Improve Risk Assessment Research Articles

Histopathologic Evaluation of Atypical Fibroxanthoma or Pleomorphic Dermal Sarcoma Debulk Specimen from Mohs Surgery: A Requirement for Their Proper Distinction.

Pleomorphic dermal sarcomas can be clinically aggressive, with a higher tendency to cause local recurrence, metastasis, and death. Atypical fibroxanthoma and pleomorphic dermal sarcoma are histopathologically similar, and their distinction requires a systematic examination of the entire excised tumor. Since Mohs micrographic surgery is commonly utilized to treat atypical fibroxanthoma, a histopathologic evaluation of debulk specimens by permanent pathology is prudent to avoid underdiagnosing pleomorphic dermal sarcoma. This approach can improve risk assessment and treatment decisions, ultimately enhancing patient outcomes. Also, the proper distinction will facilitate the future development of accurate staging criteria and additional treatment modalities.

Cytokine Dynamics and Herpesvirus Interactions in Pediatric Liver and Kidney Transplant Recipients: The Distinct Behavior of HCMV, HHV6, HHV7 and EBV.

Pediatric solid organ transplant (SOT) recipients face a challenging balance between immunosuppression and graft rejection. While Epstein-Barr Virus (EBV) and cytomegalovirus (HCMV) are known contributors to post-transplant lymphoproliferative disease and graft rejection, respectively, the roles of herpesvirus 6 and 7 (HHV6 and HHV7) and the impact of these herpesviruses on cytokine levels remain unclear, leading to gaps in clinical practice. In this associative study, we measured 17 cytokines using a Bio-Plex assay in a meticulously curated plasma sample pool (N = 158) from pediatric kidney and liver transplant recipients over a one-year follow-up period. The samples included virus-negative and virus-positive cases, either individually or in combination, along with episodes of graft rejection. We observed that the elevation of IL-4, IL-8, and IL-10 correlated with graft rejection. These cytokines were elevated in samples where HCMV or HHV6 were detected alone or where EBV and HHV7 were co-detected. Interestingly, latent EBV, when detected independently, exhibited an immunomodulatory effect by downregulating cytokine levels. However, in co-detection scenarios with β-herpesviruses, EBV transitioned to a lytic state, also associating with heightened cytokinemia and graft rejection. These findings highlight the complex interactions between the immune response and herpesviruses in transplant recipients. The study advocates for enhanced monitoring of not only EBV and HCMV but also HHV6 and HHV7, providing valuable insights for improved risk assessment and targeted interventions in pediatric SOT recipients.

Prediction of postoperative dysphagia in patients with oral cancer: A prospective cohort study

ObjectivesThis study aims to identify autonomous risk factors for postoperative dysphagia in oral cancer patients and construct a nomogram prediction model to improve risk assessment accuracy and feasibility in clinical settings. MethodsA prospective cohort study was conducted from March to July 2022 among oral cancer patients undergoing surgical interventions at the Department of Head and Neck Surgery. Clinical data were collected using the Postoperative Dysphagia Risk Factor Questionnaire. Swallowing function was assessed with the Mann Assessment of Swallowing Ability-Oral Cancer (MASA-OC). Lasso regression identified potential predictor variables, followed by univariate and multivariate logistic regression analyses. A predictive model was developed using R Studio 4.1.2 and rigorously evaluated with ROC curves, Hosmer-Lemeshow tests, and calibration curves. Internal validation utilized Bootstrap methodology with 1000 repetitive samples. ResultsThe cohort included 257 oral cancer patients, with 73.9 % experiencing postoperative dysphagia. Independent predictors included functional status, depressive symptoms, pT stage, surgical techniques, glossoplasty, maxillectomy, and post-surgery nasopharyngeal tube retention. The predictive model achieved an AUC of 0.933, sensitivity of 90.9 %, and specificity of 81.7 %. Hosmer-Lemeshow test (P = 0.715) and C-index (0.934) indicated satisfactory model fit. Internal validation yielded an AUC of 0.912, sensitivity of 93.3 %, and specificity of 63.8 %. Calibration curves demonstrated alignment between predicted and observed outcomes. ConclusionA nomogram integrating recognized risk factors shows promise in predicting postoperative dysphagia in oral cancer patients, enhancing precision and aiding healthcare professionals in risk evaluation and patient care strategies.

THE FUTURE OF AUDITING PROFESSION IN THE FACE OF AUDITORS’ LIABILITY: A REVIEW OF LITERATURE

This study examined the global issue of auditors’ negligence cases, which have led to doubts about the reliability of audit reports and raised concerns about the future of auditing profession. This research conducted a thorough literature review to deeply explore auditors' roles and responsibilities. It investigated the legal responsibilities auditors face and their impact on the credibility of their reports, while considering approaches to mitigate these liabilities and the involvement of professional bodies. Findings indicated that auditors' legal liabilities significantly impair audit quality, the trustworthiness of audit reports, and overall professional skepticism. The research revealed the urgent need for improved risk assessment techniques to uphold audit integrity and credibility, while effectively addressing liability concerns. The conclusion is that in order to promote resiliency, trust, and adaptability in the profession, the future of auditing depends on redefining auditors' culpability. The study suggested collaboration among policymakers, professional associations, and standard-setting entities to establish legal and regulatory frameworks. Additionally, the study emphasized the value of ongoing professional development for audit firms and the cultivation of a culture of professional skepticism within the auditing profession. Key words: Audit reports; Auditors’ liability; Auditors’ negligence; Credibility; Reliability

Guideline-based venous thromboprophylaxis in postpartum women: An in-depth evaluation of ACOG and RCOG recommendations and real-life implementation

ObjectivesVenous thromboembolism is one of the most serious complications of the postpartum period, and international societies have various thromboprophylaxis guidelines for its prevention. This study compares postpartum venous thromboprophylaxis recommendations from the American College of Obstetrics and Gynecology (ACOG) and the Royal College of Obstetricians and Gynecologists (RCOG) with real-life clinical practices. Study DesignData analysis of 1000 postpartum women at a tertiary care center focused on patient demographics, venous thromboembolism risk factors, and clinical thromboprophylaxis practices. Patient-specific risk factors were compared between ACOG and RCOG guidelines, assessing Low-Molecular-Weight-Heparin dosages and durations. Guideline compliance, undertreatment/overtreatment rates, and the required number of prefilled Low-Molecular-Weight-Heparin syringes were evaluated. ResultsSignificant discrepancies were observed between ACOG and RCOG guidelines, particularly in Low Molecular Weight Heparin dosages and durations. Consensus rates with clinical approaches were around 53%, with inconsistencies leaning towards undertreatment (RCOG) and overtreatment (ACOG). The number of required prefilled Low-Molecular-Weight-Heparin syringes was notably higher according to RCOG compared to ACOG guidelines. ConclusionPostpartum Venous thromboembolism prophylaxis guidelines from American College of Obstetrics and Gynecology and Royal College of Obstetricians and Gynecologists exhibit substantial differences, leading to variations in clinical practice. Further research on the significance of Venous thromboembolism risk factors is essential for improving risk assessment tools and refining guideline recommendations for pregnancy-related Venous thromboembolism prevention.

Application of improved risk assessment methods to best management practices research for non-point source pollution: From a hilly mountainous-dominated region of Southern China

