Objective: Countertransference (CT) research has demonstrated the importance of CT management for successful therapy outcomes. Over time, CT research began to take this aspect more and more seriously and expanded the concept of CT management to include aspects, such as stable characteristics of the therapist and his or her theoretical model, that do not necessarily pertain the single session (e.g., therapist’s self-integration, theoretical framework). In this study we further expand the boundaries of CT management by investigating the relationship between CT experienced within sessions and the elaborative activity taking place between sessions. Method: Twenty-three in-training psychotherapists were asked to rate their emotional reactions through the Therapist Response Questionnaire at the end of 69 counselling sessions and to fill the Post Session Therapist Questionnaire, an instrument assessing three relevant reflexivity activities in the post-session time. The 69 sessions were also recorded, transcribed and evaluated by three external raters, who applied the Countertransference Behavior Measure. Results: Results showed significant correlations between some dimensions of CT experienced in the session and some dimensions of the therapist’ post-session working through. Conclusion: Results highlighted the centrality of the reflexivity processes that therapists put in place between sessions and not only within the session to increase awareness of their countertransference, a prerequisite for its handling during the session for therapeutic purposes.

UCL, Queen Square, Institute of NeurologyRunning Title: Puberty blockers and cognitive function

Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clients and servers, data and models are susceptible to different data-poisoning attacks. In this study, our motivation is to explore the severity of data poisoning attacks in the computer network domain because they are easy to implement but difficult to detect. We considered two types of data-poisoning attacks, label flipping (LF) and feature poisoning (FP), and applied them with a novel approach. In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data. For FP, we randomly manipulated the highly contributing features determined using the Random Forest algorithm. The datasets used in this experiment were CIC [1] and UNSW [2] related to computer networks. We generated adversarial samples using the two attacks mentioned above, which were applied to a small percentage of datasets. Subsequently, we trained and tested the accuracy of the model on adversarial datasets. We recorded the results for both benign and manipulated datasets and observed significant differences between the accuracy of the models on different datasets. From the experimental results, it is evident that the LF attack failed, whereas the FP attack showed effective results, which proved its significance in fooling a server. With a 1% LF attack on the CIC, the accuracy was approximately 0.0428 and the ASR was 0.9564; hence, the attack is easily detectable, while with a 1% FP attack, the accuracy and ASR were both approximately 0.9600, hence, FP attacks are difficult to detect. We repeated the experiment with different poisoning percentages.

In this article, an unsupervised IDS (Intrusion Detection System) is presented for the detection of zero-day DDoS (Distributed Denial of Service) attacks for IoT (Internet of Things) networks that can detect anomalies without the need for prior knowledge or training in attack information. Attackers exploit existing undiscovered vulnerabilities in the system to launch zero-day attacks. There exist many traditional deep learning and machine learning based attack detection systems that cannot deal with and detect new zero-day attacks and mostly misclassify those attacks. Zero-day attacks are often new unknown threats that have not been encountered before, in addition, labelling of data is a time-consuming task for security experts, So there exists a need for unsupervised methods that can detect unseen cyber-attacks on the zero-day. DDoS attacks have recently adversely affected many organisations in terms of finance and services, as these attacks have become more sophisticated and damaging. The growth of IoT networks has facilitated the launch of more DDoS attacks. In this work, an unsupervised approach-based algorithm is proposed for the detection of zero-day DDoS attacks in IoT networks by exploiting random projection for the feature selection process to reduce the dimensionality of the network data and ensemble model consisting of K-means, GMM and one-class SVM for the unsupervised classification of the data as attack and normal using the hard voting technique. The CIC-DDoS2019 datasets are used for an extensive evaluation of the proposed method. The proposed method has obtained an accuracy of 94.55%, which is better than the other state-of-the-art unsupervised learning-based methods.

Aim: To examine the effect of implementing the Patient Complexity Instrument (PCI) in addition to usual-care on complexity detection, clinical-care time allocation, and referrals to supportive services compared to usual-assessment alone.Design: A parallel-group-blocked pragmatic randomised controlled trial.Methods: A mixed-method study conducted within a regional Australian community nursing service. Randomisation occurred at the initial client assessment following referral acceptance for community nursing support. Older people aged 65 years and over (client participants), referred to the service from 1st of July 2020 to 30th of September 2020, eligible for Commonwealth-Home-Support-Programme funding. A convenience sample of community nurses conducting client-assessments were recruited. The intervention group included usual-assessment plus the PCI and the control group was usual-assessment alone. Chi-square test independence compared complexity ratings (low, medium, high) between groups for the hypothesis that adding the PCI to usual-assessment, has no effect on nurses’ complexity detection compared to usual-assessment alone.Results: Compared to usual-assessment alone, adding the PCI did not change nurses’ level detected complexity rating. However, for older people initially assessed with low levels of complexity, the PCI indicated a need for additional clinical-care-time. The nurses feedback showed the PCI useful in prompting to identify other care factors such as level of engagement, psychosocial which were not identified by usual-assessment alone thus, enhanced complexity detection.Conclusion: The addition of PCI to nurses’ usual-assessment did not improve levels of complexity ratings. However, the PCI enhanced complexity detection by pinpointing areas of care requiring referrals for additional care and extra-time required. The ImPaCt trial demonstrated the PCI as a useful tool for enhancing care for older people receiving nursing supports in the community. The PCI is a beneficial guidance resource for those new to Community Nursing role caring for older people.Key words: district or community nurse, older people, ageing, care complexity, clinical decision-making, clinical judgement, quality of care, assessment, community aged-care.ImpactWhat problem did the study address? The trial addresses the limited evidence and use of standardised tools for detecting care complexity and greater dependence on nurses’ clinical judgment for people living in the community.    What were the main findings? The trial highlighted that the PCI could enhance community-based care for complex older people, and serves as a valuable guide for elder care, especially for those new to the community nurse role.  Where and on whom will the research have an impact?

