mdpi-ai/citelens huggingface dataset:
Impact of the COVID-19 pandemic on oncological care in Germany: rapid review
Field: ONCOLOGY

Objectives The COVID-19 pandemic affected medical care for chronic diseases. This study aimed to systematically assess the pandemic impact on oncological care in Germany using a rapid review. Methods MEDLINE, Embase, study and preprint registries and study bibliographies were searched for studies published between 2020 and 2 November 2022. Inclusion was based on the PCC framework: population (cancer), concept (oncological care) and context (COVID-19 pandemic in Germany). Studies were selected after title/abstract and full-text screening by two authors. Extracted data were synthesized using descriptive statistics or narratively. Risk of bias was assessed and summarized using descriptive statistics. Results Overall, 77 records (59 peer-reviewed studies and 18 reports) with administrative, cancer registry and survey data were included. Disruptions in oncological care were reported and varied according to pandemic-related factors (e.g., pandemic stage) and other (non-pandemic) factors (e.g., care details). During higher restriction periods fewer consultations and non-urgent surgeries, and delayed diagnosis and screening were consistently reported. Heterogeneous results were reported for treatment types other than surgery (e.g., psychosocial care) and aftercare, while ongoing care remained mostly unchanged. The risk of bias was on average moderate. Conclusions Disruptions in oncological care were reported during the COVID-19 pandemic in Germany. Such disruptions probably depended on factors that were insufficiently controlled for in statistical analyses and evidence quality was on average only moderate. Research focus on patient outcomes (e.g., longer term consequences of disruptions) and pandemic management by healthcare systems is potentially relevant for future pandemics or health emergencies.
Construction and validation of prognostic nomogram and clinical characteristics for ovarian endometrioid carcinoma: an SEER-based cohort study
Field: ONCOLOGY

Background: Ovarian endometrioid carcinoma (OEC) is the second most commonly occurring ovarian epithelial malignancy, but the associated prognostic factors remain obscure. This study aimed to analyze independent prognostic factors for patients with OEC and to develop and validate a nomogram to predict the overall survival (OS) of these patients. Methods: Clinical information of patients with OEC (2000–2019) was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors, and nomogram models were constructed using independent prognostic factors. Receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA) were used to verify the accuracy and validity of the nomogram. Kaplan–Meier curves were used to compare the differences in OS and cancer-specific survival (CSS) among subgroups. Results: A total of 4628 patients with OEC were included, being divided into training (n = 3238) and validation (n = 1390) sets (7:3 ratio). On multivariate Cox analysis, AJCC stage, age, tumor size, differentiation, chemotherapy, and lymph node resection were significant predictors of survival outcomes (P < 0.05). Resection of 1–3 lymph nodes in early-stage OEC patients did not significantly prolong OS (P > 0.05), but resection of ≥ 4 lymph nodes in early-stage improved OS and CSS (P < 0.05). The OS of early-stage patients was not related to whether or not they received chemotherapy (P > 0.05). Lymph node resection and chemotherapy significantly improved the prognosis of patients with advanced OEC (P < 0.05). The c-index of nomogram prediction model was 0.782. ROC with good discrimination, calibration plots with high consistency, and DCA with large net benefit rate result in large clinical value. Conclusion: AJCC stage, differentiation, tumor size, age, chemotherapy, and lymph node dissection were prognostic factors of OEC. The constructed nomogram prediction model can effectively predict the prognosis of OEC patients and improve the accuracy of clinical decision-making.
Parent Chats in Education System: During and after the Pandemic Outbreak
Field: EDUCATION

Digital technology has significantly changed the face of education by, among other things, creating many communication channels between the participants in the process. This study reveals the role of parent chat rooms in supporting the learning process. The pandemic experience has shown that parent chat rooms can serve a controlling and regulatory function, noting problems and inconsistencies in the learning system. The chats contain background routine messages related to informing and “events”, that is, messages of bewilderment, resistance, elation, and other emotional reactions. A total of 143 chats were analyzed, and 326 communication events were identified. During the regular period, the basic topics, including chat rules and regulations (22%), homework (20%), school activities and holidays (24%), and behavioral problems (24%), were evenly distributed, while during the distance learning period, the assignment problems (36%) and technical problems (28%) came to the fore. In the traditional offline period, parents are not direct participants in the educational process, so the information in the chats sometimes comes in a one-sided or distorted form; however, parental activity can serve to improve the educational system and monitor the processes taking place.
Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification
Field: AI

Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a real-world problem where labels are hard to obtain. To assist a large study on chimpanzee hunting activities, we aim to classify various animal species that appear in our in-the-wild camera traps located in Senegal. Using the philosophy of FSL, we aim to train an FSL network to learn to separate animal species using large public datasets and implement the network on our data with its novel species/classes and unseen environments, needing only to label a few images per new species. Here, we first discuss constraints and challenges caused by having in-the-wild uncurated data, which are often not addressed in benchmark FSL datasets. Considering these new challenges, we create two experiments and corresponding evaluation metrics to determine a network’s usefulness in a real-world implementation scenario. We then compare results from various FSL networks, and describe how factors may affect a network’s potential real-world usefulness. We consider network design factors such as distance metrics or extra pre-training, and examine their roles in a real-world implementation setting. We also consider additional factors such as support set selection and ease of implementation, which are usually ignored when a benchmark dataset has been established.
Mental illness stigma among indigenous communities in Bangladesh: a cross-sectional study
Field: PSYCHOLOGY

Background: Mental illnesses stigma is a universal and transcultural phenomenon. While mental illnesses stigma is pervasive in Bangladesh, very little research exists on stigma toward mental illnesses among indigenous communities. This study aimed to investigate the prevailing stigma and the risk factors among different indigenous communities in the Chattogram Hill Tracts (CHT) in Bangladesh. Methods: A cross-sectional survey was carried out and participants were recruited purposively from Rangamati, a South-Eastern district of Bangladesh in the CHT. Participants from various indigenous communities including Chakma, Marma, Rakhine, Tripura, and Pangkhua were recruited. The 28- item Bangla translated version of the Mental Illnesses Stigma Scale was used. Independent-samples t-test, ANOVA, and multiple regression were performed. Results: The results indicate evidence of a gender difference with females reporting more stigma than their male counterparts. Age, gender, socioeconomic status, and monthly income are associated with stigma among indigenous people. Further analyses of the subscales indicated significant differences among sociodemographic variables. Conclusions: The results provide an insight into the prevailing stigma and associate risk factors among indigenous communities. The results may help inform anti-stigma interventions targeting indigenous communities in Bangladesh.
An Interval-Valued Pythagorean Fuzzy AHP and COPRAS Hybrid Methods for the Supplier Selection Problem
Field: AI

Companies must be able to identify their suppliers appropriately and effectively in order to survive in the competitive market conditions. In order to fulfill and surpass the expectations of the consumers and clients, companies need to interact with the relevant suppliers. It is a tough manner for companies to select the best supplier from a large number of relevant alternatives. The selection process of the appropriate supplier involves multiple interacting and competing factors. Generally, the selection process and its results cause a waste of time and money. For this purpose, MCDM methodologies are utilized to manage this complex process efficiently. MCDMs allows for consistent and accurate decision-making as well as the selection of the most appropriate supplier. MCDM is one the most preferred tool to select the best alternative under the conflicting and competitive criteria when the evaluations are made in crisp numbers. Therefore, MCDM methods are preferred in various applications in academia and real life. However, the evaluations could not be always possible with crisp numbers, especially in vague environments or evaluations needs qualitative data. This study is one of the first to combine the AHP and COPRAS supplier selection methods with interval-valued Pythagorean fuzzy (IPF) logic. The effectiveness of these IPF-AHP and IPF-COPRAS evaluations for the supplier selection problem is compared and examined. The experimental results of case scenarios show that IPF is an effective way to apply in decision-making applications. In addition, sensitivity analysis is conducted to evaluate the proposed methodologies. According to sensitivity analysis, the IPF-AHP and IPF-COPRAS be able to illustrate the effects of small changings in criteria weights. Therefore, companies can use the IPF-AHP and IPF-COPRAS to assist their decision-makers in identifying and selecting the best suppliers.
Positive and Negative Impacts of Gamification on the Fitness Industry
Field: PSYCHOLOGY

Gamification features to motivate individuals to exercise have become a trend in the fitness sector that is gaining popularity. It is based on the idea that adding fun and competitive components to workout routines will inspire people to achieve their fitness objectives and maintain a healthy lifestyle. This research study attempts to analyze the literature that explores this concept of gamification in detail, and create a picture of how its implementation has changed fitness and healthy habits. This research incorporated the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach as its research methodology. Search strategy used a set of inclusion-exclusion criteria that helped us examine through hundreds of articles identified in the Web of Science and SCOPUS databases. After exclusive and inclusion criteria, 48 articles were selected to be reviewed in detail. Results have indicated that gamification strategy is a supporting factor to overcome the difficulties of executing exercises. Also, to improve the willingness towards fitness regimens.
Personality Traits and Physical Activity: Insights from German University Students
Field: PSYCHOLOGY

