In clinical practice, colorectal cancer (CRC) is difficult to distinguish from ulcerative colitis and colon polyps. Practical markers are useful for diagnosing and treating patients with CRC. Carcinoembryonic antigen (CEA) is a biomarker for diagnosing patients with CRC. However, the diagnostic sensitivity and specificity of CEA are not high. Interleukin (IL)-10, IL-17A, tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), and transforming growth factor beta (TGF-β) are assumed to be closely related to the occurrence and development of human cancer. Some have been used as diagnostic markers in CRC. It remains unclear whether cytokines in combination with CEA could be used as biomarkers for the diagnosis of CRC. Serum levels of IL-10, IL-17, TNF-α, IFN-γ, and TGF-β in patients with CRC, ulcerative colitis, colonic polyps, stomach cancer, and healthy controls were measured by enzyme-linked immunosorbent assay. The serum level of CEA was detected using electrochemiluminescence. The value of the cytokines combined with CEA as a biomarker panel for the diagnosis of CRC was assessed. CEA, IL-10, IL-17A, TNF-α, and TGF-β levels were significantly increased in CRC. CEA displayed a higher specificity than the other cytokines. IL-17A, TNF-α, and TGF-β displayed higher sensitivities than CEA, IL-10, and IFN-γ in the diagnosis of CRC. The combination of serum CEA, IL-17A, and TNF-α achieved higher diagnostic efficacy for CRC (area under the curve = 0.935). The combination of CEA, IL-17, and TNF-α has better diagnostic efficacy than CEA alone in CRC. A panel containing IL-17A, TNF-α, and CEA could be a promising molecular biomarker panel to diagnostically differentiate CRC from ulcerative colitis, colon polyps, and stomach cancer.

Leishmaniasis is a neglected tropical disease with significant global public health implications, leading to diverse clinical manifestations. It disproport

The mecA gene confers methicillin resistance in both MRSA and MR-CoNS by encoding the PBP2a protein and poses a significant public health threat due to its resistance to beta-lactam antibiotics. Rapid and accurate detection of mecA is critical for timely treatment, reducing morbidity, and preventing its spread in healthcare settings. In this study, we developed an advanced one-pot recombinase polymerase amplification (RPA)-CRISPR/Cas12a system, enhanced with glycerol and betaine, for ultrasensitive detection of the mecA gene. Glycerol's viscosity effect prevents premature interaction between Cas12a and early amplification products, while betaine enhances nucleic acid amplification. The assay demonstrated superior sensitivity, detecting as low as 5 copies/μL of mecA DNA within 60 min. Specificity testing against a panel of bacterial species confirmed the high selectivity of the assay for mecA-carrying strains with negligible cross-reactivity. Furthermore, this method exhibited excellent performance across various clinical samples, including blood, urine, and bronchoalveolar lavage fluid. Our findings underscore the potential of this advanced RPA-CRISPR/Cas12a assay as a powerful diagnostic tool for rapid, cost-effective, and highly sensitive mecA detection, offering a promising solution for clinical diagnostics and infection control.

Background: Patient-based real-time quality control (PBRTQC) can be a valuable tool in clinical laboratories due to its cost-effectiveness and constant monitoring. More focus is placed on discovering and improving algorithms that compliment conventional internal control techniques. The practical implementation of PBRTQC with a biochemical instrument comparison is lacking. We aim to evaluate PBRTQC’s efficacy and practicality by comparing low-density lipoprotein cholesterol (LDL-C) test results to ensure consistent real-time monitoring across biochemical instrumentations in clinical laboratories. Method: From 1 September 2021 to 30 August 2022, the First Affiliated Hospital of Xi’an Jiaotong University collected data from 158,259 both healthy and diseased patients, including 84,187 male and 74,072 female patients, and examined their LDL-C results. This dataset encompassed a group comprising 50,556 individuals undergoing health examinations, a group comprising 42,472 inpatients (IP), and a group comprising 75,490 outpatients (OP) for the PBRTQC intelligent monitoring platform to conduct daily tests, parameter configuration, program development, real-time execution, and performance validation of the patients’ data. Moreover 40 patients’ LDL-C levels were assessed using two biochemical analyzers, designated as the reference and comparator instruments. A total of 160 LDL-C results were obtained from 40 both healthy and diseased patients, including 14 OP, 16 IP, and 10 health examination attendees, who were selected to represent LDL-C levels broadly. Two biochemical instruments measured LDL-C measurements from the same individuals to investigate consistency and reproducibility across patient statuses and settings. We employed exponentially weighted moving average (EWMA) and moving median (MM) methods to calculate inter-instrument bias and ensure analytical accuracy. Inter-instrument bias for LDL-C measurements was determined by analyzing fresh serum samples, different concentrations of quality control (QC), and commercialized calibrators, employing both EWMA and MM within two assay systems. The assessment of inter-instrumental bias with five different methods adhered to the external quality assessment standards of the Clinical Laboratory Center of the Health Planning Commission, which mandates a bias within ±15.0%. Result: We calculated inter-instrument comparison bias with each of the five methods based on patient big data. The comparison of fresh serum samples, different concentrations of QC, commercialized calibrators, and EWMA were all in the permissive range, except for MM. MM showed that the bias between two biochemical instruments in the concentration ranges of 1.5 mmoL/L–6.2 mmoL/L exceeded the permissible range. This was mainly due to the small number of specimens, affected by variations among individual patients, leading to increased false alarms and reduced effectiveness in monitoring the consistency of the inter-instrumental results. Moreover, the inter-comparison bias derived from EWMA was less than 3.01%, meeting the 15% range assessment criteria. The bias result for MM was lower than 24.66%, which was much higher than EWMA. Thus, EWMA is better than MM for monitoring inter-instrument comparability. PBRTQC can complement the use of inter-comparison bias between biochemical analyzers. EWMA has comparable inter-instrument comparability monitoring efficacy. Conclusions: The utilization of AI-based PBRTQC enables the automated real-time comparison of test results across different biochemical instruments, leading to a reduction in laboratory operating costs, enhanced work efficiency, and improved QC. This advanced technology facilitates seamless data integration and analysis, ultimately contributing to a more streamlined and efficient laboratory workflow in the biomedical field.