Clinical Diagnostic Tool Research Articles

Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model

This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human body is virtually impossible due to the many internal mechanisms that ensure the kinetics of metals and the wide variety of organs, tissues, cellular structures, and secretions that ensure their functional redistribution, transport, and cumulation. We have developed an intelligent multi-neural network model capable of calculating the content of metals in the human body based on data on their environmental content. The model is two interconnected neural networks trained on actual measurement data. Since metals enter the body from the environment, the predictors of the model are metal content in drinking water and soil. In this case, water characterizes the short-term impact on the organism, and drinking water, combined with metal contents in soil, is a depository medium that accumulates metals from anthropogenic sources—the long-term impact. In addition, human physiological characteristics are taken into account in the calculations. Each period of exposure is taken into account by its neural network. Two variants of the model are proposed: open loop, where the calculation is performed by each neural network separately, and closed loop, where neural networks work together. The model built in this way was trained and tested on the data of real laboratory studies of 242 people living in different districts of Kazan. As a result, the accuracy of the neural network block for calculating long-term impact was 90% and higher, and the accuracy of the block for calculating short-term impact was 92% and higher. The closed double-loop model showed an accuracy of at least 96%. Conclusions: Our proposed method of assessing and quantifying metal accumulation in the body has high accuracy and reliability. It does not require expensive laboratory tests and allows quantifying the body’s metal accumulation content based on readily available information. The calculation results can be used as a tool for clinical diagnostics and operational and planned management to reduce the levels of polymetallic contamination in urban areas.

ADVANCED PREDICTIVE MODELING FOR THE BIOFIRE FILMARRAY SYSTEM IN INFECTIOUS DISEASE DIAGNOSTICS

The Bio Fire Film Array System has emerged as a powerful tool in clinical diagnostics, offering rapid and multiplexed detection of infectious pathogens. This study presents the development of a comprehensive mathematical model aimed at predicting the accuracy and sensitivity of the BioFire FilmArray System under diverse conditions. Key factors such as sample type, pathogen load, specimen characteristics, and environmental variables were systematically analyzed to elucidate their impact on the system's performance. The model incorporates a range of parameters including sample volume, specimen complexity, microbial diversity, and system-specific variables to generate probabilistic estimates of diagnostic outcomes. By leveraging large datasets encompassing diverse clinical scenarios and pathogen profiles, the model achieves robustness and generalizability, thereby facilitating its applicability across different healthcare settings and populations. Validation of the model against independent datasets demonstrates its efficacy in accurately predicting the performance of the BioFire FilmArray System across a spectrum of conditions. Overall, this mathematical model represents a significant advancement in optimizing the utility of the BioFire FilmArray System for infectious disease diagnosis. Its integration into clinical practice holds promise for improving patient care, guiding sample processing protocols, and informing decision-making processes in infectious disease management.

Proteomic analysis of extracellular vesicles derived from canine mammary tumour cell lines identifies protein signatures specific for disease state

BackgroundCanine mammary tumours (CMT) are among the most common types of tumours in female dogs. Diagnosis currently requires invasive tissue biopsies and histological analysis. Tumour cells shed extracellular vesicles (EVs) containing RNAs and proteins with potential for liquid biopsy diagnostics. We aimed to identify CMT subtype-specific proteome profiles by comparing the proteomes of EVs isolated from epithelial cell lines derived from morphologically normal canine mammary tissue, adenomas, and carcinomas.MethodsWhole-cell protein lysates (WCLs) and EV-lysates were obtained from five canine mammary cell lines: MTH53A (non-neoplastic); ZMTH3 (adenoma); MTH52C (simple carcinoma); 1305, DT1406TB (complex carcinoma); and their proteins identified by LC-MS/MS analyses. Gene Ontology analysis was performed on differentially abundant proteins from each group to identify up- and down-regulated biological processes. To establish CMT subtype-specific proteomic profiles, weighted gene correlation network analysis (WGCNA) was carried out.ResultsWCL and EVs displayed distinct protein abundance signatures while still showing the same increase in adhesion, migration, and motility-related proteins in carcinoma-derived cell lines, and of RNA processing and RNA splicing factors in the adenoma cell line. WGCNA identified CMT stage-specific co-abundant EV proteins, allowing the identification of adenoma and carcinoma EV signatures not seen in WCLs.ConclusionsEVs from CMT cell lines exhibit distinct protein profiles reflecting malignancy state, allowing us to identify potential biomarkers for canine mammary carcinomas, such as biglycan. Our dataset could therefore potentially serve as a basis for the development of a less invasive clinical diagnostic tool for the characterisation of CMT.

