AI-assisted literature exploration of innovative Chinese medicine formulas.

Objective: Our study provides an innovative approach to exploring herbal formulas that contribute to the promotion of sustainability and biodiversity conservation. We employ data mining, integrating keyword extraction, association rules, and LSTM-based generative models to analyze classical Traditional Chinese Medicine (TCM) texts. We systematically decode classical Chinese medical literature, conduct statistical analyses, and link these historical texts with modern pharmacogenomic references to explore potential alternatives. Methods: We present a novel iterative keyword extraction approach for discerning diverse herbs in historical TCM texts from the Pu-Ji Fang copies. Utilizing association rules, we uncover previously unexplored herb pairs. To bridge classical TCM herbal pairs with modern genetic relationships, we conduct gene-herb searches in PubMed and statistically validate this genetic literature as supporting evidence. We have expanded on the present work by developing a generative language model for suggesting innovative TCM formulations based on textual herb combinations. Results: We collected associations with 7,664 PubMed cross-search entries for gene-herb and 934 for Shenqifuzheng Injection as a positive control. We analyzed 16,384 keyword combinations from Pu-Ji Fang's 426 volumes, employing statistical methods to probe gene-herb associations, focusing on examining differences among the target genes and Pu-Ji Fang herbs. Conclusion: Analyzing Pu-Ji Fang reveals a historical focus on flavor over medicinal aspects in TCM. We extend our work on developing a generative model from classical textual keywords to rapidly produces novel herbal compositions or TCM formulations. This integrated approach enhances our comprehension of TCM by merging ancient text analysis, modern genetic research, and generative modeling.

REG3A overexpression functions as a negative predictive and prognostic biomarker in rectal cancer patients receiving CCRT.

BACKGROUND: Concurrent chemoradiotherapy (CCRT) is suggested before resection surgery in the control of rectal cancer. Unfortunately, treatment outcomes are widely variable and highly patient-specific. Notably, rectal cancer patients with distant metastasis generally have a much lower survival rate. Accordingly, a better understanding of the genetic background of patient cohorts can aid in predicting CCRT efficacy and clinical outcomes for rectal cancer before distant metastasis. METHODS: A published transcriptome dataset (GSE35452) (n=46) was utilized to distinguish prospective genes concerning the response to CCRT. We recruited 172 rectal cancer patients, and the samples were collected during surgical resection after CCRT. Immunohistochemical (IHC) staining was performed to evaluate the expression level of regenerating family member 3 alpha (REG3A). Pearson's chi-squared test appraised the relevance of REG3A protein expression to clinicopathological parameters. The Kaplan-Meier method was utilized to generate survival curves, and the log-rank test was performed to compare the survival distributions between two given groups. RESULTS: Employing a transcriptome dataset (GSE35452) and focusing on the inflammatory response (GO: 0006954), we recognized that REG3A is the most significantly upregulated gene among CCRT nonresponders (log2 ratio=1.2472, p=0.0079). Following IHC validation, high immunoexpression of REG3A was considerably linked to advanced post-CCRT tumor status (p<0.001), post-CCRT lymph node metastasis (p=0.042), vascular invasion (p=0.028), and low-grade tumor regression (p=0.009). In the multivariate analysis, high immunoexpression of REG3A was independently correlated with poor disease-specific survival (DSS) (p=0.004) and metastasis-free survival (MeFS) (p=0.045). The results of the bioinformatic analysis also supported the idea that REG3A overexpression is implicated in rectal carcinogenesis. CONCLUSION: In the current study, we demonstrated that REG3A overexpression is correlated with poor CCRT effectiveness and inferior patient survival in rectal cancer. The predictive and prognostic utility of REG3A expression may direct patient stratification and decision-making more accurately for those patients.

Multi-label classification and features investigation of antimicrobial peptides with various functional classes.

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.

Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data.

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.

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra.

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.

High SLC28A2 expression endows an inferior survival for rectal cancer patients managed by neoadjuvant CCRT.

