Effects of Compound Danshen Dripping Pills on Ventricular Remodeling and Cardiac Function after Acute Anterior Wall ST-Segment Elevation Myocardial Infarction (CODE-AAMI): Protocol for a Randomized Placebo-Controlled Trial.

BACKGROUND: Ventricular remodeling after acute anterior wall ST-segment elevation myocardial infarction (AAMI) is an important factor in occurrence of heart failure which additionally results in poor prognosis. Therefore, the treatment of ventricular remodeling needs to be further optimized. Compound Danshen Dripping Pills (CDDP), a traditional Chinese medicine, exerts a protective effect on microcirculatory disturbance caused by ischemia-reperfusion injury and attenuates ventricular remodeling after myocardial infarction. OBJECTIVE: This study is designed to evaluate the efficacy and safety of CDDP in improving ventricular remodeling and cardiac function after AAMI on a larger scale. METHODS: This study is a multi-center, randomized, double-blind, placebo-controlled, parallel-group clinical trial. The total of 268 patients with AAMI after primary percutaneous coronary intervention (pPCI) will be randomly assigned 1:1 to the CDDP group (n=134) and control group (n=134) with a follow-up of 48 weeks. Both groups will be treated with standard therapy of ST-segment elevation myocardial infarction (STEMI), with the CDDP group administrating 20 tablets of CDDP before pPCI and 10 tablets 3 times daily after pPCI, and the control group treated with a placebo simultaneously. The primary endpoint is 48-week echocardiographic outcomes including left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume index (LVEDVI), and left ventricular end-systolic volume index (LVESVI). The secondary endpoint includes the change in N terminal pro-B-type natriuretic peptide (NT-proBNP) level, arrhythmias, and cardiovascular events (death, cardiac arrest, or cardiopulmonary resuscitation, rehospitalization due to heart failure or angina pectoris, deterioration of cardiac function, and stroke). Investigators and patients are both blinded to the allocated treatment. DISCUSSION: This prospective study will investigate the efficacy and safety of CDDP in improving ventricular remodeling and cardiac function in patients undergoing pPCI for a first AAMI. Patients in the CDDP group will be compared with those in the control group. If certified to be effective, CDDP treatment in AAMI will probably be advised on a larger scale. (Trial registration No. NCT05000411).

Dynamic risk for first onset of depressive disorders in adolescence: does change matter?

BACKGROUND: Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The 'Risk Escalation hypothesis' posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the 'Chronic Risk hypothesis' posits that the average level rather than change predicts first-onset DD. METHODS: We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5-15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset. RESULTS: Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors. CONCLUSIONS: Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative.

Efficient gradient boosting for prognostic biomarker discovery.

MOTIVATION: A gradient boosting decision tree (GBDT) is a powerful ensemble machine-learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as extreme gradient boosting (XGB) and light gradient boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies. RESULTS: In this paper, we present a new R package 'Xsurv' as an integrated solution that applies two modern GBDT training frameworks namely, XGB and LGB, for the modeling of right-censored survival outcomes. Based on our simulations, we benchmark the new approaches against traditional methods including the stepwise Cox regression model and the original gradient boosting function implemented in the package 'gbm'. We also demonstrate the application of Xsurv in analyzing a melanoma methylation dataset. Together, these results suggest that Xsurv is a useful and computationally viable tool for screening a large number of prognostic candidate biomarkers, which may facilitate future translational and clinical research. AVAILABILITY AND IMPLEMENTATION: 'Xsurv' is freely available as an R package at: https://github.com/topycyao/Xsurv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fenchel duality of Cox partial likelihood with an application in survival kernel learning.

The Cox proportional hazard model is one of the most widely used methods in modeling time-to-event data in the health sciences. Due to the simplicity of the Cox partial likelihood function, many machine learning algorithms use it for survival data. However, due to the nature of censored data, the optimization problem becomes intractable when more complicated regularization is employed, which is necessary when dealing with high dimensional omic data. In this paper, we show that a convex conjugate function of the Cox loss function based on Fenchel duality exists, and provide an alternative framework to optimization based on the primal form. Furthermore, the dual form suggests an efficient algorithm for solving the kernel learning problem with censored survival outcomes. We illustrate performance and properties of the derived duality form of Cox partial likelihood loss in multiple kernel learning problems with simulated and the Skin Cutaneous Melanoma TCGA datasets.

