Availability Of Tools Research Articles

Impact of Running Clothes on Accuracy of Smartphone-Based 2D Joint Kinematic Assessment During Treadmill Running Using OpenPifPaf

The assessment of running kinematics is essential for injury prevention and rehabilitation, including anterior cruciate ligament sprains. Recent advances in computer vision have enabled the development of tools for quantifying kinematics in research and clinical settings. This study evaluated the accuracy of an OpenPifPaf-based markerless method for assessing sagittal plane kinematics of the ankle, knee, and hip during treadmill running using smartphone video footage and examined the impact of clothing on the results. Thirty healthy participants ran at 2.5 and 3.6 m/s under two conditions: (1) wearing minimal clothing with markers to record kinematics by using both a smartphone and a marker-based system, and (2) wearing usual running clothes and recording kinematics by only using a smartphone. Joint angles, averaged over 20 cycles, were analysed using SPM1D and RMSE. The markerless method produced kinematic waveforms closely matching the marker-based results, with RMSEs of 5.6° (hip), 3.5° (ankle), and 2.9° (knee), despite some significant differences identified by SPM1D. Clothing had minimal impact, with RMSEs under 2.8° for all joints. These findings highlight the potential of the OpenPifPaf-based markerless method as an accessible, simple, and reliable tool for assessing running kinematics, even in natural attire, for research and clinical applications.

A Pilot Study on Video Game Training Effects on Visual Working Memory: Behavioral and Neural Insights

Background/Objectives: Recent research suggests that video games may serve as cognitive training tools to enhance visual working memory (VWM) capacity. However, the effectiveness of game-based cognitive training remains debated, and the underlying neural mechanisms, as well as the relationship between training efficacy and game design factors, are unclear. This study aimed to evaluate the impact of video game training on VWM capacity and explore its neural correlates. Methods: Two groups underwent 56 daily 20 min training sessions with two distinct video games targeting different cognitive skills: a reaction-time training game and a VWM-specific training game. Behavioral assessments included accuracy, hit response times, correct rejection response times, and Cowan’s K values. Neural correlates were measured through Negative Slow Wave (NSW) activity using EEG. Decision tree classification analyses were applied to NSW data across sessions and set sizes to identify patterns linked to VWM capacity. Results: Preliminary results are that both groups showed improvements in behavioral measures (accuracy, response times, and Cowan’s K values). NSW analyses revealed a main effect of set size in both groups, and classification results indicated that NSW patterns differed between groups, across sessions, and set sizes, supporting the relationship between NSW and VWM capacity. Conclusions: These findings contribute to understanding NSW as a neurophysiological correlate of VWM capacity, demonstrating its plasticity through video game training. Simple video games could effectively enhance behavioral and neural aspects of VWM, encouraging their potential as accessible cognitive training tools.

Development and Evaluation of a Deep Learning-Based Pulmonary Hypertension Screening Algorithm Using a Digital Stethoscope.

Despite the poor outcomes related to the presence of pulmonary hypertension, it often goes undiagnosed in part because of low suspicion and screening tools not being easily accessible such as echocardiography. A new readily available screening tool to identify elevated pulmonary artery systolic pressures is needed to help with the prognosis and timely treatment of underlying causes such as heart failure or pulmonary vascular remodeling. We developed a deep learning-based method that uses phonocardiograms (PCGs) for the detection of elevated pulmonary artery systolic pressure, an indicator of pulmonary hypertension. Approximately 6000 PCG recordings with the corresponding echocardiogram-based estimated pulmonary artery systolic pressure values, as well as ≈169 000 PCG recordings without associated echocardiograms, were used for training a deep convolutional network to detect pulmonary artery systolic pressures ≥40 mm Hg in a semisupervised manner. Each 15-second PCG, recorded using a digital stethoscope, was processed to generate 5-second mel-spectrograms. An additional labeled data set of 196 patients was used for testing. GradCAM++ was used to visualize high importance segments contributing to the network decision. An average area under the receiver operator characteristic curve of 0.79 was obtained across 5 cross-validation folds. The testing data set gave a sensitivity of 0.71 and a specificity of 0.73, with pulmonic and left subclavicular locations having higher sensitivities. GradCAM++ technique highlighted physiologically meaningful PCG segments in example pulmonary hypertension recordings. We demonstrated the feasibility of using digital stethoscopes in conjunction with deep learning algorithms as a low-cost, noninvasive, and easily accessible screening tool for early detection of pulmonary hypertension.

