Tag: statistics

Acta Biochim Biophys Sin (Shanghai). 2023 Apr 25. doi: 10.3724/abbs.2023041. Online ahead of print.

ABSTRACT

Fatty acid oxidation (FAO) has been proven to be an accomplice in tumor progression. Carnitine palmitoyltransferase 1C (CPT1C), a rate-limiting enzyme in FAO, mainly functions to catalyze fatty acid carnitinylation and guarantee subsequent entry into the mitochondria for FAO in colorectal cancer (CRC). Gene expression data and clinical information extracted from The Cancer Genome Atlas (TCGA) database show significantly higher expression of CPT1C in patients with metastatic CRC ( P=0.005). Moreover, overexpression of CPT1C is correlated with worse relapse-free survival in CRC (HR 2.1, P=0.0006), while no statistical significance is indicated for CPT1A and CPT1B. Further experiments demonstrate that downregulation of CPT1C expression leads to a decrease in the FAO rate, suppression of cell proliferation, cell cycle arrest and repression of cell migration in CRC, whereas opposite results are obtained when CPT1C is overexpressed. Furthermore, an FAO inhibitor almost completely reverses the enhanced cell proliferation and migration induced by CPT1C overexpression. In addition, analysis of TCGA data illustrates a positive association between CPT1C expression and HIF1α level, suggesting that CPT1C is a transcriptional target of HIF1α. In conclusion, CPT1C overexpression indicates poor relapse-free survival of patients with CRC, and CPT1C is transcriptionally activated by HIF1α, thereby promoting the proliferation and migration of CRC cells.

PMID:37078750 | DOI:10.3724/abbs.2023041

IEEE/ACM Trans Comput Biol Bioinform. 2023 Apr 20;PP. doi: 10.1109/TCBB.2023.3268661. Online ahead of print.

ABSTRACT

Protein-protein interaction (PPI) plays an important role in almost all life activities. Many protein interaction sites have been confirmed by biological experiments, but these PPI site identification methods are time-consuming and expensive. In this study, a deep learning-based PPI prediction method, named DeepSG2PPI, is developed. Firstly, the protein sequence information is retrieved and the local context information of each amino acid residue is calculated. A two-dimensional convolutional neural network (2D-CNN) model is employed to extract features from a two-channel coding structure, in which an attention mechanism is embedded to assign higher weights to key features. Secondly, the global statistical information of each amino acid residue and the relationship graph between the protein and GO (Gene Ontology) function annotation are built, and the graph embedding vector is constructed to represent the biological features of the protein. Finally, a 2D-CNN model and two 1D-CNN models are combined for PPI prediction. The comparison analysis with existing algorithms shows that the DeepSG2PPI method has better performance. It provides more accurate and effective PPI site prediction, which will be helpful in reducing the cost and failure rate of biological experiments.

PMID:37079417 | DOI:10.1109/TCBB.2023.3268661

IEEE J Biomed Health Inform. 2023 Apr 20;PP. doi: 10.1109/JBHI.2023.3268729. Online ahead of print.

ABSTRACT

This work represents the first attempt to provide an overview of how to face data integration as the result of a dialogue between neuroscientists and computer scientists. Indeed, data integration is fundamental for studying complex multifactorial diseases, such as the neurodegenerative diseases. This work aims at warning the readers of common pitfalls and critical issues in both medical and data science fields. In this context, we define a road map for data scientists when they first approach the issue of data integration in the biomedical domain, highlighting the challenges that inevitably emerge when dealing with heterogeneous, large-scale and noisy data and proposing possible solutions. Here, we discuss data collection and statistical analysis usually seen as parallel and independent processes, as cross-disciplinary activities. Finally, we provide an exemplary application of data integration to address Alzheimer’s Disease (AD), which is the most common multifactorial form of dementia worldwide. We critically discuss the largest and most widely used datasets in AD, and demonstrate how the emergence of machine learning and deep learning methods has had a significant impact on disease’s knowledge particularly in the perspective of an early AD diagnosis.

PMID:37079415 | DOI:10.1109/JBHI.2023.3268729

IEEE Trans Neural Netw Learn Syst. 2023 Apr 20;PP. doi: 10.1109/TNNLS.2023.3264740. Online ahead of print.

