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Abstract Introduction Most studies on body composition in kidney cancer have been conducted among patients with metastatic disease. Given that aggressive tumours can adversely impact body composition and even non‐metastatic tumours can be aggressive, we evaluated associations between pre‐surgical body composition features and tumour pathological features in patients with non‐metastatic clear cell renal cell cancer (ccRCC). Methods The Resolve Cohort consists of 1239 patients with non‐metastatic ccRCC who underwent nephrectomy at Memorial Sloan Kettering Cancer Center between 2000 and 2020. The cross‐sectional areas and radiodensities of skeletal muscle, visceral adipose, and subcutaneous adipose tissues were determined from pre‐surgical computed tomography (CT) scans at the third lumbar vertebrae using Automatica software. Pearson's correlation coefficients describe inter‐relationships among BMI and body composition variables, while odds ratios (OR) and 95% confidence intervals (CI) estimate associations between continuous body composition features (per 1‐standard deviation) and advanced stage (Stage III vs. Stages I–II) and high Fuhrman grade (Grades 3–4 vs. 1–2) from multivariable logistic regression models that considered the potential impact of biological sex, contrast enhanced CTs, and early age at onset of ccRCC. Results The cohort was predominantly male (69%), white (89%), and had a median age of 58. The proportion of patients presenting with advanced stage and high‐grade disease were 31% and 51%, respectively. In models that adjusted for demographics and all body composition variables simultaneously, decreasing skeletal muscle radiodensity (i.e., more fat infiltration) but increasing visceral adipose tissue radiodensity (i.e., more lipid depletion) were associated with advanced tumour features. Per 8.4 HU decrease in skeletal muscle radiodensity, the odds of presenting with advanced stage was 1.61 (95% CI: 1.34–1.93). Per 7.22 HU increase in visceral adipose tissue radiodensity, the odds of presenting with advanced stage was 1.45 (95% CI: 1.22–1.74). Skeletal muscle index (i.e., sarcopenia) was not associated with either tumour feature. Similar associations were observed for Fuhrman grade, a more direct marker of tumour aggressiveness. Associations did not differ by sex, contrast use, or age at onset of ccRCC. Conclusions Lipid infiltrated skeletal muscle, but lipid depleted visceral adipose tissue were independently associated with advanced tumour features in non‐metastatic ccRCC. Findings highlight the importance of evaluating the full range of body composition features simultaneously in multivariable models. Interpreting pre‐surgical CTs for body composition for patients may be a novel and non‐invasive way to identify patients with aggressive renal tumours, which is clinically relevant as renal biopsies are not routinely performed.

Recent advancements in machine learning have played a crucial role in the healthcare industry, particularly in predicting heart disease with assorted datasets. Despite the registration results indicating promising accuracy and resilience in heart disease prediction models, it is still low compared to what we expected. In this article, a new technique has been demonstrated to predict heart disease based on state-of-the-art machine learning with a focus mostly on Hyper Adaboost classifiers. We evaluate the performance of several key machine learning algorithms, including Random Forest, Extra Trees, LightGBM, Decision Tree, and Hyper Adaboost, on well-known heart disease datasets. Our results are mixed, but they suggest that the Hyper Adaboost classifier provides stronger accuracy, with performance metrics consistently exceeding 97%, much better than similar models. This research highlights the promise of newer packages under Hyper for detecting and screening individuals with heart disease early, which can offer insights into advancing methodologies in future studies as well as clinical use. Our technique exhibited strong predictive power, encouraging additional study with a larger and more comprehensive dataset for the validation of our model.

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines (“MRI-QAMPER”, current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER’s functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test–retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

IntroductionMore than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.MethodsTen-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis.ResultsThe UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at

Abstract Little is known about electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case–control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months–1 year, 6 months–3 years, and 6 months–5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

BackgroundCardiovascular diseases contribute to premature mortality globally, resulting in substantial social and economic burdens. The Global Burden of Disease (GBD) Study reported that in 2019 alone, heart attack and strokes accounted for the deaths of 18.6 million individuals. Ischemic heart diseases, including acute myocardial infarction (AMI), accounted for 182 million disability-adjusted life years (DALYs) and it is leading cause of death worldwide.AimThe aim of this study is to present the burden of AMI in Kazakhstan and describe the outcome of hospitalized patients.MethodsThe data of 79,172 people admitted to hospital with ICD-10 diagnosis I21 between 2014 and 2019 was derived from the Unified National Electronic Health System and retrospectively analyzed.ResultsThe majority of the cohort (53,285, 67%) were men, with an average age of 63 (±12) years, predominantly of Kazakh (38,057, 48%) and Russian (24,583, 31%) ethnicities. Hypertension was the most common comorbidity (61,972, 78%). In males, a sharp increase in incidence is present after 40 years, while for females, the morbidity increases gradually after 55. Throughout the observation period, all-cause mortality rose from 101 to 210 people per million population (PMP). In 2019, AMI account for 169,862 DALYs in Kazakhstan, with a significant proportion (79%) attributed to years of life lost due to premature death (YLDs). Approximately half of disease burden due to AMI (80,794 DALYs) was in age group 55–69 years. Although incidence is higher for men, they have better survival rates than women. In terms of revascularization procedures, coronary artery bypass grafting yielded higher survival rates compared to percutaneous coronary intervention (86.3% and 80.9% respectively) during the 5-year follow-up.ConclusionThis research evaluated the burden and disability-adjusted life years of AMI in Kazakhstan, the largest Central Asian country. The results show that more effective disease management systems and preventive measures at earlier ages are needed.

