Screening Model Research Articles

Hospital-based health technology assessment of a screening rapid test MTBI (GFAP and UCH-L1 blood biomarkers) for mild traumatic brain injury.

The assessment of technology in hospital settings is a crucial step towards ensuring the delivery of efficient, effective, and safe healthcare. This study conducts a Hospital-Based Health Technology Assessment to evaluate the efficacy of a screening rapid test for mild Traumatic Brain Injury (mild TBI) utilizing blood biomarkers, specifically Glial Fibrillary Acidic Protein (GFAP) and Ubiquitin C-terminal Hydrolase L1 (UCH-L1). The assessment focuses on the clinical utility and performance characteristics of the proposed rapid test within a hospital setting. The screening model was meticulously examined for its ability to accurately detect mild TBI, considering the sensitivity and specificity of GFAP and UCH-L1 as blood biomarkers. The study involved a thorough evaluation of the test's diagnostic accuracy, comparing its outcomes with established standards for mild TBI diagnosis.Results from the Hospital-Based Health Technology Assessment highlight the potential of the GFAP and UCH-L1 blood biomarker-based rapid test as an efficient screening tool for mild TBI within a hospital environment. The evidence results show that the test is highly sensitive (91 percent to 100 percent) for the prediction of acute traumatic intracranial lesions, which helps rule out injury when the result is negative. When used within 12hours of injury in adult patients with mild TBI, this test holds promise in reducing the utilization of CT. The findings contribute valuable insights into the feasibility and reliability of implementing this technology for timely and accurate identification of mild TBI, enhancing clinical decision making and patient care in hospital settings.

The fruit fly Drosophila melanogaster as a screening model for antiseizure medications

ObjectiveResistance to antiseizure medications (ASMs) is a major challenge in the treatment of patients with epilepsy. Despite numerous newly marketed ASMs, the proportion of drug-resistant people with epilepsy has not significantly decreased over the years. Therefore, novel and innovative seizure models for preclinical drug screening are highly desirable. Here, we explore the efficacy of a broad spectrum of ASMs in suppressing seizure activity in two established Drosophila melanogaster bang-sensitive mutants. These mutants respond with seizures to mechanical stimulation, providing a promising platform for screening novel ASMs.MethodsSeven frequently used ASMs (brivaracetam, cenobamate, lacosamide, lamotrigine, levetiracetam, phenytoin, and valproate) were administered to the bang-sensitive mutants easily shocked2F (eas2F) and paralyticbss1 (parabss1). After 48 h of treatment, the flies were vortexed to induce mechanical stimulation. The seizure probability (i.e., ratio of seizing and non-seizing flies) as well as the seizure duration were analyzed.ResultsIn case of eas2F mutants, treatment with the sodium channel blockers phenytoin and lamotrigine resulted in a robust reduction of seizure probability, whereas flies treated with lacosamide showed a decrease in seizure duration. Treatment with valproate resulted in both a reduction in seizure probability and in seizure duration. In contrast, levetiracetam, brivaracetam and cenobamate had no effect on the bang-sensitive phenotype of eas2F flies. In case of parabss1 flies, none of the tested medications significantly reduced seizure activity, supporting its role as a model of intractable epilepsy.SignificanceOur results show that particularly sodium channel blockers as well as valproate are effective in suppressing seizure activity in the bang-sensitive mutant eas2F. These findings demonstrate the usability of Drosophila for screening drugs with antiseizure properties. Due to fewer ethical concerns, the short life cycle, and low maintenance costs, Drosophila might provide an attractive and innovative high-throughput model for the discovery of novel antiseizure compounds.

Psoriatic arthritis in psoriasis: optimizing the current screening system for psoriatic arthritis based on serum data from U.S. and Chinese populations.

