Tag: pubmed

Arch Womens Ment Health. 2022 Oct 6. doi: 10.1007/s00737-022-01269-x. Online ahead of print.

ABSTRACT

This study aimed to characterize the relationship between cannabis use, ACE score, and pregnancy outcomes. Pregnant patients in Baltimore, MD, completed the 17-point ACE checklist. Charts of the birth parent and neonate were reviewed for urine toxicology testing at initiation of care and delivery, prenatal care metrics, and birth statistics. Multivariable logistic regression analysis was performed to assess the relationship between ACE score, cannabis use, and pregnancy outcomes. Of 256 birth parents, 87 (34.0%) tested positive for cannabis at initial visit and 39 (15.2%) tested positive for cannabis at delivery. Testing positive for cannabis at initial visit or delivery was associated with higher ACE score (15.1 vs 13.7, p = 0.04; 16.2 vs 13.8, p = 0.01). Of those who tested positive for cannabis at initial visit, 39/87 (45.0%) tested positive at delivery. Continued cannabis use at delivery was associated with lower maternal weight gain (7.9 kg vs 13.3 kg, p = 0.003), fewer prenatal visits (7 vs 8, p = 0.010), and numerically higher mean ACE score. Cannabis use at delivery was associated with 10% lower birthweight (2665 g vs 3014 g p < 0.05) but not with pre-term birth. Total ACE score was not significantly associated with any birth outcome. Worse pregnancy outcomes were associated with cannabis use throughout pregnancy but not with cannabis use at prenatal care initiation. The interplay of ACE and continued cannabis use during pregnancy warrants further research on the physiologic effects of cannabis and interventions to decrease substance use during pregnancy.

PMID:36203114 | DOI:10.1007/s00737-022-01269-x

World J Urol. 2022 Oct 6. doi: 10.1007/s00345-022-04171-4. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare perioperative outcomes following robot-assisted partial nephrectomy (RAPN) in patients with age ≥ 70 years to age < 70 years.

METHODS: Using Vattikuti Collective quality initiative (VCQI) database for RAPN we compared perioperative outcomes following RAPN between the two age groups. Primary outcome of the study was to compare trifecta outcomes between the two groups. Propensity matching using nearest neighbourhood method was performed with trifecta as primary outcome for sex, body mass index (BMI), solitary kidney, tumor size and Renal nephrometery score (RNS).

RESULTS: Group A (age ≥ 70 years) included 461 patients whereas group B included 1932 patients. Before matching the two groups were statistically different for RNS and solitary kidney rates. After propensity matching, the two groups were comparable for baselines characteristics such as BMI, tumor size, clinical symptoms, tumor side, face of tumor, solitary kidney and tumor complexity. Among the perioperative outcome parameters there was no difference between two groups for operative time, blood loss, intraoperative transfusion, intraoperative complications, need for radical nephrectomy, positive margins and trifecta rates. Warm ischemia time was significantly longer in the younger age group (18.1 min vs. 16.3 min, p = 0.003). Perioperative complications were significantly higher in the older age group (11.8% vs. 7.7%, p = 0.041). However, there was no difference between the two groups for major complications.

CONCLUSION: RAPN in well-selected elderly patients is associated with comparable trifecta outcomes with acceptable perioperative morbidity.

PMID:36203102 | DOI:10.1007/s00345-022-04171-4

Aesthetic Plast Surg. 2022 Oct 6. doi: 10.1007/s00266-022-03121-0. Online ahead of print.

ABSTRACT

BACKGROUND: The positive benefits of immediate prosthesis breast reconstruction (IPBR) are incontrovertible. During the COVID-19 pandemic, health care resources became scarce. The implementation of outpatient immediate prosthesis breast reconstruction (OIPBR) can improve the efficiency of medical care and reduce viral exposure. Very few studies have focused on OIPBR and this study aimed to fill this gap by evaluating outcomes of OIPBR compared with traditional hospitalization IPBR (THIPBR) in terms of complications and quality of life.

MATERIAL AND METHODS: The study enrolled patients undergoing IPBR at Tianjin Medical University Cancer Institute and Hospital between January 1, 2020, and September 30, 2021. Outcomes were defined as postoperative complications and quality of life before reconstruction and at 3-month follow-up. Quality of life was assessed by BREAST-Q questionnaire. Inverse probability of treatment weighting and propensity score matching (PSM) were applied to adjust for confounders.

RESULTS: A total of 135 patients were enrolled, including 110 with THIPBR and 25 with OIPBR. After matching, baseline characteristics were well balanced. Patients with OIPBR had lower rates of lymphedema on the surgery side (p = 0.041) and readmission (p = 0.040) than patients with THIPBR. No statistically significant differences in the quality of life metrics of psychosocial well-being, sexual well-being, satisfaction with breast and physical well-being of the chest were found between the two groups.

