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Aging Cell Jun 2024Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a...
Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to inform the development of brain aging-related disease mechanisms and identify potential anti-aging therapeutic targets. Several epigenetic clocks exist based on plasma and neuronal tissues; however, plasma may not reflect brain aging specifically and tissue-based clocks require samples that are difficult to obtain from living participants. To address these problems, we developed a machine learning clock that uses CSF proteomics to predict the chronological age of individuals with a 0.79 Pearson correlation and mean estimated error (MAE) of 4.30 years in our validation cohort. Additionally, we analyzed proteins highly weighted by the algorithm to gain insights into changes in CSF and uncover novel insights into brain aging. We also demonstrate a novel method to create a minimal protein clock that uses just 109 protein features from the original clock to achieve a similar accuracy (0.75 correlation, MAE 5.41). Finally, we demonstrate that our clock identifies novel proteins that are highly predictive of age in interactions with other proteins, but do not directly correlate with chronological age themselves. In conclusion, we propose that our CSF protein aging clock can identify novel proteins that influence the rate of aging of the central nervous system (CNS), in a manner that would not be identifiable by examining their individual relationships with age.
PubMed: 38923730
DOI: 10.1111/acel.14230 -
Pharmacy (Basel, Switzerland) Jun 2024Research on associations between student performance in pharmacy programs and entry-to-practice milestones has been limited in Canada and in programs using a...
Research on associations between student performance in pharmacy programs and entry-to-practice milestones has been limited in Canada and in programs using a co-operative (co-op) education model. Co-op exposes students to a variety of opportunities both within direct patient care roles and in non-traditional roles for pharmacists, such as policy, advocacy, insurance, research, and the pharmaceutical industry. The purpose of this research is to analyze associations between student grades and evaluations achieved in the University of Waterloo (UW) Doctor of Pharmacy (PharmD) co-op program and success rates on entry-to-practice milestones, including the Pharmacy Examining Board of Canada (PEBC) Pharmacist Qualifying Examination and performance on final-year clinical rotations. Grades and evaluations from courses, co-op work terms, clinical rotations, and PEBC exam data from three graduating cohorts were obtained. A multiple regression analysis was performed to explore associations between student evaluations and PEBC Pharmacist Qualifying Examination and clinical rotation performance. Holding all other variables constant, grades in anatomy/physiology were negatively correlated with scores on the PEBC Pharmacist Qualifying Examination, while grades in one of the professional practice courses showed a positive relationship with the same examination. Students with higher grades in a problem-based learning capstone therapeutics course, in their first co-op work term, and in the direct patient care co-op work term tended to score higher on clinical rotations. Co-op performance was not significant in predicting PEBC performance. However, complimentary descriptive analysis underscored that students with a co-op rating of good or below were more likely to fail courses, midpoint evaluations, Objective Structured Clinical Examinations (OSCEs), and PEBC measures. Multiple predictors of performance on final-year clinical rotations and the PEBC Pharmacist Qualifying Examination were identified. This predictive model may be utilized to identify students at risk of underperforming and to facilitate early intervention and remediation programs, while also informing curricular revision.
PubMed: 38921966
DOI: 10.3390/pharmacy12030090 -
Tomography (Ann Arbor, Mich.) Jun 2024In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we...
In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.
Topics: Humans; Parkinson Disease; Magnetic Resonance Imaging; Male; Female; Machine Learning; Middle Aged; Aged; Algorithms; Brain
PubMed: 38921945
DOI: 10.3390/tomography10060068 -
Behavioral Sciences (Basel, Switzerland) Jun 2024Oppositional defiant symptoms are some of the most common developmental symptoms in children and adolescents with and without oppositional defiant disorder. Research has...
Oppositional defiant symptoms are some of the most common developmental symptoms in children and adolescents with and without oppositional defiant disorder. Research has addressed the close association of the parent-child relationship (PCR) with oppositional defiant symptoms. However, it is necessary to further investigate the underlying mechanism for forming targeted intervention strategies. By using a machine learning-based causal forest (CF) model, we investigated the heterogeneous causal effects of the PCR on oppositional defiant symptoms in children in Chinese elementary schools. Based on the PCR improvement in two consecutive years, 423 children were divided into improved and control groups. The assessment of oppositional defiant symptoms (AODS) in the second year was set as the dependent variable. Additionally, several factors based on the multilevel family model and the baseline AODS in the first year were included as covariates. Consistent with expectations, the CF model showed a significant causal effect between the PCR and oppositional defiant symptoms in the samples. Moreover, the causality exhibited heterogeneity. The causal effect was greater in those children with higher baseline AODS, a worse family atmosphere, and lower emotion regulation abilities in themselves or their parents. Conversely, the parenting style played a positive role in causality. These findings enhance our understanding of how the PCR contributes to the development of oppositional defiant symptoms conditioned by factors from a multilevel family system. The heterogeneous causality in the observation data, established using the machine learning approach, could be helpful in forming personalized family-oriented intervention strategies for children with oppositional defiant symptoms.
