Potential Screening Research Articles

Abstract PR006: The potential of multi-cancer early detection screening for reducing cancer mortality

Abstract Background: Currently, routine screening is recommended for only four cancer types (i.e., breast, cervical, colorectal, and lung) by the United States Preventive Services Task Force (USPSTF), and two-thirds of incident cancers have no screening guidelines. Emerging liquid biopsy multi-cancer early detection (MCED) tests have the potential to revolutionize early cancer detection. Their impact on cancer mortality remains uncertain. Computer models are needed to forecast long-term outcomes. Methods: We developed Simulation Model for MCED (SiMCED), a continuous-time, discrete-event microsimulation model of 14 solid tumor cancers: breast, cervical, colorectal, endometrial, esophageal, gastric, head and neck, kidney, liver, lung, ovarian, pancreatic, prostate, and urinary bladder. Cancer type- and stage-specific dwell times informed the transitions between cancer stages I-IV. Cancer type- and stage-specific MCED test sensitivities were derived from a large, multi-center, prospective, case-control study: Ascertaining Serial Cancer patients to Enable New Diagnostic 2 (ASCEND-2). The model was calibrated to reproduce annual incidence rates of cancer diagnosis as captured in the Surveillance, Epidemiology, and End Results (SEER) database, while accounting for the unobserved cancer burden. Using a 10-year time horizon, we simulated the life course of 5 million US adults aged 50-84 years, representative of the US population age distribution. Cancer diagnosis could arise from usual care or annual MCED screening. The MCED test was administered at the beginning of each year to individuals aged 50-84 years. After a cancer diagnosis, individuals followed SEER survival curves to determine the time and cause of death (cancer- or non-cancer-related). Results: Over the 10-year time horizon, the supplemental use of MCED screening reduced stage IV incidence by 876 (42%) per 100,000, relative to usual care. The 10-year reduction in cancer mortality was 439 (17%) per 100,000, which translates to 505,600 cancer-related deaths averted among US adults aged 50-84 years. Lung cancer had the highest absolute 10-year cancer mortality reduction at 126 (13%) per 100,000. Among the cancer types for which there is no routine screening, the 10-year cancer mortality reduction was 175 (15%) per 100,000, equating to 201,500 cancer-related deaths averted among US adults aged 50-84 years. Within the 10-year time window, earlier diagnosis by MCED led to an aggregated life-year gain of 1,165 per 100,000, which translates to a gain of 1,340,000 life-years among US adults aged 50-84 years. Conclusion: Our study suggests that MCED screening could be effective for reducing both stage IV incidence and cancer mortality. The real-world impact of MCED tests and their cost-effectiveness require further investigation. Citation Format: Jagpreet Chhatwal, Jade Xiao, Andrew ElHabr, Christopher Tyson, Xiting Cao, Sana Raoof, A. Mark Fendrick, A. Burak Ozbay, Paul Limburg, Tomasz M. Beer, Ashish Deshmukh, Andrew Briggs. The potential of multi-cancer early detection screening for reducing cancer mortality [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR006.

Abstract A073: The potential of multi-cancer early detection screening for reducing cancer mortality

Abstract This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR006) of the Conference Program/Proceedings. Citation Format: Jagpreet Chhatwal, Jade Xiao, Andrew ElHabr, Christopher Tyson, Xiting Cao, Sana Raoof, A. Mark Fendrick, A. Burak Ozbay, Paul Limburg, Tomasz M. Beer, Ashish Deshmukh, Andrew Briggs. The potential of multi-cancer early detection screening for reducing cancer mortality [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A073.

Abstract A006: Development of a circulating tumor cell-derived cancer organoid platform for drug screening against metastatic tumors and longitudinal monitoring of drug resistance