A document by Sargun Kaur. Click on the document to view its contents.

Allergic contact dermatitis (ACD) is a prevalent immune-mediated skin condition, affecting millions of people worldwide. Significant diagnostic challenges occur due to the subjectivity inherent in the current diagnostic approach, which involves skin patches. To address this limitation, the present study explores the potential of machine and deep learning algorithms in automating ACD diagnosis, thereby facilitating more objective and accurate assessments. A dataset comprising 1579 skin patch images from 200 patients was collected, to train and evaluate the proposed diagnostic models. The dataset underwent extensive feature extraction, resulting in 732 distinct features. These features were utilized to train traditional machine learning models, such as Random Forest, Support Vector Machines, and XGBoost, with the objective of identifying correlations related to ACD. In a second approach, Convolutional Neural Network (CNN) architectures such as EfficientNet, ResNet, and MobileNet, were evaluated in recognizing patterns in different image types, such as Texture and Redness, correlated with ACD cases. The results have indicated that machine learning algorithms can achieve a success rate of 83% in ACD detection, with the fusion algorithm of the two approaches boosting the success rate to 85%. The significance of this research lies in the enhancement of overall diagnostic accuracy achieved through the combination of information from various sources. It highlights that machine learning and CNNs can automate ACD diagnosis, making it more objective and efficient. This advancement can greatly assist clinical diagnosis, and benefit regions with limited medical resources and many patients worldwide.

Integrating a (Zr3Si2) Composite Neutron Reflector and Concrete-Lead Alloy Bio shield in a Boiling Water SMR. This research demonstrates the potential of a dual-layered system to enhance safety in nuclear reactors, showcasing synergistic performance in neutron reflection and radiation absorption. The thick concrete-lead bio shield effectively contains neutrons formed during fission, maintaining optimal neutron economy. The study evaluates various composites for neutron reflection and supports the use of Zirconium-Silicon alloy due to its proven efficacy. The selected (Zr3Si2) composite-based neutron reflector ensures the containment and reflection of electrons during the fission process. The research delves into microstructure and phase diagrams of the alloys, emphasizing a meticulous rolling procedure in manufacturing for a uniformly integrated structure. Real-world applicability is highlighted within BWRX-300 SMR reactors. This work represents a significant step in fortifying safety standards and explores innovative solutions for neutron reflection in nuclear reactors.

Water utilities today need to reinvigorate aging infrastructure, finance capital improvements, and reduce energy use. Self-funding-using internal funds to invest in capital projects that save energy, and then pay back the investment with the savings-is a strategy that hits all three needs. An ideal self-funded project is one where other funding is insufficient, an internal loan is available, the project is permanent, and savings are ongoing. An example is discussed. Concepts Water professionals have identified aging infrastructure and capital financing as two of the top three challenges facing the water industry (AWWA 2023). At the same time, water utilities are motivated to reduce their energy use, whether to save money, meet climate goals, or adjust to an evolving electricity market (Sowby et al. 2023; Patel et al. 2022). But investing in energy-efficiency projects can be expensive, even if the payback is obvious.

In the coming years, climate models forecast mountainous watersheds to undergo a reduction in snowpack, early season melt, and increases in evapotranspiration. As a result, dry soil conditions will stress vegetation at elevations of 1,850 to 2,900 meters above sea level. In this study we investigate infiltration patterns and root water uptake in response to drying within the East River catchment in Colorado. Our group collected soil cores, measured matric potential and sap flow, and monitored tree xylem and soil for stable isotopes of water (2H, 18O) along two profiles to 90 cm depth—with three Engelmann spruce and three aspen trees instrumented, respectively. Field isotope dynamics were analyzed on a daily basis between mid-July and late October using an in situ cavity ring-down spectrometer. The numerical model HYDRUS-1D was trained and calibrated with pressure head and isotope data, simulating the response to late summer dry spells and monsoonal rainfall for a 128 day period. Lab measured and model derived rates of saturated hydraulic conductivity are consistent for both soil profiles, with a median rate of 1,410 cm d-1. Model simulations reflect the three distinct dry down events from late July to September, each followed by rapid infiltration of rainfall (42, 47, and 68 mm of cumulative precipitation per event). Compared to aspen trees, shallow soil under Engelmann spruce repeatedly dries out beyond the permanent wilting point of -1.5 MPa, likely due to higher rates of canopy interception for spruce. This study highlights the benefits of coupling tracer data and commonly used hydrometric data to better constrain parameters used in numerical modeling. That said, these efforts aim to help predict and better understand quantification of certain plant water responses during ecosystem changes and future climate conditions.