Non-point source pollution (NPSP) significantly affects regional water environments worldwide. Identifying critical source areas (CSAs) and implementing best management practices (BMPs) are essential to prevent and control NPSP. This study used the Soil and Water Assessment Tool model to simulate and evaluate the characteristics of NPSP in the Gongjiang Watershed, a typical, hilly mountainous area in Southern China. The study identified CSAs of NPSP using total nitrogen (TN), total phosphorus (TP), water quality level, and population density factors. The reduction effect of different BMPs on the NPSP load was evaluated, and the cost-effectiveness (CE) of various BMPs was evaluated for the control of NPSP in the watershed. The interannual variation in NPSP load was high from 2013 to 2018, with the largest pollution load in 2016 (wet year) and the smallest in 2018 (dry year). The pollution load fluctuated throughout the year and was related to fertilization and precipitation runoff. The spatial heterogeneity of NPSP was large, with TN and TP losses showing a spatial pattern of high in the south, low in the north, and highest in the center. The CSAs of the NPSP covered 1566.66 km2, representing 9.77 % of the total watershed area. However, TN and TP loads accounted for 19.39 % and 23.53 % of total watershed production. The order of effectiveness of individual management measures in reducing TN was stubble coverage (SC) > vegetative buffer strips (VBS) > terrace engineering (TE) > grassed waterways (GW) > 20 % fertilizer reduction (FR20) > 10 % fertilizer reduction (FR10). The order of effectiveness of TP reduction was SC > VBS > TE > FR20 > GW > FR10. Combined management measures were much more effective in reducing TN and TP than individual measures, with an average increase in TN and TP reduction of 20.59 % and 25.23 %, respectively. Based on the results of the CE evaluation of BMPs, a combination of FR20, GW, and VBS was more effective than individual measures. We suggest a combination of FR20, GW, and VBS measures for effective NPSP prevention and control in Southern China’s typical, hilly mountainous areas.

Evaluation of a polygenic risk score as a predictor of early onset triple-negative breast cancer in Black women.

10501 Background: Black women in the U.S. often develop early, biologically aggressive breast cancer (BC). Triple-negative breast cancer (TNBC) is a particularly aggressive type that occurs more frequently in Black than white women and often develops before recommended regular screening. More accurate risk prediction methods are urgently needed to identify young Black women with elevated risk of TNBC. Incorporating polygenic risk scores (PRS) into clinical models can substantially improve risk assessment, but most PRS have demonstrated poor performance among non-European ancestries. We previously described a multiple-ancestry PRS (MA-PRS), based on 56 ancestry-informative and 329 BC-associated single-nucleotide polymorphisms (SNPs), that predicts overall BC risk for diverse populations by characterizing genetic ancestry at each BC SNP and applying ancestry-specific SNP risks and frequencies. Here, we evaluated the extent to which MA-PRS improves upon clinical factors for the prediction of TNBC and early onset ( < 50 years) TNBC in a large, independent cohort of self-reported Black women. Methods: We examined clinical and genetic records from self-reported Black women referred for hereditary cancer testing from 8/22 - 9/23 and negative for pathogenic variants in BC-associated genes. The association of MA-PRS with TNBC was analyzed using multivariable logistic regression adjusted for personal and family cancer history, age and genetic ancestry. Analyses were conducted within the full cohort and the subpopulation of patients < 50 years old. Odds ratios (OR) are reported per standard deviation (SD) with 95% confidence intervals (CI). Results: We identified 14728 Black women who met study eligibility criteria. Nearly 70% (9895/14728) of patients were under the age of 50. In the full cohort, 629 (4.3%) patients were diagnosed with TNBC, and 173 (1.7%) were younger than 50 years old. MA-PRS significantly improved TNBC risk prediction over clinical factors in the full cohort and in the subpopulation of women under 50 (Table). The effect of MA-PRS on risk stratification was comparable to the 1.4 OR per SD reported in current literature for mammographic density, which is widely recognized as an important risk factor. Women in the top 5% of the MA-PRS distribution were at roughly 2-fold increased risk of TNBC (Table). Conclusions: MA-PRS substantially improved upon clinical factors for the prediction of TNBC and early-onset TNBC in a large cohort of Black women. MA-PRS appears to be equally as important for managing risk of early-onset TNBC as mammographic density for overall BC. Incorporation of MA-PRS into BC risk assessment has the potential to improve TNBC survival through more accurate identification of women at high risk. [Table: see text]

Assessing radon adsorption capacity in adsorbents using solid state nuclear track detectors (SSNTDs)

Radon, specifically the isotope 222Rn, poses health risks, particularly in concentrated indoor environments. Recent years have underscored the significance of measuring low radon concentrations outdoors, offering crucial insights into radon priority areas and climate-related processes. To achieve the necessary high sensitivity, one approach involves placing radiation detectors in close proximity to efficient radon adsorbers that function as radiators. While activated charcoal has been employed for over a century, recent years have seen the production of highly efficient synthetic adsorbents and activated carbon materials. A vital parameter essential for designing and modeling sensitive radon detectors/monitors is the adsorption capacity of the adsorbent for radon. This study introduces a simple and cost-effective method enabling the determination of the adsorption capacity of numerous prospective adsorbents simultaneously. The method entails placing SSNTDs in close contact with the adsorbent to generate tracks from the alpha particles emitted by adsorbed 222Rn and its progeny. A theoretical model is presented, facilitating the determination of adsorption capacity using SSNTDs with a well-defined response function to alpha particles. Experimental validation involves comparing theoretical estimates with published data from studies on activated charcoals, demonstrating very good agreement. The method's versatility is underscored by its application to activated carbon fabrics, showcasing its potential to optimize detector designs for enhanced sensitivity. This proposed approach holds promise for advancing radon monitoring technology, contributing to improved risk assessment and environmental studies related to radon.

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

Species Difference? Bovine, Trout, and Human Plasma Protein Binding of Per- and Polyfluoroalkyl Substances.

Per- and polyfluoroalkyl substances (PFAS) strongly bind to proteins and lipids in blood, which govern their accumulation and distribution in organisms. Understanding the plasma binding mechanism and species differences will facilitate the quantitative in vitro-to-in vivo extrapolation and improve risk assessment of PFAS. We studied the binding mechanism of 16 PFAS to bovine serum albumin (BSA), trout, and human plasma using solid-phase microextraction. Binding of anionic PFAS to BSA and human plasma was found to be highly concentration-dependent, while trout plasma binding was linear for the majority of the tested PFAS. At a molar ratio of PFAS to protein ν < 0.1 molPFAS/molprotein, the specific protein binding of anionic PFAS dominated their human plasma binding. This would be the scenario for physiological conditions (ν < 0.01), whereas in in vitro assays, PFAS are often dosed in excess (ν > 1) and nonspecific binding becomes dominant. BSA was shown to serve as a good surrogate for human plasma. As trout plasma contains more lipids, the nonspecific binding to lipids affected the affinities of PFAS for trout plasma. Mass balance models that are parameterized with the protein-water and lipid-water partitioning constants (chemical characteristics), as well as the protein and lipid contents of the plasma (species characteristics), were successfully used to predict the binding to human and trout plasma.

Quantitative Abdominal Arterial Calcification Correlates with Kidney Transplant Waitlist Mortality

Introduction: The scarcity of available organs for kidney transplantation has resulted in a substantial waiting time for patients with end-stage kidney disease. This prolonged wait contributes to an increased risk of cardiovascular mortality. Calcification of large arteries is a high-risk factor in the development of cardiovascular diseases, and it is common among candidates for kidney transplant. The aim of this study was to correlate abdominal arterial calcification (AAC) score value with mortality on the waitlist. Methods: We modified the coronary calcium score and used it to quantitate the AAC. We conducted a retrospective clinical study of all adult patients who were listed for kidney transplant, between 2005 and 2015, and had abdominal computed tomography scan. Patients were divided into two groups: those who died on the waiting list group and those who survived on the waiting list group. Results: Each 1,000 increase in the AAC score value of the sum score of the abdominal aorta, bilateral common iliac, bilateral external iliac, and bilateral internal iliac was associated with increased risk of death (HR 1.034, 95% CI: 1.013, 1.055) (p = 0.001). This association remained significant even after adjusting for various patient characteristics, including age, tobacco use, diabetes, coronary artery disease, and dialysis status. Conclusion: The study highlights the potential value of the AAC score as a noninvasive imaging biomarker for kidney transplant waitlist patients. Incorporating the AAC scoring system into routine imaging reports could facilitate improved risk assessment and personalized care for kidney transplant candidates.

#913 Impact of retinal photography-based deep learning system on risk stratification for chronic kidney disease progression

Abstract Background and Aims We had previously developed a deep-learning-based risk evaluation system from retinal photographs, Reti-CKD, for stratifying chronic kidney (CKD) development risk in kidney function preserved people. This study aims to evaluate whether Reti-CKD can improve risk assessment of kidney disease progression in diabetic patients with prevalent CKD. Method Total of 5348 diabetic patients from two tertiary hospitals in Korea were evaluated. Patients with estimated glomerular filtration rate (eGFR) &amp;lt;90 ml/min/1.73 m2 or albuminuria were included. Those with missing data for retinal photograph, serum creatinine, or albuminuria were excluded. Patients were categorized into low-risk, moderate-risk, and high-risk groups according to the KDIGO criteria for prognosis of CKD. The KDIGO groups were additionally dichotomized based on Reti-CKD score (Reti-CKD &amp;lt;20 and ≥20). CKD progression was compared between the categories using Cox regression models. Primary outcome was CKD progression, defined as incremental progression to a higher NKF-KDOQI CKD stage. Results The mean age of the patients was 62.4 ± 11.4 years and 60.6% were male. Mean eGFR was 86.6 ± 15.3 mL/min per 1.73 m2 and albuminuria was present in 46.9%. During a median follow-up of 5.0 (interquartile range, 2.5-7.8) years, primary outcome developed in 1379 (25.8%) patients. The primary outcome incidence rate gradually increased with higher KDIGO and Reti-CKD combined risk categories. The risk for CKD progression progressively increased in KDIG moderate-risk and high-risk groups compared to low-risk. When Reti-CKD was incorporated to the KDIGO category, significant stratification of CKD progression risk was noted in the KDIGO low-risk and moderate-risk groups (Table and Figure). Additionally, the combination of KDIGO and Reti-CKD classification showed better discrimination power compared to the KDIGO only classification (delta c-statistics, 0.03; 95% CI 0.02 to 0.040). Conclusion Retinal photography-based deep learning system (Reti-CKD) further stratifies the risk of CKD progression and improves predictability in diabetic patients with reduced renal function.

A Case Study of Ergonomic Risk Assessment in Slovakia with Respect to EU Standard.

Attention on work-related musculoskeletal disorders (WMSDs) involves statistical surveys showing an increasing trend in the incidence of WMSDs. Technological development has led to new tools and methods for the assessment of physical load at work. These methods are mostly based on the direct sensing of appropriate parameters, which allows more precise quantification. The aim of this paper is to compare several commonly used methods in Slovakia for the assessment of ergonomic risk reflecting current EU and Slovak legislative regulations. A Captiv wireless sensory system was used at a car headlight quality control assembly workplace for sensing, data acquisition and data processing. During the evaluation of postures and movements at work, we discovered differences in the applicable standards: Decree 542/2007 Coll. (Slovak Legislation), the STN EN 1005-4+A1, and the French standards default in the Captiv system. Standards define the thresholds for hazardous postures with significant differences in several evaluated body segments, which affects the final evaluation of the measurements. Our experience from applying improved risk assessment methodology may have an impact on Slovak industrial workplaces. It was confirmed that there is a need to create uniform standards for the ergonomic risk assessment of body posture, including a detailed description of the threshold values for individual body segments.