ABSTRACT

Federated learning (FL) is a distributed machine learning framework that allows resource-constrained clients to train a global model jointly without compromising data privacy. Although FL is widely adopted, high degrees of systems and statistical heterogeneity are still two main challenges, which leads to potential divergence and nonconvergence. Clustered FL handles the problem of statistical heterogeneity straightly by discovering the geometric structure of clients with various data generation distributions and getting multiple global models. The number of clusters contains prior knowledge about the clustering structure and has a significant impact on the performance of clustered FL methods. Existing clustered FL methods are inadequate for adaptively inferring the optimal number of clusters in environments with high systems’ heterogeneity. To address this issue, we propose an iterative clustered FL (ICFL) framework in which the server dynamically discovers the clustering structure by successively performing incremental clustering and clustering in one iteration. We focus on the average connectivity within each cluster and give incremental clustering and clustering methods that are compatible with ICFL based on mathematical analysis. We evaluate ICFL in experiments on high degrees of systems and statistical heterogeneity, multiple datasets, and convex and nonconvex objectives. Experimental results verify our theoretical analysis and show that ICFL outperforms several clustered FL baseline methods.

PMID:37079405 | DOI:10.1109/TNNLS.2023.3264740

JMIR Form Res. 2023 Apr 20;7:e45305. doi: 10.2196/45305.

ABSTRACT

BACKGROUND: Early treatment dropout among youths and young adults (28%-75%) puts them at risk for poorer outcomes. Family engagement in treatment is linked to lower dropout and better attendance in outpatient, in-person treatment. However, this has not been studied in intensive or telehealth settings.

OBJECTIVE: We aimed to examine whether family members’ participation in telehealth intensive outpatient (IOP) therapy for mental health disorders in youths and young adults is associated with patient’s treatment engagement. A secondary aim was to assess demographic factors associated with family engagement in treatment.

METHODS: Data were collected from intake surveys, discharge outcome surveys, and administrative data for patients who attended a remote IOP for youths and young adults, nationwide. Data included 1487 patients who completed both intake and discharge surveys and either completed or disengaged from treatment between December 2020 and September 2022. Descriptive statistics were used to characterize the sample’s baseline differences in demographics, engagement, and participation in family therapy. Mann-Whitney U and chi-square tests were used to explore differences in engagement and treatment completion between patients with and those without family therapy. Binomial regression was used to explore significant demographic predictors of family therapy participation and treatment completion.

RESULTS: Patients with family therapy had significantly better engagement and treatment completion outcomes than clients with no family therapy. Youths and young adults with ≥1 family therapy session were significantly more likely to stay in treatment an average of 2 weeks longer (median 11 weeks vs 9 weeks) and to attend a higher percentage of IOP sessions (median 84.38% vs 75.00%). Patients with family therapy were more likely to complete treatment than clients with no family therapy (608/731, 83.2% vs 445/752, 59.2%; P<.001). Different demographic variables were associated with an increased likelihood of participating in family therapy, including younger age (odds ratio 1.3) and identifying as heterosexual (odds ratio 1.4). After controlling for demographic factors, family therapy remained a significant predictor of treatment completion, such that each family therapy session attended was associated with a 1.4-fold increase in the odds of completing treatment (95% CI 1.3-1.4).

CONCLUSIONS: Youths and young adults whose families participate in any family therapy have lower dropout, greater length of stay, and higher treatment completion than those whose families do not participate in services in a remote IOP program. The findings of this quality improvement analysis are the first to establish a relationship between participation in family therapy and an increased engagement and retention in remote treatment for youths and young patients in IOP programing. Given the established importance of obtaining an adequate dosage of treatment, bolstering family therapy offerings is another tool that could contribute to the provision of care that better meets the needs of youths, young adults, and their families.

PMID:37079372 | DOI:10.2196/45305

J Med Internet Res. 2023 Apr 20;25:e43664. doi: 10.2196/43664.

ABSTRACT

BACKGROUND: Although evidence supporting the feasibility of large-scale mobile health (mHealth) systems continues to grow, privacy protection remains an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved will inevitably attract unwanted attention from adversarial actors seeking to compromise user privacy. Although privacy-preserving technologies such as federated learning (FL) and differential privacy (DP) offer strong theoretical guarantees, it is not clear how such technologies actually perform under real-world conditions.

OBJECTIVE: Using data from the University of Michigan Intern Health Study (IHS), we assessed the privacy protection capabilities of FL and DP against the trade-offs in the associated model’s accuracy and training time. Using a simulated external attack on a target mHealth system, we aimed to measure the effectiveness of such an attack under various levels of privacy protection on the target system and measure the costs to the target system’s performance associated with the chosen levels of privacy protection.

