Miracle-dz/UniFAR-FTUs huggingface dataset:
BCDnet: Parallel heterogeneous eight-class classification model of breast pathology

Breast cancer is the cancer with the highest incidence of malignant tumors in women, which seriously endangers women’s health. With the help of computer vision technology, it has important application value to automatically classify pathological tissue images to assist doctors in rapid and accurate diagnosis. Breast pathological tissue images have complex and diverse characteristics, and the medical data set of breast pathological tissue images is small, which makes it difficult to automatically classify breast pathological tissues. In recent years, most of the researches have focused on the simple binary classification of benign and malignant, which cannot meet the actual needs for classification of pathological tissues. Therefore, based on deep convolutional neural network, model ensembleing, transfer learning, feature fusion technology, this paper designs an eight-class classification breast pathology diagnosis model BCDnet. A user inputs the patient’s breast pathological tissue image, and the model can automatically determine what the disease is (Adenosis, Fibroadenoma, Tubular Adenoma, Phyllodes Tumor, Ductal Carcinoma, Lobular Carcinoma, Mucinous Carcinoma or Papillary Carcinoma). The model uses the VGG16 convolution base and Resnet50 convolution base as the parallel convolution base of the model. Two convolutional bases (VGG16 convolutional base and Resnet50 convolutional base) obtain breast tissue image features from different fields of view. After the information output by the fully connected layer of the two convolutional bases is fused, it is classified and output by the SoftMax function. The model experiment uses the publicly available BreaKHis data set. The number of samples of each class in the data set is extremely unevenly distributed. Compared with the binary classification, the number of samples in each class of the eight-class classification is also smaller. Therefore, the image segmentation method is used to expand the data set and the non-repeated random cropping method is used to balance the data set. Based on the balanced data set and the unbalanced data set, the BCDnet model, the pre-trained model Resnet50+ fine-tuning, and the pre-trained model VGG16+ fine-tuning are used for multiple comparison experiments. In the comparison experiment, the BCDnet model performed outstandingly, and the correct recognition rate of the eight-class classification model is higher than 98%. The results show that the model proposed in this paper and the method of improving the data set are reasonable and effective.
Pelvicalyceal Volume and Fluid Temperature Elevation During Laser Lithotripsy.

BACKGROUND While high-power laser systems facilitate successful ureteroscopic treatment of larger and more complex stones, they can substantially elevate collecting system fluid temperatures with potential thermal injury of adjacent tissue. The volume of fluid in which laser activation occurs is an important factor when assessing temperature elevation. The aim of this study was to measure fluid temperature elevation and calculate thermal dose from laser activation in fluid-filled glass bulbs simulating varying calyx/pelvis volumes. MATERIALS AND METHODS Glass bulbs of volumes 0.5, 2.8, 4.0, 7.0, 21.0, and 60.8 ml were submerged in a 16 L tank of 37˚C deionized water. A 230-µm laser fiber extending 5mm from the tip of a ureteroscope was positioned in the center of each glass bulb. Irrigation with 0, 8, 15, and 40 ml/min of room temperature DI water was applied. Once steady state temperature was achieved, a Ho:YAG laser was activated for 60 seconds at 40W (0.5J x 80Hz, SP). Temperature was measured from a thermocouple affixed to the external tip of the ureteroscope. Thermal dose was calculated using the Dewey and Sapareto t43 methodology. RESULTS The extent of temperature elevation and thermal dose from laser activation were inversely related to the volume of fluid in each model and the irrigation rate. The time to threshold of thermal injury was only 3 seconds for the smallest model (0.5ml) without irrigation but was not reached in the largest model (60.8ml) regardless of irrigation rate. Irrigation delivered at 40 ml/min maintained safe temperatures below the threshold of tissue injury in all models with 1 minute of continuous laser activation. CONCLUSIONS The volume of fluid in which laser activation occurs is an important factor in determining the extent of temperature elevation. Smaller volumes receive greater thermal dose and reach threshold of tissue injury more rapidly than larger volumes.
Elucidation of the Kinetic Behavior of Quercetin, Isoquercitrin, and Rutin Solubility by Physicochemical and Thermodynamic Investigations