Psoriatic arthritis (PSA) is an inflammatory joint disease associated with psoriasis (PSO) that can be easily missed. Existing PSA screening tools ignore objective serologic indicators. The aim of this study was to develop a disease screening model and the Psoriatic Arthritis Inflammation Index (PSAII) based on serologic data to enhance the efficiency of PSA screening. A total of 719 PSO and PSA patients from the National Health and Nutrition Examination Survey (NHANES) (as training set and test set) and 135 PSO and PSA patients who were seen at The First Affiliated Hospital of Zhejiang Chinese Medical University (as external validation set) were selected, 31 indicators for these patients were collected as potential input features for the model. Least Absolute Shrinkage and Selection Operator (LASSO) was used to identify PSA-related features. Five models of logistic regression (LR), random forest, k-nearest neighbor, gradient augmentation and neural network were developed in the training set using quintuple cross validation. And we developed PSAII based on the results of LASSO regression and weights of logistic model parameters. All performance metrics are derived on the test set and the external validation set. Five variables were selected to build models, including age, lymphocyte percentage, neutrophil count, eosinophilic count, and C-reactive protein. In all established models, the LR model performed the best, with an Area Under Curve (AUC) of 0.87 (95% confidence interval (CI): 0.83-0.90) on the test set; on the external validation set the AUC was 0.82 (95%CI: 0.74-0.90). The PSAII formula was PSAII = percentage of lymphocytes × C-reactive protein/(neutrophil count × eosinophilic count × 10). The AUC of PSAII in the test is 0.93 (95%CI: 0.88-0.97), and the cutoff value is 18. The AUC of the external validation set is 0.81 (95%CI: 0.72-0.89). This study developed and validated five models to assist screening for PSA by analyzing serum data from NHANES and Chinese populations. The LR model demonstrated the best performance. We created PSAII for PSA screening. However, the high false positive rate of PSAII makes it necessary to combine it with other PSA screening tools when applied.

Simple approximate model for pion masses

Closed expressions for the masses of the charged and neutral pions are derived based on a simple approximate model for electrostatic screening when a proton is located adjacent to a neutron. The estimated mass values agree to a few percent of experimentally measured values. The electric dipole moment of the deuteron is also estimated based on the phenomenological toy model used here.

IDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer.

Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting for 15-20% of breast cancer cases. It is challenging due to low therapeutic response, heterogeneity, and aggressiveness. The PI3Ka isoform is a promising therapeutic target, often hyperactivated in TNBC, contributing to uncontrolled growth and cancer cell formation. We have proposed an interpretable deep convolutional neural network prediction (iDCNNPred) system using 2D molecular images to classify bioactivity and identify potential PI3Ka inhibitors. We built Custom-DCNN models and pre-trained models such as AlexNet, SqueezeNet, and VGG19 by using the Bayesian optimization algorithm, and found that our Custom-DCNN model performed better than a pre-trained model with lower complexity and memory usage. All top-performed models were screened with the Maybridge Chemical library to find predictive hit molecules. The screened molecules were further evaluated for protein-ligand interaction with molecular docking and finally 12 promising hits were shortlisted for biological validation using in-vitro PI3K inhibition studies. After biological evaluation, 4 potent molecules with different structural moieties were identified, and these molecules present new starting scaffolds for further improvement in terms of their potency and selectivity as PI3K inhibitors with the help of medicinal chemistry efforts. Furthermore, we also showed the significance of the interpretation and visualization of the model's predictions by the Grad-CAM technique, enhancing the robustness, transparency, and interpretability of the model's predictions. The data and script files and prediction run of models used for this study to reproduce the experiment are available in the GitHub repository at https://github.com/ravishankar1307/iDCNNPred.git .

Integrative Machine Learning, Virtual Screening, and Molecular Modeling for BacA-Targeted Anti-Biofilm Drug Discovery Against Staphylococcal Infections

