enterococcus faecium , which is one of the leading pathogens in the world. We developed a CNN model to rapidly and accurately predict vancomycin-resistant Enterococcus faecium (VRE fm ) samples from the whole mass spectra profiles of clinical samples. The CNN models demonstrated good classification performances with the average area under the receiver operating characteristic curve (AUROC) of 0.887 when using external validation data independently. Additionally, we employed the score-class activation mapping (CAM) method to identify the important features of our CNN models and found some discriminative signals that can substantially contribute to detecting the ion of resistance. This study not only utilized the complete information of MALTI-TOF MS data directly but also provided a practical means for rapid detection of VRE fm using a deep learning algorithm., Competing Interests: T-TH, H-CC, and J-HH were employed by Taiwan AI Labs. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Hsieh, Chung, Chang, Horng, Lu and Huang.)

While infections by enterovirus A71 (EV-A71) are generally self-limiting, they can occasionally lead to serious neurological complications and death. No licensed therapies against EV-A71 currently exist. Using anti-virus-induced cytopathic effect assays, 3,4-dicaffeoylquinic acid (3,4-DCQA) from Ilex kaushue extracts was found to exert significant anti-EV-A71 activity, with a broad inhibitory spectrum against different EV-A71 genotypes. Time-of-drug-addition assays revealed that 3,4-DCQA affects the initial phase (entry step) of EV-A71 infection by directly targeting viral particles and disrupting viral attachment to host cells. Using resistant virus selection experiments, we found that 3,4-DCQA targets the glutamic acid residue at position 98 (E98) and the proline residue at position 246 (P246) in the 5-fold axis located within the VP1 structural protein. Recombinant viruses harboring the two mutations were resistant to 3,4-DCQA-elicited inhibition of virus attachment and penetration into human rhabdomyosarcoma (RD) cells. Finally, we showed that 3,4-DCQA specifically inhibited the attachment of EV-A71 to the host receptor heparan sulfate (HS), but not to the scavenger receptor class B member 2 (SCARB2) and P-selectin glycoprotein ligand-1 (PSGL1). Molecular docking analysis confirmed that 3,4-DCQA targets the 5-fold axis to form a stable structure with the E98 and P246 residues through noncovalent and van der Waals interactions. The targeting of E98 and P246 by 3,4-DCQA was found to be specific; accordingly, HS binding of viruses carrying the K242A or K244A mutations in the 5-fold axis was successfully inhibited by 3,4-DCQA.The clinical utility of 3,4-DCQA in the prevention or treatment of EV-A71 infections warrants further scrutiny. IMPORTANCE The canyon region and the 5-fold axis of the EV-A71 viral particle located within the VP1 protein mediate the interaction of the virus with host surface receptors. The three most extensively investigated cellular receptors for EV-A71 include SCARB2, PSGL1, and cell surface heparan sulfate. In the current study, a RD cell-based anti-cytopathic effect assay was used to investigate the potential broad spectrum inhibitory activity of 3,4-DCQA against different EV-A71 strains. Mechanistically, we demonstrate that 3,4-DCQA disrupts the interaction between the 5-fold axis of EV-A71 and its heparan sulfate receptor; however, no effect was seen on the SCARB2 or PSGL1 receptors. Taken together, our findings show that this natural product may pave the way to novel anti-EV-A71 therapeutic strategies.

Multidrug resistance has become a phenotype that commonly exists among Staphylococcus aureus and is a serious concern for infection treatment. Nowadays, to detect the antibiotic susceptibility, antibiotic testing is generated based on the level of genomic for cure decision consuming huge of time and labor, while matrix-assisted laser desorption-ionization (MALDI) time-of-flight mass spectrometry (TOF/MS) shows its possibility in high-speed and effective detection on the level of proteomic. In this study, on the basis of MALDI-TOF spectra data of discovery cohort with 26,852 samples and replication cohort with 4,963 samples from Taiwan area and their corresponding susceptibilities to oxacillin and clindamycin, a multi-label prediction model against double resistance using Lowest Power set ensemble with XGBoost is constructed for rapid susceptibility prediction. With the output of serial susceptibility prediction, the model performance can realize 77% of accuracy for the serial prediction, the area under the receiver characteristic curve of 0.93 for oxacillin susceptibility prediction, and the area under the receiver characteristic curve of 0.89 for clindamycin susceptibility prediction. The generated multi-label prediction model provides serial antibiotic resistance, such as the susceptibilities of oxacillin and clindamycin in this study, for S. aureus -infected patients based on MALDI-TOF, which will provide guidance in antibiotic usage during the treatment taking the advantage of speed and efficiency., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Wang, Wang, Chung, Horng, Lu and Lee.)

