Exploring patterns in mental health treatment and interests of single adults in the United States: a secondary data analysis.

Objective: The objective of this study is to examine mental health treatment utilization and interest among the large and growing demographic of single adults in the United States, who face unique societal stressors and pressures that may contribute to their heightened need for mental healthcare. Method: We analyzed data from 3,453 single adults, focusing on those with possible mental health treatment needs by excluding those with positive self-assessments. We assessed prevalence and sociodemographic correlates of mental health treatment, including psychotherapy and psychiatric medication use, and interest in attending psychotherapy among participants who had never attended. Results: 26% were in mental health treatment; 17% were attending psychotherapy, 16% were taking psychiatric medications, and 7% were doing both. Further, 64% had never attended psychotherapy, of which 35% expressed interest in future attendance. There were differences in current psychotherapy attendance and psychiatric medication use by gender and sexual orientation, with women and gay/lesbian individuals more likely to engage in both forms of mental health treatment. Additionally, interest in future psychotherapy among those who had never attended varied significantly by age, gender, and race. Younger individuals, women, and Black/African-American participants showed higher likelihoods of interest in psychotherapy. Conclusion: Our research highlights a critical gap in mental health treatment utilization among single adults who may be experiencing a need for those services. Despite a seemingly higher likelihood of engagement in mental health treatment compared to the general population, only a minority of single adults in our sample were utilizing mental health treatment. This underutilization and the observed demographic disparities in mental health treatment underscore the need for targeted outreach, personalized treatment plans, enhanced provider training, and policy advocacy to ensure equitable access to mental healthcare for single adults across sociodemographic backgrounds.

[Clinical data analysis of 117 patients with ruptured abdominal aortic aneurysm].

Objective: To examine the perioperative clinical features and prognosis of patients with ruptured abdominal aortic aneurysms (rAAA) who received surgical repair. Methods: The clinical data of rAAA patients who underwent surgical repair and were admitted to the Surgical Intensive Care Unit of Beijing Anzhen Hospital, Capital Medical University from August 2005 to November 2020 were retrospectively analyzed, including the general clinical features, surgical mode, intraoperative conditions, postoperative complications, and fatality rate. Results: There were 117 patients with rAAA, with a median age of 68 (62,77) years, including 93 men (79.5%) and 24 women (20.5%). The main clinical manifestation was abdominal pain (n=115, 98.3%). Among them, 65 (55.6%) patients underwent endovascular aneurysm repair (EVAR), while 52 (44.4%) underwent open surgical repair (OSR). The common postoperative complications include acute gastrointestinal dysfunction (n=116, 99.1%), shock (n=89, 76.1%), acute respiratory distress syndrome (n=85, 72.6%), pancreatic injury (n=56, 47.9%), coagulation dysfunction (n=55, 47.0%), disseminated intravascular coagulation (n=46, 39.3%), acute kidney injury (n=39, 33.3%), infection/sepsis (n=28, 23.9%), gastrointestinal bleeding (n=17, 14.5%), and abdominal compartment syndrome (n=12, 10.3%). The overall postoperative in-hospital fatality rate was 10.3% (12/117). Preoperative use of vasopressors and inotropes, retroperitoneal hematoma, and postoperative abdominal compartment syndrome, gastrointestinal hemorrhage, acute kidney injury, and diffuse intravascular coagulation significantly increased the fatality rate [5/11, 6/24, 5/16, 6/12, 6/17, 23.1%(9/39), 19.6%(9/46), respectively]. Conclusion: The postoperative mortality of rAAA patients is still high in the era of EVAR, especially in patients with preoperative existence of shock and retroperitoneal hematoma, and with postoperative abdominal compartment syndrome, coagulation dysfunction, and acute kidney injury. It is necessary to strengthen perioperative monitoring and management of these patients to reduce the fatality rate.

Mitigating the affective and cognitive consequences of social exclusion: an integrative data analysis of seven social disconnection interventions.

