Tag: pubmed

BMC Med Imaging. 2023 Jan 10;23(1):7. doi: 10.1186/s12880-022-00955-7.

ABSTRACT

OBJECTIVES: Myocardial strain is reported to be a sensitive indicator of myocardial mechanical changes in patients with hypertrophic cardiomyopathy (HCM). The changes in the mechanics of the myocardium of normal wall thickness (< 12 mm) have yet to be well studied. This study aimed to evaluate the function of myocardial segments of normal thickness in patients with HCM.

METHODS: Sixty-three patients with HCM and 30 controls were retrospectively enrolled in this retrospective study. Cine imaging, native and post-contrast T1 maps, T2 maps, and late gadolinium enhancement were performed. In addition, regional myocardial strain was assessed by cardiac magnetic resonance-tissue tracking. Strain parameters were compared between the controls and HCM patients with segments of the myocardium of normal thickness. Subgroup analysis was conducted in obstructive and non-obstructive HCM. Lastly, p < 0.05 was considered statistically significant.

RESULTS: In normal-thickness myocardial segments of HCM (n = 716), diastolic peak strain rates (PSRs) were significantly lower than in the control group (n = 480) (radial, – 2.43 [- 3.36, – 1.78] vs. – 2.67 [- 3.58, – 1.96], p = 0.002; circumferential, 1.28 [1.01,1.60] vs. 1.39 [1.14, 1.78], p < 0.001; and longitudinal, 1.16 [0.75,1.51] vs. 1.28 [0.90, 1.71], p < 0.001). The normal-thickness segments showed no significant difference in systolic PSRs between HCM and the controls. In the subgroup analysis, significantly decreased diastolic PSRs were noted in both obstructive and non-obstructive HCM, compared with the controls (p < 0.05).

CONCLUSIONS: Diastolic changes in myocardial mechanics were observed in normal-thickness segments of HCM, occurring before morphological remodeling and systolic dysfunction developed. This finding contributed to a better understanding of the mechanical pathophysiology of HCM with preserved left ventricular ejection fraction. It may potentially aid in predicting disease progression and risk stratification.

PMID:36624416 | DOI:10.1186/s12880-022-00955-7

BMC Med Imaging. 2023 Jan 9;23(1):5. doi: 10.1186/s12880-023-00962-2.

ABSTRACT

PURPOSE: To evaluate whether deep learning reconstruction (DLR) accelerates the acquisition of 1.5-T magnetic resonance imaging (MRI) knee data without image deterioration.

MATERIALS AND METHODS: Twenty-one healthy volunteers underwent MRI of the right knee on a 1.5-T MRI scanner. Proton-density-weighted images with one or four numbers of signal averages (NSAs) were obtained via compressed sensing, and DLR was applied to the images with 1 NSA to obtain 1NSA-DLR images. The 1NSA-DLR and 4NSA images were compared objectively (by deriving the signal-to-noise ratios of the lateral and the medial menisci and the contrast-to-noise ratios of the lateral and the medial menisci and articular cartilages) and subjectively (in terms of the visibility of the anterior cruciate ligament, the medial collateral ligament, the medial and lateral menisci, and bone) and in terms of image noise, artifacts, and overall diagnostic acceptability. The paired t-test and Wilcoxon signed-rank test were used for statistical analyses.

RESULTS: The 1NSA-DLR images were obtained within 100 s. The signal-to-noise ratios (lateral: 3.27 ± 0.30 vs. 1.90 ± 0.13, medial: 2.71 ± 0.24 vs. 1.80 ± 0.15, both p < 0.001) and contrast-to-noise ratios (lateral: 2.61 ± 0.51 vs. 2.18 ± 0.58, medial 2.19 ± 0.32 vs. 1.97 ± 0.36, both p < 0.001) were significantly higher for 1NSA-DLR than 4NSA images. Subjectively, all anatomical structures (except bone) were significantly clearer on the 1NSA-DLR than on the 4NSA images. Also, in the former images, the noise was lower, and the overall diagnostic acceptability was higher.