The rise in antibiotic-resistant Staphylococcal infections necessitates innovative approaches to identify new therapeutic agents. This study investigates the application of machine learning models to identify potential phytochemical inhibitors against BacA, a target related to Staphylococcal infections. Active compounds were retrieved from BindingDB while the decoy was generated from DUDE. The RDKit was utilized for feature engineering. Machine learning models such as k-nearest neighbors (KNN), the support vector machine (SVM), random forest (RF), and naive Bayes (NB) were trained on an initial dataset consisting of 226 active chemicals and 2550 inert compounds. Accompanied by an MCC of 0.93 and an accuracy of 96%, the RF performed better. Utilizing the RF model, a library of 9000 phytochemicals was screened, identifying 300 potentially active compounds, of which 192 exhibited drug-like properties and were further analyzed through molecular docking studies. Molecular docking results identified Ergotamine, Withanolide E, and DOPPA as top inhibitors of the BacA protein, accompanied by interaction affinities of −8.8, −8.1, and −7.9 kcal/mol, respectively. Molecular dynamics (MD) was applied for 100 ns to these top hits to evaluate their stability and dynamic behavior. RMSD, RMSF, SASA, and Rg analyses showed that all complexes remained stable throughout the simulation period. Binding energy calculations using MMGBSA analysis revealed that the BacA_Withanolide E complex exhibited the most favorable binding energy profile with significant van der Waals interactions and a substantial reduction in gas-phase energy. It also revealed that van der Waals interactions contributed significantly to the binding stability of Withanolide E, while electrostatic interactions played a secondary role. The integration of machine learning models with molecular docking and MD simulations proved effective in identifying promising phytochemical inhibitors, with Withanolide E emerging as a potent candidate. These findings provide a pathway for developing new antibacterial agents against Staphylococcal infections, pending further experimental validation and optimization.

Risk factor screening and prediction modeling of gastrointestinal adverse reactions caused by GLP-1RAs

ObjectiveThis study aimed to explore the risk factors for gastrointestinal side effects (GISEs) in patients with type 2 diabetes mellitus (T2DM) during treatment with glucagon-like peptide-1 receptor agonists (GLP-1RAs) based on real-world data and to develop a prediction model for GLP-1RA-related GISEs.MethodsA total of 855 patients who attended the First Affiliated Hospital of Shandong First Medical University from January 2020 to May 2023 were selected as the study participants, who were divided into the training set (598 cases) and the validation set (297 cases) using a simple random sampling method at a ratio of 7:3. The general information and biochemical indicators of the participants were collected to assess the risk factors for GLP-1RA-related GISEs, and multifactorial logistic regression analysis was used to obtain the best predictors. A nomogram prediction model was constructed. The Hosmer–Lemeshow test was used to assess the differentiation and calibration of the nomogram model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the model.ResultsAge, gender, history of gastrointestinal disorders, and number of combined oral medications were found as risk factors for the occurrence of GISEs in patients with T2DM using GLP-1RAs (p < 0.05). The nomogram prediction model based on these four factors had good discriminability (AUC values of the training and validation sets of 0.855 and 0.836, respectively) and accuracy (Hosmer–Lemeshow test: p > 0.05 for the validation set). DCA showed that the prediction model curve had clinical utility in the threshold probability interval of >5%.ConclusionsThe established nomogram model has an excellent predictive effect on GISEs induced by GLP-1RAs in patients with T2DM.

P21 Development of a novel multidisciplinary care model for screening and managing patients with psoriatic arthritis: consensus guidance for primary care providers in Canada

Abstract There is an unmet need for timely assessment and diagnosis of psoriatic arthritis (PsA) in patients with psoriasis (PsO) and a lack of standardized clinical guidance for primary care providers (PCPs) for supporting the screening and management of these patients. Our study aimed to develop consensus guidance for screening and managing PsA in patients diagnosed with PsO, and identify potential roles for pharmacists within the multidisciplinary care team to facilitate timely specialist access. We used a modified DELPHI process to gather insights on screening practices, quality of life (QoL) monitoring, and lifestyle counselling from 11 Key Opinion Leaders across Canada, including pharmacists, rheumatologists, and dermatologists. Key questions that address current care gaps within each category were identified, and the extent of agreement was determined through two rounds of voting. The threshold for designating consensus for each question was ≥75% agreement. After two rounds of voting, the following recommendations for the multidisciplinary screening and management of PsA in patients with PsO were identified: (i) screening for PsA should be performed by dermatologists or family physicians; (ii) all patients diagnosed with PsO should be screened every 6–12 months; (iii) screening should rely on clinical judgement with a focus on swollen and tender joints, though validated screening tools can be used in the absence of adequate clinical judgement, which can be administered via virtual/telephone appointments; (iv) patients flagged through virtual screening should have an in-person follow-up assessment with their PCP or dermatologist, followed by referral to a rheumatologist if deemed necessary; (v) dermatologists and rheumatologists should be responsible for monitoring QoL in patients with PsO and PsA, respectively, with the method of assessment at the discretion of the responsible HCP; (vi) pharmacists are best positioned to review adherence to biologic therapy at each medication refill, manage injection site reactions, monitor drug safety, and assist with navigating reimbursement; and (vii) pharmacists should counsel patients on health and lifestyle factors such as smoking, alcohol consumption, and maintaining healthy body weight, advise patients on all appropriate vaccines, and counsel patients on the importance of staying up to date on vaccinations. This consensus-derived guidance provides a valuable and novel framework for the multidisciplinary management of PsA in Canada, with pharmacists being uniquely positioned to help address some of the current gaps in care. Implementing these best practices has the potential to reduce delays in diagnosing and initiating treatment for PsA in patients with PsO and improve overall patient ­outcomes.