Accessible Non-Invasive Techniques for Museums: Extending Sustainability to Resource-Limited Institutions

This work provides a synthesis of an initial experience in the development of accessible imaging techniques and their implementation on a real case: the analysis of colonial Hispano-American paintings at the Complejo Museográfico Provincial “Enrique Udaondo” (Luján, Buenos Aires). It discusses different aspects related to the possibilities of obtaining, using, and reusing equipment and materials locally, as well as details of the ways of acquiring images for photography on site. It also provides information about the composition and conservation state of selected artworks, complementing image analysis with portable X-ray fluorescence (XRF) data, and reflects on articulated/collaborative work in situ as a methodology for transferring knowledge and skills. The project aims to contribute to strengthening Latin American sustainability by creating accessible non-invasive tools for heritage conservation institutions, highlighting the value of regional capacities to approach heritage studies from collaborative and ethical proposals that promote sovereignty and reduce dependence on external inputs.

Embracing Industry 4.0: Navigating the Evolution of Entrepreneurship in Romania

Entrepreneurship constitutes one of the oldest and most profitable occupations. It has evolved alongside the Industrial Revolutions (IR), directly influencing each other. One of the fundamental pillars of technological development is represented by inventive and innovative entrepreneurs who had the courage to investigate and implement what initially seemed to be mere dreams. The first three IRs brought major and decisive changes on a global level. The fourth revolution, called Revolution 4.0 - R 4.0 (or Industry 4.0 - I 4.0), which we consider an evolution, has brought to the forefront new fields of interest and heterogeneous areas of concern: artificial intelligence, robotics, automation, the Internet of Things, 3D printing, etc. In our research, we aimed, on the one hand, to analyze the perspective of Romanian entrepreneurs regarding IR 4.0, whether they implement measures specific to new technologies, and what the impact of these measures is. On the other hand, following the analysis of the collected data, we sought to identify possibilities for stimulating local entrepreneurship under the new R 4.0 conditions. Noting that, still, in Romania, there is insufficient investment in the necessary technological infrastructure, we began our research with a theoretical analysis of the data and the state of the specialized literature. The specific infrastructure that Romania lacks includes reliable broadband internet connectivity, advanced digital technologies such as IoT devices, and accessible automation tools. These gaps significantly hinder the ability of entrepreneurs to leverage Industry 4.0 advancements, thereby limiting their capacity to innovate and compete globally. Addressing these issues will require targeted government investments in digital infrastructure, incentives for technology adoption in key sectors, and partnerships between the private and public sectors to accelerate the deployment of Industry 4.0 technologies. In the next stage, we analyzed the data collected through a semi-structured questionnaire disseminated via crowdsourcing, to which 1026 subjects responded. Before conducting the econometric analysis of the data, three research hypotheses were formulated, which were ultimately validated. The working methodology included quantitative data analysis, descriptive statistics methods, categorical principal component analysis (CATPCA), and correspondence analysis. Data processing was performed using the IBM SPSS 20 application. A limitation of our research is that we did not select the questionnaire participants based on their competencies, allowing individuals with still limited knowledge to intuitively express opinions on a field they do not know or only think they know. The lack of competency-based selection for survey participants means that respondents may not have had the requisite knowledge to provide informed opinions, potentially skewing the results. This limitation impacts the reliability of the data and could mean that the findings are not fully reflective of expert perspectives in Industry 4.0. Future studies should consider a more rigorous selection criterion, ensuring participants possess relevant competencies. This approach would help in obtaining more accurate insights, leading to more actionable conclusions. As a future action direction, we aim to analyze the issue of local entrepreneurship by involving respondents from different professional categories who possess the necessary competencies, so that their opinions can form the basis of a more detailed analysis. This would facilitate establishing viable action directions as a basis for the sustainable and enduring development of entrepreneurship in Romania in the context of the Fourth Industrial Revolution Keywords: Entrepreneurship, Industrial Revolution, Revolution 4.0, Artificial Intelligence, Technology, Investments