The solubility of flavonoids in organic media is a complex and important process. It is a key factor in extraction, formulation, and biocatalysis processes of flavonoids. In this work, dissolution experiments showed that the solubility of quercetin, isoquercitrin, and rutin in tert-amyl alcohol is higher (20, 30, and 36 g/L) than in acetonitrile (1.7 g/L). Moreover, dissolution kinetic of quercetin in tert-amyl alcohol showed an unusual behavior of the amount dissolved over time. High concentrations of dissolved quercetin were measured at the beginning of the experiment. The maximum concentration was followed by a decrease later on in time until the saturation concentration was obtained. To explain these results, thermodynamic (enthalpy of dissolution and mixing) and physicochemical (microscopy, particle size measurement) experiments were carried out. The enthalpies of dissolution (ΔHdiss) and mixing (ΔHmix), measured in this work, are consistent with the experimental solubility scale for quercetin and ru.
Effects of Withania somnifera (Ashwagandha) on Stress and the Stress-Related Neuropsychiatric Disorders Anxiety, Depression, and Insomnia

Background Withania somnifera (WS), also known as Ashwagandha, is commonly used in Ayurveda and other traditional medicine systems. WS has seen an increase in worldwide usage due to its reputation as an adaptogen. This popularity has elicited increased scientific study of its biological effects, including a potential application for neuropsychiatric and neurodegenerative disorders. Objective This review aims to provide a comprehensive summary of preclinical and clinical studies examining the neuropsychiatric effects of WS, specifically its application in stress, anxiety, depression, and insomnia. Methods Reports of human trials and animal studies of WS were collected primarily from the PubMed, Scopus, and Google Scholar databases. Results WS root and leaf extracts exhibited noteworthy anti-stress and anti-anxiety activity in animal and human studies. WS also improved symptoms of depression and insomnia, though fewer studies investigated these applications. WS may alleviate these conditions predominantly through modulation of the hypothalamic-pituitary-adrenal and sympathetic-adrenal medullary axes, as well as through GABAergic and serotonergic pathways. While some studies link specific withanolide components to its neuropsychiatric benefits, there is evidence for the presence of additional yet unidentified active compounds in WS. Conclusion While benefits were seen in the reviewed studies, significant variability in the WS extracts examined prevents a consensus on the optimum WS preparation or dosage for treating neuropsychiatric conditions. WS generally appears safe for human use; however, it will be important to investigate potential herb-drug interactions involving WS if used alongside pharmaceutical interventions. Further elucidation of active compounds of WS is also needed.
Liquides ioniques sous confinement nanométrique unidimensionnel

L'idee a la base de ce projet est de mettre a profit le confinement nanometrique unidimensionnel pour decupler la conduction ionique des electrolytes et donc la puissance des accumulateurs au lithium. Nous avons concentre nos efforts sur une classe particuliere d'electrolytes qui, en raison de leurs stabilites physique et electrochimique, ont ete identifies comme prometteurs: les Liquides Ioniques (LI). Nous avons confine les LI dans deux systemes poreux presentant une topologie commune (pores cylindriques macroscopiquement orientes) mais aux proprietes physico-chimiques complementaires: i) des alumines poreuses (AAO, interface hydrophile, diametre des pores de 25 a 160 nm) et ii) des membranes de NanoTubes de Carbone (NTC, interface hydrophobe, diametre des pores 4 nm). Nous avons developpe un modele microscopique multi-echelle original, prenant en compte la dynamique complexe des cations des LI: combinaison i) de la dynamique de reorientation rapide des chaines laterales alkyle, ii) de la diffusion de la molecule au sein des agregats nanometriques spontanement formes par les LI, puis iii) de la diffusion entre ces agregats. Ce modele reproduit de facon remarquablement robuste les donnees de diffusion quasi-elastique de neutrons sur une gamme etendue de vecteurs de diffusion (0,1 a 2,5 A-1) et de temps (10-1 a 2.103 ps). A cette echelle locale, nous ne detectons pas d'influence du confinement sur la dynamique du LI confine au sein des AAO et des CNT. Nous montrons cependant qu'a l'echelle microscopique (PFG-NMR) et macroscopique (spectroscopie d'impedance) le confinement des LI au sein des NTC permet d'obtenir un gain de conductivite d'un facteur 3. Un brevet est depose.
Fetal Genotype-Phenotype Sex Discordance: A Case of 5-Alpha-Reductase Deficiency