Abstract Metastatic disease remains the primary cause of cancer-related deaths, presenting a significant challenge in oncology drug development. Current drug screening efforts often rely on cell lines or primary tumor-derived models, which may not adequately mimic the behavior of metastatic tumors. To address this, we aimed to develop a system that more accurately reflects metastatic tumors for improved drug screening. We leveraged a specialized culture platform featuring patented coated plates and proprietary media developed by AcroCyte (Taiwan) to cultivate circulating tumor cells (CTCs) into cancer organoids without pre-selection or enrichment. Building on our previous work, where we successfully cultivated CTCs from fresh blood and cryopreserved peripheral blood mononuclear cells (PBMCs) from renal, colon, and lung cancer patients into cancer organoids, we have now demonstrated the ability to freeze these CTC- derived organoids and thaw them for new culture when needed. We also present new data extending this success to breast and prostate cancers. CTCs from five frozen PBMC samples from breast cancer patients and three from prostate cancer patients formed visible colonies, with positive immunostaining for EpCAM, cytokeratin, and CD44. The immune cells in the PBMC samples gradually died off within three weeks of culture, as confirmed by the absence of CD45- positive cells. Interestingly, CTC-derived organoids from breast and prostate cancers did not form the spheroids observed in renal, colon, and lung cancer models. Instead, they appeared as colonies or clusters of CTCs. After 6-8 weeks of active growth, these clusters exhibited a slower growth rate, although they remained viable in the proprietary culture system. We are actively evaluating various culture media supplements, tailored to breast and prostate cancers, to extend the active growth period of these CTC-derived organoids for longer durations. Importantly, these CTC-derived colonies retained drug sensitivity, demonstrating the system's potential for effective drug screening against metastatic tumors. Our platform now supports organoid development from renal, colon, lung, breast, and prostate cancers, and we are actively expanding to include all major solid tumors. Since this technology uses blood, which can be obtained non-invasively, it enables longitudinal monitoring of CTCs as a functional liquid biopsy. Additionally, frozen PBMCs can be used as a source material for CTCs, and the resulting organoids can be utilized for drug sensitivity testing. This platform could be instrumental in oncology drug development and holds promise for monitoring emerging drug resistance in clinical trials by analyzing PBMC samples collected before and after treatment, offering a functional liquid biopsy-based approach to aid in developing therapies targeting metastatic disease. Citation Format: Yi-chun Han, David Hsieh, Ruobo Zhang, Emily Miyashita-Lin, Shian-Jiun Shih. Development of a circulating tumor cell-derived cancer organoid platform for drug screening against metastatic tumors and longitudinal monitoring of drug resistance [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A006.

Abstract A075: Circulating T-cell receptor repertoire analysis improves cancer early detection

Abstract Introduction: Cancer screening by liquid biopsy promises to detect cancer early when it can be cured. However, current ctDNA-based approaches detect only a minority of early-stage cancers. For liquid biopsy to realize its full potential for cancer early detection, sensitivity for earliest stage disease must be improved. Methods: We set out to improve sensitivity of liquid biopsy by exploiting tumor recognition by T cells through sequencing of the circulating T-cell receptor repertoire. Using gDNA extracted from blood buffy coats, we studied a cohort of 471 lung cancer patients (85% stage I) and 600 subjects without cancer enriched for older individuals with a history of smoking. We performed TCR beta chain sequencing to yield a median of 101,899 TCR clonotypes per sample (median 81,602 cancer / 112,966 controls) and built a TCR sequence similarity graph to cluster clonotypes into TCR repertoire functional units (RFUs). The TCR frequencies of RFUs were tested for association with cancer status and significantly associated RFUs were combined using an SVM model into a cancer score. Cancer RFU levels were also correlated to imputed subject HLA types for 32 HLA Class I and 38 Class II alleles. The model was evaluated by 10-fold cross-validation and compared to a ctDNA panel of 237 mutation hotspots in 154 lung cancer driver genes and to 17 lung cancer related protein biomarkers in 86 subjects. Results: We identified 372 cancer-associated TCR RFUs at FDR≤0.1, including 198 enriched in cancer samples (fold-change range 1.03 to 3.13) and 174 enriched in controls (fold- change 0.96 to 0.30). Levels of 265/372 (71%) RFUs were correlated to presence of an HLA allele at FDR ≤ 0.1. Of the total 2,770 significant HLA-RFU associations, 479 (17%) involved an HLA Class I allele, while 2,291 (83%) involved an HLA Class II allele, highlighting a potential role for CD4 helper or regulatory T cell antigen recognition in the cancer RFUs found. The RFU cancer score achieved a cross-validated test ROC AUC of 0.71 for cancer status prediction (0.71 Stage I / 0.70 Stage II-IV) with 49% of Stage I cancers detected at a specificity of 80%. Importantly, the cancer score was not dependent on sample source site, technical or biological variation in TCR repertoire depth, or lung cancer histology subtype. The TCR RFU score also distinguished lung cancer patients from control subjects with other conditions such as benign lung nodules and COPD. We observed an up to ∼20%-point gain in sensitivity for stage I cancer when TCR RFUs were added to ctDNA and proteins in a potential multi-analyte cancer screening test. By contrast, TCR analysis did not provide additional sensitivity for stage II-IV disease. Conclusion: We demonstrate detection of a significant fraction of early lung cancer cases from blood by analyzing the circulating TCR repertoire and show that this signal is complementary to established analytes such as proteins and ctDNA. A similar study for the detection of early colorectal cancer and advanced adenomas is under way. Citation Format: Yilong Li, Michelle Nahas, Dennis Stephens, Kate Froburg, Emma Hintz, Devin Champagne, Amaneet Lochab, Markus Brown, Jasper Braun, María Antonia Fortuño, María-del-Mar Ocón, Andrea Pasquier, Inés María Luque, Christopher W. Seder, Jeffrey A. Borgia, Luis Miguel Seijo, Luis M Montuenga, Roman Yelensky. Circulating T-cell receptor repertoire analysis improves cancer early detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A075.

