Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Simple Summary This review discusses the heterogeneity of breast cancer and strategies to understand it using multi-omics profiling methods at the traditional bulk and single-cell levels. It begins with the exploration of genomics and its role in classifying breast cancer into molecular subtypes. Following that, it delves into the contribution of transcriptomics in illuminating the diverse gene expression patterns within individual breast cancer cells, further underscoring the disease’s complexity. The narrative then addresses the impact of proteomics, which unveils intricate protein expression patterns and modifications. Despite these advancements, it is essential to integrate multiple omics approaches to fully unravel breast cancer heterogeneity. The integration of genomics, transcriptomics, and proteomics at the single-cell level holds great promise for advancing breast cancer research and management in the future. Abstract Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple ‘omics’ strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer’s molecular complexities, but also promotes the development of personalized treatment strategies.
Disney boys to men: erotic gaze and masculine gender capital of former Disney boy actors

ABSTRACT This research examines the nature of gender presentation of men who were the stars on Disney Channel shows. Research has already examined how young women who were Disney stars become quickly sexualised and perceived as women under the male gaze. However, there is little corresponding research on boys who are subject to the scrutiny of the public. I engage in a phenomenological content analysis of the social media of three adult male actors who starred on the show Wizards of Waverly Place. The nature of masculine gender capital encourages these men to move towards a masculine ideal while recognising they are under the erotic gaze. These men push to be severed from their youth identities, forming a strategic boundary that nods to the homoeroticism surrounding their identity.
Rao, Wald, and Gradient Tests for Adaptive Detection of Swerling I Targets

We consider adaptive detection of a Gaussian rank-one component known up to a scaling factor buried in Gaussian noise with unknown statistics, a problem which arises when detecting Swerling I targets in radar systems. From the joint distribution of the samples under test and the training samples, the score function and the Fisher information matrix are derived, which enables us to formulate the Rao, Wald and gradient tests associated with the composite hypotheses problem. The Rao test is shown to bear resemblance with its deterministic counterpart and the Wald test is essentially based on the target power maximum likelihood estimate. The gradient test sort of blends the two other tests. All of them are shown to have a constant false alarm rate. Their performance is evaluated through numerical simulations in matched and mismatched cases.
Deep Carious Lesions Management with Stepwise, Selective, or Non-Selective Removal in Permanent Dentition: A Systematic Review of Randomized Clinical Trials

Objective: The goal of this systematic study was to investigate the effectiveness of selective, stepwise, and non-selective removal techniques for caries removal in permanent teeth with deep carious lesions. The primary focus was the results found comparing techniques for caries removal to check whether there was pulp exposition; the secondary was the materials used for pulp protection and clinical findings reported within the included studies. Methods: The search was performed in two databases (PubMed/MEDLINE and Web Of Science). The studies included in this systematic review were selected based on eligibility criteria. The inclusion criteria were: (1) randomized controlled trials (RCTs), (2) that compared the total removal of carious tissue with selective removal in permanent teeth with deep carious lesions, (3) with a follow-up period of at least 6 months, and (4) publications in English. Regarding the exclusion criteria, the following were not considered: (1) articles published in other languages, (2) articles that did not compare the different types of total/selective decay removal, and (3) articles published before January 2008. The risk of bias and the quality of the included studies were independently assessed by two reviewers using the RoB 2 tool. Results: 5 out of 105 potentially eligible studies were included. Regarding the teeth included in the study, three articles performed management only on permanent molars, while other studies also performed management on incisors/canines/premolars/molars. Management protocols were divided into nonselective caries removal and partial caries removal (selective/stepwise). The theory of non-selective caries removal was considered an excessive, unnecessarily invasive option and a form of outdated management, and selective removal was preferred. Conclusion: The selective removal technique presented a higher success rate and fewer incidences of pulpal exposure than total removal, after up to 18 months of follow up. Moreover, only one session seemed to be a better management choice compared to two sessions because the cavity re-opening procedure is more prone to pulp exposure and highly depends on patient commitment. Otherwise, at 5 years of follow up, there was no difference between selective removal and total removal in management longevity. In addition, there were also no differences between the success of the materials used for definitive restorations in teeth subjected to any of the techniques evaluated.
Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection

The recent increase in credit card fraud is rapidly has caused huge monetary losses for individuals and financial institutions. Most credit card frauds are conducted online by illegally obtaining payment credentials through data breaches, phishing, or scamming. Many solutions have been suggested to address the credit card fraud problem for online transactions. However, the high-class imbalance is the major challenge that faces the existing solutions to construct an effective detection model. Most of the existing techniques used for class imbalance overestimate the distribution of the minority class, resulting in highly overlapped or noisy and unrepresentative features, which cause either overfitting or imprecise learning. In this study, a credit card fraud detection model (CCFDM) is proposed based on ensemble learning and a generative adversarial network (GAN) assisted by Ensemble Synthesized Minority Oversampling techniques (ESMOTE-GAN). Multiple subsets were extracted using under-sampling and SMOTE was applied to generate less skewed sets to prevent the GAN from modeling the noise. These subsets were used to train diverse sets of GAN models to generate the synthesized subsets. A set of Random Forest classifiers was then trained based on the proposed ESMOTE-GAN technique. The probabilistic outputs of the trained classifiers were combined using a weighted voting scheme for decision-making. The results show that the proposed model achieved 1.9%, and 3.2% improvements in overall performance and the detection rate, respectively, with a 0% false alarm rate. Due to the massive number of transactions, even a tiny false positive rate can overwhelm the analysis team. Thus, the proposed model has improved the detection performance and reduced the cost needed for manual analysis.
Hydrologic effects of phreatophyte control, Acme-Artesia reach of the Pecos River, New Mexico, 1967-82

The U.S. Bureau of Reclamation began a phreatophyte clearing and control program in the bottom land of the Acme-Artesia reach of the Pecos River in March 1967. The initial cutting of 19,000 acres of saltcedar trees, the dominant phreatophyte in the area, was completed in May 1969. Saltcedar regrowth continued each year until July 1975, when root plowing eradicated most of the regrowth. The major objective of the clearing and control program was to salvage water that could be put to beneficial use. Measurements of changes in the water table in the bottom land and changes in the base flow of the Pecos River were made in order to determine the hydrologic effects of the program. Some salvage of water was indicated, but it is not readily recognized as an increase in base flow. The quantity of salvage probably is less than the average annual base-flow gain of 19,110 acre-feet in the reach during 1967-82.
Analysis of matrisome expression patterns in murine and human dorsal root ganglia

The extracellular matrix (ECM) is a dynamic structure of molecules that can be divided into six different categories and are collectively called the matrisome. The ECM plays pivotal roles in physiological processes in many tissues, including the nervous system. Intriguingly, alterations in ECM molecules/pathways are associated with painful human conditions and murine pain models. Nevertheless, mechanistic insight into the interplay of normal or defective ECM and pain is largely lacking. The goal of this study was to integrate bulk, single-cell, and spatial RNA sequencing (RNAseq) datasets to investigate the expression and cellular origin of matrisome genes in male and female murine and human dorsal root ganglia (DRG). Bulk RNAseq showed that about 65% of all matrisome genes were expressed in both murine and human DRG, with proportionally more core matrisome genes (glycoproteins, collagens, and proteoglycans) expressed compared to matrisome-associated genes (ECM-affiliated genes, ECM regulators, and secreted factors). Single cell RNAseq on male murine DRG revealed the cellular origin of matrisome expression. Core matrisome genes, especially collagens, were expressed by fibroblasts whereas matrisome-associated genes were primarily expressed by neurons. Cell–cell communication network analysis with CellChat software predicted an important role for collagen signaling pathways in connecting vascular cell types and nociceptors in murine tissue, which we confirmed by analysis of spatial transcriptomic data from human DRG. RNAscope in situ hybridization and immunohistochemistry demonstrated expression of collagens in fibroblasts surrounding nociceptors in male and female human DRG. Finally, comparing human neuropathic pain samples with non-pain samples also showed differential expression of matrisome genes produced by both fibroblasts and by nociceptors. This study supports the idea that the DRG matrisome may contribute to neuronal signaling in both mouse and human, and that dysregulation of matrisome genes is associated with neuropathic pain.
Effective Coping with Academic Stress Is a Matter of Personality Types: Revisiting the Person-Centred Approach

