Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model.

BACKGROUND: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a clinically critical pathogen that causes severe infection. Due to improper antibiotic administration, the prevalence of CRKP infection has been increasing considerably. In recent years, the utilization of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has enabled the identification of bacterial isolates at the families and species level. Moreover, machine learning (ML) classifiers based on MALDI-TOF MS have been recently considered a novel method to detect clinical antimicrobial-resistant pathogens. METHODS: A total of 2683 isolates (369 CRKP cases and 2314 carbapenem-susceptible Klebsiella pneumoniae [CSKP]) collected in the clinical laboratories of Taipei Medical University Hospital (TMUH) were included in this study, and 80% of data was split into the training data set that were submitted for the ML model. The remaining 20% of data was used as the independent data set for external validation. In this study, we established an artificial neural network (ANN) model to analyze all potential peaks on mass spectrum simultaneously. RESULTS: Our artificial neural network model for detecting CRKP isolates showed the best performance of area under the receiver operating characteristic curve (AUROC = 0.91) and of area under precision-recall curve (AUPRC = 0.90). Furthermore, we proposed the top 15 potential biomarkers in probable CRKP isolates at 2480, 4967, 12,362, 12,506, 12,855, 14,790, 15,730, 16,176, 16,218, 16,758, 16,919, 17,091, 18,142, 18,998, and 19,095 Da. CONCLUSIONS: Compared with the prior MALDI-TOF and machine learning studies of CRKP, the amount of data in our study was more sufficient and allowing us to conduct external validation. With better generalization abilities, our artificial neural network model can serve as a reliable screening tool for CRKP isolates in clinical practice. Integrating our model into the current workflow of clinical laboratories can assist the rapid identification of CRKP before the completion of traditional antimicrobial susceptibility testing. The combination of MADLI-TOF MS and machine learning techniques can support physicians in selecting suitable antibiotics, which has the potential to enhance the patients' outcomes and lower the prevalence of antimicrobial resistance.

Exploring Gut Microenvironment in Colorectal Patient with Dual-Omics Platform: A Comparison with Adenomatous Polyp or Occult Blood.

The gut mucosa is actively absorptive and functions as the physical barrier to separate the gut ecosystem from host. Gut microbiota-utilized or food-derived metabolites are closely relevant to the homeostasis of the gut epithelial cells. Recent studies widely suggested the carcinogenic impact of gut dysbiosis or altered metabolites on the development of colorectal cancer (CRC). In this study, liquid chromatography coupled-mass spectrometry and long-read sequencing was applied to identify gut metabolites and microbiomes with statistically discriminative abundance in CRC patients (n = 20) as compared to those of a healthy group (n = 60) ofenrolled participants diagnosed with adenomatous polyp (n = 67) or occult blood (n = 40). In total, alteration in the relative abundance of 90 operational taxonomic units (OTUs) and 45 metabolites were identified between recruited CRC patients and healthy participants. Among the candidates, the gradual increases in nine OTUs or eight metabolites were identified in healthy participants, patients diagnosed with occult blood and adenomatous polyp, and CRC patients. The random forest regression model constructed with five OTUs or four metabolites achieved a distinct classification potential to differentially discriminate the presence of CRC (area under the ROC curve (AUC) = 0.998 or 0.975) from the diagnosis of adenomatous polyp (AUC = 0.831 or 0.777), respectively. These results provide the validity of CRC-associated markers, including microbial communities and metabolomic profiles across healthy and related populations toward the early screening or diagnosis of CRC.

Evaluation of Blood Glucose Monitoring System in Screening for Neonatal Hypoglycemia: Tighter Accuracy Standard.

Neonatal hypoglycemia may cause severe neurological damages; therefore, tight glycemic control is crucial to identify neonate at risk. Previous blood glucose monitoring system (BGMS) failed to perform well in neonates; there are calls for the tightening of accuracy requirements. It remains a need for accurate BGMS for effective bedside diabetes management in neonatal care within a hospital population. A total of 300 neonates were recruited from local hospitals. Accuracy performance of a commercially available BGMS was evaluated against reference instrument in screening for neonatal hypoglycemia, and assessment was made based on the ISO15197:2013 and a tighter standard. At blood glucose level < 47 mg/dl, BGMS assessed met the minimal accuracy requirement of ISO 15197:2013 and tighter standard at 100% and 97.2%, respectively.

Differential Impacts of Alternative Splicing Networks on Apoptosis.

Apoptosis functions as a common mechanism to eliminate unnecessary or damaged cells during cell renewal and tissue development in multicellular organisms. More than 200 proteins constitute complex networks involved in apoptotic regulation. Imbalanced expressions of apoptosis-related factors frequently lead to malignant diseases. The biological functions of several apoptotic factors are manipulated through alternative splicing mechanisms which expand gene diversity by generating discrete variants from one messenger RNA precursor. It is widely observed that alternatively-spliced variants encoded from apoptosis-related genes exhibit differential effects on apoptotic regulation. Alternative splicing events are meticulously regulated by the interplay between trans-splicing factors and cis-responsive elements surrounding the regulated exons. The major focus of this review is to highlight recent studies that illustrate the influences of alternative splicing networks on apoptotic regulation which participates in diverse cellular processes and diseases.

Evaluation of accuracy of FAD-GDH- and mutant Q-GDH-based blood glucose monitors in multi-patient populations.

BACKGROUND: Glucose dehydrogenases have been highly promoted to high-accuracy blood glucose (BG) monitors. The flavin adenine dinucleotide glucose dehydrogenase (FAD-GDH) and mutant variant of quinoprotein glucose dehydrogenase (Mut. Q-GDH) are widely used in high-performance BG monitors for multi-patient use. Therefore we conducted accuracy evaluation of the GDH monitors, FAD-GDH-based GM700 and Mut. Q-GDH-based Performa. METHODS: Different patients were enrolled: patients with and without diabetes, patients receiving respiratory therapies, hemodialysis (HD) and peritoneal dialysis (PD) patients, and neonates. The accuracy evaluation of FAD-GDH- and Mut. Q-GDH-based monitors referred to ISO 15197:2013 which applies new criteria for the minion accuracy requirements: more than 95% of the blood glucose readings shall fall within ±15mg/dL of the reference method at glucose concentration <100mg/dL and within ±15% of the reference method at glucose concentration ≥100mg/dL. Bland-Altman plots were used to evaluate the 2 GDH monitors as well. RESULTS: Bland-Altman plots visualized excellent precision of the BG monitors. The 95% limit agreement of overall results for the FAD-GDH-based monitors was within ±12% and that for the Mut. Q-GDH-based monitors was from -10 to +17%. Both BG monitors met the accuracy requirements of ISO 15197:2013. The FAD-GDH-based monitor performed better with neonates and patients with and without diabetes, and the Mut. Q-GDH-based monitor performed better with HD and PD patients. CONCLUSIONS: Analytical results prove that the GDH-based monitors tolerate a broad BG concentration range, are oxygen independent, have BG specificity, and have minimal interference from hematocrit. The GDH-based monitors are reliable for multi-patient use.