Schizophrenia polygenic risk score and 20-year course of illness in psychotic disorders.

Understanding whether and how the schizophrenia polygenic risk score (SZ PRS) predicts course of illness could improve diagnosis and prognostication in psychotic disorders. We tested whether the SZ PRS predicts symptoms, cognition, illness severity, and diagnostic changes over the 20 years following first admission. The Suffolk County Mental Health Project is an inception cohort study of first-admission patients with psychosis. Patients were assessed six times over 20 years, and 249 provided DNA. Geographically- and demographically-matched never psychotic adults were recruited at year 20, and 205 provided DNA. Symptoms were rated using the Schedule for the Assessment of Positive Symptoms and Schedule for the Assessment of Negative Symptoms. Cognition was evaluated with a comprehensive neuropsychological battery. Illness severity and diagnosis were determined by consensus of study psychiatrists. SZ PRS was significantly higher in first-admission than never psychotic groups. Within the psychosis cohort, the SZ PRS predicted more severe negative symptoms (ß = 0.21), greater illness severity (ß = 0.28), and worse cognition (ß = -0.35), across the follow-up. The SZ PRS was the strongest predictor of diagnostic shifts from affective to non-affective psychosis over the 20 years (AUC = 0.62). The SZ PRS predicts persistent differences in cognition and negative symptoms. The SZ PRS also predicts who among those who appear to have a mood disorder with psychosis at first admission will ultimately be diagnosed with a schizophrenia spectrum disorder. These findings show potential for the SZ PRS to become a tool for diagnosis and treatment planning.

Multiple-kernel learning for genomic data mining and prediction.

BACKGROUND: Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be under-utilized by genomic researchers partly due to the lack of unified guidelines for its use, and benchmark genomic datasets. RESULTS: We provide three implementations of MKL in R. These methods are applied to simulated data to illustrate that MKL can select appropriate models. We also apply MKL to combine clinical information with miRNA gene expression data of ovarian cancer study into a single analysis. Lastly, we show that MKL can identify gene sets that are known to play a role in the prognostic prediction of 15 cancer types using gene expression data from The Cancer Genome Atlas, as well as, identify new gene sets for the future research. CONCLUSION: Multiple kernel learning coupled with modern optimization techniques provides a promising learning tool for building predictive models based on multi-source genomic data. MKL also provides an automated scheme for kernel prioritization and parameter tuning. The methods used in the paper are implemented as an R package called RMKL package, which is freely available for download through CRAN at https://CRAN.R-project.org/package=RMKL .

Are fit indices used to test psychopathology structure biased? A simulation study.

Structural models of psychopathology provide dimensional alternatives to traditional categorical classification systems. Competing models, such as the bifactor and correlated factors models, are typically compared via statistical indices to assess how well each model fits the same data. However, simulation studies have found evidence for probifactor fit index bias in several psychological research domains. The present study sought to extend this research to models of psychopathology, wherein the bifactor model has received much attention, but its susceptibility to bias is not well characterized. We used Monte Carlo simulations to examine how various model misspecifications produced fit index bias for 2 commonly used estimators, WLSMV and MLR. We simulated binary indicators to represent psychiatric diagnoses and positively skewed continuous indicators to represent symptom counts. Across combinations of estimators, indicator distributions, and misspecifications, complex patterns of bias emerged, with fit indices more often than not failing to correctly identify the correlated factors model as the data-generating model. No fit index emerged as reliably unbiased across all misspecification scenarios. Although, tests of model equivalence indicated that in one instance fit indices were not biased-they favored the bifactor model, albeit not unfairly. Overall, results suggest that comparisons of bifactor models to alternatives using fit indices may be misleading and call into question the evidentiary meaning of previous studies that identified the bifactor model as superior based on fit. We highlight the importance of comparing models based on substantive interpretability and their utility for addressing study aims, the methodological significance of model equivalence, as well as the need for implementation of statistical metrics that evaluate model quality. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Depression Severity Over 27 Months in Adolescent Girls Is Predicted by Stress-Linked Cortical Morphology.