CONCLUSION: Compared with the 4NSA images, the 1NSA-DLR images exhibited less noise, higher overall image quality, and allowed more precise visualization of the menisci and ligaments.

PMID:36624404 | DOI:10.1186/s12880-023-00962-2

BMC Psychiatry. 2023 Jan 9;23(1):18. doi: 10.1186/s12888-023-04516-2.

ABSTRACT

BACKGROUND: Individuals with type 1 diabetes (T1DM) may experience sleep problems, usually due to low blood sugar levels during sleep or performance of blood sugar management (e.g., blood sugar monitoring). This study aimed to identify the disease-related characteristics, psychosocial aspects, and related factors underlying sleep quality in patients with T1DM.

METHODS: This study employed a descriptive research design. The participants were 159 individuals with T1DM who completed online questionnaires. The data were analyzed using descriptive statistics, correlations, and multiple regression analyses.

RESULTS: The average score for depression in T1DM patients was 23.77 (SD 5.31), and sleep quality received a score of 4.58 (SD 3.22). Depression was positively correlated with sleep quality and negatively correlated with the total resilience score. The factors linked to depression in T1DM patients were duration of disease, sleep latency, sleep duration, sleep disturbance, and resilience-acceptance of self and life sub-factors, with an explanatory power of 44.4% for the depression variance. The associated factors with sleep quality in T1DM patients were complications, resilience-personal competence sub-factors, and depression, with an explanatory power of 37.4% for sleep quality variance.

CONCLUSIONS: The results of this study suggest that to improve sleep quality in patients with T1DM, it is necessary to develop and support disease management to prevent complications and implement interventions for improving resilience and reducing negative emotions such as depression.

PMID:36624402 | DOI:10.1186/s12888-023-04516-2

BMC Bioinformatics. 2023 Jan 9;24(1):8. doi: 10.1186/s12859-022-05123-w.

ABSTRACT

BACKGROUND: The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a pseudo-value regression approach for network analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information criteria, followed by pseudo-value calculations, which are then entered into a robust regression model.

RESULTS: This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus database to identify DC genes that are associated with chronic obstructive pulmonary disease to demonstrate its utility.

CONCLUSION: To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.

PMID:36624383 | DOI:10.1186/s12859-022-05123-w

BMC Cardiovasc Disord. 2023 Jan 9;23(1):8. doi: 10.1186/s12872-022-03015-8.

ABSTRACT

BACKGROUND: Pulmonary vein isolation (PVI) is recommended to treat paroxysmal and persistent atrial fibrillation (AF). This analysis aimed to assess the hospital efficiency of single-shot cryoballoon ablation (CBA) and point-by-point radiofrequency ablation (RFA).

METHODS: The discrete event simulation used PVI procedure times from the FREEZE Cohort study to establish the electrophysiology (EP) lab occupancy time. 1000 EP lab days were simulated according to an illustrative German hospital, including 3 PVI cases per day using CBA at one site and RFA at the other.

RESULTS: The analysis included 1560 CBA patients and 1344 RFA patients from the FREEZE Cohort. Some baseline patients’ characteristics were different between groups (age, AF type, and some concomitant diseases), without being statistically associated to ablation procedure time. Mean procedure time was 122.2 ± 39.4 min for CBA and 160.3 ± 53.5 min for RFA (p < 0.0001). RFA was associated with a more than five-fold increase of cumulative overtime compared to CBA over the simulated period (1285 h with RFA and 253 h with CBA). 70.7% of RFA lab days included overtime versus 25.7% for CBA. CBA was associated with more days with an additional hour at the end of the EP lab shift compared to RFA (47.8% vs 11.5% days with one hour left, respectively).

CONCLUSION: CBA is faster and more predictable than point-by-point RFA, and enables improvements in EP lab efficiency, including: fewer cumulative overtime hours, more days where overtime is avoided and more days with remaining time for the staff or for any EP lab usage. Clinical trial registration NCT01360008 (first registration 25/05/2011).