Klebsiella pneumoniae is one of the most common causes of hospital- and community-acquired pneumoniae. Resistance to the extensively used quinolone antibiotic, such as ciprofloxacin, has increased in Klebsiella pneumoniae , which leads to the increase in the risk of initial antibiotic selection for Klebsiella pneumoniae treatment. Rapid and precise identification of ciprofloxacin-resistant Klebsiella pneumoniae (CIRKP) is essential for clinical therapy. Nowadays, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is another approach to discover antibiotic-resistant bacteria due to its shorter inspection time and lower cost than other current methods. Machine learning methods are introduced to assist in discovering significant biomarkers from MALDI-TOF MS data and construct prediction models for rapid antibiotic resistance identification. This study examined 16,997 samples taken from June 2013 to February 2018 as part of a longitudinal investigation done by Change Gung Memorial Hospitals (CGMH) at the Linkou branch. We applied traditional statistical approaches to identify significant biomarkers, and then a comparison was made between high-importance features in machine learning models and statistically selected features. Large-scale data guaranteed the statistical power of selected biomarkers. Besides, clustering analysis analyzed suspicious sub-strains to provide potential information about their influences on antibiotic resistance identification performance. For modeling, to simulate the real antibiotic resistance predicting challenges, we included basic information about patients and the types of specimen carriers into the model construction process and separated the training and testing sets by time. Final performance reached an area under the receiver operating characteristic curve (AUC) of 0.89 for support vector machine (SVM) and extreme gradient boosting (XGB) models. Also, logistic regression and random forest models both achieved AUC around 0.85. In conclusion, models provide sensitive forecasts of CIRKP, which may aid in early antibiotic selection against Klebsiella pneumoniae . The suspicious sub-strains could affect the model performance. Further works could keep on searching for methods to improve both the model accuracy and stability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Wang, Wang, Chung, Horng, Lu and Lee.)

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Enterococcus faecium is a clinically important pathogen that can cause significant morbidity and death. In this study, we aimed to develop a machine learning (ML) algorithm-based rapid susceptibility method to distinguish vancomycin-resistant E. faecium (VRE fm ) and vancomycin-susceptible E. faecium (VSE fm ) strains. A predictive model was developed and validated to distinguish VRE fm and VSE fm strains by analyzing the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) spectra of unique E. faecium isolates from different specimen types. The algorithm used 5,717 mass spectra, including 2,795 VRE fm and 2,922 VSE fm mass spectra, and was externally validated with 2,280 mass spectra of isolates (1,222 VRE fm and 1,058 VSE fm strains). A random forest-based algorithm demonstrated overall good classification performances for the isolates from the specimens, with mean accuracy, sensitivity, and specificity of 0.78, 0.79, and 0.77, respectively, with 10-fold cross-validation, timewise validation, and external validation. Furthermore, the algorithm provided rapid results, which would allow susceptibility prediction prior to the availability of phenotypic susceptibility results. In conclusion, an ML algorithm designed using mass spectra obtained from the routine workflow may be able to rapidly differentiate VRE fm strains from VSE fm strains; however, susceptibility results must be confirmed by routine methods, given the demonstrated performance of the assay. IMPORTANCE A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on a large-scale MS data set of clinical VRE fm and VSE fm isolates, including 2,795 VRE fm and 2,922 VSE fm isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, the accuracy of which was 0.50, based on the local epidemiology. The algorithm improved the accuracy of antibiotic administration, compared to empirical antibiotic prescription. An ML algorithm designed using MALDI-TOF MS spectra obtained from the routine workflow accurately differentiated VRE fm strains from VSE fm strains, especially in blood and sterile body fluid samples, and can be applied to facilitate the rapid and accurate clinical testing of pathogens.

