Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer

Abstract Background Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments. Results ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle. Conclusions The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.Peer Reviewe

Factors Predicting Emotional Cue-Responding Behaviors of Nurses in Taiwan: An Observational Study

Objective Responding to emotional cues is an essential element of therapeutic communication. The purpose of this study is to examine nurses' competence of responding to emotional cues (CRE) and related factors while interacting with standardized patients with cancer. Methods This is an exploratory and predictive correlational study. A convenience sample of registered nurses who have passed the probationary period in southern Taiwan was recruited to participate in 15-minute videotaped interviews with standardized patients. The Medical Interview Aural Rating Scale was used to describe standardized patients' emotional cues and to measure nurses' CRE. The State-Trait Anxiety Inventory was used to evaluate nurses' anxiety level before the conversation. We used descriptive statistics to describe the data and stepwise regression to examine the predictors of nurses' CRE. Results A total of 110 nurses participated in the study. Regardless of the emotional cue level, participants predominately responded to cues with inappropriate distancing strategies. Prior formal communication training, practice unit, length of nursing practice, and educational level together explain 36.3% variances of the nurses' CRE. Conclusions This study is the first to explore factors related to Taiwanese nurses' CRE. Compared to nurses in other countries, Taiwanese nurses tended to respond to patients' emotional cues with more inappropriate strategies. We also identified significant predictors of CRE that show the importance of communication training. Future research and education programs are needed to enhance nurses' CRE and to advocate for emotion-focused communication

Comparison of neostigmine and sugammadex for hemodynamic parameters in neurointerventional anesthesia

IntroductionHemodynamic stability is important during neurointerventional procedures. However, ICP or blood pressure may increase due to endotracheal extubation. The aim of this study was to compare the hemodynamic effects of sugammadex and neostigmine with atropine in neurointerventional procedures during emergence from anesthesia.MethodsPatients undergoing neurointerventional procedures were allocated to the sugammadex group (Group S) and the neostigmine group (Group N). Group S was administered IV 2 mg/kg sugammadex when a train-of-four (TOF) count of 2 was present, and Group N was administered neostigmine 50 mcg/kg with atropine 0.2 mg/kg at a TOF count of 2. We recorded heart rate, systolic blood pressure, diastolic blood pressure, mean blood pressure (MAP), and peripheral arterial oxygen saturation during administration of the reverse agent and at 2, 5, 10, 15, 30, 120 min, and 24 h thereafter. The primary outcome was blood pressure and heart rate change after the reversal agent was given. The secondary outcomes were systolic blood pressure variability standard deviation (a measure of the amount of variation or dispersion of a set of values), systolic blood pressure variability-successive variation (square root of the average squared difference between successive blood pressure measurements), nicardipine use, time-to-TOF ratio ≥0.9 after the administration of reversal agent, and time from the administration of the reversal agent to tracheal extubation.ResultsA total of 31 patients were randomized to sugammadex, and 30 patients were randomized to neostigmine. Except for anesthesia time, there were no significant differences in any of the clinical characteristics between the two groups. The results demonstrated that the increase in MAP from period A to B was significantly greater in Group N than in Group S (regression coefficient = −10, 95% confidence interval = −17.3 to −2.7, P = 0.007). The MAP level was significantly increased from period A to B in the neostigmine group (95.1 vs. 102.4 mm Hg, P = 0.015), but it was not altered in Group S. In contrast, the change in HR from periods A to B was not significantly different between groups.ConclusionWe suggest that sugammadex is a better option than neostigmine in interventional neuroradiological procedures due to the shorter extubation time and more stable hemodynamic change during emergence

Topological Pattern Recognition of Severe Alzheimer's Disease via Regularized Supervised Learning of EEG Complexity

Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters (m = 2, r = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD

Long working range light field microscope with fast scanning multifocal liquid crystal microlens array

The light field microscope has the potential of recording the 3D information of biological specimens in real time with a conventional light source. To further extend the depth of field to broaden its applications, in this paper, we proposed a multifocal high-resistance liquid crystal microlens array instead of the fixed microlens array. The developed multifocal liquid crystal microlens array can provide high quality point spread function in multiple focal lengths. By adjusting the focal length of the liquid crystal microlens array sequentially, the total working range of the light field microscope can be much extended. Furthermore, in our proposed system, the intermediate image was placed in the virtual image space of the microlens array, where the condition of the lenslets numerical aperture was considerably smaller. Consequently, a thin-cell-gap liquid crystal microlens array with fast response time can be implemented for time-multiplexed scanning

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion