Category: Nevin Manimala

Psychosom Med. 2023 Jan 16. doi: 10.1097/PSY.0000000000001164. Online ahead of print.

ABSTRACT

OBJECTIVE: Type D personality, a joint tendency toward negative affectivity (NA) and social inhibition (SI), has been linked to adverse events in patients with heart disease, though with inconsistent findings. Here, we apply an individual patient-data meta-analysis to data from 19 prospective cohort studies (N = 11151), to investigate the prediction of adverse outcomes by Type D personality in acquired cardiovascular disease (CVD) patients.

METHOD: For each outcome (all-cause mortality, cardiac mortality, myocardial infarction (MI), coronary artery bypass grafting, percutaneous coronary intervention, major adverse cardiac event (MACE), any adverse event), we estimated Type D’s prognostic influence and the moderation by age, sex, and disease type.

RESULTS: In CVD patients, evidence for a Type D effect in terms of the Bayes factor (BF) was strong for MACE (BF = 42.5; OR = 1.14) and any adverse event (BF = 129.4; OR = 1.15). Evidence for the null hypothesis was found for all-cause mortality (BF = 45.9; OR = 1.03), cardiac mortality (BF = 23.7; OR = 0.99) and MI (BF = 16.9; OR = 1.12), suggesting Type D had no effect on these outcomes. This evidence was similar in the subset of coronary artery disease (CAD) patients, but inconclusive for heart failure (HF) patients. Positive effects were found for NA on cardiac- and all-cause mortality, the latter being more pronounced in males than females.

CONCLUSION: Across 19 prospective cohort studies, Type D predicts adverse events in CAD patients, while evidence in HF patients was inconclusive. In both CAD and HF patients, we found evidence for a null effect of Type D on cardiac- and all-cause mortality.

PMID:36640440 | DOI:10.1097/PSY.0000000000001164

Catheter Cardiovasc Interv. 2023 Jan 14. doi: 10.1002/ccd.30535. Online ahead of print.

ABSTRACT

BACKGROUND: Invasive cardiac catheterization (CC) temporarily increases pain, discomfort, and anxiety. Procedural sedation is deployed to mitigate these symptoms, though practice varies. Research evaluating peri-procedural patient-reported outcomes is lacking.

METHODS AND RESULTS: We randomized 175 patients undergoing CC to short interval ([SI] group, <6 min) or long interval ([LI] group, ≥6 min) time intervals between initial intravenous sedation and local anesthetic administration. Outcomes included: (1) total pain medication use, (2) patient-reported and behaviorally assessed pain and (3) patient satisfaction during outpatient CC. Generalized linear mixed effect models were used to evaluate the impact of treatment time interval on total medication utilization, pain, and satisfaction. Among enrollees the mean age was 62 (standard deviation [SD] = 13.4), a majority were male (66%), white (74%), and overweight (mean body mass index = 28.5 [SD = 5.6]). Total pain medication use did not vary between treatment groups (p = 0.257), with no difference in total fentanyl (p = 0.288) or midazolam (p = 0.292). Post-treatment pain levels and nurse-observed pain were not statistically significant between groups (p = 0.324 & p = 0.656, respectively. No significant differences with satisfaction with sedation were found between the groups (p = 0.95) Patient-reported pain, satisfaction and nurse-observed measures of pain did not differ, after adjustment for demographic and procedural factors. Analyses of treatment effect modification revealed that postprocedure self-reported pain levels varied systematically between individuals undergoing percutaneous coronary intervention (PCI) (SI = 2.2 [0.8, 3.6] vs. LI = 0.7 [-0.6, 2.0]) compared with participants not undergoing PCI (SI = 0.4 [-0.8, 1.7] vs. LI = 0.7 [-0.3, 1.6]) (p = 0.043 for interaction).

CONCLUSION: No consistent treatment differences were found for total medication dose, pain, or satisfaction regardless of timing between sedation and local anesthetic. Treatment moderations were seen for patients undergoing PCI. Further investigation of how procedural and individual factors impact the patient experience during CC is needed.