For rectal cancer patients with stage T3-4 disease or positive lymph node, neoadjuvant concurrent chemoradiotherapy (CCRT) has become the standard treatment, but the clinical outcomes are still far from satisfactory. Accordingly, a more precise predictive tool such as genetic biomarkers is urgently required to optimize therapy decisions. Colorectal cancer (CRC) development has been considerably correlated with cellular metabolic process involving nucleotides, but the underlying molecular mechanisms remain unclear. In this study, we employed a transcriptome dataset comprising 46 rectal adenocarcinoma patients undergoing preoperative CCRT and focused on nucleobase-containing compound metabolic process (GO: 0055134) for data mining. We identified solute carrier family 28 member 2 (SLC28A2) as the most considerably upregulated gene among rectal cancer patients with CCRT resistance. Afterwards, there were a total of 172 rectal cancer tissue blocks procuring from our biobank, and the immunointensity of SLC28A2 was appraised utilizing immunohistochemical staining. Strong SLC28A2 immunointensity was significantly linked to female patients (p = 0.032), vascular invasion (p = 0.021), and post-CCRT tumor invasion and regional lymph node involvement (p < 0.001 and p = 0.005). Notably, patients with strong SLC28A2 immunointensity had no tumor downstaging (p < 0.001). Univariate analysis revealed that high SLC28A2 immunoexpression was considerably unfavorably linked to all three endpoints: local recurrence-free survival (LRFS), metastasis-free survival (MeFS), and disease-specific survival (DSS) (all p ≤ 0.0333). Moreover, both high SLC28A2 immunoexpression and low tumor regression grade were independently unfavorable prognostic factors for all three endpoints (all p ≤ 0.013) in the multivariate analysis. Utilizing function prediction analysis, SLC28A2 upregulation was more likely to be linked with stem cell homeostasis in rectal cancer. In brief, we demonstrated that high SLC28A2 immunoexpression is substantially linked to an advanced stage, poor response to CCRT, and worse patient survival. Consequently, SLC28A2 expression can be a valuable predictive and prognostic marker for rectal cancer patients and be an encouraging therapeutic target for those with CCRT resistance.

Overexpression of Dehydrogenase/Reductase 9 Predicts Poor Response to Concurrent Chemoradiotherapy and Poor Prognosis in Rectal Cancer Patients.

Objective: To reduce the risk of locoregional recurrence, the addition of neoadjuvant concurrent chemoradiotherapy (CCRT) is recommended before surgical management for rectal cancer patients. However, despite identical tumor histology, individual patient response to neoadjuvant CCRT varies greatly. Accordingly, a comprehensive molecular characterization that is used to predict CCRT efficacy is instantly needed. Methods: Pearson's chi-squared test was utilized to correlate dehydrogenase/reductase 9 (DHRS9) expression with clinicopathological features. Survival curves were created applying the Kaplan-Meier method, and the log-rank test was conducted to compare prognostic utility between high and low DHRS9 expression groups. Multivariate Cox proportional hazards regression analysis was applied to identify independent prognostic biomarkers based on variables with prognostic utility at the univariate level. Results: Utilizing a public transcriptome dataset, we identified that the DHRS9 gene is the most considerably upregulated gene related to epithelial cell differentiation (GO: 0030855) among rectal cancer patients with CCRT resistance. Employing immunohistochemical staining, we also demonstrated that high DHRS9 immunoexpression is considerably associated with an aggressive clinical course and CCRT resistance in our rectal cancer cohort. Among all variables with prognostic utility at the univariate level, only high DHRS9 immunoexpression was independently unfavorably prognostic of all three endpoints (all p ≤ 0.048) in the multivariate analysis. In addition, applying bioinformatic analysis, we also linked DHRS9 with unrevealed functions, such as keratan sulfate and mucin synthesis which may be implicated in CCRT resistance. Conclusion: Altogether, DHRS9 expression may serve as a helpful predictive and prognostic biomarker and assist decision-making for rectal cancer patients who underwent neoadjuvant CCRT.

Suboptimal Plasma Vitamin C Is Associated with Lower Bone Mineral Density in Young and Early Middle-Aged Men: A Retrospective Cross-Sectional Study.

