Clinical Diagnostic Tool Research Articles

Intelligent imaging technology applications in multidisciplinary hospitals: A systematic review.

With the rapid development of artificial intelligence technology, its applications in medical imaging have become increasingly extensive. This review aimed to analyze the current development status and future direction of intelligent imaging technology by investigating its application in various medical departments. To achieve this, we conducted a comprehensive search of various data sources up to 2024, including PubMed, Web of Science, and Google Scholar, based on the principle of comprehensive search. A total of 332 articles were screened, and after applying the inclusion and exclusion criteria, 56 articles were selected for this study. According to the findings, intelligent imaging technology exhibits robust image recognition capabilities, making it applicable across diverse medical imaging modalities within hospital departments. This technology offers an efficient solution for the analysis of various medical images by extracting and accurately identifying complex features. Consequently, it significantly aids in the detection and diagnosis of clinical diseases. Its high accuracy, sensitivity, and specificity render it an indispensable tool in clinical diagnostics and related tasks, thereby enhancing the overall quality of healthcare services. The application of intelligent imaging technology in healthcare significantly enhances the efficiency of clinical diagnostics, resulting in more accurate and timely patient assessments. This advanced technology offers a faster and more precise diagnostic approach, ultimately improving patient care and outcomes. This review analyzed the socioeconomic changes brought about by intelligent imaging technology to provide a more comprehensive evaluation. Also, we systematically analyzed the current shortcomings of intelligent imaging technology and its future development directions, to enable future research.

C‐TUnet: A CNN‐Transformer Architecture‐Based Ultrasound Breast Image Classification Network

ABSTRACTUltrasound breast image classification plays a crucial role in the early detection of breast cancer, particularly in differentiating benign from malignant lesions. Traditional methods face limitations in feature extraction and global information capture, often resulting in lower accuracy for complex and noisy ultrasound images. This paper introduces a novel ultrasound breast image classification network, C‐TUnet, which combines a convolutional neural network (CNN) with a Transformer architecture. In this model, the CNN module initially extracts key features from ultrasound images, followed by the Transformer module, which captures global context information to enhance classification accuracy. Experimental results demonstrate that the proposed model achieves excellent classification performance on public datasets, showing clear advantages over traditional methods. Our analysis confirms the effectiveness of combining CNN and Transformer modules—a strategy that not only boosts the accuracy and robustness of ultrasound breast image classification but also offers a reliable tool for clinical diagnostics, holding substantial potential for real‐world application.

To Study the Thyroid Hormone Levels in Neonates of Rural and Urban Vidarbha Region

ABSTRACT Background: This study aimed to evaluate thyroid hormone levels in neonates from the rural and urban Vidarbha region. Over the past decade, neonatal hypothyroidism screening has been conducted using filter paper techniques to identify congenital hypothyroidism (CH). In some screening programs, only T4 is measured initially, with TSH assessed if the T4 levels fall below normal. Conversely, some programs exclusively measure TSH to screen for CH. Currently, initial TSH screening can identify subclinical hypothyroidism in cases where T4 levels are normal, while preliminary T4 testing can reveal congenital hypothyroidism when TSH is elevated. If either T4 or TSH is found to be abnormal during preliminary screening, the other parameter should be monitored. Early diagnosis and prompt treatment are crucial in preventing intellectual disabilities in newborns. Material and Method: The present cross-sectional research will be conducted in collaboration with the Departments of Biochemistry, Obstetrics and Gynecology, Community Medicine, and Pediatrics at Jawaharlal-Nehru Medical College Sawangi (Meghe) Wardha, along with the Datta-Meghe Institute of Medical Science (Deemed University) in Maharashtra, India, and Datta-Meghe Medical College, Shalinitai Meghe Hospital and Research Centre, Nagpur. Results: A total of 272 neonates were tested for Congenital Hypothyroidism (CH) from the study area of the Vidarba region in which 138 neonates were from the rural area and 134 neonates were from the urban area. A total of 144 (53%) males and 128 (47%) females were born in rural and urban areas. In the neonates, it was observed that the Mean ± SD for free T4 and TSH was 0.84 ± 0.15 and 3.83 ± 2.12, respectively. If the free T4 value was less than the lower limit, the TSH value CB ≥25 μIU/ml and plasma ≥10 μIU/ml was considered as screen positive in the present study. Conclusion: Newborn screening programs were established to assess neonates at birth by measuring (TSH) levels. In many screening programs, TSH is the primary marker for evaluating thyroid function and is the preferred test for clinicians diagnosing various thyroid disorders. However, TSH levels can vary significantly and exhibit a broad reference range within the general population, which presents challenges in interpreting the results during newborn screening and in using it later as a clinical diagnostic tool for thyroid function.