PMID:36640418 | DOI:10.1002/ccd.30535

World Psychiatry. 2023 Feb;22(1):86-104. doi: 10.1002/wps.21068.

ABSTRACT

Empirical evidence indicates a significant bidirectional association between mental disorders and physical diseases, but the prospective impact of men-tal disorders on clinical outcomes of physical diseases has not been comprehensively outlined. In this PRISMA- and COSMOS-E-compliant umbrella review, we searched PubMed, PsycINFO, Embase, and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, up to March 15, 2022, to identify systematic reviews with meta-analysis that examined the prospective association between any mental disorder and clinical outcomes of physical diseases. Primary outcomes were disease-specific mortality and all-cause mortality. Secondary outcomes were disease-specific incidence, functioning and/or disability, symptom severity, quality of life, recurrence or progression, major cardiac events, and treatment-related outcomes. Additional inclusion criteria were further applied to primary studies. Random effect models were employed, along with I² statistic, 95% prediction intervals, small-study effects test, excess significance bias test, and risk of bias (ROBIS) assessment. Associations were classified into five credibility classes of evidence (I to IV and non-significant) according to established criteria, complemented by sensitivity and subgroup analyses to examine the robustness of the main analysis. Statistical analysis was performed using a new package for conducting umbrella reviews (https://metaumbrella.org). Population attributable fraction (PAF) and generalized impact fraction (GIF) were then calculated for class I-III associations. Forty-seven systematic reviews with meta-analysis, encompassing 251 non-overlapping primary studies and reporting 74 associations, were included (68% were at low risk of bias at the ROBIS assessment). Altogether, 43 primary outcomes (disease-specific mortality: n=17; all-cause mortality: n=26) and 31 secondary outcomes were investigated. Although 72% of associations were statistically significant (p<0.05), only two showed convincing (class I) evidence: that between depressive disorders and all-cause mortality in patients with heart failure (hazard ratio, HR=1.44, 95% CI: 1.26-1.65), and that between schizophrenia and cardiovascular mortality in patients with cardiovascular diseases (risk ratio, RR=1.54, 95% CI: 1.36-1.75). Six associations showed highly suggestive (class II) evidence: those between depressive disorders and all-cause mortality in patients with diabetes mellitus (HR=2.84, 95% CI: 2.00-4.03) and with kidney failure (HR=1.41, 95% CI: 1.31-1.51); that between depressive disorders and major cardiac events in patients with myocardial infarction (odds ratio, OR=1.52, 95% CI: 1.36-1.70); that between depressive disorders and dementia in patients with diabetes mellitus (HR=2.11, 95% CI: 1.77-2.52); that between alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C (RR=3.15, 95% CI: 2.87-3.46); and that between schizophrenia and cancer mortality in patients with cancer (standardized mean ratio, SMR=1.74, 95% CI: 1.41-2.15). Sensitivity/subgroup analyses confirmed these results. The largest PAFs were 30.56% (95% CI: 27.67-33.49) for alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C, 26.81% (95% CI: 16.61-37.67) for depressive disorders and all-cause mortality in patients with diabetes mellitus, 13.68% (95% CI: 9.87-17.58) for depressive disorders and major cardiac events in patients with myocardial infarction, 11.99% (95% CI: 8.29-15.84) for schizophrenia and cardiovascular mortality in patients with cardiovascular diseases, and 11.59% (95% CI: 9.09-14.14) for depressive disorders and all-cause mortality in patients with kidney failure. The GIFs confirmed the preventive capacity of these associations. This umbrella review demonstrates that mental disorders increase the risk of a poor clinical outcome in several physical diseases. Prevention targeting mental disorders – particularly alcohol use disorders, depressive disorders, and schizophrenia – can reduce the incidence of adverse clinical outcomes in people with physical diseases. These findings can inform clinical practice and trans-speciality preventive approaches cutting across psychiatric and somatic medicine.

PMID:36640414 | DOI:10.1002/wps.21068

World Psychiatry. 2023 Feb;22(1):48-74. doi: 10.1002/wps.21056.

