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.

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.

Characterization and identification of antimicrobial peptides with different functional activities.

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.

High prevalence of genotype 6 hepatitis C virus infection in Southern Taiwan using Abbott genotype assays.

BACKGROUND/PURPOSE: Abbott RealTime Genotype II assay can effectively identify hepatitis C virus (HCV) genotypes (GTs), but some GT 6 subtypes might not be differentiated from GT 1. Abbott RealTime Genotype II PLUS and sequencing might be needed to resolve these ambiguous results. Unlike the high prevalence of GT 6 in Southeast Asia, GT 6 had rarely been reported in Taiwan except in intravenous drug abusers (IDU). But the prevalence of GT 6 in Taiwan might be underestimated. We conducted this study to determine the GTs in a HCV endemic area in Southern Taiwan. METHODS: A total of 1147 patients with hepatitis C viremia for direct acting antivirals (DAA) treatment at the Chi Mei medical system in Tainan were enrolled. Genotype was determined using a working flow consisted of Abbott GT II, PLUS assays and 5' untranslated region (5' UTR)/core sequencing. RESULTS: Among the 1147 patients, 883 (77.0%) obtained GT results by GT II, 264 (23.0%) samples with ambiguous results by GT II assay received further tests, including 194 (73.5%) with PLUS assay and 70 (26.5%) with 5'UTR/core sequencing. Nearly three-quarters (73.5%) of ambiguous results by GT II assay were GT 6. Overall, 18.3% of samples were GT 6. Phylogenetic study of 11 samples of GT 6 subtypes showed 7 (63.6%) were 6 g. CONCLUSION: GT 6 is the major factor for high ambiguous rate by GT II. Unexpected high prevalence of GT 6 (18.3%) in Southern Taiwan, especially subtype 6 g, closely related to Indonesian strains, is first reported.

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.

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.

The E2F transcription factor 1 transactives stathmin 1 in hepatocellular carcinoma.

BACKGROUND: Through data mining the Stanford Microarray Database, the stathmin 1 (STMN1) transcript was found to be frequently upregulated in the hepatocellular carcinoma (HCC) with low alpha-fetoprotein level. The molecular mechanism of STMN1 upregulation in HCCs remained unclear. METHODS: Quantitative RT-PCR, immunoblotting, immunohistochemistry, and transfection of expression or small hairpin RNA interference plasmids, chromatin immunoprecipitation (ChIP), and quantitative ChIP assays were performed in HCC specimens or 2 distinct HCC-derived cell lines. Dual luciferase assay and site-directed mutagenesis were applied to analyze the activities of STMN1 proximal promoter region. RESULTS: STMN1 mRNA and proteins were significantly associated with several clinicopathological features. High STMN1 or E2F1 immunoexpression was predictive of poor overall survival (OS) rate (P < .01). In HCC-derived cell lines, E2F1 was elevated before STMN1 mRNA during the cell cycle. Exogenous expression of E2F1 or both transcription factor DP-1 (TFDP1) and E2F1 genes induced E2F1 and STMN1 mRNA (P < .01). Knockdown of the E2F1 gene suppressed E2F1 and STMN1 mRNA and E2F1 and STMN1 protein levels (P < .05). The promoter activity of STMN1 gene increased with overexpression of both E2F1 and TFDP1 genes (P < .05); however, it decreased when mutations were introduced in the E2F1-binding sites (P < .05). CONCLUSIONS: Upregulation of E2F1 and STMN1 proteins associate with worse outcomes in patients with HCC. E2F1 significantly correlates with STMN1 protein level in HCC lesions and in vitro transactivation assays, suggesting that STMN1 gene is transactivated by the E2F1 protein.

EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC.

The function of a protein is generally related to its subcellular localization. Therefore, knowing its subcellular localization is helpful in understanding its potential functions and roles in biological processes. This work develops a hybrid method for computationally predicting the subcellular localization of eukaryotic protein. The method is called EuLoc and incorporates the Hidden Markov Model (HMM) method, homology search approach and the support vector machines (SVM) method by fusing several new features into Chou's pseudo-amino acid composition. The proposed SVM module overcomes the shortcoming of the homology search approach in predicting the subcellular localization of a protein which only finds low-homologous or non-homologous sequences in a protein subcellular localization annotated database. The proposed HMM modules overcome the shortcoming of SVM in predicting subcellular localizations using few data on protein sequences. Several features of a protein sequence are considered, including the sequence-based features, the biological features derived from PROSITE, NLSdb and Pfam, the post-transcriptional modification features and others. The overall accuracy and location accuracy of EuLoc are 90.5 and 91.2 %, respectively, revealing a better predictive performance than obtained elsewhere. Although the amounts of data of the various subcellular location groups in benchmark dataset differ markedly, the accuracies of 12 subcellular localizations of EuLoc range from 82.5 to 100 %, indicating that this tool is much more balanced than other tools. EuLoc offers a high, balanced predictive power for each subcellular localization. EuLoc is now available on the web at http://euloc.mbc.nctu.edu.tw/.