Navigating the Bayes maze: The psychologist's guide to Bayesian statistics, a hands-on tutorial with R code.

Bayesian statistics has gained substantial popularity in the social sciences, particularly in psychology. Despite its growing prominence in the psychological literature, many researchers remain unacquainted with Bayesian methods and their advantages. This tutorial addresses the needs of curious applied psychology researchers and introduces Bayesian analysis as an accessible and powerful tool. We begin by comparing Bayesian and frequentist approaches, redefining fundamental terms from both perspectives with practical illustrations. Our exploration of Bayesian statistics includes Bayes's Theorem, likelihood, prior and posterior distributions, various prior types, and Markov-Chain Monte Carlo (MCMC) methods, supplemented by graphical aids for clarity. To bridge theory and practice, we employ a psychological research example with real, open data. We analyse the data using both frequentist and Bayesian approaches, providing R code and comprehensive supporting information, and emphasising best practices for interpretation and reporting. We discuss and demonstrate how to interpret parameter estimates and credible intervals, among other essential topics. Throughout, we maintain an accessible and user-friendly language, focusing on practical implications, intuitive examples, and actionable recommendations.

GBMPurity: A Machine Learning Tool for Estimating Glioblastoma Tumour Purity from Bulk RNA-seq Data.

Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumour purity, the proportion of malignant cells within a tumour, is an important covariate for understanding the disease, having direct clinical relevance or obscuring signal of the malignant portion in molecular analyses of bulk samples. However, current methods for estimating tumour purity are non-specific and technically demanding. Therefore, we aimed to build a reliable and accessible purity estimator for GBM. We developed GBMPurity, a deep-learning model specifically designed to estimate the purity of IDH-wildtype primary GBM from bulk RNA-seq data. The model was trained using simulated pseudobulk tumours of known purity from labelled single-cell data acquired from the GBmap resource. The performance of GBMPurity was evaluated and compared to several existing tools using independent datasets. GBMPurity outperformed existing tools, achieving a mean absolute error of 0.15 and a concordance correlation coefficient of 0.88 on validation datasets. We demonstrate the utility of GBMPurity through inference on bulk RNA-seq samples and observe reduced purity of the Proneural molecular subtype relative to the Classical, attributed to the increased presence of healthy brain cells. GBMPurity provides a reliable and accessible tool for estimating tumour purity from bulk RNA-seq data, enhancing the interpretation of bulk RNA-seq data and offering valuable insights into GBM biology. To facilitate the use of this model by the wider research community, GBMPurity is available as a web-based tool at: https://gbmdeconvoluter.leeds.ac.uk/.

Advancing Sustainable Mushroom Production in Developing Countries: A Pathway to Nutritional Security and Economic Growth

Mushroom production represents a promising industry with significant economic and environmental benefits and offers a viable strategy for addressing the nutritional deficits prevalent in these regions. Its low capital requirements make it an accessible tool for poverty alleviation and wealth creation, providing extensive job opportunities and contributing to national economic growth. The utilization of agro-forestry waste as raw material for mushroom cultivation aligns with the waste-to-wealth approach, fostering environmental sustainability. Despite the popularity of mushrooms as a food source, most of the mushrooms consumed in developing countries are gathered from the wild. The commercialization of mushroom production, which has the potential to become a sustainable and profitable agro-industrial sector, remains largely untapped, underexplored, underutilized, underexploited, and underrepresented. Various challenges impede the growth of the mushroom industry in developing countries, including socio-economic and cultural barriers, inadequate technical and scientific expertise, lack of supportive government policies, fluctuating market prices, and inefficient marketing systems. However, increased awareness of the nutritional, health, and economic benefits of mushrooms, coupled with the establishment of mushroom farming enterprises, could significantly enhance the industry's visibility and accelerate its industrial development in a short time. This review examines the development and sustainability of mushroom production for nutritional security and national economic development in developing nations, along with its many opportunities and challenges.

