Background
Peripartum depression is a common disorder with very high potential hazards for both the patients and their babies. The typical treatment options include antidepressants and electroconvulsive therapy. However, these treatments do not ensure the safety of the fetus. Recently, repetitive transcranial magnetic stimulation has emerged as a promising treatment for neuropathies as well as depression. Nevertheless, many studies excluded pregnant women. This systematic review was conducted to confirm whether repetitive transcranial magnetic stimulation was a suitable treatment option for peripartum depression.
Methods
We performed a systematic review that followed the PRISMA guidelines. We searched for studies in the MEDLINE, PsycINFO, EMBASE, and Cochrane library databases published until the end of September 2020. Eleven studies were selected for the systematic review, and five studies were selected for quantitative synthesis. Data analysis was conducted using Comprehensive Meta-Analysis 3 software. The effect size was analyzed using the standardized mean difference, and the 95% confidence interval (CI) was determined by the generic inverse variance estimation method.
Results
The therapeutic effect size of repetitive transcranial magnetic stimulation for peripartum depression was 1.394 (95% CI: 0.944–1.843), and the sensitivity analysis effect size was 1.074 (95% CI: 0.689–1.459), indicating a significant effect. The side effect size of repetitive transcranial magnetic stimulation for peripartum depression was 0.346 (95% CI: 0.214–0.506), a meaningful result. There were no severe side effects to the mothers or fetuses.
Conclusions
From various perspectives, repetitive transcranial magnetic stimulation can be considered an alternative treatment to treat peripartum depression to avoid exposure of fetuses to drugs and the severe side effects of electroconvulsive therapy. Further research is required to increase confidence in the results.
BackgroundIVUS is widely used to quantitatively assess coronary artery disease. The purpose of this study was to automatically characterize dense calcium (DC) tissue in the gray scale intravascular ultrasound (IVUS) images using the image textural features.MethodsA total of 316 Gy-scale IVUS and corresponding virtual histology images from 26 patients with acute coronary syndrome who underwent IVUS along with X-ray angiography between October 2009 to September 2014 were retrospectively acquired and analyzed. One expert performed all procedures and assessed their IVUS scans. After image acquisition, the DC candidate and corresponding acoustic shadow regions were automatically determined. Then, nine image-base feature groups were extracted from the DC candidates. In order to reduce the dimensionalities, principal component analysis (PCA) was performed, and selected feature sets were utilized as an input for a deep belief network. Classification results were validated using 10-fold cross validation.ResultsThe dimensionality of the feature map was efficiently reduced by 50% (from 66 to 33) without any performance decrease using PCA method. Sensitivity, specificity, and accuracy of the proposed method were 92.8 ± 0.1%, 85.1 ± 0.1%, and 88.4 ± 0.1%, respectively (p < 0.05). We found that the window size could largely influence the characterization results, and selected the 5 × 5 size as the best condition. We also validated the performance superiority of the proposed method with traditional classification methods.ConclusionsThese experimental results suggest that the proposed method has significant clinical applicability for IVUS-based cardiovascular diagnosis.
Background
The purpose of this study was to assess the effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes.
Methods
We searched PubMed, EMBASE, Cochrane Central, and the Web of Science to December 2021. The eligibility criteria for study selection were randomized controlled trials comparing artificial pancreas systems (MPC, PID, and fuzzy algorithms) with conventional insulin therapy in type 1 diabetes patients. The heterogeneity of the overall results was identified by subgroup analysis of two factors including the intervention duration (overnight and 24 h) and the follow-up periods (< 1 week, 1 week to 1 month, and > 1 month).
Results
The meta-analysis included a total of 41 studies. Considering the effect on the percentage of time maintained in the target range between the MPC-based artificial pancreas and conventional insulin therapy, the results showed a statistically significantly higher percentage of time maintained in the target range in overnight use (10.03%, 95% CI [7.50, 12.56] p < 0.00001). When the follow-up period was considered, in overnight use, the MPC-based algorithm showed a statistically significantly lower percentage of time maintained in the hypoglycemic range (−1.34%, 95% CI [−1.87, −0.81] p < 0.00001) over a long period of use (> 1 month).
Conclusions
Overnight use of the MPC-based artificial pancreas system statistically significantly improved glucose control while increasing time maintained in the target range for outpatients with type 1 diabetes. Results of subgroup analysis revealed that MPC algorithm-based artificial pancreas system was safe while reducing the time maintained in the hypoglycemic range after an overnight intervention with a long follow-up period (more than 1 month).
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