BACKGROUND: Being socially excluded has detrimental effects, with prolonged exclusion linked to loneliness and social isolation. Social disconnection interventions that do not require direct support actions (e.g., "how can I help?") offer promise in mitigating the affective and cognitive consequences of social exclusion. We examine how various social disconnection interventions involving friends and unknown peers might mitigate social exclusion by buffering (intervening before) and by promoting recovery (intervening after). METHODS: We present an integrative data analysis (IDA) of five studies (N = 664) that systematically exposed participants to exclusion (vs. inclusion) social dynamics. Using a well-validated paradigm, participants had a virtual interaction with two other people. Unbeknownst to participants, the other people's behavior was programmed to either behave inclusively toward the participant or for one to behave exclusively. Critically, our social disconnection interventions experimentally manipulated whether a friend was present (vs. an unknown peer vs. being alone), the nature of interpersonal engagement (having a face-to-face conversation vs. a reminder of an upcoming interaction vs. mere presence), and the timing of the intervention in relation to the social dynamic (before vs. during vs. after). We then assessed participants' in-the-moment affective and cognitive responses, which included mood, feelings of belonging, sense of control, and social comfort. RESULTS: Experiencing exclusion (vs. inclusion) led to negative affective and cognitive consequences. However, engaging in a face-to-face conversation with a friend before the exclusion lessened its impact (p < .001). Moreover, a face-to-face conversation with a friend after exclusion, and even a reminder of an upcoming interaction with a friend, sped-up recovery (ps < .001). There was less conclusive evidence that a face-to-face conversation with an unknown peer, or that the mere presence of a friend or unknown peer, conferred protective benefits. CONCLUSIONS: The findings provide support for the effectiveness of social disconnection interventions that involve actual (i.e., face-to-face) or symbolic (i.e., reminders) interactions with friends. These interventions target momentary vulnerabilities that arise from social exclusion by addressing negative affect and cognitions before or after they emerge. As such, they offer a promising approach to primary prevention prior to the onset of loneliness and social isolation.

Traffic trajectory data analysis technology based on HMM model map matching algorithm.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

Spatial distribution and determinants of intermittent preventive treatment for malaria during pregnancy: a secondary data analysis of the 2019 Ghana malaria indicators survey.

BACKGROUND: Malaria during pregnancy is associated with poor maternal, foetal, and neonatal outcomes. To prevent malaria infection during pregnancy, the World Health Organization recommended the use of intermittent preventive therapy with sulfadoxine-pyrimethamine (IPTp-SP) in addition to vector control strategies. Although Ghana's target is to ensure that all pregnant women receive at least three (optimal) doses of SP, the uptake of SP has remained low; between 2020 and 2022, only 60% of pregnant women received optimal SP during their most recent pregnancy. This study sought to map the geospatial distribution and identify factors associated with SP uptake during pregnancy in Ghana. METHODS: Secondary data analysis was conducted using the 2019 Ghana Malaria Indicator Survey dataset. The data analysed were restricted to women aged 15-49 years who reported having a live birth within the two years preceding the survey. A modified Poisson regression model was used to determine factors associated with SP uptake during pregnancy. Geospatial analysis was employed to map the spatial distribution of optimal SP uptake across the ten regions of Ghana using R software. RESULTS: The likelihood that pregnant women received optimal SP correlated with early initiation of first antenatal care (ANC), number of ANC contacts, woman's age, region of residence, and family size. Overall, the greater the number of ANC contacts, the more likely for pregnant women to receive optimal SP. Women with four or more ANC contacts were 2 times (aPR: 2.16; 95% CI: [1.34-3.25]) more likely to receive optimal SP than pregnant women with fewer than four ANC contacts. In addition, early initiation and a high number of ANC contacts were associated with a high number of times a pregnant woman received SP. Regarding spatial distribution, a high uptake of optimal SP was significantly observed in the Upper East and Upper West Regions, whereas the lowest was observed in the Eastern Region of Ghana. CONCLUSIONS: In Ghana, there were regional disparities in the uptake of SP during pregnancy, with the uptake mainly correlated with the provision of ANC services. To achieve the country's target for malaria control during pregnancy, there is a need to strengthen intermittent preventive treatment for malaria during pregnancy by prioritizing comprehensive ANC services.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

Characterizing human postprandial metabolic response using multiway data analysis.