BACKGROUND: Evidence supports the notion that early-life stress and trauma impact cortical development and increase vulnerability to depression. However, it remains unclear whether common stressful life events in community-dwelling adolescents have similar consequences for cortical development. METHODS: A total of 232 adolescent girls (mean age 15.29 ± 0.65 years) were assessed with the Stressful Life Events Schedule (a semistructured interview of stressors in the previous 9 months) and underwent a magnetic resonance imaging scan. FreeSurfer 5.3.0 was used to perform whole-brain surface-based morphometry. Dysphoria was assessed at the time of imaging and prospectively at three 9-month follow-up appointments using the Inventory of Depression and Anxiety Symptoms II. RESULTS: At least one stressful life event was reported in 90% of the adolescent participants during the 9 months preceding imaging. Greater burden of recent life stress was associated with less left precuneus and left postcentral cortical thickness and smaller left superior frontal and right inferior parietal volume (all p < .05 after multiple comparisons correction). Left precuneus thickness in the stress-associated cluster significantly predicted dysphoria for 27 months after imaging controlling for prior dysphoria (ß = -.11, p = .004). Left precuneus cortical thickness accounted for 17.0% of the association between stress and dysphoric mood for 27 months after imaging (ß = .04, p = .05). CONCLUSIONS: Consistent with evidence from imaging studies of trauma-exposed youths and preclinical stress models, a heavy burden of recent common life stress in community-dwelling adolescent girls was associated with altered frontal/parietal cortical morphology. Stress-linked precuneus cortical thickness represents a candidate prospective biomarker of adolescent depression.

Common Taxonomy of Traits and Symptoms: Linking Schizophrenia Symptoms, Schizotypy, and Normal Personality.

The associations among normal personality and many mental disorders are well established, but it remains unclear whether and how symptoms of schizophrenia and schizotypal traits align with the personality taxonomy. This study examined the joint factor structure of normal personality, schizotypy, and schizophrenia symptoms in people with psychotic disorders (n = 288) and never-psychotic adults (n = 257) in the Suffolk County Mental Health Project. First, we evaluated the structure of schizotypal (positive schizotypy, negative schizotypy, and mistrust) and normal traits. In both the psychotic-disorder and never-psychotic groups, the best-fitting model had 5 factors: neuroticism, extraversion, conscientiousness, agreeableness, and psychoticism. The schizotypy traits were placed on different dimensions: negative schizotypy went on (low) extraversion, whereas positive schizotypy and mistrust went on psychoticism. Next, we added symptoms to the model. Numerous alternatives were compared, and the 5-factor model remained best-fitting. Reality distortion (hallucinations and delusions) and disorganization symptoms were placed on psychoticism, and negative symptoms were placed on extraversion. Models that separated symptom dimensions from trait dimensions did not fit well, arguing that taxonomies of symptoms and traits are aligned. This is the first study to show that symptoms of psychosis, schizotypy, and normal personality reflect the same underlying dimensions. Specifically, (low) extraversion, negative schizotypy, and negative symptoms form one spectrum, whereas psychoticism, positive schizotypy, and positive and disorganized symptoms form another. This framework helps to understand the heterogeneity of psychosis and comorbidity patterns found in psychotic disorders. It also underscores the importance of traits to understanding these disorders.

The role of modifiable health-related behaviors in the association between PTSD and respiratory illness.

BACKGROUND: Posttraumatic stress disorder (PTSD) increases risk of future respiratory illness. However, mechanisms that underpin the association between these common and debilitating conditions remain unknown. The aim of this study was to identify modifiable, health-related behaviors they may explain the link between PTSD and respiratory problems. METHODS: World Trade Center responders (N = 452, 89% male, mean age = 55 years) completed baseline PTSD and sleep questionnaires, followed by 2-weeks of daily diaries, actigraphy and ambulatory spirometry to monitor lower respiratory symptoms, pulmonary function, activity levels, stressors, and sleep. Lipid levels were obtained from electronic medical records. RESULTS: Cross-sectional mediation analyses revealed that the association between PTSD and self-reported respiratory symptoms was explained by poor sleep, low activity, and daily stressors. The association between PTSD symptoms and pulmonary function was explained by insomnia and low activity. CONCLUSIONS: A range of health-related daily behaviors and experiences, especially sleep disturbances and inactivity, may explain excess respiratory illness morbidity in PTSD. The findings were generally consistent across daily self-report and spirometry measures of respiratory problems. Targeting these behaviors might enhance prevention of and intervention in respiratory problems in traumatized populations.

