Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Radiomic Analysis of MRI Related to Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia, causing progressive impairment of memory and cognitive functions. Radiomic features obtained from brain MRI have shown a great potential as non-invasive biomarkers for this disease; however, their usefulness has not yet been explored for individual brain regions. In this paper, we hypothesize that distinct regions are affected differently by AD and, thus, that shape or texture changes occurring in separate regions can be expressed by different radiomic features. Moreover, to improve the classification of AD and healthy control (HC) subjects, we propose novel features based on the entropy of the convolution neural network (CNN) feature maps. The proposed approach is evaluated comprehensively using the Open Access Series of Imaging Studies database. Our experiments assess the significance of 45 different radiomic features from individual subcortical regions, via the Wilcoxon test. We also use the random forest classifier to identify the subcortical regions that best differentiate AD patients from HC subjects. Our analysis identified the features derived from several subcortical regions that show significant differences between AD and HC (corrected $p < 0.01$ ). Specifically, we found correlation and volume features from the hippocampus (AUC = 81.19% – 84.09%) and amygdala (AUC = 79.70% – 80.27%) regions to have the greatest discriminative power. Furthermore, the proposed entropy features derived from CNN layers yielded the highest classification AUC of 92.58%, compared to 84.45% for the combined radiomic features of all subcortical. These results suggest that the proposed CNN entropy features could be used as an effective biomarker for AD.
Epithelial-mesenchymal transition-induced metastasis could be a bait for natural killer cells.

Recent research has shown that several mechanisms induce tumor progression and a novel cause is the escape of tumor cells from immune surveillance (1). Natural killer (NK) cells play an important role in immunosurveillance against cancer cells (2), and the immunosurveillance by NK cells involves NK cell-mediated tumor cytotoxicity, which depends on the balance between NK cell-activating and -inhibiting ligands expressed in tumor cells (3).
Deep Learning for Continuous Electronic Fetal Monitoring in Labor

Continuous electronic fetal monitoring (EFM) is used worldwide to visually assess whether a fetus is exhibiting signs of distress during labor, and may benefit from an emergency operative delivery (e.g. Cesarean section). Previously, computerized EFM assessment that mimics clinical experts showed no benefit in randomized clinical trials. However, as an example of routinely collected `big’ data, EFM interpretation should benefit from data-driven computational approaches, such as deep learning, which allow automated evaluation based on large clinical datasets.Here we report our investigation of long short term memory (LSTM) and convolutional neural networks (CNN) in analyzing EFM traces from over 35,000 labors for the prediction of fetal compromise. Of these, 85% are used for training with crossvalidation and the remainder are set aside for testing. The results are compared with Clinical practice (reason for operative deliveryrecorded as fetal distress) and an earlier prototype system for computerized analysis of EFM (OxSys 1.5), developed on the same data. We demonstrate that CNN outperforms LSTM, Clinical practice, and OxSys 1.5 in predicting fetal compromise, with a sensitivity of 42% (30%, 34%, and 36% for the others, respectively), at comparable or lower false positive rates. We also show that increasing the size of the training set improves the sensitivity and stability of CNN’s performance on the testing set. When tested on a small open-access external database, CNN moderately improves on the performance of published feature extraction based methods.We conclude that CNN could play an important role in the field of automated EFM analysis, but requires further work.
Immunological biomarkers of tolerance in human kidney transplantation: An updated literature review

The half‐life of transplanted kidneys is <10 years. Acute or chronic rejections have a negative impact on transplant outcome. Therefore, achieving to allograft tolerance for improving long‐term transplant outcome is a desirable goal of transplantation field. In contrast, there are evidence that distinct immunological characteristics lead to tolerance in some transplant recipients. In contrast, the main reason for allograft loss is immunological responses. Various immune cells including T cells, B cells, dendritic cells, macrophages, natural killer, and myeloid‐derived suppressor cells damage graft tissue and, thereby, graft loss happens. Therefore, being armed with the comprehensive knowledge about either preimmunological or postimmunological characteristics of renal transplant patients may help us to achieve an operational tolerance. In the present study, we are going to review and discuss immunological characteristics of renal transplant recipients with rejection and compare them with tolerant subjects.
Prevalence of anxious and depressive symptoms in college students of a public institution.

OBJECTIVE Identify the prevalence of anxious and depressive symptoms and their correlations with sociodemographic and occupational characteristics in university students. METHOD This is census, cross-sectional and analytical study, developed with nursing students of a federal public university in the Northeast of Brazil in the months of September and October 2016. 205 university students of all the periods of the course attended the study. Beck's inventories for anxiety and depression were applied. RESULTS Most of the participants were female, single, native of the state capital and living with his parents. The prevalence of depression was 30.2% and of anxiety, 62.9%. Association between the level of depressive symptoms, work, sex and leisure was identified. CONCLUSION The prevalence of symptoms of anxiety and depression was quite expressive, lacking, thus, more attention to the promotion of mental health of nursing students.
Levofloxacin Versus Ceftriaxone and Azithromycin Combination in the Treatment of Community Acquired Pneumonia in Hospitalized Patients.