This study explores the intriguing relationship between personality traits, self-rated fitness (SRF), and physical activity (PA) variables among German university students (N = 4244) and sheds light on the impact of personality on adherence to PA guidelines. Employing an online cross-sectional study, the short-form of the Big Five Inventory-2 assessed five domains of personality traits (Extraversion, Negative Emotionality, Agreeableness, Conscientiousness, and Open-Mindedness). PA, including sitting time, was assessed using the International Physical Activity Questionnaire (short-form). SRF and muscle-strengthening activities (MSA) were assessed with one item each. Multiple linear and logistic regression analyses examined associations of individual personality trait domains and all domains combined with SFR, PA variables, and adherence to PA guidelines, controlling for sociodemographic, behavioral, and (mental) health covariates. Most reliably, Extraversion and Conscientiousness revealed positive associations with PA variables, while Negative Emotionality yielded inverse relationships with PA variables. For instance, each unit increase in Extraversion corresponded to an additional 17 min of weekly MSA. On the contrary, daily sitting time was unrelated to personality. Of note, high Open-Mindedness was associated with lower odds for adhering to current PA guidelines. The findings have implications for developing targeted interventions that promote a physically active lifestyle and support students’ well-being and academic success.
Abrogating Metastatic Properties of Triple-Negative Breast Cancer Cells by EGFR and PI3K Dual Inhibitors
Field: ONCOLOGY

Triple-negative breast cancer (TNBC) is a devastating BC subtype. Its aggressiveness, allied to the lack of well-defined molecular targets, usually culminates in the appearance of metastases that account for poor prognosis, particularly when they develop in the brain. Nevertheless, TNBC has been associated with epidermal growth factor receptor (EGFR) overexpression, leading to downstream phosphoinositide 3-kinase (PI3K) signaling activation. We aimed to unravel novel drug candidates for TNBC treatment based on EGFR and/or PI3K inhibition. Using a highly metastatic TNBC cell line with brain tropism (MDA-MB-231 Br4) and a library of 27 drug candidates in silico predicted to inhibit EGFR, PI3K, or EGFR plus PI3K, and to cross the blood–brain barrier, we evaluated the effects on cell viability. The half maximal inhibitory concentration (IC50) of the most cytotoxic ones was established, and cell cycle and death, as well as migration and EGFR pathway intervenient, were further evaluated. Two dual inhibitors emerged as the most promising drugs, with the ability to modulate cell cycle, death, migration and proliferation, morphology, and PI3K/AKT cascade players such as myocyte enhancer factor 2C (MEF2C) and forkhead box P1 (FOXP1). This work revealed EGFR/PI3K dual inhibitors as strong candidates to tackle brain metastatic TNBC cells.
Glial cell-derived soluble factors increase the metastatic potential of pancreatic adenocarcinoma cells and induce epithelial-to-mesenchymal transition
Field: ONCOLOGY

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive types of cancer, characterized by the spreading of highly metastatic cancer cells, including invasion into surrounding nerves and perineural spaces. Nerves, in turn, can invade the tumor tissue and, through the secretion of neurotrophic factors, chemokines, and cytokines, contribute to PDAC progression. However, the contribution of the nerve-associated glial cells to PDAC progression is not well characterized. Methods: Two murine PDAC cell lines were cultured with the conditioned media (CM) of primary enteric glial cells or IMS32 Schwann cells (SCs). Different properties of PDAC cells, such as invasiveness, migratory capacity, and resistance to gemcitabine, were measured by RT-qPCR, microscopy, and MTT assays. Using a neuronal cell line, the observed effects were confirmed to be specific to the glial lineage. Results: Compared to the control medium, PDAC cells in the glial cell-conditioned medium showed increased invasiveness and migratory capacity. These cells showed reduced E-cadherin and increased N-cadherin and Vimentin levels, all markers of epithelial–mesenchymal transition (EMT). Primary enteric glial cell CM inhibited the proliferation of PDAC cells but preserved their viability, upregulated transcription factor Snail, and increased their resistance to gemcitabine. The conditioned medium generated from the IMS32 SCs produced comparable results. Conclusion: Our data suggest that glial cells can increase the metastatic potential of PDAC cells by increasing their migratory capacity and inducing epithelial-to-mesenchymal transition, a re-programming that many solid tumors use to undergo metastasis. Glial cell-conditioned medium also increased the chemoresistance of PDAC cells. These findings may have implications for future therapeutic strategies, such as targeting glial cell-derived factor signaling in PDAC.
Construction of the survival nomograms for colon cancer patients of different ages based on the SEER database
Field: ONCOLOGY

Introduction: Three nomograms for predicting the outcomes of early- and late-onset colon cancer (COCA) among patients not stratified by age were constructed using data in the Epidemiology and End Results (SEER) database (1975–2019). The accuracy of the nomogram was then assessed. Method: Clinical data of 6107 patients with COCA were obtained from the SEER database. The patients were randomly divided into training and validation cohorts in a ratio of 7:3. Univariate and multivariate COX analyses of factors that could independently impact the prognosis of COCA were performed, and the corresponding nomograms for early-onset and late-onset COCA were constructed. Calibration curves, ROC curves, and C-index were used to determine the predictive accuracy. The discriminatory ability of the nomograms to assess their clinical utility, which was compared with the TNM staging system of the 8th edition of AJCC, was verified using survival analysis. Result: Tumor primary site, ethnicity, and serum carcinoembryonic antigen (CEA) level significantly impacted the prognosis of colon cancer. Race, brain metastasis, and CEA were independent factors for predicting COCA prognosis. C-index, ROC, and calibration curves demonstrated that the three nomograms were accurate and superior to the traditional TNM staging system. Among the three nomograms, the early-onset COCA nomogram had the highest predictive accuracy, followed by that of colon cancer not stratified by age. Conclusion: Three nomograms for patients not stratified by age, early-onset colon cancer, and late-onset colon cancer were constructed. The accuracies of the nomograms were good and were all superior to the conventional TNM staging system. The early- and late-onset COCA nomograms are useful for clinical management and individualized treatment of COCA patients at different ages.
RETRACTED ARTICLE: Research on the Prediction of the Inauguration Development Direction of College Students’ Entrepreneurship Education Based on Educational Data Mining
Field: AI

In many related studies, educational data mining technology has been proven to play an important role in predicting the development direction of entrepreneurship education for college students. To further improve the accuracy of the prediction, we chose the grey prediction model as the basic prediction model and automatically optimized the weighting method to improve the model. To solve the problem of predicting the development direction of students’ employment in the guidance of entrepreneurship and employment in colleges and universities, the study selects the grey prediction model as the basic prediction model and chooses the automatic optimization and weighting method to improve the model. Meanwhile, the study establishes a variable system containing six dimensions: academic achievement; physical and mental development; cultural, physical, and artistic quantified status; ideological and political quantified status; scientific and technological innovation quantified status; social work quantified status. The final study used the actual prediction test to analyze the prediction effect. We have selected a variable system consisting of six dimensions, which are the results of extensive research. These dimensions include academic achievement, physical and mental development, cultural/sports/art quantitative status, ideological and political quantitative status, technological innovation quantitative status, and social work quantitative status. Each dimension provides us with important predictions about student entrepreneurship and employment. The results show that the model designed by the survey has only two cases of error in the prediction of 20 actual samples. At the same time, there is no prediction error in the two prediction directions of entrepreneurship and social employment. This shows that the model designed by the study is stable and accurate, and the prediction results are more reliable in the prediction directions of entrepreneurship and social employment. Compared with other relevant research results, our model performs well in predicting accuracy, especially in predicting entrepreneurial and social employment directions, without any prediction errors, indicating that our model has superior performance in predicting stability and accuracy compared to other studies.
RETRACTED ARTICLE: E-commerce User Recommendation Algorithm Based on Social Relationship Characteristics and Improved K-Means Algorithm
Field: AI

In the era of the Internet, information data continue to accumulate, and the explosive growth of network information explosion leads to the reduction of the accuracy of users’ access to information. To enhance the user experience and purchasing desire of e-commerce users, a e-commerce user recommendation algorithm based on social relationship characteristics and improved K-means algorithm is proposed. It combines the Automatic Time Division Dynamic Topic Model based on adaptive time slice division for building a strength calculation model in view of the characteristics of social relations. Then, it proposes an e-commerce user recommendation algorithm in view of the improved K-means algorithm to improve the accuracy of topic feature extraction and user recommendation. The experiment illustrates that there is no fluctuation in the clustering function of the improved K-means algorithm, and the highest, lowest, and average accuracy remain consistent under the three datasets, with average accuracy of 78.9%, 84.5%, and 95.9%, respectively. The community discovery-based friend recommendation algorithm presented in the study has the highest accuracy, illustrating that improving the K-means algorithm can further improve recommendation accuracy. The accuracy of the feature extraction method in view of alternative cost is 0.63, which improves the accuracy by about 9%. The results indicate that this study can provide technical support for user recommendations on e-commerce platforms.