Abstract Objective To describe the prenatal and postnatal diagnostic workup leading to the diagnosis of 5-alpha-reductase type 2 deficiency (5AR2D) in a case of 46,XY disorder of sex development (DSD). Case report A first-trimester noninvasive prenatal test (NIPT) on maternal blood revealed a male fetus with a low risk of aneuploidy. However, a female fetus was identified at the second-trimester scan. A repeat sample revealed similar results and ruled out the possibility of both a sample swap or a vanishing twin. At birth, phenotypically female external genitalia were evident, with testes noted in the labioscrotal area. Neonatal blood confirmed a 46,XY complement and a 46,XY DSD genetic panel revealed a 5AR2D. Conclusion Our case and others described in the literature demonstrate that fetal sex discordance detected by a combination of NIPT and subsequent ultrasound examination can be associated with several biological conditions, with DSD being the most significant.
Advances in pharmacotherapy for neuroblastoma

ABSTRACT Introduction Neuroblastoma is the most prevalent cancer type diagnosed within the first year after birth and accounts for 15% of deaths from pediatric cancer. Despite the improvements in survival rates of patients with neuroblastoma, the incidence of the disease has increased over the last decade. Neuroblastoma tumor cells harbor a vast range of variable and heterogeneous histochemical and genetic alterations which calls for the need to administer individualized and targeted therapies to induce tumor regression in each patient. Areas covered This paper provides reviews the recent clinical trials which used chemotherapeutic and/or targeted agents as either monotherapies or in combination to improve the response rate in patients with neuroblastoma, and especially high-risk neuroblastoma. It also reviews some of the prominent preclinical studies which can provide the rationale for future clinical trials. Expert opinion Although some distinguished advances in pharmacotherapy have been made to improve the survival rate and reduce adverse events in patients with neuroblastoma, a more comprehensive understanding of the mechanisms of tumorigenesis, resistance to therapies or relapse, identifying biomarkers of response to each specific drug, and developing predictive preclinical models of the tumor can lead to further breakthroughs in the treatment of neuroblastoma.
Barriers to and Facilitators for Using Nutrition Apps: Systematic Review and Conceptual Framework

Background Nutrition apps are effective in changing eating behavior and diet-related health risk factors. However, while they may curb growing overweight and obesity rates, widespread adoption is yet to be achieved. Hence, profound knowledge regarding factors motivating and hindering (long-term) nutrition app use is crucial for developing design guidelines aimed at supporting uptake and prolonged use of nutrition apps. Objective In this systematic review, we synthesized the literature on barriers to and facilitators for nutrition app use across disciplines including empirical qualitative and quantitative studies with current users, ex-users, and nonusers of nutrition apps. Methods A systematic literature search including 6 databases (PubMed, Web of Science, PsychINFO, PSYNDEX, PsycArticles, and SPORTDiscus) as well as backward and forward citation search was conducted. Search strategy, inclusion and exclusion criteria, and the planned data extraction process were preregistered. All empirical qualitative and quantitative studies published in German or English were eligible for inclusion if they examined adolescents (aged 13-18) or adults who were either current users, ex-users, and nonusers of nutrition apps. Based on qualitative content analysis, extracted individual barriers and facilitators were grouped into categories. Results A total of 28 publications were identified as eligible. A framework with a 3-level hierarchy was designed which grouped 328 individual barriers and facilitators into 23 subcategories, 12 categories, and 4 clusters that focus on either the individual user (goal setting and goal striving, motivation, routines, lack of awareness of knowledge), different aspects of the app and the smartphone (features, usability of the app or food database, technical issues, data security, accuracy/trustworthiness, costs), positive and negative outcomes of nutrition app use, or interactions between the user and their social environment. Conclusions The resulting conceptual framework underlines a pronounced diversity of reasons for (not) using nutrition apps, indicating that there is no “one-size-fits-all” approach for uptake and prolonged use of nutrition apps. Hence, tailoring nutrition apps to needs of specific user groups seems promising for increasing engagement.
Data‐driven Protein Engineering