Background: This study was conducted to evaluate associations between bone mineral density (BMD) and four selected circulating nutrients, particularly vitamin C, among adults aged 20−49 years. Methods: In this retrospective cross-sectional study, the lumbar spine BMD of 866 men and 589 women were measured by dual-energy X-ray absorptiometry and divided into tertiles, respectively. Logistic regressions were used to identify the predictors of low BMD by comparing subjects with the highest BMD to those with the lowest. Results: Multivariate logistic regressions identified suboptimal plasma vitamin C (adjusted odds ratio (AOR) 1.64, 95% confidence interval (CI) 1.16, 2.31), suboptimal serum vitamin B12 (AOR 2.05, 95% CI 1.02, 4.12), and low BMI (BMI < 23) (AOR 1.68, 95% CI 1.12, 2.53) as independent predictors for low BMD in men. In women, low BMI was the only independent predictor for low BMD. Plasma vitamin C, categorized as suboptimal (≤8.8 mg/L) and sufficient (>8.8 mg/L), was positively significantly correlated with the lumbar spine BMD in men, but there was no association in women. Conclusions: Plasma vitamin C, categorized as suboptimal and sufficient, was positively associated with the lumbar spine BMD in young and early middle-aged men. A well-designed cohort study is needed to confirm the findings.

Discrimination of Methicillin-resistant Staphylococcus aureus by MALDI-TOF Mass Spectrometry with Machine Learning Techniques in Patients with Staphylococcus aureus Bacteremia.

Early administration of proper antibiotics is considered to improve the clinical outcomes of Staphylococcus aureus bacteremia (SAB), but routine clinical antimicrobial susceptibility testing takes an additional 24 h after species identification. Recent studies elucidated matrix-assisted laser desorption/ionization time-of-flight mass spectra to discriminate methicillin-resistant strains (MRSA) or even incorporated with machine learning (ML) techniques. However, no universally applicable mass peaks were revealed, which means that the discrimination model might need to be established or calibrated by local strains' data. Here, a clinically feasible workflow was provided. We collected mass spectra from SAB patients over an 8-month duration and preprocessed by binning with reference peaks. Machine learning models were trained and tested by samples independently of the first six months and the following two months, respectively. The ML models were optimized by genetic algorithm (GA). The accuracy, sensitivity, specificity, and AUC of the independent testing of the best model, i.e., SVM, under the optimal parameters were 87%, 75%, 95%, and 87%, respectively. In summary, almost all resistant results were truly resistant, implying that physicians might escalate antibiotics for MRSA 24 h earlier. This report presents an attainable method for clinical laboratories to build an MRSA model and boost the performance using their local data.

High Stromal SFRP2 Expression in Urothelial Carcinoma Confers an Unfavorable Prognosis.

Background: Urothelial carcinoma (UC) patients often bear clinical and genetic heterogeneity, which may differ in management and prognosis. Especially, patients with advanced/metastatic UC generally have a poor prognosis and survive for only few months. The Wnt/ß-catenin signaling is found to be highly activated in several cancers, including UC. However, accumulated evidence has shown discordance between the Wnt/ß-catenin signaling and UC carcinogenesis. Accordingly, we aim to get a better understanding of the molecular characterization of UC, focusing on the Wnt signaling, which may add value to guiding management more precisely. Patients and Methods: Clinical data and pathological features were retrospectively surveyed. The correlations of secreted Frizzled-related protein 2 (SFRP2) immunoexpression with clinicopathological features were analyzed by Pearson's chi-square test. The Kaplan-Meier method with a log-rank test was employed to plot survival curves. All significant features from the univariate analysis were incorporated into the Cox regression model for multivariate analysis. Results: Following data mining on a transcriptome dataset (GSE31684), we identified that 8 transcripts in relation to the Wnt signaling pathway (GO: 0016055) were significantly upregulated in advanced/metastatic bladder tumors. Among these transcripts, the SFRP2 level showed the most significant upregulation. Additionally, as SFRP2 is a putative Wnt inhibitor and may be expressed by stroma, we were interested in examining the immunoexpression and clinical relevance of stromal and tumoral SFRP2 in our urothelial carcinoma cohorts containing 295 urinary bladder UC (UBUC) and 340 upper urinary tract UC (UTUC) patients. We observed that high SFRP2 expression in stroma but not in tumors is significantly linked to aggressive UC features, including high tumor stage and histological grade, positive nodal metastasis, the presence of vascular and perineural invasion, and high mitotic activity in UBUC and UTUC. Moreover, high stromal SFRP2 expression significantly and independently predicted worse clinical outcomes in UBUC and UTUC. Utilizing bioinformatic analysis, we further noticed that stromal SFRP2 may link epithelial-mesenchymal transition (EMT) to UC progression. Conclusion: Collectively, these results imply that stromal SFRP2 may exert oncogenic function beyond its Wnt antagonistic ability, and stromal SFRP2 expression can provide prognostic and therapeutic implications for UC patients.