As antibiotics resistance on superbugs has risen, more and more studies have focused on developing rapid antibiotics susceptibility tests (AST). Meanwhile, identification of multiple antibiotics resistance on Staphylococcus aureus provides instant information which can assist clinicians in administrating the appropriate prescriptions. In recent years, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a powerful tool in clinical microbiology laboratories for the rapid identification of bacterial species. Yet, lack of study devoted on providing efficient methods to deal with the MS shifting problem, not to mention to providing tools incorporating the MALDI-TOF MS for the clinical use which deliver the instant administration of antibiotics to the clinicians. In this study, we developed a web tool, MDRSA, for the rapid identification of oxacillin-, clindamycin-, and erythromycin-resistant Staphylococcus aureus . Specifically, the kernel density estimation (KDE) was adopted to deal with the peak shifting problem, which is critical to analyze mass spectra data, and machine learning methods, including decision trees, random forests, and support vector machines, which were used to construct the classifiers to identify the antibiotic resistance. The areas under the receiver operating the characteristic curve attained 0.8 on the internal (10-fold cross validation) and external (independent testing) validation. The promising results can provide more confidence to apply these prediction models in the real world. Briefly, this study provides a web-based tool to provide rapid predictions for the resistance of antibiotics on Staphylococcus aureus based on the MALDI-TOF MS data. The web tool is available at: http://fdblab.csie.ncu.edu.tw/mdrsa/., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Chung, Wang, Weng, Wang, Wu, Tseng, Chen, Lu, Horng and Lee.)

The Michael addition reaction is a spontaneous and quick chemical reaction that is widely applied in various fields. This reaction is performed by conjugating an addition of nucleophiles with α, β-unsaturated carbonyl compounds, resulting in the bond formation of C-N, C-S, C-O, and so on. In the development of molecular materials, the Michael addition is not only used to synthesize chemical compounds but is also involved in the mechanism of drug action. Several covalent drugs that bond via Michael addition are regarded as anticarcinogens and anti-inflammatory drugs. Although drug development is mainly focused on pharmaceutical drug discovery, target-based discovery can provide a different perspective for drug usage. However, considerable time and labor are required to define a molecular target through molecular biological experiments. In this review, we systematically examine the chemical structures of current FDA-approved antiviral drugs for potential Michael addition moieties with α, β-unsaturated carbonyl groups, which may exert an unidentified broad-spectrum inhibitory mechanism to target viral or host factors. We thus propose that profiling the targets of antiviral agents, such as Michael addition products, can be achieved by employing a high-throughput LC-MS approach to comprehensively analyze the interaction between drugs and targets, and the subsequent drug responses in the cellular environment to facilitate drug repurposing and/or identify potential adverse effects, with a particular emphasis on the pros and cons of this shotgun proteomic approach.

Background: A mass spectrometry-based assessment of methicillin resistance in Staphylococcus aureus would have huge potential in addressing fast and effective prediction of antibiotic resistance. Since delays in the traditional antibiotic susceptibility testing, methicillin-resistant S. aureus remains a serious threat to human health., Results: Here, linking a 7 years of longitudinal study from two cohorts in the Taiwan area of over 20 000 individually resolved methicillin susceptibility testing results, we identify associations of methicillin resistance with the demographics and mass spectrometry data. When combined together, these connections allow for machine-learning-based predictions of methicillin resistance, with an area under the receiver operating characteristic curve of >0.85 in both the discovery [95% confidence interval (CI) 0.88-0.90] and replication (95% CI 0.84-0.86) populations., Conclusions: Our predictive model facilitates early detection for methicillin resistance of patients with S. aureus infection. The large-scale antibiotic resistance study has unbiasedly highlighted putative candidates that could improve trials of treatment efficiency and inform on prescriptions., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

In December 2020, the U.K. authorities reported to the World Health Organization (WHO) that a new COVID-19 variant, considered to be a variant under investigation from December 2020 (VUI-202012/01), was identified through viral genomic sequencing. Although several other mutants were previously reported, VUI-202012/01 proved to be about 70% more transmissible. Hence, the usefulness and effectiveness of the newly U.S. Food and Drug Administration (FDA)-approved COVID-19 vaccines against these new variants are doubtfully questioned. As a result of these unexpected mutants from COVID-19 and due to lack of time, much research interest is directed toward assessing secondary metabolites as potential candidates for developing lead pharmaceuticals. In this study, a marine-derived fungus Aspergillus terreus was investigated, affording two butenolide derivatives, butyrolactones I ( 1 ) and III ( 2 ), a meroterpenoid, terretonin ( 3 ), and 4-hydroxy-3-(3-methylbut-2-enyl)benzaldehyde ( 4 ). Chemical structures were unambiguously determined based on mass spectrometry and extensive 1D/2D NMR analyses experiments. Compounds ( 1 - 4 ) were assessed for their in vitro anti-inflammatory, antiallergic, and in silico COVID-19 main protease (M pro ) and elastase inhibitory activities. Among the tested compounds, only 1 revealed significant activities comparable to or even more potent than respective standard drugs, which makes butyrolactone I ( 1 ) a potential lead entity for developing a new remedy to treat and/or control the currently devastating and deadly effects of COVID-19 pandemic and elastase-related inflammatory complications.