From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings.

This article includes a literature review and a case study of artificial intelligence (AI) heart murmur detection models to analyse the opportunities and challenges in deploying AI in cardiovascular healthcare in low- or medium-income countries (LMICs). This study has two parallel components: (1) The literature review assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- and low-income countries. Reasons for the limited deployment of machine learning models are discussed, as well as model generalisation. Moreover, the literature review discusses how emerging human-centred deployment research is a promising avenue for overcoming deployment barriers. (2) A predictive AI screening model is developed and tested in a case study on heart murmur detection in rural Brazil. Our binary Bayesian ResNet model leverages overlapping log mel spectrograms of patient heart sound recordings and integrates demographic data and signal features via XGBoost to optimise performance. This is followed by a discussion of the model's limitations, its robustness, and the obstacles preventing its practical application. The difficulty with which this model, and other state-of-the-art models, generalise to out-of-distribution data is also discussed. By integrating the results of the case study with those of the literature review, the NASSS framework was applied to evaluate the key challenges in deploying AI-supported heart murmur detection in low-income settings. The research accentuates the transformative potential of AI-enabled healthcare, particularly for affordable point-of-care screening systems in low-income settings. It also emphasises the necessity of effective implementation and integration strategies to guarantee the successful deployment of these technologies.

Antiedematogenic and Anti-Inflammatory Effects of the Essential Oil from the Fruits of Piper tuberculatum Jacq. (Piperaceae) in Animal Models.

Piper tuberculatum, traditionally known in Brazil as 'pimenta-d'arda,' 'pimenta-longa,' or 'pimenta de macaco,' has been used in Brazilian folk medicine to treat inflammatory symptoms. Considering this species' significant essential oil content, the present study aimed to evaluate the anti-edematogenic and anti-inflammatory effects of the crucial oil Piper tuberculatum (EOPT) in vivo. To this end, Swiss mice (Mus musculus) of both sexes were treated orally with the EOPT at 50, 100, and 250 mg/Kg. The rotarod and open field evaluated the potential activity in the central nervous system. At the same time, formalin and abdominal writhing tests were carried out to perform the pharmacological screening of the essential oil. Next, the anti-edematogenic effect was assessed using paw edema models induced by carrageenan, dextran, histamine, and arachidonic acid. The anti-inflammatory activity was then characterized in peritonitis (acute) and granuloma (chronic) models. The EOPT treatment at the highest dose (250 mg/kg) showed no indication of central activity. All the EOPT doses (50, 100, and 250 mg/kg) had analgesic effects associated with anti-inflammatory mechanisms in screening models. Accordingly, the treatment (EOPT 100 mg/Kg) inhibited the inflammatory process in acute and chronic models. In conclusion, EOPT has analgesic, antiedematogenic, and anti-inflammatory effects, highlighting its potential use in developing anti-inflammatory drugs.

