Assessment of Failure Risks in Laboratories During the Pandemic

Clinical laboratories produce test results that support the diagnosis, prognosis, and patient treatment. Test results must be relevant, accurate, and reliable for patient care. International bibliographic data estimate that approximately 62.0% of the errors made in clinical laboratories are due to errors during the pre-analytical stage. This chapter presents a failure modes and effects analysis (FMEA) to analyze potential failure risks within the pre-analytical phase and classify them according to severity and likelihood. FMEA allows molecular laboratories to lower costs and drive better outcomes through high-quality nucleic acid extraction, sensitive detection, and accurate quantification. RT-PCR technology continues to be the gold standard for the clinical detection of SARS-CoV-2 RNA in individuals suspected of COVID-19. It is essential to use highly sensitive assays to detect active infections and reduce the likelihood of false-negative results.

dnaJ : a New Approach to Identify Species within the Genus Enterobacter

We propose a new approach for Enterobacter species identification based on the diversity of the gene encoding the heat shock protein DnaJ. This new tool can be easily implemented in clinical laboratories in addition to identification by MALDI-TOF.

Early prediction of COVID-19 patient survival by targeted plasma multi-omics and machine learning

The recent surge of COVID-19 hospitalizations severely challenges healthcare systems around the globe and demands for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of 100s of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and non-survivors. With increasing length of hospitalization, the survivors’ samples showed a trend towards normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of ten proteins and five metabolites we could predict patient survival with 92% accuracy (AUC 0.97) at the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.

Molecular diversity survey on diarrheagenic Escherichia coli isolates among children with gastroenteritis in Fars, Iran

Aim: To differentiate Escherichia coli isolates from diarrheal pediatric patients in clinical laboratories. Materials & methods: Patients with watery diarrhea were selected for sampling and tested for Diarrheagenic E. coli (DEC) by API kit. DEC isolates were tested for phylotyping, pathotyping and presence of determined virulence-encoding genes by specific molecular methods. Results: About 50% of isolates were detected as DECs (>55 and >31% were categorized B2 and D phylotypes respectively). Enterotoxigenic E. coli was the most and Enteroinvasive E. coli was the lowest prevalent pathotypes. csg and fim genes were the most present virulence factors. Conclusion: Typing of E. coli isolates from stool specimens will help to determine the diversity of diarrheal pathogens and take proper decisions to reduce the health burden of diarrheal diseases.

Integrating Circulating Tumor DNA Features and Plasma Protein Markers to Detected Early Lymphoid Neoplasm

Introduction The disease burden of lymphoid neoplasm has been rising in China over the last decade. But most patients manifest with advanced stage disease at initial diagnosis, and the prognosis is poor with a 5-year survival rate of 38.3%. Here we reported a novel multivariate cancer risk score (CRS) model which is used to detect early lymphoid neoplasm from the peripheral blood. It incorporates three cancer hallmarks, copy number aberrations (CNA) and fragment size (FS) via shallow whole genome sequencing (sWGS) from cell-free DNA (cfDNA), and a panel of seven tumor protein markers in a single blood draw (10ml). Methods 44 newly diagnosed and untreated stage I-IV lymphoid neoplasm patients and 247 healthy individuals with no cancer diagnosis were enrolled in this study. 10ml peripheral blood was collected from each participant after enrollment. cfDNA was extracted and subjected to sWGS whereas plasma was subjected to measure the levels of 7 PTMs. The cancer risk score (CRS) of a subject was calculated via an established CRS model [1]. Results Firstly, genomic and epigenetic features were explored from cfDNA sWGS results. CNA is a ubiquitous genomic hallmark in a wide spectrum of cancers. In this study, 26 of the 44 (59.1%) lymphoid neoplasm patients had CNA in at least one genomic segment (>5Mb). FS feature of cfDNA bears the correspondence of the epigenetic landscapes of cells that give rise to those cfDNA fragments. When CNA was combined with FS, 29 (65.9%) patients were able to be detected by the CNA+FS classifier. On the other hand, 13 (29.5%) patients were tested positive by PTMs alone, indicating non-DNA molecular surrogates can also serve as cancer biomarkers with acceptable performance. When CNA, FS and PTM were incorporated into a multidimensional and multivariate CRS model, it achieved the best performance allowing 31 (70.0%) lymphoid neoplasm cases to be identified with a positive predictive value (PPV) of 86.1% at 98.0% specificity. The sensitivity of CRS model increases with the advances of disease with a sensitivity of 50.0% in early stage (stage I -Ⅱ) and 90.0% in late stage (stage Ⅲ-Ⅳ). Conclusion In summary, this study provides an efficient and non-invasive method to detect lymphoid neoplasm. Instead of relying only on one dimension of cancer markers, the multidimensional approach which incorporating CNA, fragment size and protein markers is plausible in early detection of lymphoid neoplasm with sufficient accuracy and robustness. Disclosures Zhu: Clinical Laboratories, Shenyou Bio: Current Employment. Li: SeekIn Inc.: Current Employment, Current holder of individual stocks in a privately-held company. Geng: Clinical Laboratories, Shenyou Bio: Current Employment. Chang: Clinical Laboratories, Shenyou Bio: Current Employment. Chen: SeekIn Inc: Current Employment, Current holder of individual stocks in a privately-held company. Mao: SeekIn Inc: Current Employment, Current holder of individual stocks in a privately-held company.