Background: It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly became an epidemic due to the absence of apparent symptoms and causes for patients, confusion appears due to the lack of sufficient laboratory results, and its intelligent algorithms were used to make decisions on clinical outcomes. Methods: This study developed a new framework for medical datasets with high missing values based on deep-learning optimization models. The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN). Results: The experiment results conducted on three medical datasets showed the effectiveness of our hybrid approach and an improvement in accuracy and efficiency since all the evaluation metrics were close to ideal for all deep learning classifiers. We got the best evaluation in terms of accuracy 98%, precession 98.5%, F1-score 98.6%, and ROC Curve (95% to 99%) for the COVID-19 dataset provided by GitHub. The second dataset is also Covid-19 provided by Albert Einstein Hospital with high missing data after applying our approach the accuracy reached more than 91%. Third dataset for Cervical Cancer provided by Kaggle all the evaluation metrics reached more than 95%. Conclusions: The proposed formula for processing this type of data can replace the traditional formats in optimization while providing high accuracy and less time to classify patients. Whereas, the experimental results of our approach, supported by comprehensive statistical analysis, can improve the overall evaluation performance of the problem of classifying medical data sets with high missing values. Therefore, this approach can be used in many areas such as energy management, environment, and medicine.

Addressing the challenges in diagnosing and classifying self-care difficulties in exceptional children’s healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry.

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging–Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

The research aims to disclose academicians' perceptions of "Online Teaching Tools" (OTT) within the UTAUT-2. In the study, a qualitative research method was implemented. The case study pattern has also been used for the study. The study group of the research was determined through maximum variation sampling from purposive sampling methods. Twenty-six academicians working at the faculties of education of two universities in the Western Black Sea Region of Turkey constitute the study group of the study. A semi-structured interview form was employed as a data collection tool. The data were analyzed inductively. MAXQDA 20 program was implemented in data analysis. In terms of performance expectancy, OTT can be integrated into teaching methods and techniques. In terms of effort expectancy, it has also been found that the use of OTT is mostly straightforward, and the fact that it is time-consuming, and challenging does not mean negative and technical problems might occur. Regarding social influence, academicians receive support and advice on using OTT. In terms of facilitating conditions, most academicians perceive themselves as sufficient. In terms of hedonic motivation, academicians are of the opinion that that OTT become enjoyable, primarily if it is used via student participation. It is shown that the difference in the exchange rate within Turkey increases fees as a price value. In further studies, it is possible to refer to the opinions of academicians from various fields based on the relevant theory via the following qualitative and quantitative methods.

The primary purpose of this research was to determine the digital literacy proficiency perceptions of primary school teachers working in Eskisehir and compare them based on certain variables. The mixed method was used as a research method; the data were collected using the Digital Literacy Assessment Scale developed by Acar (2015) for quantitative findings and in-depth interviews for qualitative results. The quantitative study group consisted of 733 primary school teachers. In the qualitative dimension of the research, there were data from 7 primary school teachers who agreed to fill out the structured interview form and then take part in an in-depth interview. Quantitative data of the study were analyzed using SPSS software. Qualitative data were analyzed with the descriptive analysis method and interpreted according to their relationship with the research questions. The result showed that the primary school teachers perceive themselves as highly sufficient in regard to the different competence areas of digital literacy and that they dominate these competence areas to a great extent. There were significant differences between the digital literacy levels of the primary school teachers and their age, gender, educational status, seniority in the profession, personal computer possession, constant internet connection, technologies/practices used in education, and technological education. However, the grade level taught was not an influential factor in their digital literacy levels. Moreover, in-service training should be organized to provide digital literacy, using Web 2.0 tools, and integrating technology into the classroom environment, starting with those who have more service than their years of seniority in the profession.

