Quantitative Bayesian Network Analyses of Mitochondrial Toxicity

Abstract Background. Greater levels of insight may be linked with depressive symptoms among patients with schizophrenia, however, it would be useful to characterize this association at symptom-level, in order to inform research on interventions. Methods. Data on depressive symptoms (Calgary Depression Scale for Schizophrenia) and insight (G12 item from the Positive and Negative Syndrome Scale) were obtained from 921 community-dwelling, clinically-stable individuals with a DSM-IV diagnosis of schizophrenia, recruited in a nationwide multicenter study. Network analysis was used to explore the most relevant connections between insight and depressive symptoms, including potential confounders in the model (neurocognitive and social-cognitive functioning, positive, negative and disorganization symptoms, extrapyramidal symptoms, hostility, internalized stigma, and perceived discrimination). Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association between insight and depression. Results. After adjusting for confounders, better levels of insight were associated with greater self-depreciation, pathological guilt, morning depression and suicidal ideation. No difference in global network structure was detected for socioeconomic status, service engagement or illness severity. The DAG confirmed the presence of an association between greater insight and self-depreciation, suggesting the more probable causal direction was from insight to depressive symptoms. Conclusions. In schizophrenia, better levels of insight may cause self-depreciation and, possibly, other depressive symptoms. Person-centered and narrative psychotherapeutic approaches may be particularly fit to improve patient insight without dampening self-esteem.

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Multivariate and Bayesian Network Analyses of the Factors Causing Belief Conflicts That Deteriorate Teamwork and Cause Stress among Pharmacists during Proposals and Inquiries of Prescription

YAKUGAKU ZASSHI ◽

10.1248/yakushi.20-00013 ◽

2020 ◽

Vol 140 (9) ◽

pp. 1151-1164

Author(s):

Tsuyoshi Hayashi ◽

Tomoya Tachi ◽

Mirai Takaoka ◽

Yoshihiro Noguchi ◽

Hitomi Teramachi

Keyword(s):

Bayesian Network ◽

Network Analyses

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Probabilistic Graphical Model using Bayesian Networks for Predicting Clinical Outcome after Posterior Decompression in Patients with Cervical Spondylotic Myelopathy

10.21203/rs.3.rs-45605/v1 ◽

2020 ◽

Author(s):

Sung Bae Park ◽

Sohee Oh ◽

Changwon Yoo ◽

Dong Ah Shin ◽

Sun-Ho Lee ◽

...

Keyword(s):

Clinical Outcome ◽

Bayesian Network ◽

Cervical Spondylotic Myelopathy ◽

Graphical Model ◽

Probabilistic Graphical Model ◽

Symptom Duration ◽

Posterior Decompression ◽

Causal Factors ◽

Gait Impairment ◽

Network Analyses

Abstract BackgroundThe objective of this study was to develop a probabilistic graphical model (PGM) to show the personalized prediction of clinical outcome in patients with cervical spondylotic myelopathy (CSM) with different clinical conditions after posterior decompression and to use the PGM to identify causal predictors of the outcome.MethodsWe included data from 59 patients who had undergone cervical posterior decompression for CSM. The candidate predictive parameters were age, sex, body mass index, trauma history, symptom duration, preoperative and last Japanese Orthopaedic Association (JOA) scores, gait impairment, claudication, bladder dysfunction, Nurick grade, American Spinal Injury Association (ASIA) grade, smoking, diabetes mellitus, cardiopulmonary disorders, hypertension, stroke, Parkinson disease, dementia, psychiatric disorders, arthritis, ossification of the posterior longitudinal ligament, cord signal change in T1-weighted images, postoperative kyphosis, and cord compression ratio. Statistical and Bayesian network analyses were used to create the PGM and identify predictive factors.ResultsIn multiple linear regression analysis, preoperative JOA score, presence of a psychiatric disorder, and ASIA grade were identified as significant factors associated with the last JOA score. Dementia, sex, preoperative JOA score, and gait impairment were causal factors in the PGM with 93.2% accuracy. Sex, dementia, and preoperative JOA score were direct causal factors related to the last JOA score. Being female, having dementia, and a low preoperative JOA score were significantly related to having a low last JOA score. The PGM showed that preoperative JOA score and sex did not affect the last JOA score in patients with dementia. The probability of having a high last JOA score was higher in men with a high preoperative JOA score than in women with the same preoperative state (74% vs. 2%, respectively).ConclusionsThe causal predictors of surgical outcome for CSM were sex, dementia, and preoperative JOA score. Use of the PGM with the Bayesian network may be useful personalized medicine tool for predicting the outcome for each patient with CSM.