Performance of Multimodal Artificial Intelligence Chatbots Evaluated on Clinical Oncology Cases

Multimodal artificial intelligence (AI) chatbots can process complex medical image and text-based information that may improve their accuracy as a clinical diagnostic and management tool compared with unimodal, text-only AI chatbots. However, the difference in medical accuracy of multimodal and text-only chatbots in addressing questions about clinical oncology cases remains to be tested. To evaluate the utility of prompt engineering (zero-shot chain-of-thought) and compare the competency of multimodal and unimodal AI chatbots to generate medically accurate responses to questions about clinical oncology cases. This cross-sectional study benchmarked the medical accuracy of multiple-choice and free-text responses generated by AI chatbots in response to 79 questions about clinical oncology cases with images. A unique set of 79 clinical oncology cases from JAMA Network Learning accessed on April 2, 2024, was posed to 10 AI chatbots. The primary outcome was medical accuracy evaluated by the number of correct responses by each AI chatbot. Multiple-choice responses were marked as correct based on the ground-truth, correct answer. Free-text responses were rated by a team of oncology specialists in duplicate and marked as correct based on consensus or resolved by a review of a third oncology specialist. This study evaluated 10 chatbots, including 3 multimodal and 7 unimodal chatbots. On the multiple-choice evaluation, the top-performing chatbot was chatbot 10 (57 of 79 [72.15%]), followed by the multimodal chatbot 2 (56 of 79 [70.89%]) and chatbot 5 (54 of 79 [68.35%]). On the free-text evaluation, the top-performing chatbots were chatbot 5, chatbot 7, and the multimodal chatbot 2 (30 of 79 [37.97%]), followed by chatbot 10 (29 of 79 [36.71%]) and chatbot 8 and the multimodal chatbot 3 (25 of 79 [31.65%]). The accuracy of multimodal chatbots decreased when tested on cases with multiple images compared with questions with single images. Nine out of 10 chatbots, including all 3 multimodal chatbots, demonstrated decreased accuracy of their free-text responses compared with multiple-choice responses to questions about cancer cases. In this cross-sectional study of chatbot accuracy tested on clinical oncology cases, multimodal chatbots were not consistently more accurate than unimodal chatbots. These results suggest that further research is required to optimize multimodal chatbots to make more use of information from images to improve oncology-specific medical accuracy and reliability.

Exploring the connection between EU-funded research and methodological approaches: insights from a retrospective analysis

BackgroundOver the last two decades, substantial investments have been directed towards supporting fundamental and applied research in Alzheimer’s disease (AD), breast cancer (BC), and prostate cancer (PC), which continue to pose significant health challenges. Recently, the Joint Research Centre (JRC) of the European Commission (EC) conducted a retrospective analysis to examine the major scientific advancements resulting from EU-funded research in these disease areas and their impact on society.MethodsBuilding upon this analysis, our subsequent investigation delves into the methodological approaches—both animal and non-animal models and methods—employed in AD, BC, and PC research funded under past EU framework programs (FP5, FP6, FP7, and H2020), and explored the notable research outputs associated with these approaches.ResultsOur findings indicate a prevalent use of animal-based methodologies in AD research, particularly evident in projects funded under H2020. Notably, projects focused on drug development, testing, or repurposing heavily relied on animal models. Conversely, research aimed at clinical trial design, patient stratification, diagnosis and diagnostic tool development, lifestyle interventions, and prevention—outputs with potential societal impact—more frequently utilised non-animal methods. Advanced investigations leveraging imaging, computational tools, biomarker discovery and organ/tissue chip technologies predominantly favoured non-animal strategies.ConclusionsThese insights highlight a correlation between methodological choices and the translational potential of research outcomes, suggesting the need for a reconsideration of research strategy planning in future framework programs.

