Harmonic Mean Research Articles

USE OF NATURAL LANGUAGE PROCESSING TECHNIQUES TO PREDICT PATIENT SELECTION FOR TOTAL HIP ARTHROPLASTY: RESULTS FROM THE AI TO REVOLUTIONISE THE PATIENT CARE PATHWAY IN HIP AND KNEE ARTHROPLASTY (ARCHERY) PROJECT

To examine whether Natural Language Processing (NLP) using a state-of-the-art clinically based Large Language Model (LLM) could predict patient selection for Total Hip Arthroplasty (THA), across a range of routinely available clinical text sources.Data pre-processing and analyses were conducted according to the Ai to Revolutionise the patient Care pathway in Hip and Knee arthroplasty (ARCHERY) project protocol (https://www.researchprotocols.org/2022/5/e37092/). Three types of deidentified Scottish regional clinical free text data were assessed: Referral letters, radiology reports and clinic letters. NLP algorithms were based on the GatorTron model, a Bidirectional Encoder Representations from Transformers (BERT) based LLM trained on 82 billion words of de-identified clinical text. Three specific inference tasks were performed: assessment of the base GatorTron model, assessment after model-fine tuning, and external validation.There were 3911, 1621 and 1503 patient text documents included from the sources of referral letters, radiology reports and clinic letters respectively. All letter sources displayed significant class imbalance, with only 15.8%, 24.9%, and 5.9% of patients linked to the respective text source documentation having undergone surgery. Untrained model performance was poor, with F1 scores (harmonic mean of precision and recall) of 0.02, 0.38 and 0.09 respectively. This did however improve with model training, with mean scores (range) of 0.39 (0.31–0.47), 0.57 (0.48–0.63) and 0.32 (0.28–0.39) across the 5 folds of cross-validation. Performance deteriorated on external validation across all three groups but remained highest for the radiology report cohort.Even with further training on a large cohort of routinely collected free-text data a clinical LLM fails to adequately perform clinical inference in NLP tasks regarding identification of those selected to undergo THA. This likely relates to the complexity and heterogeneity of free-text information and the way that patients are determined to be surgical candidates.

Clinical pharmacokinetics and pharmacodynamics of venetoclax, a selective B-cell lymphoma-2 inhibitor.

Venetoclax, a highly potent BCL-2 inhibitor, is indicated for treatment of some hematologic malignancies as monotherapy, and/or in combination with other agents. Venetoclax pharmacokinetics has been extensively characterized in patients and healthy participants. After oral dosing, the median time to reach maximum plasma concentration ranged from 5 to 8 h and harmonic mean half-life ranged from 14 to 18 h. Food increases venetoclax bioavailability by 3-5-fold and venetoclax should be administered with food to ensure adequate and consistent bioavailability. Venetoclax is eliminated via cytochrome P450 (CYP)3A metabolism, and a negligible amount of unchanged drug is excreted in urine. Strong CYP3A/P-glycoprotein inhibitors increased venetoclax exposures (AUC) by 1.44- to 6.90-fold while a significant decrease (71%) has been observed when dosed with strong CYP3 inducers. Venetoclax does not inhibit or induce CYP enzymes or transporters. Venetoclax pharmacokinetics is not appreciably altered by age, weight, sex, but the exposure is up to twofold higher in participants from Asian countries. Mild-to-severe renal impairment or end-stage renal disease do not alter venetoclax exposures, and venetoclax is not cleared by dialysis. Although mild-to-moderate hepatic impairment does not affect venetoclax exposures, twofold higher exposure was observed in subjects with severe hepatic impairment. Venetoclax exposure is comparable across patients with different hematologic malignancies and healthy participants. Overall, venetoclax exposure is only affected by food and CYP3A modulators and is only higher in Asian subjects and subjects with severe hepatic impairment. Venetoclax exposure-response relationships are malignancy-dependent and can be different between monotherapy and combination therapy.

New multivariable mean from nonlinear matrix equation associated to the harmonic mean

New multivariable mean from nonlinear matrix equation associated to the harmonic mean

Surface Thermodynamic Properties of Poly Lactic Acid by Inverse Gas Chromatography.