Recent literature provides alarming data on the increase in university academic stress. The role of personality in understanding and addressing this problem is well established. However, this evidence could be improved by adopting a person-centred approach (e.g., types), as opposed to the usual variable-centred approach (e.g., traits), and considering the role of gender. Our aim was to explore how personality types and gender relate to coping strategies and perceived coping efficacy for academic stress. A total of 810 university psychology students completed the NEO-FFI Inventory and the Coping Strategies Inventory. Post hoc tests for MANOVA and ANOVA were performed. Types and gender were used as predictors and coping strategies, and perceived coping efficacy as criteria. There was no type-gender interaction. Types combining low neuroticism-high conscientiousness (e.g., entrepreneur) chose the most adaptive coping strategies and showed the highest levels of perceived coping efficacy, while high neuroticism-low conscientiousness types (e.g., insecure) opted for maladaptive coping strategies and presented the lowest perceived coping efficacy. Gender was not associated with perceived coping efficacy but with use (e.g., women prefer emotional expression). The personality typology provided useful information on individual differences in coping with academic stress, which can help guide specific strategies to manage it.
Genetic Influences on Outcomes of Psychotherapy in Borderline Personality Disorder: A Narrative Review of Implications for Personalized Treatment

Borderline personality disorder (BPD) manifests as instability in mood, relationships, self-image, and behavior, representing a challenging mental health issue. This review scrutinizes genetic factors influencing BPD and the corresponding treatment outcomes. The primary objective of this narrative review is to illuminate the association between genetic factors and BPD treatment outcomes, discussing the potential of genetic testing for personalized therapy. The review is derived from observational and experimental studies on BPD, genetic factors, and psychotherapy from 2000 to 2023, sourced primarily through PubMed. Reviews and meta-analyses were excluded. Our review suggests that genetic factors account for 40-60% of BPD variation, with significant roles played by epigenetic alterations like DNA methylation and microRNAs, particularly in the context of childhood trauma. Gene-environment interactions are also vital for BPD's development. Treatments such as dialectical behavior therapy, mentalization-based therapy, and schema therapy have shown efficacy, with success variability possibly linked to genetic factors. However, existing research is constrained by recall bias, diverse methodologies, and limited sample sizes. Future research necessitates long-term follow-up, diverse populations, and controlled variables to enhance our comprehension of BPD treatment outcomes' genetic foundations. The review underlines the promise of personalized medicine in BPD treatment, driven by genetic insights.
On some relations in the class of multi-index Mittag-Leffler functions

In this paper, some properties related to the -parametric Mittag-Leffler (M-L) functions are investigated. More precisely, three families of such functions with different kinds of indices are examined. Appropriate estimates and asymptotic formulae are obtained and which are further used in proving the convergence of series of functions of these families in the whole complex plane, as well as in its compact subsets. Using these auxiliary results, different relations referring to the discussed families are obtained; generalizing the ones already known for particular cases of the parameters. Those are considered different representations of the -parametric M-L functions pertaining to the families discussed, and with which various equalities are established relating different sums that involve matrix coefficients. Some interesting properties are reached for particular choices of the matrices. Finally, bounds of the -parametric M-L function are given under a perturbed matrix argument.
UTILITY OF THE PRODUCT VALUE OF SERUM ALBUMIN AND PROTHROMBIN TIME IN ASSESSING THE SEVERITY OF ACUTE PANCREATITIS

Background: Acute pancreatitis (AP) is an inflammatory disorder characterised by local or systemic immuno-inflammation, which could lead to organ dysfunction and death. The present study aimed to study the utility of the product value of serum albumin and prothrombin time in assessing the severity of acute pancreatitis. Materials and Methods: This cross-sectional observational study was done in ESIC Medical College & PGIMSR, Chennai, for 18 months on 120 patients aged 20-75 years with acute pancreatitis without complications. The product value of serum albumin and prothrombin time were compared with the clinical severity, which was graded according to the APACHE II scoring system and compared with the clinical outcome. Result: Among 120 patients, the maximum of study participants was male, 119 (99.2%), and 41-50 years (34.2 %). We found that around 23 (19.2 %) of the participants had a product value of < 35 (severe pancreatitis), 87 (72.5 %) of the participants and a product value of 35-70, and 10 (8.3 %) had a product value of more than 70. About 20 (16.7 %) participants had an Apache II score of more than eight, indicating severe pancreatitis. Further, 10 (8.3%) patients expired during the study. The sensitivity and specificity of the product value in severe pancreatitis are 90 % and 95 %, and the positive and negative predictive values are 78% and 97%, respectively. Conclusion: In conclusion, our present study reveals that the product value of serum albumin and prothrombin time on admission is independently associated with the severity of acute pancreatitis.
Identifying relevant asymmetry features of EEG for emotion processing