PubMed: 38920836
DOI: 10.3390/bs14060504 -
Frontiers in Nutrition 2024Although micronutrients (MNs) are important for children's growth and development, their intake has not received enough attention. MN deficiency is a significant public...
BACKGROUND
Although micronutrients (MNs) are important for children's growth and development, their intake has not received enough attention. MN deficiency is a significant public health problem, especially in developing countries like Ethiopia. However, there is a lack of empirical evidence using advanced statistical methods, such as machine learning. Therefore, this study aimed to use advanced supervised algorithms to predict the micronutrient intake status in Ethiopian children aged 6-23 months.
METHODS
A total weighted of 2,499 children aged 6-23 months from the Ethiopia Demographic and Health Survey 2016 data set were utilized. The data underwent preprocessing, with 80% of the observations used for training and 20% for testing the model. Twelve machine learning algorithms were employed. To select best predictive model, their performance was assessed using different evaluation metrics in Python software. The Boruta algorithm was used to select the most relevant features. Besides, seven data balancing techniques and three hyper parameter tuning methods were employed. To determine the association between independent and targeted feature, association rule mining was conducted using the algorithm in R software.
RESULTS
According to the 2016 Ethiopia Demographic and Health Survey, out of 2,499 weighted children aged 12-23 months, 1,728 (69.15%) had MN intake. The random forest, catboost, and light gradient boosting algorithm outperformed in predicting MN intake status among all selected classifiers. Region, wealth index, place of delivery, mothers' occupation, child age, fathers' educational status, desire for more children, access to media exposure, religion, residence, and antenatal care (ANC) follow-up were the top attributes to predict MN intake. Association rule mining was identified the top seven best rules that most frequently associated with MN intake among children aged 6-23 months in Ethiopia.
CONCLUSION
The random forest, catboost, and light gradient boosting algorithm achieved a highest performance and identifying the relevant predictors of MN intake. Therefore, policymakers and healthcare providers can develop targeted interventions to enhance the uptake of micronutrient supplementation among children. Customizing strategies based on identified association rules has the potential to improve child health outcomes and decrease the impact of micronutrient deficiencies in Ethiopia.
PubMed: 38919392
DOI: 10.3389/fnut.2024.1397399 -
Annals of Laboratory Medicine Jun 2024In recent decades, the analytical quality of clinical laboratory results has substantially increased because of collaborative efforts. To effectively utilize laboratory...
BACKGROUND
In recent decades, the analytical quality of clinical laboratory results has substantially increased because of collaborative efforts. To effectively utilize laboratory results in applications, such as machine learning through big data, understanding the level of harmonization for each test would be beneficial. We aimed to develop a quantitative harmonization index that reflects the harmonization status of real-world laboratory tests.
METHODS
We collected 2021-2022 external quality assessment (EQA) results for eight tests (HbA1c, creatinine, total cholesterol, HDL-cholesterol, triglyceride, alpha-fetoprotein [AFP], carcinoembryonic antigen [CEA], and prostate-specific antigen [PSA]). This EQA was conducted by the Korean Association of External Quality Assessment Service, using commutable materials. The total analytical error of each test was determined according to the bias% and CV% within peer groups. The values were divided by the total allowable error from biological variation (minimum, desirable, and optimal) to establish a real-world harmonization index (RWHI) at each level (minimum, desirable, and optimal). Good harmonization was arbitrarily defined as an RWHI value ≤ 1 for the three levels.
RESULTS
Total cholesterol, triglyceride, and CEA had an optimal RWHI of ≤ 1, indicating an optimal harmonization level. Tests with a desirable harmonization level included HDL-cholesterol, AFP, and PSA. Creatinine had a minimum harmonization level, and HbA1c did not reach the minimum harmonization level.
CONCLUSIONS
We developed a quantitative RWHI using regional EQA data. This index may help reflect the actual harmonization level of laboratory tests in the field.
