Miracle-dz/UniFAR-FTUs huggingface dataset:
Thyroseq v3, Afirma GSC, and microRNA Panels Versus Previous Molecular Tests in the Preoperative Diagnosis of Indeterminate Thyroid Nodules: A Systematic Review and Meta-Analysis

Background Molecular tests are being used increasingly as an auxiliary diagnostic tool so as to avoid a diagnostic surgery approach for cytologically indeterminate thyroid nodules (ITNs). Previous test versions, Thyroseq v2 and Afirma Gene Expression Classifier (GEC), have proven shortcomings in malignancy detection performance. Objective This study aimed to evaluate the diagnostic performance of the established Thyroseq v3, Afirma Gene Sequencing Classifier (GSC), and microRNA-based assays versus prior iterations in ITNs, in light of “rule-in” and “rule-out” concepts. It further analyzed the impact of noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) reclassification and Bethesda cytological subtypes on the performance of molecular tests. Methods Pubmed, Scopus, and Web of Science were the databases used for the present research, a process that lasted until September 2020. A random-effects bivariate model was used to estimate the summary sensitivity, specificity, positive (PLR) and negative likelihood ratios (NLR), and area under the curve (AUC) for each panel. The conducted sensitivity analyses addressed different Bethesda categories and NIFTP thresholds. Results A total of 40 eligible studies were included with 7,831 ITNs from 7,565 patients. Thyroseq v3 showed the best overall performance (AUC 0.95; 95% confidence interval: 0.93–0.97), followed by Afirma GSC (AUC 0.90; 0.87–0.92) and Thyroseq v2 (AUC 0.88; 0.85–0.90). In terms of “rule-out” abilities Thyroseq v3 (NLR 0.02; 95%CI: 0.0–2.69) surpassed Afirma GEC (NLR 0.18; 95%CI: 0.10–0.33). Thyroseq v2 (PLR 3.5; 95%CI: 2.2–5.5) and Thyroseq v3 (PLR 2.8; 95%CI: 1.2–6.3) achieved superior “rule-in” properties compared to Afirma GSC (PLR 1.9; 95%CI: 1.3–2.8). Evidence for Thyroseq v3 seems to have higher quality, notwithstanding the paucity of studies. Both Afirma GEC and Thyroseq v2 performance have been affected by NIFTP reclassification. ThyGenNEXT/ThyraMIR and RosettaGX show prominent preliminary results. Conclusion The newly emerged tests, Thyroseq v3 and Afirma GSC, designed for a “rule-in” purpose, have been proved to outperform in abilities to rule out malignancy, thus surpassing previous tests no longer available, Thyroseq 2 and Afirma GEC. However, Thyroseq v2 still ranks as the best rule-in molecular test. Systematic Review Registration http://www.crd. york.ac. uk/PROSPERO, identifier CRD42020212531.
Carnosol Is a Novel Inhibitor of p300 Acetyltransferase in Breast Cancer

Carnosol, a natural polyphenol abundant in edible plants such as sage, rosemary, and oregano, has shown promising anticancer activity against various types of cancers. Nonetheless, very little is known about its molecular mechanism of action or its downstream target(s). We have previously shown that carnosol inhibits cellular proliferation, migration, invasion, and metastasis as well as triggers autophagy and apoptosis in the highly invasive MDA-MB-231 breast cancer cells. Here, we report that carnosol induces histone hypoacetylation in MDA-MB-231 and Hs578T breast cancer cells. We show that, while carnosol does not affect HDACs, it promotes a ROS-dependent proteasome degradation of p300 and PCAF histone acetyl transferases (HATs) without affecting other HATs such as GCN5 and hMOF. Carnosol-induced histone hypoacetylation remains persistent even when p300 and PCAF protein levels were rescued from degradation by (i) the inhibition of the proteasome activity by the proteasome inhibitors MG-132 and bortezomib, and (ii) the inhibition of ROS accumulation by the ROS scavenger, N-acetylcysteine. In addition, we report that, in a cell-free system, carnosol efficiently inhibits histone acetyltransferase activity of recombinant p300 but not that of PCAF or GCN5. Molecular docking studies reveal that carnosol inhibits p300 HAT activity by blocking the entry of the acetyl-CoA binding pocket of the catalytic domain. The superimposition of the docked conformation of the p300 HAT domain in complex with carnosol shows a similar orientation as the p300 structure with acetyl-CoA. Carnosol occupies the region where the pantetheine arm of the acetyl-CoA is bound. This study further confirms carnosol as a promising anti-breast cancer therapeutic compound and identifies it as a novel natural p300 inhibitor that could be added to the existing panel of inhibitors.
Natural Compounds as Medical Strategies in the Prevention and Treatment of Psychiatric Disorders Seen in Neurological Diseases

