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The 8q24 region harbors multiple risk variants for distinct cancers, including >8 for prostate cancer. In this study, we conducted fine mapping of the 8q24 risk region (127.8-128.8Mb) in search of novel associations with common and rare variation in 4853 prostate cancer case patients and 4678 control subjects of African ancestry. All statistical tests were two-sided. We identified three independent associations at P values of less than 5.00×10(-8), all of which were replicated in studies from Ghana and Uganda (combined sample = 5869 case patients, 5615 control subjects; rs114798100: risk allele frequency [RAF] = 0.04, per-allele odds ratio [OR] = 2.31, 95% confidence interval [CI] = 2.04 to 2.61, P = 2.38×10(-40); rs72725879: RAF = 0.33, OR = 1.37, 95% CI = 1.30 to 1.45, P = 3.04×10(-27); and rs111906932: RAF = 0.03, OR = 1.79, 95% CI = 1.53 to 2.08, P = 1.39×10(-13)). Risk variants rs114798100 and rs111906923 are only found in men of African ancestry, with rs111906923 representing a novel association signal. The three variants are located within or near a number of prostate cancer-associated long noncoding RNAs (lncRNAs), including PRNCR1, PCAT1, and PCAT2. These findings highlight ancestry-specific risk variation and implicate prostate-specific lncRNAs at the 8q24 prostate cancer susceptibility region.

Prostate cancer is the most common non-skin cancer in males, with a ∼1.5-2-fold higher incidence in African American men when compared with whites. Epidemiologic evidence supports a large heritable contribution to prostate cancer, with over 100 susceptibility loci identified to date that can explain ∼33% of the familial risk. To explore the contribution of both rare and common variation in coding regions to prostate cancer risk, we sequenced the exomes of 2165 prostate cancer cases and 2034 controls of African ancestry at a mean coverage of 10.1×. We identified 395 220 coding variants down to 0.05% frequency [57% non-synonymous (NS), 42% synonymous and 1% gain or loss of stop codon or splice site variant] in 16 751 genes with the strongest associations observed in SPARCL1 on 4q22.1 (rs13051, Ala49Asp, OR = 0.78, P = 1.8 × 10(-6)) and PTPRR on 12q15 (rs73341069, Val239Ile, OR = 1.62, P = 2.5 × 10(-5)). In gene-level testing, the two most significant genes were C1orf100 (P = 2.2 × 10(-4)) and GORAB (P = 2.3 × 10(-4)). We did not observe exome-wide significant associations (after correcting for multiple hypothesis testing) in single variant or gene-level testing in the overall case-control or case-case analyses of disease aggressiveness. In this first whole-exome sequencing study of prostate cancer, our findings do not provide strong support for the hypothesis that NS coding variants down to 0.5-1.0% frequency have large effects on prostate cancer risk in men of African ancestry. Higher-coverage sequencing efforts in larger samples will be needed to study rarer variants with smaller effect sizes associated with prostate cancer risk.

Background Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias. Results We propose the Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several precomputed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on 4 publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance. Conclusions By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a Python package at https://pypi.org/project/MLcps/. [ABSTRACT FROM AUTHOR]

Background In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional 2-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. Results To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. Conclusion SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan. [ABSTRACT FROM AUTHOR]

Clinical Scenario: Mild traumatic brain injury, or concussion, has been associated with physical, cognitive, and emotional sequelae. Little is understood in regard to many characteristics, such as anxiety, and their effect on post-concussion symptoms. Clinical Question: Is state anxiety, trait anxiety, or anxiety sensitivity a clinical predictor of symptoms in those presenting with mild traumatic brain injury or concussion? Summary of Key Findings: A literature search returned 3 possible studies; 3 studies met inclusion criteria and included. One study reported in athletes that greater social support was associated with decreased state-anxiety, lower state anxiety post-concussion was associated with increased social support, and that those with greater social support may experience reduced anxiety, regardless of injury type sustained. One study reported baseline trait anxiety in athletes was not significantly associated with post-concussion state anxiety, but that symptoms of depression at baseline was the strongest predictor for post-concussion state anxiety. Three studies reported that state and trait anxiety are not related to increased post-concussion symptom scores. One study reported that greater anxiety sensitivity is related to higher reported post-concussion symptom scores, which may manifest as somatic symptoms following concussion, and revealed that anxiety sensitivity may be a risk factor symptom development. Clinical Bottom Line: There is low-level to moderate evidence to support that anxiety sensitivity is linked to post-concussion symptoms. State and trait anxiety do not appear to be related to post-concussion symptoms alone. Post-concussion state anxiety may occur if post-concussion symptoms of depression are present or if baseline symptoms of depression are present. Better social support may improve state anxiety post-concussion. Strength of Recommendation: There is grade B evidence to support that state and trait anxiety are not risk factors for post-concussion symptom development. There is grade C evidence to support anxiety sensitivity as a risk factor for developing post-concussion symptoms. [ABSTRACT FROM AUTHOR]

Lesbian, gay, and bisexual older adults are more likely than their heterosexual peers to experience health disparities, discrimination from healthcare providers based on sexual orientation, and rejection from their family of origin, all of which can complicate medical care and decision making, as well as end-of-life arrangements. Yet, relatively few studies of lesbian, gay, and bisexual seniors have looked at motivations for advance care and end-of-life planning, which are strategies that can help ensure that healthcare treatment and end-of-life wishes are enacted as desired. The present qualitative study investigated this topic with a purposive sample of nine lesbian, gay, and bisexual and same-gender-loving adults in a metropolitan region of the Southeastern United States. The study involved in-depth face-to-face interviews, followed by a brief pen-and-paper survey. Participants' ages ranged from 65 to 77; the sample included five men and four women. Six individuals were white/Caucasian, while three were African American/Black. We identified three themes related to motivations for advance care and end-of-life planning: wanting a sense of agency, learning from others, and reducing conflict and confusion for loved ones. We discuss the importance of these findings for social work practice with lesbian, gay, and bisexual older adults and for social work education, as well as implications for future research. [ABSTRACT FROM AUTHOR]