Background: While severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection presents with mild or no symptoms in most cases, a significant number of patients become critically ill. Remdesivir has been approved for the treatment of coronavirus disease 2019 (COVID-19) in several countries, but its use as monotherapy has not substantially lowered mortality rates. Because agents from traditional Chinese medicine (TCM) have been successfully utilized to treat pandemic and endemic diseases, we designed the current study to identify novel anti-SARS-CoV-2 agents from TCM., Methods: We initially used an antivirus-induced cell death assay to screen a panel of herbal extracts. The inhibition of the viral infection step was investigated through a time-of-drug-addition assay, whereas a plaque reduction assay was carried out to validate the antiviral activity. Direct interaction of the candidate TCM compound with viral particles was assessed using a viral inactivation assay. Finally, the potential synergistic efficacy of remdesivir and the TCM compound was examined with a combination assay., Results: The herbal medicine Perilla leaf extract (PLE, approval number 022427 issued by the Ministry of Health and Welfare, Taiwan) had EC 50 of 0.12 ± 0.06 mg/mL against SARS-CoV-2 in Vero E6 cells - with a selectivity index of 40.65. Non-cytotoxic PLE concentrations were capable of blocking viral RNA and protein synthesis. In addition, they significantly decreased virus-induced cytokine release and viral protein/RNA levels in the human lung epithelial cell line Calu-3. PLE inhibited viral replication by inactivating the virion and showed additive-to-synergistic efficacy against SARS-CoV-2 when used in combination with remdesivir., Conclusion: Our results demonstrate for the first time that PLE is capable of inhibiting SARS-CoV-2 replication by inactivating the virion. Our data may prompt additional investigation on the clinical usefulness of PLE for preventing or treating COVID-19., Competing Interests: Conflicts of interest The authors declare they have no actual or potential competing financial interests., (Copyright © 2021 Chang Gung University. Published by Elsevier B.V. All rights reserved.)

Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due to an increasingly clinical need to classify between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) efficiently and effectively, we were motivated to develop a systematic pipeline based on a large-scale dataset of MS spectra. However, the shifting problem of peaks in MS spectra induced a low effectiveness in the classification between MRSA and MSSA isolates. Unlike previous works emphasizing on specific peaks, this study employs a binning method to cluster MS shifting ions into several representative peaks. A variety of bin sizes were evaluated to coalesce drifted or shifted MS peaks to a well-defined structured data. Then, various machine learning methods were performed to carry out the classification between MRSA and MSSA samples. Totally 4858 MS spectra of unique S. aureus isolates, including 2500 MRSA and 2358 MSSA instances, were collected by Chang Gung Memorial Hospitals, at Linkou and Kaohsiung branches, Taiwan. Based on the evaluation of Pearson correlation coefficients and the strategy of forward feature selection, a total of 200 peaks (with the bin size of 10 Da) were identified as the marker attributes for the construction of predictive models. These selected peaks, such as bins 2410-2419, 2450-2459 and 6590-6599 Da, have indicated remarkable differences between MRSA and MSSA, which were effective in the prediction of MRSA. The independent testing has revealed that the random forest model can provide a promising prediction with the area under the receiver operating characteristic curve (AUC) at 0.8450. When comparing to previous works conducted with hundreds of MS spectra, the proposed scheme demonstrates that incorporating machine learning method with a large-scale dataset of clinical MS spectra may be a feasible means for clinical physicians on the administration of correct antibiotics in shorter turn-around-time, which could reduce mortality, avoid drug resistance and shorten length of stay in hospital in the future., (© The Author(s) 2020. Published by Oxford University Press.)

The proteostasis network guarantees successful protein synthesis, folding, transportation, and degradation. Mounting evidence has revealed that this network maintains proteome integrity and is linked to cellular physiology, pathology, and virus infection. Human enterovirus A71 (EV-A71) and EV-D68 are suspected causative agents of acute flaccid myelitis, a severe poliomyelitis-like neurologic syndrome with no known cure. In this context, further clarification of the molecular mechanisms underlying EV-A71 and EV-D68 infection is paramount. Here, we summarize the components of the proteostasis network that are intercepted by EV-A71 and EV-D68, as well as antivirals that target this network and may help develop improved antiviral drugs., (Copyright © 2021. Published by Elsevier B.V.)

