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Introduction: Pulsed Field Ablation (PFA) is a novel non-thermal method for cardiac ablation, relying on irreversible electroporation induced by high-energy pulsed electric fields (PEFs) to create localized lesions in the heart atria. A significant challenge in optimizing PFA treatments is determining the lethal electric field threshold (EFT), which governs ablation volume and varies with PEF waveform parameters. However, the proprietary nature of device developer’s waveform characteristics and the lack of standardized nonclinical testing methods have left optimal EFTs for cardiac ablation uncertain. Methods: To address this gap, we introduced a laboratory protocol employing human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in monolayer format to evaluate the impact of a range of clinically relevant biphasic pulse parameters on lethal EFT and adiabatic heating (AH). Cell death areas were assessed using fluorescent dyes and confocal microscopy, while lethal EFTs were quantified through comparison with electric field numerical simulations. Results and conclusion: Our study confirmed a strong correlation between cell death in hiPSC-CMs and the number and duration of pulses in each train, with pulse repetition frequency exerting a comparatively weaker influence. Fitting of these results through machine learning algorithms were used to develop an open-source online calculator. By estimating lethal EFT and associated temperature increases for diverse pulse parameter combinations, this tool, once validated, has the potential to significantly reduce reliance on animal models during early-stage device de-risking and performance assessment. This tool also offers a promising avenue for advancing PFA technology for cardiac ablation medical devices to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

Purpose: Extrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking. Methods: A total of 3,921 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database, which form the training and internal validation cohorts of the survival prediction model. The endpoint was an overall survival of 0.5-5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation (n = 68) was performed to evaluate the generalization ability of the selected model. Results: Among machine learning algorithms, the random forest model performs best on internal validation, whose area under the curve (AUC) is 0.736-0.800. The net benefit is higher than the TNM classification in decision curve analysis. The AUC of this model on the external validation cohort is 0.739-0.811. This model was then deployed online as a free, publicly available prediction tool of EPSCC (http://42.192.80.13:4399/). Conclusion: This study provides an excellent online survival prediction tool for EPSCC with machine learning and large-scale data. Age, TNM stages, and surgery (including potential performance status information) are the most critical factors for the prediction model. [ABSTRACT FROM AUTHOR]

Power calculation is a necessary step when planning genome-wide association studies (GWAS) to ensure meaningful findings. Statistical power of GWAS depends on the genetic architecture of phenotype, sample size, and study design. While several computer programs have been developed to perform power calculation for single SNP association testing, it might be more appropriate for GWAS power calculation to address the probability of detecting any number of associated SNPs. In this paper, we derive the statistical power distribution across causal SNPs under the assumption of a point-normal effect size distribution. We demonstrate how key outcome indices of GWAS are related to the genetic architecture (heritability and polygenicity) of the phenotype through the power distribution. We also provide a fast, flexible and interactive power calculation tool which generates predictions for key GWAS outcomes including the number of independent significant SNPs, the phenotypic variance explained by these SNPs, and the predictive accuracy of resulting polygenic scores. These results could also be used to explore the future behaviour of GWAS as sample sizes increase further. Moreover, we present results from simulation studies to validate our derivation and evaluate the agreement between our predictions and reported GWAS results. [ABSTRACT FROM AUTHOR]

Purpose: To establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle. Methods: We have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with <5 oocytes retrieved during assisted reproductive technology treatment cycles. Least Absolute Shrinkage and Selection Operator logistic regression with 5-fold cross validation methods was applied to construct the model, and that with the lowest scaled log-likelihood was selected as the final one. Results: The areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed. Conclusions: Based on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models. [ABSTRACT FROM AUTHOR]

Background: Predicting the number of oocytes retrieved (NOR) following controlled ovarian stimulation (COS) is the only way to ensure effective and safe treatment in assisted reproductive technology (ART). To date, there have been limited studies about predicting specific NOR, which hinders the development of individualized treatment in ART. Objective: To establish an online tool for predicting NOR. Materials and Methods: In total, 621 prospective routine gonadotropin releasing hormone (GnRH) antagonist COS cycles were studied. Independent variables included age, body mass index, antral follicle counts, basal FSH, basal and increment of anti-mullerian hormone, Luteinizing hormon, estradiol, testosterone, androstenedione, and inhibin B. The outcome variable was NOR. The independent variables underwent appropriate transformation to achieve a better fit for a linear relationship with NOR. Pruned forward selection with holdback validation was then used to establish predictive models. Corrected Akaike's information criterion, Schwarz–Bayesian information criterion, scaled – log [likelihood], and the generalized coefficient of determination (R 2) were used for model evaluation. Results: A multiple negative binomial regression model was used for predicting NOR because it fitted a negative binomial distribution. We established Model 1, using basal ovarian reserve markers, and Model 2, using both basal and early dynamic markers for predicting NOR following COS. The generalized R 2 values were 0.54 and 0.51 for Model 1 and 0.64 and 0.62 for Model 2 in the training and validation sets, respectively. Conclusion: Models 1 and 2 could be applied to different scenarios. For directing the starting dose of recombinant follicle stimulation hormone (rFSH), Model 1 using basic predictors could be used prior to COS. Model 2 could be used for directing the adjustment of rFSH dosages during COS. An online tool (http://121.43.113.123:8002/) based on these two models is also developed. We anticipate that the clinical application of this tool could help the ART clinics to reduce iatrogenic ovarian under- or over-responses, and could reduce costs during COS for ART. [ABSTRACT FROM AUTHOR]