CONCLUSION: OIPBR is a safe and efficient alternative to THIBPR during the COVID-19 pandemic. It is recommended when medical conditions allow to conserve medical resources. Accelerated technical training for the performance of OIPBR at the hospital level should be expedited.

LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

PMID:36203096 | DOI:10.1007/s00266-022-03121-0

Commun Biol. 2022 Oct 6;5(1):1061. doi: 10.1038/s42003-022-03978-6.

ABSTRACT

The contribution of germline copy number variants (CNVs) to risk of developing cancer in individuals with pathogenic BRCA1 or BRCA2 variants remains relatively unknown. We conducted the largest genome-wide analysis of CNVs in 15,342 BRCA1 and 10,740 BRCA2 pathogenic variant carriers. We used these results to prioritise a candidate breast cancer risk-modifier gene for laboratory analysis and biological validation. Notably, the HR for deletions in BRCA1 suggested an elevated breast cancer risk estimate (hazard ratio (HR) = 1.21), 95% confidence interval (95% CI = 1.09-1.35) compared with non-CNV pathogenic variants. In contrast, deletions overlapping SULT1A1 suggested a decreased breast cancer risk (HR = 0.73, 95% CI 0.59-0.91) in BRCA1 pathogenic variant carriers. Functional analyses of SULT1A1 showed that reduced mRNA expression in pathogenic BRCA1 variant cells was associated with reduced cellular proliferation and reduced DNA damage after treatment with DNA damaging agents. These data provide evidence that deleterious variants in BRCA1 plus SULT1A1 deletions contribute to variable breast cancer risk in BRCA1 carriers.

PMID:36203093 | DOI:10.1038/s42003-022-03978-6

J Exp Psychol Appl. 2022 Oct 6. doi: 10.1037/xap0000450. Online ahead of print.

ABSTRACT

Although theories of emotion associate negative emotional symptoms with cognitive biases in information processing, they rarely specify the details. Here, we characterize cognitive biases in information processing of pleasant and unpleasant information, and how these biases covary with anxious and depressive symptoms, while controlling for general stress and cognitive ability. Forty undergraduates provided emotional symptom scores (Depression Anxiety Stress Scale-21) and performed a statistical learning task that required predicting the next sound in a long sequence of either pleasant or unpleasant naturalistic sounds (blocks). We used an information weights framework to determine if the degree of behavioral change associated with observing either confirmatory (“B” follows “A”) or disconfirmatory (“B” does not follow “A”) transitions differs for pleasant and unpleasant sounds. Bayesian mixed-effects models revealed that negative emotional symptom scores predicted performance as well as processing biases of pleasant and unpleasant information. Further, information weights differed between pleasant and unpleasant information, and importantly, this difference varied based on symptom scores. For example, higher depressive symptom scores predicted a bias of underutilizing disconfirmatory information in unpleasant content. These findings have implications for models of emotional disorders by offering a mechanistic explanation and formalization of the associated cognitive biases. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

PMID:36201842 | DOI:10.1037/xap0000450

J Proteome Res. 2022 Oct 6. doi: 10.1021/acs.jproteome.2c00278. Online ahead of print.

ABSTRACT

Combining robust proteomics instrumentation with high-throughput enabling liquid chromatography (LC) systems (e.g., timsTOF Pro and the Evosep One system, respectively) enabled mapping the proteomes of 1000s of samples. Fragpipe is one of the few computational protein identification and quantification frameworks that allows for the time-efficient analysis of such large data sets. However, it requires large amounts of computational power and data storage space that leave even state-of-the-art workstations underpowered when it comes to the analysis of proteomics data sets with 1000s of LC mass spectrometry runs. To address this issue, we developed and optimized a Fragpipe-based analysis strategy for a high-performance computing environment and analyzed 3348 plasma samples (6.4 TB) that were longitudinally collected from hospitalized COVID-19 patients under the auspice of the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study. Our parallelization strategy reduced the total runtime by ∼90% from 116 (theoretical) days to just 9 days in the high-performance computing environment. All code is open-source and can be deployed in any Simple Linux Utility for Resource Management (SLURM) high-performance computing environment, enabling the analysis of large-scale high-throughput proteomics studies.

PMID:36201825 | DOI:10.1021/acs.jproteome.2c00278

J Pers Soc Psychol. 2022 Nov;123(5):1089-1137. doi: 10.1037/pspp0000437.