Determining the Educational Needs of Students Regarding the Use of Augmented Reality Applications in Museums

<p dir="ltr"><span>In recent years, augmented reality (AR) applications, which have emerged as an indispensable technological element in many fields, have also begun to be used as an important learning tool in museum environments. It is seen that AR applications, which enable permanent learning especially by using technology, have positive effects on students. In this context, the aim of this research is to determine the educational needs of students regarding the use of augmented reality applications in museums. The research was conducted using the screening model from quantitative method designs. The study group consists of university students (n=99). In the spring semester of 2023-2024, students taking the museum education applications course and the Digital Literacies and Ethics course were determined from purposeful samples using the criterion sampling method. The quantitative data of the study were obtained with a 5-point Likert-type educational needs survey. Quantitative data were interpreted according to the mean score (MEAN) values in the distribution of the survey items. According to the research findings, students need training for the use of Smartify, Museum Alive, Layar, Wonderscope, ARLOOPA, ARKit, Vuforia Engine, ARCore applications. Based on these findings, in future studies, students will be asked to design augmented reality applications for museums by providing training on the relevant applications.</span></p>

Zebrafish model for functional screening of flow-responsive genes controlling endothelial cell proliferation

Local haemodynamics control arterial homeostasis and dysfunction by generating wall shear stress (WSS) which regulates endothelial cell (EC) physiology. Here we use a zebrafish model to identify genes that regulate EC proliferation in response to flow. Suppression of blood flow in zebrafish embryos (by targeting cardiac troponin) reduced EC proliferation in the intersegmental vessels (ISVs) compared to controls exposed to flow. The expression of candidate regulators of proliferation was analysed in EC isolated from zebrafish embryos by qRT-PCR. Genes shown to be expressed in EC were analysed for the ability to regulate proliferation in zebrafish vasculature exposed to flow or no-flow conditions using a knockdown approach. wnk1 negatively regulated proliferation in no-flow conditions, whereas fzd5, gsk3β, trpm7 and bmp2a promoted proliferation in EC exposed to flow. Immunofluorescent staining of mammalian arteries revealed that WNK1 is expressed at sites of low WSS in the murine aorta, and in EC overlying human atherosclerotic plaques. We conclude that WNK1 is expressed in EC at sites of low WSS and in diseased arteries and may influence vascular homeostasis by reducing EC proliferation.

Assessment of CPME as Sustainable Low VOC Alternative to Hexane: Optimization of Extraction Efficiency and Bioactive Compound Yield from Fenugreek Seed Oil Using Computational and Experimental Methods.

This study investigates the performance of cyclopentyl methyl ether (CPME) in the extraction of fenugreek seed oil, aiming to replace the conventionally used hexane. The efficiency of this alternative solvent was evaluated first through in silico methods (based on Hansen Solubility Parameters (HSPs) and Conductor-like Screening Model for Real Solvent (COSMO-RS) simulations), followed by experimental studies. Solubility computational predictions analysis revealed that CPME exhibits superior solvation power compared to hexane. Experimentally, CPME demonstrated a significantly higher oil yield (7.23%) compared to hexane (4.25%) and a better retention of beneficial unsaturated fatty acids than hexane. Additionally, the physicochemical properties of oils extracted with CPME showed enhanced oxidative stability, sterol, tocopherol, and phenolic contents, leading to superior antioxidant and antibacterial activities. Importantly, CPME's low volatile organic compound (VOC) emissions further establish it as a more sustainable and environmentally friendly alternative to hexane, aligning with contemporary goals of reducing harmful emissions in extraction processes. Thus, this paper highlights the functional advantages of CPME, focusing on its efficiency, selectivity, and enhanced retention of bioactive compounds, positioning it as a superior extraction solvent for fenugreek seed oil compared to hexane.