Bismaleimide (BMI) is a high-performance thermosetting resin widely used in the aerospace, automotive, and mechanical and electronics industries due to its chemical and mechanical stability. It can act as a protective barrier against for steel and other metallic components which serve as magnets, aircraft components. Carbon Nanofibre (CNF) reinforced BMI has shown improved performance as advanced material which has been shown here with surface morphological changes along with the chemical protection imposed by BMI.

A document by Shekhar Kumar Yadav. Click on the document to view its contents.

This research paper delves into the meticulous modeling of temperature changes during the preparation of Italian meringue buttercream. The primary focus is on controlling the cooking time for a crucial step in the process, the creation of simple syrup. The aim is to ensure the syrup reaches precisely 235°F to attain the desired buttercream consistency without crystallization issues. The study investigates various mathematical methods to model the temperature-time relationship based on collected experimental data points. The analysis involves rigorous mathematical computations to determine the parameters of the best-fitting functions. The study ultimately presents detailed insights into the complexities of this culinary process and provides a comprehensive exploration of mathematical methodologies for function approximation in similar scenarios.

This research was conducted as part of ENGIN-298 at Contra Costa College by Ali Ahmad Siddiqui with the assistance of Dr. Chao Liu from Fall 2022 to Fall 2023. Drug-use is a societal crisis that is in-need of desperate mitigation and alleviation. In this paper, we claim that machine learning algorithms are highly effective in assessing the likelihood for an individual to engage in the use of certain drugs. In particular, we show how machine learning algorithms can be trained using real-world data collected from users who consume a variety of different drugs in order to give insight into the drug-use for unknown individuals.

Introduction: In the realm of human-computer interaction and behavioral research, accurate real-time gaze estimation is critical. Traditional methods often rely on expensive equipment or large datasets, which are impractical in many scenarios. This paper introduces a novel, geometrybased approach to address these challenges, utilizing consumer-grade hardware for broader applicability. Methods: We leverage novel face landmark detection neural networks capable of fast inference on consumer-grade chips to generate accurate and stable 3D landmarks of the face and iris. From these, we derive a small set of geometry-based descriptors, forming an 8-dimensional manifold representing the eye and head movements. These descriptors are then used to formulate linear equations for predicting eye-gaze direction. Results: Our approach demonstrates the ability to predict gaze with an angular error of less than 1.9 degrees, rivaling state-of-the-art systems while operating in real-time and requiring negligible computational resources. Conclusion: The developed method marks a significant step forward in gaze estimation technology, offering a highly accurate, efficient, and accessible alternative to traditional systems. It opens up new possibilities for real-time applications in diverse fields, from gaming to psychological research.

A document by Md. Al-Amin. Click on the document to view its contents.

10.5281/zenodo.8412496Basal cell carcinoma (BCC) is the most prevalent form of cancer in the globe. The occurrence of BCC is the result of a complex interaction between genetic and environmental factors. BCC's clinical and histopathological characteristics can vary depending on the tumor's histological subtype, ranging from low-risk tumours such as pigmented, superficial, and nodular tumours to high-risk types such as the basosquamous subtype with sarcomatoid differentiation, micronodular, infiltrating, sclerosing, and morphoeic tumours. This paper aims to provide an overview of the histopathological types of basal cell carcinoma.

The remarkable progress and growth in the fields of Artificial Intelligence (AI) and BioChemical sciences in recent times have been continuously solving many complex problems. AI has shown a measurable impact in almost all areas of life including healthcare. The exponential growth of data, technologies for efficient storage, retrieval of this huge data, and ever-increasing computational power have given rise to data-driven AI-based approaches like Deep Learning (DL). Productive use of Big Data has been recognized in many phases of drug discovery. The availability of massive biochemical data catalyzed by the fast processing abilities of GPUs is pushing up the huge success of deep learning approaches for some key activities in drug discovery like drug-target interaction or virtual screening. DL techniques have shown the potential to strategically reduce the time and huge cost spent in the drug discovery pipeline. This paper discusses the significance of DL in the context of biochemical Big Data and the drug discovery process.