Measuring Eco-Anxiety with the Polish Version of the 13-Item Hogg Eco-Anxiety Scale (HEAS-13): Latent Structure, Correlates, and Psychometric Performance

Background/Objectives: The Hogg Eco-Anxiety Scale (HEAS-13) is a thirteen-item measure of eco-anxiety, with four dimensions: (1) affective symptoms, (2) rumination, (3) behavioural symptoms, and (4) anxiety about personal impact. Being a recently developed questionnaire, data on its psychometrics are limited. The aim of this study was to introduce a Polish version of the HEAS-13 and examine its psychometric properties. Methods: Our sample consisted of 634 Polish-speaking adults, with ages ranging from 18 to 67 years. We assessed the HEAS-13’s factor structure, internal consistency, test–retest reliabilities, and its concurrent validity via relationships with climate-related variables, psychopathology symptoms, and well-being. We put emphasis on examining the discriminant validity of the HEAS-13 against general psychological distress. Results: As expected, the Polish HEAS-13 demonstrated strong factorial validity with an intended four-factor structure. The internal consistency and test–retest reliabilities of the scale were good and moderate, respectively. Higher levels of eco-anxiety were associated with higher environmental concerns, the experience of climate change (i.e., one’s perception of being affected by climate change), pro-environmental behavioural engagement, climate change worry, anxiety, and depressive symptoms, as well as lower levels of well-being. We empirically supported the strong discriminant validity of the HEAS-13, demonstrating that eco-anxiety was separable from general psychological distress. We also noted that females, younger people, and those with lower educational levels experienced higher eco-anxiety. To facilitate the use of this measure, we propose a potential screening cut-off value for the HEAS-13, which can indicate meaningfully elevated levels of eco-anxiety. Conclusions: Overall, the Polish version of the HEAS-13 has strong psychometric properties, usefully enabling the examination of climate-related anxiety. Our findings highlight its potential in cross-cultural research and healthcare practice.

Revolutionizing early breast cancer screening: Advanced multi-spectral transmission imaging classification with improved Otsu's method and K-means clustering

Breast cancer stands as a formidable danger to the health of women worldwide, underscoring the critical need for effective screening methods. Multispectral transmission imaging offers a promising avenue due to its non-invasive potential for early screening. Some researchers already suggested registration to solve the problem of jitters due to respiration and movement and frame accumulation technology to solve the low grayscale problem. However, the classification of blood vessels and breast tissue in breast images often suffers from low signal-to-noise ratio (SNR) and low contrast, hindering accurate classification. This paper proposes a novel improved Otsu's method with K-Means clustering to address this challenge. The proposed method aims to enhance the foundation for classification using multispectral transmission images. The study utilizes multispectral transmission images captured at four wavelengths, representing an innovative avenue for early, affordable breast cancer screening research. Initially, 300 images are registered and accumulated to prove the efficiency of the suggested methodology. Then, median filtering is applied to reduce noise in the images. Improved Otsu's segmentation method is then employed to separate blood vessels from breast tissue. After that, K-means clustering is utilized to accurately classify these components. The results of the proposed method demonstrate significant improvements in classification accuracy and grayscale contrast of multispectral breast images. By effectively distinguishing blood vessels and breast tissue, the proposed methodology addresses the inherent challenges of low contrast in multispectral transmission imaging. This advancement offers a clearer pathway for early breast cancer screening.