METHODS: A neural network classifier that attempts to predict IHS participant daily mood ecological momentary assessment score from sensor data served as our target system. An external attacker attempted to identify participants whose average mood ecological momentary assessment score is lower than the global average. The attack followed techniques in the literature, given the relevant assumptions about the abilities of the attacker. For measuring attack effectiveness, we collected attack success metrics (area under the curve [AUC], positive predictive value, and sensitivity), and for measuring privacy costs, we calculated the target model training time and measured the model utility metrics. Both sets of metrics are reported under varying degrees of privacy protection on the target.

RESULTS: We found that FL alone does not provide adequate protection against the privacy attack proposed above, where the attacker’s AUC in determining which participants exhibit lower than average mood is over 0.90 in the worst-case scenario. However, under the highest level of DP tested in this study, the attacker’s AUC fell to approximately 0.59 with only a 10% point decrease in the target’s R² and a 43% increase in model training time. Attack positive predictive value and sensitivity followed similar trends. Finally, we showed that participants in the IHS most likely to require strong privacy protection are also most at risk from this particular privacy attack and subsequently stand to benefit the most from these privacy-preserving technologies.

CONCLUSIONS: Our results demonstrated both the necessity of proactive privacy protection research and the feasibility of the current FL and DP methods implemented in a real mHealth scenario. Our simulation methods characterized the privacy-utility trade-off in our mHealth setup using highly interpretable metrics, providing a framework for future research into privacy-preserving technologies in data-driven health and medical applications.

PMID:37079370 | DOI:10.2196/43664

Elife. 2023 Apr 20;12:e83118. doi: 10.7554/eLife.83118.

ABSTRACT

BACKGROUND: Genome-wide association studies (GWASs) have identified genetic susceptibility variants for both leukocyte telomere length (LTL) and lung cancer susceptibility. Our study aims to explore the shared genetic basis between these traits and investigate their impact on somatic environment of lung tumours.

METHODS: We performed genetic correlation, Mendelian randomisation (MR), and colocalisation analyses using the largest available GWASs summary statistics of LTL (N=464,716) and lung cancer (N=29,239 cases and 56,450 controls). Principal components analysis based on RNA-sequencing data was used to summarise gene expression profile in lung adenocarcinoma cases from TCGA (N=343).

RESULTS: Although there was no genome-wide genetic correlation between LTL and lung cancer risk, longer LTL conferred an increased risk of lung cancer regardless of smoking status in the MR analyses, particularly for lung adenocarcinoma. Of the 144 LTL genetic instruments, 12 colocalised with lung adenocarcinoma risk and revealed novel susceptibility loci, including MPHOSPH6, PRPF6, and POLI. The polygenic risk score for LTL was associated with a specific gene expression profile (PC2) in lung adenocarcinoma tumours. The aspect of PC2 associated with longer LTL was also associated with being female, never smokers, and earlier tumour stages. PC2 was strongly associated with cell proliferation score and genomic features related to genome stability, including copy number changes and telomerase activity.

CONCLUSIONS: This study identified an association between longer genetically predicted LTL and lung cancer and sheds light on the potential molecular mechanisms related to LTL in lung adenocarcinomas.

FUNDING: Institut National du Cancer (GeniLuc2017-1-TABAC-03-CIRC-1-TABAC17-022), INTEGRAL/NIH (5U19CA203654-03), CRUK (C18281/A29019), and Agence Nationale pour la Recherche (ANR-10-INBS-09).

PMID:37079368 | DOI:10.7554/eLife.83118

JMIR Res Protoc. 2023 Apr 20;12:e46187. doi: 10.2196/46187.

ABSTRACT

BACKGROUND: End-stage kidney disease (ESKD) is treated with dialysis or kidney transplantation, with most patients with ESKD receiving in-center hemodialysis treatment. This life-saving treatment can result in cardiovascular and hemodynamic instability, with the most common form being low blood pressure during the dialysis treatment (intradialytic hypotension [IDH]). IDH is a complication of hemodialysis that can involve symptoms such as fatigue, nausea, cramping, and loss of consciousness. IDH increases risks of cardiovascular disease and ultimately hospitalizations and mortality. Provider-level and patient-level decisions influence the occurrence of IDH; thus, IDH may be preventable in routine hemodialysis care.

OBJECTIVE: This study aims to evaluate the independent and comparative effectiveness of 2 interventions-one directed at hemodialysis providers and another for patients-in reducing the rate of IDH at hemodialysis facilities. In addition, the study will assess the effects of interventions on secondary patient-centered clinical outcomes and examine factors associated with a successful implementation of the interventions.

