The association between periodontal disease and the risk of myocardial infarction: a pooled analysis of observational studies

Quality scores of case–control and cohort studies using Newcastle-Ottawa Scale. (PDF 37 kb

Alterations of Sub-cortical Gray Matter Volume and Their Associations With Disease Duration in Patients With Restless Legs Syndrome

Object: The purpose of this study was to uncover the pathology of restless legs syndrome (RLS) by exploring brain structural alterations and their corresponding functional abnormality.Method: Surface-based morphometry (SBM) and voxel-based morphometry (VBM) were performed to explore the alterations in cortical and sub-cortical gray matter volume (GMV) in a cohort of 20 RLS and 18 normal controls (NC). Furthermore, resting-state functional connectivity (RSFC) was also performed to identify the functional alterations in patients with RLS.Results: We found significant alterations of sub-cortical GMV, especially the bilateral putamen (PUT), rather than alterations of cortical GMV in patients with RLS compared to NC using both SBM and VBM. Further sub-regional analysis revealed that GMV alterations of PUT was mostly located in the left dorsal caudal PUT in patients with RLS. In addition, altered RSFC patterns of PUT were identified in patients with RLS compared to NC. Moreover, correlation analyses showed that the GMV of the left caudate and the left ventral rostral PUT were positively correlated with disease duration in patients with RLS.Conclusions: The alterations of subcortical GMV might imply that the primarily affected areas are located in sub-cortical areas especially in the sub-region of PUT by the pathologic process of RLS, which might be used as potential biomarkers for the early diagnosis of RLS

Preoperative CT-guided ICG injection locating SPNs

Background: Localization of small pulmonary nodules (SPNs) is challenging in minimally invasive pulmonary resection, and it is unknown whether computer tomography (CT) guided by indocyanine green (ICG) can provide accurate localization with minimal complications. Methods: We performed a retrospective study of patients who underwent thoracoscopic resection of pulmonary nodules after CT-guided preoperative localization with ICG from May 2019 to May 2020. Demographics, procedural data, postoperative complications, and pathologic information, were collected, and an analysis of the accuracy and complications after surgery was conducted. Results: In 471 patients, there was a total of 512 peripheral pulmonary nodules that were ≤2 cm in size. The average time for CT-guided percutaneous ICG injection for localization was 18 minutes, and 98.4% (504/512) of the nodules were successfully localized. The average size of the nodules was 9.1 mm, and the average depth from the pleural surface was 8.9 mm. Overall, 5.9% (28/471) of the patients had asymptomatic pneumothorax after localization, but none needed a tube thoracostomy. All the nodules were resected using video-assisted thoracoscopy technique. Conclusions: Preoperative CT-guided transthoracic ICG injection is safe and feasible for localization of small lung nodules for minimally invasive pulmonary resection. This technique should be considered for preoperative CT-guided localization of small lung nodules

Virtual–real fusion maintainability verification based on adaptive weighting and truncated spot method

Maintainability is an important general quality characteristic of products. Insufficient maintainability will lead to long maintenance time and high maintenance cost, thus affecting the availability of products. Maintainability verification is an important means to ensure maintainability meets design requirements. However, the cost of traditional real maintainability verification method is very high, and the virtual maintenance method has insufficient verification accuracy due to the lack of large maintenance force feedback when the human body is moving. In order to reduce the evaluation error and test sample size, the paper conducts maintainability verification based on the mixed physical and virtual maintainability test scenarios. Aiming at the problem that traditional methods are difficult to deal with the real test information and synchronous virtual simulation information in the test process, this study proposes a virtual–real fusion maintainability evaluation algorithm based on adaptive weighting and truncated SPOT (Sequential Posterior Odd Test) method. It can weigh real test information and virtual human simulation information adaptively to obtain a virtual–real fusion maintainability test sample. Then, the SPOT method is used to evaluate the maintainability of small samples. The adjustment of valve clearance, replacement of air filter element and replacement of starting motor maintenance tasks of ship engine are taken as examples for demonstration. The virtual–real fusion and virtual maintainability verification methods are respectively used for verification, and compared with the physical maintenance scenario constructed by 3D printing, indicating that the accuracy of virtual–real fusion maintainability test verification is 89%, while the virtual maintainability verification is only 33%

FP-Outlier: frequent pattern based outlier detection

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the outlier transactions and propose the FindFPOF algorithm to discover outliers. The experimental results have shown that our approach outperformed the existing methods on identifying interesting outliers

Altered Resting-State Brain Activities in Drug-Naïve Major Depressive Disorder Assessed by fMRI: Associations With Somatic Symptoms Defined by Yin-Yang Theory of the Traditional Chinese Medicine

Identification of biological markers for defining subtypes of major depressive disorder (MDD) is critical for better understanding MDD pathophysiology and finding effective treatment intervention. The “Yin and Yang” theory is a fundamental concept of traditional Chinese Medicine (TCM). The theory differentiates MDD patients into two subtypes, Yin and Yang, based on their somatic symptoms, which had empirically been used for the delivery of effective treatment in East Asia. Nonetheless, neural processes underlying Yin and Yang types in MDD are poorly understood. In this study, we aim to provide physiological evidence using functional magnetic resonance imaging (fMRI) to identify altered resting-state brain activity associated with Yin and Yang types in drug-naïve MDD patients. The Yin type and Yang type MDD patients showed increased amplitude of low-frequency fluctuation (ALFF) in different cortical brain areas in the parietal, temporal, and frontal lobe, compared to matched healthy controls. Differential ALFF is also observed in several cortical areas in frontal lobe and insula between Yin and Yang type group. Of note, although ALFF is increased in the inferior parietal lobe in both Yin and Yang type group, inferior parietal lobe-centered functional connectivity (FC) is increased in Yang type, but is decreased in Ying type, compared with matched healthy controls. These results suggest that differential resting-state brain activity and functional connectivity in Yin and Yang types may contribute to biological measures for better stratification of heterogeneous MDD patients