Phytochemical screening and antimicrobial potential of some foliose lichens from Pir Panjal range of Jammu & Kashmir, India

Phytochemical screening and antimicrobial potential of some foliose lichens from Pir Panjal range of Jammu & Kashmir, India

Common molecular basis for MASH and hepatitis C revealed via systems biology approach

BackgroundMetabolic dysfunction-associated steatohepatitis (MASH) is characterized by liver inflammation and damage caused by a buildup of fat in the liver. Hepatitis C, caused by hepatitis C virus (HCV), is a disease that can lead to liver cirrhosis, liver cancer, and liver failure. MASH and hepatitis C are the common causes of liver cirrhosis and hepatocellular carcinoma. Several studies have shown that hepatic steatosis is also a common histological feature of liver in HCV infected patients. However, the common molecular basis for MASH and hepatitis C remains poorly understood.MethodsFirstly, differentially expressed genes (DEGs) for MASH and hepatitis C were extracted from the GSE89632, GSE164760 and GSE14323 datasets. Subsequently, the common DEGs shared among these datasets were determined using the Venn diagram. Next, a protein-protein interaction (PPI) network was constructed based on the common DEGs and the hub genes were extracted. Then, gene ontology (GO) and pathway analysis of the common DEGs were performed. Furthermore, transcription factors (TFs) and miRNAs regulatory networks were constructed, and drug candidates were identified. After the MASH and hepatitis C cell model was treated with predicted drug, the expression levels of the signature genes were measured by qRT-PCR and ELISA.Results866 common DEGs were identified in MASH and hepatitis C. The GO analysis showed that the most significantly enriched biological process of the DEGs was the positive regulation of cytokine production. 10 hub genes, including STAT1, CCL2, ITGAM, PTPRC, CXCL9, IL15, SELL, VCAM1, TLR4 and CCL5, were selected from the PPI network. By constructing the TF-gene and miRNA-gene network, most prominent TFs and miRNAs were screened out. Potential drugs screening shows that Budesonide and Dinoprostone may benefit patients, and cellular experiments showed that Budesonide effectively inhibited the expression of genes related to glycolipid metabolism, fibrosis, and inflammatory factors.ConclusionWe extracted 10 hub genes between MASH and hepatitis C, and performed a series of analyses on the genes. Molecular docking and in vitro studies have revealed that Budesonide can effectively suppress the progression of MASH and hepatitis C. This study can provide novel insights into the potential drug targets and biomarkers for MASH and hepatitis C.

Self-consistent Strong Screening Applied to Thermonuclear Reactions

Abstract Self-consistent strong plasma screening around light nuclei is implemented in the Big Bang nucleosynthesis (BBN) epoch to determine the short-range screening potential, e ϕ(r)/T ≥ 1, relevant for thermonuclear reactions. We numerically solve the nonlinear Poisson–Boltzmann equation incorporating Fermi–Dirac statistics, adopting a generalized screening mass to find the electric potential in the cosmic BBN electron–positron plasma for finite-sized α particles (4He++) as an example. Although the plasma follows Boltzmann statistics at large distances, Fermi–Dirac statistics is necessary when work performed by ions on electrons is comparable to their rest-mass energy. While self-consistent strong screening effects are generally minor owing to the high BBN temperatures, they can enhance the fusion rates of high-Z (Z > 2) elements while leaving fusion rates of lower-Z (Z ≤ 2) elements relatively unaffected. Our results also reveal a pronounced spatial dependence of the self-consistent strong screening potential near the nuclear surface. These findings about the electron–positron plasma’s role refine BBN theory predictions and offer broader applications for studying weakly coupled plasmas in diverse cosmic and laboratory settings.

