王雲銘教授研究團隊發表研究成果於 Sensors and Actuators B: Chemical

連結網址：

https://www.sciencedirect.com/science/article/pii/S0925400523006755

Abstract

The COVID-19 pandemic has become a global catastrophe, affecting the health and economy of the human community. It is required to mitigate the impact of pandemics by developing rapid molecular diagnostics for SARS-CoV-2 virus detection. In this context, developing a rapid point-of-care (POC) diagnostic test is a holistic approach to the prevention of COVID-19. In this context, this study aims at presenting a real-time, biosensor chip for improved molecular diagnostics including recombinant SARS-CoV-2 spike glycoprotein and SARS-CoV-2 pseudovirus detection based on one-step-one-pot hydrothermally derived CoFeBDCNH2-CoFe2O4 MOF-nanohybrids. This study was tested on a PalmSens-EmStat Go POC device, showing a limit of detection (LOD) for recombinant SARS-CoV-2 spike glycoprotein of 6.68 fg/mL and 6.20 fg/mL in buffer and 10% serum-containing media, respectively. To validate virus detection in the POC platform, an electrochemical instrument (CHI6116E) was used to perform dose dependent studies under similar experimental conditions to the handheld device. The results obtained from these studies were comparable indicating the capability and high detection electrochemical performance of MOF nanocomposite derived from one-step-one-pot hydrothermal synthesis for SARS-CoV-2 detection for the first time. Further, the performance of the sensor was tested in the presence of Omicron BA.2 and wild-type D614G pseudoviruses.

李宗夷教授研究團隊發表研究成果於Briefings in Bioinformatics

連結網址：https://pubmed.ncbi.nlm.nih.gov/36810579/

Abstract

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time- consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase-kinase similarity network was generated by merging
the sequence-, functional-, protein-domain- and ‘STRING’-related similarities. Thus, besides sequence data, protein-protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the ‘TK’, ‘Other’, ‘STE’, ‘CAMK’, ‘TKL’, ‘CMGC’, ‘AGC’, ‘CK1’ and ‘Atypical’ groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.

何信瑩教授研究團隊發表研究成果於Human Genetics and Genomics Advances

連結網址：https://pubmed.ncbi.nlm.nih.gov/37124139/

Abstract

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection.

李宗夷教授研究團隊發表研究成果於Microbiology Spectrum

連結網址：https://pubmed.ncbi.nlm.nih.gov/37042778/

Abstract

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.