Western Laboratories (United States)

Aiming at the challenges of complexity of anatomical structure, noise interference and multi-scale target processing faced in the field of lung medical image segmentation, this paper proposes a novel PDU-Net model. The model takes U-Net as the basic architecture and innovatively integrates path aggregation network (PANet) and dual attention mechanism (DAN). Specifically, PANet enhances the model's capability in multi-scale context fusion and small target localization by constructing a bidirectional feature pyramid (i.e., top-down semantic information transfer and bottom-up spatial detail supplementation) and adaptive feature pooling techniques, while DAN integrates spatial attention (for focusing on critical regions) and channel attention (for reinforcing semantic features) in parallel, thus improve the model's anti-noise performance and structural adaptation. Experimental results on the LIDC-IDRI and LUNA16 lung nodule CT datasets show that PDU-Net achieves 83.78%, 99.63%, and 77.34% in terms of MIoU, accuracy, and precision, respectively, which is an improvement of 7.44%, 1.14%, and 10.77% compared to the original U-Net model. This significant performance improvement provides technical support for the accurate segmentation of lung nodule contours, which in turn provides reliable technical support for the early diagnosis of lung cancer.

We introduce Trillion-7B, the most token-efficient Korean-centric multilingual LLM available. Our novel Cross-lingual Document Attention (XLDA) mechanism enables highly efficient and effective knowledge transfer from English to target languages like Korean and Japanese. Combined with optimized data mixtures, language-specific filtering, and tailored tokenizer construction, Trillion-7B achieves competitive performance while dedicating only 10\% of its 2T training tokens to multilingual data and requiring just 59.4K H100 GPU hours (\$148K) for full training. Comprehensive evaluations across 27 benchmarks in four languages demonstrate Trillion-7B's robust multilingual performance and exceptional cross-lingual consistency.

Clinical laboratories are facing severe shortages of qualified medical laboratory technologists (MLT). Given the vital role of the laboratory within the healthcare system, patient care in acute care settings, especially within emergency departments, are at risk if there are insufficient MLTs available to staff hospital laboratories. To mitigate this human resource challenge and reduce the overall risk to laboratory operations, clinical laboratories are exploring novel strategies to ensure continuous services that are crucial to patient care. One strategy being employed is leveraging medical laboratory assistant (MLA) staff to perform certain laboratory testing under the direction of MLTs. Options include testing in the central laboratory and point of care testing (POCT). Here, several recommendations have been developed by consensus of the authors, who are several clinical biochemists from across Canada and members of the Canadian Society of Clinical Chemists (CSCC) POCT Special Interest Group, with expertise in central laboratory and POCT oversight. These recommendations are aimed at clinical laboratory and healthcare system clinical and administrative leaders who are exploring alternative staffing models. The recommendations refer to two models of testing performed by MLAs, one whereby MLAs perform a menu of lower complexity tests within the central laboratory and one in which MLAs perform POCT outside of the laboratory in a true point of care setting.

Background: β-lactamases (BLs) are the leading cause of antimicrobial resistance in gram-negative bacteria. Metallo-β-lactamases (MBL) play a critical role in hydrolyzing a wide range of β-lactam drugs, including carbapenems. Recent reports have highlighted an increase in Pseudomonas aeruginosa abundance during acute SARS-CoV-2 infections, potentially complicating viral treatment. Objectives: This study aims to present the discovery of novel inhibitors targeting P. aeruginosa Verona integrin-encoded (VIM)-2 MBL using a combination of computational and experimental methods. Methods: A total of 61 953 natural compounds and captopril (used as a positive control) were screened as potential inhibitors. Additionally, the 3D structure of the enzyme was obtained from the Protein Data Bank (PDB). The most promising compounds were selected through molecular docking, and further analysis of conformational changes in the protein-inhibitor complex was conducted using the GROMACS molecular dynamics package. Enzyme assays were performed to validate the results obtained through molecular modeling. Results: Two compounds, namely ZINC98363781 and indolebutyroyl aspartic acid (ZINC04090499), demonstrated strong inhibitory potential with docking energies of -14.1 and -12.7 kcal mol-1, respectively. Captopril exhibited a docking energy of -10.684 kcal mol-1. Molecular dynamics analysis indicated good stability and flexibility in the studied systems. According to the binding energies calculated by mechanics-Poisson Boltzmann surface area (MM-PBSA), captopril, ZINC98363781, and ZINC04090499 displayed binding energies of -29.39 ± 5.92 kcal mol-1, -79.74 ± 67.51 kcal mol-1, and -99.65 ± 26.52 kcal mol-1, respectively. Enzyme assays confirmed that the IC50 value of ZINC04090499 against VIM-2 MBL was 25 μM. Conclusions: Our findings suggest that ZINC04090499 is a promising inhibitor of VIM-2 MBL and warrant further investigation in laboratory studies.