Investigating the correlation between second grade primary school students fluent reading skills and parental involvement in home literacy activities

To stay up-to-date with the rapid changes in technology and science in today's world, reading skills are still essential. Reading skills, in addition to being a means of acquiring knowledge in social life, have a key role in success in academic life. The rich knowledge encountered in school life needs to be analyzed, synthesized and understood. Fluent reading skill is directly related to an individual's capacity to make sense of the text, and this skill is one of the cornerstones of children's success in educational life. Primary school is a period in which children acquire reading and writing skills and progress by building on those skills. The support that parents provide by participating in literacy activities at home in this process plays a crucial role in the development of their children's fluent reading skills. In this context, the comparative study of parental involvement in literacy activities with fluent reading skills at primary school level has formed a significant area of research. The aim of the research is to investigate the relationship between the fluent reading skills of the students studying in the second grade and the level of parental involvement in home literacy activities. One of the quantitative research methods, the relational screening model was conducted in the research. Participants are second-grade students who are studying in two different primary schools in the province of Ordu and their parents. In data collection, the speed and accuracy components of the students from fluent reading components were evaluated and the parental involvement levels were examined using the “Parental Involvement in Home Literacy Activities” scale. The findings from the analysis of the data were discussed in terms of the fluent reading skill levels of primary school second-grade students and parental involvement level in home literacy activities.

Stepwise identification of prodromal dementia: Testing a practical model for primary care.

Prodromal dementia is largely underdiagnosed in primary care. To develop a clinical model for detecting prodromal dementia within the operative boundaries of primary care practice. The study employed the Functional Activities Questionnaire (FAQ) and Montreal Cognitive Assessment (MoCA) to evaluate a "functional-cognitive" step-down screening model, in which the MoCA is administered subsequent to reported symptoms on the FAQ. It classified participants from the Alzheimer's Disease Imaging Initiative to three diagnostic categories: (1) healthy cognition (n = 396), (2) mild cognitive impairment without conversion (n = 430), and (3) prodromal dementia assessed 24 months before diagnosis (n = 164). Analyses indicated that the step-down model (Model 1) performed significantly better than an alternative model that applied the FAQ as a single measure (Model 2) and compared well with another model that administered both screening measures to all participants (Model 3). Gradient Boosting Trees classifications yielded the following estimations for Model 1/Model 2/ Model 3, respectively: Sensitivity = 0.87/0.77/0.89, Specificity = 0.68/0.47/0.70, PPV = 0.73/0.40/0.75, NVP = 0.84/0.81/0.87, F1 Score = 0.79/0.52/0.81, AUC = 0.78/0.67/0.79. These analyses support the proposed model. The study offers algorithms for validated measures, which were developed from a well characterized clinical sample. Their accuracy will likely improve further with new data from diverse clinical settings. These results can serve primary care in a timely manner in light of the recent advances in pharmacological treatment of dementia and the expected increase in demand for screening.

Heavy Metals Screening Model in Primary Care: Experience in the Sierra Minera de Cartagena (Spain)

Soils contaminated by heavy metals such as lead, cadmium, and arsenic represent a significant health risk. The Sierra Minera of Cartagena (Spain) is an area historically contaminated by mining activities. This study evaluates the exposure to heavy metals and proposes a clinical screening model for its management in primary care. Descriptive cross-sectional study conducted between 2017 and 2020 with volunteers from the Sierra Minera of Cartagena who provided blood and urine samples. Primary care health professionals were trained in sample collection and analysis, risk communication, and clinical protocols on heavy metals were implemented. 203 participants, 66.5% women and 38 (18.7%) under 16 years old. The majority resided in Zone 0 (contaminated area). Mean blood lead level was 1.78μg/dl and 2.22μg/dl in those under 16 years old, with. Metal concentrations, particularly lead, increased with age, male sex, Arab ethnicity, and proximity to contaminated areas. Tobacco smoke was identified as a main source of lead exposure in children under 16 years. The primary care clinical screening model identified 12 (7%) and 22 (11%) participants exceeding 5μg/dl and 3.5μg/dl respectively, particularly six children and one pregnant woman, with significant levels that normalized within 2-3 months following PEHSU's clinical guidelines. The implementation of clinical and analytical screening for heavy metals in primary care, supported by pediatric environmental health units (PEHSU), proved effective in screening and reducing in children blood lead levels in a short period. Training health professionals is crucial to adequately address environmental risks and protect the health of affected populations.

472 Construction of spheroids from melanocytes of different ethnic origins: screening model and variation

472 Construction of spheroids from melanocytes of different ethnic origins: screening model and variation

Evaluating the Influence of Clinical Data on Inter-Observer Variability in Optic Disc Analysis for AI-Assisted Glaucoma Screening.