Rapid advancements in machine learning and artificial intelligence have raised important concerns regarding fairness and biases in algorithmic decision-making. This paper evaluates the biases present in major machine learning models–Random Forest, K-nearest neighbors, XGBoost, and Naive Bayes - by comparing their performance on different subgroups of health data, including across genders and racial groups. We also analyze the models for strong calibration: if the average predicted probability corresponds to the actual observed rate for all subgroups. Through a t-test and ANOVA test of model performance on different subgroups, we demonstrate that the models have significantly different True Positive Rates across racial groups, but not across gender groups. A comparison of parity metrics and testing for strong calibration through changepoint detection of the random forest model further showed that there were disparities in model performance. Cross validation and the implementation of a paired t-test showed that Brier scores differed across k -folds, indicating inconsistencies in model calibration. When we employed the bias mitigation strategy of reweighting, however, the disparity was reduced. We also suggest a new method for bias mitigation which jointly employs reweighting and adversarial training. This work serves to caution the use of machine learning models in medical contexts, especially for clinical outcome prediction where risk or mortality likelihood may be significantly impacted by biases in the model or data. It is imperative that creators and users of machine learning models prioritize diversity and representation in both datasets and trained models concerning sensitive attributes to ensure equity and ethical practices in this field. We also suggest future work.

In the field of dentistry, there is a growing demand for increased precision in diagnostic tools, with a specific focus on advanced imaging techniques such as computed tomography, cone beam computed tomography, magnetic resonance imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep learning has emerged as a pivotal tool in this context, enabling the implementation of automated segmentation techniques crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions, which, if left undetected, can have a significant impact on human health. The impressive track record of deep learning across various domains, including dentistry, underscores its potential to revolutionize early detection and treatment of oral health issues. Objective: Having demonstrated significant results in diagnosis and prediction, deep convolutional neural networks (CNNs) represent an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize the current debate, and establish baselines for future research. Method: In this study, a systematic literature review is employed as a methodology to identify and select relevant studies that specifically investigate the deep learning technique for dental imaging analysis. This study elucidates the methodological approach, including the systematic collection of data, statistical analysis, and subsequent dissemination of outcomes. Results: In incorporating 45 studies, we identified selection criteria and research objectives, addressing significant gaps in the existing literature. These studies assist clinicians in examining dental conditions and classifying dental structures, including caries detection and the identification of various tooth types. We evaluated model performance, addressing the identified gaps, using diverse metrics that we strive to list and explain. Conclusion: This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies. Although this research acknowledged some limitations, CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall.

Title: Occurrence of Low Diastolic Pressure and Cardiovascular Disease are More Common in Elderly that Could Explain Higher Mortality Rate in this populationMohammad Reza Movahed,1,2Department of Medicine, University of Arizona, Tucson, AZDepartment of Medicine, University of Arizona, Phoenix, AZCorrespondence to:Mohammad Reza Movahed, MD, PhDClinical Professor of MedicineCareMore Regional Director of Arizona7901 E SpeedwayTucson, AZ 85710Email: rmova@aol.comTel: 949 400 0091Key words:Diastolic hypertension; hypertension; elderly; high blood pressure; cardiovascular risk factorConflict of interest: NoneLetter to Editor:With great interest, I read the paper entitled “Evaluation of Optimal Diastolic Blood Pressure (BP) Range Among Adults With Treated Systolic Blood Pressure Less Than 130 mmHg” by Li et al. (1) They found that diastolic BP less than 60 is associated with worse outcome in patients with age of over 50. (1) However, they have a major flaw in their data analysis as they did not adjust for age and any other risk factors in any multivariate analysis. We know that age is the most important factor for all cause or any mortality and hypertension rate increases with age. Patients with diastolic BP of < 60 in this study had much higher age that can clearly explain why this group had higher mortality. It is surprising that no multivariate adjustment was done in this study. As it can be seen from the table, mean age in patients with diastolic BP < 60 was 77.1 which was much higher than in other groups (66.9, 62.2 and 59.0). This is a very strong and highly significant difference. Looking at different age groups, I found similar issue with age cut offs. Patients with an age of <65 were only representing 23.1% of the population with diastolic BP <60 vs 44.8% in the next group of patients with diastolic BP between 60-70. Furthermore, not only they did not adjust their data for age, they also did not look at many other risk factors that are associated with hypertension needing adjustment. In large meta-analysis that was performed by Riaz et al. (2), they found that risk factors for hypertension includes smoking, obesity, diabetes mellitus, stress and anxiety which are all also risk factors for higher mortality. In their table, the authors document that the patients with diastolic BP of < 60 also had much higher significant history of cardiovascular disease as another unadjusted risk factor. Furthermore, they did not include other important risk factors commonly occuring in elderly such as presence of renal disease or chronic obstructive lung disease. I really hope that the authors will perform multivariate adjustment after reading this letter. Based on such a large difference in age distribution, we may not observe higher mortality rate in patients with diastolic BP of < 60 vs others after appropriate multivariate adjustment. Otherwise, the result of this study will be meaningless and very misleading.

