Tag: pubmed

Front Digit Health. 2023 Aug 9;5:1187578. doi: 10.3389/fdgth.2023.1187578. eCollection 2023.

ABSTRACT

INTRODUCTION: In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult.

METHODS: By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost.

RESULTS: Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

PMID:37621964 | PMC:PMC10445490 | DOI:10.3389/fdgth.2023.1187578

Front Comput Neurosci. 2023 Aug 9;17:1223258. doi: 10.3389/fncom.2023.1223258. eCollection 2023.

ABSTRACT

Reservoir computing provides a time and cost-efficient alternative to traditional learning methods. Critical regimes, known as the “edge of chaos,” have been found to optimize computational performance in binary neural networks. However, little attention has been devoted to studying reservoir-to-reservoir variability when investigating the link between connectivity, dynamics, and performance. As physical reservoir computers become more prevalent, developing a systematic approach to network design is crucial. In this article, we examine Random Boolean Networks (RBNs) and demonstrate that specific distribution parameters can lead to diverse dynamics near critical points. We identify distinct dynamical attractors and quantify their statistics, revealing that most reservoirs possess a dominant attractor. We then evaluate performance in two challenging tasks, memorization and prediction, and find that a positive excitatory balance produces a critical point with higher memory performance. In comparison, a negative inhibitory balance delivers another critical point with better prediction performance. Interestingly, we show that the intrinsic attractor dynamics have little influence on performance in either case.

PMID:37621962 | PMC:PMC10445160 | DOI:10.3389/fncom.2023.1223258

Ultrasound Int Open. 2023 Aug 23;9(1):E11-E17. doi: 10.1055/a-2044-2855. eCollection 2023 Jan.

ABSTRACT

Objectives The discrimination of ovarian lesions presents a significant problem in everyday clinical practice with ultrasonography appearing to be the most effective diagnostic technique. The aim of our study was to externally evaluate the performance of different diagnostic models when applied by examiners with various levels of experience. Methods This was a diagnostic accuracy study including women who were admitted for adnexal masses, between July 2018 and April 2021, to a Greek tertiary oncology center. Preoperatively sonographic data were evaluated by an expert gynecologist, a 6 ^th and a 1 ^st year gynecology resident, who applied the International Ovarian Tumor Analysis (IOTA) Simple Rules (SR) and Assessment of Different NEoplasias in the adneXa (ADNEX) model to discriminate between benign and malignant ovarian tumors. The explant pathology report was used as the reference diagnosis. Kappa statistics were used for the investigation of the level of agreement between the examined systems and the raters. Results We included 66 women, 39 with benign and 27 with malignant ovarian tumors. ADNEX (with and without “CA-125”) had high sensitivity (96-100%) when applied by all raters but a rather low specificity (36%) when applied by the 1st year resident. SR could not be applied in 6% to 17% of the cases. It had slightly lower sensitivity, higher specificity, and higher overall accuracy, especially when applied by the 1st year resident (61% vs. 92%), compared to ADNEX. Conclusion Both ADNEX and SR can be utilized for screening in non-oncology centers since they offer high sensitivity even when used by less experienced examiners. In the hands of inexperienced examiners, SR appears to be the best model for assessing ovarian lesions.

PMID:37621952 | PMC:PMC10446913 | DOI:10.1055/a-2044-2855

Front Psychol. 2023 Aug 9;14:1108093. doi: 10.3389/fpsyg.2023.1108093. eCollection 2023.

ABSTRACT

BACKGROUND: Studies examining weight change patterns and depression are scarce and report inconsistent findings. This study-aimed to elucidate the association between weight change patterns and the risk of depression in a large, representative sample of US adults.

METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) 2005-2018 was analyzed. Five weight change groups were categorized: stable normal, weight loss, weight gain, maximum overweight, and stable obesity. Depression was ascertained using the validated Patient Health Questionnaire (PHQ-9) and depression was defined as PHQ score ≥ 10.

RESULTS: A total of 17,556 participants were included. Compared with participants who maintained normal weight, stable obesity participants had increased risks of depression across adulthood from age 25 years to 10 years before the survey (OR = 1.61, 95% CI =1.23 to 2.11), in the 10 years period before the survey (OR = 2.15, 95% CI =1.71 to 2.70), and from age 25 years to survey (OR = 1.88, 95% CI =1.44 to 2.44). Weight gain was associated with an increased risk of depression from age 25 years to 10 years before the survey (OR = 1.71, 95% CI = 1.41 to 2.04), in the 10 years period before the survey (OR = 1.73, 95% CI = 1.35 to 2.21), and for the period from age 25 years to survey (OR = 1.83, 95% CI = 1.49 to 2.24). In the stratified analyses, we found statistically significant interactions with sex.