Low-cost digital colorimetric sensor for rapid on-site determination of ascorbic acid in vegetable- and fruit-based purees and juices

Ascorbic acid is essential for human health due to its potent antioxidant properties; however, excessive intake can lead to adverse effects. This has created a high demand for simple and cost-effective analytical techniques to determine this analyte outside of laboratory settings for food quality control. In this study, a portable colorimetric sensor was developed for the quantitative on-site determination of ascorbic acid. The technique is based on the reduction of phosphorus heteropoly acid by ascorbic acid, resulting in the formation of a blue-colored product. The heteropoly acid was immobilized on a polymer template in the sensor's indicator zone with menthol used as an environmentally friendly extractant to enhance the device’s stability and extend its shelf life. The colorimetric sensor demonstrated reliable detection with a low limit of detection of 5mgkg-1, calculated based on a blank test using 3σ. Under optimized conditions, the linear detection range was 15-150mgkg-1. The disposable sensors were applied to determine ascorbic acid in fruit and vegetable juices and purees for baby food, with recovery rates between 88 and 110%. This easy-to-produce and ready-to-use technique presents significant potential for mass on-site screening of ascorbic acid in food samples.

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

Phenytoin-induced DRESS syndrome: A multidisciplinary case study in precision medicine and advanced therapeutics

This case report aims to examine the diagnostic and therapeutic approach for phenytoin-induced Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS) syndrome in a pediatric patient, emphasizing the importance of early recognition and intervention. A 17-year-old male presented with a pruritic rash, fever, lymphadenopathy, and elevated liver enzymes one month after starting phenytoin for epilepsy. Diagnosis was confirmed using the RegiSCAR scoring system, which objectively assessed clinical and laboratory findings. The patient received high-dose corticosteroids and N-acetylcysteine (NAC) after discontinuing phenytoin, with regular monitoring of clinical and laboratory parameters. Timely withdrawal of phenytoin, along with corticosteroid and NAC therapy, led to the resolution of symptoms without long-term complications. The RegiSCAR scoring system proved invaluable for accurate and swift diagnosis, supporting its utility in pediatric DRESS cases. This case also suggests the potential of genetic screening, particularly HLA typing, for identifying high-risk individuals, although it was not utilized here. Early diagnosis and multidisciplinary management are critical for favorable outcomes in pediatric DRESS cases. Routine HLA screening could help prevent DRESS syndrome in patients prescribed high-risk medications, though further studies are needed to assess feasibility and cost-effectiveness. This case underlines the value of standardized treatment protocols and personalized medicine in managing drug hypersensitivity reactions effectively.

Plasma levels of sCASP3 predicts carotid intima media thickness progression

Abstract Background Studies on human atherosclerotic plaques have shown that apoptosis is associated with rupture prone plaques and that apoptosis is a key process for fast plaque progression. In line with this, we showed that circulating levels of soluble Caspase-3 (sCaspase-3) predict atherosclerotic events. However, the relationship between circulating levels of apoptosis markers and plaque progression remains to be explored. Purpose Here we aimed to investigate if circulating levels of apoptosis markers could predict mean intima-media thickness in the common carotid artery (IMT-CCA) progression among individuals with atherosclerotic carotid plaques. Methods The Malmö Diet and Cancer (MDC) cardiovascular cohort was used. MDC is a prospective population-based cohort study. Baseline variables (blood, clinical information and carotid ultrasound measurements) were collected between 1991-1994 and the right carotid ultrasound re-examinations were performed between 1994-2001. IMT was measured at by ultrasound at baseline as well as at the re-examinations. The Olink Proximity Extension Assay (SciLifeLab, Uppsala, Sweden) was used to analyze plasma levels of sCaspase-3, sFADD, sCaspase-8, sTRAIL-R2 and sTNFR1 at baseline. Correlation and multiple linear regression analyses, adjusted for age, sex, and cardiovascular risk factors, were employed to evaluate the relationships between apoptosis biomarkers and IMT-CCA as well as average annual change in IMT-CCA. Results In total, 1929 participants (median age 60 years) with an ultrasound confirmed carotid plaque (focal IMT >1.2mm) at baseline were included. Subjects who underwent ultrasound IMT re-examination over a period of >4.5 years were divided into a discovery (n=406) and a validation (n=355) cohort. sCaspase-3 was identified as the marker with the strongest correlation to IMT-CCA at baseline (p=0.02) and upon re-examination (p=0.04), among the five apoptosis markers examined, in both the discovery and validation cohorts. sCaspase-3 also showed a correlation with the average annual change of IMT-CCA (r=0.12, p=8×10-4) in both cohorts. Furthermore, when diving the cohort into tertiles based on plasma levels of sCapase-3, subjects with high levels of sCaspase-3 (3rd tertile) had greater annual changes in IMT-CCA compared to the 1st tertile (p=0.03). Multiple linear regression analyses confirmed that sCapase-3 was associated with the average annual IMT-CCA change in both the discovery (beta=0.14, 95% confidence interval (CI) 0.04–0.24) cohort and the validation cohort (beta=0.11, 95%CI 0.01–0.21). Conclusion Using a large, population-based study with repeated IMT measurements, we demonstrated that circulating levels of sCapase-3 were associated with increasing IMT in the CCA. While further studies are warranted, these results suggest that sCapase-3 levels could serve as potential screening marker to identify individuals at higher risk for plaque progression.

Ridge-type covariance and precision matrix estimators of the multivariate normal distribution

AbstractWe consider ridge-type estimation of the multivariate normal distribution’s covariance matrix and its inverse, the precision matrix. While several ridge-type covariance and precision matrix estimators have been presented in the literature, their respective inverses are often not considered as precision and covariance matrix estimators even though their estimands are one-to-one related through the matrix inverse. We study which estimator is to be preferred in what case. Hereto we compare the ridge-type covariance matrix estimators and their properties to that of the inverse of the ridge-type precision matrix estimators, and vice versa. The comparison, in which we take all ridge-type estimators along, is limited to a specific case that is illustrative of the difference between the covariance and precision matrix estimators. The comparison addresses the estimators’ estimating equation, analytic expression, analytic properties like positive definiteness and penalization limit, mean squared error, consistency, Bayesian formulation, and their loss and potential for marginal and partial correlation screening.

Maternal Fasting Plasma Glucose, Age and Body Mass Index as Prediction of Gestational Diabetes Mellitus in Iran

Background: Gestational diabetes mellitus (GDM) is a common complication of pregnancy that can lead to adverse outcomes. In this retrospective cohort study, maternal fasting blood sugar (FBS) in the second trimester of pregnancy, body mass index (BMI), and age were assessed as potential screening indicators of later GDM. Methods: The study population included information on 4007 Iranian pregnant women documented by the Integrated Health Record System (SIB) record system (2019-2020). Results: In the adjusted analysis, FBS maintained a significant relationship with GDM (P ≤0.001). In the simple ROC analysis, the AUC (SE) of FBS for the prediction of GDM was 0.905(0.09), and considering the cut-off point as 85.95, sensitivity (Se) and specificity (Sp) were equal to 0.81 and 0.71, respectively, but by stillbirth, abortion, prematurity, neonatal weight, height, and head circumference not obtained acceptable AUC (≥.60) for detection of FBS cut-off point. The cut-off point of FBS in the presence of maternal age (AUC>0.6) and BMI (AUC>0.6) by GDM was 83.75(Se= 86.4%, Sp= 80.0%). Conclusion: Based on the evidence presented, maternal weight and BMI are important in predicting hyperglycemia leading to GDM. It is necessary to conduct more precise national studies to standardize the FBS cut-off point by controlling age and BMI variables.

Single molecule tracking based drug screening

The single-molecule tracking of transmembrane receptors in living cells has provided significant insights into signaling mechanisms, such as mobility and clustering upon their activation/inactivation, making it a potential screening method for drug discovery. Here we show that single-molecule tracking-based screening can be used to explore compounds both detectable and undetectable by conventional methods for disease-related receptors. Using an automated system for a fast large-scale single-molecule analysis, we screen for epidermal growth factor receptor (EGFR) from 1134 of FDA approved drugs. The 18 hit compounds include all EGFR-targeted tyrosine kinase inhibitors (TKIs) in the library that suppress any phosphorylation-dependent mobility shift of EGFR, proving the concept of this approach. The remaining hit compounds are not reported as EGFR-targeted drugs and do not inhibit EGF-induced EGFR phosphorylation. These non-TKI compounds affect the mobility and/or clustering of EGFR without EGF and induce EGFR internalization, to impede EGFR-dependent cell growth. Thus, single-molecule tracking provides an alternative modality for discovering therapeutics on various receptor functions with previously untargeted mechanisms.