Introduction:Epidemiology:Each year, there are millions of procedures done in the United States that utilize iodinated contrast agents. The main risk of iodinated contrast is kidney injury which could lead to morbidity and mortality (1–6). Contrast-induced nephropathy (CIN), also known as contrast-induced acute kidney injury (CI-AKI), is one of the most common causes of impairment of renal function in the United States (7–10) and is the third common cause of hospital-acquired renal insufficiency (11). Different studies have used various definitions for contrast-induced nephropathy including an increase in serum creatinine ≥0.5 mg/dl or ≥25% from baseline creatinine within 24 to  72 hours after contrast medium administration (3,6,12–16). The mortality rate is increased among patients who developed CIN during and after hospitalization, especially among those who required dialysis (6,17,18). Therefore, any preventive measures that can reduce CIN risk can be lifesaving with a reduction in mortality and morbidity in patients undergoing iodinated contrast exposure.Brief Summary of CIN preventive measures:●      Hydration and fluid optimizationSeveral methods have been proposed to prevent CIN in clinical settings. However, none of them has been proved to be consistently effective except for hydration and reduction in the amount of contrast exposure. Hydration is the most common prophylactic technique to reduce CIN occurrence in a way that all high-risk patients undergoing contrast exposure should receive appropriate hydration if possible before the procedure in high-risk patients and after the procedure in all patients if feasible without contraindication to hydration (14,19–23). It has been shown that intravenous fluid administration with isotonic saline is more effective compared to the half saline infusion (14,22). However, the optimal fluid volume and infusion rate is controversial. Current guidelines recommend intravenous administration of 1-1.5 ml/kg/h of normal saline six hours before and after contrast injection (24). With respect to proper fluid administration, left ventricular end-diastolic pressure (LVEDP) can be assessed and adjusted accordingly. This theory was assessed in the POSEIDON trial. Their findings suggested patients who received adjusted fluid based on LVEDP had a significantly lower risk of CIN after cardiac catheterization (relative risk: 0.41, 95% confidence interval (CI): 0.22 – 0.79, P= 0.005). However, three cases of shortness of breath, probably in the context of pulmonary edema, were reported in both intervention and control groups (25). Also, Maioliet et al. used bioimpedance vector analysis (BIVA) for the assessment of body fluid status. After randomization of low BIVA patients to normal or double volume normal saline administration, they found no significant difference in CIN occurrence defined by standard criteria (increase serum creatinine by ≥ 0.3 mg/dl within 48 hours) between those groups (10.8% vs. 4.7%, P= 0.08, respectively) (26).●      Avoidance of nephrotoxic agentsAnother factor that can raise CIN risk might be related to nephrotoxic drugs. Although there are not enough trials to strongly prove the benefit of nephrotoxic drugs discontinuation before contrast exposure, it is generally recommended to hold potentially nephrotoxic drugs including nonsteroidal anti-inflammatory drugs, aminoglycosides, vancomycin, sulfonamides, penicillins, amphotericin, loop diuretics, and metformin in high-risk patients. The latter drug has been associated with metabolic acidosis which might predispose kidneys to the development of CIN but this concept has not been proven (27,28).●      N-Acetylcysteine administrationN-Acetylcysteine (N-AC) was initially reported by Tepel et al. to be protective against contrast-induced nephropathy in a small trial (29). However, numerous trials and meta-analyses have completely failed to show any benefit and therefore its use is not recommended (29–31).●      Type of contrast mediaContrast media typed based on osmolality is thought to be important for CIN pathogenesis and has been categorized into three different types based on the osmolality (high osmolar, low osmolar, and iso-osmolar) (32). Initially, several studies have shown that iso-osmolar contrast media have the lowest risk of CIN incidence in comparison to low-osmolar contrast agents (33–36), but numerous other trials failed to show any significant differences in the occurrence of contrast-induced nephropathy (37–40).●      Dialysis and hemofiltrationIn terms of dialysis and hemofiltration which directly removes the contrast from the systemic circulation, there is no clinical evidence suggesting prophylactic use of dialysis can prevent CIN (41). No benefit has been reported for post-procedural dialysis either (42). Marenzi et al. reported the use of hemofiltration might be beneficial in the prevention of CIN (43). However, it remains unclear whether it was related to increased clearance through dialysis or due to alkalinizing agents used during filtration.●      Treatment of hypoperfusionDue to the negative effect of renal hypoperfusion, regardless of its etiology, with contrast administration resulting in increased CIN risk, utilization of short time assisted devices increasing cardiac output might reduce this risk. Flaherty and colleagues performed a randomized clinical trial and found usage of a Microaxial percutaneous assist device (Impella) was associated with a lower likelihood of acute kidney injury among high risk percutaneous coronary intervention (PCI) patients with reduced left ventricular ejection fraction ≤ 35% (odds ratio (OR): 0.13, 95% CI: 0.09 – 0.31, P< 0.001) (44). These findings might be associated with resultant reduced CIN risk. However, larger studies are warranted.