PubMed: 38919008
DOI: 10.3343/alm.2024.0082 -
Biology Direct Jun 2024Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients....
BACKGROUND
Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions.
METHODS
Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequent functional analysis of these genes was conducted on the gene set cancer analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy. Additionally, multivariate COX regression analysis highlighted the significant role of POLD1 and examined its association with DNA methylation. Finally, molecular docking and immunohistochemistry experiments were carried out to assess the binding affinity of POLD1 to PCa drugs and its impact on PCa prognosis.
RESULTS
The study identified 21 metastasis-related genes using the WGCNA method, which were found to be associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms showed strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results. POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients.
CONCLUSIONS
The developed diagnostic and metastasis models provide substantial value for patients with prostate cancer. The discovery of POLD1 as a novel biomarker related to prostate cancer metastasis offers a promising avenue for enhancing treatment of prostate cancer metastasis.
Topics: Humans; Male; Prostatic Neoplasms; Machine Learning; Immunotherapy; Neoplasm Metastasis; Biomarkers, Tumor; Prognosis; Molecular Docking Simulation; Gene Expression Regulation, Neoplastic
PubMed: 38918844
DOI: 10.1186/s13062-024-00494-x -
Scientific Reports Jun 2024Understanding the genetic basis of complex diseases is one of the most important challenges in current precision medicine. To this end, Genome-Wide Association Studies...
Understanding the genetic basis of complex diseases is one of the most important challenges in current precision medicine. To this end, Genome-Wide Association Studies aim to correlate Single Nucleotide Polymorphisms (SNPs) to the presence or absence of certain traits. However, these studies do not consider interactions between several SNPs, known as epistasis, which explain most genetic diseases. Analyzing SNP combinations to detect epistasis is a major computational task, due to the enormous search space. A possible solution is to employ deep learning strategies for genomic prediction, but the lack of explainability derived from the black-box nature of neural networks is a challenge yet to be addressed. Herein, a novel, flexible, portable, and scalable framework for network interpretation based on transformers is proposed to tackle any-order epistasis. The results on various epistasis scenarios show that the proposed framework outperforms state-of-the-art methods for explainability, while being scalable to large datasets and portable to various deep learning accelerators. The proposed framework is validated on three WTCCC datasets, identifying SNPs related to genes known in the literature that have direct relationships with the studied diseases.
Topics: Epistasis, Genetic; Polymorphism, Single Nucleotide; Humans; Genome-Wide Association Study; Deep Learning; Neural Networks, Computer; Computational Biology; Algorithms
PubMed: 38918413
DOI: 10.1038/s41598-024-65317-5 -
JAMA Network Open Jun 2024The ELEKT-D: Electroconvulsive Therapy (ECT) vs Ketamine in Patients With Treatment Resistant Depression (TRD) (ELEKT-D) trial demonstrated noninferiority of intravenous... (Randomized Controlled Trial)
Randomized Controlled Trial
IMPORTANCE
The ELEKT-D: Electroconvulsive Therapy (ECT) vs Ketamine in Patients With Treatment Resistant Depression (TRD) (ELEKT-D) trial demonstrated noninferiority of intravenous ketamine vs ECT for nonpsychotic TRD. Clinical features that can guide selection of ketamine vs ECT may inform shared decision-making for patients with TRD.
OBJECTIVE
To evaluate whether selected clinical features were associated with differential improvement with ketamine vs ECT.
DESIGN, SETTING, AND PARTICIPANTS
This secondary analysis of an open-label noninferiority randomized clinical trial was a multicenter study conducted at 5 US academic medical centers from April 7, 2017, to November 11, 2022. Analyses for this study, which were not prespecified in the trial protocol, were conducted from May 10 to Oct 31, 2023. The study cohort included patients with TRD, aged 21 to 75 years, who were in a current nonpsychotic depressive episode of at least moderate severity and were referred for ECT by their clinicians.
EXPOSURES
Eligible participants were randomized 1:1 to receive either 6 infusions of ketamine or 9 treatments with ECT over 3 weeks.
MAIN OUTCOMES AND MEASURES
Association between baseline factors (including 16-item Quick Inventory of Depressive Symptomatology Self-Report [QIDS-SR16], Montgomery-Asberg Depression Rating Scale [MADRS], premorbid intelligence, cognitive function, history of attempted suicide, and inpatient vs outpatient status) and treatment response were assessed with repeated measures mixed-effects model analyses.