An all-statistics, high-speed algorithm for the analysis of copy number variation in genomes.

Detection of copy number variation (CNV) in DNA has recently become an important method for understanding the pathogenesis of cancer. While existing algorithms for extracting CNV from microarray data have worked reasonably well, the trend towards ever larger sample sizes and higher resolution microarrays has vastly increased the challenges they face. Here, we present Segmentation analysis of DNA (SAD), a clustering algorithm constructed with a strategy in which all operational decisions are based on simple and rigorous applications of statistical principles, measurement theory and precise mathematical relations. Compared with existing packages, SAD is simpler in formulation, more user friendly, much faster and less thirsty for memory, offers higher accuracy and supplies quantitative statistics for its predictions. Unique among such algorithms, SAD's running time scales linearly with array size; on a typical modern notebook, it completes high-quality CNV analyses for a 250 thousand-probe array in ∼1 s and a 1.8 million-probe array in ∼8 s.

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.

Recurrent amplification at 7q21.2 Targets CDK6 gene in primary myxofibrosarcomas and identifies CDK6 overexpression as an independent adverse prognosticator.

BACKGROUND: Myxofibrosarcoma is genetically complex and remains obscure in molecular determinants of clinical aggressiveness. Our prior study revealed recurrent gains of 7q in myxofibrosarcomas where MET and CDK6 genes displayed increased DNA copies. Previously, we demonstrated the implication of MET overexpression, prompting us to further elucidate the roles of CDK6 in myxofibrosarcomas. MATERIALS: On tissue microarrays, CDK6 immunoexpression was assessable in 77 primary tumors, 55 of which were successfully quantified for CDK6 and MET genes by real-time PCR using genomic DNA extracted from laser-microdissected tumor cells. Gene status and protein expression of CDK6 were correlated with each other, clinicopathological variables, metastasis-free survival (MFS), and disease-specific survival (DSS). RESULTS: Protein overexpression and gene amplification of CDK6, which were detected in 21 of 77 (27.2 %) and 13 of 55 cases (23.6 %), respectively, were highly related to each other (p < .001) and associated with higher grades (overexpression, p = .004; amplification, p = .014). There was a strong correlation between CDK6 and MET gene copies (p < .001, r = 0.0714). Importantly, CDK6 protein overexpression (MFS, p = .0002; DSS, p = .0015) and gene amplification (MFS, p = .0001; DSS, p = .0083) were both univariately associated with worse outcomes. Together with nonextremity location and AJCC stage III disease, CDK6 overexpression independently portended inferior MFS (p = .0015, risk ratio [RR] = 7.411). This aberration, along with nonextremity location, was also an independent adverse prognosticator of DSS (p = .0069, RR = 6.006). CONCLUSIONS: In approximately a quarter of primary myxofibrosarcomas, CDK6 overexpression is mostly driven by gene amplification on 7q, associated with adverse prognosticators, and independently predictive of worse outcomes, highlighting its possible causative role in tumor aggressiveness.

Purification and properties of an insecticidal metalloprotease produced by Photorhabdus luminescens strain 0805-P5G, the entomopathogenic nematode symbiont.

A total of 13 Photorhabdus luminescens strains were screened for proteolytic activity. The P. luminescens strain 0805-P5G had the highest activity on both skim milk and gelatin plates. The protease was purified to electrophoretical homogeneity by using a two-step column chromatographic procedure. It had a molecular weight of 51.8 kDa, as determined by MALDI-TOF mass spectrometry. The optimum pH, temperature, as well as pH and thermal stabilities were 8, 60 °C, 5-10, and 14-60 °C, respectively. It was completely inhibited by EDTA and 1,10-phenanthroline. Bioassay of the purified protease against Galleria mellonella by injection showed high insecticidal activity. The protease also showed high oral toxicity to the diamondback moth (Plutella xylostella) of a Taiwan field-collected strain, but low toxicity to an American strain. To our knowledge, this is the first report to demonstrate that the purified protease of P. luminescens has direct toxicity to P. xylostella and biopesticide potentiality.

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.

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.

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.

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.

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.

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.