Abstract IA002: Molecular subtypes of NMIBC

Abstract Non-muscle-invasive bladder cancers (NMIBCs) show large variation in disease aggressiveness which is not fully explained by differences in clinical and pathological factors. It is therefore essential to increase our understanding of the molecular landscape of NMIBC to improve risk assessment and identify potential therapeutic targets. In 2021, we published the four transcriptomic UROMOL2021 classes of NMIBC: class 1, class 2a, class 2b and class 3 (Lindskrog et al., Nature Communications, 2021). Multiple other subtyping schemes for NMIBC have been published, including, among others, the Lund taxonomy (Marzouka et al., The Journal of Molecular Diagnostics, 2022), the stage-stratified subtypes from Leeds (Hurst et al., Cell Reports Medicine, 2021) and BRS1-3 from Rotterdam (de Jong et al., Science Translational Medicine, 2023). In my presentation, I will give an overview of the development and status of NMIBC subtypes and discuss clinical utility. Furthermore, I will present novel data from the UROMOL cohort, where we have now performed a comprehensive genomic characterization of tumors using whole exome sequencing (n=438) and shallow whole genome sequencing (n=362). Overall, we observe a large genomic variation within NMIBC that is associated with different gene expression profiles (UROMOL2021 classes). We performed integrative clustering analysis of somatic mutations, copy number alterations and gene expression data for 230 tumors and identified four “iClusters”, which were highly prognostic and went beyond the single-layer stratifications of tumors. Multi-omics subtyping may improve our understanding of differences in disease aggressiveness within NMIBC further, which could ultimately be used to improve risk assessment of patients and identify potential therapeutic targets. Citation Format: Sia V. Lindskrog. Molecular subtypes of NMIBC [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr IA002.

Clinical complexity of an Italian cardiovascular intensive care unit: the role of mortality and severity risk scores.

The identification of patients at greater mortality risk of death at admission into an intensive cardiovascular care unit (ICCU) has relevant consequences for clinical decision-making. We described patient characteristics at admission into an ICCU by predicted mortality risk assessed with noncardiac intensive care unit (ICU) and evaluated their performance in predicting patient outcomes. A total of 202 consecutive patients (130 men, 75 ± 12 years) were admitted into our tertiary-care ICCU in a 20-week period. We evaluated, on the first 24 h data, in-hospital mortality risk according to Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score 3 (SAPS 3); Sepsis related Organ Failure Assessment (SOFA) Score and the Mayo Cardiac intensive care unit Admission Risk Score (M-CARS) were also calculated. Predicted mortality was significantly lower than observed (5% during ICCU and 7% at discharge) for APACHE II and SAPS 3 (17% for both scores). Mortality risk was associated with older age, more frequent comorbidities, severe clinical presentation and complications. The APACHE II, SAPS 3, SOFA and M-CARS had good discriminative ability in distinguishing deaths and survivors with poor calibration of risk scores predicting mortality. In a recent contemporary cohort of patients admitted into the ICCU for a variety of acute and critical cardiovascular conditions, scoring systems used in general ICU had good discrimination for patients' clinical severity and mortality. Available scores preserve powerful discrimination but the overestimation of mortality suggests the importance of specific tailored scores to improve risk assessment of patients admitted into ICCUs.

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review.

Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Alzheimer's disease: insights into pathology, molecular mechanisms, and therapy.

Alzheimer's disease (AD), the leading cause of dementia, is characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. This condition casts a significant shadow on global health due to its complex and multifactorial nature. In addition to genetic predispositions, the development of AD is influenced by a myriad of risk factors, including aging, systemic inflammation, chronic health conditions, lifestyle and environmental exposures. Recent advancements in understanding the complex pathophysiology of AD are paving the way for enhanced diagnostic techniques, improved risk assessment, and potentially effective prevention strategies. These discoveries are crucial in the quest to unravel the complexities of AD, offering a beacon of hope for improved management and treatment options for the millions affected by this debilitating disease.

Gynecological Cancer and Venous Thromboembolism: A Narrative Review to Increase Awareness and Improve Risk Assessment and Prevention.

The prevention and appropriate management of venous thromboembolism in cancer patients is of paramount importance. However, the literature data report an underestimation of this major problem in patients with gynecological cancers, with an inconsistent venous thromboembolism risk assessment and prophylaxis in this patient setting. This narrative review provides a comprehensive overview of the available evidence regarding the management of venous thromboembolism in cancer patients, focusing on the specific context of gynecological tumors, exploring the literature discussing risk factors, risk assessment, and pharmacological prophylaxis. We found that the current understanding and management of venous thromboembolism in gynecological malignancy is largely based on studies on solid cancers in general. Hence, further, larger, and well-designed research in this area is needed.

Progress and Prospects for a Nucleic Acid Screening Test Set.

