Notes

Clinician's commentary: Atypia in salivary sweat gland great pin faith.
001). Serum GOLPH3 concentrations in CRC patients correlated positively with CEA and CA19-9 concentrations (
< 0.05).

Serum GOLPH3 concentrations in GC and CRC patients are related to TNM stage. GOLPH3 may represent a novel biomarker for the diagnosis of GC and CRC. The combination of serum GOLPH3, CEA, and CA19-9 concentrations can improve diagnostic efficiency for GC and CRC. GOLPH3 is expected to become an indicator for the early diagnosis and evaluation of surgical effects.
Serum GOLPH3 concentrations in GC and CRC patients are related to TNM stage. GOLPH3 may represent a novel biomarker for the diagnosis of GC and CRC. The combination of serum GOLPH3, CEA, and CA19-9 concentrations can improve diagnostic efficiency for GC and CRC. GOLPH3 is expected to become an indicator for the early diagnosis and evaluation of surgical effects.Detecting COVID-19 from medical images is a challenging task that has excited scientists around the world. COVID-19 started in China in 2019, and it is still spreading even now. Chest X-ray and Computed Tomography (CT) scan are the most important imaging techniques for diagnosing COVID-19. All researchers are looking for effective solutions and fast treatment methods for this epidemic. To reduce the need for medical experts, fast and accurate automated detection techniques are introduced. Deep learning convolution neural network (DL-CNN) technologies are showing remarkable results for detecting cases of COVID-19. In this paper, deep feature concatenation (DFC) mechanism is utilized in two different ways. In the first one, DFC links deep features extracted from X-ray and CT scan using a simple proposed CNN. The other way depends on DFC to combine features extracted from either X-ray or CT scan using the proposed CNN architecture and two modern pre-trained CNNs ResNet and GoogleNet. The DFC mechanism is applied to form a definitive classification descriptor. The proposed CNN architecture consists of three deep layers to overcome the problem of large time consumption. For each image type, the proposed CNN performance is studied using different optimization algorithms and different values for the maximum number of epochs, the learning rate (LR), and mini-batch (M-B) size. Experiments have demonstrated the superiority of the proposed approach compared to other modern and state-of-the-art methodologies in terms of accuracy, precision, recall and f_score.Coronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. selleck products A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https//github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy 98.16%, sensitivity 98.93%, specificity 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.How does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible-infected-removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the 'global' level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.Drama pedagogy training (DPT) is a drama-based-pedagogy focused on socio-emotional-learning (SEL) development, over academic or artistic. This study aims to see if DPT promotes theory of mind (ToM) and collaborative behavior in 126 French children aged 9-10 years old, randomly assigned to an experimental group (DPT), either a control group for 6 weeks. Post-tests showed large effects of training on ToM, F(1, 124) = 24.36, p less then .001, η² =.16, and collaborative behavior, F(1, 124) = 29.8, p less then .001, η² = .19. T-test showed significant differences on ToM (t = -4.94, p less then .001) and collaborative behavior (t = -5.46, p less then .001), higher for DPT. Effects of type of school and grade are discussed. Results confirm the hypotheses.Loscalzo and Giannini (Loscalzo, Y., & Giannini, M. [2017]. Studyholism or Study Addiction? A comprehensive model for a possible new clinical condition. In A. M. Columbus (Ed.), Advances in psychological research, (Vol. 125, pp. 19-37). Hauppauge, NY, USA Nova Science) recently proposed a theoretical model for a new potential clinical condition Studyholism, or obsession toward studying. This study aims to analyze the psychometric properties of the instrument that has been created based on their theory, namely the Studyholism Inventory (SI-10). The participants are 1296 Italian college students aged between 19 and 55 years. We analyzed its factor structure, as well as its convergent and divergent validity, and we proposed the cut-off scores of the SI-10. Moreover, we investigated some demographic and study-related differences in studyholism and study engagement and the correlations with academic indicators. The results showed that the SI-10 is a ten-item (2 fillers) and 2-factor instrument (GFI = .98, CFI = .97, RMSEA = .07) with good psychometric properties. The SI-10 could be used in future research to analyze the features and correlates of studyholism, and for both clinical and preventive purposes, pointing to favor students' well-being and academic success.Recent research has begun to focus on positive body image and how this can be supported in adolescence. Body appreciation is a key element of positive body image, and has been associated with self-reported health status, weight-related concern, family factors and psychological variables such as self-esteem. In this study we explored these associations among Hungarian adolescent females. Female high school students from two major towns in Csongrád county, Hungary (N = 454; age range from 14 to 20; M = 16.3 years, SD = 1.2) completed questionnaires assessing body appreciation, self-esteem, optimism, life satisfaction and health- and weight-related variables. Analyses revealed that body appreciation was most strongly related to self-esteem, as well as being positively associated with life satisfaction, self-perceived health, being in control of diet, and engagement in sport. Conversely, binge drinking, engaging in slimming behaviors and having eating disorders in the family were negatively associated with body appreciation. These findings provide some indications of factors that might be targetted in health education programs aiming to promote positive body image and to develop resilience against body dissatisfaction in this demographic group. Such programs should also include information of nutrition and media literacy.The present study represents an effort to expand and deepen the scant literature on Adolescent Dating Aggression (ADA) within the Italian context; adolescent dating aggression is a public health issue of interest due to its increasing frequency among adolescents. The prevalence of verbal-emotional and physical ADA was examined as well as gender and age differences in a sample of Italian adolescents. Participants included 436 adolescents (47.7% males; 52.3% females) living in northern Italy, aged 16 to 18 years (M = 17.11). Participants completed the Conflict in Adolescent Dating Relationships Inventory measuring abusive behaviors between adolescent dating partners. Non-parametric analyses were computed. Verbal-emotional ADA perpetration and victimization were much more common than physical ADA perpetration and victimization. Females reported higher levels of verbal-emotional and physical ADA perpetration than males. To fully investigate gender differences single behaviors were analyzed and described. Finally, age differences emerged only for perpetrated verbal-emotional abuse with such aggression being highest at age 18.
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