Tag: statistics

Subst Abus. 2022 Dec;43(1):1277-1285. doi: 10.1080/08897077.2022.2095080.

ABSTRACT

Background: The objectives of this study were to: (1) estimate the prevalence of family history of alcohol and other drug (AOD) misuse (positive family history [FH+]) in first- and second-degree relatives across sexual identity subgroups (i.e., lesbian, gay, bisexual, heterosexual); (2) compare AOD misuse among offspring of sexual minority and heterosexual parents; and (3) examine the relationships between FH+ and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) alcohol use disorder (AUD) and other drug use disorder (ODUD) across sexual identity subgroups. Methods: Data were from the National Epidemiologic Survey on Alcohol and Related Conditions-III (n = 36,309 non-institutionalized U.S. adults aged ≥ 18 years). Data collection occurred in households using structured diagnostic face-to-face interviews during 2012-2013. Results: The presence of FH+ in first- and second-degree relatives was most prevalent among bisexual women relative to all other sexual orientation subgroups. Multivariable regression analyses indicated that the odds of AUD and ODUD were higher among FH+ adults relative to negative family history (FH-) adults. Lesbian and bisexual women had higher odds of AUD compared to heterosexual women, controlling for any FH+; this sexual identity difference was not found for men. There were no significant differences in ODUD between heterosexual FH- men and gay FH- men. We found differences in AOD misuse among offspring of bisexual parents, but not gay or lesbian parents compared to heterosexual parents. Conclusions: Health professionals should consider the higher likelihood of a family history of AOD misuse among sexual minorities, especially bisexual women, when treating these individuals. The lack of differences in AOD misuse among offspring of gay or lesbian parents relative to heterosexual parents warrants attention for legal, policy, and clinical decisions.

PMID:35849748 | DOI:10.1080/08897077.2022.2095080

J Proteome Res. 2022 Jul 18. doi: 10.1021/acs.jproteome.2c00156. Online ahead of print.

ABSTRACT

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

PMID:35849720 | DOI:10.1021/acs.jproteome.2c00156

Harv Rev Psychiatry. 2022 Jul-Aug 01;30(4):226-237. doi: 10.1097/HRP.0000000000000342.

ABSTRACT

Malignant self-regard (MSR) is a self-representation that encompasses the shared features of depressive personality disorder, masochistic/self-defeating personality disorder, depressive-masochistic personality, and vulnerable narcissism. In this review we begin by describing the construct’s historical precursors, which begin in early psychoanalytic/dynamic theory, and then trace its development across iterations of the Diagnostic and Statistical Manual of Mental Disorders. Special attention is paid to differentiating MSR from vulnerable narcissism. We then consider MSR’s place within transdiagnostic, transtheoretical, and dimensional models of personality pathology. We focus heavily on MSR’s impact on various personality systems (e.g., thought and affect systems) and also on overall personality functioning. The empirical research on MSR in relation to these systems is thoroughly reviewed and largely supports its psychometric properties and clinical significance. We suggest that MSR may map onto the distress subfactor in the hierarchical taxonomy of psychopathology (HiTOP) and that MSR seems to occupy the shared internalizing space across the neurotic and borderline level of personality organization in Kernberg’s model of personality disorders. We also identify four major directions for future research: the possible benefits of self-defeating tendencies that involve pathological narcissism and self-esteem; MSR’s relationship to overall health and well-being; depressive states and MSR severity; and how MSR fits within the Alternative Model for Personality Disorders and the personality disorder framework of the International Classification of Diseases.

PMID:35849740 | DOI:10.1097/HRP.0000000000000342

Brain Behav. 2022 Jul 18:e2694. doi: 10.1002/brb3.2694. Online ahead of print.

ABSTRACT

OBJECTIVE: Military members and veterans are at elevated risk of treatment-resistant posttraumatic stress disorder (TR-PTSD) due to higher rates of exposure to potentially traumatic events during the course of duty. Knowledge of TR-PTSD is limited, and specific protocols or evidence-based TR-PTSD therapies are lacking. Multimodal motion-assisted memory desensitization and reconsolidation (3MDR) therapy is an emerging intervention for combat-related TR-PTSD. The purpose of this study was to preliminarily assess the effectiveness of 3MDR in addressing TR-PTSD in Canadian military members and veterans.

METHODS: This study is a longitudinal mixed-methods clinical trial. English-speaking military members and veterans aged 18-60 with TR-PTSD were recruited to participate. The intervention consisted of six sessions of 3MDR therapy. Quantitative data were collected pretreatment, posttreatment, and longitudinally at 1, 3, and 6 months after completion of 3MDR.