CONCLUSION: Our study suggested that stable obesity and weight gain across adulthood were associated with increased risks of depression.

PMID:37621933 | PMC:PMC10446764 | DOI:10.3389/fpsyg.2023.1108093

Influenza Other Respir Viruses. 2023 Aug 22;17(8):e13174. doi: 10.1111/irv.13174. eCollection 2023 Aug.

ABSTRACT

BACKGROUND: The severity of Severe Acute Respiratory Syndrome Coronavirus 2 infection varies with age and time. Here, we quantify how age-specific risks of hospitalization, intensive care unit (ICU) admission, and death upon infection changed from February 2020 to June 2021 in the Netherlands.

METHODS: A series of large representative serology surveys allowed us to estimate age-specific numbers of infections in three epidemic periods (late-February 2020 to mid-June 2020, mid-June 2020 to mid-February 2021, and mid-February 2021 to late-June 2021). We accounted for reinfections and breakthrough infections. Severity measures were obtained by combining infection numbers with age-specific numbers of hospitalization, ICU admission, and excess all-cause deaths.

RESULTS: There was an accelerating, almost exponential, increase in severity with age in each period. The rate of increase with age was the highest for death and the lowest for hospitalization. In late-February 2020 to mid-June 2020, the overall risk of hospitalization upon infection was 1.5% (95% confidence interval [CI] 1.3-1.8%), the risk of ICU admission was 0.36% (95% CI: 0.31-0.42%), and the risk of death was 1.2% (95% CI: 1.0-1.4%). The risk of hospitalization was significantly increased in mid-June 2020 to mid-February 2021, while the risk of ICU admission remained stable over time. The risk of death decreased over time, with a significant drop among ≥70-years-olds in mid-February 2021 to late-June 2021; COVID-19 vaccination started early January 2021.

CONCLUSION: Whereas the increase in severity of Severe Acute Respiratory Syndrome Coronavirus 2 with age remained stable, the risk of death upon infection decreased over time. A significant drop in risk of death among elderly coincided with the introduction of COVID-19 vaccination.

PMID:37621921 | PMC:PMC10444602 | DOI:10.1111/irv.13174

Ups J Med Sci. 2022 May 30;127. doi: 10.48101/ujms.v127.8577. eCollection 2022.

ABSTRACT

BACKGROUND: Although neuropathic pain is a significant problem in polyneuropathy, the underlying molecular mechanisms are poorly understood. The endogenous bioactive lipids 2-arachidonoyl-glycerol (2-AG), oleoylethanolamide (OEA), palmitoylethanolamide (PEA), and stearoylethanolamide (SEA) are known to influence pain and inflammation in the peripheral nervous system. The aim of this study was to explore the plasma levels of endocannabinoids and related lipids and health-related quality of life in patients with polyneuropathy with and without pain.

METHODS: Patients (n = 48) with Chronic Idiopathic Axonal Neuropathy were included. Clinical data were retrieved from medical files. All patients filled out the SF-36 and EQ-5D questionnaires. In addition, blood samples were analyzed for 2-AG, OEA, PEA, and SEA.

RESULTS: Neuropathic pain was reported in 21 of the patients. There were significantly lower levels of 2-AG in patients with neuropathic pain (P = 0.03), but there were no significant differences in OEA (P = 0.61), PEA (P = 0.95), or SEA (P = 0.97) levels. The patients reporting pain in the hands had significantly lower SEA levels, 10.0 versus 15.0 (P = 0.03). The levels of 2-AG were significantly higher among patients reporting paresthesia in their feet (80.1 vs. 56.3; P = 0.02). Levels of PEA, SEA, and 2-AG were decreased in patients with loss of vibration. PEA and SEA were decreased in patients with loss of pain and temperature, and SEA decreased in patients with loss of sense of touch. However, the differences in the levels of bioactive endogenous lipids were not statistically significant when corrected for multiple comparisons.

CONCLUSION: Alterations of 2-AG levels between polyneuropathy patients with and without neurogenic pain indicate that it could play an essential role. Further studies are warranted.

PMID:37621890 | PMC:PMC10445423 | DOI:10.48101/ujms.v127.8577

BMJ Lead. 2023 Aug 24:leader-2023-000737. doi: 10.1136/leader-2023-000737. Online ahead of print.

ABSTRACT

BACKGROUND: Allied health professionals (AHPs) are an important group within the National Health Service (NHS) in the UK and make up a large portion of the workforce. Investment in AHP leadership is believed to lead to improvements in patient care, resource use, collaboration and innovation. This study aims to assess the current state of AHP strategic leadership within the NHS.

