Impact of Wound Dressing Changes on Nursing Workload in an Intensive Care Unit.

The objective of this study is to understand how the type of wound dressing changes (routine or frequent) in patients admitted to intensive care units influences nurses' workload. This study used a database of retrospective and analytical observational study from one Portuguese intensive care unit. The sample included 728 adult patients admitted between 2015 and 2019. The nursing workload was assessed by the TISS-28 scale, both at admission and at discharge. The linear regression results show that patients with frequent dressing changes are associated with a higher nursing workload, both at admission (Coef. 1.65; 95% CI [0.53; 2.77]) and discharge (Coef. 1.27; 95% CI [0.32; 2.22]). In addition, age influences the nursing workload; older people are associated with a higher nursing workload (at admission Coef. 0.07; 95% CI [0.04; 0.10]; at discharge Coef. 0.08; 95% CI [0.05; 0.10]). Additionally, an increase in nursing workload at admission would significantly increase the nursing workload at discharge (Coef. 0.27; 95% CI [0.21; 0.33]). The relative stability of the nursing workload over the studied years is also another important finding (the influence of studied years is non-significant). In conclusion, patients with frequent dressing changes presented higher TISS-28 scores when compared with patients with an exchange of routine dressings, which leads to a higher nursing workload.

Unveiling New Druggable Pockets in Influenza Non-Structural Protein 1: NS1-Host Interactions as Antiviral Targets for Flu.

Influenza viruses are responsible for significant morbidity and mortality worldwide in winter seasonal outbreaks and in flu pandemics. Influenza viruses have a high rate of evolution, requiring annual vaccine updates and severely diminishing the effectiveness of the available antivirals. Identifying novel viral targets and developing new effective antivirals is an urgent need. One of the most promising new targets for influenza antiviral therapy is non-structural protein 1 (NS1), a highly conserved protein exclusively expressed in virus-infected cells that mediates essential functions in virus replication and pathogenesis. Interaction of NS1 with the host proteins PI3K and TRIM25 is paramount for NS1's role in infection and pathogenesis by promoting viral replication through the inhibition of apoptosis and suppressing interferon production, respectively. We, therefore, conducted an analysis of the druggability of this viral protein by performing molecular dynamics simulations on full-length NS1 coupled with ligand pocket detection. We identified several druggable pockets that are partially conserved throughout most of the simulation time. Moreover, we found out that some of these druggable pockets co-localize with the most stable binding regions of the protein-protein interaction (PPI) sites of NS1 with PI3K and TRIM25, which suggests that these NS1 druggable pockets are promising new targets for antiviral development.

Nursing workload assessment in an intensive care unit: A retrospective observational study using the Nursing Activities Score.

BACKGROUND: Nursing Activities Score (NAS) is a promising tool for calculating the nursing workload in intensive care units (ICU). However, data on intensive care nursing activities in Portugal are practically non-existent. AIM: To assess the nursing workload in a Portuguese ICU using the NAS. STUDY DESIGN: Retrospective cohort study developed throughout the analysis of the electronic health record database from 56 adult patients admitted to a six-bed Portuguese ICU between 1 June-31 August 2020. The nursing workload was assessed by the Portuguese version of the NAS. The study was approved by the Hospital Council Board and Ethics Committee. The study report followed the STROBE guidelines. RESULTS: The average occupancy rate was 73.55% (±16.60%). The average nursing workload per participant was 67.52 (±10.91) points. There was a correlation between the occupancy rate and the nursing workload. In 35.78% of the days, the nursing workload was higher than the available human resources, overloading nurse staffing/team. CONCLUSIONS: The nursing workload reported follows the trend of the international studies and the results reinforce the importance of adjusting the nursing staffing to the complexity of nursing care in this ICU. This study highlighted periods of nursing workload that could compromise patient safety. RELEVANCE TO CLINICAL PRACTICE: This was one of the first studies carried out with the NAS after its cross-cultural adaptation and validation for the Portuguese population. The nursing workload at the patient level was higher in the first 24 h of ICU stays. Because of the 'administrative and management activities' related to the 'patient discharge procedures', the last 24 h of ICU stays also presented high levels of nursing workload. The implementation of a nurse-to-patient ratio of 1:1 may contribute to safer nurse staffing and to improve patient safety in this Tertiary (level 3) ICU.

Designing optimized drug candidates with Generative Adversarial Network.

Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder-Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model's ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine [Formula: see text] receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

Explainable deep drug-target representations for binding affinity prediction.

