Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Trends on Protein Driven Bioinks for 3D Printing

In the last decade, three-dimensional (3D) extrusion bioprinting has been on the top trend for innovative technologies in the field of biomedical engineering. In particular, protein-based bioinks such as collagen, gelatin, silk fibroin, elastic, fibrin and protein complexes based on decellularized extracellular matrix (dECM) are receiving increasing attention. This current interest is the result of protein’s tunable properties, biocompatibility, environmentally friendly nature and possibility to provide cells with the adequate cues, mimicking the extracellular matrix’s function. In this review we describe the most relevant stages of the development of a protein-driven bioink. The most popular formulations, molecular weights and extraction methods are covered. The different crosslinking methods used in protein bioinks, the formulation with other polymeric systems or molecules of interest as well as the bioprinting settings are herein highlighted. The cell embedding procedures, the in vitro, in vivo, in situ studies and final applications are also discussed. Finally, we approach the development and optimization of bioinks from a sequential perspective, discussing the relevance of each parameter during the pre-processing, processing, and post-processing stages of technological development. Through this approach the present review expects to provide, in a sequential manner, helpful methodological guidelines for the development of novel bioinks.
Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help diminish the burden and chaos of medical management and operations. Previous studies contributed tremendous progress on the dependability quantification of existing computing paradigms (e.g., cloud, grid computing) at remote data centers, while a few works investigated the performance of provided medical services under the constraints of operational availability of devices and systems at local medical centers. Therefore, it is critical to rapidly develop appropriate models to quantify the operational metrics of medical services provided and sustained by medical information systems (MIS) even before practical implementation. In this paper, we propose a comprehensive performability SRN model of an edge/fog based MIS for the performability quantification of medical data transaction and services in local hospitals or medical centers. The model elaborates different failure modes of fog nodes and their VMs under the implementation of fail-over mechanisms. Sophisticated behaviors and dependencies between the performance and availability of data transactions are elaborated in a comprehensive manner when adopting three main load-balancing techniques including: (i) probability-based, (ii) random-based and (iii) shortest queue-based approaches for medical data distribution from edge to fog layers along with/without fail-over mechanisms in the cases of component failures at two levels of fog nodes and fog virtual machines (VMs). Different performability metrics of interest are analyzed including (i) recover token rate, (ii) mean response time, (iii) drop probability, (iv) throughput, (v) queue utilization of network devices and fog nodes to assimilate the impact of load-balancing techniques and fail-over mechanisms. Discrete-event simulation results highlight the effectiveness of the combination of these for enhancing the performability of medical services provided by an MIS. Particularly, performability metrics of medical service continuity and quality are improved with fail-over mechanisms in the MIS while load balancing techniques help to enhance system performance metrics. The implementation of both load balancing techniques along with fail-over mechanisms provide better performability metrics compared to the separate cases. The harmony of the integrated strategies eventually provides the trustworthiness of medical services at a high level of performability. This study can help improve the design of MIS systems integrated with different load-balancing techniques and fail-over mechanisms to maintain continuous performance under the availability constraints of medical services with heavy computing workloads in local hospitals/medical centers, to combat with new waves of virus pandemics.
TB and SIV Coinfection; a Model for Evaluating Vaccine Strategies against TB Reactivation in Asian Origin Cynomolgus Macaques: A Pilot Study Using BCG Vaccination

This pilot study aimed to determine the utility of a cynomolgus macaque model of coinfection with simian immunodeficiency virus (SIV) for the assessment of vaccines designed to prevent reactivation of TB. Following infection caused by aerosol exposure to an ultralow dose of Mycobacterium tuberculosis (M. tb), data trends indicated that subsequent coinfection with SIVmac32H perturbed control of M. tb infection as evidenced by the increased occurrence of progressive disease in this group, higher levels of pathology and increased frequency of progressive tuberculous granulomas in the lung. BCG vaccination led to improved control of TB-induced disease and lower viral load in comparison to unvaccinated coinfected animals. The M. tb-specific IFNγ response after exposure to M. tb, previously shown to be associated with bacterial burden, was lower in the BCG-vaccinated group than in the unvaccinated groups. Levels of CD4+ and CD8+ T cells decreased in coinfected animals, with counts recovering more quickly in the BCG-vaccinated group. This pilot study provides proof of concept to support the use of the model for evaluation of interventions against reactivated/exacerbated TB caused by human immunodeficiency virus (HIV) infection.
Identification and validation of circulating miRNAs as potential new biomarkers for severe liver disease in patients with leptospirosis