ABSTRACT

Despite considerable progress in pharmacotherapy over the past seven decades, many mental disorders remain insufficiently treated. This situation is in part due to the limited knowledge of the pathophysiology of these disorders and the lack of biological markers to stratify and individualize patient selection, but also to a still restricted number of mechanisms of action being targeted in monotherapy or combination/augmentation treatment, as well as to a variety of challenges threatening the successful development and testing of new drugs. In this paper, we first provide an overview of the most promising drugs with innovative mechanisms of action that are undergoing phase 2 or 3 testing for schizophrenia, bipolar disorder, major depressive disorder, anxiety and trauma-related disorders, substance use disorders, and dementia. Promising repurposing of established medications for new psychiatric indications, as well as variations in the modulation of dopamine, noradrenaline and serotonin receptor functioning, are also considered. We then critically discuss the clinical trial parameters that need to be considered in depth when developing and testing new pharmacological agents for the treatment of mental disorders. Hurdles and perils threatening success of new drug development and testing include inadequacy and imprecision of inclusion/exclusion criteria and ratings, sub-optimally suited clinical trial participants, multiple factors contributing to a large/increasing placebo effect, and problems with statistical analyses. This information should be considered in order to de-risk trial programmes of novel agents or known agents for novel psychiatric indications, increasing their chances of success.

PMID:36640403 | DOI:10.1002/wps.21056

World Psychiatry. 2023 Feb;22(1):43-45. doi: 10.1002/wps.21039.

NO ABSTRACT

PMID:36640399 | DOI:10.1002/wps.21039

J Assist Reprod Genet. 2023 Jan 14. doi: 10.1007/s10815-022-02707-6. Online ahead of print.

ABSTRACT

PURPOSE: To determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non-invasive method for predicting aneuploidy.

METHODS: A cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method. Patient characteristics such as maternal age, AMH level, paternal sperm quality, and total number of normally fertilized (2PN) embryos were processed using SVM and NN. To improve model performance, we created voting ensembles using CNN, SVM, and NN to combine our imaging data with clinical parameter variations. Statistical significance was evaluated with a one-sample t-test with 2 degrees of freedom.

RESULTS: When assessing blastocyst images alone, the CNN test accuracy was 61.2% (± 1.32% SEM, n = 3 models) in correctly classifying euploid/aneuploid embryos (n = 140 embryos). When the best CNN model was assessed as a voting ensemble, the test accuracy improved to 65.0% (AMH; p = 0.1), 66.4% (maternal age; p = 0.06), 65.7% (maternal age, AMH; p = 0.08), 66.4% (maternal age, AMH, number of 2PNs; p = 0.06), and 71.4% (maternal age, AMH, number of 2PNs, sperm quality; p = 0.02) (n = 140 embryos).

CONCLUSIONS: By combining CNNs with patient characteristics, voting ensembles can be created to improve the accuracy of classifying embryos as euploid/aneuploid from CNN alone, allowing for AI to serve as a potential non-invasive method to aid in karyotype screening and selection of embryos.

PMID:36640251 | DOI:10.1007/s10815-022-02707-6

Environ Sci Pollut Res Int. 2023 Jan 14. doi: 10.1007/s11356-023-25194-3. Online ahead of print.

ABSTRACT

In recent years, traditional energy sources have caused a variety of negative impacts on the environment, and reducing carbon emissions is a top priority. The development of renewable energy technology is the key to transform the energy structure. Renewable energy represented by wind energy and photovoltaics has abundant reserves so they are connected to the grid system on a large scale. However, because of natural energy’s randomness, renewable energy power generation poses potential risks to energy production and grid security. By making short-term forecasts of renewable energy generation power, the uncertainty of energy generation can be reduced, and it is crucial to study renewable energy forecasting techniques. This paper proposes an integrated forecasting system for renewable energy sources. Firstly, ensemble empirical mode decomposition is used for data preprocessing, and stationarity analysis is used for modal identification; then, support vector regression optimized by sparrow search algorithm and statistical methods are combined to make forecast according to different characteristics of the series respectively; finally, the feasibility of this method in renewable energy time series prediction is verified by experiments. The experiments prove that the proposed model effectively improves the accuracy and prediction performance on ultra-short-term renewable energy forecasting; and it has good applicability and competitiveness with different forecasting scenarios and characteristics, which satisfy the actual forecasting requirements in terms of operational efficiency and accuracy, thus providing a technical basis for the effective utilization of renewable energy.