DNA synthesis companies screen orders to detect controlled sequences with misuse risks. Assessing screening accuracy is challenging owing to the breadth of biological risks and ambiguities in risk definitions. Here, we detail an International Gene Synthesis Consortium working group's rationale and process to develop a prototype DNA synthesis screening test dataset, aiming to establish a baseline of screening system accuracy to compare with various screening approaches. Construction of the prototype test dataset involved four tool developers screening nucleic acid sequences from three taxonomic clusters of controlled organisms (Orbivirus, Francisella tularensis, and Coccidioides). Results were mapped onto predefined, comparable categories, checking for consensus or conflicts. Conflicts were grouped based on gene annotation and resolved through discussion. The process highlighted several long-standing challenges in DNA synthesis screening, including the qualitative differences in approaches taken by screening tools. Our findings highlight the lack of clarity in assessing pathogen sequences with respect to regulatory control language, compounded by scientific uncertainty. We illustrate the current degree of consensus and existing challenges using classification statistics and specific examples. This prototype underscores the necessity of expert-regulator coordination in assessing gene-associated risks, offering a template for creating test sets across all taxonomic groups on international control lists. Expanding the working group would enrich dataset comprehensiveness, enabling a transition from species-focused to function-focused regulatory controls. This sets the foundation for quality control, certification, and improved risk assessment in DNA synthesis screening.

Abstract PO5-13-03: Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling

Abstract Background: Breast cancer (BC) is the second leading cause of cancer death among women. Accordingly, early diagnosis is key to the successful treatment, management, and care of BC. Recent studies confirmed that plasma metabolites could be reliable cancer biomarkers, allowing for the development of a minimally invasive routine blood test that can be used for screening, as well as for monitoring disease evolution in patients. The exact metabolic pathways involved in early BC development remain unclear. Metabolomic profiling of women with BC may help to identify new biomarkers to predict breast cancer long before symptoms appear. The purpose of this study was to validate a plasma metabolomic biomarker panel for an improved risk assessment for early detection BC in 241 patients, and to understand the potential role and the relationship between BC subtypes and hormone receptor status. Methods: Our study included a total of 185 plasma samples from women with biopsy-confirmed BC and 56 plasma samples from healthy controls. A targeted, quantitative mass spectrometry (MS)-based metabolomics approach was used to analyze 138 metabolites in plasma samples using a combination of direct injection (DI) MS and reverse-phase high performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS). The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and hormones receptor status were used to determine optimal biomarker sets. The same biomarkers and regression models were used and assessed on the validation models. The area under the receiver operator characteristic curves (AUROC), sensitivities and specificities at selected cut off points were calculated for each subgroup. Results: A large proportion of BC patients were at an early stage, with 98 at stage I (53.0 %), 70 at stage II (37.8%), and 17 at stage III (9.2%). The BC patients were of all subtypes: 138 luminal A (76,2%), 23 luminal B (12.7%), 5 hormone receptor-negative/HER2-positive (2.8%), and 15 triples negative (8.3%). A feature selection was performed on the training set using Partial Least-Squares Discriminant Analysis (PLS-DA), and the top performing metabolites were identified as the most important in discriminating BC from healthy subjects. The impact of age was also investigated. Features with &amp;gt;80% missing values were removed. To predict BC, the best signature comprised 9 variables implicated in fatty acid metabolism, amino acid metabolism, polyamine biosynthesis and related signaling pathways We further developed and validated a logistic regression with AUROC &amp;gt; 0.9 using these metabolites and other clinical data for detecting different stages and subtypes of BC. Conclusion: This study identified and validated a simple, high-performing, metabolite-based test for the early detection of BC. After confirmation in other independent cohort studies, our findings could provide the foundations for the development of a blood-based routine test for women at the highest risk for BC that is cost-effective, accurate and reliable. In addition, this approach could be used to complement other modalities especially for BC patients with dense breasts. Citation Format: Jean-Francois Haince, Lun Zhang, Rashid Ahmed Bux, Paramjit S. Tappia, Bram Ramjiawan, David Wishart, Andrew Maksymiuk. Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-03.