Poly lactic acid (PLA) is one of the most commonly used bio-derived thermoplastic polymers in 3D and 4D printing applications. The determination of PLA surface properties is of capital importance in 3D/4D printing technology. The surface thermodynamic properties of PLA polymers were determined using the inverse gas chromatography (IGC) technique at infinite dilution. The determination of the retention volume of polar and non-polar molecules adsorbed on the PLA particles filling the column allowed us to obtain the dispersive, polar, and Lewis's acid-base surface properties at different temperatures from 40 °C to 100 °C. The applied surface method was based on our recent model that used the London dispersion equation, the new chromatographic parameter function of the deformation polarizability, and the harmonic mean of the ionization energies of the PLA polymer and organic molecules. The application of this new method led to the determination of the dispersive and polar free surface energy of the adsorption of molecules on the polymeric material, as well as the glass transition and the Lewis acid-base constants. Four interval temperatures were distinguished, showing four zones of variations in the surface properties of PLA as a function of the temperature before and after the glass transition. The acid-base parameters of PLA strongly depend on the temperature. The accurate determination of the dispersive and polar surface physicochemical properties of PLA led to the work of adhesion of the polar organic solvents adsorbed on PLA. These results can be very useful for achieving reliable and functional 3D and 4D printed components.

Radio Contra Harmonic Mean Number of Graphs

Objectives: To explore the least upper bound of graphs by radio contra harmonic labeling. Methods: Contra harmonic mean function or , radio mean labeling condition and radio harmonic mean labeling are used. Findings: Here we introduce radio contra harmonic mean labeling and its least upper bound, designated as radio contra harmonic mean number, by formulating the constraints. Novelty: Based on radio mean labeling and contra harmonic mean labeling, the new concept of radio contra harmonic mean labeling was established. The bounds of some special graphs are encountered here. This kind of labeling is employed in secure communication networks and is also applicable in X-rays, crystallography, coding theory, computing etc. Keywords: Radio contra harmonic mean labeling, rchmn (G), Order, Diameter, Smallest span

Look4LTRs: a Long terminal repeat retrotransposon detection tool capable of cross species studies and discovering recently nested repeats

Plant genomes include large numbers of transposable elements. One particular type of these elements is flanked by two Long Terminal Repeats (LTRs) and can translocate using RNA. Such elements are known as LTR-retrotransposons; they are the most abundant type of transposons in plant genomes. They have many important functions involving gene regulation and the rise of new genes and pseudo genes in response to severe stress. Additionally, LTR-retrotransposons have several applications in biotechnology. Due to the abundance and the importance of LTR-retrotransposons, multiple computational tools have been developed for their detection. However, none of these tools take advantages of the availability of related genomes; they process one chromosome at a time. Further, recently nested LTR-retrotransposons (multiple elements of the same family are inserted into each other) cannot be annotated accurately — or cannot be annotated at all — by the currently available tools. Motivated to overcome these two limitations, we built Look4LTRs, which can annotate LTR-retrotransposons in multiple related genomes simultaneously and discover recently nested elements. The methodology of Look4LTRs depends on techniques imported from the signal-processing field, graph algorithms, and machine learning with a minimal use of alignment algorithms. Four plant genomes were used in developing Look4LTRs and eight plant genomes for evaluating it in contrast to three related tools. Look4LTRs is the fastest while maintaining better or comparable F1 scores (the harmonic average of recall and precision) to those obtained by the other tools. Our results demonstrate the added benefit of annotating LTR-retrotransposons in multiple related genomes simultaneously and the ability to discover recently nested elements. Expert human manual examination of six elements — not included in the ground truth — revealed that three elements belong to known families and two elements are likely from new families. With respect to examining recently nested LTR-retrotransposons, three out of five were confirmed to be valid elements. Look4LTRs — with its speed, accuracy, and novel features — represents a true advancement in the annotation of LTR-retrotransposons, opening the door to many studies focused on understanding their functions in plants.

Temperature Dependence of the Polar and Lewis Acid-Base Properties of Poly Methyl Methacrylate Adsorbed on Silica via Inverse Gas Chromatography.

The adsorption of polymers on solid surfaces is common in many industrial applications, such as coatings, paints, catalysis, colloids, and adhesion processes. The properties of absorbed polymers commonly vary with temperature. In this paper, inverse gas chromatography at infinite dilution was used to determine the physicochemical characterization of PMMA adsorbed on silica. A new method based on the London dispersion equation was applied with a new parameter associating the deformation polarizability with the harmonic mean of the ionization energies of the solvent. More accurate values of the dispersive and polar interaction energies of the various organic solvents adsorbed on PMMA in bulk phase and PMMA/silica at different recovery fractions were obtained, as well as the Lewis acid-base parameters and the transition temperatures of the different composites. It was found that the temperature and the recovery fraction have important effects on the various physicochemical and thermodynamic properties. The variations in all the interaction parameters showed the presence of three transition temperatures for the different PMMA composites adsorbed on silica with various coverage rates, with a shift in these temperatures for a recovery fraction of 31%. An important variation in the polar enthalpy and entropy of adsorption, the Lewis acid-base parameters and the intermolecular separation distance was highlighted as a function of the temperature and the recovery fraction of PMMA on silica.