Enterovirus A71 (EV-A71), a positive-stranded RNA virus of the Picornaviridae family, may cause neurological complications or fatality in children. We examined specific factors responsible for this virulence using a chemical genetics approach. Known compounds from an anti-EV-A71 herbal medicine, Salvia miltiorrhiza (Danshen), were screened for anti-EV-A71. We identified a natural product, rosmarinic acid (RA), as a potential inhibitor of EV-A71 by cell-based antiviral assay and in vivo mouse model. Results also show that RA may affect the early stage of viral infection and may target viral particles directly, thereby interfering with virus-P-selectin glycoprotein ligand-1 (PSGL1) and virus-heparan sulfate interactions without abolishing the interaction between the virus and scavenger receptor B2 (SCARB2). Sequencing of the plaque-purified RA-resistant viruses revealed a N104K mutation in the five-fold axis of the structural protein VP1, which contains positively charged amino acids reportedly associated with virus-PSGL1 and virus-heparan sulfate interactions via electrostatic attraction. The plasmid-derived recombinant virus harbouring this mutation was confirmed to be refractory to RA inhibition. Receptor pull-down showed that this non-positively charged VP1-N104 is critical for virus binding to heparan sulfate. As the VP1-N104 residue is conserved among different EV-A71 strains, RA may be useful for inhibiting EV-A71 infection, even for emergent virus variants. Our study provides insight into the molecular mechanism of virus-host interactions and identifies a promising new class of inhibitors based on its antiviral activity and broad spectrum effects against a range of EV-A71.

Background and Objectives: Fenofibrate, a PPAR-α agonist, has been demonstrated to reduce the progression of diabetic retinopathy (DR) and the need for laser treatment in a FIELD (Fenofibrate Intervention and Event Lowering in Diabetes) study. However, in the subgroup of patients without pre-existing DR, there was no significant difference in the progression of DR between the fenofibrate group and the placebo group. In this study, we aim to investigate whether fenofibrate can decrease the risk of incident DR in a population-based cohort study of type 2 diabetic patients in Taiwan. Materials and Methods: A total of 32,253 type 2 diabetic patients without previous retinopathy were retrieved from 892,419 patients in 2001-2002. They were then divided into two groups based on whether they were exposed to fenofibrate or not. The patients were followed until a diagnosis of diabetic retinopathy was made or until the year 2008. Results: With a follow-up period of 6.8 ± 1.5 years and 5.4 ± 2.6 years for 2500 fenofibrate users and 29,753 non-users, respectively, the Cox proportional hazard regression analysis revealed that the hazard ratio (HR) of new onset retinopathy was 0.57 (95% CI 0.57-0.62, p < 0.001). After adjusting for hypertension; the Charlson comorbidity index (CCI); and medications such as angiotensin-converting enzyme inhibitors (ACE-I), angiotensin receptor blockers (ARB), anticoagulants, gemfibrozil, statins, and hypoglycemic agents, the adjusted HR was 0.75 (95% CI 0.68-0.82, p < 0.001). The need for laser treatment has an HR and adjusted HR of 0.59 (95% CI 0.49-0.71, p < 0.001) and 0.67 (95% CI 0.56-0.81, p < 0.001), respectively. Conclusion: Our study showed that the long-term and regular use of fenofibrate may decrease the risk of incident retinopathy and the need for laser treatment in type 2 diabetic patients. Since there are limitations associated with our study, further investigations are necessary to confirm such an association.

Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level, the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information, and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo (https://fdblab.csie.ncu.edu.tw/kmalo/home.html), which can help facilitate the functional investigation of protein malonylation on mammals and plants.

Because of the rapid development of multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to the antibiotic-resistance crisis. However, the identification of AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) and physicochemical properties with different sequence lengths against different organisms to predict AMPs. Therefore, the major purpose of this study is to identify AMPs on seven categories of organisms, including amphibians, humans, fish, insects, plants, bacteria, and mammals. According to the one-rule attribute evaluation, the selected features were used to construct the predictive models based on the random forest algorithm. Compared to the accuracies of iAMP-2L (a web-server for identifying AMPs and their functional types), ADAM (a database of AMP), and MLAMP (a multi-label AMP classifier), the proposed method yielded higher than 92% in predicting AMPs on each category. Additionally, the sensitivities of the proposed models in the prediction of AMPs of seven organisms were higher than that of all other tools. Furthermore, several physicochemical properties (charge, hydrophobicity, polarity, polarizability, secondary structure, normalized van der Waals volume, and solvent accessibility) of AMPs were investigated according to their sequence lengths. As a result, the proposed method is a practical means to complement the existing tools in the characterization and identification of AMPs in different organisms., Competing Interests: The authors declare no conflict of interest.