RESULTS: Results from the first 11 participants to complete the 3MDR protocol exhibited statistically significant improvement (surviving multiple comparison correction) in clinically administered and self-reported scores for PTSD (CAPS-5 and PCL-5), moral injury (MISS-M-SF), depression (PHQ-9), anxiety (GAD-7), emotional regulation (DERS-18), and resilience (CD-RS-25).

CONCLUSION: The preliminary and exploratory results from this clinical trial support the growing body of literature illustrating 3MDR as an effective treatment for military-related TR-PTSD. These results are notable given participants’ previous lack of success with frontline psychotherapeutic and pharmacological interventions. Given that there are currently very limited treatment options for TR-PTSD, 3MDR could prove to be a valuable treatment option for military members and veterans with TR-PTSD.

PMID:35849703 | DOI:10.1002/brb3.2694

J Infect Dis. 2022 Jul 18:jiac294. doi: 10.1093/infdis/jiac294. Online ahead of print.

ABSTRACT

BACKGROUND: The frequency and clinical presentation of malaria infections show marked heterogeneity in epidemiological studies. However, deeper understanding of this variability is hampered by the difficulty in quantifying all relevant factors. Here, we report the history of malaria infections in twins, who are exposed to the same in utero milieu, share genetic factors and are similarly exposed to vectors.

METHODS: Data were obtained from a Malian longitudinal birth cohort. Samples from 25 twin pairs were examined for malaria infection and antibody responses. Bayesian models were developed on the number of infections during follow-up.

RESULTS: In 16/25 pairs, both children were infected and often developed symptoms. In 8/25 pairs, only one twin was infected, but usually only once or twice. Statistical models suggest this pattern is not inconsistent with twin siblings having the same underlying infection rate. In a pair with discordant hemoglobin genotype, parasite densities were consistently lower in the child with hemoglobin AS, but antibody levels were similar.

CONCLUSIONS: By using a novel design, we describe residual variation in malaria phenotypes in naturally-matched children and confirm the important role of environmental factors, as suggested by the between twin pair heterogeneity in malaria history.

PMID:35849702 | DOI:10.1093/infdis/jiac294

IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul 18;PP. doi: 10.1109/TCBB.2022.3191972. Online ahead of print.

ABSTRACT

Numerous studies have reported that micro RNAs (miRNAs) play pivotal roles in disease pathogenesis based on the deregulation of the expressions of target messenger RNAs. Therefore, the identification of disease-related miRNAs is of great significance in understanding human complex diseases, which can also provide insight into the design of novel prognostic markers and disease therapies. Considering the time and cost involved in wet experiments, most recent works have focused on the effective and feasible modeling of computational frameworks to uncover miRNA-disease associations. In this study, we propose a novel framework called node2vec-based neural collaborative filtering for predicting miRNA-disease association (NCMD) based on deep neural networks. Initially, NCMD exploits Node2vec to learn low-dimensional vector representations of miRNAs and diseases. Next, it utilizes a deep learning framework that combines the linear ability of generalized matrix factorization and nonlinear ability of a multilayer perceptron. Experimental results clearly demonstrate the comparable performance of NCMD relative to the state-of-the-art methods according to statistical measures. In addition, case studies on breast cancer, lung cancer and pancreatic cancer validate the effectiveness of NCMD. Extensive experiments demonstrate the benefits of modeling a neural collaborative-filtering-based approach for discovering novel miRNA-disease associations.

PMID:35849666 | DOI:10.1109/TCBB.2022.3191972

IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul 18;PP. doi: 10.1109/TCBB.2022.3191395. Online ahead of print.

ABSTRACT

Protein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein structure. In this study, IGPRED-Multitask, a deep learning model with multi task learning architecture based on deep inception network, graph convolutional network and a bidirectional long short-term memory is proposed. Moreover, hyper-parameters of the model are fine-tuned using Bayesian optimization, which is faster and more effective than grid search. The same benchmark test data sets as in the OPUS-TASS paper including TEST2016, TEST2018, CASP12, CASP13, CASPFM, HARD68, CAMEO93, CAMEO93_HARD, as well as the train and validation sets, are used for fair comparison with the literature. Statistically significant improvements are observed in secondary structure prediction on 4 datasets, in phi angle prediction on 2 datasets and in psi angel prediction on 3 datasets compared to the state-of-the-art methods. For solvent accessibility prediction, TEST2016 and TEST2018 datasets are used only to assess the performance of the proposed model. The IGPRED-Multitask method is available at PSP server, which can be accessed by visiting http://psp.agu.edu.tr.