Prevalence and Predictors of Insufficient Plasma Vitamin C in a Subtropical Region and Its Associations with Risk Factors of Cardiovascular Diseases: A Retrospective Cross-Sectional Study.

Background: to evaluate the prevalence and predictors of insufficient plasma vitamin C among adults in a subtropical region and its associations with cardiovascular disease risk factors including dyslipidemia and lipid-independent markers, namely homocysteine, high-sensitivity C-reactive protein (hs-CRP) and lipoprotein(a). Methods: Data of this retrospective cross-sectional study were extracted from electronic medical database of a Medical Center. Based on plasma vitamin C status, subjects were split into two groups­subjects with sufficient and insufficient plasma vitamin C levels (<50 µmol/L, ≤8.8 mg/L). Results: Prevalence of insufficient plasma vitamin C in 3899 adults was 39%. Multivariate logistic regression identified male gender, high body mass index, age 20−39, and winter/spring as independent predictors of insufficient vitamin C among all subjects. Greater proportions of subjects with insufficient plasma vitamin C had lower high-density lipoprotein cholesterol levels and elevated levels of triglyceride, homocysteine and hs-CRP (all p < 0.001). There were no differences in total cholesterol, low-density lipoprotein cholesterol and lipoprotein(a) between groups. Conclusions: There was a high prevalence of insufficient plasma vitamin C in the subtropical region, which indicates that insufficient plasma vitamin C remains a public health issue. Further study is needed to confirm these findings and to determine the underlying mechanisms.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data.

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.

MDRSA: A Web Based-Tool for Rapid Identification of Multidrug Resistant Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry.

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/.

Overexpression of Pyruvate Dehydrogenase Kinase-3 Predicts Poor Prognosis in Urothelial Carcinoma.

BACKGROUND: The mitochondrial pyruvate dehydrogenase complex (PDC) link glycolysis to the tricarboxylic acid cycle by decarboxylating pyruvate to acetyl coenzyme A irreversibly. Cancer cells are characterized by a shift in cellular metabolism from mitochondrial respiration to glycolysis. PDC activity inhibition mediated by phosphorylation via pyruvate dehydrogenase kinase (PDK) has been linked to cancer. However, the clinical significance of PDKs in urothelial cancer prognosis is not clear. We investigated the role and prognostic value of PDK3 expression in patients with upper urinary tract urothelial carcinoma (UTUC) and urinary bladder urothelial carcinoma (UBUC). PATIENTS AND METHODS: We retrospectively analyzed clinical data and pathological features. Formalin-fixed urothelial carcinoma (UC) tissues were collected and embedded in paraffin. The correlation of PDK3 expression with clinical characteristics, pathological findings and patient outcomes, including metastasis-free survival (MFS) and disease-specific survival (DSS) were analyzed by Pearson's chi-square test, Kaplan-Meier analysis, and the multivariate Cox proportional hazards model. RESULTS: Data from 295 patients with UBUC and 340 patients with UTUC were evaluated. High PDK3 expression significantly correlated with several pathologic variables such as high T stage, lymph node metastases, high tumor grade, vascular invasion, and high mitotic rate (all P < 0.001). High PDK3 expression was associated with poor disease-specific survival (DSS) (P < 0.0001) and metastatic free survival (MFS) (P < 0.0001) in a Kaplan-Meier analysis. Additionally, multivariate analysis demonstrated increased PDK3 expression is a significant predictive risk factor for DSS [hazard ratio (HR) in UBUC, 2.79, P = 0.009; in UTUC, 2.561, P = 0.03] and MFS (HR in UBUC, 1.907, P = 0.024; in UTUC, 1.793, P = 0.044). The gene co-expression analysis showed abundant PDK3 co-upregulated genes were involved in the processes of DNA replication and repair through the Gene Ontology classification system. CONCLUSION: High PDK3 expression has been linked to negative pathologic characteristics and poor oncological outcomes, suggesting that it could be used as a predictive biomarker for UC. PDK3 mRNA levels and its co-upregulated genes were strongly associated with DNA replication and repair. These results suggest that PDK3 may play a key role in tumor proliferation and development.