BACKGROUND In Asia, an estimated one million deaths are caused by communityacquired pneumonia (CAP) each year. Despite the high mortality in elderly people, a large number of CAP patients have been treated and survived with optimal life expectancy. A few studies have been done on adult CAP therapeutic approaches in Asia. Moreover, differences have been noted between these studies and European data. We aimed to investigate the efficacy of oral Levofloxacin (TAVANEX), 750 mg, once daily for five days versus parenteral Ceftriaxone 1gr BD, plus oral Azithromycin (250 mg, once daily) for seven to ten days (standard regimen) in CAP treatment. MATERIALS AND METHODS We conducted a prospective randomized trial among 150 patients with CAP in Qaem Hospital of Alborz city from December 2016 to June 2017. A group of CAP patients were randomized in two treatment groups. One group was treated with oral Levofloxacin (TAVANEX), 750 mg, once daily for five days and the other group with parenteral Ceftriaxone 1gr BD plus oral Azithromycin (250 mg, once daily) for seven to ten days (standard regimen). The efficacy and side effects of the assigned drugs were compared between two groups. The probability level for statistical significance was set at P ≤ 0.05. RESULTS The body temperature (P value=0.09), WBC count (P value=0.15), respiratory sounds (P value=0.18) and admission duration (P value=0.15) showed no significant differences after treatment between two groups. There was no report of hospital mortality, clinical deterioration and antibiotic escalation during hospital admission in both groups of study. In standard regimen group, only two (2.7%) patients had skin rash while in Levofloxacin group one case (1.3%) had skin rash, two patients (2.7%) had gastrointestinal problems and three (4%) patients showed central nervous system (CNS) complications. In both groups, the reticulonodular pattern was more frequently observed in Chest X-ray. Although standard regimen group (n=27, 36%) showed more consolidation than patients in Levofloxacin group (n=22, 29.3%), and the ground glass pattern was observed more in Levofloxacin group. CONCLUSION We concluded that monotherapy with oral Levofloxacin was as effective as treatment with Ceftriaxone plus Azithromycin combination in patients with CAP who required hospitalization.
Viscous control of cellular respiration by membrane lipid composition

How membrane viscosity affects respiration In bacteria, energy production by the electron transport chain occurs at cell membranes and can be influenced by the lipid composition of the membrane. Budin et al. used genetic engineering to influence the concentration of unsaturated branched-chain fatty acids and thus control membrane viscosity (see the Perspective by Schon). Experimental measurements and mathematical modeling indicated that rates of respiratory metabolism and rates of cell growth were dependent on membrane viscosity and its effects on diffusion. Experiments on yeast mitochondria also showed similar effects. Maintaining efficient respiration may thus place evolutionary constraints on cellular lipid composition. Science, this issue p. 1186; see also p. 1114 Metabolic engineering of membrane viscosity can regulate respiration rates in Escherichia coli and in yeast mitochondria. Lipid composition determines the physical properties of biological membranes and can vary substantially between and within organisms. We describe a specific role for the viscosity of energy-transducing membranes in cellular respiration. Engineering of fatty acid biosynthesis in Escherichia coli allowed us to titrate inner membrane viscosity across a 10-fold range by controlling the abundance of unsaturated or branched lipids. These fluidizing lipids tightly controlled respiratory metabolism, an effect that can be explained with a quantitative model of the electron transport chain (ETC) that features diffusion-coupled reactions between enzymes and electron carriers (quinones). Lipid unsaturation also modulated mitochondrial respiration in engineered budding yeast strains. Thus, diffusion in the ETC may serve as an evolutionary constraint for lipid composition in respiratory membranes.
Cilostazol Improves HFD-Induced Hepatic Steatosis by Upregulating Hepatic STAMP2 Expression through AMPK

