Hydrogel Bioinks of Alginate and Curcumin-Loaded Cellulose Ester-Based Particles for the Biofabrication of Drug-Releasing Living Tissue Analogs.

3D bioprinting is a versatile technique that allows the fabrication of living tissue analogs through the layer-by-layer deposition of cell-laden biomaterials, viz. bioinks. In this work, composite alginate hydrogel-based bioinks reinforced with curcumin-loaded particles of cellulose esters (CEpCUR) and laden with human keratinocytes (HaCaT) are developed. The addition of the CEpCUR particles, with sizes of 740 ± 147 nm, improves the rheological properties of the inks, increasing their shear stress and viscosity, while preserving the recovery rate and the mechanical and viscoelastic properties of the resulting fully cross-linked hydrogels. Moreover, the presence of these particles reduces the degradation rate of the hydrogels from 26.3 ± 0.8% (ALG) to 18.7 ± 1.3% (ALG:CEpCUR_10%) after 3 days in the culture medium. The 3D structures printed with the ALG:CEpCUR inks reveal increased printing definition and the ability to release curcumin (with nearly 70% of cumulative release after 24 h in PBS). After being laden with HaCaT cells (1.2 × 106 cells mL-1), the ALG:CEpCUR bioinks can be successfully 3D bioprinted, and the obtained living constructs show good dimensional stability and high cell viabilities at 7 days post-bioprinting (nearly 90%), confirming their great potential for application in fields like wound healing.

A decision support system based on artificial intelligence and systems biology for the simulation of pancreatic cancer patient status.

Oncology treatments require continuous individual adjustment based on the measurement of multiple clinical parameters. Prediction tools exploiting the patterns present in the clinical data could be used to assist decision making and ease the burden associated to the interpretation of all these parameters. The goal of this study was to predict the evolution of patients with pancreatic cancer at their next visit using information routinely recorded in health records, providing a decision-support system for clinicians. We selected hematological variables as the visit's clinical outcomes, under the assumption that they can be predictive of the evolution of the patient. Multivariate models based on regression trees were generated to predict next-visit values for each of the clinical outcomes selected, based on the longitudinal clinical data as well as on molecular data sets streaming from in silico simulations of individual patient status at each visit. The models predict, with a mean prediction score (balanced accuracy) of 0.79, the evolution trends of eosinophils, leukocytes, monocytes, and platelets. Time span between visits and neutropenia were among the most common factors contributing to the predicted evolution. The inclusion of molecular variables from the systems-biology in silico simulations provided a molecular background for the observed variations in the selected outcome variables, mostly in relation to the regulation of hematopoiesis. In spite of its limitations, this study serves as a proof of concept for the application of next-visit prediction tools in real-world settings, even when available data sets are small.

A full-length infectious cDNA clone of a dsRNA totivirus-like virus.

Totivirus-like viruses are a group of non-segmented double-stranded (ds)RNA viruses with two open reading frames, which were recently discovered and provisionally assigned to the Totiviridae family. Unlike yeast and protozoan Totiviridae viruses, these totivirus-like viruses infect a diverse spectrum of metazoan hosts and currently have enormous impacts on fisheries and agriculture. We developed the first infectious full-length cDNA clone of a totivirus-like virus, the Omono River virus (OmRV), and produced infectious particles using an RNA-transcript-based method. Compared with the parent wild-type particles from nature, the infectious-cloning OmRV particles have presented strong cytopathic effects, infectivity and similar morphology. Thus far, the established system is one of the few reported systems for generating a non-segmented dsRNA virus cDNA clone.

SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning.

BACKGROUND: In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories. RESULTS: Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers. CONCLUSIONS: The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.

Changes in Muscle Thickness after 8 Weeks of Strength Training, Electromyostimulation, and Both Combined in Healthy Young Adults.

The aim of this study was to verify and compare the effects of electromyostimulation training (EMS), strength training (ST), and both combined (STEMS), through the analysis of the elbow flexors muscle thickness. Forty subjects (24.45 ± 3.53 years), were randomly divided equally in 4 groups: 3 experimental groups and 1 control group. Each experimental group was submitted to one of three interventions, either an ST protocol, an EMS protocol, or a STEMS protocol. The control group (CG) did not perform any type of physical activity. Ultrasonography (US) was used to measure muscle thickness (MT) at 50 and 60% of the distance between the acromion and the olecranon. The results showed a significant difference in the elbow flexors muscle thickness after 8 weeks, both in the STG, EMSG, and STEMSG, but not in the CG. However, no significant differences were observed between the intervention protocols. It seems that an increase in MT can be obtained using either with ST, EMS, or both combined, however, the results doesn't support the overlap of one method in relation to the others. EMS can be another interesting tool to induce muscle hypertrophy, but not necessarily better.