Background Leptospirosis, a global zoonotic infectious disease, has various clinical manifestations ranging from mild self-limiting illness to life-threatening with multi-organ damage, including liver involvement. This study was aimed at identifying circulating microRNAs (miRNAs) as novel biomarkers for predicting severe liver involvement in patients with leptospirosis. Methods In a discovery set, 12 serum samples of patients with anicteric and icteric leptospirosis at initial clinical presentation were used for miRNA profiling by a NanoString nCounter miRNA assay. In a validated cohort, top candidate miRNAs were selected and further tested by qRT-PCR in serum samples of 81 and 16 individuals with anicteric and icteric leptospirosis, respectively. Results The discovery set identified 38 significantly differential expression miRNAs between the two groups. Among these, miR-601 and miR-630 were selected as the top two candidates significantly up-regulated expressed in the icteric group. The enriched KEGG pathway showed that these miRNAs were mainly involved in immune responses and inflammation. In the validated cohort, miR-601 and miR-630 levels were significantly higher in the icteric group compared with the anicteric group. Additionally, these two miRNAs displayed good predictors of subsequent acute liver failure with a high sensitivity of 100%. On regression analysis, elevated miR-601 and miR-630 expression were also predictive of multi-organ failures and poor overall survival. Conclusion Our data indicated that miRNA expression profiles were significantly differentiated between the icteric and anicteric groups. Serum miR-601 and miR-630 at presentation could potentially serve as promising biomarkers for predicting subsequent acute liver failure and overall survival in patients with leptospirosis.
Reflections on psychological resilience: a comparison of three conceptually different operationalizations in predicting mental health

ABSTRACT Background: Psychological resilience refers to the ability to maintain mental health or recover quickly after stress. Despite the popularity of resilience research, there is no consensus understanding or operationalization of resilience. Objective: We plan to compare three indicators of resilience that each involve a different operationalization of the construct: a) General resilience or one’s self-reported general ability to overcome adversities; b) Daily resilience as momentarily experienced ability to overcome adversities; and c) Recovery speed evident in the pattern of negative affect recovery after small adversities in daily life. These three indicators are constructed per person to investigate their cross-sectional associations, stability over time, and predictive validity regarding mental health. Methods: Data will be derived from the prospective MIRORR study that comprises 96 individuals at different levels of psychosis risk and contains both single-time assessed questionnaires and 90-days intensive longitudinal data collection at baseline (T0) and three yearly follow-up waves (T1–T3). General resilience is assessed using the Brief Resilience Scale (BRS) at baseline. Daily resilience is measured by averaging daily resilience scores across 90 days. For recovery speed, vector-autoregressive models with consecutive impulse response simulations will be applied to diary data on negative affect and daily stressors to calculate pattern of affect recovery. These indicators will be correlated concurrently (at T0) to assess their overlap and prospectively (between T0 and T1) to estimate their stability. Their predictive potential will be assessed by regression analysis with mental health (SCL-90) as an outcome, resilience indicators as predictors, and stressful life events as a moderator. Conclusion: The comparison of different conceptualizations of psychological resilience can increase our understanding of its multifaceted nature and, in future, help improve diagnostic, prevention and intervention strategies aimed at increasing psychological resilience. HIGHLIGHTS We develop three different indicators of psychological resilience, each involving a different operationalization of the concept, using the data from the same sample. We will compare how these indicators relate to each other and how they predict future level of mental health.
Amelioration of Hepatic Steatosis in Mice through Bacteroides uniformis CBA7346-Mediated Regulation of High-Fat Diet-Induced Insulin Resistance and Lipogenesis

Dietary habits and gut microbiota play an essential role in non-alcoholic fatty liver disease (NAFLD) and related factors such as insulin resistance and de novo lipogenesis. In this study, we investigated the protective effects of Bacteroides uniformis CBA7346, isolated from the gut of healthy Koreans, on mice with high-fat diet (HFD)-induced NAFLD. Administration of B. uniformis CBA7346 reduced body and liver weight gain, serum alanine aminotransferase and aspartate aminotransferase levels, liver steatosis, and liver triglyceride levels in mice on an HFD; the strain also decreased homeostatic model assessment for insulin resistance values, as well as serum cholesterol, triglyceride, lipopolysaccharide, leptin, and adiponectin levels in mice on an HFD. Moreover, B. uniformis CBA7346 controlled fatty liver disease by attenuating steatosis and inflammation and regulating de novo lipogenesis-related proteins in mice on an HFD. Taken together, these findings suggest that B. uniformis CBA7346 ameliorates HFD-induced NAFLD by reducing insulin resistance and regulating de novo lipogenesis in obese mice.
A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis

Liver cirrhosis poses a major risk for the development of hepatocellular carcinoma (HCC). This retrospective study investigated to what extent radiomic features allow the prediction of emerging HCC in patients with cirrhosis in contrast-enhanced computed tomography (CECT). A total of 51 patients with liver cirrhosis and newly detected HCC lesions (n = 82) during follow-up (FU-CT) after local tumor therapy were included. These lesions were not to have been detected by the radiologist in the chronologically prior CECT (PRE-CT). For training purposes, segmentations of 22 patients with liver cirrhosis but without HCC-recurrence were added. A total of 186 areas (82 HCCs and 104 cirrhotic liver areas without HCC) were analyzed. Using univariate analysis, four independent features were identified, and a multivariate logistic regression model was trained to classify the outlined regions as “HCC probable” or “HCC improbable”. In total, 60/82 (73%) of segmentations with later detected HCC and 84/104 (81%) segmentations without HCC were classified correctly (AUC of 81%, 95% CI 74–87%), yielding a sensitivity of 72% (95% CI 57–83%) and a specificity of 86% (95% CI 76–96%). In conclusion, the model predicted the occurrence of new HCCs within segmented areas with an acceptable sensitivity and specificity in cirrhotic liver tissue in CECT.
All-Opto Plasmonic-Controlled Bulk and Surface Sensitivity Analysis of a Paired Nano-Structured Antenna with a Label-Free Detection Approach

Gold nanoantennas have been used in a variety of biomedical applications due to their attractive electronic and optical properties, which are shape- and size-dependent. Here, a periodic paired gold nanostructure exploiting surface plasmon resonance is proposed, which shows promising results for Refractive Index (RI) detection due to its high electric field confinement and diffraction limit. Here, single and paired gold nanostructured sensors were designed for real-time RI detection. The Full-Width at Half-Maximum (FWHM) and Figure-Of-Merit (FOM) were also calculated, which relate the sensitivity to the sharpness of the peak. The effect of different possible structural shapes and dimensions were studied to optimise the sensitivity response of nanosensing structures and identify an optimised elliptical nanoantenna with the major axis a, minor axis b, gap between the pair g, and heights h being 100 nm, 10 nm, 10 nm, and 40 nm, respectively. In this work, we investigated the bulk sensitivity, which is the spectral shift per refractive index unit due to the change in the surrounding material, and this value was calculated as 526–530 nm/RIU, while the FWHM was calculated around 110 nm with a FOM of 8.1. On the other hand, the surface sensing was related to the spectral shift due to the refractive index variation of the surface layer near the paired nanoantenna surface, and this value for the same antenna pair was calculated as 250 nm/RIU for a surface layer thickness of 4.5 nm.
A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. Two Well-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results.
Modelling Nonalcoholic Steatohepatitis In Vivo—A Close Transcriptomic Similarity Supports the Guinea Pig Disease Model

The successful development of effective treatments against nonalcoholic steatohepatitis (NASH) is significantly set back by the limited availability of predictive preclinical models, thereby delaying and reducing patient recovery. Uniquely, the guinea pig NASH model develops hepatic histopathology and fibrosis resembling that of human patients, supported by similarities in selected cellular pathways. The high-throughput sequencing of guinea pig livers with fibrotic NASH (n = 6) and matched controls (n = 6) showed a clear separation of the transcriptomic profile between NASH and control animals. A comparison to NASH patients with mild disease (GSE126848) revealed a 45.2% overlap in differentially expressed genes, while pathway analysis showed a 34% match between the top 50 enriched pathways in patients with advanced NASH (GSE49541) and guinea pigs. Gene set enrichment analysis highlighted the similarity to human patients (GSE49541), also when compared to three murine models (GSE52748, GSE38141, GSE67680), and leading edge genes THRSP, CCL20 and CD44 were highly expressed in both guinea pigs and NASH patients. Nine candidate genes were identified as highly correlated with hepatic fibrosis (correlation coefficient > 0.8), and showed a similar expression pattern in NASH patients. Of these, two candidate genes (VWF and SERPINB9) encode secreted factors, warranting further investigations as potential biomarkers of human NASH progression. This study demonstrates key similarities in guinea pig and human NASH, supporting increased predictability when translating research findings to human patients.
Isolation and Functional Characterization of Two SHORT VEGETATIVE PHASE Homologous Genes from Mango