PMID:36640232 | DOI:10.1007/s11356-023-25194-3

Funct Integr Genomics. 2023 Jan 14;23(1):38. doi: 10.1007/s10142-023-00963-y.

ABSTRACT

Breast cancer is the most common tumor and the leading cause of cancer death in women. Cuproptosis is a new type of cell death, which can induce proteotoxic stress and eventually lead to cell death. Therefore, regulating copper metabolism in tumor cells is a new therapeutic approach. Long non-coding RNAs play an important regulatory role in immune response. At present, cuproptosis-related lncRNAs in breast cancer have not been reported. Breast cancer RNA sequencing, genomic mutations, and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Patients with breast cancer were randomly assigned to the train group or the test group. Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-related lncRNAs and to construct a risk prognostic model. The prediction performance of the model is verified and recognized. In addition, the nomogram was used to predict the prognosis of breast cancer patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassay were used to detect the differences in biological function. Tumor mutation burden (TMB) was used to measure immunotherapy response. A total of 19 cuproptosis genes were obtained and a prognostic model based on 10 cuproptosis-related lncRNAs was constructed. Kaplan-Meier survival curves showed statistically significant overall survival (OS) between the high-risk and low-risk groups. Receiver operating characteristic curve (ROC) and principal component analysis (PCA) show that the model has accurate prediction ability. Compared with other clinical features, cuproptosis-related lncRNAs model has higher diagnostic efficiency. Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor for breast cancer patients. In addition, the nomogram model analysis showed that the tumor mutation burden was significantly different between the high-risk and low-risk groups. Of note, the additive effect of patients in the high-risk group and patients with high TMB resulted in reduced survival in breast cancer patients. Our study identified 10 cuproptosis-related lncRNAs, which may be promising biomarkers for predicting the survival prognosis of breast cancer.

PMID:36640225 | DOI:10.1007/s10142-023-00963-y

Environ Monit Assess. 2023 Jan 14;195(2):299. doi: 10.1007/s10661-023-10922-6.

ABSTRACT

Use of medicinal herbs is now gaining popularity especially among the low-income people because it is cheap, readily available and its “seeming” lack of side effects. However, environmental pollution is a potential threat to its continued use. This study examines the effect of air pollution on the soil and consequently on the medicinal herbs grown on such soils. Soil and four medicinal herbs, Chromolaena odorata, Vernonia amygdalina, Carica papaya and Ocimum gratissimum, commonly used in the south western part of Nigeria either as purely medicinal herbs, soup vegetables or both were carefully harvested from Fasina, a polluted area, and Moro, a relatively unpolluted area, all in Ile-Ife, Nigeria. Samples were prepared following standard practice and analysed for nickel, chromium, cadmium and lead using atomic absorption spectroscopy (AAS). The results showed that elemental concentrations at the two locations were within the permissible limit for both soil and herbs, the statistical test also established no significant difference between the two locations. However, toxic metals concentrations (chromium, cadmium and lead) were found higher at the polluted site while that of the essential metal, nickel, was higher at the unpolluted site. Of the four metals, cadmium has the highest transfer ratio (0.39 and 0.34) while lead has the least (0.21 and 0.25) for Moro and Fasina sites respectively. Similarly, Chromolaena odorata has the highest transfer ratio (0.34) while Carica papaya has the least (0.28). In conclusion, gradual build-up of the toxic metals at the polluted site is evident and may eventually contaminate the herbs.

PMID:36640219 | DOI:10.1007/s10661-023-10922-6

J Med Syst. 2023 Jan 14;47(1):9. doi: 10.1007/s10916-023-01907-6.

ABSTRACT

Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen’s kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection.

PMID:36640212 | DOI:10.1007/s10916-023-01907-6