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Carbapenemase-producing organisms (CPOs) present a major threat to public health, demanding precise diagnostic techniques for their detection. Discrepancies among the CPO tests have raised concerns, partly due to limitations in detecting bacterial diversity within host specimens. We explored the impact of an unbiased colony selection on carbapenemase testing and assessed its relevance to various tests. Using the FirstAll method for unbiased colony selection to reduce bias, we compared the results from different methods, namely the modified carbapenem inactivation method/EDTA-modified carbapenem inactivation method (mCIM/eCIM), the Carba5, the CPO panel, and the multiplex PCR (MPCR). We compared the FirstAll method to the conventional colony selection for MPCR with seven CPO species. In addition, we evaluated the test performance on seven CPO species using MPCR as a reference and the FirstAll method as the colony-selection method. The results revealed that the selections from the FirstAll method have improved rates of carbapenemase detection, in comparison to approximately 11.2% of the CPO isolates that were noted to be false negatives in the conventional colony-selection methods. Both the Carba5 test and the CPO panel showed suboptimal performance (sensitivity/specificity: Carba5 74.6%/89.5%, CPO panel 77.2%/74.4%) in comparison to the FirstAll method. The Carba5 test provided specific carbapenemase class assignments, but the CPO panel failed in 18.7% of the cases. The Carba5 test and the CPO panel results correlated well with ceftazidime-avibactam minimal inhibitory concentrations (MICs). The concordance for Class A/D with MICs was 94.7% for Carba5 and 92.7% for the CPO panel; whereas for Class B, it was 86.5% for Carba5 and 75.9% for the CPO panel. In conclusion, FirstAll, as the unbiased colony-selection method, was shown to impact carbapenemase testing. With FirstAll, the diagnostic performance of both the Carba5 and the CPO panel was found to be lower. Furthermore, the utilization of ceftazidime-avibactam guided by either the CPO panel or Carba5 was appropriate.

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Background: Machine learning (ML) has emerged as a superior method for the analysis of large datasets. Application of ML is often hindered by incompleteness of the data which is particularly evident when approaching disease screening data due to varied testing regimens across medical institutions. Here we explored the utility of multiple ML algorithms to predict cancer risk when trained using a large but incomplete real-world dataset of tumor marker (TM) values., Methods: TM screening data were collected from a large asymptomatic cohort (n = 163,174) at two independent medical centers. The cohort included 785 individuals who were subsequently diagnosed with cancer. Data included levels of up to eight TMs, but for most subjects, only a subset of the biomarkers were tested. In some instances, TM values were available at multiple time points, but intervals between tests varied widely. The data were used to train and test various machine learning models to evaluate their robustness for predicting cancer risk. Multiple methods for data imputation were explored and models were developed for both single time-point as well as time-series data., Results: The ML algorithm, long short-term memory (LSTM), demonstrated superiority over other models for dealing with irregular medical data. A cancer risk prediction tool was trained and validated for a single time-point test of a TM panel including up to four biomarkers (AUROC = 0.831, 95% CI: 0.827-0.835) which outperformed a single threshold method using the same biomarkers. A second model relying on time series data of up to four time-points for 5 TMs had an AUROC of 0.931., Conclusions: A cancer risk prediction tool was developed by training a LSTM model using a large but incomplete real-world dataset of TM values. The LSTM model was best able to handle irregular data compared to other ML models. The use of time-series TM data can further improve the predictive performance of LSTM models even when the intervals between tests vary widely. These risk prediction tools are useful to direct subjects to further screening sooner, resulting in earlier detection of occult tumors., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Tumor markers are used to screen tens of millions of individuals worldwide at annual health check-ups, especially in East Asia. Machine learning (ML)-based algorithms that improve the diagnostic accuracy and clinical utility of these tests can have substantial impact leading to the early diagnosis of cancer., Methods: ML-based algorithms, including a cancer screening algorithm and a secondary organ of origin algorithm, were developed and validated using a large real world dataset (RWD) from asymptomatic individuals undergoing routine cancer screening at a Taiwanese medical center between May 2001 and April 2015. External validation was performed using data from the same period from a separate medical center. The data set included tumor marker values, age, and gender from 27,938 individuals, including 342 subsequently confirmed cancer cases., Results: Separate gender-specific cancer screening algorithms were developed. For men, a logistic regression-based algorithm outperformed single-marker and other ML-based algorithms, with a mean area under the receiver operating characteristic curve (AUROC) of 0.7654 in internal and 0.8736 in external cross validation. For women, a random forest-based algorithm attained a mean AUROC of 0.6665 in internal and 0.6938 in external cross validation. The median time to cancer diagnosis (TTD) in men was 451.5, 204.5, and 28 days for the mild, moderate, and high-risk groups, respectively; for women, the median TTD was 229, 132, and 125 days for the mild, moderate, and high-risk groups. A second algorithm was developed to predict the most likely affected organ systems for at-risk individuals. The algorithm yielded 0.8120 sensitivity and 0.6490 specificity for men, and 0.8170 sensitivity and 0.6750 specificity for women., Conclusions: ML-derived algorithms, trained and validated by using a RWD, can significantly improve tumor marker-based screening for multiple types of early stage cancers, suggest the tissue of origin, and provide guidance for patient follow-up.