PMID:36624380 | DOI:10.1186/s12872-022-03015-8

BMC Med Res Methodol. 2023 Jan 9;23(1):7. doi: 10.1186/s12874-022-01794-4.

ABSTRACT

BACKGROUND: The proportion of patients with diabetic retinopathy (DR) has grown with increasing number of diabetes mellitus patients in the world. It is among the major causes of blindness worldwide. The main objective of this study was to identify contributing risk factors of DR among people with type II diabetes mellitus.

METHOD: A sample of 191 people with type II diabetes mellitus was selected from the Black Lion Specialized Hospital diabetic unit from 1 March 2018 to 1 April 2018. A multivariate stochastic regression imputation technique was applied to impute the missing values. The response variable, DR is a categorical variable with two outcomes. Based on the relationship derived from the exploratory analysis, the odds of having DR were not necessarily linearly related to the continuous predictors for this sample of patients. Therefore, a semiparametric model was proposed to identify the risk factors of DR.

RESULT: From the sample of 191 people with type II diabetes mellitus, 98 (51.3%) of them had DR. The results of semiparametric regression model revealed that being male, hypertension, insulin treatment, and frequency of clinical visits had a significant linear relationships with the odds of having DR. In addition, the log- odds of having DR has a significant nonlinear relation with the interaction of age by gender (for female patients), duration of diabetes, interaction of cholesterol level by gender (for female patients), haemoglobin A1c, and interaction of haemoglobin A1c by fasting blood glucose with degrees of freedom [Formula: see text], respectively. The interaction of age by gender and cholesterol level by gender appear non significant for male patients. The result from the interaction of haemoglobin A1c (HbA1c) by fasting blood glucose (FBG) showed that the risk of DR is high when the level of HbA1c and FBG were simultaneously high.

CONCLUSION: Clinical variables related to people with type II diabetes mellitus were strong predictive factors of DR. Hence, health professionals should be cautious about the possible nonlinear effects of clinical variables, interaction of clinical variables, and interaction of clinical variables with sociodemographic variables on the log odds of having DR. Furthermore, to improve intervention strategies similar studies should be conducted across the country.

PMID:36624377 | DOI:10.1186/s12874-022-01794-4

Neural Netw. 2023 Jan 3;160:97-107. doi: 10.1016/j.neunet.2022.12.024. Online ahead of print.

ABSTRACT

This article focuses on predefined time synchronization problem for a class of signal switching neural networks with time-varying delays. In the network models, we not only consider the coupling characteristics in the following networks, but also consider the disturbance with standard Brownian motion. In the design of the controller, the control gain is designed as 1ɛ+T_p-t (t∈[T₀,T_p), ɛ is an optional smaller positive number), which avoids the infinite gain (the control gain is designed as 1T_p-t in other reference). In order to get the predefined time control law, a power function is multiplied to the Lyapunov functional, from which it can get an exponential upper bound function via the derivative and mathematical expectation operation. Utilizing the martingale theory and the method of Laplace matrix, some novel predefined time synchronization criteria are obtained for the leader-following neural networks, meanwhile the following networks can maintain the leader network after achieved synchronization. Based on the special network of the main system, five corollaries separately develop the predefined time synchronization results from different perspectives. An example with some simulation figures and computing results fully exhibits the effectiveness of the achieved synchronization scheme. In this case, although the error signal is disturbed by Brownian motion, the trace signal can still stably converge to zero by this control scheme, meanwhile the predefined-time control effect is achieved.

PMID:36623446 | DOI:10.1016/j.neunet.2022.12.024

Heart Lung. 2023 Jan 7;58:217-222. doi: 10.1016/j.hrtlng.2023.01.003. Online ahead of print.

ABSTRACT

BACKGROUND: Observational studies have found an association between iron deficiency anemia (IDA) and chronic obstructive pulmonary disease (COPD) risk. However, whether IDA plays a role in COPD development remains unclear.

OBJECTIVES: This study was performed to explore the causal association between IDA and COPD.

