Successful Validation Research Articles

Individual Burden of Illness Index in Bipolar Disorder Remission: A Cross-Sectional Study.

A population-based method for estimating disease burden is commonly used. Nevertheless, these measurements do not entirely capture the comprehensive burden of illness on an individual patient. To address the problem, the Individual Burden of Illness Index (IBI index) Index was created and validated, specifically for major depressive disorder. The IBI represents the overall influence of the condition, encompassing distress from symptom intensity, functional impairment, and the patient's quality of life. The aim of the study was to approve and validate the IBI index for the integral assessment of disease burden in patients with bipolar disorder (BD) in remission. The cross-sectional study was conducted in the outpatient psychiatric services in Saint Petersburg, Russia, from April through October 2020. Eighty-five patients aged 18 to 45 (mean age 36.6±5.7 years) with BD (type I - 75%, n=64; type II - 25%, n=21) in remission were examined. The study procedure included a structured clinical interview and the use of clinical scales: the World Health Organization's Quality of Life Questionnaire, Hamilton Rating Scale for Depression (HDRS), Young Mania Rating Scale (YMRS), and Personal and the Social Performance Scale. The principal component analysis in accordance with the adjusted one showed that the burden of illness in patients with BD in remission is directly related to the severity of residual depressive symptoms, reflected in the HDRS score: as the HDRS score increases (0.27, p <0.001), residual mania (-0.14, p <0.001), social functioning (-0.06, p <0.001), and quality of life (-0.04, p <0.001) decrease. In contrast, when there are remaining residual mania symptoms, as indicated by the YMRS score, the result tends to be a lower burden, better social functioning, and enhanced quality of life. The study has demonstrated through statistical means a successful adaptation and validation of the previously calculated IBI index for patients with BD in remission. Residual affective symptoms were shown to have different impacts on the social functioning of patients with BD in remission, indicating the need for a timely assessment and targeted therapy of these symptoms in such patients.

681 Application of Low-Cost Automated Progress Tracking System (APS) on Laparoscopic Appendicectomy Simulation Models (LaApSi) for the Laparoscopic Simulation Training in the Lower-Middle-Income-Countries (LMICs)

Abstract Aim To assess the effectiveness of the Low-Cost Automated Progress Tracking System on Laparoscopic Appendicectomy (LA) Simulation Models (LaApSi) for Laparoscopic Training in Lower-Middle-Income-Countries (LMICs). To assess and record the progression of trainees’ performance during simulation training. Method After the successful application and validation of the LaApSi model during laparoscopic courses at LMICs, the APS system has been prototyped to assess the trainee’s progression of LA. This system helps to assess surgical steps during simulated LA with the help of the LaApSi model integrated with wires at various anatomical positions giving real-time computer feedback and allowing users to assess the timing of operation, blood vessel excision and injury to the base of the appendix/caecum. Results The prototype version tested in LMIC showed excellent results in the detection of the above-mentioned parameters (shown in photos). This cost-effective model (LaApSi&amp;lt; £5 and docking system &amp;lt;£15) showed huge potential as a laparoscopic training model in LMICs. There will be additional validation of 16 APS models during the Laparoscopic Surgery Course in Nepal in Jan 2024 in collaboration with Nepalese Doctors Association-UK which we believe will further solidify its application in the LMICs Conclusions There is a need for the transition to high-fidelity low-cost laparoscopic simulation models from the traditional training methods in the LMICs which in the long-term address global surgery values. These systems have the potential for remote proctorship in the rural parts adapting the Hub &amp; Spoke model and integrating AI and Machine Learning which eliminates the use of wires and sensors in the models.