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Nutraceutical potentialities of Tunisian Argan oil based on its physicochemical properties and fatty acid content as assessed through Bayesian network analyses

Lipids in Health and Disease ◽

10.1186/s12944-018-0782-9 ◽

2018 ◽

Vol 17 (1) ◽

Cited By ~ 1

Author(s):

Mohsen Hanana ◽

Hajer Mezghenni ◽

Rayda Ben Ayed ◽

Ali Ben Dhiab ◽

Slim Jarradi ◽

...

Keyword(s):

Fatty Acid ◽

Physicochemical Properties ◽

Bayesian Network ◽

Acid Content ◽

Fatty Acid Content ◽

Network Analyses ◽

Argan Oil

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Disparities in pregnancy-related deaths: spatial and Bayesian network analyses of maternal mortality ratio in 54 African countries

BMJ Global Health ◽

10.1136/bmjgh-2020-004233 ◽

2021 ◽

Vol 6 (2) ◽

pp. e004233

Author(s):

Sanni Yaya ◽

Seun Stephen Anjorin ◽

Sunday A Adedini

Keyword(s):

Spatial Distribution ◽

Maternal Mortality ◽

Bayesian Network ◽

Social Determinants ◽

Maternal Mortality Ratio ◽

Public Health Problem ◽

Cold Spot ◽

African Countries ◽

Network Analyses ◽

Mortality Ratio

BackgroundMaternal mortality remains a public health problem despite several global efforts. Globally, about 830 women die of pregnancy-related death per day, with more than two-third of these cases occurring in Africa. We examined the spatial distribution of maternal mortality in Africa and explored the influence of SDoH on the spatial distribution.MethodsWe used country-level secondary data of 54 African countries collected between 2006 and 2018 from three databases namely, World Development Indicator, WHO’s Global Health Observatory Data and Human Development Report. We performed descriptive analyses, presented in tables and maps. The spatial analysis involved local indicator of spatial autocorrelation maps and spatial regression. Finally, we built Bayesian networks to determine and show the strength of social determinants associated with maternal mortality.ResultsWe found that the average prevalence of maternal mortality ratio (MMR) in Africa was 415 per 100 000 live births. Findings from the spatial analyses showed clusters (hotspots) of MMR with seven countries (Guinea-Bissau, Guinea, Sierra Leone, Cote d’Ivoire, Chad and Cameroon, Mauritania), all within the Middle and West Africa. On the other hand, the cold spot clusters were formed by two countries; South Africa and Namibia; eight countries (Algeria, Tunisia, Libya, Ghana, Gabon and Congo, Equatorial Guinea and Cape Verde) formed low–high clusters; thus, indicating that these countries have significantly low MMR but within the neighbourhood of countries with significantly high MMR. The findings from the regression and Bayesian network analysis showed that gender inequities and the proportion of skilled birth attendant are strongest social determinants that drive the variations in maternal mortality across Africa.ConclusionMaternal mortality is very high in Africa especially in countries in the middle and western African subregions. To achieve the target 3.1 of the sustainable development goal on maternal health, there is a need to design effective strategies that will address gender inequalities and the shortage of health professionals.

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Superior outcomes with Argatroban for heparin-induced thrombocytopenia: a Bayesian network meta-analysis

International Journal of Clinical Pharmacy ◽

10.1007/s11096-021-01260-z ◽

2021 ◽

Author(s):

Giorgia Colarossi ◽

Nicola Maffulli ◽

Andromahi Trivellas ◽

Heike Schnöring ◽

Nima Hatam ◽

...