BACKGROUND: Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug-target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing explanations to the decision-making process of deep learning architectures. In this research study, we explore the reliability of convolutional neural networks (CNNs) at identifying relevant regions for binding, specifically binding sites and motifs, and the significance of the deep representations extracted by providing explanations to the model's decisions based on the identification of the input regions that contributed the most to the prediction. We make use of an end-to-end deep learning architecture to predict binding affinity, where CNNs are exploited in their capacity to automatically identify and extract discriminating deep representations from 1D sequential and structural data. RESULTS: The results demonstrate the effectiveness of the deep representations extracted from CNNs in the prediction of drug-target interactions. CNNs were found to identify and extract features from regions relevant for the interaction, where the weight associated with these spots was in the range of those with the highest positive influence given by the CNNs in the prediction. The end-to-end deep learning model achieved the highest performance both in the prediction of the binding affinity and on the ability to correctly distinguish the interaction strength rank order when compared to baseline approaches. CONCLUSIONS: This research study validates the potential applicability of an end-to-end deep learning architecture in the context of drug discovery beyond the confined space of proteins and ligands with determined 3D structure. Furthermore, it shows the reliability of the deep representations extracted from the CNNs by providing explainability to the decision-making process.

Human forager response to abrupt climate change at 8.2 ka on the Atlantic coast of Europe.

The cooling and drying associated with the so-called '8.2 ka event' have long been hypothesized as having sweeping implications for human societies in the Early Holocene, including some of the last Mesolithic hunter-gatherers in Atlantic Europe. Nevertheless, detailed 'on-site' records with which the impacts of broader climate changes on human-relevant environments can be explored have been lacking. Here, we reconstruct sea surface temperatures (SST) from δ18O values measured on subfossil topshells Phorcus lineatus exploited by the Mesolithic human groups that lived at El Mazo cave (N Spain) between 9 and 7.4 ka. Bayesian modelling of 65 radiocarbon dates, in combination with this δ18O data, provide a high-resolution seasonal record of SST, revealing that colder SST during the 8.2 ka event led to changes in the availability of different shellfish species. Intensification in the exploitation of molluscs by humans indicates demographic growth in these Atlantic coastal settings which acted as refugia during this cold event.

Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo, Brazil.

As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~2 million are absent from large public databases. WGS enables identification of ~2,000 previously undescribed mobile element insertions without previous description, nearly 5 Mb of genomic segments absent from the human genome reference, and over 140 alleles from HLA genes absent from public resources. We reclassify and curate pathogenicity assertions for nearly four hundred variants in genes associated with dominantly-inherited Mendelian disorders and calculate the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observe that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS.

Conformational Dynamics of the Soluble and Membrane-Bound Forms of Interleukin-1 Receptor Type-1: Insights into Linker Flexibility and Domain Orientation.

Interleukin-1 receptor type 1 (IL-1R1) is a key player in inflammation and immune responses. This receptor regulates IL-1 activity in two forms: as a membrane-bound form and as a soluble ectodomain. The details and differences between the conformational dynamics of the membrane-bound and the soluble IL-1R1 ectodomains (ECDs) remain largely elusive. Here, we study and compare the structural dynamics of the soluble and membrane-bound IL-1R1-ECDs using molecular dynamics (MD) simulations, focusing on the flexible interdomain linker of the ECD, as well as the spatial rearrangements between the Ig-like domains of the ECD. To explore the membrane-bound conformations, a full-length IL-1R1 structural model was developed and subjected to classical equilibrium MD. Comparative analysis of multiple MD trajectories of the soluble and the membrane-bound IL-1R1-ECDs reveals that (i) as somewhat expected, the extent of the visited "open-to-closed" transitional states differs significantly between the soluble and membrane-bound forms; (ii) the soluble form presents open-closed transitions, sampling a wider rotational motion between the Ig-like domains of the ECD, visiting closed and "twisted" conformations in higher extent, whereas the membrane-bound form is characterized by more conformationally restricted states; (iii) interestingly, the backbone dihedral angles of residues Glu202, Glu203 and Asn204, located in the flexible linker, display the highest variations during the transition between discrete conformational states detected in IL-1R1, thus appearing to work as the "central wheel of a clock's movement". The simulations and analyses presented in this contribution offer a deeper insight into the structure and dynamics of IL-1R1, which may be explored in a drug discovery setting.

The Therapeutic Prospects of Targeting IL-1R1 for the Modulation of Neuroinflammation in Central Nervous System Disorders.