Green decorated of gold nanoparticles over kaolin mediated by plant extract and investigation of its anti-bile duct cancer properties in the in vitro condition

In the current research, kaolin-decorated Au nanoparticles were produced by a green medication method by using Strawberry root’s extract as a natural coating and reducing component for capping and stabilizing Au NPs over the kaolin. The Au NPs/kaolin material was thoroughly characterized using XRD, FE-SEM, EDX-elemental mapping, TEM, and ICP. Characterization data confirmed the well-supporting of Au NPs over the kaolin. TEM images displayed the spherical shaped gold nanoparticles averaging 20–40 nm in size over the kaolin surface. The cytotoxicity and anti-bile duct cancer properties of Au NPs/kaolin were investigated through the utilization of MTT assay on three bile duct cancer cell lines, namely OCUCHLM1, EGI1, and GBCSD. Au nanoparticles (Au NPs) combined with kaolin exhibited dose-dependent anti-bile duct cancer properties and significantly reduced cell viability in OCUCHLM1, EGI1, and GBCSD cell lines. Importantly, these effects were observed without causing any cytotoxicity in the normal cell line (HUVECs). The OCUCHLM1 cell line exhibited the most significant anti-bile duct cancer effects when treated with Au NPs/kaolin, surpassing the results observed in other cell lines. Upon the successful validation of the aforementioned findings in the clinical trial investigations, Au NPs/kaolin could potentially serve as an effective chemotherapeutic agent in combating bile duct cancer.

Multi-phase SPH-FDM and experimental investigations on the hydrodynamics of an oscillating water column wave energy device

In this paper, the hydrodynamic performance of a bottom-seated type oscillating water column (OWC) device is investigated by numerical and experimental methods. A coupled SPH (Smoothed Particle Hydrodynamics) - FDM (Finite Difference Method) is proposed to simulate the interaction between waves and the OWC device. The current SPH-FDM model improves grid encryption method, and expand the single-phase SPH-FDM to multiphase SPH-FDM. The results from SPH-FDM align closely with those from the published experimental measurements. Following the successful validations, SPH-FDM and experiment are employed for a series of simulations to investigate the performance of the OWC device. Compared to experimental method, SPH-FDM can obtain global flow field information and avoid the influence of free surface identification and capillarity on vortex determination. In addition, SPH-FDM can naturally captures free surfaces, so it supports further studies on the non-uniformity of water surface in the chamber of OWC which cannot be studied by PIV or pneumatic model.

Development, Validation, and Implementation of an Augmented Multiwell, Multitarget Quantitative PCR for the Analysis of Human Papillomavirus Genotyping through Software Automation, Data Science, and Artificial Intelligence

The value of HPV testing for cervical cancer screening is well established, where its use as primary screening option or as reflex test after atypical cytology results has recently gained wide acceptance. The importance of full genotyping and viral load determination has been demonstrated to enhance the clinical understanding of the viral infection progression during follow-up or after treatment, thereby providing clinicians with supplementary tools for optimized patient management.In this study a new analysis method for the RIATOL quantitative PCR assay was developed, validated and implemented in the laboratory of clinical molecular pathology at A.M.L. (Sonic Healthcare, Belgium), under national accreditation and following the international ISO guidelines. It presents the successful validation of a high throughput, multi-target HPV analysis method, with enhanced accuracy on both qualitative and quantitative end-results. This is achieved by software standardization and automation of PCR curve analysis and interpretation, using data science and artificial intelligence (DS/AI). Moreover, the user-centric functionality of the platform was demonstrated to enhance both staff training and routine analysis workflows, thereby saving time and laboratory personnel resources.Overall, the integration of the FastFinder plugin semi-automatic analysis algorithm with the RIATOL qPCR assay proved to be a remarkable advancement in high throughput HPV quantification, with demonstrated capability to provide highly accurate clinical-grade results and to reduce manual variability and analysis time.

Multi-Objective Model Updating of Tower Structures Featuring Closely Spaced Modes