●      Balanced hydration systemAnother proposed mechanism in CIN prevention has been attributed to a balanced hydration procedure. This process has been suggested based on the theory that as urine output becomes higher, the contrast concentration in kidneys would become lower ultimately resulting in decreasing CIN risk. Briguori et al. implemented Renal Insufficiency After Contrast Media Administration Trial II (REMEDIAL II) trial to assess the feasibility of the RenalGuard system (PLC Medical Systems, Inc, Franklin, MA) in the prevention of CIN. Briefly, the mentioned system consists of closed-loop fluid management that consistently monitors and evaluates hydration status and urine output. 294 candidates for coronary or peripheral angiography/angioplasty with an estimated glomerular filtration rate (eGFR) of ≤ 30 ml/min/1.73 m2 and/or risk score of at least 11 were selected and randomly allocated to control (sodium bicarbonate and N-AC administration) or RenalGuard (hydration with saline and N-AC under RenalGuard system control with furosemide administration) group. The intervention group received an initial bolus for 30 minutes and furosemide (0.25 mg/kg) would be prescribed to increase urine output to ≥ 300 ml/h. They found CIN was significantly decreased in the RenalGuard arm compared to controls (11% (16 out of 146 subjects) vs. 20.5% (30 out of 146 subjects), OR: 0.47, 95% CI: 0.24 – 0.92). Different administration routes of N-AC (oral agent for controls and intravenous route for intervention group) resulting in probable variable bioavailability of the drug as well as their reported data applicable to a subset of chronic kidney disease (CKD) patients might be considered for extension of the outcomes (45).Likewise, the Induced Diuresis With Matched Hydration Compared to Standard Hydration for Contrast-Induced Nephropathy Prevention (MYTHOS) trial using the RenalGuard system was performed for CKD patients who underwent coronary procedures. 170 subjects with eGFR< 60 ml/min/1.73 m2 were randomly assigned to standard intravenous saline hydration as a control group (n= 83) or furosemide with matched hydration as an intervention group (n= 87). The intervention arm received 250 ml of normal saline as well as 0.5 mg/kg of furosemide to reach a urine output of more than 300 ml/h. Patients in the intervention group experienced CIN less frequently rather than controls (4.6% vs. 18%, P= 0.005). Single-center and not-blinded study design, as well as pre-determined hydration protocol in the intervention group, were some limitations related to the mentioned project (46).●      Renal coolingAlso, a cooling renal method based on the theory of decreasing oxidative injury in lower temperatures in the context of contrast injection has been announced. However, it did not show any promising outcome in terms of CIN prevention. For instance, Stone and colleagues performed a randomized trial and allocated 128 cardiac catheterization candidates with CKD (estimated creatinine clearance: 20-50 ml/min) to control (n= 70) and intervention (n= 58) groups. In addition to hydration, the latter group underwent systemic hypothermia at 33-34 °C starting before contrast injection toward three hours post-procedure followed by rewarming to 36 °C with a rate of 1 °C per hour afterward. CIN was observed in 18.6% and 22.4% of normothermia and hypothermia groups, respectively. However, there was no significant association neither in unadjusted nor in adjusted models (OR: 1.27, 95% CI: 0.53 – 3.00, P= 0.59 and OR: 0.83, 95% CI: 0.18 – 3.78, P= 0.81, respectively) (47).●      Ischemic preconditioningThe hypothesis of ischemic preconditioning, as multiple short cycles of ischemia and reperfusion in one organ, could be effective on another organ, on reduction of CIN has been tested in a randomized clinical trial on 100 subjects which revealed four 5-minute inflation-deflation cycles of blood pressure cuff to 50 mmHg above each patient systolic blood pressure before coronary angiography (CA) had been associated with a decreased likelihood of CIN compared to controls (OR: 0.21, 95% CI: 0.07 – 0.57, P= 0.002) (48). Although this procedure can be applied in all clinical settings, further studies with a larger sample size are required.●       Other agentsOne small study showed infusion of sodium bicarbonate might be more effective in the prevention of CIN rather than isotonic saline (49). However, subsequent larger trials failed to prove this association (25,26). Therefore, sodium bicarbonate is not recommended to be used for this purpose by the Consensus Working Panel (22).Other pharmacologic agents include ascorbic acid, diuretics, mannitol, calcium channel blockers, fenoldopam, dopamine, atrial natriuretic peptide, L-arginine, theophylline, and statins have been reported in the literature in terms of CIN prevention with controversial results (50–62).The role of contrast volume●       Contrast volume as a risk for CINContrast volume has been shown to be an independent risk factor for CIN (63–65). It has been previously proved the amount of contrast correlates with the incidence of CIN (66). After a data analysis of 53780 vascular interventions, Lee et al. indicated CIN was correlated with CKD stage in a way that the incidence of AKI in the context of contrast administration raised with each CKD stage (CKD stage 1: 0.39%, CKD stage 2: 0.45%, CKD stage 3: 1.5%, CKD stage 4: 4.3% and CKD stage 5: 7.5%). They suggested the risk of post-contrast AKI could be reduced by using safe thresholds of contrast volume (67).Rihal et al.’s study