Drachmanngunn9270

4% points higher under multicenter lymph node data. The pixel-level cross-domain image mapping and the semantic-level cycle consistency provided a stable confounding representation with class-conditioning information to achieve effective domain adaptation under complex feature distribution.Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.Traditionally, for diagnosing patellar dislocation, clinicians make manual geometric measurements on computerized tomography (CT) images taken in the knee area, which is often complex and error-prone. Therefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly segment the patella and the femur regions on the CT images and then measure two geometric quantities, patellar tilt angle (PTA), and patellar lateral shift (PLS) automatically on the segmentation results, which are finally used to assist in diagnoses. The proposed quantities are proved valid and the proposed algorithms are proved effective by experiments.Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, such that drugs were less likely to have corresponding side effects, as negative samples. Through several tests with a fixed feature extraction scheme and different machine-learning algorithms, models with selected negative samples produced high performance. The best model even yielded nearly perfect performance. These models had much higher performance than those without such strategy or with another selection strategy. Furthermore, it is not necessary to consider the balance of positive and negative samples under such a strategy.[This corrects the article DOI 10.1155/2019/1282085.].Background Mahai capsules (MHC) have been deemed to be an effective herb combination for treatment of cardiovascular diseases (CVD) development and improvement of the life quality of CVD patients. To systematically explore the mechanisms of MHC in CVD, a network pharmacology approach mainly comprising target prediction, network construction, biological process and pathway analysis, and related diseases was adopted in this study. Methods We collected the bioactive compounds and potential targets of MHC through the TCMSP servers. Candidate targets related to CVD were collected from Therapeutic Targets Database and PharmGkb database and analyzed using ClueGO plugin in Cytoscape. KEGG pathway was enriched and analyzed through the EnrichR platform, and protein-protein interaction networks were calculated by STRING platform. The compound-target, target-disease, and compound-target-disease networks were constructed using Cytoscape. Results A total of 303 targets of the 57 active ingredients in MHC were obtained. The network analysis showed that PTGS2, PTGS1, HSP90, Scn1a, estrogen receptor, calmodulin, and thrombin were identified as key targets of MHC in the treatment of CVD. The functional enrichment analysis indicated that MHC probably produced the therapeutic effects against CVD by synergistically regulating many biological pathways, such as PI3K-Akt, TNF, HIF-1, FoxO, apoptosis, calcium, T-cell receptor, VEGF, and NF-kappa B signaling pathway. Conclusions In summary, the analysis of the complete profile of the pharmacological properties, as well as the elucidation of targets, networks, and pathways, can further illuminate that the underlying mechanisms of MHC in CVD might be strongly associated with its synergic regulation of inflammation, apoptosis, and immune function, and provide new clues for its future development of therapeutic strategies and basic research.Gastric precancerous lesions (GPLs) are an essential precursor in the occurrence and development of gastric cancer, known to be one of the most common and lethal cancers worldwide. Traditional Chinese medicine (TCM) has a positive prospect for the prevention and therapy of GPL owing to several advantages including a definite curative effect, fewer side effects compared to other treatments, multiple components, and holistic regulation. Despite these characteristic advantages, the mechanisms of TCM in treating GPL have not been fully elucidated. In this review, we summarize the current knowledge with respect to herbal formulations and the therapeutic mechanisms of TCM active ingredients for GPL. This paper elaborates on the mechanisms of TCM underlying the prevention and treatment of GPL, specifically those that are linked to anti-H. pylori, anti-inflammation, antiproliferation, proapoptotic, antioxidation, antiglycolytic, and antiangiogenesis effects.Objective The objective of this study was to compare the effectiveness of different combinations of interventions in patients with stroke at the convalescence stage based on the electronic health records (EHRs) by using the Markov decision process (MDP) theory and explore the feasibility of the Markov model in the real-world study (RWS). Methods Screening was conducted for