Background: A panel of 3 serum proteins and 1 autoantibody has been developed to assist with the detection of lung cancer. We aimed to validate the accuracy of the biomarker panel in an independent test set and explore the impact of adding a fourth serum protein to the panel, as well as the impact of combining molecular and clinical variables., Methods: The training set of serum samples was purchased from commercially available biorepositories. The testing set was from a biorepository at the Cleveland Clinic. All lung cancer and control subjects were >50 years old and had smoked a minimum of 20 pack-years. A panel of biomarkers including CEA (carcinoembryonic antigen), CYFRA21-1 (cytokeratin-19 fragment 21-1), CA125 (carbohydrate antigen 125), HGF (hepatocyte growth factor), and NY-ESO-1 (New York esophageal cancer-1 antibody) was measured using immunoassay techniques. The multiple of the median method, multivariate logistic regression, and random forest modeling was used to analyze the results., Results: The training set consisted of 604 patient samples (268 with lung cancer and 336 controls) and the testing set of 400 patient samples (155 with lung cancer and 245 controls). With a threshold established from the training set, the sensitivity and specificity of both the 4- and 5-biomarker panels on the testing set was 49% and 96%, respectively. Models built on the testing set using only clinical variables had an area under the receiver operating characteristic curve of 0.68, using the biomarker panel 0.81 and by combining clinical and biomarker variables 0.86., Conclusions: This study validates the accuracy of a panel of proteins and an autoantibody in a population relevant to lung cancer detection and suggests a benefit to combining clinical features with the biomarker results., Competing Interests: Declaration of conflicting interests:The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: R.S. and V.D. are employees of 20/20 GeneSystems, the proprietor of the biomarker panel being studied. P.J.M. has served on clinical advisory boards for companies that have developed or are developing other lung cancer biomarkers (Oncimmune, InDi, Grail, Exact Sciences). All other authors do not have reported conflicts.

Objectives: "PAULA's" test (Protein Assays Utilizing Lung cancer Analytes) is a novel multiplex immunoassay blood test that incorporates both tumor antigens and autoantibodies to determine the risk that lung cancer (LC) is present in individuals from a high-risk population. The test's performance characteristics were evaluated in a study using 380 retrospective clinical serum samples., Methods: PAULA's test is performed on the Luminex xMAP technology platform, and detects a panel of 3 tumor antigens (CEA, CA-125, and CYFRA 21-1) and 1 autoantibody marker (NY-ESO-1). A training set (n = 230) consisting of 115 confirmed diagnoses of non-small cell lung carcinoma (NSCLC) cases and 115 age- and smoking history-matched controls was used to develop the LC predictive model. Data from an independent matched validation set (n = 150) was then used to evaluate the model developed, and determine the ability of the test to distinguish NSCLC cases from controls., Results: The 4-biomarker panel was able to discriminate NSCLC cases from controls with 74% sensitivity, 80% specificity, and 0.81 AUC in the training set and with 77% sensitivity, 80% specificity, and 0.85 AUC in the independent validation set. The use of NY-ESO-1 autoantibodies substantially increased the overall sensitivity of NSCLC detection as compared to the 3 tumor markers alone. Overall, the multiplexed 4-biomarker panel assay demonstrated comparable performance to a previously employed 8-biomarker non-multiplexed assay., Conclusions: These studies confirm the value of using a mixed panel of tumor antigens and autoantibodies in the early detection of NSCLC in high-risk individuals. The results demonstrate that the performance of PAULA's test makes it suitable for use as an aid to determine which high-risk patients need to be directed to appropriate noninvasive diagnostic follow-up testing, especially low-dose CT (LDCT).