Introduction A protein’s sequence of amino acids encodes its function. This “function” could refer to a protein’s natural biological function, or it could also be any other property including binding affinity toward a particular ligand, thermodynamic stability, or catalytic activity. A detailed understanding of how these functions are encoded would allow us to more accurately reconstruct the tree of life and possibly predict future evolutionary events, diagnose genetic diseases before they manifest symptoms, and design new proteins with useful properties. We know that a protein sequence folds into a three-dimensional structure, and this structure positions specific chemical groups to perform a function; however, we’re missing the quantitative details of this sequence-structure-function mapping. This mapping is extraordinarily complex because it involves thousands of molecular interactions that are dynamically coupled across multiple length and time scales. Computational methods can be used to model the mapping from sequence to structure to function. Tools such as molecular dynamics simulations or Rosetta use atomic representations of protein structures and physics-based energy functions to model structures and functions (1–3). While these models are based on well-founded physical principles, they often fail to capture a protein’s overall global behavior and properties. There are numerous challenges associated with physics-based models including consideration of conformational dynamics, the requirement to make energy function approximations for the sake of computational efficiency, and the fact that, for many complex properties such as enzyme catalysis, the molecular basis is simply unknown (4). In systems composed of thousands of atoms, the propagation of small errors quickly overwhelms any predictive accuracy. Despite tremendous breakthroughs and research progress over the last century, we still lack the key details to reliably predict, simulate, and design protein function. In this chapter, we present the emerging field of data-driven protein engineering. Instead of physically modeling the relationships between protein sequence, structure, and function, data-driven methods use ideas from statistics and machine learning to infer these complex relationships from data. This top-down modeling approach implicitly captures the numerous and possibly unknown factors that shape the mapping from sequence to function. Statistical models have been used to understand the molecular basis of protein function and provide exceptional predictive accuracy for protein design.
Adaptive evolvability through direct selection instead of indirect, second‐order selection

Abstract Can evolvability itself be the product of adaptive evolution? To answer this question is challenging, because any DNA mutation that alters only evolvability is subject to indirect, “second order” selection on the future effects of this mutation. Such indirect selection is weaker than “first‐order” selection on mutations that alter fitness, in the sense that it can operate only under restrictive conditions. Here I discuss a route to adaptive evolvability that overcomes this challenge. Specifically, a recent evolution experiment showed that some mutations can enhance both fitness and evolvability through a combination of direct and indirect selection. Unrelated evidence from gene duplication and the evolution of gene regulation suggests that mutations with such dual effects may not be rare. Through such mutations, evolvability may increase at least in part because it provides an adaptive advantage. These observations suggest a research program on the adaptive evolution of evolvability, which aims to identify such mutations and to disentangle their direct fitness effects from their indirect effects on evolvability. If evolvability is itself adaptive, Darwinian evolution may have created more than life's diversity. It may also have helped create the very conditions that made the success of Darwinian evolution possible.
Patients’ E-Readiness to use E-Health technologies for oral health