In this contribution, we present a multi-objective model updating approach for the purpose of damage localisation and quantification on a girder mast structure based on closely spaced modes. In structural health monitoring (SHM), modal quantities, such as eigenfrequencies and mode shapes, serve as monitoring parameters. Specifically, model updating strategies adjust the local structural stiffness of a simulated structure to reduce the difference between the simulation results and the experimentally identified modal quantities. The resulting structural stiffness represents the damaged state of the structure, thus providing valuable information about the occurred damage. Symmetrical tower structures are challenging for model updating approaches due to their closely spaced bending modes, which are prone to uncertain alignments between the identified mode shapes and the model mode shapes. As a result, the naive comparison between the mode shapes of the real structure and the model often yields poor results. In order to overcome this problem, we adapt the concept of the subspace of order 2 modal assurance criterion (S2MAC), i.e., finding the best fit to a mode shape vector in a subspace spanned by two bending modes, hence enabling a meaningful comparison of the changes in the dominant mode shapes. The proposed method is validated using an experimental 9 m tall girder mast structure featuring reversible damage mechanisms. Mode shapes and eigenfrequencies, identified using the Bayesian operational modal analysis (BayOMA) method, are employed for multi-objective model updating, allowing for the estimation of several Pareto-optimal solutions. The structure is parameterised with two damage parameters, accounting for the location and severity, which limits the optimisation to only one damaged beam at any given time. The damage localisation is performed for data sets recorded over a time span of several months, including three different damage positions, resulting in a successful validation of the proposed

Effects of the filter media pack configurations on the air filtration performance

Abstract High efficiency particulate air (HEPA) filters are widely used in various industries to contain airborne contamination. The pressure drop of the filters is a crucial performance parameter when designing a HEPA filter as it is the resistance that the filter offers to the flow of air. Thus, it is critical to find the effect of each configuration and integrate an optimal setup for the filter to avoid high-pressure drops. Set against these backgrounds, this study aims to study the effect of different configurations on the total pressure drop of the filter, and to review how each configuration can affect each other using a commercial software ANSYS Fluent. The different filter media pack configurations and boundary conditions investigated here are including pleat shape (V-shaped and U-shaped), pleat density (1-7 pleat/cm), pleat height (1cm, 1.3cm), and inlet velocity (0.02-0.2 m/s) on the pressure drop of the filter. A two-dimensional geometrical model is developed, and then validated with respect to the data obtained from Lydall M3004-06 property sheet. Upon successful validation exercise, a series of parametric studies is conducted to numerically examine the impact of changing each configuration on the total pressure drop of the filter. Results show that an optimal pleat density is attained where the total pressure drop is minimized, i.e., 3 pleat/cm for V-shaped and 4 pleat/cm for U-shaped for inlet velocity of 0.02 m/s. At a constant inlet velocity, the U-shaped pleat has a lower pressure drop than the V-shaped pleat at low pleat densities, but a higher pressure drop at higher densities. This behaviour can be attributed to the balance between inertial and viscous resistance. Increasing the inlet velocity increases the total pressure drop for all pleat heights and decreases the optimal pleat density. This effect is less pronounced on the V-shaped pleat. Increasing the pleat height for the U-shaped pleat decreases the pressure drop and optimal pleat density but increases pressure drop in the viscous dominated region. Conversely, increasing the pleat height for the V-shaped pleat decreases the pressure drop for all pleat counts and only decreases the optimal pleat density at high velocity.

DeepRing: Convolution Neural Network based on Blockchain Technology

Background: This paper addresses specific challenges in predictive modeling, namely transparency issues, susceptibility to data manipulation, and fairness concerns. To overcome these obstacles, the study introduces DeepRing, approach that combines Convolutional Neural Networks (CNNs) and blockchain technology. Objective: DeepRing aims to enhance prediction integrity, data security, and fairness, thereby improving the ethical considerations, reliability, and accountability of predictive models. Methods: involves iterative training of a CNN model on five diverse datasets, including CIFAR-10, Fashion-MNIST, MNIST, CIFAR-100, and a Hands dataset. The CNN architecture incorporates Conv2D layers, MaxPooling2D layers, and Dense layers. Training metrics such as accuracy and sparse categorical cross-entropy loss are monitored, with the Adam optimizer employed. While achieving high accuracy on Plam (0.5300), MNIST (0.9978) and Fashion MNIST (0.9673), DeepRing exhibits moderate performance on CIFAR-10 (0.9296) and lower accuracy on CIFAR-100 (0.5973). Results: demonstrate the effectiveness of DeepRing in improving accuracy and enhancing model performance across various datasets. However, further development and validation are essential for successful model implementation, further development and validation are essential for successful model implementation. Conclusions: Introduces DeepRing as an innovative solution to address key challenges in predictive modeling, specifically focusing on transparency issues, susceptibility to data manipulation, and fairness concerns. By combining Convolutional Neural Networks (CNNs) with blockchain technology, DeepRing aims to elevate prediction integrity, enhance data security, and promote fairness, thereby contributing to the improvement of ethical considerations, reliability, and accountability in predictive modelling.