This study aims to evaluate the inter-observer variability in assessing the optic disc in fundus photographs and its implications for establishing ground truth in AI research. Seventy subjects were screened during a screening campaign. Fundus photographs were classified into normal (NL) or abnormal (GS: glaucoma and glaucoma suspects) by two masked glaucoma specialists. Referrals were based on these classifications, followed by intraocular pressure (IOP) measurements, with rapid decisions simulating busy outpatient clinics.In the second stage, four glaucoma specialists independently categorized images as normal, suspect, or glaucomatous. Reassessments were conducted with access to IOP and contralateral eye data. In the first stage, the agreement between senior and junior specialists in categorizing patients as normal or abnormal was moderately high. Knowledge of IOP emerged as an independent factor influencing the decision to refer more patients. In the second stage, agreement among the four specialists varied, with greater concordance observed when additional clinical information was available. Notably, there was a statistically significant variability in the assessment of optic disc excavation. The inclusion of various risk factors significantly influences the classification accuracy of specialists. Risk factors like IOP and bilateral data influence diagnostic consistency among specialists. Reliance solely on fundus photographs for AI training can be misleading due to inter-observer variability. Comprehensive datasets integrating multimodal clinical information are essential for developing robust AI models for glaucoma screening.

Automated non-invasive laser speckle imaging of the chick heart rate and extraembryonic blood vessels and their response to Nifedipine and Amlodipine drugs.

Using our recently developed laser speckle contrast imaging (LSCI) to visualize blood vessels and monitor blood flow noninvasively, we test the utility of the developing chick heart as a functional model for drug screening. To this end, we examined the effects of antihypertensive agents Nifedipine and Amlodipine, belonging to the L-type calcium channel antagonist family, on blood flow visualized noninvasively through the intact shell. Guided by the live view mode, the drugs were injected through the shell and ventral to HH16-19 chick embryos. Our results show a significant reduction in the chick's heart rate, blood flow, and vascular size within 5-20min after Nifedipine or Amlodipine injection. For moderate Nifedipine concentrations, these parameters returned to initial values within 2-3h. Nifedipine showed a rapid reduction in heart rate and blood flow dynamics at a concentration ten times lower than Amlodipine. These findings show that our LSCI system can monitor and distinguish the chick heart's response to injected drugs from the same family. This serves as proof-of-concept, paving the way for a rapid, cost-effective, and quantitative test system for screening drugs that affect the cardiovascular system of live chick embryos. Live noninvasive imaging may also provide insights into the development and functioning of the vertebrate heart.

Investigating the relationship between mathematics motivation and mathematics anxiety in 4th grade primary students with and without a diagnosis of giftedness

The aim of this study is to examine the relationship between mathematics motivation and mathematics anxiety in 4th grade primary school students who are diagnosed as gifted and those who are not, in terms of some variables. A descriptive and relational screening model was used in the study. The sample of the study consisted of 500 students, 250 of whom were diagnosed as gifted (SAC students) and 250 of whom were not diagnosed as gifted, selected from different regions of Turkey. The Mathematics Motivation Scale and the Mathematics Anxiety Scale were used as data collection instruments. As a result of the analyses, a significant negative relationship was found between mathematics anxiety and intrinsic motivation, extrinsic motivation and total motivation, and a significant positive relationship was found between mathematics anxiety and motivation. In the regression analysis it was found that all motivation sub-dimensions predicted mathematics anxiety. There was no significant relationship between mathematics motivation and mathematics anxiety in 4th grade female students with and without giftedness. A significant negative relationship was found between mathematics anxiety and intrinsic motivation, extrinsic motivation and total motivation in 4th grade male students with and without giftedness. A positive significant relationship was found between maths anxiety and motivation. When the results of the regression analysis were analyses, it was found that all motivation sub-dimensions predicted mathematics anxiety. A significant difference was found in the sub-dimensions of intrinsic motivation and extrinsic motivation of the participating students' mathematics motivation, but no significant difference was found in the sub-dimension of motivation. There was a significant difference between the mathematics anxiety levels of 4th grade students diagnosed as gifted and those not diagnosed as gifted.