CFO: Calibration-Free Odds Bayesian Designs for Dose Finding in Clinical Trials.

Calibration-free odds type (CFO-type) designs have been demonstrated to be robust, model-free, and practically useful, which have become the state-of-the-art approach for dose finding. However, a key challenge for implementing such designs is a lack of accessible tools. We develop a user-friendly R package and Shiny web-based software to facilitate easy implementation of CFO-type designs. Moreover, we incorporate randomization into the CFO framework. We created the R package CFO and leveraged R Shiny to build an interactive web application, CFO suite, for implementing CFO-type designs. We introduce the randomized CFO (rCFO) design by integrating the exploration-exploitation mechanism into the CFO framework. The CFO package and CFO suite encompass various variants tailored to different clinical settings. Beyond the fundamental CFO design, these include the two-dimensional CFO (2dCFO) for drug-combination trials, accumulative CFO (aCFO) for accommodating all dose information, rCFO for integrating exploration-exploitation via randomization, time-to-event CFO (TITE-CFO), and fractional CFO (fCFO) for addressing late-onset toxicity. Using all information and addressing delayed toxicity outcomes, TITE-aCFO and fractional-aCFO are also included. The package provides functions for determining the subsequent cohort dose, selecting the maximum tolerated dose, and conducting simulations to evaluate performance, with results presented through textual and graphical outputs. The CFO package and CFO suite provide comprehensive and flexible tools for implementing CFO-type designs in phase I clinical trials. This work is highly significant as it integrates all existing CFO-type designs to facilitate novel trial designs with enhanced performance. In addition, this promotes the spread of statistical methods using a user-friendly R package and Shiny software. It strengthens collaborations between biostatisticians and clinicians, further enhancing trial performance in terms of efficiency and accuracy.

PHMD: An easy data access tool for prognosis and health management datasets

PHMD: An easy data access tool for prognosis and health management datasets

Threads That Become Tendrils

The settler-state of Canada continues to reconcile with the genocide of the original Indigenous custodians of the lands on which we operate, alongside the underserving and discriminating against racialized, black, disabled, and LGBTQ2+ peoples all while navigating a climate crisis, the proliferation of equity, diversity, and inclusion (EDI) nomenclature and plans permeates every social and financial sector. EDI initiatives, while called many names throughout history such as social inclusion and affirmative action, experienced a rise in creation in 2020 because of public outcries for the acknowledgement of systemic racial injustice and pressure to address this ongoing form of violence. Canada’s arts and culture sector is not immune from this scrutiny. Having a long history of engaging in social services, the arts and culture sector is often tasked with “fixing” issues when funding is cut to education, health, and community programs, yet arts institutions are not equipped to do this. This paper follows one resident researcher’s journey as they were tasked with developing an arts civic impact framework suggesting equity practices in the arts. The study used a mixed-methods approach, drawing from the walking interview, reverse photo-elicitation, feminist manifestos and research-creation to bring cultural workers across the country together to develop an accessible tool to carefully engage in equity practices within the sector. As a critique and response to flat and prescriptive EDI plans, what was created based on this cross-country collaboration was a non-linear, spiraling framework existing online that arts organizations can make use of and adapt based on their circumstances. Weaving together a historical account of arts administration, Western managerialism, and the EDI in the arts sector, this article responds to the research question: How can access to the arts and culture sector from coast-to-coast-to-coast be more equitable?