B-154 Development and Evaluation of an Automated High-Throughput Magnetic Bead-Based pre-treatment Platform for Lipid-Soluble Vitamins in Clinical Mass Spectrometry

Abstract Background Mass spectrometry has become a critical tool in clinical diagnostics due to its unparalleled sensitivity, specificity, and ability for multiplex analysis, redefining standards in small molecule detection. However, its efficacy is limited by complex sample preparation requirements, such as purification and enrichment. Traditional pre-treatment methods often involve labor-intensive, error-prone procedures that can dilute samples, thereby necessitating enhanced sensitivity in mass spectrometry. Addressing these challenges, we developed an automated, high-throughput pre-treatment platform using magnetic bead technology to streamline the preparation of small molecules for analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS). This study assesses the platform's effectiveness and reliability using lipid-soluble vitamins as a test case. Methods We developed a magnetic bead-based extraction process for lipid-soluble vitamins (Vitamins A, 25-hydroxyvitamin D2, 25-hydroxyvitamin D3, E, and K1) from clinical serum samples, utilizing Zybio's EXM3000 for automation. Protein-bound vitamins were dissociated using methanol and isopropanol, followed by magnetic bead extraction. The extracted vitamins underwent LC-MS/MS analysis. Comparative analysis with traditional liquid-liquid extraction methods, using 20 clinical samples and commercially available kits, evaluated this novel approach's analytical efficacy. Results The method showed excellent linearity (r > 0.99) for the target compounds. The imprecision ranged from 1.5% to 9.3%, and the accuracy (recovery rate) was between 91.5% and 113.4%. The limit of quantification, repeated six times, had a deviation of -8.5% to 13% and a CV of 1.5%-6.4%, meeting industry standards and EP document requirements. Comparisons with commercial kits in 20 clinical samples showed deviations within ±15%, and the 95% confidence interval for expected bias met the testing requirements. The method also demonstrated high correlation with the commercial kits, as evidenced by regression equations for Vitamin A: y = 0.9732x - 1.7985, 25-hydroxyvitamin D2: y = 1.0163x - 0.0055, 25-hydroxyvitamin D3: y = 1.0597x - 0.7968, Vitamin E: y = 1.0569x - 0.2917, and Vitamin K1: y = 1.0055x - 0.0039. Conclusions This study showed excellent performance of a novel automated high-throughput magnetic bead sample preparation platform for small molecules in clinical mass spectrometry. This technology overcomes the complexities associated with sample pre-treatment in clinical mass spectrometry and holds broad clinical application prospects in mass spectrometric analysis.

Two Decades of High-Resolution Peripheral Quantitative Computed Tomography: Present and Future Clinical Perspectives.

Twenty years have passed since the introduction of high-resolution peripheral quantitative computed tomography (HR-pQCT) to assess human bone microarchitecture. During that time, the technique has emerged as an important research tool used by clinicians and scientists to learn about the pathophysiology of bone adaptation in the context of osteoporosis and many other bone-affected conditions. Its rich three-dimensional data is well suited for precise longitudinal monitoring of bone microarchitecture and associated patient-specific estimated bone strength.However, uptake of HR-pQCT as a clinical diagnostic tool has been limited, in part due to challenges such as availability, regulatory approvals, and demonstrated cost effectiveness. New research suggests fracture risk assessment using HR-pQCT is comparable with current standards based on traditional bone densitometry, but its contribution to clinical care is best suited to two areas: (1) leveraging microarchitectural information to assist in treatment decisions for the large subset of patients who lie in the so-called gray zone by current fracture risk assessment, and (2) longitudinal monitoring that establishes highly refined trajectories of bone adaptation and can inform decisions to initiate treatment, monitor treatment effects, and inform cessation.