Beyond the two-trials rule.

The two-trials rule for drug approval requires "at least two adequate and well-controlled studies, each convincing on its own, to establish effectiveness." This is usually implemented by requiring two significant pivotal trials and is the standard regulatory requirement to provide evidence for a new drug's efficacy. However, there is need to develop suitable alternatives to this rule for a number of reasons, including the possible availability of data from more than two trials. I consider the case of up to three studies and stress the importance to control the partial Type-I error rate, where only some studies have a true null effect, while maintaining the overall Type-I error rate of the two-trials rule, where all studies have a null effect. Some less-known -value combination methods are useful to achieve this: Pearson's method, Edgington's method and the recently proposed harmonic mean -test. I study their properties and discuss how they can be extended to a sequential assessment of success while still ensuring overall Type-I error control. I compare the different methods in terms of partial Type-I error rate, project power and the expected number of studies required. Edgington's method is eventually recommended as it is easy to implement and communicate, has only moderate partial Type-I error rate inflation but substantially increased project power.

Positioning multiple optical network units in fiber-wireless networks: An efficient hybrid [formula omitted]-harmonic means clustering approach

Wireless-optical broadband access network (WOBAN) or fiber-wireless (FiWi) is a robust and cost-effective solution to provide high bandwidth network connectivity to mobile subscribers. Optical network unit (ONU) placement is a topological optimization problem that plays a vital role in effective deployment of FiWi network. Recently, some artificial intelligence-based approaches for positioning ONUs were proposed and generated promising results. However, the reported performance in the literature is far from the ideal state. This issue emphasizes the necessity of introducing new placement methods or improving the performance of existing methods. This paper presents a hybrid clustering algorithm for finding the optimal position of multiple ONUs. The proposed backtracking K-harmonic means (BKHM) is an integration of K-harmonic means (KHM) and a backtracking search algorithm (BSA). BKHM not only efficiently specifies the optimal positions of ONUs but also significantly decreases the implementation cost. To demonstrate the performance of the BKHM, we evaluate it on well-known placement benchmarks compared with BSA, KHM, whale optimization algorithm (WOA), enhanced backtracking search (EBS), harris hawks optimization (HHO), and chaotic local search-based levy flight distribution (CLF) algorithms. The experimental results and statistical analysis proved that the proposed BKHM algorithm outperformed its counterparts on most benchmarks in terms of cost optimization, convergence accuracy, and solution quality.

Infrared Maritime Small-Target Detection Based on Fusion Gray Gradient Clutter Suppression

The long-distance ship target turns into a small spot in an infrared image, which has the characteristics of small size, weak intensity, limited texture information, and is easily affected by noise. Moreover, the presence of heavy sea clutter, including sun glints that exhibit local contrast similar to small targets, negatively impacts the performance of small-target detection methods. To address these challenges, we propose an effective detection scheme called fusion gray gradient clutter suppression (FGGCS), which leverages the disparities in grayscale and gradient between the target and its surrounding background. Firstly, we designed a harmonic contrast map (HCM) by using the two-dimensional difference of Gaussian (2D-DoG) filter and eigenvalue harmonic mean of the structure tensor to highlight high-contrast regions of interest. Secondly, a local gradient difference measure (LGDM) is designed to distinguish isotropic small targets from background edges with local gradients in a specific direction. Subsequently, by integrating the HCM and LGDM, we designed a fusion gray gradient clutter suppression map (FGGCSM) to effectively enhance the target and suppress clutter from the sea background. Finally, an adaptive constant false alarm threshold is adopted to extract the targets. Extensive experiments on five real infrared maritime image sequences full of sea glints, including a small target and sea–sky background, show that FGGCS effectively increases the signal-to-clutter ratio gain (SCRG) and the background suppression factor (BSF) by more than 22% and 82%, respectively. Furthermore, its receiver operating characteristic (ROC) curve has an obviously more rapid convergence rate than those of other typical detection algorithms and improves the accuracy of small-target detection in complex maritime backgrounds.

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis.

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.