reported the volume of contrast media administered during the PCI was correlated with acute renal failure (6). Kooiman and colleagues analyzed data from 82,120 PCI procedures and found patients who received high contrast, as defined by division of contrast volume over calculated creatinine clearance resulting in more than 3, had increased CIN odds in both univariate and multivariate regression models (OR: 1.61, 95% CI: 1.46 – 1.79, P< 0.001 and OR: 1.77, 95% CI: 1.58 – 1.98, P< 0.001, respectively) (68). Likewise, another observational study on 561 patients suffering from myocardial infarction who underwent PCI revealed CIN was significantly higher among those with a contrast ratio (measured by administered contrast volume divided by calculated maximum contrast agent dose) of more than 1 in comparison to the ratio of less than one (34.6% vs. 3%, P< 0.001) (65). Kane et al. reported the rate of CIN in patients with CKD undergoing CA could be reduced by ultra-low contrast volumes (69). However, even small amounts of contrast can deteriorate renal function, especially among high-risk patients (70). A small study on 30 patients with eGFR< 45 ml/min/1.73m2 underwent CA/PCI with ultra-low volume contrast media showed utilization of this kind of agent was safe with no reported increased serum creatinine 48 hours post-procedure (71). However, a single study design and small sample size are potential limitations needed to be considered. 123 subjects with at least stage 3 of CKD experienced CA/PCI was selected by Kelly and colleagues. They used a novel ultra-low contrast delivery technique with an automated contrast injector for their procedures and reported a CIN rate of 3.3%. Quite a small sample size, as well as retrospective study design and performance in a single-center, should be considered for the generalization of their findings (72). Although the CIN rate was lower among CKD patients who underwent PCI with ultra-low contrast (n= 8) compared with the conventional group (n= 103) in another retrospective study, the difference was not statistically significant (0 vs. 15.5%, P= 0.28). Asymmetric sample distribution between groups and their small cohort size might limit their outcomes (73).Mariani et al. proposed the theory of zero contrast volume and performed MOZART (Minimizing cOntrast utiliZation With IVUS Guidance in coRonary angioplasTy) trial to assess whether intravascular ultrasound (IVUS) could decrease contrast exposure compared to the routine method. 83 PCI candidate patients were selected and randomly assigned to routine angiography (n= 42) or IVUS method (n= 41) with matched clinical and laboratory data. The median contrast volume was significantly lower in IVUS rather than in the routine angiography group (20 ml, interquartile range (IQR): 12.5 – 30 ml vs. 64.5 ml, IQR: 42.8 – 97 ml, P< 0.001). Also, the ratio of contrast volume to creatinine clearance was remarkably lower in the IVUS group (0.4, IQR: 0.2 – 0.6 vs. 1.0, IQR: 0.6 – 1.9, P< 0.001) (74). Although they found a promising outcome, the higher cost of IVUS might be a limiting factor for usage in clinical settings. On the other hand, it has been suggested that contrast volume reduction before contrast exposure may lower the risk of CIN (75).●      Methods for reducing contrast volume administrationIn terms of reducing contrast volume administration, few studies are available. Mehran et al. performed a randomized clinical trial to assess the efficiency of contrast reduction in patients with underlying renal diseases who underwent CA. 578 patients in stage III (eGFR between 30 and 60 ml/min) and IV (eGFR between 20 and 30 ml/min) of CKD with at least two further criteria of New York heart association (NYHA) functional class of III or IV of heart failure, diabetes mellitus (treated with either insulin or oral agents), anemia, hypertension, albuminuria or age of at least 75 years were randomly assigned to hydration (n= 286) or hydration plus AVERT system group (n= 292). The latter system is a contrast modulation system designed to adjust the pressure of contrast injection toward the patient. The relative reduction in contrast volume was 15.5% (hydration group: 101.3 ± 71.1 ml vs. hydration plus AVERT group: 85.6 ± 50.5 ml, P= 0.02). The distribution of AKI induced by contrast did not differ significantly (26.6% vs. 27%, P= 0.70, respectively) (76). Likewise, Gurm and colleagues performed an observational study on 114 patients with eGFR of 20 – 60 ml/min/1.73m2 to assess the feasibility of contrast volume reduction during CA or PCI using DyeVertTM Plus Contrast Reduction System (DyeVert Plus System, Osprey Medical). Data analysis of 105 successfully recruited patients revealed the contrast volume saving of 40.1 ± 8.8% per each performed procedure. AKI induced by contrast agent was observed in three (2.6%) of patients (77). The small sample size and observational design of the study should be considered for the generalization of reported data.●      Automated contrast injection devicesAlthough data analysis of 60,884 candidates who underwent PCI revealed contrast agent usage was lower in centers that used automated contrast injectors compared to those centers not used this method (199 ± 84 ml vs. 204 ± 82 ml, P< 0.0001), no difference had been found in terms of CIN occurrence (3.11% vs. 3.42%, P= 0.15) (78). On the other hand, Minsinger and colleagues performed a meta-analysis and found automated contrast injectors decreased contrast volume up to 45 ml per subject (95% CI: 0.78 – 0.93, P< 0.001), and it was associated with a 15% reduction in CIN compared to manual injection methods (OR: 0.85, 95% CI: 0.78 – 0.93, P< 0.001) (79).

IntroductionHypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease (1,2)  and is the most frequent cause of sudden cardiac death (SCD) in young individuals, particularly athletes with a high level of training (3–5).HCM is a prevalent hereditary cardiovascular condition that affects one in 500 people in the general population (6,7). The cumulative proportion of sudden cardiac death (SCD) events in childhood hypertrophic cardiomyopathy (HCM) within five years of diagnosis ranged from 8% to 10% (8,9). It is characterized by inadequate relaxation, hypercontractility, reduced compliance, and left ventricle hypertrophy (10–12). HCM manifests as a chronic, progressive illness that can have a severe, transformative effect on a person’s life and significantly lower quality of life. Data on the cost to society associated with HCM has shown significant increases in all-cause hospitalizations, hospital days, outpatient visits, and total healthcare costs. The majority of cost increases can be attributed to increased hospitalizations and hospital days among symptomatic patients (13). The most often reported symptoms include syncope, palpitations, exertional dyspnea, shortness of breath, ankle swelling, exhaustion, sense of disorientation, and lightheadedness (14,15).Among the estimated 700,000 patients with HCM, only 100,000 have been diagnosed in the United States (16). Underdiagnosis may be due in part to challenges in the diagnosis of asymptomatic HCM patients, who typically receive a diagnosis by chance or via systematic screening efforts (12). However, developments in the understanding of genetic and phenotypic characteristics of HCM have promise for improving the identification of the condition. Over the last twenty years, the condition has been linked to abnormalities in genes that encode proteins of the cardiomyocyte’s contractile machinery (6,17,18). It appears that significant progress has been made in understanding the illness from both a genetic and clinical standpoint (19).Despite new developments, HCM remains underdiagnosed. Although the population prevalence of HCM is between 1:200 and 1:500, only 10–20% of cases are found by clinical means (20). Patients with HCM can have a normal life expectancy but a notable percentage can develop HCM-related complications including heart failure, atrial fibrillation (AF), and cardioembolic stroke, while a smaller percentage have SCD or life-threatening ventricular arrhythmias (21). SCD is the most common cause of mortality among these patients and frequently occurs during exercise. However, it often goes undetected until death, as many individuals experience minimal or no significant symptoms (6,11). Consequently, a high index of diagnostic suspicion, accurate identification, and a thorough clinical examination of patients and family members are crucial for early identification and treatment (20). Identifying high-risk patients is crucial to lowering the risk of SCD in young individuals with HCM, as effective treatment has the potential to significantly reduce HCM mortality and morbidity (22,23) This can be achieved through exercise limitation, medication therapies, and the use of implantable cardioverter defibrillators (ICDs) (24) . Therefore, there has been considerable interest in improving diagnostic accuracy among HCM patients, especially young patients, to inform intervention (21).The HCM diagnosis is based on imaging techniques, such as echocardiography or cardiovascular magnetic resonance (CMR), that reveal increasing LV wall thickness (21). Thorough investigation has led to a better comprehension of risk categorization for patients with HCM. The latest European Society of Cardiology (ESC) guidelines propose evaluating clinical examination, family history, 48-hour electrocardiography (ECG), echocardiography, and exercise testing for this purpose (6,11). The European Society of Cardiology (ESC) has proposed specific cardiac screening guidelines for young competitive athletes (25), which include assessing symptoms and family medical history (e.g., premature death, HCM), conducting a physical examination, and performing a resting 12-lead ECG. A recent Danish study revealed that a large proportion of individuals who experienced SCD due to HCM had previous symptoms, and most of them had sought medical attention before their death, in contrast to the control group (26). These findings suggest there is an opportunity to improve the identification of HCM among at-risk patients, as many patients seek treatment.The ECG continues to be a fundamental aspect of evaluating patients with HCM. Moreover, it is experiencing a ”renaissance” in the realm of cardiomyopathies, not only due to its cost-effectiveness and widespread accessibility, but also because it offers information pertinent to morphology, function, and genetic foundation simultaneously (21) HCM has diagnosis so far relied on identification of left ventricular hypertrophy (LVH) with a wall thickness greater than 15 mm using echocardiography or CMR. However, this degree of LVH is not exclusive to HCM and may stem from various other pathological conditions, widening the differential. In such instances, the ECG is highly valuable in assisting with the differentiation between sarcomeric HCM and its phenocopies (21). There is a growing body of literature evaluating the accuracy of ECG markers in predicting HCM, however, there remains a need for research on the extent to which ECG findings are predictive of HCM identified on echocardiography. Therefore, the aim of this study was to evaluate the prevalence of abnormal ECG findings, including LVH, T wave inversion, left bundle branch block (LBBB), and left atrial enlargement in participants with suspected HCM detected during screening echocardiography.

Skateboarders tend to be young and healthy with a disregard for authority and societal norms. During the SARS-CoV-2 pandemic, they tended to disregard social distancing directives from politicians and public health authorities. While our immediate reaction may be to condemn their behavior, it’s possible that their defiance may help society and save lives by building up herd immunity in the safest possible manner.