INTRODUCTION: Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time. OBJECTIVES: Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles. METHODS: We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences. RESULTS: Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state. CONCLUSION: The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.

What predicts response to sertraline for people with depression in primary care? a secondary data analysis of moderators in the PANDA trial.

PURPOSE: Antidepressants are a first-line treatment for depression, yet many patients do not respond. There is a need to understand which patients have greater treatment response but there is little research on patient characteristics that moderate the effectiveness of antidepressants. This study examined potential moderators of response to antidepressant treatment. METHODS: The PANDA trial investigated the clinical effectiveness of sertraline (n = 326) compared with placebo (n = 329) in primary care patients with depressive symptoms. We investigated 11 potential moderators of treatment effect (age, employment, suicidal ideation, marital status, financial difficulty, education, social support, family history of depression, life events, health and past antidepressant use). Using multiple linear regression, we investigated the appropriate interaction term for each of these potential moderators with treatment as allocated. RESULTS: Family history of depression was the only variable with weak evidence of effect modification (p-value for interaction = 0.048), such that those with no family history of depression may have greater benefit from antidepressant treatment. We found no evidence of effect modification (p-value for interactions≥0.29) by any of the other ten variables. CONCLUSION: Evidence for treatment moderators was extremely limited, supporting an approach of continuing discuss antidepressant treatment with all patients presenting with moderate to severe depressive symptoms.

mbDecoda: a debiased approach to compositional data analysis for microbiome surveys.

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Propensity score matching as an effective strategy for biomarker cohort design and omics data analysis.

BACKGROUND: Analysis of omics data that contain multidimensional biological and clinical information can be complex and make it difficult to deduce significance of specific biomarker factors. METHODS: We explored the utility of propensity score matching (PSM), a statistical technique for minimizing confounding factors and simplifying the examination of specific factors. We tested two datasets generated from cohorts of colorectal cancer (CRC) patients, one comprised of immunohistochemical analysis of 12 protein markers in 544 CRC tissues and another consisting of RNA-seq profiles of 163 CRC cases. We examined the efficiency of PSM by comparing pre- and post-PSM analytical results. RESULTS: Unlike conventional analysis which typically compares randomized cohorts of cancer and normal tissues, PSM enabled direct comparison between patient characteristics uncovering new prognostic biomarkers. By creating optimally matched groups to minimize confounding effects, our study demonstrates that PSM enables robust extraction of significant biomarkers while requiring fewer cancer cases and smaller overall patient cohorts. CONCLUSION: PSM may emerge as an efficient and cost-effective strategy for multiomic data analysis and clinical trial design for biomarker discovery.

12-Month Trajectories of Health-Related Quality of Life Following Hospitalization in German Cancer Centers-A Secondary Data Analysis.

Patient-reported outcomes (PROs) offer a diverse array of potential applications within medical research and clinical practice. In comparative research, they can serve as tools for delineating the trajectories of health-related quality of life (HRQoL) across various cancer types. We undertook a secondary data analysis of a cohort of 1498 hospitalized cancer patients from 13 German cancer centers. We assessed the Physical and Mental Component Scores (PCS and MCS) of the 12-Item Short-Form Health Survey at baseline (t0), 6 (t1), and 12 months (t2), using multivariable generalized linear regression models. At baseline, the mean PCS and MCS values for all cancer patients were 37.1 and 44.3 points, respectively. We observed a significant improvement in PCS at t2 and in MCS at t1. The most substantial and significant improvements were noted among patients with gynecological cancers. We found a number of significant differences between cancer types at baseline, t1, and t2, with skin cancer patients performing best across all time points and lung cancer patients performing the worst. MCS trajectories showed less pronounced changes and differences between cancer types. Comparative analyses of HRQoL scores across different cancer types may serve as a valuable tool for enhancing health literacy, both among the general public and among cancer patients themselves.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications.

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

The future of rapid and automated single-cell data analysis using reference mapping.

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

SEDA 2024 update: enhancing the SEquence DAtaset builder for seamless integration into automated data analysis pipelines.