Automated and code-free development of a risk calculator using ChatGPT-4 for predicting diabetic retinopathy and macular edema without retinal imaging

BackgroundDiabetic retinopathy (DR) and macular edema (DME) are critical causes of vision loss in patients with diabetes. In many communities, access to ophthalmologists and retinal imaging equipment is limited, making screening for diabetic retinal complications difficult in primary health care centers. We investigated whether ChatGPT-4, an advanced large-language-model chatbot, can develop risk calculators for DR and DME using health check-up tabular data without the need for retinal imaging or coding experience.MethodsData-driven prediction models were developed using medical history and laboratory blood test data from diabetic patients in the Korea National Health and Nutrition Examination Surveys (KNHANES). The dataset was divided into training (KNHANES 2017–2020) and validation (KNHANES 2021) datasets. ChatGPT-4 was used to build prediction formulas for DR and DME and developed a web-based risk calculator tool. Logistic regression analysis was performed by ChatGPT-4 to predict DR and DME, followed by the automatic generation of Hypertext Markup Language (HTML) code for the web-based tool. The performance of the models was evaluated using areas under the curves of receiver operating characteristic curve (ROC-AUCs).ResultsChatGPT-4 successfully developed a risk calculator for DR and DME, operational on a web browser without any coding experience. The validation set showed ROC-AUCs of 0.786 and 0.835 for predicting DR and DME, respectively. The performance of the ChatGPT-4 developed models was comparable to those created using various machine-learning tools.ConclusionBy utilizing ChatGPT-4 with code-free prompts, we overcame the technical barriers associated with using coding skills for developing prediction models, making it feasible to build a risk calculator for DR and DME prediction. Our approach offers an easily accessible tool for the risk prediction of DM and DME in diabetic patients during health check-ups, without the need for retinal imaging. Based on this automatically developed risk calculator using ChatGPT-4, health care workers will be able to effectively screen patients who require retinal examinations using only medical history and laboratory data. Future research should focus on validating this approach in diverse populations and exploring the integration of more comprehensive clinical data to enhance predictive performance.Graphical

Reliability and Validity of the Duke Activity Status Index in Bariatric Surgery Individuals Living with Obesity: Insights into Prognostic Value.

Obesity is a global health issue that complicates surgical outcomes, particularly in bariatric/metabolic surgery candidates. Assessing functional capacity is critical for optimizing preoperative planning and postoperative care. The Duke Activity Status Index (DASI) is a simple, self-administered questionnaire that estimates functional capacity based on daily activities. This study evaluates the reliability and clinical applicability of the DASI as an alternative to the 6-min walk test (6-MWT) for bariatric/metabolic surgery individuals living with obesity. This cross-sectional study included 126 individuals who are candidates for bariatric/metabolic surgery. Participants completed the DASI and 6-MWT. Test-retest reliability was assessed using intraclass correlation coefficients (ICCs), and validity was evaluated through correlations with 6-MWT results. The agreement was measured using the standard error of measurement (SEM) and minimal detectable change (MDC). The DASI demonstrated excellent internal consistency (Cronbach's alpha = 0.86) and test-retest reliability (ICC = 0.97, 95% CI: 0.96-0.98). A strong correlation was observed between DASI and 6-MWT scores (r = 0.603; p < 0.001). ROC analysis showed an optimal cut-off score of 24.1 (AUC = 0.836), with 72.2% sensitivity and 74.1% specificity. The SEM and MDC values were 2.36 and 6.54 points, respectively. The DASI is a reliable, accessible tool for bariatric surgeons to assess functional capacity in candidates for bariatric/metabolic surgery. Its simplicity, strong correlation with performance-based measures, and minimal resource requirements make it ideal for preoperative evaluations, particularly in resource-limited settings.

Aproximación a la capacitación en competencias digitales de docentes en servicio en contextos de pobreza

This study had like main objective to develop recommendations for teacher training in digital competencies in contexts of poverty in Latin America. Using a qualitative approach that combined documentary review and participatory methods, key strategies for the adaptation of accessible digital tools and the development of practical skills in teachers were identified. The findings highlight the importance of creativity and continuous training in educational technology to overcome technological limitations and promote educational equity.