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

Hemophagocytic Lymphohistiocytosis: Clinical Characteristics and Diagnostic Prediction Model

To investigate the clinical characteristics of patients with hemophagocytic lymphohistiocytosis (HLH) and quantify the diagnostic value of various indexes in patients with elevated soluble interleukin-2 receptor (sCD25), so as to construct a diagnostic prediction model of HLH. The clinical characteristics of 121 patients with elevated sCD25 (≥2 400 U/ml) in the Third Affiliated Hospital of Sun Yat-Sen University were analyzed retrospectively. The patients were divided into HLH group and non-HLH group according to the diagnostic criteria of HLH. The patients with HLH were divided into infection group, tumor group, macrophage activation syndrome (MAS) group and unknown etiology group according to their etiology. The basic data and treatment of the patients were collected for univariate and multivariate logistic analysis to establish a diagnostic prediction model of HLH. Among the 121 enrolled patients with elevated sCD25, 68 were diagnosed as HLH. The proportion of patients using vasopressors, the incidence rate of disseminated intravascular coagulation (DIC), and the HScore in the HLH group were higher than those in the non-HLH group (P < 0.05). Hepatomegaly, splenomegaly, and hemophagocytosis were more common in HLH patients(P < 0.05). Compared with the patients in non-HLH group, patients in HLH group had lower levels of neutrophils, platelets, fibrinogen, IgG, and IgM, while the levels of triglycerides, ferritin (FER), sCD25, serum glutamic oxaloacetic transaminase (SGOT), alkaline phosphatase (ALP), total bilirubin (TBil), lactate dehydrogenase (LDH), and D-dimer were higher (P < 0.05). In subgroup analysis, the level of sCD25 in tumor group was higher than that in infection group. The level of sCD25/ferritin in tumor group was higher than that in infection group and MAS group. Compared with HLH patients in the tumor group, the procalcitonin (PCT) level, proportion of patients using vasopressors, positive rate of hemophagocytosis, and incidence rate of DIC were all higher in the infection group, and the differences were statistically significant (P < 0.05). The results of multivariate analysis showed that fever, splenomegaly, hemophagocytosis, cytopenias, IgM, M.sCD25 [multiple of sCD25 detection value relative to the diagnostic threshold (2400 U/ml)], fibrinogen, and triglycerides were independent predictive factors for HLH (P < 0.05).The diagnostic prediction model H constructed based on temperature, splenomegaly, hemophagocytosis, cytopenias, IgM, M.sCD25, fibrinogen, triglycerides showed good predictive accuracy. The optimal cutoff value of H was 39.45, the sensitivity of the model was 94.12%, the specificity was 83.02%. sCD25, sCD25/FER, PCT, hemophagocytosis, hemodynamic instability and DIC could help to distinguish the underlying etiology of HLH. The prediction model H has high discrimination and calibration, which could be used as a relatively accurate clinical diagnostic tool for HLH.

Analyte-induced hindrance in the RCA-assisted CRISPR/Cas12a system for homogeneous protein assays

Heterogeneous assays, such as enzyme-linked immunosorbent assays, have become indispensable for in vitro diagnostics. However, the simple, sensitive, and accurate detection is limited by their multiple washing and incubation steps, and limited amplification methods. In this study, we design a novel approach utilizing analyte-induced hindrance within the rolling circle amplification (RCA)-assisted CRISPR/Cas12a system for simple and highly sensitive homogenous protein detection. Streptavidin (SA) and digoxin antibody (anti-Dig) are employed as representative detection models. The specific recognition of target proteins using primers modified with small molecules hinders the RCA process, preventing the activation of Cas12a′s trans-cleavage activity, thereby leading to a reduction in fluorescence intensity. Our developed platform exhibites exceptional detection performance characterized by high sensitivity, robust specificity, and significant potential for application in complex samples. By expanding the recognition elements, this platform can evolve into a versatile clinical diagnostic tool with universal applicability. In addition, this platform provides a novel direction for quantifying ultralow-concentration disease biomarkers in clinical practice.

Establishment and Application of Mismatch Amplification Mutation Assay-PCR for Rapid Detection and Differentiation of Duck Hepatitis A Virus-1 Attenuated Vaccine and Wild Strains.

Duck hepatitis A virus type 1 (DHAV-1) is the main pathogen causing viral hepatitis in ducks, marked by high contagion and acute mortality. Live attenuated DHAV-1 vaccines are widely used to control the disease. This study aims to develop a mismatch amplification mutation assay (MAMA)-PCR for the rapid detection and differentiation of Korean DHAV-1 wild-type strains from vaccine strains. A MAMA primer was designed to target a single nucleotide polymorphism (SNPs) at position 2276 within the VP1 gene, allowing differentiation in a single PCR reaction. The MAMA-PCR accurately identified both strains, with detection limits of 100.5 ELD50/mL and 102.3 ELD50/mL, respectively. The MAMA-PCR demonstrated specificity, showing no cross-reactivity with 12 other viral and bacterial pathogens. The MAMA-PCR was applied to 89 farms, yielding results consistent with nested-PCR and sequence determination, identifying four positive farms for DHAV-1 vaccine strains. In conclusion, this study is the first to employ the MAMA-PCR method to distinguish between DHAV-1 wild-type and vaccine strains. The developed method is rapid, simple, specific, and sensitive, thereby serving as an effective tool for clinical diagnostics in identifying and differentiating between Korean DHAV-1 wild-type and vaccine strains.

Unveiling Microbial Diversity: Raman Spectroscopy's Discrimination of Clostridium and Related Genera.

In the clinical environment, the identification of phylogenetic closely related genera and species like Clostridium and Bacillus spp. is challenging. Both genera contain representatives of pathogenic and nonpathogenic species that need to be distinguished for a proper diagnostic read-out. Therefore, reliable and accurate detection methods must be employed for the correct identification of these genera and species. Despite their high pathogenicity, clostridial infections and food contaminations present significant challenges due to their unique cultivation conditions and developmental needs. Therefore, in many diagnostic protocols, the toxins are used for microbiological documentation. However, the applied laboratory methods suffer in accuracy, sometimes require large bacterial loads to provide reliable results, and cannot differentiate pathogenic from nonpathogenic strains. Here, Raman spectroscopy was employed to create an extensive Raman database consisting of pathogenic and nonpathogenic Bacillus and Clostridium species. These genera, as well as representatives of Paraclostridium and Clostridioides were specifically selected for their phylogenetic relation, cultivation conditions, and metabolic activity. A chemometric evaluation of the Raman spectra of single vegetative cells revealed a high discriminating power at the genus level. However, bacilli are considerably easier to classify at the species level than clostridia. The discrimination between the genera and species was based on their phylogeny and not their aerobic and anaerobic cultivation conditions. These encouraging results demonstrated that Raman spectroscopy coupled with chemometrics is a robust and helpful method for differentiating Clostridium species from Bacillus, Clostridioides, and Paraclostridium species. This approach has the potential to be a valuable tool in clinical diagnostics.

PD-ARnet: a deep learning approach for Parkinson’s disease diagnosis from resting-state fMRI

Objective. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.Approach.This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification.Main results.Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%).Significance.The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.

Split CRISPR-Cas12a for enhanced detection of NF-κB p50: A novel electrochemiluminescence biosensor approach

NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) plays a pivotal role in regulating immune responses, inflammation, and cell survival. The detection of its subunit, NF-κB p50, is crucial for understanding its involvement in various pathological conditions, including chronic inflammation and cancer. Traditional detection methods, such as EMSA, ELISA, Western blotting, and qPCR, have limitations regarding sensitivity, specificity, and quantification. To address these challenges, we developed a CRISPR-Cas12a-based electrochemiluminescence (ECL) biosensor. This biosensor utilizes the high specificity of the CRISPR-Cas12a system, which can be programmed to target specific DNA sequences, coupled with an ECL detection mechanism that amplifies the signal upon target recognition. The ECL biosensor showed excellent assay performance. It exhibited a linear correlation between ECL intensity and NF-κB p50 concentration, offering a precise and sensitive method for quantifying this protein. The limit of detection (LOD) was determined to be 0.235 fM, which is superior to or comparable with existing detection techniques. Specificity tests confirmed the biosensor’s ability to distinguish NF-κB p50 from non-specific proteins such as BSA, Siglec-5, and PSA, ensuring high accuracy and minimal false positives. This study highlights the potential of CRISPR-Cas12a-based ECL biosensors as powerful tools for clinical diagnostics and research applications, offering a sensitive, specific, and versatile platform for detecting NF-κB p50 and potentially other biomarkers.

Fast Multiphoton Microscopic Imaging Joint Image Super-Resolution for Automated Gleason Grading of Prostate Cancers.