Aims:   To perform an international trial to derive alert and action levels for the use of quantitative PCR (qPCR) in the monitoring of Legionella to determine the effectiveness of control measures against legionellae., Methods and Results:   Laboratories (7) participated from six countries. Legionellae were determined by culture and qPCR methods with comparable detection limits. Systems were monitored over ≥10 weeks. For cooling towers (232 samples), there was a significant difference between the log mean difference between qPCR (GU l(-1) ) and culture (CFU l(-1) ) for Legionella pneumophila (0·71) and for Legionella spp. (2·03). In hot and cold water (506 samples), the differences were less, 0·62 for Leg. pneumophila and 1·05 for Legionella spp. Results for individual systems depended on the nature of the system and its treatment. In cooling towers, Legionella spp. GU l(-1) always exceeded CFU l(-1) , and usually Legionella spp. were detected by qPCR when absent by culture. The pattern of results by qPCR for Leg. pneumophila followed the culture trend. In hot and cold water, culture and qPCR gave similar results, particularly for Leg. pneumophila. There were some marked exceptions with temperatures ≥50°C, or in the presence of supplementary biocides. Action and alert levels for qPCR were derived that gave results comparable to the application of the European Guidelines based on culture. Algorithms are proposed for the use of qPCR for routine monitoring., Conclusions:   Action and alert levels for qPCR can be adjusted to ensure public health is protected with the benefit that remedial actions can be validated earlier with only a small increase in the frequency of action being required., Significance and Impact of the Study:   This study confirms it is possible to derive guidelines on the use of qPCR for monitoring the control of legionellae with consequent improvement to response and public health protection., (© 2011 The Authors. Journal of Applied Microbiology © 2011 The Society for Applied Microbiology.)

Alterations in eIF3-p48/INT6 gene expression have been implicated in murine and human mammary carcinogenesis. We examined levels of INT6 protein in human tumors and determined that breast and colon tumors clustered into distinct groups based on levels of INT6 expression and clinicopathological variables. We performed multiplex tissue immunoblotting of breast, colon, lung, and ovarian tumor tissues and found that INT6 protein levels positively correlated with those of TID1, Patched, p53, c-Jun, and phosphorylated-c-Jun proteins in a tissue-specific manner. INT6 and TID1 showed significant positive correlation in all tissue types tested. These findings were confirmed by immunohistochemical staining of INT6 and TID1. Further evidence supporting a cooperative role for INT6 and TID1 is the presence of endogenous INT6 and TID1 proteins as complexes. We detected co-immunoprecipitation between INT6 and TID1, as well as between INT6 and Patched. These findings suggest potential integrated roles for INT6, TID1, and Patched proteins in cell growth, development, and tumorigenesis. Additionally, these data suggest that the combination of INT6, TID1, and Patched protein levels may be useful biomarkers for the development of diagnostic assays.

Background: There is a need for a technology that can quantitatively assay multiple proteins from a single hair follicle while preserving the morphology of the follicle. For proteomic profiling, the technology should be less labor intensive, with a higher throughput, more quantitative and more reproducible than immunohistochemistry., Objective: To test the ability of a novel method, layered expression scanning of hair (LES-hair) to detect the levels and localization of proteins in plucked hair follicles., Methods: LES-hair was used to assay proteins in the plucked hair follicle., Results: LES-hair detected differential expression of proteins within discrete regions of the plucked hair follicle. These proteins included cleaved caspase 3, Ki-67 and the phosphorylated forms of c-Kit, epidermal growth factor receptor and vascular endothelial growth factor receptor., Conclusion: LES-hair provides a research tool for studying the basic biology of plucked hair follicles and has potential clinical applications such as monitoring treatment of alopecia or using plucked hair follicles as a surrogate tissue to monitor pharmacodynamic effects of targeted cancer therapies., (2005 S. Karger AG, Basel)

The molecular profiles of protein expression from hundreds of cell lysates can be determined in a high-throughput manner by using fluorescent bead technologies, enzyme-linked immunosorbent assays (ELISAs), and protein microarrays. Although powerful, these tools are costly and technically challenging and thus have limited accessibility for many research groups. We propose a modification of traditional dot blotting that increases throughput of this approach and provides a simple and cost-effective technique for profiling multiple samples. In contrast to traditional blotting that uses a single membrane, we introduce blotting onto a stack of novel, thin, sieve-like membranes. These membranes have a high affinity for binding proteins, but have a lower capacity of protein binding compared to traditional (nitrocellulose) membranes. We compare the linear binding capacity and variability of these novel membranes with nitrocellulose membranes. Also, we describe the use of these membranes in a multilayer dot blot format for profiling mitogen-mediated signal transduction pathways in T cells.