Introduction Scientific evidence highlights the importance of E-Readiness in the adoption and implementation of E-Oral Health technologies. However, to our knowledge, there is no study investigating the perspective of patients in this regard. Therefore, the objective of this study was to explore patients’ E-Readiness in the field of dentistry. Materials and methods A qualitative study was conducted using interpretive descriptive methodology. Purposeful sampling with maximum variation and snowball techniques were used to recruit the study participants via McGill University dental clinics and affiliated hospitals, as well as private or public dental care organizations. A total of 15 face-to-face, semi-structured and 60 to 90-minute audio recorded interviews were conducted. Data collection and analyses were performed concurrently, and interviews were continued until saturation was reached. Activity theory was used as the conceptual framework, and thematic analysis was used to analyze data. Data analysis was conducted both manually and with the use of “ATLAS-ti” software. Results Four major themes emerged from the study; unlocking barriers, E-Oral Health awareness, inquisitiveness for E-Oral Health technology and enduring oral health benefits. These themes correspond with all three types of readiness (core, engagement and structural). Conclusion The study results suggest that dental patients consider E-Oral Health as a facilitator to access to care, and they are ready to learn and use E-Oral Health technology. There is a need to implement and support E-Oral Health technologies to improve patient care.
Papaya: Global Typestate Analysis of Aliased Objects

Typestates are state machines used in object-oriented programming to specify and verify correct order of method calls on an object. To avoid inconsistent object states, typestates enforce linear typing, which eliminates—or at best limits—aliasing. However, aliasing is an important feature in programming, and the state-of-the-art on typestates is too restrictive if we want typestates to be adopted in real-world software systems. In this paper, we present a type system for an object-oriented language with typestate annotations, which allows for unrestricted aliasing, and as opposed to previous approaches it does not require linearity constraints. The typestate analysis is global and tracks objects throughout the entire program graph, which ensures that well-typed programs conform and complete the declared protocols. We implement our framework in the Scala programming language and illustrate our approach using a running example that shows the interplay between typestates and aliases.
12-month trajectory and predictors of affect balance in nursing home residents living with dementia

Abstract Objectives:Emotional expressions in late-stage dementia have traditionally been studied within a deficit paradigm. Moving the narrative of the dementia trajectory from a solely negative pathological experience to one that acknowledges the potential for positive experiences aligns with international recommendations for living well with dementia. The purpose of this study was to extend prior research by examining the pattern of well-being using affect balance, the ratio of positive to negative affect, in nursing home residents living with dementia over 12 months and its association to factors that could potentially influence resident well-being. Method:This study was a secondary analysis of baseline, 4 and 12-month data from a pragmatic clinical trial. A total of 536 residents with moderate to severe cognitive impairments from 55 nursing homes were included in the multivariable linear mixed model regression analyses. Results:Resident function, the number of registered nurse hours devoted to care in the facility, and the quality of staff interaction predicted higher affect balance over time after controlling for other variables. Conclusion:The findings provide support for the utility of affect balance as a meaningful outcome measure of well-being for persons living with dementia. In addition, results point to specific interventions (i.e. maintaining/improving resident function, providing adequate nurse staffing levels, and improving staff communications skills) that can serve as the focus for both research and practice to help residents live well with dementia. Clinicaltrials.gov (NCT03014570).
Immune checkpoint inhibitors for the treatment of myeloma: novel investigational options

ABSTRACT Introduction Multiple myeloma (MM) is still considered incurable and the outcome of patients with triple-class refractory remains very poor. Immunotherapy is considered as a standard of care for the treatment of MM. Among immunotherapeutic approaches, the PD-1/PD-L1 axis is an attractive target because PD-L1 is highly expressed in most myeloma plasma cells. While many types of cancer benefit from checkpoint inhibitor treatment, their relevance in multiple myeloma needs to be defined. Areas covered The authors evaluate the published data regarding the mechanism of action, safety profile, and clinical efficacy of the immune checkpoint inhibitors (ICI) for the treatment of multiple myeloma. Expert opinion The use of ICI monotherapy does not offer any clinical benefit in myeloma patients. In combination with immunomodulatory drugs (IMID), ICI failed to demonstrate clinical benefit and were associated with increased toxicity. Given the toxicities of these treatments, predictive markers would be useful to select patients who would benefit most. Clinical studies are necessary to evaluate the safety and efficacy of checkpoint inhibitors in combination with other standards of care such as proteasome inhibitors and monoclonal antibodies. The combination of anti-PD-1 with T-cell engager (TCE) or CAR-T cells seems theoretically attractive and should be explored in clinical trials.