The School Years Screening Test for Evaluation of Mental Status-Revised (SYSTEMS-R) as a cognitive function screening tool in children with epilepsy

Background Children with epilepsy are at high risk of cognitive impairment that can affect quality of life. Intelligence quotient (IQ) measurement using the Wechsler Intelligence Scale for Children (WISC) is the gold standard test of cognitive function, but it is time-consuming and costly. The School Years Screening Test for Evaluation of Mental Status-Revised (SYSTEMS-R) is a potential cognitive function screening tool that can be used in children with epilepsy. Objective To assess the performance of SYSTEMS-R as a cognitive function screening tool in children aged 6-15 years with epilepsy. Methods This cross-sectional diagnostic test study was conducted in children aged 6-15 years with epilepsy. All subjects were assessed using both SYSTEMS-R and WISC 4th edition. The sensitivity, specificity, positive and negative predictive value, likelihood ratios of the positive and negative tests, and accuracy of SYSTEMS-R was calculated, with WISC as the gold standard test. Results Based on the SYSTEMS-R, the prevalence of cognitive impairment in children aged 6-15 years with epilepsy in our population was 86.4%. With WISC as the gold SYSTEMS-R had 84% sensitivity, 91% specificity, 98% positive predictive value, and 47% negative predictive value. The likelihood ratio of a positive SYSTEMS-R test was 10.11 and the likelihood ratio of a negative test was 0.17. The overall accuracy of SYSTEMS-R to detect cognitive impairment was 85%. Conclusion SYSTEMS-R has good sensitivity and specificity to assess cognitive function in children 6-15 years with epilepsy. It can be considered for widespread use in the early detection of cognitive impairment in pediatric epilepsy patients aged 6-15 years.

Development of a transformer-based deep learning algorithm for diabetic peripheral neuropathy classification using corneal confocal microscopy images.

Diabetic peripheral neuropathy (DPN) is common and can go unnoticed until it is firmly developed. This study aims to establish a transformer-based deep learning algorithm (DLA) to classify corneal confocal microscopy (CCM) images, identifying DPN in diabetic patients. Our classification model differs from traditional convolutional neural networks (CNNs) using a Swin transformer network with a hierarchical architecture backbone. Participants included those with (DPN+, n = 57) or without (DPN-, n = 37) DPN as determined by the updated Toronto consensus criteria. The CCM image dataset (consisting of 570 DPN+ and 370 DPN- images, with five images selected from each participant's left and right eyes) was randomly divided into training, validation, and test subsets at a 7:1:2 ratio, considering individual participants. The effectiveness of the algorithm was assessed using diagnostic accuracy measures, such as sensitivity, specificity, and accuracy, in conjunction with Grad-CAM visualization techniques to interpret the model's decisions. In the DPN + group (n = 12), the transformer model successfully predicted all participants, while in the DPN- group (n = 7), one participant was misclassified as DPN+, with an area under the curve (AUC) of 0.9405 (95% CI 0.8166, 1.0000). Among the DPN + images (n = 120), 117 were correctly classified, and among the DPN- images (n = 70), 49 were correctly classified, with an AUC of 0.8996 (95% CI 0.8502, 0.9491). For single-image predictions, the transformer model achieved a superior AUC relative to the ResNet50 model (0.8761, 95% CI 0.8155, 0.9366), the Inception_v3 model (0.8802, 95% CI 0.8231, 0.9374), and the DenseNet121 model (0.8965, 95% CI 0.8438, 0.9491). Transformer-based networks outperform CNN-based networks in rapid binary DPN classification. Transformer-based DLAs have clinical DPN screening potential.

Cost-effective identification of Barrett's esophagus in the community: A first step towards screening.