METHODS: We obtained summary statistics for IDA from 6087 cases and 211,115 controls of European ancestry in an open genome-wide association study (GWAS) to select strongly associated single nucleotide polymorphisms that could serve as instrumental variables for IDA (P < 5 × 10-8). Additional summary statistics for COPD were obtained from 6915 COPD cases and 186,723 controls of European ancestry from a publicly available GWAS. A bidirectional Mendelian randomization analysis was performed using inverse variance weighting as the primary method of analysis. The reliability of the results was verified by heterogeneity and sensitivity analysis.

RESULTS: IDA increased the risk of COPD, with an odds ratio (OR) of 1.15 (95% confidence interval (CI: 1.04-1.25, p = 0.002). There was no evidence of a causal effect of COPD on IDA risk, with an OR of 0.99 (95% CI: 0.87-1.13, p = 0.91). The sensitivity analysis showed no evidence of heterogeneity or horizontal pleiotropy.

CONCLUSIONS: We found that IDA increases the risk of COPD. Additionally, there was no evidence that COPD increases the risk of IDA. Therefore, IDA should be considered in future COPD risk studies and reintroduced as a potential therapeutic target. The relationship between COPD and IDA risk requires further study using indirect mechanisms.

PMID:36623443 | DOI:10.1016/j.hrtlng.2023.01.003

Curr Opin Biotechnol. 2023 Jan 7;79:102884. doi: 10.1016/j.copbio.2022.102884. Online ahead of print.

ABSTRACT

Statistical methods, especially machine learning, learning(ML), are pivotal for the analyses of large data generated by multiomics human gut microbiota study. These analyses lead to the discovery of microbe-disease associations. Furthermore, recent efforts for more data transparency and accessible analytical tools improved data availability and study reproducibility. Our recent accumulated knowledge on microbe-disease associations brings light to the next questions: what is the role of microbes in disease progression and how can we apply our knowledge of microbiome in clinical settings? Here, we introduce recent studies that implemented ML to answer the questions of causal inference and clinical translation.

PMID:36623442 | DOI:10.1016/j.copbio.2022.102884

Theriogenology. 2022 Dec 13;198:264-272. doi: 10.1016/j.theriogenology.2022.12.012. Online ahead of print.

ABSTRACT

The stem cell-based research for reproductive biotechnology has been widely studied and shows promise for repairing defective tissue or degenerated cells to treat different diseases. The adipose tissue and amniotic membrane have awakened great interest in regenerative medicine and arises as a promising source of mesenchymal stem cells. Both types, adipose and amniotic derived mesenchymal stem cells (AMSCs) are multipotent cells with an enhanced ability to differentiate into multiple lineages.. We aimed to evaluate the effect of basal supplementation of exosomes in cell cultures with canine amniotic mesenchymal stem cells (MSCs). Mesenchymal stem cells derived from canine amniotic and adipose tissue were isolated and cultured performing cell passages until 80-90% confluence was reached. The growth curve was determined and peak cell growth was observed in the second passage. The cells were then characterized and differentiated into adipogenic, chondrogenic and osteogenic lineages. Extracellular vesicles from amnion were isolated using an ultracentrifugation protocol and characterized by nanosight analysis. To evaluate their ability to improve cellular viability in naturally inefficient passages, exosomes were co-cultures to the MSC cells. The results showed a 15-20% increase in the expansion rate of cultures supplemented with vesicles extracted in the first and second passages when compared to the control group. Statistical analysis using the Dunnett test (p ≤ 0.05) corroborated this result, showing a positive correlation between supplementation and expansion rate. These results indicate not only the importance of exosomes in the cell communication process but also the feasibility of the culture supplementation protocol for therapeutic purposes. The potential of the AMSCs for reproductive biotechnology is undoubted, however, their application to repair reproductive disorders and the involved mechanisms remain elusive. The strategies to enable the Adipose Stem Cells and AMSCs application in reproductive biotechnology and optimize their use for tissue regeneration open new venues using exosomes interactions.

PMID:36623429 | DOI:10.1016/j.theriogenology.2022.12.012