Keyword(s):

Mortality Rate ◽

Bayesian Network ◽

Meta Analysis ◽

Oral Anticoagulants ◽

Random Effects Model ◽

Thromboembolic Events ◽

Heparin Induced Thrombocytopenia ◽

Bayesian Hierarchical ◽

Network Analyses ◽

Anticoagulant Drugs

AbstractBackground Argatroban, lepirudin, desirudin, bivalirudin, and danaparoid are commonly used to manage heparin-induced thrombocytopenia related complications. However, the most suitable drug for this condition still remains controversial. Aim of the review This Bayesian network meta-analysis study compared the most common anticoagulant drugs used in the management of heparin-induced thrombocytopenia. Method All clinical trials comparing two or more anticoagulant therapies for suspected or confirmed heparin-induced thrombocytopenia were considered for inclusion. Studies concerning the use of heparins or oral anticoagulants were not considered. Data concerning hospitalisation length, thromboembolic, major, and minor haemorrhagic events, and mortality rate were collected. The network analyses were made through the STATA routine for Bayesian hierarchical random-effects model analysis with standardised mean difference (SMD) and log odd ratio (LOR) effect measures. Results Data from a total of 4338 patients were analysed. The overall mean age was 62.31 ± 6.6 years old. Hospitalization length was considerably shorter in favour of the argatroban group (SMD: − 1.70). Argatroban evidenced the lowest rate of major (LOR: − 1.51) and minor (LOR: − 0.57) haemorrhagic events. Argatroban demonstrated the lowest rate of thromboembolic events (LOR: 0.62), and mortality rate (LOR: − 1.16). Conclusion Argatroban performed better overall for selected patients with HIT. Argatroban demonstrated the shortest hospitalization, and lowest rate of haemorrhages, thromboembolisms, and mortality compared to bivalirudin, lepirudin, desirudin, and danaparoid.

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Questionnaire Investigation on Jewelry/Accessory Internet Shopping and Its Analysis Utilizing Bayesian Network

International Business Research ◽

10.5539/ibr.v9n1p165 ◽

2015 ◽

Vol 9 (1) ◽

pp. 165

Author(s):

Tsuyoshi Aburai ◽

Kazuhiro Takeyasu ◽

Chie Ishio

Keyword(s):

Sensitivity Analysis ◽

Bayesian Network ◽

Odds Ratio ◽

The Internet ◽

Internet Shopping ◽

Network Analyses ◽

Sample Data ◽

On Line ◽

Log Odds ◽

Line Network

<p>Recently, the numbers of jewelry/accessories buying via the Internet are increasing, especially for young people. They often have difficulty deciding what kinds of jewelry/accessories, because there are many kinds of jewelry/accessories to choose from. Consulting service to support decisions is required for these matters. In this paper, a questionnaire investigation is executed for the purchasing on-line network, used for jewelry/accessory purchasing in order to get instructions for an on-line network consulting service. Nearly 500 sample data are collected. In this research, we construct the model utilizing Bayesian Network and causal relationship is sequentially chained by the characteristic of customer, the purchase budget and the accessory type. We analyzed them by sensitivity analysis and log odds ratio was also calculated. Hypothesis testing result was compared with this one. The method to utilize log odds ratio in the sensitivity analysis under the Bayesian Network proved to be sensitive and useful. This method would be applicable in many Bayesian Network analyses. These are utilized for constructing a much more effective and useful on-line network consulting service.<strong></strong></p>

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Bayesian network analyses of resistance pathways against efavirenz and nevirapine

AIDS ◽

10.1097/qad.0b013e32830fe940 ◽

2008 ◽

Vol 22 (16) ◽

pp. 2107-2115 ◽

Cited By ~ 16

Author(s):

Koen Deforche ◽

Ricardo J Camacho ◽

Zehave Grossman ◽

Marcelo A Soares ◽

Kristel Van Laethem ◽

...

Keyword(s):

Bayesian Network ◽

Network Analyses

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Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

International Journal of Molecular Sciences ◽

10.3390/ijms22052316 ◽

2021 ◽

Vol 22 (5) ◽

pp. 2316

Author(s):

Andrei S. Rodin ◽

Grigoriy Gogoshin ◽

Seth Hilliard ◽

Lei Wang ◽

Colt Egelston ◽

...

Keyword(s):

Flow Cytometry ◽

Bayesian Network ◽

Cancer Immunotherapy ◽

Mononuclear Cells ◽

Machine Learning Techniques ◽

Computational Pipeline ◽

Network Analyses ◽

Research Priority ◽

Immune Biomarkers ◽

Clinical Responses

Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline.

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