RESULTS
Among the 365 participants included in this study (mean [SD] age, 46.0 [14.5] years; 191 [52.3%] female), 195 were randomized to the ketamine group and 170 to the ECT group. In repeated measures mixed-effects models using depression levels over 3 weeks and after false discovery rate adjustment, participants with a baseline QIDS-SR16 score of 20 or less (-7.7 vs -5.6 points) and those starting treatment as outpatients (-8.4 vs -6.2 points) reported greater reduction in the QIDS-SR16 with ketamine vs ECT. Conversely, those with a baseline QIDS-SR16 score of more than 20 (ie, very severe depression) and starting treatment as inpatients reported greater reduction in the QIDS-SR16 earlier in course of treatment (-8.4 vs -6.7 points) with ECT, but scores were similar in both groups at the end-of-treatment visit (-9.0 vs -9.9 points). In the ECT group only, participants with higher scores on measures of premorbid intelligence (-14.0 vs -11.2 points) and with a comorbid posttraumatic stress disorder diagnosis (-16.6 vs -12.0 points) reported greater reduction in the MADRS score. Those with impaired memory recall had greater reduction in MADRS during the second week of treatment (-13.4 vs -9.6 points), but the levels of MADRS were similar to those with unimpaired recall at the end-of-treatment visit (-14.3 vs -12.2 points). Other results were not significant after false discovery rate adjustment.
CONCLUSIONS AND RELEVANCE
In this secondary analysis of the ELEKT-D randomized clinical trial of ECT vs ketamine, greater improvement in depression was observed with intravenous ketamine among outpatients with nonpsychotic TRD who had moderately severe or severe depression, suggesting that these patients may consider ketamine over ECT for TRD.
Topics: Humans; Ketamine; Electroconvulsive Therapy; Female; Male; Middle Aged; Depressive Disorder, Treatment-Resistant; Adult; Aged; Treatment Outcome
PubMed: 38916891
DOI: 10.1001/jamanetworkopen.2024.17786 -
Clinical and Experimental Medicine Jun 2024Dysregulated lipid metabolism in the bone marrow microenvironment (BMM) plays a vital role in multiple myeloma (MM) development, progression, and drug resistance....
Dysregulated lipid metabolism in the bone marrow microenvironment (BMM) plays a vital role in multiple myeloma (MM) development, progression, and drug resistance. However, the exact mechanism by which lipid metabolism impacts the BMM, promotes tumorigenesis, and triggers drug resistance remains to be fully elucidated.By analyzing the bulk sequencing and single-cell sequencing data of MM patients, we identified lipid metabolism-related genes differential expression significantly associated with MM prognosis, referred to as LMRPgenes. Using a cohort of ten machine learning algorithms and 117 combinations, LMRPgenes predictive models were constructed. Further exploration of the effects of the model risk score (RS) on the survival status, immune status of patients with BMM, and response to immunotherapy was conducted. The study also facilitated the identification of personalized therapeutic strategies targeting specified risk categories within patient cohorts.Analysis of the scRNA-seq data revealed increased lipid metabolism-related gene enrichment scores (LMESs) in erythroblasts and progenitor, malignant, and Tprolif cells but decreased LMESs in lymphocytes. LMESs were also strongly correlated with most of the 50 hallmark pathways within these cell populations. An elevated malignant cell ratio and reduced lymphocytes were observed in the high LMES group. Moreover, the LMRPgenes predictive model, consisting of 14 genes, showed great predictive power. The risk score emerged as an independent indicator of poor outcomes. Inverse relationships between the RS and immune status were noted, and a high RS was associated with impaired immunotherapy responses. Drug sensitivity assays indicated the effectiveness of bortezomib, buparlisib, dinaciclib, staurosporine, rapamycin, and MST-312 in the high-RS group, suggesting their potential for treating patients with high-RS values and poor response to immunotherapy. Ultimately, upon verification via qRT-PCR, we observed a significant upregulation of ACBD6 in NDMM group compared to the control group.Our research enhances the knowledge base regarding the association between lipid metabolism-related genes (LMRGs) and the BMM in MM patients, offering substantive insights into the mechanistic effects of the BMM mediated by LMRGs.
Topics: Humans; Lipid Metabolism; Tumor Microenvironment; Multiple Myeloma; Bone Marrow; Transcriptome; Gene Expression Profiling; Prognosis; Gene Expression Regulation, Neoplastic
PubMed: 38916672
DOI: 10.1007/s10238-024-01398-w