PMID:35849663 | DOI:10.1109/TCBB.2022.3191395

PLoS Negl Trop Dis. 2022 Jul 18;16(7):e0010622. doi: 10.1371/journal.pntd.0010622. Online ahead of print.

ABSTRACT

BACKGROUND: The results of the latest national survey on important human parasitic diseases in 2015-2016 showed Guangdong Province is still a moderately endemic area, with the weighted prevalence of soil-transmitted helminths (STHs) higher than national average. High-resolution age- and gender-specific spatial-temporal risk maps can support the prevention and control of STHs, but not yet available in Guangdong.

METHODOLOGY: Georeferenced age- and gender-specific disease data of STH infections in Guangdong Province was derived from three national surveys on important human parasitic diseases, conducted in 1988-1992, 2002-2003, and 2015-2016, respectively. Potential influencing factors (e.g., environmental and socioeconomic factors) were collected from open-access databases. Bayesian geostatistical models were developed to analyze the above data, based on which, high-resolution maps depicting the STH infection risk were produced in the three survey years in Guangdong Province.

PRINCIPAL FINDINGS: There were 120, 31, 71 survey locations in the first, second, and third national survey in Guangdong, respectively. The overall population-weighted prevalence of STH infections decreased significantly over time, from 68.66% (95% Bayesian credible interval, BCI: 64.51-73.06%) in 1988-1992 to 0.97% (95% BCI: 0.69-1.49%) in 2015-2016. In 2015-2016, only low to moderate infection risk were found across Guangdong, with hookworm becoming the dominant species. Areas with relatively higher risk were (>5%) mostly distributed in the western region. Females had higher infection risk of STHs than males. The infection risk of A. lumbricoides and T. trichiura were higher in children, while middle-aged and elderly people had higher infection risk of hookworm. Precipitation, elevation, land cover, and human influence index (HII) were significantly related with STH infection risk.

CONCLUSIONS/SIGNIFICANCE: We produced the high-resolution, age- and gender-specific risk maps of STH infections in the three national survey periods across nearly 30 years in Guangdong Province, which can provide important information assisting the control and prevention strategies.

PMID:35849623 | DOI:10.1371/journal.pntd.0010622

PLoS Comput Biol. 2022 Jul 18;18(7):e1010287. doi: 10.1371/journal.pcbi.1010287. Online ahead of print.

ABSTRACT

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

PMID:35849618 | DOI:10.1371/journal.pcbi.1010287

PLoS One. 2022 Jul 18;17(7):e0271216. doi: 10.1371/journal.pone.0271216. eCollection 2022.

ABSTRACT

BACKGROUND: Globally, postpartum hemorrhage is the leading preventable cause of maternal mortality. To decrease postpartum hemorrhage-related maternal mortalities, identifying its risk factors is crucial to suggest interventions. In this regard, little is known about the link between primary postpartum hemorrhage and inter-pregnancy interval in Ethiopia, where more than half of pregnancies occur shortly after the preceding childbirth. Therefore, we aimed to elucidate the association of primary postpartum hemorrhage with an inter-pregnancy interval in urban South Ethiopia.

METHODS: A community-based matched nested case-control study was conducted among a cohort of 2548 pregnant women. All women with primary postpartum hemorrhage during the follow-up (n = 73) were taken as cases. Women who were randomly selected from those without primary postpartum hemorrhage (n = 292) were taken as controls. Cases were individually matched with controls (1:4 ratio) for age group and location. A conditional logistic regression analysis was done using R version 4.0.5 software. Statistically, a significant association was declared using 95% CI and p-value. Attributable fraction (AF) and population attributable fraction (PAF) were used to estimate the public health impacts of the inter-pregnancy interval.

RESULTS: This study found out that more than half (66%) of primary postpartum hemorrhage was attributed to inter-pregnancy interval <24 months (AF = 66.3%, 95% CI: 37.5, 82.5%). This could be prevented if the inter-pregnancy interval was increased to 24-60 months. Likewise, nearly half (49%) of primary postpartum hemorrhage in the study population could be prevented if the inter-pregnancy interval <24 months was prevented. Additionally, primary postpartum hemorrhage was attributed to antepartum hemorrhage, prolonged labour and multiple pregnancies.

CONCLUSIONS: Primary postpartum hemorrhage was associated with inter-pregnancy interval under 24 months, highlighting the need to improve postpartum modern contraceptive utilization in the community. Counseling couples about how long to wait until subsequent pregnancy and the risk when the inter-pregnancy interval is short need to be underlined.

PMID:35849596 | DOI:10.1371/journal.pone.0271216