Hepatitis C Virus Subtypes Novel 6g-Related Subtype and 6w Could Be Indigenous in Southern Taiwan with Characteristic Geographic Distribution.

Hepatitis C virus (HCV) genotype (GT) 6 is the most genetically diverse GT and mainly distributed in Southeast Asia and south China but not Taiwan. Earlier studies showed the major HCV GTs in Taiwan were GT 1b and 2 with very rare GT 6 except in injection drug users (IDUs), and subtype 6a is the main GT 6 subtype among IDUs. Recently, we reported a much higher prevalence (18.3%) of GT 6 in Tainan City, southern Taiwan. This study was designed to clarify the subtypes of GT 6 in this endemic area. A total of 3022 (1343 men and 1679 women) HCV viremic patients were enrolled. Subtypes of GT 6 were determined by sequencing of core/E1 and nonstructural protein 5B in 322 of 518 GT 6 patients. The overall GT 6 prevalence rate was 17.1% (518/3022), with higher prevalence districts (>25%) located in northern Tainan. A novel 6g-related subtype is the most prevalent subtype (81.0%), followed by 6w (10.8%), 6a (7.5%), and 6n (0.7%). The high GT 6 prevalence in Tainan was mainly due to a novel 6g-related subtype and 6w. These two subtypes could be indigenous in Tainan with characteristic geographic distribution.

Prognostic Significance of ROR2 Expression in Patients with Urothelial Carcinoma.

We investigated the association of receptor tyrosine kinase-like orphan receptor 2 (ROR2) expression with clinicopathological features and oncologic outcomes in large urothelial carcinoma (UC) of the upper tract (UTUC) and urinary bladder (UBUC) cohorts. Through transcriptomic profiling of a published dataset (GSE31684), ROR2 was discovered to be the most upregulated gene during UC progression, focusing on the JNK cascade (GO:0007254). Initially, the evaluation of ROR2 mRNA expression in 50 frozen UBUCs showed significantly upregulated levels in high-stage UC. Moreover, high ROR2 immunoexpression significantly correlated with high tumor stage, high tumor grade, lymph node metastasis, and vascular invasion (all p < 0.05). In multivariate analysis, after adjusting for standard clinicopathological features, ROR2 expression status was an independent prognosticator of cancer-specific survival and metastasis-free survival in UTUC and UBUC (all p < 0.01). In the subgroup analysis, it also significantly predicted bladder tumor recurrence in non-muscle invasive UBUC. Furthermore, the GO enrichment analysis showed that fatty acid, monocarboxylic acid, carboxylic acid metabolic processes, negative regulation of neutrophil migration, and negative regulation of granulocyte and neutrophil chemotaxis were significantly enriched by ROR2 dysregulation. In conclusion, high ROR2 immunoexpression was associated with aggressive pathological characteristics in UC and independently predicted worse prognosis, suggesting it could play roles in clinical risk stratification and therapy decisions.

Shortening the Time of the Identification and Antimicrobial Susceptibility Testing on Positive Blood Cultures with MALDI-TOF MS.