Psychiatric disorders are frequently encountered in many neurological disorders, such as Alzheimer’s and Parkinson diseases along with epilepsy, migraine, essential tremors, and stroke. The most common comorbid diagnoses in neurological diseases are depression and anxiety disorders along with cognitive impairment. Whether the underlying reason is due to common neurochemical mechanisms or loss of previous functioning level, comorbidities are often overlooked. Various treatment options are available, such as pharmacological treatments, cognitive-behavioral therapy, somatic interventions, or electroconvulsive therapy. However oral antidepressant therapy may have some disadvantages, such as interaction with other medications, low tolerability due to side effects, and low efficiency. Natural compounds of plant origin are extensively researched to find a better and safer alternative treatment. Experimental studies have shown that phytochemicals such as alkaloids, terpenes, flavonoids, phenolic acids as well as lipids have significant potential in in vitro and in vivo models of psychiatric disorders. In this review, various efficacy of natural products in in vitro and in vivo studies on neuroprotective and their roles in psychiatric disorders are examined and their neuro-therapeutic potentials are shed light.
Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.
Theoretical Predictions of Surface Light Element Abundances in Protostellar and Pre-Main Sequence Phase

Theoretical prediction of surface stellar abundances of light elements–lithium, beryllium, and boron–represents one of the most interesting open problems in astrophysics. As well known, several measurements of 7Li abundances in stellar atmospheres point out a disagreement between predictions and observations in different stellar evolutionary phases, rising doubts about the capability of present stellar models to precisely reproduce stellar envelope characteristics. The problem takes different aspects in the various evolutionary phases; the present analysis is restricted to protostellar and pre-Main Sequence phases. Light elements are burned at relatively low temperatures (T from ≈ 2 to ≈ 5 million degrees) and thus in the early evolutionary stages of a star they are gradually destroyed at different depths of stellar interior mainly by (p, α) burning reactions, in dependence on the stellar mass. Their surface abundances are strongly influenced by the nuclear cross sections, as well as by the extension toward the stellar interior of the convective envelope and by the temperature at its bottom, which depend on the characteristics of the star (mass and chemical composition) as well as on the energy transport in the convective stellar envelope. In recent years, a great effort has been made to improve the precision of light element burning cross sections. However, theoretical predictions surface light element abundance are challenging because they are also influenced by the uncertainties in the input physics adopted in the calculations as well as the efficiency of several standard and non-standard physical processes active in young stars (i.e. diffusion, radiative levitation, magnetic fields, rotation). Moreover, it is still not completely clear how much the previous protostellar evolution affects the pre-Main Sequence characteristics and thus the light element depletion. This paper presents the state-of-the-art of theoretical predictions for protostars and pre-Main Sequence stars and their light element surface abundances, discussing the role of (p, α) nuclear reaction rates and other input physics on the stellar evolution and on the temporal evolution of the predicted surface abundances.
Moving Past Species Classifications for Risk-Based Approaches to Food Safety: Salmonella as a Case Study

Classification of bacterial pathogens into species has traditionally been an essential prerequisite for many aspects of infection control and food safety. Consequently, control measures, regulations, industry specifications, and other guidance documents for foodborne pathogens typically specify a pathogen species (or genus) as target. For example, current Codex Alimentarius guidelines recommend absence of Salmonella in two 375 g samples for infant formula (FAO, 2020). For other ready-to-eat (RTE) foods, absence in smaller samples size (e.g., 25 g) is typically expected. However, there is increasing evidence for Salmonella, as well as other foodborne pathogens, that subgroups, serovars, and/or clonal groups within a given species may differ considerably in their ability to cause human disease. Importantly, there is emerging evidence that use of “sub-species” (or “sub-genus”) classification for defining food safety hazards may allow for more effective management of foodborne pathogens. The genus Salmonella, a pathogen where all members of the genus are considered a food safety hazard, represents one key foodborne pathogen where there is increasing evidence that subtypes differ considerably in their likelihood of causing human disease and where science-based subtype-specific and risk-based approaches of control may have a positive public health impact. There is clear evidence that certain Salmonella subtypes (e.g., certain subtypes within serovars Kentucky and Cerro) show reduced human virulence, while being frequently isolated from food animal species (specifically poultry and cattle) (Rodriguez-Rivera et al., 2014; Haley et al., 2016). Additionally, under the current approach of considering all Salmonella equally likely to cause human disease, limited progress has been made worldwide to reduce foodborne salmonellosis cases (Havelaar et al., 2015; Tack et al., 2020). In some situations, there are counterproductive incentives to prioritize control of highly frequent Salmonella strains with limited human public health relevance (e.g., through poultry vaccines targeting S. Kentucky) over control of less frequent Salmonella strains commonly linked to human salmonellosis cases. We outline a new paradigm for incorporating strain-specific assessment of human public health significance into regulations and control strategies, which we propose should be used to incentivize control of Salmonella subtypes with the greatest public health impact, with a focus on pre-harvest control in meat and poultry. This paradigm could subsequently be applied to other foodborne pathogens, such as Listeria monocytogenes.
The First Insight Into the Supramolecular System of D,L-α-Difluoromethylornithine: A New Antiviral Perspective