ABSTRACT

Attachment theory provides a framework for understanding the correlations among interpersonal relationships, stress, and health. Moreover, adult attachment is an important predictor of mental health. However, there is a lack of systematic reviews that simultaneously examine the associations between adult attachment and both positive and negative indicators of mental health. Consequently, we meta-analyzed 224 studies examining the associations between adult attachment and mental health, using robust variance estimation with random effects. The results (k = 245 samples, N = 79,722) showed that higher levels of attachment anxiety and avoidance were positively correlated with negative affect (e.g., depression, anxiety, loneliness) and they were negatively correlated with positive affect (e.g., life satisfaction, self-esteem). More specifically, there were moderate associations between attachment avoidance and negative mental health (r = .28) and positive mental health (r = -.24). Likewise, there were moderate associations between attachment anxiety and negative mental health (r = .42) and positive mental health (r = -.29). Furthermore, the association between the attachment dimensions and mental health outcomes was also moderated by several variables (e.g., gender, age). Finally, these associations remained statistically significant even when the attachment dimensions were mutually controlled using meta-analytic structural equation modeling. Overall, attachment anxiety had larger associations with mental health than did attachment avoidance. Thus, the current results support robust links between adult attachment and mental health. This may have implications for future research and mental health treatments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

PMID:36201836 | DOI:10.1037/pspp0000437

Psychol Methods. 2022 Oct 6. doi: 10.1037/met0000524. Online ahead of print.

ABSTRACT

Structural equation modeling is one of the most popular statistical frameworks in the social and behavioral sciences. Often, detection of groups with distinct sets of parameters in structural equation models (SEM) are of key importance for applied researchers, for example, when investigating differential item functioning for a mental ability test or examining children with exceptional educational trajectories. In the present article, we present a new approach combining subgroup discovery-a well-established toolkit of supervised learning algorithms and techniques from the field of computer science-with structural equation models termed SubgroupSEM. We provide an overview and comparison of three approaches to modeling and detecting heterogeneous groups in structural equation models, namely, finite mixture models, SEM trees, and SubgroupSEM. We provide a step-by-step guide to applying subgroup discovery techniques for structural equation models, followed by a detailed and illustrated presentation of pruning strategies and four subgroup discovery algorithms. Finally, the SubgroupSEM approach will be illustrated on two real data examples, examining measurement invariance of a mental ability test and investigating interesting subgroups for the mediated relationship between predictors of educational outcomes and the trajectories of math competencies in 5th grade children. The illustrative examples are accompanied by examples of the R package subgroupsem, which is a viable implementation of our approach for applied researchers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

PMID:36201823 | DOI:10.1037/met0000524

Psychol Methods. 2022 Oct 6. doi: 10.1037/met0000529. Online ahead of print.

ABSTRACT

Model selection is an omnipresent issue in structural equation modeling (SEM). When deciding among competing theories instantiated as formal statistical models, a trade-off is often sought between goodness-of-fit and model parsimony. Whereas traditional fit assessment in SEM quantifies parsimony solely as the number of free parameters, the ability of a model to account for diverse data patterns-known as fitting propensity-also depends on the functional form of a model. The present investigation provides a systematic assessment of the fitting propensity of models typically considered and compared in SEM, namely, exploratory and confirmatory factor analysis models positing a different number of latent factors or a different hierarchical structure (single-factor, correlated factors, higher-order, and bifactor models). Furthermore, the behavior of commonly used fit indices (CFI, SRMR, RMSEA, TLI) and information criteria (AIC, BIC) in accounting for fitting propensity was assessed. Although the results demonstrated varying degrees of fitting propensity for the models under scrutiny, these differences were mostly driven by the number of free parameters. There was little evidence for additional differences in the functional form of the compared models. Fit indices adjusting for the number of free parameters such as the RMSEA and TLI thus adequately accounted for differences in fitting propensity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

PMID:36201821 | DOI:10.1037/met0000529

Psychol Methods. 2022 Oct 6. doi: 10.1037/met0000532. Online ahead of print.

ABSTRACT

Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable. We begin by illustrating that, for a variety of noncontinuously distributed outcomes (i.e., binary and count outcomes), attempts to use the linear model for recovery leads to catastrophic levels of bias and false discovery. Next, focusing on transformations of normally distributed variables (i.e., censoring and noninterval scaling), we show that linear models again produce spurious interaction effects. We provide explanations offering geometric and algebraic intuition as to why interactions are a challenge for these incorrectly specified models. In light of these findings, we make two specific recommendations. First, a careful consideration of the outcome’s distributional properties should be a standard component of interaction studies. Second, researchers should approach research focusing on interactions with heightened levels of scrutiny. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

PMID:36201820 | DOI:10.1037/met0000532