Nonalcoholic fatty liver disease (NAFLD) is an increasingly studied condition that can progress to end-stage liver disease. Although NAFLD was first described in 1980, a complete understanding of the mechanism and causes of this disease is still lacking. Six-transmembrane protein of prostate 2 (STAMP2) plays a role in integrating inflammatory and nutritional signals with metabolism. Our previous study suggested that STAMP2 may be a suitable target for treating NAFLD. In the current study, we performed a focused drug-screening and found that cilostazol could be a potential STAMP2 enhancer. Thus, we examined whether cilostazol alleviates NAFLD through STAMP2. The in vivo and in vitro pharmacological efficacies of cilostazol on STAMP2 expression and lipid accumulation were analyzed in NAFLD mice induced by high-fat diet (HFD) and in HepG2 cell lines treated by oleic acid (OA), respectively. Cilostazol increased the expression of STAMP2 through transcriptional regulation in vivo and in vitro. Cilostazol also dampened the STAMP2 downregulation caused by the HFD and by OA in vivo and in vitro, respectively. Cilostazol activated AMP-activated protein kinase (AMPK) in vivo and in vitro, and AMPK functions upstream of STAMP2, and reversed downregulation of STAMP2 expression through AMPK in the NAFLD model. Cilostazol ameliorates hepatic steatosis by enhancing hepatic STAMP2 expression through AMPK. Enhancing STAMP2 expression with cilostazol represents a potential therapeutic avenue for treatment of NAFLD.
Removing Hidden Confounding by Experimental Grounding

Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from observational data is hidden confounding, whose presence cannot be tested in data and can invalidate any causal conclusion. Experimental data does not suffer from confounding but is usually limited in both scope and scale. We introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data. Our method makes strictly weaker assumptions than existing approaches, and we prove conditions under which it yields a consistent estimator. We demonstrate our method's efficacy using real-world data from a large educational experiment.
A Circuit Compatible Accurate Compact Model for Ferroelectric-FETs

In this work we develop a compact model of ferroelectric field-effect-transistors (FeFET) for memory applications, enabling their exploration at the circuit and architecture level. In contrast to Landau-Khalatnikov (L-K) based approaches, the presented model is founded on the combination of a nucleation dominated multi-domain Presiach theory of ferroelectric switching with a conventional transistor model. The model successfully reproduces the evolution of the FeFET memory window as a function of the program and erase conditions (amplitude, pulse width, and history). To calibrate the model, we fabricated 10nm thick Hf0.4Zr0. 6O2 (HZO) MFM capacitors and FeFETs and characterized the polarization switching dynamics. Our results highlight the importance of accounting for the switching history, minor loop trajectory, and coupled time-voltage response of the ferroelectric to quantitatively reproduce the measured FeFET characteristics.
Hand-Exoskeleton Assisted Progressive Neurorehabilitation Using Impedance Adaptation Based Challenge Level Adjustment Method

This paper presents an underactuated design of a robotic hand exoskeleton, and a challenge based neurorehabilitation strategy. The exoskeleton is designed to reproduce natural human fingertip paths during extension and grasping, keeping minimal kinematic complexity. It facilitates an impedance adaptation based trigged assistance control strategy by switching between active non-assist and passive assistance modes. In the active non-assist mode, the exoskeleton motion follows the applied fingertip forces based on an impedance model. If the applied fingertip forces are inadequate, the passive assistance mode is triggered. The impedance parameters are updated at regular intervals based on the user performance, to implement a challenge based rehabilitation strategy. A six-week long hand therapy, conducted on four chronic stroke patients, resulted in significant ($p$-value$<0.05$) increase in force generation capacity and decrease ($p$-value$<0.05$) in the required assistance. Also, there was a significant ($p$-value$<0.05$) increase in the system impedance parameters which adequately challenged the patients. The change in the Action-Research-Arm-Test (ARAT) scores from baseline was also found to be significant ($p$-value$<0.05$) and beyond the minimal clinically important difference (MCID) limit. Thus, the results prove that the proposed control strategy with has the potential to be a clinically effective solution for personalized rehabilitation of poststroke hand functionality.
Effects of Tolvaptan on Volume Overload in Patients with Heart Failure.

The present meta-analysis aimed to evaluate effects of tolvaptan on fluid retention in patients with heart failure who were non-responsive to conventional treatment and to assess differences between effects of low (≤ 15 mg/day) and high (> 15 mg/day) tolvaptan doses.Randomized controlled trials comparing add-on tolvaptan therapy and placebo or therapy with other diuretics in patients with heart failure were identified through a database search. The primary outcomes were changes in body weight and urine volume, and the secondary outcomes were changes in serum sodium and creatinine levels.In total, 14 reports were analyzed using a random effects model. Add-on tolvaptan was associated with increased urine volume [mean difference (MD), 1.44 L; 95% confidence interval (CI), 0.96 to 1.92], decreased body weight (MD, -0.99 kg; 95% CI, -1.24 to -0.74), and increased serum sodium levels (MD, 3.66 mEq/L; 95% CI, 3.43 to 3.88) within 2 days. Serum creatinine levels on day 7 were not different between the groups (MD, -0.03 mg/dL; 95% CI, -0.09 to 0.03). The high-dose group showed greater changes in urine volume, body weight, and serum sodium levels than the low-dose group. Serum creatinine levels slightly increased in the high-dose group (MD, 0.06; 95% CI, 0.04 to 0.08) and slightly decreased in the low-dose group (MD, -0.10; 95% CI, -0.19 to -0.01). Our findings suggest that add-on tolvaptan therapy for heart failure improves fluid retention in the early therapy phase. However, this drug should be properly used to avoid the worsening of renal function, which may occur at high doses.
I recognise your name but I can’t remember your face: An advantage for names in recognition memory