P-2112. Development and Validation of a Cheap, Portable Assay to Rapidly Profile Bacterial Infections of the Bloodstream

Abstract Background Antimicrobial resistant (AMR) bacterial infections pose a significant and growing health threat worldwide, and their burden is most severe in lower income areas where diagnostic infrastructure is lacking. The development of cost-effective and accessible diagnostic tools for the rapid identification of resistant infections in low-resource settings is crucial. Specific High-sensitivity Enzymatic Reporter Unlocking, or SHERLOCK, is a promising CRISPR-based system that can aid in the diagnosis of infections and support clinical decision making. Here, we present an assay termed BADLOCK (Bacteria and AMR Detection by SHERLOCK) that uses this technology to rapidly identify bacterial species and AMR genes on blood cultures. BADLOCK workflow demonstrating simplicity of approach and rapid turnaround. Methods We combine isothermal amplification of targets with CRISPR/Cas13-based detection into a single step, allowing for a streamlined workflow and a total time-to-identification of less than two hours from positive cultures using lateral flow strips (Fig. 1). The assay primarily targets topA for species identification, a highly conserved gene within species with sufficient variability between them to enable species-level typing. A panel of clinically important AMR genes, including the ESBL blaCTX-M-15, three major carbapenemases, and the GPC resistance genes mecA and vanA, is also included. Pilot study assessing assay performance. A) each column represents a different isolate while each row represents a different CRISPR gene target, which are enumerated at the bottom. Each species name refers to the topA gene sequence unique to that species. The top box shows the expected gene content based on prior sequencing, while the bottom box shows the results of each assay. The blue shading shows relative fluorescence intensity of the generated signal. B) Basic test characteristics for each target gene. Results In addition to the AMR resistance genes, we have validated a panel of 9 gram-negative rods, 6 staphylococci, 2 enterococci, and 2 streptococcal species with further targets being developed, and have performed a laboratory pilot study on a subset of these (Fig. 2). We have additionally collected over 600 sequential positive blood cultures and are in the process of performing a large clinical validation study on patient samples, thereby elucidating the precise test characteristics of our assay. Conclusion We have developed an assay capable of rapidly detecting bacterial species causing bloodstream infections and their major epidemic AMR genes on low-cost paper strips. Our work has the potential to improve the diagnostic landscape for resistant infections in low-and-middle-income countries and serve as an adjunctive diagnostic to traditional approaches in highly resourced setting, which would represent a major step forward in infectious disease diagnostics. Disclosures All Authors: No reported disclosures

Melanoma Detection Using Deep Learning Techniques

Skin Cancer, particularly melanoma, is one of the most prevalent and deadly forms of cancer globally. Early detection significantly improves survival rates, but manual diagnosis by dermatologists is often subjective and time-consuming. This study proposes a deep learning-based approach to skin cancer prediction, focusing on the classification of malignant and benign lesions. Leveraging a convolutional neural network (CNN) architecture, the model is trained on a large dataset of dermoscopic images. The system automates feature extraction, reducing human bias and error. Our model achieves state-of-the-art performance by incorporating techniques such as data aug- mentation, transfer learning, and fine-tuning on pre-trained networks (such as ResNet &amp; VGG ), which improve the model’s ability to generalize across diverse skin tones and lesion types. Evaluation metrics such as accuracy, sensitivity, specificity, and AUC (Area Under the Curve) are used to assess the model’s efficacy. The results demonstrate the potential of deep learning to en- hance early diagnosis, aiding dermatologists in clinical decision-making and providing a foundation for scalable, accessible skin cancer screening tools. Keywords: Skin Cancer, Deep Learning, CNN, Early Detection, Healthcare AI

Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study.