Effect of Rest Interval Between Sets in the Muscle Function During a Sequence of Strength Training Exercises for the Upper Body.

ABSTRACT: Matos, F, Ferreira, B, Guedes, J, Saavedra, F, Reis, VM, and Vilaça-Alves, J. Effect of rest interval between sets in the muscle function during a sequence of strength training exercises for the upper body. J Strength Cond Res 35(6): 1628-1635, 2021-The objective of this study was to observe the ideal recovery time between sets and exercises, for both chest and back, which allowed for maintaining muscle function with the initial load previously established. Sixty young men recreationally trained in strength training (ST) were divided into 2 groups: (a) 30 subjects were included in the GC group (the group that performed ST for the chest) and (b) 30 subjects were included in the GB group (the group that performed ST for the back). Each group was submitted to 3 experimental sessions, performing an ST sequence with 3 sets of 8 repetition maximum: GC performed a chest barbell press (CBP), an inclined CBP, and a chest butterfly; GB performed a lat pull-down, a back row, and a shoulder extension on the high pulley. The experimental sessions differed in rest time between sets performed (60, 90, and 120 seconds). For both groups in each sequence, significantly higher numbers of repetitions were observed with the rest time of 120 seconds relative to the rest time of 90 seconds (p = 0.004), 120 seconds in relation to the rest time of 60 seconds (p = 0.001), and in the rest interval of 90 seconds in relation to the rest time of 60 seconds (p < 0.0001). The results showed that 120 seconds was sufficient to maintain muscle function and perform the total number of repetitions per set. The data seem to show that for the ST methodology applied, it is not appropriate to assume that a certain relative intensity will translate into a similar number of repetitions in different exercises, especially with shorter rest intervals such as 60 and 90 seconds.

MENSAdb: a thorough structural analysis of membrane protein dimers.

Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration-approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π-π stacking, T-stacking and cation-π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.

Predicting Hot Spots Using a Deep Neural Network Approach.

Targeting protein-protein interactions is a challenge and crucial task of the drug discovery process. A good starting point for rational drug design is the identification of hot spots (HS) at protein-protein interfaces, typically conserved residues that contribute most significantly to the binding. In this chapter, we depict point-by-point an in-house pipeline used for HS prediction using only sequence-based features from the well-known SpotOn dataset of soluble proteins (Moreira et al., Sci Rep 7:8007, 2017), through the implementation of a deep neural network. The presented pipeline is divided into three steps: (1) feature extraction, (2) deep learning classification, and (3) model evaluation. We present all the available resources, including code snippets, the main dataset, and the free and open-source modules/packages necessary for full replication of the protocol. The users should be able to develop an HS prediction model with accuracy, precision, recall, and AUROC of 0.96, 0.93, 0.91, and 0.86, respectively.

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm.

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

Prediction and targeting of GPCR oligomer interfaces.

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

An Overview of Antiretroviral Agents for Treating HIV Infection in Paediatric Population.

Paediatric Acquired ImmunoDeficiency Syndrome (AIDS) is a life-threatening and infectious disease in which the Human Immunodeficiency Virus (HIV) is mainly transmitted through Mother-To- Child Transmission (MTCT) during pregnancy, labour and delivery, or breastfeeding. This review provides an overview of the distinct therapeutic alternatives to abolish the systemic viral replication in paediatric HIV-1 infection. Numerous classes of antiretroviral agents have emerged as therapeutic tools for downregulation of different steps in the HIV replication process. These classes encompass Non- Nucleoside Analogue Reverse Transcriptase Inhibitors (NNRTIs), Nucleoside/Nucleotide Analogue Reverse Transcriptase Inhibitors (NRTIs/NtRTIs), INtegrase Inhibitors (INIs), Protease Inhibitors (PIs), and Entry Inhibitors (EIs). Co-administration of certain antiretroviral drugs with Pharmacokinetic Enhancers (PEs) may boost the effectiveness of the primary therapeutic agent. The combination of multiple antiretroviral drug regimens (Highly Active AntiRetroviral Therapy - HAART) is currently the standard therapeutic approach for HIV infection. So far, the use of HAART offers the best opportunity for prolonged and maximal viral suppression, and preservation of the immune system upon HIV infection. Still, the frequent administration of high doses of multiple drugs, their inefficient ability to reach the viral reservoirs in adequate doses, the development of drug resistance, and the lack of patient compliance compromise the complete HIV elimination. The development of nanotechnology-based drug delivery systems may enable targeted delivery of antiretroviral agents to inaccessible viral reservoir sites at therapeutic concentrations. In addition, the application of Computer-Aided Drug Design (CADD) approaches has provided valuable tools for the development of anti-HIV drug candidates with favourable pharmacodynamics and pharmacokinetic properties.