The acquisition of the competencies of sustainability literacy through education requires an understanding of how sustainability literacy is shaped. In this connection, in the current study, the interaction between the following dimensions of sustainability literacy was investigated; SA (sustainability attitude), SB (sustainability behavior) and SK (sustainability knowledge). The study was conducted as a survey study on university students in the UK and Turkey within the context of a project entitled "Ecological Literacy Education" and supported by TUBITAK (Turkish Scientific and Technological Research Organization). The participants (n:1023) of the study are students attending several departments of Plymouth University in the UK and Mugla Sitki Kocman University in Turkey. The data were collected by using the Scale of Sustainability Literacy and analyzed using AMOS version 24. Although it was understood that there were mutual and positive correlations between the dimensions of sustainability literacy, it was revealed that the correlations between some sub-dimensions of these dimensions showed variation. The results of the study are expected to contribute to revealing the interaction between the dimensions of sustainability literacy in more detail and accordingly to the effectiveness of sustainability education.

This study aims to investigate pre-service teachers' integration of technology and how the integration of technology influences the level of cognitive demands of the mathematics tasks in their mathematics technology activities. The purpose of this study was to investigate the various levels of cognitive demands of mathematical tasks created or modified by pre-service mathematics teachers for technology activities. This study presents case studies of pre-service teachers chosen from a group of participants, and these PSTs come from a variety of backgrounds. Showcase Portfolios and lesson plans were gathered in order to comprehend the selection/creation of mathematical tasks and how they were intended to be implemented in the classroom by PSTs. This study offers suggestions for teacher preparation to integrate technology into mathematics instruction in ways that support students' learning through a review of results, the cognitive demands of mathematical tasks in PSTs' technology activities.

This study examines the effects of digital game-based and different educational programs on the phonological awareness skills of 60-72-month-old children. The study group of the research consists of 60-72-month-old children studying in kindergartens affiliated with primary schools. The study group was divided into three experimental groups and one control group. There were 22 children in experimental group 1, 16 in experimental group 2, 17 in experimental group 3, and 17 in the control group, totaling 72 children. In the study, a phonological awareness education program was applied to experimental group 1, a digital game-supported phonological awareness education program was applied to experimental group 2, a digital game-based phonological awareness education program was applied to experimental group 3, and no intervention was applied to the control group. A quasiexperimental design was used as the research model. The Study used the Early Literacy Skills Assessment Tool (ELSA) and the Early Literacy Test for Preschoolers (ELTKC) as data collection tools. A pre-test was administered to the study and control groups, and after the pre-test, educational programs were applied to the experimental groups for ten weeks. After the educational programs were implemented, a post-test was administered to the experimental and control groups, and a retention test was administered to the experimental groups. According to the results of the study, it was determined that there was a significant difference between the experimental groups and between the experimental groups and the control group.

Medical imaging data annotation is expensive and time-consuming. Supervised deep learning approaches may encounter overfitting if trained with limited medical data, and further affect the robustness of computer-aided diagnosis (CAD) on CT scans collected by various scanner vendors. Additionally, the high false-positive rate in automatic lung nodule detection methods prevents their applications in daily clinical routine diagnosis. To tackle these issues, we first introduce a novel self-learning schema to train a pre-trained model by learning rich feature representatives from large-scale unlabeled data without extra annotation, which guarantees a consistent detection performance over novel datasets. Then, a 3D feature pyramid network (3DFPN) is proposed for high-sensitivity nodule detection by extracting multi-scale features, where the weights of the backbone network are initialized by the pre-trained model and then fine-tuned in a supervised manner. Further, a High Sensitivity and Specificity (HS2) network is proposed to reduce false positives by tracking the appearance changes among continuous CT slices on Location History Images (LHI) for the detected nodule candidates. The proposed method’s performance and robustness are evaluated on several publicly available datasets, including LUNA16, SPIE-AAPM, LungTIME, and HMS. Our proposed detector achieves the state-of-the-art result of 90.6% sensitivity at 1/8 false positive per scan on the LUNA16 dataset. The proposed framework’s generalizability has been evaluated on three additional datasets (i.e., SPIE-AAPM, LungTIME, and HMS) captured by different types of CT scanners.

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.

Abstract Combatting the current global epidemic of obesity requires that people have a realistic understanding of what a healthy body size looks like. This is a particular issue in different population sub-groups, where there may be increased susceptibility to obesity-related diseases. Prior research has been unable to systematically assess body size judgement due to a lack of attention to gender and race; our study aimed to identify the contribution of these factors. Using a data-driven multi-variate decision tree approach, we varied the gender and race of image stimuli used, and included the same diversity among participants. We adopted a condition-rich categorization visual task and presented participants with 120 unique body images. We show that gender and weight categories of the stimuli affect accuracy of body size perception. The decision pattern reveals biases for male bodies, in which participants showed an increasing number of errors from leaner to bigger bodies, particularly under-estimation errors. Participants consistently mis-categorized overweight male bodies as normal weight, while accurately categorizing normal weight. Overweight male bodies are now perceived as part of an expanded normal: the perceptual boundary of normal weight has become wider than the recognized BMI category. For female bodies, another intriguing pattern emerged, in which participants consistently mis-categorized underweight bodies as normal, whilst still accurately categorizing normal female bodies. Underweight female bodies are now in an expanded normal, in opposite direction to that of males. Furthermore, an impact of race type and gender of participants was also observed. Our results demonstrate that perceptual weight categorization is multi-dimensional, such that categorization decisions can be driven by ultiple factors.