High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients. Nine ML algorithms for PFS prediction were developed using a prospective cohort of 310 HGSOC patients consecutively enrolled from a large Chinese tertiary hospital between January 2017 and December 2020. The optimal model was internally validated using the 1000 bootstrap method. The SHapley Additive exPlanations (SHAP) method was employed to interpret the model in terms of feature importance and feature effects. The final model, constructed with the optimal feature subset, was deployed as an interactive web-based Shiny app. The random survival forest (RSF) model demonstrated superior predictive performance compared to other ML models, the RFS model constructed with an optimal feature subset in the optimal imputed dataset achieved a superior 1000 bootstrap C-index of 0.755 (95% CI: 0.750-0.780) and a Brier score of 0.183 (95% CI: 0.175-0.190). SHAP analysis identified tumor residual, HE4, FIGO stage, T stage, CA125, age, ascites volume, platelet counts, and BMI as the top nine contributing factors. It also revealed potential nonlinear relationships and important thresholds between HE4, CA125, age, ascites volume, platelet counts, the body mass index, and PFS risk. Additionally, interaction effects were found between tumor residual and age, HE4, and CA125. Finally, an interactive web-based Shiny app for the model was developed and accessible at https://rsfmodels.shinyapps.io/ocRSF/. An explainable ML model for PFS prediction in HGSOC patients was developed with superior results. The publicly accessible web tool based on the optimized model facilitates its utility in clinical settings, potentially improving individualized patient management and treatment decision-making in HGSOC.

Relationship of left ventricular longitudinal strain to coronary artery calcium load in patients with newly diagnosed type 2 diabetes mellitus

Abstract Introduction Cardiovascular complications commonly manifest after longstanding Type 2 Diabetes Mellitus. Newly diagnosed Type 2 Diabetes Mellitus (NDT2DM) is much less studied. The coronary artery computed tomography calcium score (CTCS) has become a widely accessible, accurate, and reliable tool for assessing the risk of major cardiovascular events, particularly in asymptomatic individuals. Additionally, myocardial strain, specifically echocardiographic Global Longitudinal Strain (GLS), measures the systolic function of the left ventricle (LV), enabling the early detection of systolic dysfunction. GLS is also increasingly recognized for its prognostic value in coronary artery disease. Purpose The objective of this study was to analyze the relationship and predictors of subclinical calcified atherosclerotic plaques in coronary arteries, measured by CTCS, to LV longitudinal strain in patients with NDT2DM. Methods In this prospective cross-sectional study 101 patients with NDT2DM, according to the criteria of the American Diabetes Association, were included. CTCS was performed on all patients by 64-slice cardiac CT, with a thickness of 2-5 mm. The Agatston method was used to determine the CTCS. For Speckle Tracking Echocardiography, images from the apical four-chamber, three-chamber and two-chamber views with ECG gating were attained. The endocardial border was manually adjusted at end-systole. The software system automatically generated the strain values for each segment, the average LV GLS, end-diastolic, end-systolic LV volumes and LV ejection fraction. With Spearman’s correlation test we analysed the relationship between LV longitudinal strain parameters and the level of CTCS. We also performed the multiple regression analysis to identify LV longitudinal strain predictors for high CTCS. Results Mean age was 54.4 ± 11.6 years, 53 (52.5%) were males, and mean time since diagnosis of diabetes was 2.8 ± 5.3 months. Only 8 (7.9%) patients did not have calcified atherosclerotic plaques or a CTCS of zero, 7 (6.9%) a CTCS of 1-10, 43 (42.6%) a CTCS of 11-100, 23 (22.8%) a CTCS 101-400 and 20 (19.8%) patients had CTCS &amp;gt;400. The mean total volume of calcium in these patients was 210. LAD had the highest CTCS, followed by RCA, LCx and left main coronary artery. Table 1 shows the correlation between CTCS and LV longitudinal strain parameters, while table 2 depicts predictors for higher CTCS. Conclusions LVGLS does not appear to correlate with the coronary artery calcium burden in patients with NDT2DM. However, regional LV longitudinal strain may serve as a predictor for asymptomatic calcified coronary artery plaques. Given that the LAD exhibits the highest CTCS, the predictive regions might correspond to this affected artery. Thus, a comprehensive analysis of both global and regional LV longitudinal strain could be beneficial for the early detection of patients at risk for cardiovascular complications. Correlation of CTCS and LV strain LV strain predictors for CTCS