Background: Group B streptococcus (GBS) is an important pathogen that is responsible for invasive infections, including sepsis and meningitis. GBS serotyping is an essential means for the investigation of possible infection outbreaks and can identify possible sources of infection. Although it is possible to determine GBS serotypes by either immuno-serotyping or geno-serotyping, both traditional methods are time-consuming and labor-intensive. In recent years, the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported as an effective tool for the determination of GBS serotypes in a more rapid and accurate manner. Thus, this work aims to investigate GBS serotypes by incorporating machine learning techniques with MALDI-TOF MS to carry out the identification., Results: In this study, a total of 787 GBS isolates, obtained from three research and teaching hospitals, were analyzed by MALDI-TOF MS, and the serotype of the GBS was determined by a geno-serotyping experiment. The peaks of mass-to-charge ratios were regarded as the attributes to characterize the various serotypes of GBS. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), were then used to construct predictive models for the five different serotypes (Types Ia, Ib, III, V, and VI). After optimization of feature selection and model generation based on training datasets, the accuracies of the selected models attained 54.9-87.1% for various serotypes based on independent testing data. Specifically, for the major serotypes, namely type III and type VI, the accuracies were 73.9 and 70.4%, respectively., Conclusion: The proposed models have been adopted to implement a web-based tool (GBSTyper), which is now freely accessible at http://csb.cse.yzu.edu.tw/GBSTyper/, for providing efficient and effective detection of GBS serotypes based on a MALDI-TOF MS spectrum. Overall, this work has demonstrated that the combination of MALDI-TOF MS and machine intelligence could provide a practical means of clinical pathogen testing.

Background: Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available., Objective: As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature., Methods: In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function., Results: Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches., Conclusions: To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches., (©Po-Ting Lai, Wei-Liang Lu, Ting-Rung Kuo, Chia-Ru Chung, Jen-Chieh Han, Richard Tzong-Han Tsai, Jorng-Tzong Horng. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.11.2019.)

Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus . The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus ., (Copyright © 2019 Chung, Wang, Lien, Tseng, Chen, Lee, Liu, Horng and Lu.)

In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues of proteins are dominant against infections. Importantly, AMPs are produced by virtually all known living organisms and are able to target a wide range of pathogenic microorganisms, including viruses, parasites, bacteria and fungi. Although several studies have proposed different machine learning methods to predict peptides as being AMPs, most do not consider the diversity of AMP activities. On this basis, we specifically investigated the sequence features of AMPs with a range of functional activities, including anti-parasitic, anti-viral, anti-cancer and anti-fungal activities and those that target mammals, Gram-positive and Gram-negative bacteria. A new scheme is proposed to systematically characterize and identify AMPs and their functional activities. The 1st stage of the proposed approach is to identify the AMPs, while the 2nd involves further characterization of their functional activities. Sequential forward selection was employed to extract potentially informative features that are possibly associated with the functional activities of the AMPs. These features include hydrophobicity, the normalized van der Waals volume, polarity, charge and solvent accessibility-all of which are essential attributes in classifying between AMPs and non-AMPs. The results revealed the 1st stage AMP classifier was able to achieve an area under the receiver operating characteristic curve (AUC) value of 0.9894. During the 2nd stage, we found pseudo amino acid composition to be an informative attribute when differentiating between AMPs in terms of their functional activities. The independent testing results demonstrated that the AUCs of the multi-class models were 0.7773, 0.9404, 0.8231, 0.8578, 0.8648, 0.8745 and 0.8672 for anti-parasitic, anti-viral, anti-cancer, anti-fungal AMPs and those that target mammals, Gram-positive and Gram-negative bacteria, respectively. The proposed scheme helps facilitate biological experiments related to the functional analysis of AMPs. Additionally, it was implemented as a user-friendly web server (AMPfun, http://fdblab.csie.ncu.edu.tw/AMPfun/index.html) that allows individuals to explore the antimicrobial functions of peptides of interest., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The incidence of urothelial carcinoma (UC) is higher in patients undergoing chronic dialysis than in the general population. This study investigated plasma miRNA profiling as the ancillary diagnosis biomarker associated with UC in patients undergoing chronic hemodialysis. We successfully screened out and detected miRNA expression from plasma in eight patients undergoing dialysis through quantitative real-time PCR array analysis and identified eight candidate miRNAs. The candidate miRNAs were then validated using single quantitative RT-PCR assays from 52 plasma samples. The miRNA classifier for ancillary UC detection was developed by multiple logistic regression analyses. Moreover, we validated the classifier by testing another nine samples. Expression levels of miR-150-5p, miR-150-5p/miR-155-5p, miR-378a-3p/miR-150-5p, miR-636/miR-150-5p, miR-150-5p/miR-210-3p, and miR-19b-1-5p/miR-378a-3p were shown to be significantly different between UC and non-UC samples ( P = 0.035, 0.0048, 0.016, 0.024, 0.038, and 0.048). Kaplan-Meier curve analysis also showed that low miR-19b-1-5p expression was associated with a worse prognosis ( P = 0.0382). We also developed a miRNA classifier based on five miRNA expression levels to predict UC and found that the area under curve was 0.882. The classifier had a sensitivity of 80% (95% confidence interval: 0.5191% to 0.9567%) and a specificity of 83.7% (95% confidence interval: 0.6799% to 0.9381%). This classifier was tested by nine samples with 100% accuracy. The miRNA classifier offers higher sensitivity and specificity than the existing makers. Thus, this approach will improve the prospective diagnosis of UC in patients undergoing chronic hemodialysis.