METHODS: This study is a pragmatic, cluster randomized trial to be conducted in 20 hemodialysis facilities in the United States. Hemodialysis facilities will be randomized using a 2 × 2 factorial design, such that 5 sites will receive a multimodal provider education intervention, 5 sites will receive a patient activation intervention, 5 sites will receive both interventions, and 5 sites will receive none of the 2 interventions. The multimodal provider education intervention involved theory-informed team training and the use of a digital, tablet-based checklist to heighten attention to patient clinical factors associated with increased IDH risk. The patient activation intervention involves tablet-based, theory-informed patient education and peer mentoring. Patient outcomes will be monitored during a 12-week baseline period, followed by a 24-week intervention period and a 12-week postintervention follow-up period. The primary outcome of the study is the proportion of treatments with IDH, which will be aggregated at the facility level. Secondary outcomes include patient symptoms, fluid adherence, hemodialysis adherence, quality of life, hospitalizations, and mortality.

RESULTS: This study is funded by the Patient-Centered Outcomes Research Institute and approved by the University of Michigan Medical School’s institutional review board. The study began enrolling patients in January 2023. Initial feasibility data will be available in May 2023. Data collection will conclude in November 2024.

CONCLUSIONS: The effects of provider and patient education on reducing the proportion of sessions with IDH and improving other patient-centered clinical outcomes will be evaluated, and the findings will be used to inform further improvements in patient care. Improving the stability of hemodialysis sessions is a critical concern for clinicians and patients with ESKD; the interventions targeted to providers and patients are predicted to lead to improvements in patient health and quality of life.

TRIAL REGISTRATION: ClinicalTrials.gov NCT03171545; https://clinicaltrials.gov/ct2/show/NCT03171545.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46187.

PMID:37079365 | DOI:10.2196/46187

J Med Internet Res. 2023 Apr 20;25:e45249. doi: 10.2196/45249.

ABSTRACT

BACKGROUND: The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods.

OBJECTIVE: This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community’s experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases.

METHODS: We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of “1” was assigned to months in 2020 and “0” otherwise and tested for its statistical significance.

RESULTS: A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community’s experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period.

CONCLUSIONS: There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them.

PMID:37079359 | DOI:10.2196/45249

J Med Internet Res. 2023 Apr 20;25:e46254. doi: 10.2196/46254.

ABSTRACT

BACKGROUND: Previous studies have investigated the association between suicide and internet search volumes of terms related to suicide or self-harm. However, the results varied by people’s age, period, and country, and no study has exclusively investigated suicide or self-harm rates among adolescents.

OBJECTIVE: This study aims to determine the association between the internet search volumes of terms related to suicide/self-harm and the number of suicides among South Korean adolescents. We investigated gender differences in this association and the time lag between the internet search volumes of the terms and the connected suicide deaths.

METHODS: We selected 26 search terms related to suicide and self-harm among South Korean adolescents, and the search volumes of these terms for adolescents aged 13-18 years were obtained from the leading internet search engine in South Korea (Naver Datalab). A data set was constructed by combining data from Naver Datalab and the number of suicide deaths of adolescents on a daily basis from January 1, 2016, to December 31, 2020. Spearman rank correlation and multivariate Poisson regression analyses were performed to identify the association between the search volumes of the terms and the suicide deaths during that period. The time lag between suicide death and the increasing trend in the search volumes of the related terms was estimated from the cross-correlation coefficients.

RESULTS: Significant correlations were observed within the search volumes of the 26 terms related to suicide/self-harm. The internet search volumes of several terms were associated with the number of suicide deaths among South Korean adolescents, and this association differed by gender. The search volume for “dropout” showed a statistically significant correlation with the number of suicides in all adolescent population groups. The correlation between the internet search volume for “dropout” and the connected suicide deaths was the strongest for a time lag of 0 days. In females, self-harm and academic score showed significant associations with suicide deaths, but academic score showed a negative correlation, and the time lags with the strongest correlations were 0 and -11 days, respectively. In the total population, self-harm and suicide method were associated with the number of suicides, and the time lags with the strongest correlations were +7 and 0 days, respectively.

CONCLUSIONS: This study identifies a correlation between suicides and internet search volumes related to suicide/self-harm among South Korean adolescents, but the relatively weak correlation (incidence rate ratio 0.990-1.068) should be interpreted with caution.

PMID:37079349 | DOI:10.2196/46254