Developing an entrustable professional activity for providing health education and consultation in occupational therapy and examining its validity

BackgroundEntrustable Professional Activities (EPA)-based assessment is easily and intuitively used in evaluating the learning outcomes of competency-based medical education (CBME). This study aimed to develop an EPA for occupational therapy focused on providing health education and consultation (TP-EPA3) and examine its validity.MethodsNineteen occupational therapists who had completed online training on the EQual rubric evaluation participated in this study. An expert committee identified six core EPAs for pediatric occupational therapy. TP-EPA3 was developed following the EPA template and refined through consensus meetings. The EQual rubric, a 14-item, five-point criterion-based anchor system, encompassing discrete units of work (DU), entrustable, essential, and important tasks of the profession (EEIT), and curricular role (CR), was used to evaluate the quality of TP-EPA3. Overall scores below 4.07, or scores for DU, EEIT, and CR domains below 4.17. 4.00, and 4.00, respectively, indicate the need for modifications.ResultsThe TP-EPA3 demonstrated good validity, surpassing the required cut-off score with an average overall EQual score of 4.21 (SD = 0.41). Specific domain scores for DU, EEIT, and CR were 3.90 (SD = 0.69), 4.46 (SD = 0.44), and 4.42 (SD = 0.45), respectively. Subsequent revisions clarified observation contexts, enhancing specificity and focus. Further validation of the revised TP-EPA3 and a thorough examination of its reliability and validity are needed.ConclusionThe successful validation of TP-EPA3 suggests its potential as a valid assessment tool in occupational therapy education, offering a structured approach for developing competency in providing health education and consultation. This process model for EPA development and validation can guide occupational therapists in creating tailored EPAs for diverse specialties and settings.

Comparative assessment of heel rise detection for consistent gait phase separation

BackgroundAccurate identification of gait events is crucial to reliable gait analysis. Heel rise, a key event marking the transition from mid-stance to terminal stance, poses challenges in precise detection due to its gradual nature. This leads to variability in accuracy across studies utilizing diverse measuring techniques. Research questionHow do different HR detection methods compare when assessed against the underlying heel motion pattern and visual detection across varying speed, footwear conditions, and individuals? MethodsLeveraging data from over 10,000 strides in diverse scenarios with 15 healthy subjects, we evaluated methods based on measurements from optical motion capture (OMC), force plates, and shank-mounted inertial measurement units (IMUs). The evaluation of these methods included an assessment of their precision and consistency with the heel marker's motion pattern and agreement with visually detected heel rise. ResultsOMC-based heel rise detection methods, utilizing the heel marker's vertical acceleration and jerk, consistently identified the same point in the heel motion pattern, outperforming velocity-based methods and our new position-based method resembling traditional footswitch-based heel rise detection. Variability in velocity and position-based methods derives from subtle heel rise variations after mid-stance, exhibiting individual differences. Our proposed IMU-based methods show promise by closely matching OMC-based accuracy. SignificanceThe results have significant implications for gait analysis, providing insights into heel rise event detection's complexities. Accurate HR identification is crucial for gait phase separation, and our findings, especially with the robust heel marker's jerk-based method, enhance precision and consistency across walking conditions. Moreover, our successful development and validation of IMU-based algorithm offer cost-effective and mobile alternative for HR detection, expanding their potential use in comprehensive gait analysis.