The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisphere processes positive emotions, whereas the right hemisphere processes negative emotions. Previous studies have used these asymmetry models to enhance the classification of emotions in machine learning models. However, little research has been conducted to explore how machine learning models can help identify associations between hemisphere asymmetries and emotion processing. To address this gap, we conducted two experiments using a subject-independent approach to explore how the asymmetry of the brain hemispheres is involved in processing happiness, sadness, fear, and neutral emotions. We analyzed electroencephalogram (EEG) signals from 15 subjects collected while they watched video clips evoking these four emotions. We derived asymmetry features from the recorded EEG signals by calculating the log ratio between the relative energy of symmetrical left and right nodes. Using the asymmetry features, we trained four binary logistic regressions, one for each emotion, to identify which features were more relevant to the predictions. The average AUC-ROC across the 15 subjects was 56.2, 54.6, 51.6, and 58.4% for neutral, sad, fear, and happy, respectively. We validated these results with an independent dataset, achieving comparable AUC-ROC values. Our results showed that brain lateralization was observed primarily in the alpha frequency bands, whereas for the other frequency bands, both hemispheres were involved in emotion processing. Furthermore, the logistic regression analysis indicated that the gamma and alpha bands were the most relevant for predicting emotional states, particularly for the lateral frontal, parietal, and temporal EEG pairs, such as FT7-FT8, T7-T8, and TP7-TP8. These findings provide valuable insights into which brain areas and frequency bands need to be considered when developing predictive models for emotion recognition.
The in vitro Activity of Echinocandins Against Clinical Trichophyton rubrum Isolates and Review of the Susceptibility of T. rubrum to Echinocandins Worldwide

Introduction The emergence of resistance in Trichophyton rubrum to azoles and terbinafine has become increasingly evident in recent years, necessitating the development of novel antifungal drugs and the exploration of new indications for existing agents. Methods In this study, we retrospectively evaluated the in vitro antifungal activity of 3 echinocandins (anidulafungin, caspofungin, and micafungin) against 73 clinical isolates of T. rubrum collected from a teaching hospital in Shanghai, China, using EUCAST E.DEF 9.3. 1 with minor modification. We also reviewed the susceptibility of T. rubrum to echinocandins globally by literature searching. Results Our findings revealed that micafungin exhibited the lowest modal minimum effective concentration (MEC) value (0.08 mg/L, n = 28) and the lowest geometric mean (GM) MEC value (0.014 mg/L) among the 73 isolates of T. rubrum tested, followed by anidulafungin with a modal MEC value of 0.016 mg/L (n = 67) and a GM of 0.018 mg/L. Caspofungin displayed a higher modal MEC value of 0.5 mg/L (n = 35) and a GM of 0.308 mg/L. Despite variations in methodologies, similar results were obtained from the review of five relevant studies included in our analysis. Discussion Echinocandins exhibited excellent in vitro activity against T. rubrum isolates, with micafungin and anidulafungin demonstrating greater potency than caspofungin. These findings suggest that echinocandins could be considered as potential treatment options for managing recalcitrant dermatophytoses resulting from the emergence of resistance. However, it is important to note that the clinical efficacy of these in vitro findings has yet to be established and warrants further investigation.
Real-World Analysis of Durvalumab after Chemoradiation in Stage III Non-Small-Cell Lung Cancer

The 2017 PACIFIC trial heralded the incorporation of routine adjuvant durvalumab following curative-intent chemoradiation for stage III non-small-cell lung cancer (NSCLC). However, carefully selected clinical trial populations can differ significantly from real-world populations, which can have implications on treatment toxicities and outcomes, making it difficult to accurately counsel patients. Consequently, we performed a real-world, retrospective analysis of outcomes and toxicities in 118 patients with stage III NSCLC treated with durvalumab after platinum-based chemoradiotherapy. The data were collected from patients who underwent treatment at a single, tertiary-level Canadian cancer centre from May 2018 to October 2020. The variables collected included patient demographics, treatment specifics, progression-free survival, overall survival, and immune-related adverse events (IRAE) from durvalumab. Descriptive statistics were used for toxicity analysis, and progression-free survival and overall survival estimates were calculated using the Kaplan–Meier method. The statistical analyses indicated a 64.4% (n = 76) toxicity rate, with a 21% (n = 25) toxicity rate of grade 3+ IRAEs. The most common documented IRAEs were pneumonitis (n = 44; 40%), followed by rash (n = 20; 18%) and thyroid dysfunction (n = 17; 15%). FEV1 and DLCO were not found to be associated predictors of pneumonitis toxicity. The median PFS and OS were estimated to be >1.7 years and >2.7 years, respectively.