Study objective: To determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields. Design/setting: To evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed. Results: Findings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on “machine learning” (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to “artificial intelligence” (n = 4535), “natural language processing” (n = 2608), and “deep learning” (n = 4459). Conclusion: Trends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.

Several observation protocols in different theoretical frameworks and components have been designed and validated by teacher trainers and professional development providers to capture and categorize observational data on the characteristics and level of inquiry in science practices. However, certain constraints limit their wide use, such as the neglect of certain indicators of scientific inquiry, the scoring of just summative goals, or the necessity for extensive observer training. The present study, therefore, aims to propose a tool that assists professional development providers and mentors in diagnosing the closeness of practices to scientific inquiry in the context of various settings, from traditional didactic lectures to scientific inquiry, making it easily operational for self and peer evaluations as well as 360-degree video feedback. The method concentrated on multiple observations provides supportive evidence about the tool's internal consistency coefficient, the relationship between its components, and the inter-rater reliability coefficient between multiple scorers. The results highlight that the tool, which includes the components of course structure, course overview, and teacher-student communication, has the potential to contribute to the growing library of observation protocols.

Early intervention in kidney cancer helps to improve survival rates. Abdominal computed tomography (CT) is often used to diagnose renal masses. In clinical practice, the manual segmentation and quantification of organs and tumors are expensive and time-consuming. Artificial intelligence (AI) has shown a significant advantage in assisting cancer diagnosis. To reduce the workload of manual segmentation and avoid unnecessary biopsies or surgeries, in this paper, we propose a novel end-to-end AI-driven automatic kidney and renal mass diagnosis framework to identify the abnormal areas of the kidney and diagnose the histological subtypes of renal cell carcinoma (RCC). The proposed framework first segments the kidney and renal mass regions by a 3D deep learning architecture (Res-UNet), followed by a dual-path classification network utilizing local and global features for the subtype prediction of the most common RCCs: clear cell, chromophobe, oncocytoma, papillary, and other RCC subtypes. To improve the robustness of the proposed framework on the dataset collected from various institutions, a weakly supervised learning schema is proposed to leverage the domain gap between various vendors via very few CT slice annotations. Our proposed diagnosis system can accurately segment the kidney and renal mass regions and predict tumor subtypes, outperforming existing methods on the KiTs19 dataset. Furthermore, cross-dataset validation results demonstrate the robustness of datasets collected from different institutions trained via the weakly supervised learning schema.

Background: COVID-19 (Coronavirus Disease-2019) is a pandemic disease, infecting more than 26.5 million people. Since there is no specific and effective treatment; early diagnosis and optimal isolation of the patient are of vital importance. Real-time polymerase chain reaction-based (RT-PCR) analyses do not achieve sufficient sensitivity in the diagnosis of the disease. Methods: The data from 2217 patients diagnosed as COVID-19 between March 2020 and June 2020 and hospitalized or discharged with home isolation were retrospectively analyzed. Demographic data, comorbidities, PCR results, initial computed tomography (CT), laboratory values, Lactate Dehydrogenase (LDH)/Lymphocyte ratio, initial treatments and last status were recorded. The diagnostic sensitivity of LDH/Lymphocyte ratio, which is the main purpose of the study, was analyzed statistically. Results: In order to test the effectiveness of LDH/Lymphocyte ratio for COVID-19 for diagnostic purposes, CT results were considered as gold standard. The area under the curve (AUC) was found to be 0.706 (p < 0.001; cut-off > 0.06) (Sensitivity: 76.4, specificity: 59.60). For the evaluation of LDH/Lymphocyte ratio in terms of survival, AUC was found to be 0.749 (p < 0.001; cut-off > 0.21) (Sensitivity: 70.59, specificity: 73.88). Conclusion: Studies based on radiological findings have demonstrated that CT involvement has higher sensitivity. LDH/Lymphocyte ratio was analyzed in terms of diagnosis and mortality with using specific CT involvement as gold standard method which was found to be a more sensitive due to PCR false negativity; 0.06 and 0.21 were obtained as cut off values for diagnosis and mortality.

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.