Enhancing UAV‐HetNet security through functional encryption framework

SummaryIn the current landscape, the rapid expansion of the internet has brought about a corresponding surge in the number of data consumers. As user volume and diversity have escalated, the shift from conventional, uniform networks to Heterogeneous Networks (HetNets) has emerged. HetNets are designed with a primary objective: enhancing Quality of Service (QoS) standards for users. In the context of HetNets facilitated by Unmanned Aerial Vehicles (UAVs), a substantial influx of users and devices is observed. Within this multifaceted environment, the potential for malicious intruder nodes to efficiently execute and propagate harmful actions across the network is a distinct concern. Consequently, the entirety of network communication becomes susceptible to a multitude of security threats. To address these vulnerabilities and safeguard communication, the Functional Encryption (FE) technique is employed. FE empowers the protection of data against intrusion attacks. This paper presents a comprehensive methodology for implementing FE within UAV‐integrated HetNets, executed in two sequential phases. The initial phase secures communication between User Equipment (UE) and Micro Base Station (MBS), followed by the second phase, which focuses on securing communication among MBS and UAV. The viability of the proposed approach is substantiated through validation using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The validation process involves the development of High‐Level Protocol Specification Language (HLPSL) codes. The successful security validation outcome underscores the capacity of the proposed methodology to provide the intended security measures and robustness to the network environment.

Flight verification of cooling self-sustaining high-temperature superconducting motor

The global shift towards sustainable development and technological advancements has propelled the energy transition trend. Recognizing the substantial environmental impact of conventional commercial airplanes, there is a growing urgency to develop a sophisticated superconducting motor system for commercial aviation. The advent of high-temperature superconducting motors presents a transformative leap, offering significant advantages in power density and efficiency when compared to traditional motors. To validate the issues that future liquid-hydrogen superconducting electric airplanes may encounter, a kilowatt-class aerospace high-temperature superconducting motor is designed. Based on the requirements of airborne applications, critical parameters such as electromagnetic characteristics, operating characteristics, and AC losses have been analyzed. Furthermore, extensive research and testing have been conducted on the superconducting motor magnet, leading to the successful assembly of a prototype. The superconducting motor has a rated output power of 2.7 kW and a rated speed of 5000 rpm. Rigorous ground operation performance tests have also been conducted to ensure the feasibility and reliability of the motor in practical applications. Benefiting from the topological structure design, the superconducting motor has an excellent sealing performance at low temperatures. The superconducting motor can maintain low temperature and high vacuum for a long time, when the vacuum pump is removed and the liquid nitrogen inlet is closed after the motor is completely cooled. The culmination of these endeavors is the realization of a successful flight validation of an unmanned aerial vehicle equipped with a high-temperature superconducting motor, demonstrating a sustained flight of nearly one hour.

A novel efficient three-stage electrochemical pre-lithiation method for the amorphous carbon negative electrodes of lithium-ion capacitors

Electrochemical pre-lithiation stands as a practical and widely adopted technique for precisely controlling the degree of pre-lithiation, but the process is usually time-consuming. Herein, we introduce a novel three-stage electrochemical pre-lithiation method, comprising an initial stage of a high current density, a second stage of a low current density, and an additional stage of constant voltage to ensure the complete formation of solid electrolyte interphase (SEI) on the amorphous carbons: soft carbon (SC) and hard carbon (HC). Lithium-ion capacitors (LICs) are configured with the pre-lithiated SC or HC as the negative electrode and activated carbon as the positive electrode to assess the efficacy and adaptability of this three-stage pre-lithiation approach. Our findings demonstrate that this method can reduce the pre-lithiation time from 1114 to 604 min for SC, and from 1913 to 1080 min for HC, achieving nearly 46 % and 44 % decreases, respectively. Furthermore, the three-stage pre-lithiation method can simultaneously increase reversible Li-ion storage capacity within the same operating potential window, thereby enhancing the electrochemical performance of the LIC cells. Specifically, the LIC cell employing the three-stage pre-lithiated SC exhibits an energy density of 96.5 Wh kg-1 at 0.15 kW kg-1 and 76.5 Wh kg-1 at 5.98 kW kg-1. These cells demonstrate remarkable reversibility, retaining 89.6 % charge capacity after 10,000 cycles. Similarly, the LIC with the three-stage pre-lithiated HC achieves even higher energy densities of 105.3 Wh kg-1 at 0.15 kW kg-1 and 86.42 Wh kg-1 at 5.98 kW kg-1 with a retention of 85.2 % after 5000 cycles. The successful implementation and validation of this three-stage pre-lithiation method pave the way for further development of LICs.