Influenza A viruses (IAVs) have caused worldwide epidemics and pandemics by reassortment and generation of drug-resistant mutants, which render antivirals and current vaccinations no longer usable. In this study, an itaconic acid derivative 1 was identified from a chemical library of 20 000 compounds, by performing a cell-based screening assay, as a lead agent exhibiting anti-influenza A activity. Accordingly, a series of itaconic acid derivatives were designed and synthesized by adopting a rational design strategy to obtain more potent anti-influenza agents. The results of an in vitro pharmacological study showed that compounds 4 and 8 exhibited the most potent anti-IAV effect with half-maximal effective concentration values of 0.14 and 0.11 μM, respectively, in Madin-Darby canine kidney cells. The mechanism of action studies showed that lead agents 1 and 4 reduced virus replication by directly targeting IAV nucleoproteins and disrupting virus ribonucleoprotein export from the nucleus to the cytosol. On the basis of its high potential as an anti-IAV agent and its selectivity index >785, compound 4 was found to be a promising candidate for further development against IAVs.

Pathogenic bacteremia portends a high mortality risk in adult patients admitted to an Emergency Department (ED). This study aims to investigate the effect of adding high-sensitivity C-reactive protein (hs-CRP) to procalcitonin (PCT) and lactate in predicting bacteremia, Gram-negative (GNB) and Gram-positive bacteremia (GPB), using the optimal cutoff derived from the receiver operating characteristics analysis. We evaluated the diagnostic measures, including the positive-test likelihood (LR+), the negative-test likelihood (LR-), and the diagnostic odds ratio (DOR) using a single-center retrospective analysis design. This Standards for Reporting Diagnostic-compliant study comprised 886 consecutive adults who were admitted to the ED in 2010; to this cohort, a 22.2% prevalence of true bacteremia was subsequently confirmed. At the cutoff of 3.9 μg/L, PCT had a DOR of 5.3 (95% confidence interval [CI]: 3.76-7.61) and LR + of 2.8 (95% CI: 2.3-3.4) in predicting overall bacteremia. Elevated PCT and lactate (cutoff at 2 mmol/L), increased the DOR and LR + to 6.3 (95% CI: 4.27-9.29) and 4.0 (95% CI: 3.1-5.2). The DOR and LR + were further improved to 7.1 (95% CI: 4.2-11.95) and 5.6 (95% CI: 3.7-8.6), respectively, when hs-CRP at the cutoff of 1238 nmol/L was added to PCT plus lactate. High-sensitivity CRP at the cutoff of 1,255 nmol/L can enhance the discriminative power raising DOR and LR + values for GPB. The elevation of hs-CRP at the optimal cutoff might improve the diagnostic performance to predict unspecified bacteremia and GPB, but not GNB.