Rotavirus molecular surveillance remains important in the post vaccine era in order to monitor the changes in transmission patterns, identify vaccine induced antigenic changes and discover potentially pathogenic vaccine related strains. The Canadian province of Alberta introduced rotavirus vaccination into its provincial vaccination schedule June 2015. To evaluate the impact of this program on stool rotavirus positivity rate, strain diversity and seasonal trends, we analyzed a prospective cohort of children with acute gastroenteritis recruited between December 2014 and August 2018. We identified dynamic changes in rotavirus positivity and genotype trends during pre- and post- rotavirus vaccine introduction periods. Genotypes G9P[8], G1P[8], G2P[4] and G12P[8] predominated consecutively each season with overall lower rotavirus incidence rates in 2016 and 2017. The demographic and clinical features of rotavirus gastroenteritis were comparable among wild type rotaviruses; however, children with G12P[8] infections were older (P<0.001). Continued efforts to monitor changes in the molecular epidemiology of rotavirus using whole genome sequence characterization is needed to further understand the impact of the selection pressure of vaccination on rotavirus evolution.

Abstract Introduction: Highly anticipated advancements in biotechnology and instrumentation now allows for simultaneous detection of multiple biomarkers. This is accelerating the cancer research field towards the development of multianalyte-based in vitro diagnostic tests for early detection and/or monitoring of cancer. These multianalyte liquid biopsy tests are often based on detection of cancer-related proteins and/or tumor-specific genomic and transcriptomic changes (circulating cell-free nucleic acids) in plasma. A specialized and proprietary stabilization reagent has been developed for use in a blood collection tube with the capability to simultaneously stabilize protein biomarkers of interest and cell-free nucleic acids. Methods: Blood samples from self-proclaimed healthy donors were drawn into evacuated blood collection tubes containing a new stabilization reagent and stored at room temperature. Plasma was isolated using a general double-spin protocol (1800 × g for 15 min and 2800 × g for 15 min) and frozen at -80 °C until use. Plasma levels of proteins of interest including cancer markers and immuno-oncology checkpoint markers were measured by Ella Simple Plex Assays (Bio-Techne) and Luminex xMAP® Technology Assays (Bio-Techne, samples tested by LuminexPLORE Lab). Total cell-free nucleic acids were isolated using the QIAamp Circulating Nucleic Acid Kit and the resultant cfDNA or cfRNA was analyzed with fluorometric-based assay (quantitative) and Bioanalyzer/Tapestation (qualitative). Results: Compared with blood samples collected in EDTA and ACD-A tubes, the new stabilization reagent maintained the draw time levels of plasma proteins of interest such as TGF-β1, MMP-9, IL-8/CXCL8, CCL5/RANTES, Granzyme B, CEA and PD-L1 for up to 5 days of room temperature storage in whole blood. The reagent also effectively maintained draw time levels of plasma cell-free nucleic acids to near draw time levels. In addition, the new reagent also minimized in vitro hemolysis during room temperature storage. Conclusion: This new and breakthrough stabilization reagent simultaneously maintains the draw time levels of proteins of interest and cell-free nucleic acids for an extended period of time in whole blood. It provides cancer researchers and clinical assay developers confidence in blood sample integrity and flexibility in sample storage, transport, and processing at ambient temperature for multianalyte analysis or multiomics profiling. Citation Format: Jing Li, Lisa Bartron, Jordan LaRue, Sama Mehta, Nicholas M. George. Ambient storage and stabilization of proteins and cell-free nucleic acids in whole blood samples. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5605.