BackgroundPredicting the number of oocytes retrieved (NOR) following controlled ovarian stimulation (COS) is the only way to ensure effective and safe treatment in assisted reproductive technology (ART). To date, there have been limited studies about predicting specific NOR, which hinders the development of individualized treatment in ART.ObjectiveTo establish an online tool for predicting NOR.Materials and MethodsIn total, 621 prospective routine gonadotropin releasing hormone (GnRH) antagonist COS cycles were studied. Independent variables included age, body mass index, antral follicle counts, basal FSH, basal and increment of anti-mullerian hormone, Luteinizing hormon, estradiol, testosterone, androstenedione, and inhibin B. The outcome variable was NOR. The independent variables underwent appropriate transformation to achieve a better fit for a linear relationship with NOR. Pruned forward selection with holdback validation was then used to establish predictive models. Corrected Akaike’s information criterion, Schwarz–Bayesian information criterion, scaled –log[likelihood], and the generalized coefficient of determination (R2) were used for model evaluation.ResultsA multiple negative binomial regression model was used for predicting NOR because it fitted a negative binomial distribution. We established Model 1, using basal ovarian reserve markers, and Model 2, using both basal and early dynamic markers for predicting NOR following COS. The generalized R2 values were 0.54 and 0.51 for Model 1 and 0.64 and 0.62 for Model 2 in the training and validation sets, respectively.ConclusionModels 1 and 2 could be applied to different scenarios. For directing the starting dose of recombinant follicle stimulation hormone (rFSH), Model 1 using basic predictors could be used prior to COS. Model 2 could be used for directing the adjustment of rFSH dosages during COS. An online tool (http://121.43.113.123:8002/) based on these two models is also developed. We anticipate that the clinical application of this tool could help the ART clinics to reduce iatrogenic ovarian under- or over-responses, and could reduce costs during COS for ART.

Despite education about the risks of excessive sun exposure, teenagers in Australia are sun-seeking, with sunburn common in summer. Conversely, some regular (time-limited) exposure to sunlight (that avoids sunburn) is necessary for vitamin D and healthy bones and other molecules important for immune and metabolic health. New interventions are thus required to better support teenagers to make healthy and balanced decisions about their sun behaviors. This paper describes the development of a prototype online tool—a smartphone app—that aimed to foster safe sun practices in teenagers. We recruited young adolescents (aged 12–13 years, n = 24) as “co-researchers” to provide ongoing input into the nature and design of the online tool. This age group was selected, as it is a critical time when young people transition from primary education, where “SunSmart” behaviors are entrenched in Australian schools, to high school, where risky behaviors emerge. Through a series of interviews and workshops, we codesigned an Apple iOS smartphone app with the co-researchers, leading health promotion professionals, researchers, and app designers. The developed app, Sun Safe, contains educational content relevant to teenagers about safe sun behaviors, complemented by other features requested by co-researchers and stakeholders to help engage young people, including gamified quizzes to test their sun health knowledge, real-time weather data on the UV Index and temperature, a sunscreen application timer, and reminders to check the UV Index. The developed prototype app was rated well by co-researchers, suggesting it is suitable for further feasibility and efficacy testing as an intervention tool to improve knowledge and promote safe sun behaviors by young adolescents.

This study presents a web-based sentence comprehension test aimed at identifying high school students who are at risk for a language delay. By assessing linguistic skills on a sample of high school students with Italian as an L2 and their monolingual peers, attending a vocational school, we were able to identify a subgroup of L2 students with consistent difficulties in sentence comprehension, though their reading skills were within the average range. The same subgroup revealed to experience a lack of support within the school context, suggesting that poor L2 skills might be a critical variable to consider in order to identify students at risk for school exclusion. Regarding the test, accuracy to the on-line sentence comprehension task was significantly predicted by reading abilities and vocabulary skills, thus indicating that this test might represent a rapid but efficient way to assess linguistic abilities at school. We recommend that establishing a valid and practical procedure for the evaluation of linguistic skills in bilingual students who struggle with their L2 is the first step toward promoting social inclusion in the multilingual classroom, in order to increase their ability to actively participate in school and social activities.

Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test1) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals.

This study presents a web-based sentence comprehension test aimed at identifying high school students who are at risk for a language delay. By assessing linguistic skills on a sample of high school students with Italian as an L2 and their monolingual peers, attending a vocational school, we were able to identify a subgroup of L2 students with consistent difficulties in sentence comprehension, though their reading skills were within the average range. The same subgroup revealed to experience a lack of support within the school context, suggesting that poor L2 skills might be a critical variable to consider in order to identify students at risk for school exclusion. Regarding the test, accuracy to the on-line sentence comprehension task was significantly predicted by reading abilities and vocabulary skills, thus indicating that this test might represent a rapid but efficient way to assess linguistic abilities at school. We recommend that establishing a valid and practical procedure for the evaluation of linguistic skills in bilingual students who struggle with their L2 is the first step toward promoting social inclusion in the multilingual classroom, in order to increase their ability to actively participate in school and social activities. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test1) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals. [ABSTRACT FROM AUTHOR]