The SHORT VEGETATIVE PHASE (SVP) gene is a transcription factor that integrates flowering signals and plays an important role in the regulation of flowering time in many plants. In this study, two full-length cDNA sequences of SVP homologous genes—MiSVP1 and MiSVP2—were obtained from ‘SiJiMi’ mango. Sequence analysis showed that the MiSVPs had typical MADS-box domains and were highly conserved between each other. The analysis of expression patterns showed that the MiSVPs were expressed during flower development and highly expressed in vegetative tissues, with low expression in flowers/buds. The MiSVPs could responded to low temperature, NaCl, and PEG treatment. Subcellular localization revealed that MiSVP1 and MiSVP2 were localized in the nucleus. Transformation of Arabidopsis revealed that overexpression of MiSVP1 delayed flowering time, overexpression of MiSVP2 accelerated flowering time, and neither MiSVP1 nor MiSVP2 had an effect on the number of rosette leaves. Overexpression of MiSVP1 increased the expression of AtFLC and decreased the expression of AtFT and AtSOC1, and overexpression of MiSVP2 increased the expression levels of AtSOC1 and AtFT and decreased the expression levels of AtFLC. Point-to-point and bimolecular fluorescence complementation (BiFC) assays showed that MiSVP1 and MiSVP2 could interact with SEP1-1, SOC1D, and AP1-2. These results suggest that MiSVP1 and MiSVP2 may play a significant roles in the flowering process of mango.
Association of Nut Consumption with Risk of Stroke and Cardiovascular Disease: The Million Veteran Program

Cardiovascular disease (CVD), including stroke and coronary artery disease (CAD), is the major cause of mortality for Americans. Nuts have been shown to improve a variety of cardiovascular disease risk factors. This study aimed to test the hypothesis that nut consumption is inversely associated with risk of incidence of stroke, CAD, and CVD mortality in the prospective Million Veterans Program (MVP). A total of 179,827 MVP participants enrolled between 2011 and 2018 were free of CVD prior to assessment of nut consumption via the food frequency questionnaire. Incident stroke and CVD events were ascertained from the Veterans Affairs electronic medical health records and the National Death Index. We used the Cox regression model to compute multivariable adjusted hazard ratios. Over the 3.5-year median follow-up, 3362 new cases of ischemic stroke were identified. When compared with participants who rarely or never consumed nuts, those consuming nuts ≥ 5 times per week were 19% less likely to experience a stroke (95% CI: 8% to 28%); 22% less likely to suffer from CAD (95% CI: 16% to 28%); and 24% less likely to die from CVD (95% CI: 7% to 37%). Consumption of peanut butter was not associated with risk of stroke. Increased dietary intake of nuts, but not peanut butter, was associated with a lower risk of stroke, CAD, and CVD death.
Potential Role of Probiotics for Inflammaging: A Narrative Review

Background and aims: Inflammaging, a chronic, low-grade inflammation (LGI), is one of the mechanisms of adaptation of an organism to aging. Alterations in the composition of gut microbiota and gut permeability are among the main sources of LGI. They may be modulated by supplementation with live microorganisms, i.e. probiotics. This narrative review was performed with the aim to critically examine the current evidence from randomized clinical trials (RCTs) on the effects of probiotics on pro-inflammatory and anti-inflammatory cytokines and C-reactive protein (CRP) in healthy older subjects. Methodology: RCTs on the effects of probiotics on inflammatory parameters in subjects older than 65 years published in English and Italian from 1990 to October 2020 were searched in PubMed. Studies that were not RCTs, those using probiotics together with prebiotics (synbiotics), and studies performed in subjects with acute or chronic diseases were excluded. The findings of RCTs were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: A total of nine RCTs met the eligibility criteria and were included in this narrative review. Four articles reported that probiotic supplementation significantly affected inflammatory parameters, respectively, by reducing TGF-β1 concentrations, IL-8, increasing IL-5 and Il-10, and IFN-γ and IL-12. Conclusions: Based on this narrative review, probiotic supplementation showed a limited effect on inflammatory markers in healthy individuals older than 65 years. Besides being few, the studies analyzed have methodological limitations, are heterogeneous, and provide results which are incomparable.