patients with stroke at the convalescence stage who were admitted to the Third Affiliated Hospital of Zhejiang Chinese Medical University from January 2012 to January 2017 based on the EHRs. The relevant clinical data were extracted, and the appropriate conversion was made (state-action-reward) according to the Markov model. The transformed data were analysed and solved by the MDP to obtain the best interventions for patients with various stroke recovery periods. Results 926 patients with stroke at the convalescence stage were initially selected. And according to the inclusion exclusion criteria, 854 patients were screened. Through the MDP,ciency and blood stasis type. Rehabilitation, Chinese herbal decoction, and acupuncture treatment are recommended for moderate-severe injuries. Conclusions The MDP makes it possible to study the effectiveness of various treatment methods in stroke patients who are at the convalescence stage. Further exploratory studies using MDP theory in other areas in which complex interventions are common would be worthwhile.Turmeric (Curcuma longa L.) is a popular natural drug, traditionally used for the treatment of a wide range of diseases. Its root, as its most popular part used for medicinal purposes, contains different types of phytochemicals and minerals. This review summarizes what is currently known on biochemistry, safety, pharmacological activities (mechanistically), and clinical applications of turmeric. In short, curcumin is considered as the fundamental constituent in ground turmeric rhizome. Turmeric possesses several biological activities including anti-inflammatory, antioxidant, anticancer, antimutagenic, antimicrobial, antiobesity, hypolipidemic, cardioprotective, and neuroprotective effects. These reported pharmacologic activities make turmeric an important option for further clinical research. Also, there is a discussion on its safety and toxicity.Currently, consumers' demand for sunscreens derived from natural sources that provide photoprotection from ultraviolet (UV) radiation is pushing the cosmetic industry to develop breakthrough formulations of sun protection products by incorporating plant antioxidants as their active ingredients. In this context, the present study was initiated to evaluate the antioxidant and photoprotective properties of the underutilized Hylocereus polyrhizus peel extract (HPPE) using in vitro spectrophotometric techniques. The phytochemical screenings of HPPE conducted via high-performance liquid chromatography (HPLC) and ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) revealed the presence of phenolic acids and flavonoids as the major secondary metabolites in HPPE. The antioxidant potentials evaluated based on 2, 2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical and total antioxidant capacity assays were in the range of 22.16 ± 0.24%-84.67 ± 0.03% with 50% inhibitory concentration (IC50) of 36.39 ± 0.04 μg/mL and 23.76 ± 0.14%-31.87 ± 0.26% (IC50 = 21.93 ± 0.07 μg/mL), respectively. For the photoprotective evaluation, the results showed that HPPE had significantly high absorbance values (3.1-3.6) at 290-320 nm with an exceptional sun protection factor (SPF) value of 35.02 ± 0.39 at 1.00 mg/mL. HPPE also possessed a broad-spectrum shielding power against both UVA and UVB radiations. Hence, in terms of practical implications, our findings would offer an exciting avenue to develop a photoprotective formulation incorporating the ethanolic extract of Hylocereus polyrhizus peels as a synergistic active ingredient for its excellent UV absorption properties and the strong antioxidant activities.Objective To analyze the potential active ingredients and related crucial targets of the ShengDiHuang Decoction (SDHD) formula in the treatment of dysfunctional uterine bleeding (DUB) by using network pharmacology and verification experiment. Methods In this study, we determined the potential active ingredients from the traditional SDHD formula and their targets with the network pharmacology method. The network of "compound-disease-target" was constructed by the software of Cytoscape. Software of DAVID was used to enrich pathways for these 87 targets of SDHD. Further, the therapeutic effect of SDHD on DUB was verified by observing the morphological changes of the uterus and ovaries and determining the expression of ERS2 and progesterone in the plasma. selleckchem Results 52 compounds of Rheum and 5 compounds of Rehmannia were selected, and 87 potential targets were screened by network pharmacology. Furthermore, 7 main active ingredients were acquired by the ADME process. In addition, enrichment analysis of drug-target networks indicated that SDHD may play a role in overall coordination through "multicomponent and multitarget" in different organ patterns by regulating multiple pathways directly or indirectly.