The interleukin-1 receptor type 1 (IL-1R1) holds pivotal roles in the immune system, as it is positioned at the "epicenter" of the inflammatory signaling networks. Increased levels of the cytokine IL-1 are a recognized feature of the immune response in the central nervous system (CNS) during injury and disease, i.e., neuroinflammation. Despite IL-1/IL-1R1 signaling within the CNS having been the subject of several studies, the roles of IL-1R1 in the CNS cellular milieu still cause controversy. Without much doubt, however, the persistent activation of the IL-1/IL-1R1 signaling pathway is intimately linked with the pathogenesis of a plethora of CNS disease states, ranging from Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), all the way to schizophrenia and prion diseases. Importantly, a growing body of evidence is showing that blocking IL-1R1 signaling via pharmacological or genetic means in different experimental models of said CNS diseases leads to reduced neuroinflammation and delayed disease progression. The aim of this paper is to review the recent progress in the study of the biological roles of IL-1R1, as well as to highlight key aspects that render IL-1R1 a promising target for the development of novel disease-modifying treatments for multiple CNS indications.

Synthesis and structure-activity relationships of new chiral spiro-ß-lactams highly active against HIV-1 and Plasmodium.

The synthesis and antimicrobial activity of new spiro-ß-lactams is reported. The design of the new molecules was based on the structural modulation of two previously identified lead spiro-penicillanates with dual activity against HIV and Plasmodium. The spiro-ß-lactams synthesized were assayed for their in vitro activity against HIV-1, providing relevant structure-activity relationship information. Among the tested compounds, two spirocyclopentenyl-ß-lactams were identified as having remarkable nanomolar activity against HIV-1. Additionally, the same molecules showed promising antiplasmodial activity, inhibiting both the hepatic and blood stages of Plasmodium infection.

Stroke in patients infected by the novel coronavirus and its causal mechanisms: A narrative review.

OBJECTIVE: The current study aimed to evaluate the mechanisms of stroke development during the coronavirus disease 2019 (COVID-19) pandemic and analyze the related characteristics, such as etiology, age group, associated comorbidities, and prognosis. METHODS: A narrative was performed using the descriptors ["novel coronavirus"] AND ["stroke"] in the PubMed, Science Direct, Google Scholar, Lilacs, and Biblioteca Virtual em Saúde (BVS) databases, including studies published between December 1, 2019, and April 28, 2020. RESULTS: A total of 142 articles were identified, with 89 of them in the PubMed database, 46 in Science Direct, and 7 in Google Scholar. No articles were found using the defined keywords in the Lilacs and BVS databases. A total of 22 articles were included for final evaluation. We observed that infection by the novel coronavirus caused a greater risk of the occurrence of stroke, with several studies suggesting etiological mechanisms, such as the involvement of angiotensin-converting enzyme 2, viral invasion, and hypoxia as well as the increase in D-dimer and the reduction in platelets, which had been commonly observed in COVID-19 cases. The most common complication of stroke was found among the elderly with preexisting comorbidities, mainly cardiovascular disease. We detected reports of strokes among young people with no preexisting risk factors for thromboembolic events, in which the mechanism related to the viral infection was the most probable cause. In this review, we confirmed that stroke is part of the spectrum of clinical manifestations resulting from COVID-19 and is associated with a worse prognosis. Cerebrovascular lesions resulting from complications of the infection by the novel coronavirus occurred as a result of ischemic, hemorrhagic, and/or thromboembolic etiologies. CONCLUSION: The occurrence of stroke during the pandemic as a result of the novel coronavirus has a multifactorial character, and emergency physicians should focus on systematic measures for its screening and accurate diagnosis as well as on appropriate interventions based on early decisionmaking that may have a favorable impact on reducing damage and saving lives.

Spiro-ß-lactam BSS-730A Displays Potent Activity against HIV and Plasmodium.

The high burden of malaria and HIV/AIDS prevents economic and social progress in developing countries. A continuing need exists for development of novel drugs and treatment regimens for both diseases in order to address the tolerability and long-term safety concerns associated with current treatment options and the emergence of drug resistance. We describe new spiro-ß-lactam derivatives with potent (nM) activity against HIV and Plasmodium and no activity against bacteria and yeast. The best performing molecule of the series, BSS-730A, inhibited both HIV-1 and HIV-2 replication with an IC50 of 13 ± 9.59 nM and P. berghei hepatic infection with an IC50 of 0.55 ± 0.14 µM with a clear impact on parasite development. BSS-730A was also active against the erythrocytic stages of P. falciparum, with an estimated IC50 of 0.43 ± 0.04 µM. Time-of-addition studies showed that BSS-730A potentially affects all stages of the HIV replicative cycle, suggesting a complex mechanism of action. BSS-730A was active against multidrug-resistant HIV isolates, with a median 2.4-fold higher IC50 relative to control isolates. BSS-730A was equally active against R5 and X4 HIV isolates and displayed strong synergism with the entry inhibitor AMD3100. BSS-730A is a promising candidate for development as a potential therapeutic and/or prophylactic agent against HIV and Plasmodium.