Forgetting someone’s name is a common failure of memory, and often occurs despite being able to recognise that person’s face. This gives rise to the widespread view that memory for names is generally worse than memory for faces. However, this everyday error confounds stimulus class (faces vs. names) with memory task: recognition versus recall. Here we compare memory for faces and names when both are tested in the same recognition memory framework. Contrary to the common view, we find a clear advantage for names over faces. Across three experiments, we show that recognition of previously unfamiliar names exceeds recognition of previously unfamiliar faces. This advantage persists, even when the same face pictures are repeated at learning and test—a picture-memory task known to produce high levels of performance. Differential performance between names and faces disappears in recognition memory for familiar people. The results are discussed with reference to representational complexity and everyday memory errors.
A Machine Learning Framework for Automatic and Continuous MMN Detection With Preliminary Results for Coma Outcome Prediction

Mismatch negativity (MMN) is a component of the event-related potential (ERP) that is elicited through an odd-ball paradigm. The existence of the MMN in a coma patient has a good correlation with coma emergence; however, this component can be difficult to detect. Previously, MMN detection was based on visual inspection of the averaged ERPs by a skilled clinician, a process that is expensive and not always feasible in practice. In this paper, we propose a practical machine learning (ML) based approach for detection of MMN component, thus, improving the accuracy of prediction of emergence from coma. Furthermore, the method can operate on an automatic and continuous basis thus alleviating the need for clinician involvement. The proposed method is capable of the MMN detection over intervals as short as two minutes. This finer time resolution enables identification of waxing and waning cycles of a conscious state. An auditory odd-ball paradigm was applied to 22 healthy subjects and 2 coma patients. A coma patient is tested by measuring the similarity of the patient's ERP responses with the aggregate healthy responses. Because the training process for measuring similarity requires only healthy subjects, the complexity and practicality of training procedure of the proposed method are greatly improved relative to training on coma patients directly. Since there are only two coma patients involved with this study, the results are reported on a very preliminary basis. Preliminary results indicate we can detect the MMN component with an accuracy of 92.7% on healthy subjects. The method successfully predicted emergence in both coma patients when conventional methods failed. The proposed method for collecting training data using exclusively healthy subjects is a novel approach that may prove useful in future, unrelated studies where ML methods are used.
In Silico Studies on Compounds Derived from Calceolaria: Phenylethanoid Glycosides as Potential Multitarget Inhibitors for the Development of Pesticides

An increasing occurrence of resistance in insect pests and high mammal toxicity exhibited by common pesticides increase the need for new alternative molecules. Among these alternatives, bioinsecticides are considered to be environmentally friendly and safer than synthetic insecticides. Particularly, plant extracts have shown great potential in laboratory conditions. However, the lack of studies that confirm their mechanisms of action diminishes their potential applications on a large scale. Previously, we have reported the insect growth regulator and insecticidal activities of secondary metabolites isolated from plants of the Calceolaria genus. Herein, we report an in silico study of compounds isolated from Calceolaria against acetylcholinesterase, prophenoloxidase, and ecdysone receptor. The molecular docking results are consistent with the previously reported experimental results, which were obtained during the bioevaluation of Calceolaria extracts. Among the compounds, phenylethanoid glycosides, such as verbascoside, exhibited good theoretical affinity to all the analyzed targets. In light of these results, we developed an index to evaluate potential multitarget insecticides based on docking scores.
Global Sensitivity Analysis of a Cardiovascular Model for the Study of the Autonomic Response to Head-up Tilt Testing

This paper proposes the integration and analysis of a mathematical model representing the cardiovascular system and its short-term autonomic response to head-up tilt (HUT) testing. A Latin Hypercube Sampling method was applied to design an optimal experimental space, including 19 model parameters coming from the cardiovascular and baroreflex control systems. Then, a global, variance-based sensitivity analysis was applied to quantity the effects of these parameters on heart rate and systolic blood pressure. Results highlight the relevant influence of the intrinsic heart rate and the sympathetic and parasympathetic baroreflex gains on heart rate regulation, as well as the impact of left ventricle diastolic parameters on systolic blood pressure. Moreover, a significant effect of right ventricle dynamics on blood pressure was noted. These results provide valuable information for the application of such an integrated model for the analysis of the autonomic mechanisms regulating the cardiovascular response induced by postural changes. In particular, they suggest a convenient set of parameters to be identified in a subject-specific manner.