Structural Characterization of Membrane Protein Dimers.

Membrane proteins are essential vessels for cell communication both with other cells and noncellular structures. They modulate environment responses and mediate a myriad of biological processes. Dimerization and multimerization processes have been shown to further increase the already high specificity of these processes. Due to their central role in various cell and organism functions, these multimers are often associated with health conditions, such as Alzheimer's disease (AD), Parkinson's disease (PD), and diabetes, among others.Understanding the membrane protein dimers' interface takes advantage of the specificity of the structure, for which we must pinpoint the most relevant interfacial residues, since they are extremely likely to be crucial for complex formation. Here, we describe step by step our own in silico protocol to characterize these residues, making use of known experimental structures. We detail the computational pipeline from data acquisition and pre-processing to feature extraction. A molecular dynamics simulation protocol to further study membrane dimer proteins and their interfaces is also illustrated.

Chronic Metabolic Derangement-Induced Cognitive Deficits and Neurotoxicity Are Associated with REST Inactivation.

Chronic metabolic alterations may represent a risk factor for the development of cognitive impairment, dementia, or neurodegenerative diseases. Hyperglycemia and obesity are known to imprint epigenetic markers that compromise the proper expression of cell survival genes. Here, we showed that chronic hyperglycemia (60 days) induced by a single intraperitoneal injection of streptozotocin compromised cognition by reducing hippocampal ERK signaling and by inducing neurotoxicity in rats. The mechanisms appear to be linked to reduced active DNA demethylation and diminished expression of the neuroprotective transcription factor REST. The impact of the relationship between adiposity and DNA hypermethylation on REST expression was also demonstrated in peripheral blood mononuclear cells in obese children with reduced levels of blood ascorbate. The reversible nature of epigenetic modifications and the cognitive impairment reported in obese children, adolescents, and adults suggest that the correction of the anthropometry and the peripheral metabolic alterations would protect brain homeostasis and reduce the risk of developing neurodegenerative diseases.

Tetrahydrobiopterin improves hippocampal nitric oxide-linked long-term memory.

Tetrahydrobiopterin (BH4) is synthesized by the combined action of three metabolic pathways, namely de novo synthesis, recycling, and salvage pathways. The best-known function of BH4 is its mandatory action as a natural cofactor of the aromatic amino acid hydroxylases and nitric oxide synthases. Thus, BH4 is essential for the synthesis of nitric oxide, a retrograde neurotransmitter involved in learning and memory. We investigated the effect of BH4 (4-4000 pmol) intracerebroventricular administration on aversive memory, and on BH4 metabolism in the hippocampus of rodents. Memory-related behaviors were assessed in Swiss and C57BL/6 J mice, and in Wistar rats. It was consistently observed across all rodent species that BH4 facilitates aversive memory acquisition and consolidation by increasing the latency to step-down in the inhibitory avoidance task. This effect was associated with a reduced threshold to generate hippocampal long-term potentiation process. In addition, two inhibitors of memory formation (N(ω)-nitro-L-arginine methyl ester - L-Name - and dizocilpine - MK-801 -) blocked the enhanced effect of BH4 on memory, while the amnesic effect was not rescue by the co-administration of BH4 or a cGMP analog (8-Br-cGMP). The data strongly suggest that BH4 enhances aversive memory by activating the glutamatergic neurotransmission and the retrograde activity of NO. It was also demonstrated that BH2 can be converted into BH4 by activating the BH4 salvage pathway under physiological conditions in the hippocampus. This is the first evidence showing that BH4 enhances aversive memory and that the BH4 salvage pathway is active in the hippocampus.

Cardiorespiratory, enzymatic and hormonal responses during and after walking while fasting.