Gleason grading system is dependable for quantifying prostate cancer. This paper introduces a fast multiphoton microscopic imaging method via deep learning for automatic Gleason grading. Due to the contradiction between multiphoton microscopy (MPM) imaging speed and quality, a deep learning architecture (SwinIR) is used for image super-resolution to address this issue. The quality of low-resolution image is improved, which increased the acquisition speed from 7.55 s per frame to 0.24 s per frame. A classification network (Swin Transformer) was introduced for automated Gleason grading. The classification accuracy and Macro-F1 achieved by training on high-resolution images are respectively 90.9% and 90.9%. For training on super-resolution images, the classification accuracy and Macro-F1 are respectively 89.9% and 89.9%. It shows that super-resolution image can provide a comparable performance to high-resolution image. Our results suggested that MPM joint image super-resolution and automatic classification methods hold the potential to be a real-time clinical diagnostic tool for prostate cancer diagnosis.

Benchmarking whole exome sequencing in the German network for personalized medicine

IntroductionWhole Exome Sequencing (WES) has emerged as an efficient tool in clinical cancer diagnostics to broaden the scope from panel-based diagnostics to screening of all genes and enabling robust determination of complex biomarkers in a single analysis. MethodsTo assess concordance, six formalin-fixed paraffin-embedded (FFPE) tissue specimens and four commercial reference standards were analyzed by WES as matched tumor-normal DNA at 21 NGS centers in Germany, each employing local wet-lab and bioinformatics. Somatic and germline variants, copy-number alterations (CNAs), and complex biomarkers were investigated. Somatic variant calling was performed in 494 diagnostically relevant cancer genes. The raw data were collected and re-analyzed with a central bioinformatic pipeline to separate wet- and dry-lab variability. ResultsThe mean positive percentage agreement (PPA) of somatic variant calling was 76 % while the positive predictive value (PPV) was 89 % in relation to a consensus list of variants found by at least five centers. Variant filtering was identified as the main cause for divergent variant calls. Adjusting filter criteria and re-analysis increased the PPA to 88 % for all and 97 % for the clinically relevant variants. CNA calls were concordant for 82 % of genomic regions. Homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) status were concordant for 94 %, 93 %, and 93 % of calls, respectively. Variability of CNAs and complex biomarkers did not decrease considerably after harmonization of the bioinformatic processing and was hence attributed mainly to wet-lab differences. ConclusionContinuous optimization of bioinformatic workflows and participating in round robin tests are recommended.

Treatment of Acute Sore Throat in Malaysia: A Consensus of Multidisciplinary Recommendations Using Modified Delphi Methodology.

Using antibiotics in the treatment of acute sore throats has been linked with antimicrobial resistance (AMR) and needs to be addressed. The consensus sought to improve diagnostic accuracy, decrease unwarranted antibiotic prescriptions and enhance patient outcomes. A multidisciplinary panel of nine experts reviewed published literature and discussed current practices in managing sore throat. Ten evidence-based statements on sore throat and AMR, diagnostic accuracy and antibiotic prescribing, and symptomatic therapy were developed. A modified Delphi exercise was then carried out. A consensus was reached if at least 70% of the group agreed with the statement. All 10 statements for managing acute sore throat achieved consensus. The major concern of AMR caused by improper antibiotic prescribing, particularly in cases of viral sore throat, was recognized. This underscores the need for improved diagnostic tools, such as the McIsaac score, to reduce needless antibiotic prescriptions. To improve patient satisfaction, effective pain management using non-antibiotic alternatives such as paracetamol, non-steroidal anti-inflammatory drugs (NSAIDs), and flurbiprofen throat lozenges was recommended. Pain and inflammation can be adequately managed with low-dose NSAIDs. The potential benefits of topical NSAIDs were acknowledged for their milder safety profile than oral formulations. A consensus was achieved on the use of a clinical diagnostic tool, prudent use of antibiotics, and symptomatic therapy in acute sore throat management. The McIsaac score and point-of-care testing (POCT) for the presence of group A beta-hemolytic Streptococcus (GABHS) can aid in the decision-making process for antibiotic use, reducing needless prescriptions. The mainstay of therapy is symptomatic treatment, which includes the use of NSAIDs.

Evaluation of Galaxy as a User-Friendly Bioinformatics Tool for Enhancing Clinical Diagnostics in Genetics Laboratories

Bioinformatics platforms have revolutionized clinical diagnostics by facilitating the analysis of genomic data, thereby advancing personalized medicine and enhancing patient care. This study investigates the integration, usage patterns, challenges, and impact of the Galaxy platform within clinical diagnostics laboratories. Employing a convergent parallel mixed-methods design, quantitative survey data and qualitative insights from structured interviews were gathered from fifteen participants across diverse roles in clinical settings. Findings reveal widespread adoption of Galaxy, with high satisfaction reported for its user-friendly interface and significant improvements in workflow efficiency and diagnostic accuracy. Challenges such as data security and training needs were identified, emphasizing the platform's role in simplifying complex data analysis tasks. The study contributes to understanding Galaxy’s transformative potential in clinical practice, offering recommendations for optimizing its integration and functionality. These insights are crucial for advancing clinical diagnostics and enhancing patient outcomes.

Diagnosis of acute hyperglycemia based on data-driven prediction models

Acute hyperglycemia is a common endocrine and metabolic disorder that seriously threatens the health and life of patients. Exploring effective diagnostic methods and treatment strategies for acute hyperglycemia to improve treatment quality and patient satisfaction is currently one of the hotspots and difficulties in medical research. This article introduced a method for diagnosing acute hyperglycemia based on data-driven prediction models. In the experiment, clinical data from 1000 patients with acute hyperglycemia were collected. Through data cleaning and feature engineering, 10 features related to acute hyperglycemia were selected, including BMI (Body Mass Index), TG (triacylglycerol), HDL-C (High-density lipoprotein cholesterol), etc. The support vector machine (SVM) model was used for training and testing. The experimental results showed that the SVM model can effectively predict the occurrence of acute hyperglycemia, with an average accuracy of 96 %, a recall rate of 84 %, and an F1 value of 89 %. The diagnostic method for acute hyperglycemia based on data-driven prediction models has a certain reference value, which can be used as a clinical auxiliary diagnostic tool to improve the early diagnosis and treatment success rate of acute hyperglycemia patients.

Diagnostic Clinical Tool in Trauma Patients to Rule out Thoracolumbar Fracture

Abstract Introduction: The primary objective of this study was to assess the effectiveness of the clinical decision tool (CDT) in trauma patients, providing a comparable ability to rule out thoracolumbar (TL) fractures as traditional imaging methods. The goal is to facilitate early clearance of the TL spine without an immediate requirement for radiological tests, thereby minimizing unnecessary utilization of TL-spine imaging. Methods: A prospective, observational study was conducted on trauma patients with suspected TL injury. To achieve early TL clearance, the CDT assessed criteria such as absence of pain, tenderness, and pain-free axial movement and flexion. The study enrolled alert trauma patients with thoracic and/or lumbar spine injuries, defined by the Glasgow Coma Scale of 15. The study excluded patients not aligning with CDT criteria, such as those who received intravenous opioid analgesia within 4 h and those unable to stand due to suspected pelvic or lower limb injuries. Results: Following the completion of the CDT steps, there were 31 true negative cases, signifying the absence of TL fractures according to both CDT and imaging studies. The sensitivity of the CDT was 99.38% (95% confidence interval [CI]: 96.59%–99.98%), specificity 9.1% (95% CI: 6.30%–12.73%), negative predictive value (NPV) 96.87% (95% CI: 81.02%–99.56%), positive predictive value (PPV) 34.19% (95% CI: 33.38%–35.00%), negative likelihood ratio (LHR) 0.07 (95% CI: 0.01–0.49), and positive LHR 1.09 (95% CI: 1.06–1.13). The sensitivity, specificity, NPV, PPV, negative LHR, and positive LHR varied with each step in the CDT. Notably, the overall sensitivity was high; however, the stepwise sensitivity decreased, albeit with an improvement in specificity with each further step in the tool. The overall sensitivity in the study cohort (n = 500) was high; however, the stepwise sensitivity decreased, albeit with an improvement in the specificity. Conclusions: The CDT to rule out TL fracture is a feasible bedside stepwise tool in fully awake trauma patients after a thorough clinical neurological examination on arrival. The tool could help Level II or III trauma centers avoid secondary triage to the higher center.