Portable SpectroChip-Based Immunoassay Platform for Rapid and Accurate Melamine Quantification in Urine Samples

Growing concerns about the health risks of melamine adulteration in food products highlight the urgent need for reliable detection methods. However, the long-term effects of chronic low-level melamine exposure remain inadequately explored. This study introduces THE ONE InstantCare platform, a portable immunoassay analyzer integrating a SpectroChip-based spectral processing unit (SPU) with lateral flow immunoassay (LFIA) for sensitive and accurate quantification of melamine in human urine. This platform provides a cost-effective, rapid, and user-friendly point-of-care (POC) solution for melamine detection. Analytical evaluations across eight melamine concentrations (0–100 parts per billion, ppb) achieved a limit of detection (LOD) of 1.91 ppb. Validation with 24 human urine samples demonstrated strong concordance with liquid chromatography–mass spectrometry (LC-MS), yielding an intraclass correlation coefficient (ICC) of 0.9220, a Pearson correlation coefficient of 0.9389, and 95% agreement in Bland–Altman analysis. High reproducibility was observed, with an intraday coefficient of variation (CV) of 6.53% and acceptable interday CV values, while interference studies confirmed reliability in the presence of common biological substances. By delivering results in approximately 10 min, THE ONE InstantCare platform significantly reduces analysis time compared to LC-MS, which typically requires several hours. This novel platform enhances food safety surveillance and advances human health risk assessments, particularly for evaluating melamine-linked kidney damage. Its versatility and robust performance make it a promising tool for environmental monitoring and clinical diagnostics, enabling the detection of diverse biomarkers with high sensitivity and reproducibility.

Tyrosinase Oxidative Cross-Linking in the Cell-Like Crowded Microenvironment for Visible Inhibitor Screening.

Owing to many diseases and disorders being caused by dysfunctional or over/underexpressed enzymes, screening for inhibitors of pharmacologically relevant enzymes has emerged as a promising tool for drug discovery, clinical diagnostics, enzyme engineering, and other medical fields. However, despite the recent advances, most inhibitor screenings are still usually conducted in dilute media, at concentrations far from the media in which the enzymes are actually found, which may cause drugs to fail when translated to in vivo. Herein, we build a gel-like intracellular biological environment in vitro using a tyrosinase oxidative cross-linking hydrogel system that is closer to the real catalytic environment of enzymes. We report a straightforward and effective inhibitor evaluation strategy that can quickly compare the inhibitory strengths of inhibitors based on the principle that adding inhibitors causes color changes and mechanical changes in the system. Enabled by molecular docking, we further demonstrate the different performances of the inhibitors at different concentrations. By construction of the cell-like crowded environment in vitro, this system shows an appealing application prospect for new drug development.

Red Leg Dilemma: Development and Validation of Clinical Decision Tools for Non-Necrotizing Bacterial Dermohypodermitis, Necrotizing Fasciitis, and Eczematous Dermatitis.

Diagnosing red legs on first presentation is challenging. There exists a lack of robustly developed and validated diagnostic red leg tools in clinical practice. Physicians fear missing cases of infectious red legs and treat many patients unnecessarily with antibiotics. Develop and validate easy-to-use diagnostic tools applicable at bedside of patients to orient diagnosis of the commonest and most serious causes of infectious red legs (non-necrotizing bacterial dermohypodermitis (NNBDH), and necrotizing fasciitis (NF)) versus the commonest inflammatory cause (eczema). We collected data of patients presenting to our dermatology department from January first 2012 until May 17th 2017 with a diagnosis of red leg. Three models were developed using fast frugal trees. Validation was performed in a second cohort of patients. A total of 187 patients (mean age 56, SD = 21 years, 48.1% women) were included in the development phase and 62 patients (mean age 64, SD = 19, 52% women) in the validation phase. In the validation data set, sensitivity and specificity were respectively 67% and 91% for NNBDH, 83% and 66%, for NF and 88% and 93%, for eczema. Presentations of suspected lower-limb infections are commonly misdiagnosed, resulting in avoidable antibiotic prescription and hospitalization. We developed an easy-to-use clinical diagnostic tool applicable at the bedside of patients to help orient physicians in certain situations and avoid unnecessary initiation of antibiotics. Future work should focus on validating this tool in primary care to minimize misdiagnosis of red legs and overprescription of antibiotics.

Performance comparison of logistic regression and random forest model in bipolar disorder diagnosis

Abstract. Bipolar disorder (BD) is a significant psychiatric disease that has a large impact on patients living qualities. The diagnosis of BD is important for the early treatment and effective control that benefits the patients, their families, and society. With the development of machine learning algorithms, this paper makes use of the multinomial logistic regression (LR) and Random Forest (RF) models in predicting bipolar disorder (BD). The evaluation and comparison between the two models are conducted to present the possibility of using ML models to help with diagnosis of the BD. A dataset consisting of 120 individuals, including 28 BD Type I patients, 31 BD Type II patients, 31 depression patients, and 30 normal individuals was used in this study. The performance of the models was assessed using metrics such as accuracy, confusion matrix, cross-validation scores, classification report, and ROC-AUC curves. The multinomial LR model demonstrated superior performance with an accuracy of 83%, higher cross-validation scores, and better discriminative ability as indicated by ROC-AUC values. In contrast, the RF model achieved an accuracy of 79%, with lower precision and recall for certain classes. The findings suggest that the multinomial LR model is more effective in predicting bipolar disorder and its subtypes, making it a robust and reliable tool for clinical diagnostics.

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.