BACKGROUND: The initial version of SEDA assists life science researchers without programming skills with the preparation of DNA and protein sequence FASTA files for multiple bioinformatics applications. However, the initial version of SEDA lacks a command-line interface for more advanced users and does not allow the creation of automated analysis pipelines. RESULTS: The present paper discusses the updates of the new SEDA release, including the addition of a complete command-line interface, new functionalities like gene annotation, a framework for automated pipelines, and improved integration in Linux environments. CONCLUSION: SEDA is an open-source Java application and can be installed using the different distributions available ( https://www.sing-group.org/seda/download.html ) as well as through a Docker image ( https://hub.docker.com/r/pegi3s/seda ). It is released under a GPL-3.0 license, and its source code is publicly accessible on GitHub ( https://github.com/sing-group/seda ). The software version at the time of submission is archived at Zenodo (version v1.6.0, http://doi.org/10.5281/zenodo.10201605 ).

Blockchain en salud: transformando la seguridad y la gestión de datos clínicos / Blockchain in health: Transforming security and clinical data management

Introducción Los avances tecnológicos continúan transformando la sociedad, incluyendo el sector de la salud. La naturaleza descentralizada y verificable de la tecnología blockchain presenta un gran potencial para abordar desafíos actuales en la gestión de datos sanitarios. Discusión Este artículo indaga sobre cómo la adopción generalizada de blockchain se enfrenta a importantes desafíos y barreras que deben abordarse, como la falta de regulación, la complejidad técnica, la salvaguarda de la privacidad y los costos tanto económicos como tecnológicos. La colaboración entre profesionales médicos, tecnólogos y legisladores es esencial para establecer un marco normativo sólido y una capacitación adecuada. Conclusión La tecnología blockchain tiene potencial de revolucionar la gestión de datos en el sector de la salud, mejorando la calidad de la atención médica, empoderando a los usuarios y fomentando la compartición segura de datos. Es necesario un cambio cultural y regulatorio, junto a más evidencia, para concluir sus ventajas frente a las alternativas tecnológicas existentes. (AU)

Introduction Technological advances continue to transform society, including the health sector. The decentralized and verifiable nature of blockchain technology presents great potential for addressing current challenges in healthcare data management. Discussion This article reports on how the generalized adoption of blockchain faces important challenges and barriers that must be addressed, such as the lack of regulation, technical complexity, safeguarding privacy, and economic and technological costs. Collaboration between medical professionals, technologists and legislators is essential to establish a solid regulatory framework and adequate training. Conclusion Blockchain technology has the potential to revolutionize data management in the healthcare sector, improving the quality of medical care, empowering users, and promoting the secure sharing of data, but an important cultural change is needed, along with more evidence, to reveal its advantages in front of the existing technological alternative. (AU)

An XIC-Centric Strategy for Improved Identification and Quantification in Proteomic Data Analyses.

Reproducibility is a "proteomic dream" yet to be fully realized. A typical data analysis workflow utilizing extracted ion chromatograms (XICs) often treats the information path from identification to quantification as a one-way street. Here, we propose an XIC-centric approach in which the data flow is bidirectional: identifications are used to derive XICs whose information is in turn applied to validate the identifications. In this study, we employed liquid chromatography-mass spectrometry data from glycoprotein and human hair samples to illustrate the XIC-centric concept. At the core of this approach was XIC-based monoisotope repicking. Taking advantage of the intensity information for all detected isotopes across the whole range of an XIC peak significantly improved the accuracy and uncovered misidentifications originating from monoisotope assignment mistakes. It could also rescue non-top-ranked glycopeptide hits. Identification of glycopeptides is particularly susceptible to precursor mass errors for their low abundances, large masses, and glycans differing by 1 or 2 Da easily confused as isotopes. In addition, the XIC-centric strategy significantly reduced the problem of one XIC peak associated with multiple unique identifications, a source of quantitative irreproducibility. Taken together, the proposed approach can lead to improved identification and quantification accuracy and, ultimately, enhanced reproducibility in proteomic data analyses.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis.

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.