Targeting the polyamine biosynthetic pathway by inhibiting ornithine decarboxylase (ODC) is a powerful approach in the fight against diverse viruses, including SARS-CoV-2. Difluoromethylornithine (DFMO, eflornithine) is the best-known inhibitor of ODC and a broad-spectrum, unique therapeutical agent. Nevertheless, its pharmacokinetic profile is not perfect, especially when large doses are required in antiviral treatment. This article presents a holistic study focusing on the molecular and supramolecular structure of DFMO and the design of its analogues toward the development of safer and more effective formulations. In this context, we provide the first deep insight into the supramolecular system of DFMO supplemented by a comprehensive, qualitative and quantitative survey of non-covalent interactions via Hirshfeld surface, molecular electrostatic potential, enrichment ratio and energy frameworks analysis visualizing 3-D topology of interactions in order to understand the differences in the cooperativity of interactions involved in the formation of either basic or large synthons (Long-range Synthon Aufbau Modules, LSAM) at the subsequent levels of well-organized supramolecular self-assembly, in comparison with the ornithine structure. In the light of the drug discovery, supramolecular studies of amino acids, essential constituents of proteins, are of prime importance. In brief, the same amino-carboxy synthons are observed in the bio-system containing DFMO. DFT calculations revealed that the biological environment changes the molecular structure of DFMO only slightly. The ADMET profile of structural modifications of DFMO and optimization of its analogue as a new promising drug via molecular docking are discussed in detail.
Network-Based Online Survey Exploring Self-Reported Depression Among University and College Students During the Early Days of the COVID-19 Outbreak

Background: The psychology of university and college students is immature, they are thus more likely to suffer from depression due to the COVID-19 pandemic. The present study aims to investigate the self-reported depression status of Chinese university and college students and explore its influencing factors. Methods: We conducted a network-based online survey, and a total of 17,876 participants completed the questionnaire. Depression was measured by the Self-Rating Depression Scale (SDS). Univariate analysis and multivariate logistic analysis were performed to explore the influencing factors of self-reported depression symptoms. Results: The proportion of self-reported depression symptoms, mild self-reported depression symptoms, and moderate to severe (M/S) self-reported depression symptoms was 65.2, 53.7, and 11.5%, respectively. The mean score of self-reported depression was 54.8 ± 9.0. Female, personality type of partial introversion, junior college educational level, “moderate” or “high” self-perceived risk of infection, “moderately” or “highly” impacted by the outbreak, and being eager to go back to school were risk factors for M/S self-reported depression symptoms (p < 0.05). While, “moderate” or “high” concern about the outbreak, “moderate” or “high” satisfaction with pandemic prevention and control measures, and having health literacy on communicable diseases were protective factors for M/S self-reported depression symptoms (p < 0.05). Conclusion: The status of self-reported depression symptoms among university and college students was severer than expected, and the influencing factors were multifaceted. Government and school administrators should strengthen the dissemination of knowledge on disease prevention and control. Moreover, much attention should be paid to female and junior college students.
p53 Rather Than β-Catenin Mediated the Combined Hypoglycemic Effect of Cinnamomum cassia (L.) and Zingiber officinale Roscoe in the Streptozotocin-Induced Diabetic Model