METHODS: A freedom of information (FOI) request was sent to all NHS Trusts and health boards (HBs) within the UK NHS. The questions focused on the AHP workforce, with a particular interest in the chief AHPs (or equivalent roles) working in an NHS setting. Analysis of the FOI used a range of descriptive statistics.

RESULTS: Of the 217 Trusts/HBs contacted, responses were received from 160 (74%). The majority (81%) reported that they employed a Chief AHP or equivalent role, with only 14% of these having a position on the Trust/HB executive board. There were 50 different job titles reported as the titles for the chief AHP or equivalent roles: with director of AHPs (18.6%), lead AHP (13.9%) and chief AHP (11.6%) being the most reported titles. The results identified an inequity of representation of AHP professions within senior AHP leadership; with most of these roles (70%) held by physiotherapists and occupational therapists.

CONCLUSION: Changes in AHP strategic leadership are needed to address the inequities identified in this study. Addressing these issues is required to enable inclusive leadership, which is crucial to improve the contribution of AHPs to healthcare.

PMID:37620124 | DOI:10.1136/leader-2023-000737

Genome Res. 2023 Aug 24:gr.277525.122. doi: 10.1101/gr.277525.122. Online ahead of print.

ABSTRACT

Principal Component Analysis (PCA) is widely utilized in statistics, machine learning, and genomics for dimensionality reduction and uncovering low-dimensional latent structure. To address the challenges posed by ever-growing data size, fast and memory-efficient PCA methods have gained prominence. In this paper, we propose a novel Randomized Singular Value Decomposition (RSVD) algorithm implemented in PCAone, featuring a window-based optimization scheme that enables accelerated convergence while improving the accuracy. Additionally, PCAone incorporates out-of-core and multithreaded implementations for the existing Implicitly Restarted Arnoldi Method (IRAM) and RSVD. Through comprehensive evaluations using multiple large-scale real-world datasets in different fields, we demonstrate the advantage of PCAone over existing methods. The new algorithm achieves significantly faster computation time while maintaining accuracy comparable to the slower IRAM method. Notably, our analyses of UK Biobank, comprising around 0.5 million individuals and 6.1 million common SNPs, demonstrate that PCAone accurately computes the top 40 principal components within 9 hours. This analysis effectively captures population structure, signals of selection, structural variants, and low recombination regions, utilizing less than 20 GB of memory and 20 CPU threads. Furthermore, when applied to single-cell RNA sequencing data featuring 1.3 million cells, PCAone, accurately capturing the top 40 principal components in 49 minutes. This performance represents a 10-fold improvement over state-of-the-art tools.

PMID:37620119 | DOI:10.1101/gr.277525.122

Lancet Diabetes Endocrinol. 2023 Sep;11(9):637-640. doi: 10.1016/S2213-8587(23)00153-5.

NO ABSTRACT

PMID:37620062 | DOI:10.1016/S2213-8587(23)00153-5

Inj Prev. 2023 Aug 23:ip-2023-044905. doi: 10.1136/ip-2023-044905. Online ahead of print.

ABSTRACT

INTRODUCTION: Musculoskeletal injury (MSK-I) mitigation and prevention programmes (MSK-IMPPs) have been developed and implemented across militaries worldwide. Although programme efficacy is often reported, development and implementation details are often overlooked, limiting their scalability, sustainability and effectiveness. This scoping review aimed to identify the following in military populations: (1) barriers and facilitators to implementing and scaling MSK-IMPPs; (2) gaps in MSK-IMPP research and (3) future research priorities.

METHODS: A scoping review assessed literature from inception to April 2022 that included studies on MSK-IMPP implementation and/or effectiveness in military populations. Barriers and facilitators to implementing these programmes were identified.

RESULTS: From 132 articles, most were primary research studies (90; 68.2%); the remainder were review papers (42; 31.8%). Among primary studies, 3 (3.3%) investigated only women, 62 (69%) only men and 25 (27.8%) both. Barriers included limited resources, lack of stakeholder engagement, competing military priorities and equipment-related factors. Facilitators included strong stakeholder engagement, targeted programme design, involvement/proximity of MSK-I experts, providing MSK-I mitigation education, low burden on resources and emphasising end-user acceptability. Research gaps included variability in reported MSK-I outcomes and no consensus on relevant surveillance metrics and definitions.

CONCLUSION: Despite a robust body of literature, there is a dearth of information about programme implementation; specifically, barriers or facilitators to success. Additionally, variability in outcomes and lack of consensus on MSK-I definitions may affect the development, implementation evaluation and comparison of MSK-IMPPs. There is a need for international consensus on definitions and optimal data reporting elements when conducting injury risk mitigation research in the military.

PMID:37620010 | DOI:10.1136/ip-2023-044905