Apolipoprotein E-ε4 allele predicts escalation of psychotic symptoms in late adulthood.

BACKGROUND: Research on a putative link between apolipoprotein-ε4 allele (APOE-ε4) and schizophrenia has been inconclusive. However, prior studies have not investigated the association between APOE-ε4 and symptom trajectories, nor has the existing literature taken into account the potentially moderating effect of age in genetic association studies. METHODS: The association between APOE-ε4 and four symptom dimensions was investigated in a longitudinal study of 116 individuals with schizophrenia initially assessed during their first admission for psychosis and evaluated five times over the following 20years. A meta-analysis identified 29 case-control studies of APOE-ε4 allele frequency in schizophrenia, which were analyzed using random-effects meta-regression to test the potentially moderating effect of age. RESULTS: Longitudinal models identified a specific association between APOE-ε4 and symptom trajectories, showing that APOE-ε4 portends worsening severity of hallucinations and delusions in late adulthood among people with schizophrenia, at a rate of a 0.46 standard deviation increase per decade. Meta-analysis showed a significant effect of age: the association between APOE-ε4 and schizophrenia was not detectable in younger people but became pronounced with age, such that APOE-ε4 increased the odds of diagnosis by 10% per decade. CONCLUSIONS: Taken together, the meta-analysis and longitudinal analysis implicate APOE-ε4 as an age-related risk factor for worsening hallucinations and delusions, and suggest APOE-ε4 may play an age-mediated pathophysiological role in schizophrenia. The presence of an APOE-ε4 allele may also identify a subgroup of patients who require intensive monitoring and additional targeted interventions, especially in mid-to late-life.

Maladaptive Personality Traits and 10-Year Course of Psychiatric and Medical Symptoms and Functional Impairment Following Trauma.

Background: Personality is a major predictor of many mental and physical disorders, but its contributions to illness course are understudied. Purpose: The current study aimed to explore whether personality is associated with a course of psychiatric and medical illness over 10 years following trauma. Methods: World Trade Center (WTC) responders (N = 532) completed the personality inventory for DSM-5, which measures both broad domains and narrow facets. Responders' mental and physical health was assessed in the decade following the WTC disaster during annual monitoring visits at a WTC Health Program clinic. Multilevel modeling was used in an exploratory manner to chart the course of health and functioning, and examine associations of maladaptive personality domains and facets with intercepts (initial illness) and slopes (course) of illness trajectories. Results: Three maladaptive personality domains-negative affectivity, detachment and psychoticism-were uniquely associated with initial posttraumatic stress disorder (PTSD); detachment and psychoticism were also associated with initial functional impairment. Five facets-emotional lability, anhedonia, callousness, distractibility and perceptual dysregulation-were uniquely associated with initial mental and physical health and functional impairment. Anxiousness and depressivity facets were associated with worse initial levels of psychiatric outcomes only. With regard to illness trajectory, callousness and perceptual dysregulation were associated with the increase in PTSD symptoms. Anxiousness was associated with greater persistence of respiratory symptoms. Conclusions: Several personality domains and facets were associated with initial levels and long-term course of illness and functional impairment in a traumatized population. Results inform the role of maladaptive personality in the development and maintenance of chronic mental-physical comorbidity. Personality might constitute a transdiagnostic prognostic and treatment target.

Declining Clinical Course of Psychotic Disorders Over the Two Decades Following First Hospitalization: Evidence From the Suffolk County Mental Health Project.