Background: The antioxidant, hypoglycemic, and insulin-enhancing effects of ginger and cinnamon were previously confirmed in experimental and human studies, while the combined effect of ginger and cinnamon was not thoroughly investigated until now. Objectives: This study was designed to assess the antidiabetic effect of combined administration of ginger (Zingiber officinale Roscoe) and cinnamon (Cinnamomum cassia L.) in streptozotocin (STZ)-induced diabetic rats compared to metformin and to explain the mechanism behind this effect. Materials and methods: STZ was utilized to induce diabetes mellitus in male Sprague–Dawley rats. Assessments of fasting blood glucose level (BGL), the total antioxidant capacity (TAC), serum insulin, HOMA-IR, and HOMA–β cells were performed. Pancreatic gene expression of β-catenin and p53 was assessed using RT-PCR. Assessment of histopathological alterations of pancreatic islet cells was performed using routine and immunohistochemical techniques. Results: BGL significantly decreased (p = 0.01), while serum insulin and TAC significantly increased (p < 0.001) in both metformin- and ginger plus cinnamon–treated groups compared to the untreated diabetic group. HOMA–β cell index significantly increased (p = 0.001) in ginger plus cinnamon, indicating their enhancing effect on insulin secretion in diabetic conditions. p53 gene expression was significantly upregulated (p < 0.001), while β-catenin was insignificantly downregulated (p = 0.32) in ginger plus cinnamon–treated groups. Insulin immunoexpression in β cells significantly increased (p = 0.001, p = 0.004) in metformin- and ginger plus cinnamon–treated groups, respectively. Conclusions: The combined administration of ginger and cinnamon has a significant hypoglycemic and antioxidant effect in STZ-induced diabetes mostly through enhancing repair of islet cells mediated via upregulation of pancreatic p53 expression. Therefore, testing this effect in diabetic patients is recommended.
Drug Response-Related DNA Methylation Changes in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Pharmacotherapy is the most common treatment for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). Pharmacogenetic studies have achieved results with limited clinical utility. DNA methylation (DNAm), an epigenetic modification, has been proposed to be involved in both the pathology and drug treatment of these disorders. Emerging data indicates that DNAm could be used as a predictor of drug response for psychiatric disorders. In this study, we performed a systematic review to evaluate the reproducibility of published changes of drug response-related DNAm in SCZ, BD and MDD. A total of 37 publications were included. Since the studies involved patients of different treatment stages, we partitioned them into three groups based on their primary focuses: (1) medication-induced DNAm changes (n = 8); (2) the relationship between DNAm and clinical improvement (n = 24); and (3) comparison of DNAm status across different medications (n = 14). We found that only BDNF was consistent with the DNAm changes detected in four independent studies for MDD. It was positively correlated with clinical improvement in MDD. To develop better predictive DNAm factors for drug response, we also discussed future research strategies, including experimental, analytical procedures and statistical criteria. Our review shows promising possibilities for using BDNF DNAm as a predictor of antidepressant treatment response for MDD, while more pharmacoepigenetic studies are needed for treatments of various diseases. Future research should take advantage of a system-wide analysis with a strict and standard analytical procedure.
CD73+ Mesenchymal Stem Cells Ameliorate Myocardial Infarction by Promoting Angiogenesis

With multipotent differentiation potential and paracrine capacity, mesenchymal stem cells (MSCs) have been widely applied in clinical practice for the treatment of ischemic heart disease. MSCs are a heterogeneous population and the specific population of MSCs may exhibit a selective ability for tissue repair. The aim of our research was to adapt the CD73+ subgroup of adipose derived MSCs (AD-MSCs) for the therapy of myocardial infarction (MI). In this research, AD-MSCs were isolated from adipose tissue surrounding the groin of mice and CD73+ AD-MSCs were sorted using flow cytometry. To investigate the therapeutic effects of CD73+ AD-MSCs, 1.2 × 106 CD73+ AD-MSCs were transplanted into rat model of MI, and CD73– AD-MSCs, normal AD-MSCs transplantation served as control. Our results revealed that CD73+ AD-MSCs played a more effective role in the acceleration function of cardiac recovery by promoting angiogenesis in a rat model of MI compared with mixed AD-MSCs and CD73– AD-MSCs. Moreover, with the expression of CD73 in AD-MSCs, the secretion of VEGF, SDF-1α, and HGF factors could be promoted. It also shows differences between CD73+ and CD73– AD-MSCs when the transcription profiles of these two subgroups were compared, especially in VEGF pathway. These findings raise an attractive outlook on CD73+ AD-MSCs as a dominant subgroup for treating MI-induced myocardial injury. CD73, a surface marker, can be used as a MSCs cell quality control for the recovery of MI by accelerating angiogenesis.
The Case Against Registered Reports

Copyright © 2021, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602 88 AI MAGAZINE Registered reports have been touted as one of the solutions to the problems surrounding the reproducibility crisis. They promote good research practices and combat data dredging.1 What is not to like? Still, although registered reports have been embraced by many different fields, it is not the case for artificial intelligence (AI). It is hard to find traces of registered reports in the field of AI. Is this because they are not needed in AI? Is there not a case for registered reports in AI research?  Registered reports have been proposed as a way to move from eye-catching and surprising results and toward methodologically sound practices and interesting research questions. However, none of the top-twenty artificial intelligence journals support registered reports, and no traces of registered reports can be found in the field of artificial intelligence. Is this because they do not provide value for the type of research that is conducted in the field of artificial intelligence? The Case Against Registered Reports