Aroma Substances Generation and Variation of Broth in Pork Braised Processing based on Multi-Instrumental and Multiplex Analysis

The aim was to investigate and reveal the patterns of production and changes of major aroma substances in broth during quantitative repeated cycles of cooking and the effects of meat and spices on the formation of unique aroma in broth. Aroma substances were systematically identified and quantified for three groups (MS, broth cooked with meat and spices; M, broth cooked with meat; and S, broth cooked with spices) by using GC-MS, E-nose and tri-step infrared spectroscopy. The PCA model of M group E-nose showed that the total content of aroma substances reached the maximum value in the broth samples cooked for the third time. Meanwhile, the GC-MS results showed that benzaldehyde in the spices completely disappeared in the 4th brining, esters such as linalyl propionate and isopropyl myristate disappeared after the 5th and 6th brining, ketones basically disappeared in the 6th brining, and the relative content of alcohols in M group was highest in the 4th brining. Successful validation of the generation and variation of aroma substances by E-nose and GC-MS through tri-step infrared spectroscopy technique with increasing resolution. Therefore, the quality of braised meat products can be improved by replenishing the meat and spices after the 3th brining.

A Minimal Approach to Demonstrate Concordance of Digital and Conventional Microscopy in Toxicologic Pathology.

Digitalization of pathology workflows has undergone a rapid evolution and has been widely established in the diagnostic field but remains a challenge in the nonclinical safety context due to lack of regulatory guidance and validation experience for good laboratory practice (GLP) use. One means to demonstrate that digital slides are fit for purpose, that is, provide sufficient quality for pathologists to reach a diagnosis, is conduction of comparison studies, which have been published both, for veterinary and human diagnostic pathology, but not for toxicologic pathology. Here, we present an approach that uses study material from nonclinical safety studies and that allows for the statistical comparison of concordance rates for glass and digital slide evaluation while minimizing time and effort for the involved personnel. Using a benchmark study design, we demonstrate that evaluation of digital slides fits the purpose of nonclinical safety evaluation. These results add to reports of successful workflow validations and support the full adaptation of digital pathology in the regulatory field.

Cancer clinical trial participation in socioeconomically vulnerable patients: A risk model to aid in targeted interventions.

1529 Background: In patients with cancer, those with lower income are 30% less likely to participate in clinical trials. Lower income individuals face direct and indirect costs that can make participation in a clinical trial prohibitive. However, a broader evaluation of specific behavioral, information, insurance, and area-level variables that could contribute to this disparity has not been conducted. We aimed to develop and validate a model to identify socioeconomically vulnerable patients at high risk of non-participation in clinical trials. Methods: We used data from the Health Information National Trends Survey (HINTS), a national cross-sectional survey about knowledge of, attitudes toward, and use of health-related information. We analyzed HINTS survey databases that included questions about whether patients with cancer participated in a clinical trial (survey years 2014, 2017, and 2020). We examined 21 different demographic, socioeconomic, behavioral, geographic, and health information questions. We derived a risk model to predict clinical trial participation in a random set of 60% of participants using best subset selection with k-fold cross validation. The derived model was validated in the remaining 40% of participants. Logistic regression was used. Results: We examined N=1,023 participants with household income &lt;$75,000, the current median in the U.S. In the training dataset of n=614 participants, a model with 5 variables was identified. Non-Hispanic White patients and patients without a college education, with high levels of distrust, with high levels of anxiety or depression, and from non-urban areas were all at lower risk of trial participation. We summed the adverse risk factors for all individuals; a risk score with 4 levels was constructed based on distribution quartiles. In the independent validation cohort (n=409), each increase in level of adverse risk factors was associated with a 42% reduction in the odds of trial participation (OR=0.58, 95%-CI, 0.40-0.84, p=.004), indicating successful model validation. Among all individuals, trial participation rates decreased from 18.6% to 7.5% to 4.6% to 2.8%, respectively, as the number of adverse risk factors increased (in quartiles) from 0-1 to 2 to 3 to 4-5. Individuals with 4 or 5 risk factors (vs. those with 0-1 factors) were 87% less likely to participate (OR=0.13, 95% CI, 0.05-0.31, p&lt;.0001). Conclusions: We developed and validated a 5-variable risk model that identified a large set of lower income individuals at lower risk of trial participation. In a first-time observation, psychosocial variables were shown to be meaningful predictors of lower trial participation. These findings could aide in the early identification of patients who may benefit from additional support to navigate the treatment trial decision making process, in the name of more equitable participation in trials for all patients.

Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial

Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients. The algorithm is validated on a dataset of 350 patients, achieving an area under the curve of 0.75, specificity of 31.8% at 88.7% sensitivity, and projected 28.7% reduction in molecular testing. We successfully deploy the system in a non-interventional study comprising 89 global study clinical sites and demonstrate its potential to prioritize/deprioritize molecular testing resources and provide substantial cost savings in the drug development and clinical settings.

A modular strategy for the testing and assessment of non-genotoxic carcinogens

A modular strategy is described for the testing and assessment (MoSt) of non-genotoxic carcinogenicity (NGTxC) that is suitable for regulatory applications. It utilizes and builds upon work conducted by the OECD expert group on NGTxC. The approach integrates relevant test methods from the molecular- to cellular- and further to tissue level, many of which have been recently reviewed. Six progressive modules are included in the strategy. Advice is provided for the iterative selection of the next appropriate test method within each step of the strategy. Assessment is completed by a weight of evidence conclusion, which integrates the different streams of modular information. The assessment method gives higher weight to findings that are mechanistically linked with biological relevance to carcinogenesis. With a focus on EU-REACH, and pending upon successful test method validation and acceptance, this will also enable the MoSt for NGTxC to be applied for regulatory purposes across different regulatory jurisdictions.

Observer 8: Outliers, Attention, and Situated Knowledge in a Qualitative Behavioral Assessment of Laboratory Mouse Welfare

This article explores how an innovative animal welfare methodology (Qualitative Behavior Assessment) negotiates subjectivism and objectivism in its distinctive epistemology, as it strives to produce a certain kind of laboratory mouse—a complex, social subject. Through an ethnographic study of the development of a Qualitative Behavior Assessment (QBA) tool for laboratory mouse welfare, I show how QBA foregrounds the animal’s lived emotional experience by using qualitative language to assess their welfare, while also relying on statistical methods of validation. Drawing on Mol et al.’s understanding of care as something that parses, handles, and balances diverse “goods,” I argue that QBA practitioners’ care for the data must balance competing priorities and values. I take particular interest in what makes a “good” assessor as they transform between subject and object. When two observers are found to be outliers, with their divergent judgments marring the successful statistical validation of the QBA mouse tool, the situated nature of knowledge is brought to the fore. I argue that turning to the embodied practice of attention, as distinct from care, helps us understand why, and raises questions about the epistemic culture of conventional animal welfare science and the extent to which the human observer risks reification within QBA’s formal methodological practice.

Modeling Irrigation of Tomatoes with Saline Water in Semi-Arid Conditions Using Hydrus-1D

In arid and semi-arid regions like Tunisia, irrigation water is typically saline, posing a risk of soil and crop salinization and yield reduction. This research aims to study the combined effects of soil matric and osmotic potential stresses on tomato root water uptake. Plants were grown in pot and field experiments in loamy-clay soils and were irrigated with three different irrigation water qualities: 0, 3.5, and 7 dS/m. The Hydrus-1D model was used to simulate the combined dynamics of subsurface soil water and salts. Successful calibration and validation of the model against measured water and salt profiles enabled the examination of the combined effects of osmotic and matric potential stresses on root water uptake. Relative yields, indirectly estimated from actual and potential transpiration, indicated that the multiplicative stress response model effectively simulated the measured yields and the impact of saline water irrigation on crop yields. The experimental and modeling results provide information to aid in determining the salinity levels conducive to optimal crop growth. The findings indicate that the selected salinity levels affect tomato growth to varying degrees. Specifically, the salinity levels conducive to optimal tomato growth were between 0 and 3.5 dS/m, with a significant growth reduction above this salinity level. The gradual salinization of the root zone further evidenced this effect. The scenario considering a temperature increase of 2 °C had no significant impact on crop yields in the pot and field experiments.