Nursing workload assessment in an intensive care unit: A 5-year retrospective analysis.

AIMS: To study the correlation between the workload of intensive care nursing teams and the sociodemographic, anthropometric and clinical characteristics of patients in critical condition in a Portuguese Intensive Care Unit (ICU) during a 5-year period. BACKGROUND: Currently, indices of nursing workload quantification are one of the resources used for planning and evaluating ICUs. Evidence shows that there are several factors related to critical patients and their hospitalisation which potentially influence the nursing workload. DESIGN: Retrospective cohort analysis of a health record database from adult patients admitted to a Portuguese ICU between 1 January 2015-31 December 2019. METHODS: Simplified Therapeutic Intervention Scoring System (TISS-28) scores of 730 adult patients. Three TISS-28 assessments were considered: first assessment, last assessment and average. The STROBE guidelines were used in reporting this study. RESULTS: The TISS-28 has an average of 34.2 ± 6.9 points at admission, which is considered a high nursing workload. A somewhat lower result was found for the discharge and average assessments. It shows that basic activities accounted for the highest percentage of time spent (38.0%), followed by the cardiovascular support category (26.5%). The TISS-28 shows consistent results throughout the study period, despite a small trend reduction in the last 2 years. CONCLUSIONS: Lower workloads were found for age ≤44 years and with a shorter length of stay. Higher workload was more probable in patients classified in Cullen Class IV (OR = 2.5) and with a normal to higher weight percentile (OR = 1.9 and 1.5, respectively). RELEVANCE TO CLINICAL PRACTICE: Knowledge of the factors influencing the nursing workload facilitates the implementation of rules to improve performance in nursing interventions, based on the redefinition of care priorities, increased productivity, human resources management and reduction of additional costs to the organisation, related to possible adverse events, among others.

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities.

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

Predicting hydrophobic solvation by molecular simulation: 1. Testing united-atom alkane models.

We present a systematic test of the performance of three popular united-atom force fields-OPLS-UA, GROMOS and TraPPE-at predicting hydrophobic solvation, more precisely at describing the solvation of alkanes in alkanes. Gibbs free energies of solvation were calculated for 52 solute/solvent pairs from Molecular Dynamics simulations and thermodynamic integration making use of the IBERCIVIS volunteer computing platform. Our results show that all force fields yield good predictions when both solute and solvent are small linear or branched alkanes (up to pentane). However, as the size of the alkanes increases, all models tend to increasingly deviate from experimental data in a systematic fashion. Furthermore, our results confirm that specific interaction parameters for cyclic alkanes in the united-atom representation are required to account for the additional excluded volume within the ring. Overall, the TraPPE model performs best for all alkanes, but systematically underpredicts the magnitude of solvation free energies by about 6% (RMSD of 1.2 kJ/mol). Conversely, both GROMOS and OPLS-UA systematically overpredict solvation free energies (by ∼13% and 15%, respectively). The systematic trends suggest that all models can be improved by a slight adjustment of their Lennard-Jones parameters. © 2016 Wiley Periodicals, Inc.

A novel bis-furan scaffold for transthyretin stabilization and amyloid inhibition.

The design and synthesis of a novel bis-furan scaffold tailored for high efficiency at inhibiting transthyretin amyloid formation is reported. In vitro results show that the discovered compounds are more efficient inhibitors of amyloid formation than tafamidis, a drug currently used in the treatment of familial amyloid polyneuropathy (FAP), despite their lower molecular weight and lipophilicity. Moreover, ex vivo experiments with the strongest inhibitor in the series, conducted in human blood plasma from normal and FAP Val30Met-transthyretin carriers, disclose remarkable affinity and selectivity profiles. The promises and challenges facing further development of this compound are discussed under the light of increasing evidence implicating transthyretin stability as a key factor not only in transthyretin amyloidoses and several associated co-morbidities, but also in Alzheimer's disease.