Correlation between Prostate Volume and Intravesical Prostate Protrusion to Bladder Detrusor Muscle Thickness in Patients with Benign Prostate Enlargement by Transabdominal Ultrasound Examination in Banjar Jagatamu, Meliling, Kerambitan, Tabanan 2024

Benign prostate enlargement (BPE) is a common degenerative condition in elderly men, often leading to lower urinary tract symptoms (LUTS) such as urgency, frequent urination, and incomplete bladder emptying. This study investigates the correlation between prostate volume, intravesical prostate protrusion (IPP), and bladder detrusor muscle thickness in patients with BPE using transabdominal ultrasonography. Conducted in Banjar Jagatamu, Meliling Village, Tabanan Regency, the study employed a cross-sectional design with 71 participants aged 60 years and older. Inclusion criteria required an International Prostate Symptom Score (IPSS) ≥ 8 or prostate volume &gt; 20 mL. The results revealed a significant correlation between prostate volume and bladder detrusor muscle thickness (r = 0.665, p &lt; 0.001) and between IPP and detrusor thickness (r = 0.806, p &lt; 0.001). These findings highlight the utility of ultrasonography in assessing structural changes caused by chronic urinary obstruction. The study provides valuable insights into the role of prostate volume and IPP as predictive markers for bladder remodeling in BPE patients. By focusing on a rural population, this research emphasizes the need for accessible diagnostic tools to improve early detection and management of BPE, ultimately enhancing patient outcomes in underserved communities.

Regional left ventricle strain outperforms global longitudinal strain in predicting late gadolinium enhancement in arrhythmogenic cardiomyopathy

Abstract Background Arrhythmogenic cardiomyopathy (AC) is a genetically determined heart muscle disease characterized by myocyte necrosis and consequent fibro-fatty replacement. The recognition of fibrosis is a relevant finding with important diagnostic and prognostic implications. Speckle tracking echocardiography (STE) may be more accurate than conventional echocardiography in detecting early myocardial alterations that need to be confirmed with the gold standard technique, cardiac magnetic resonance (CMR). Purpose The aim of our study is to evaluate if STE-derived strain analysis can predict the presence of fibrosis on CMR in the corresponding segment. Methods 86 patients with AC (including right/left dominant and biventricular forms) underwent advanced echocardiography with STE-derived strain analysis and CMR. Using a 17-segment Bull's-eye model, the cut-off value that best predicts the presence of gadolinium enhancement on CMR was determined for each wall segment using ROC curve analysis. Results The medium age was 45 (32-58) years, male gender was prevalent 71%. Among our patients, 44.1% had a biventricular form, 26.7% had a right-dominant form, 26.7% had a left-dominant form, and 2.3% had a borderline diagnosis. Regarding cardiovascular risk factors, 11.6% of patients had arterial hypertension, 15.1% had dyslipidemia, 2% had diabetes, 1% were smokers and 1% had coronary artery disease with previous percutaneous revascularization. The mean left ventricle (LV) ejection fraction (EF) was preserved 59% (54-64), while the medium LV global longitudinal strain (GLS) was slightly reduced, -18.2% (-20.1 to -15.2). A detailed analysis of regional wall strain was performed, and ROC curve analysis was used to determine a cut-off value for each segment to predict the presence of fibrosis on CMR in the corresponding segment, as shown in Table 1. Lower-than-average values demonstrated good accuracy in predicting the presence of fibrosis on CMR. Conclusions STE-strain analysis is a clinically accessible tool that enhances the sensitivity of echocardiography in detecting myocardial tissue alterations in AC, which would otherwise be identifiable only through CMR. Notably, even when GLS is within normal limits, regional strain sub-analysis can additionally reveal myocardial involvement. Table 1