OBJECTIVE: Kraepelin considered declining course a hallmark of schizophrenia, but others have suggested that outcomes usually stabilize or improve after treatment initiation. The authors investigated this question in an epidemiologically defined cohort with psychotic disorders followed for 20 years after first hospitalization. METHOD: The Suffolk County Mental Health Project recruited first-admission patients with psychosis from all inpatient units of Suffolk County, New York (response rate, 72%). Participants were assessed in person six times over two decades; 373 completed the 20-year follow-up (68% of survivors); 175 had schizophrenia/schizoaffective disorder. Global Assessment of Functioning (GAF), psychotic symptoms, and mood symptoms were rated at each assessment. Month 6, when nearly all participants were discharged from the index hospitalization, was used as a reference. RESULTS: In the schizophrenia group, mean GAF scores declined from 49 at month 6 to 36 at year 20. Negative and positive symptoms also worsened (Cohen's d values, 0.45-0.73). Among participants without schizophrenia, GAF scores were higher initially (a mean of approximately 64) but declined by 9 points over the follow-up period. Worsening began between years 5 and 8. Neither aging nor changes in antipsychotic treatment accounted for the declines. In all disorders, depression improved and manic symptoms remained low across the 20 years. CONCLUSIONS: The authors found substantial symptom burden across disorders that increased with time and ultimately may undo initial treatment gains. Previous studies have suggested that better health care delivery models may preempt this decline. In the United States, these care needs are often not met, and addressing them is an urgent priority.

A systematic evaluation of nucleotide properties for CRISPR sgRNA design.

BACKGROUND: CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. RESULTS: By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offered marginal improvement (∼2% increase in AUC). CONCLUSION: Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna .

What do clinicians treat: Diagnoses or symptoms? The incremental validity of a symptom-based, dimensional characterization of emotional disorders in predicting medication prescription patterns.

BACKGROUND: Although practice guidelines are based on disorders specified in diagnostic manuals, such as the DSM, practitioners appear to follow symptoms when making treatment decisions. Psychiatric medication is generally prescribed in a transdiagnostic manner, further highlighting how symptoms, not diagnoses, often guide clinical practice. A quantitative approach to nosology promises to provide better guidance as it describes psychopathology dimensionally and its organization reflects patterns of covariation among symptoms. AIM: To investigate whether a quantitative classification of emotional disorders can account for naturalistic medication prescription patterns better than traditional diagnoses. METHODS: Symptom dimensions and DSM diagnoses of emotional disorders, as well as prescribed medications, were assessed using interviews in a psychiatric outpatient sample (N=318, mean age 42.5years old, 59% female, 81% Caucasian). RESULTS: Each diagnosis was associated with prescription of multiple medication classes, and most medications were associated with multiple disorders. This was largely due to heterogeneity of clinical diagnoses, with narrow, homogenous dimensions underpinning diagnoses showing different medication profiles. Symptom dimensions predicted medication prescription better than DSM diagnoses, irrespective of whether this was examined broadly across all conditions, or focused on a specific disorder and medication indicated for it. CONCLUSIONS: Psychiatric medication was prescribed in line with symptoms rather than DSM diagnoses. A quantitative approach to nosology may better reflect treatment planning and be a more effective guide to pharmacotherapy than traditional diagnoses. This adds to a diverse body of evidence about superiority of the quantitative system in practical applications and highlights its potential to improve psychiatric care.

Validating dimensions of psychosis symptomatology: Neural correlates and 20-year outcomes.

Heterogeneity of psychosis presents significant challenges for classification. Between 2 and 12 symptom dimensions have been proposed, and consensus is lacking. The present study sought to identify uniquely informative models by comparing the validity of these alternatives. An epidemiologic cohort of 628 first-admission inpatients with psychosis was interviewed 6 times over 2 decades and completed an electrophysiological assessment of error processing at year 20. We first analyzed a comprehensive set of 49 symptoms rated by interviewers at baseline, progressively extracting from 1 to 12 factors. Next, we compared the ability of resulting factor solutions to (a) account for concurrent neural dysfunction and (b) predict 20-year role, social, residential, and global functioning, and life satisfaction. A four-factor model showed incremental validity with all outcomes, and more complex models did not improve explanatory power. The 4 dimensions-reality distortion, disorganization, inexpressivity, and apathy/asociality-were replicable in 5 follow-ups, internally consistent, stable across assessments, and showed strong discriminant validity. These results reaffirm the value of separating disorganization and reality distortion, are consistent with recent findings distinguishing inexpressivity and apathy/asociality, and suggest that these 4 dimensions are fundamental to understanding neural abnormalities and long-term outcomes in psychosis. (PsycINFO Database Record