Prevalence of psychiatric disorders in patients with craniofacial malformations - a statistical analysis

IntroductionCraniofacial malformations have long been associated with a heightened risk of psychiatric disorders. Understanding this link is crucial, as it can inform early intervention and support for affected individuals, enhancing their overall well-being. Research in this area aims to shed light on the prevalence and nature of these disorders within the craniofacial population, ultimately improving healthcare and quality of life for affected individuals.ObjectivesThis study aims to establish a comprehensive understanding of the relationship between craniofacial malformations and psychiatric disorders. Specifically, our objectives include: assessing prevalence, identifying risk factors, evaluating impact and informing clinical practice. This research aims to improve the holistic care and mental well-being of individuals with craniofacial malformations, contributing to a more comprehensive approach in the field of psychiatry.MethodsThis cross-sectional study was conducted at a prominent referral hospital named Hospital de Clínicas de Porto Alegre during the month of August 2023.Participant Selection: Patients with craniofacial malformations of all ages and both genders.Data Collection: We conducted structured interviews with participants to gather demographic information, medical history, and details of their craniofacial conditions.Medical Records Review: Medical records were reviewed to corroborate craniofacial diagnoses and identify any comorbid conditions.Statistical Analysis: Data were analyzed using appropriate statistical techniques to assess the association between craniofacial malformations and psychiatric disorders.Ethical Considerations: The study adhered to all ethical guidelines, with informed consent obtained from participants or their legal guardians. Ethical approval was obtained from the hospital’s Institutional Review Board.Data Handling: Confidentiality and data security were ensured throughout the study, with all data anonymized to protect participant privacy.ResultsIn our study, we assessed 132 different patients, comprising 62 females and 70 males. The youngest patient was 2 months old, while the oldest was 56 years old. The mean age of the patients was 16.22 years, with a median of 9 years, a harmonic mean of 18 years, and a standard deviation of 15.23 years.Among the patients, 24 exhibited psychiatric disorders, evenly split between 12 males and 12 females. Their average age was 16.21 years, with a median of 10 years, a harmonic mean of 6.13, and a standard deviation of 14.57. The youngest patient with evidence of a psychiatric disorder was 2 years old.Image:Image 2:ConclusionsOur study underscores the prevalence of psychiatric disorders among craniofacial patients, which seems to be greater than the general population, emphasizing the need for integrated care that considers both medical and psychological aspects, thus enhancing the overall well-being of these individuals.Disclosure of InterestNone Declared

Pumping‐induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

AbstractIn groundwater hydrology, aquitard heterogeneity is often less considered compared to aquifers, despite its significant impact on groundwater hydraulics and groundwater resources evaluation. A semi‐analytical solution is derived for pumping‐induced well hydraulics and groundwater budget with consideration of vertical heterogeneity in aquitard hydraulic conductivity (K) and specific storage (Ss). The proposed new solution is innovative in its partitioning of the aquitard into multiple homogeneous sub‐layers to enable consideration of various forms of vertically heterogeneous K or Ss. Two scenarios of analytical investigations are explored: one is the presence of aquitard interlayers with distinct K or Ss values, a common field‐scale occurrence; another is an exponentially depth‐decaying aquitard Ss, a regional‐scale phenomenon supported by statistical analysis. Analytical investigations reveal that a low‐K interlayer can significantly increase aquifer drawdown and enhance aquifer/aquitard depletion; a high‐Ss interlayer can noticeably reduce aquifer drawdown and increase aquitard depletion. Locations of low‐K or high‐Ss interlayers also significantly impact well hydraulics and groundwater budget. In the context of an exponentially depth‐decaying aquitard Ss, a larger decay exponent can enhance aquifer drawdown. When using current models with a vertically homogeneous aquitard, half the sum of the geometric and harmonic means of exponentially depth‐decaying aquitard Ss should be used to calculate aquitard depletion and unconfined aquifer leakage.