The aim of the present study was to observe whether performing a low intensity endurance exercise following an overnight fasted (FAST) or fed (FED) condition promotes different cardiorespiratory, enzymatic and hormonal responses. Nine male physical active subjects, (age 21.89 ± 2.52 years old, height 175.89 ± 5.16 cm, weight 72.10 ± 4.31 kg, estimated body fat 7.25 ± 2.11%), randomly performed two sessions of 45 minutes' low intensity exercise (individual ventilator threshold) interspersed by seven days, differentiated only in whether they were provided with a standardized meal or not. The oxygen consumption (VO2) and heart rate (HR) were measured continuously at the 30-min rest, the 45-min during and the 30-min post-exercise. The testosterone (T) and cortisol (C) hormones were measured at rest, immediately post-exercise and 15-min post-exercise. The Glucose (GLU), Free fatty acids (FFA) and enzyme lipase activity (ELP) were measured at rest, 15-min and 30-min exercise, immediately, 15-min and 30-min post-exercise. Significantly lower values were observed in FED compared to FAST with: C (nmol/L) from pre (428.87 ± 120.41; 454.62 ± 148.33, respectively) to immediately post-exercise (285.10 ± 85.86; 465.66 ± 137.70, respectively) and 15-min post-exercise (248.00 ± 87.88; 454.31 ± 112.72, respectively) (p<0.05); and GLU at all times, with an exception at 15-min post-exercise. The testosterone/cortisol ratio (T/C) was significantly higher in the FED compared with FAST from pre (0.05 ± 0.02, 0.05 ± 0.01, respectively) to 15-min post-exercise (0.08 ± 0.03, 0.05 ± 0.02, respectively). No other significant differences were observed between conditions. We conclude that fasting prior to low intensity endurance exercise does not seem be advantageous, when it comes to fat loss, compared with the same exercise performed after a meal.

Effect of Cold-Water Immersion on Elbow Flexors Muscle Thickness After Resistance Training.

Matos, F, Neves, EB, Rosa, C, Reis, VM, Saavedra, F, Silva, S, Tavares, F, and Vilaça-Alves, J. Effect of cold-water immersion on elbow flexors muscle thickness after resistance training. J Strength Cond Res 32(3): 756-763, 2018-Cold-water immersion (CWI) is commonly applied to speed up the recovery process after exercise. Muscle damage may induce a performance reduction and consequence of the intramuscular pressure induced by the muscular swelling. The aim of the study was to verify the CWI effects on muscle thickness (MT) behavior of the elbow flexors after a strength training (ST) protocol. Eleven men were submitted to an ST, performed in 2 different weeks. In one of the weeks, subjects experienced a passive recovery. In the other, subjects were submitted to a CWI (20 minutes at 5-10° C). Ultrasound (US) images were taken before, after, as well as 24, 48, and 72 hours after exercise, to evaluate the MT. Muscle thickness in both exercise arm (EA) and control arm (CA) was significantly higher 48 and 72 hours after exercise when subjects were submitted to a passive recovery compared with the CWI (p = 0.029, p = 0.028, p = 0.009, and p = 0.001, 48 hours, 72 hours, EA, and CA, respectively). When each arm was analyzed with or without using CWI individually, significantly higher MT was observed in the EA with CWI: before exercise in relation to 72 hours after exercise (p = 0.042) and after exercise in relation to the other measurements (p = 0.003, p = 0.003, p = 0.038, and p < 0.0001, before exercise and 24, 48, 72 hours after exercise, respectively). The evaluation of MT by US provides evidence that CWI after ST (and 24 hours after exercise) may reduce muscle swelling in the postexercise days when compared with a passive recovery. Seems to be a paradox between the uses of CWI for an acute reduction of muscle swelling.

Differential effects of insulin on peripheral diabetes-related changes in mitochondrial bioenergetics: involvement of advanced glycosylated end products.

Large scale clinical trials have demonstrated that an intensive antihyperglycemic treatment in diabetes mellitus (DM) in individuals reduces the incidence of micro- and macrovascular complications, e.g. nephropathy, retinopathy, DM-accelerated atherosclerosis, myocardial infarction, or limb amputations. Here, we investigated the effect of short- and long-term insulin administration on mitochondrial function in peripheral tissues of streptozotocin (STZ)-induced hyperglycemic rats. In addition, the in vitro effect of methylglyoxal (MG), advanced glycation end products (AGEs) and human diabetic plasma on mitochondrial activity was investigated in skeletal muscle and liver mitochondria and in rat skin primary fibroblasts. Hyperglycemic STZ rats showed tissue-specific patterns of energy deficiency, evidenced by reduced activities of complexes I, II and/or IV after 30 days of hyperglycemia in heart, skeletal muscle and liver; moreover, cardiac tissue was found to be the most sensitive to the diabetic condition, since energy metabolism was impaired after 10 days of the hyperglycemia. Insulin-induced tight glycemic control was effective in protecting against the hyperglycemia-induced inhibition of mitochondrial enzyme activities. Furthermore, the long-term hormone replacement (30 days) also increased these activities in kidney from STZ-treated animals, where the hyperglycemic state did not modify the electron transport activity. Results from in vitro experiments indicate that mitochondrial impairment could result from oxidative stress-induced accumulation of MG and/or AGEs. Further investigations demonstrated that human plasma AGE accumulation elicits reduced mitochondrial function in skin fibroblast. These data suggest that persistent hyperglycemia results in tissue-specific patterns of energy deficiency and that early and continuous insulin therapy is necessary to maintain proper mitochondrial metabolism.