The first step towards developing a screening strategy for Barrett's esophagus (BE) is the identification of individuals in the community. Currently available tools include endoscopy, less-invasive non-endoscopic devices, and non-invasive risk stratification models. We evaluated the cost of potential strategies for identification of BE as a first step towards screening. Two hypothetical cohorts of the general population aged ≥50years with BE prevalence rates of 1.9% and 6.8% were modeled. Four potential screening tools were evaluated: (i) risk stratification based on non-weighted clinical factors according to US/European guidelines, (ii) weighted risk stratification using algorithmic models, (iii) less-invasive devices such as Cytosponge+trefoil factor 3 (TFF3), and (iv) endoscopy. Using a decision-analytic model, the cost per BE case identified and the cost-effectiveness were compared for six potential BE screening strategies based on combinations of the four screening tools; (i)+(iv), (ii)+(iv), (iii)+(iv), (i)+(iii)+(iv), (ii)+(iii)+(iv), and only (iv). The cost per BE case identified was lowest for the weighted risk stratification followed by Cytosponge-TFF3 then endoscopy strategy at both 1.9% and 6.8% BE prevalences (US$9282 and US$3406, respectively) although it was sensitive to the cost of less-invasive devices. This strategy was also most cost-effective for a BE prevalence of 1.9%. At BE prevalence of 6.8%, the Cytosponge-TFF3 followed by endoscopy strategy was most cost-effective. Incorporating weighted risk stratification and less-invasive devices such as Cytosponge-TFF3 into BE screening strategies has a potential to cost-effectively identify BE in the community although device cost and the community prevalence of BE will impact the optimal strategy.

Effectiveness of Risk-Adapted Upper Gastrointestinal Cancer Screening in China: Prospective Cohort Study.

Previous studies have proved the effectiveness of endoscopic screening in rural areas; however, long-term, high-quality evidence regarding the effectiveness of risk-adapted upper gastrointestinal cancer (UGC) sequential screening strategies in resource-rich regions is currently lacking. The objectives were to validate the effectiveness of risk-adapted sequential screening strategies in UGC prevention and control and assess the potential of sequential screening to lower mortality rates. Based on the Cancer Screening Program in Urban China, a prospective, large-scale cohort study based on population was conducted to recruit individuals from 4 cities in China from 2013-2019. Those identified as having a high risk of UGC according to a validated risk-score model were advised to undergo endoscopy tests. Follow-up outcomes were tracked until June 2021. Incidence of UGC, UGC-related mortality, and all-cause mortality were evaluated between the screened and nonscreened cohorts. The study included 153,079 participants at baseline. In total, 113,916 (74.42%) of the participants were designated as low risk of UGC. The remaining 39,163 (25.68%) participants were deemed to be at high risk of UGC and were offered gastroscopy tests. Among the high-risk participants, 9627 (compliance rate 24.6%) adhered to the gastroscopy tests. Over a median follow-up of 6.05 (IQR 3.06-7.06) years, 622 UGC cases, 180 UGC deaths, and 1958 all-cause death cases were traced. The screened cohort exhibited the highest cumulative incidence of UGC (119.2 per 100,000 person-years), followed by the nonscreened and low-risk cohorts. Obvious reductions in both all-cause mortality and UGC mortality were observed between those who undertook screening (153.7 and 4.7 per 100,000 person-years, respectively) and the nonscreened group (245.3 and 27 per 100,000 person-years, respectively). The screening population showed a significant 36% and 82% reduction in both all-cause mortality (hazard ratio [HR] 0.64, 95% CI 0.49-0.83, P<.001) and UGC mortality (HR 0.18, 95% CI 0.04-0.74, P=.02), respectively, compared to the nonscreened group. Reductions of 35% in all-cause mortality (HR 0.65, 95% CI 0.49-0.86, P=.003) and 81% in UGC mortality (HR 0.19, 95% CI 0.05-0.80, P=.02) were observed in participants aged older than 55 years in the screened group compared to the nonscreened group. The reductions in all-cause mortality and UGC mortality were statistically significant in males (all-cause mortality: HR 0.64, 95% CI 0.47-0.88, P=.005; UGC mortality: HR 0.10, 95% CI 0.01-0.72, P=.02), but significant reductions were not observed in females (all P values were >.05). Our study suggests the significance of one-off risk-adapted UGC screening in reducing both all-cause mortality and UGC mortality, particularly among high-risk individuals, indicating its effectiveness in UGC prevention and management.