Establishment of Obesity Diagnosis Criteria Using Body Volume Index of 3D BodyScanner

PURPOSE This study sought to establish obesity diagnosis criteria by using the Body Volume Index (BVI) by body part extracted through 3D BodyScanner.METHODS The body fat percentage was measured using Dual Energy X-ray Absorptiometer (DEXA) for 225 participants (male = 119, female = 106), and BVI for eight body parts was measured using 3D BodyScanner. Independent t-test and Receiver Operating Characteristic (ROC) analysis were conducted. ROC analysis calculated the Area Under the Curve (AUC), and the optimal cut-point by Youden's J index. Sensitivity, Specificity, Accuracy, Balanced Classification Rate (BCR), and F1-score (harmonic mean of recall and precision) values were calculated to verify the validity of the optimal cut-point.RESULTS A statistically significant difference was observed in BVI by body part according to whether obesity was present for both men and women, and the obese group higher than the normal group. The optimal cut-point for each body part to diagnose obesity was 7.96 for shoulder, 9.79 for chest, 7.15 for upper abdominal, 7.71 for lower abdominal, 14.89 for total abdominal, 9.79 for thigh, 5.70 for calf, and 74.96 for total body volume in men. In case of women, this was 6.04 for shoulder, 9.82 for chest, 4.96 for upper abdominal, 6.23 for lower abdominal, 11.63 for total abdominal, 8.88 for thigh, 4.05 for calf, and 58.15 for total body volume, and the accuracy was 0.6~0.9.CONCLUSIONS BVI is a useful indicator for diagnosing obesity. However, this can be applicable only to Asian adults since there may be differences depending on race or age.

Averaging or adding domain conductivities to calculate the unsaturated soil hydraulic conductivity

AbstractRecent models of the unsaturated hydraulic conductivity curve (UHCC) are the sum of separate UHCCs for domains of capillary water, film water, and water vapor. This requires parallel, noninteracting domains. A theoretical framework for aggregating domain conductivities to a bulk soil UHCC is presented to identify and possibly relax implicit assumptions about domain configuration. The paper develops arithmetic, harmonic, and geometric averages of the liquid‐water conductivities that can be arithmetically averaged with the vapor conductivity. However, current models for capillary and film conductivities are intrinsic, that is, valid within their respective domain. The vapor conductivity is a bulk conductivity, that is, it gives the conductivity of the gaseous domain as it manifests itself in the soil. Conversion relationships use the domain volume fractions as approximations of the as‐yet unknown weighting factors to convert between intrinsic and bulk conductivities. This facilitates consistent averaging of domain conductivities. Even with consistent averaging, a truly physically accurate model of the UHCC based on domain conductivities is fundamentally elusive. Nevertheless, models based on the three averages and the unweighted sum of the domain conductivities produce good fits to data for two soils but diverge in the dry range. The fitted curves for the capillary and film water depend on the averaging (or adding) method. Hence, they are not strictly characteristic of their respective domains. The true intrinsic domain conductivity functions may be impossible to determine.

The upper bound of the harmonic mean of the Neumann eigenvalues in curved spaces

Considering an n-dimensional Riemannian manifold M whose sectional curvature is bounded above by κ and Ricci curvature is bounded below by (n−1)K, we obtain an upper bound for the harmonic mean of the first (n−1) non-zero Neumann eigenvalues of the Laplacian for domains contained in M. This can be viewed as certain isoperimetric inequality and generalizes the results for domains in space forms [5,15].

CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues

Accurate identification of protein nucleic acid binding residues poses a significant challenge with important implications for various biological processes and drug design. Many typical computational methods for protein analysis rely on a single model that could ignore either the semantic context of the protein or the global 3D geometric information. Consequently, these approaches may result in incomplete or inaccurate protein analysis. To address the above issue, in this paper, we present CrossBind, a novel collaborative cross modal approach for identifying binding residues by exploiting both protein geometric structure and its sequence prior knowledge extracted from a large scale protein language model. Specifically, our multi modal approach leverages a contrastive learning technique and atom wise attention to capture the positional relationships between atoms and residues, thereby incorporating fine grained local geometric knowledge, for better binding residue prediction. Extensive experimental results demonstrate that our approach outperforms the next best state of the art methods, GraphSite and GraphBind, on DNA and RNA datasets by 10.8/17.3% in terms of the harmonic mean of precision and recall (F1 Score) and 11.9/24.8% in Matthews correlation coefficient (MCC), respectively. We release the code at https://github.com/BEAM-Labs/CrossBind.

Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning

We propose a generalized method for boosting the generalization ability of pre-trained vision-language models (VLMs) while fine-tuning on downstream few-shot tasks. The idea is realized by exploiting out-of-distribution (OOD) detection to predict whether a sample belongs to a base distribution or a novel distribution and then using the score generated by a dedicated competition based scoring function to fuse the zero-shot and few-shot classifier. The fused classifier is dynamic, which will bias towards the zero-shot classifier if a sample is more likely from the distribution pre-trained on, leading to improved base-to-novel generalization ability. Our method is performed only in test stage, which is applicable to boost existing methods without time-consuming re-training. Extensive experiments show that even weak distribution detectors can still improve VLMs' generalization ability. Specifically, with the help of OOD detectors, the harmonic mean of CoOp and ProGrad increase by 2.6 and 1.5 percentage points over 11 recognition datasets in the base-to-novel setting.