MicroRNAs are noncoding RNA species comprising 18-23 nucleotides that regulate host-virus interaction networks. Here, we show that enterovirus A71 infection in human rhabdomyosarcoma (RD) is regulated by miR-197 expression. Transfection of miR-197 mimic into RD cells inhibited virus replication by interfering with the viral RNA synthesis. We employed a combination of mass-spectrometry-based quantitative proteomics with the stable isotope labeling with amino acids in cell culture (SILAC) approach for the identification of the miR-197 target genes in RD cells and to investigate the differential expression of the prospective target proteins. A total of 1822 proteins were repeatedly identified in miR-197-transfected RD cells, 106 of which were predicted to have seed sites by TargetScan. Notably, seven of eight selected genes potentially related to viral replication and immune response were validated as direct miR-197 targets, using a luciferase 3'-untranslated region (UTR) reporter assay. The expression levels of three selected endogenous molecules (ITGAV, ETF1, and MAP2K1/MEK1) were significantly reduced when RD cells were transfected with a miR-197 mimic. Our results provide a comprehensive database of miR-197 targets, which might provide better insights into the understanding of host-virus interaction.

Heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) is an emerging superbug with implicit drug resistance to vancomycin. Detecting hVISA can guide the correct administration of antibiotics. However, hVISA cannot be detected in most clinical microbiology laboratories because the required diagnostic tools are either expensive, time consuming, or labor intensive. By contrast, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) is a cost-effective and rapid tool that has potential for providing antibiotics resistance information. To analyze complex MALDI-TOF mass spectra, machine learning (ML) algorithms can be used to generate robust hVISA detection models. In this study, MALDI-TOF mass spectra were obtained from 35 hVISA/vancomycin-intermediate S. aureus (VISA) and 90 vancomycin-susceptible S. aureus isolates. The vancomycin susceptibility of the isolates was determined using an Etest and modified population analysis profile-area under the curve. ML algorithms, namely a decision tree, k-nearest neighbors, random forest, and a support vector machine (SVM), were trained and validated using nested cross-validation to provide unbiased validation results. The area under the curve of the models ranged from 0.67 to 0.79, and the SVM-derived model outperformed those of the other algorithms. The peaks at m/z 1132, 2895, 3176, and 6591 were noted as informative peaks for detecting hVISA/VISA. We demonstrated that hVISA/VISA could be detected by analyzing MALDI-TOF mass spectra using ML. Moreover, the results are particularly robust due to a strict validation method. The ML models in this study can provide rapid and accurate reports regarding hVISA/VISA and thus guide the correct administration of antibiotics in treatment of S. aureus infection.

Recent studies indicate that erectile dysfunction (ED) and prostate cancer share common potential risk factors such as chronic inflammation, prostatitis, cigarette smoking, obesity, a high animal fat diet, sedentarism, and depression. There is great interest in knowing if ED is associated with prostate cancer. This study aimed to investigate if men afflicted with ED harbor an increased risk of prostate cancer, utilizing two concurrent comparison groups, constructed from the Taiwan NHIRD, with up to 8 years' follow-up. Among men with no preexisting prostate cancer, an ED group of 3,593 men ≥ 40 years of age and two non-ED comparison groups of 14,372 men from the general population, 1:4 matched by age and index date (GENPOP); and 3,594 men with clinical benign prostatic hyperplasia (BPH), matched by similar criteria were assembled. A Cox model was constructed to calculate the adjusted hazard ratio (aHR) after controlling for age, socioeconomic factors, and various medical comorbidities. During the 11,449 person-year follow-up for the ED group, 24 incident prostate cancer developed. During the 44,486 and 11,221 person-year follow-up for the GENPOP and the BPH group, respectively, there were 33 and 25 incidents of prostate cancer. The ED group demonstrated a 2.6-fold greater risk of prostate cancer than that by the GENPOP with an aHR of 2.63 (95% confidence interval [CI] [1.51, 4.59], p < .001). There was no significant difference in risk between ED and BPH group (aHR = 0.83, 95% CI [0.46, 1.48]). This concurrent, double comparison, longitudinal study revealed a positive association between ED and subsequent prostate cancer incidence.

Aim: No antiviral medications are currently approved to treat enterovirus (EV)-associated disease or prevent EV infection., Methods: In this study, a series of probenecid derivatives were designed via a rational strategy and synthesized to obtain more potent anti-EV agents., Results: Compounds 8 and 24 exhibited the most potent activity against EV D68 and A71, with half maximal effective concentration (EC 50 ) values of 2.49/2.09 and 2.59/2.41 μM, respectively, and revealed a broad inhibition spectrum toward other EV strains, with high selectivity indices. Additionally, compounds 8 and 24 showed good stability in rat serum, with half-lives of 48.39 and 60.26 min, respectively., Conclusion: Compounds 8 and 24 are the promising candidates for the development of new agents against EV D68 and A71 viruses.