Stochastic, intensity-based precursorisolation can result in isotopicallyenriched fragment ions. This problem is exacerbated for large peptidesand stable isotope labeling experiments using deuterium or 15N. For stable isotope labeling experiments, incomplete and ubiquitouslabeling strategies result in the isolation of peptide ions composedof many distinct structural isomers. Unfortunately, existing proteomicssearch algorithms do not account for this variability in isotopicincorporation, and thus often yield poor peptide and protein identificationrates. We sought to resolve this shortcoming by deriving the expectedisotopic distributions of each fragment ion and incorporating theminto the theoretical mass spectra used for peptide-spectrum-matching.We adapted the Comet search platform to integrate a modified spectralprediction algorithm we term Conditional fragment Ion DistributionSearch (CIDS). Comet-CIDS uses a traditional database searching strategy,but for each candidate peptide we compute the isotopic distributionof each fragment to better match the observed m/z distributions. Evaluating previously generated D2O and 15N labeled data sets, we found that Comet-CIDSidentified more confident peptide spectral matches and higher proteinsequence coverage compared to traditional theoretical spectra generation,with the magnitude of improvement largely determined by the amountof labeling in the sample.

Abstract BackgroundPneumonia from SARS-CoV-2 is difficult to distinguish from other viral and bacterial etiologies. Broad-spectrum antimicrobials are frequently prescribed to patients hospitalized with COVID-19 which potentially acts as a catalyst for the development of antimicrobial resistance (AMR). ObjectivesWe conducted a systematic review and meta-analysis during the first 18 months of the pandemic to quantify the prevalence and types of resistant co-infecting organisms in patients with COVID-19 and explore differences across hospital and geographic settings.MethodsWe searched MEDLINE, Embase, Web of Science (BioSIS), and Scopus from November 1, 2019 to May 28, 2021 to identify relevant articles pertaining to resistant co-infections in patients with laboratory confirmed SARS-CoV-2. Patient- and study-level analyses were conducted. We calculated pooled prevalence estimates of co-infection with resistant bacterial or fungal organisms using random effects models. Stratified meta-analysis by hospital and geographic setting was also performed to elucidate any differences. ResultsOf 1331 articles identified, 38 met inclusion criteria. A total of 1959 unique isolates were identified with 29% (569) resistant organisms identified. Co-infection with resistant bacterial or fungal organisms ranged from 0.2 to 100% among included studies. Pooled prevalence of co-infection with resistant bacterial and fungal organisms was 24% (95% CI: 8-40%; n=25 studies: I2=99%) and 0.3% (95% CI: 0.1-0.6%; n=8 studies: I2=78%), respectively. Among multi-drug resistant organisms, methicillin-resistant Staphylococcus aureus, carbapenem-resistant Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa and Candida auris were most commonly reported. Stratified analyses found higher proportions of AMR outside of Europe and in ICU settings, though these results were not statistically significant. Patient-level analysis demonstrated >50% (n=58) mortality, whereby all but 6 patients were infected with a resistant organism. ConclusionsDuring the first 18 months of the pandemic, AMR was moderately prevalent in COVID-19 patients and varied by hospital and geography although there was substantial heterogeneity. Given the variation in patient populations within these studies, clinical settings, practice patterns, and definitions of AMR, further research is warranted to quantify AMR in COVID-19 patients to improve surveillance programs, infection prevention and control practices and antimicrobial stewardship programs globally.