Slot coating of viscoplastic materials: A computational study of the effects of viscoplasticity on the flow dynamics and low-flow limit

Yield-stress materials such as structurally complex formulations of paints, slurries, and waxes have been long ubiquitous in the coating industry, though the practice of coating engineering remains largely empirical as the fundamental role of viscoplasticity due to the yield stress of the coating material in most coating applications is still unclear. Here, we couple a recent harmonic mean viscosity regularization for the Bingham model with a well-established finite element/elliptic mesh generation method for free surface flows to present a detailed computational study of slot coating applications of viscoplastic materials. By neglecting inertia and focusing on the downstream section of a slot coater, we introduce suitable dimensionless parameters to discuss a comprehensive set of results that unravels a striking impact of viscoplasticity on the flow dynamics and low-flow limit. We show that viscoplastic effects have major implications to the velocity field and recirculation pattern in the coating bead as well as to the development length and free surface in the film formation region. Most importantly, we find that viscoplastic effects markedly widen the operating window of the process, delaying the onset of the low-flow limit and thereby suggesting that structurally complex yield-stress materials may be used to coat thinner films and/or at higher speeds than predicted by the standards far established for simple Newtonian liquids.

Abstract 1070: Overcoming deficiencies in the early detection of high grade serous ovarian cancer: Evaluating exosomal proteins as novel biomarkers of disease

Abstract Introduction: High-grade serous ovarian cancer (HGSOC) accounts for over 75% of all epithelial ovarian cancer and has a high mortality rate due to a lack of early detection methods. Many biomarkers have been proposed; however, none have been shown to improve detection at an early stage in this patient population. The purpose of this study is to assess whether there are unique extracellular vesicle (EV) protein signatures of early-stage HGSOC that could serve as early detection biomarkers. Methods: 250 serum samples were obtained from a multi-institutional retrospective cohort of patients with all stages of HGSOC and healthy controls. LC-MS/MS and PEA of serum samples from patients with early-stage HGSOC identified differentially expressed EVs proteins in HGSOC versus controls. Tob candidate proteins are validated by ELISA. Results: We have identified the top 10 EVs candidate proteins based on their fold change and statistical significance. When comparing early-stage HGSOC and controls using ELISA, CFH, PCP, and CCNE1 exhibited the highest Area Under the Curve (AUC) values of 0.94, 0.91, and 0.83, respectively. In contrast, MUC16 (also known as CA-125) exhibited an AUC of only 0.42 in whole serum. Furthermore, MUC16 distinguished between controls and both early and advanced-stage HGSOC in EVs, while in whole serum, it was only differentially expressed between controls and advanced-stage disease. Our results demonstrate that the predictive performance of the combined biomarkers surpasses that of any individual biomarker. Further, we integrated the ten aforementioned biomarkers, deriving the optimal combination biomarker by maximizing the corresponding Sum of Harmonic Means (SHUM) function by utilizing data from all 70 patients, results showed the optimal coefficients of the biomarkers, which maximize the diagnostic accuracy for early-stage HGSOC. The overall true positive rate (TPR) of 0.943, false positive rate (FPR) of 0.000, and Mathew's correlation coefficient (MCC) were presented for both the combined biomarker approach and individual biomarkers. The results demonstrated that the predictive performance of the combined biomarkers surpassed that of any individual biomarker. Conclusion: Expression of EV-based protein biomarkers is significantly different in early-stage HGSOC compared to both control and late-stage HGSOC. This work can be readily translated to clinical use and potentially improve the early detection of HGSOC substantially. Citation Format: Michelle Lightfoot, Kalpana Deepa Priya Dorayappan, Lianbo Yu, Colin Hisey, Takahiko Sakaue, Muralidharan Anbalagan, Casey M. Cosgrove, Larry G. Maxwell, Premal Thaker, Beth Y. Karlan, David O’Malley, Raphael E. Pollock, David E. Cohn, Rajan Gogna, Selvendiran Karuppaiyah. Overcoming deficiencies in the early detection of high grade serous ovarian cancer: Evaluating exosomal proteins as novel biomarkers of disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1070.