The current processes used in clinical microbiology laboratories take ~24 h for incubation to identify the bacteria after the blood culture has been confirmed as positive and fa further ~24 h to report the results of antimicrobial susceptibility tests (ASTs). Patients with suspected bloodstream infection are treated with empiric broad-spectrum antibiotics but delayed targeted antimicrobial therapy. This study aimed to develop a method with a significantly shortened turnaround time for clinical application by identifying the optimal incubation period of a subculture. A total of 188 positive blood culture samples obtained from Nov. 2019 to Aug. 2020 were included. Compared to the conventional 24-h incubation for bacterial identification, our approach achieved 96.1% and 97.4% identification accuracy after shortening the incubation time to 4.5 and 3.5 h for gram-positive (GP) and gram-negative (GN) bacterial samples, respectively. Samples from short-term incubation without any intermediate step or process were directly subjected to analysis with the Phoenix M50 AST. Compared to the conventional disk diffusion AST, the category agreements for GP (excluding Streptococcus spp.), Streptococcus spp., and GN bacterial samples were 91.8%, 97.5%, and 92.7%, respectively. Our approach significantly reduced the average turnaround time from 48 h to 28 h for reporting bacterial identity and decreased average AST from 72 h to 50.3 h compared to the conventional methods. Accordingly, this approach allows a physician to prescribe the appropriate antibiotic(s) ~21.7 h earlier, thereby improving patient outcomes.

High Level of Aristolochic Acid Detected With a Unique Genomic Landscape Predicts Early UTUC Onset After Renal Transplantation in Taiwan.

BACKGROUND: The unusual high dialysis prevalence and upper urinary tract urothelial carcinoma (UTUC) incidence in Taiwan may attribute to aristolochic acid (AA), which is nephrotoxic and carcinogenic, exposure. AA can cause a unique mutagenic pattern showing A:T to T:A transversions (mutational Signature 22) analyzed by whole exome sequencing (WES). However, a fast and cost-effective tool is still lacking for clinical practice. To address this issue, we developed an efficient and quantitative platform for the quantitation of AA and tried to link AA detection with clinical outcomes and decipher the genomic landscape of UTUC in Taiwan. PATIENTS AND METHODS: We recruited 61 patients with de novo onset of UTUC after kidney transplantation who underwent radical nephroureterectomy. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform was developed for the quantitation of AA. Pearson's chi-square test, Kaplan-Meier method, and Cox proportional hazard model were utilized to assess the correlations among AA detection, clinicopathological characteristics, and clinical outcomes. Seven tumors and seven paired normal tissues were sequenced using WES (approximately 800x sequencing depth) and analyzed by bioinformatic tool. RESULTS: We found that high level of 7-(deoxyadenosin-N6-yl)aristolactam I (dA-AL-I) detected in paired normal tissues was significantly correlated with fast UTUC initiation times after renal transplantation (p = 0.035) and with no use of sirolimus (p = 0.046). Using WES analysis, we further observed that all tumor samples were featured by Signature 22 mutations, apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC)-associated gene mutations, p53 mutations, no fibroblast growth factor receptor 3 (FGFR3) mutation, and high tumor mutation burden (TMB). Especially, mammalian target of rapamycin (mTOR) activation predominated in dA-AL-I-detected samples compared with those without dA-AL-I detection and might be associated with UTUC initiation through cell proliferation and suppression of UTUC progression via autophagy inhibition. CONCLUSION: Accordingly, dA-AL-I detection can provide more direct evidence to AA exposure and serve as a more specific predictive and prognostic biomarker for patients with de novo onset of UTUC after kidney transplantation.

Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins.

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.

Geographic variation of genotype 6 hepatitis C virus infection in an endemic area of southern Taiwan.

Taiwan is a hepatitis C virus (HCV) endemic country with geographic variation of prevalence and main genotypes(GTs) are 1 b and 2a. We recently reported high GT6 prevalence in Tainan of southern Taiwan. To clarify this special genotype as a local endemic disease and its geographic variation, the prevalence rates of HCV GTs of 37 districts of Tainan were analyzed. A total of 3040 patients with HCV viremia were enrolled. The prevalence rates of HCV GT 1a, 1 b, 2, 3, 4, 6 and mixed types were 3.9%, 31.6%, 45.9%, 0.6%, 0.2%, 17.1% and 0.5% respectively. GT6 prevalence showed marked variation from 0 to 39.2%. Four districts with GT6 prevalence >30% are located between Jishui and Zengwen rivers. Preliminary subtyping data were 6 g/a/w. This geographic variation with spatial restriction by two rivers with 6 g/w is suggestive of local endemic infection of preexisting GT 6 HCV for centuries.