Development of a scale to gain insight into the experience of living with chronic heart failure: The UNAV-Experience of Living with Chronic Heart Failure Scale.

BACKGROUND: To date, there are no tools for the nursing staff to gain systematic insight on the experience lived by patients with chronic heart failure. The objective of this study was to develop a scale for this purpose. METHODS: The study was conducted between January 2018 and December 2020 in three Spanish hospitals. The process described by DeVellis was used for the development of the scale. The items were built based on a phenomenological study and a systematic review of the literature. Next, feedback from a panel of experts was obtained, the scale was administered to a sample of patients with chronic heart failure, and a cognitive interview and an observational study were conducted to create the final version of the scale. RESULTS: The first version of the scale had in seven domains and 76 items. After its evaluation by a panel of experts, it was reduced to a second version with six domains and 55 items. Following the administration of Version 2 to 17 patients (58.8% male, mean age 59.53, 70.6% classified as NYHA functional class II), five items were modified and two eliminated. Thus, the third version of the UNAV-CHF Experience Scale was composed of six domains and 53 items. CONCLUSIONS: This study presents the development of the UNAV-experience of living with chronic heart failure scale. It is an original and novel instrument that allows systematically explore this experience. A larger-scale study is necessary to confirm the validity of our scale.

Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study.

Background: The ability to predict a long duration of mechanical ventilation (MV) by clinicians is very limited. We assessed the value of machine learning (ML) for early prediction of the duration of MV > 14 days in patients with moderate-to-severe acute respiratory distress syndrome (ARDS). Methods: This is a development, testing, and external validation study using data from 1173 patients on MV ≥ 3 days with moderate-to-severe ARDS. We first developed and tested prediction models in 920 ARDS patients using relevant features captured at the time of moderate/severe ARDS diagnosis, at 24 h and 72 h after diagnosis with logistic regression, and Multilayer Perceptron, Support Vector Machine, and Random Forest ML techniques. For external validation, we used an independent cohort of 253 patients on MV ≥ 3 days with moderate/severe ARDS. Results: A total of 441 patients (48%) from the derivation cohort (n = 920) and 100 patients (40%) from the validation cohort (n = 253) were mechanically ventilated for >14 days [median 14 days (IQR 8-25) vs. 13 days (IQR 7-21), respectively]. The best early prediction model was obtained with data collected at 72 h after moderate/severe ARDS diagnosis. Multilayer Perceptron risk modeling identified major prognostic factors for the duration of MV > 14 days, including PaO2/FiO2, PaCO2, pH, and positive end-expiratory pressure. Predictions of the duration of MV > 14 days showed modest discrimination [AUC 0.71 (95%CI 0.65-0.76)]. Conclusions: Prolonged MV duration in moderate/severe ARDS patients remains difficult to predict early even with ML techniques such as Multilayer Perceptron and using data at 72 h of diagnosis. More research is needed to identify markers for predicting the length of MV. This study was registered on 14 August 2023 at ClinicalTrials.gov (NCT NCT05993377).

Embodied planning in climbing: how pre-planning informs motor execution.

Introduction: The aim of the study is to address embodied planning in climbing. Embodied planning was conceptualized as the interaction between perceptual-cognitive pre-planning and motor execution. Methods: In an experimental study, 18 climbers were asked to pre-plan a climbing route and to perform the route afterward. During pre-planning, the visual search pattern of climbers was captured using a portable eye tracker. After previewing, they were invited to climb the wall. Results: Results revealed that holds looked at during pre-planning were used twice as much during route execution than those not looked at. The duration of fixations was longer for holds used than those not used during route execution. The experience of climbers (training years) correlated with visual strategies and climbing performance, such that experienced participants climbed faster and fixated at the holds not used for a shorter time. Discussion: The visual behaviors of climbers were influenced by their past sensorimotor experiences during route previewing, impacting subsequent climbing performance.

Unearthing a Cryptic Biosynthetic Gene Cluster for the Piperazic Acid-Bearing Depsipeptide Diperamycin in the Ant-Dweller Streptomyces sp. CS113.

Piperazic acid is a cyclic nonproteinogenic amino acid that contains a hydrazine N-N bond formed by a piperazate synthase (KtzT-like). This amino acid, found in bioactive natural products synthesized by non-ribosomal peptide synthetases (NRPSs), confers conformational constraint to peptides, an important feature for their biological activities. Genome mining of Streptomyces strains has been revealed as a strategy to identify biosynthetic gene clusters (BGCs) for potentially active compounds. Moreover, the isolation of new strains from underexplored habitats or associated with other organisms has allowed to uncover new BGCs for unknown compounds. The in-house "Carlos Sialer (CS)" strain collection consists of seventy-one Streptomyces strains isolated from the cuticle of leaf-cutting ants of the tribe Attini. Genomes from twelve of these strains have been sequenced and mined using bioinformatics tools, highlighting their potential to encode secondary metabolites. In this work, we have screened in silico those genomes, using KtzT as a hook to identify BGCs encoding piperazic acid-containing compounds. This resulted in uncovering the new BGC dpn in Streptomyces sp. CS113, which encodes the biosynthesis of the hybrid polyketide-depsipeptide diperamycin. Analysis of the diperamycin polyketide synthase (PKS) and NRPS reveals their functional similarity to those from the aurantimycin A biosynthetic pathway. Experimental proof linking the dpn BGC to its encoded compound was achieved by determining the growth conditions for the expression of the cluster and by inactivating the NRPS encoding gene dpnS2 and the piperazate synthase gene dpnZ. The identity of diperamycin was confirmed by High-Resolution Mass Spectrometry (HRMS) and Nuclear Magnetic Resonance (NMR) and by analysis of the domain composition of modules from the DpnP PKS and DpnS NRPS. The identification of the dpn BGC expands the number of BGCs that have been confirmed to encode the relatively scarcely represented BGCs for depsipeptides of the azinothricin family of compounds and will facilitate the generation of new-to-nature analogues by combinatorial biosynthesis.

Paclitaxel as HIPEC-Drug after Surgical Cytoreduction for Ovarian Peritoneal Metastases: A Randomized Phase III Clinical Trial (HIPECOVA).

Multidisciplinary strategies have transformed the management of advanced ovarian cancer. We aimed to evaluate the effectiveness of paclitaxel in hyperthermic intraperitoneal chemotherapy (HIPEC) following surgical cytoreduction for ovarian peritoneal metastases in a randomized phase III trial conducted between August 2012 and December 2019. Seventy-six patients were randomized to either the HIPEC or no HIPEC group. Although median values for the primary endpoints (recurrence-free survival (RFS) and overall survival (OS)) revealed superior outcomes for the HIPEC (RFS: 23 months, OS: 48 months) over the control group (RFS: 19 months, OS: 46 months), these differences were not statistically significant (p = 0.22 and p = 0.579). Notably, the HIPEC group demonstrated significantly higher 5-year OS and 3-year RFS rates (47.2% and 47.5%) compared to patients without HIPEC (34.5% and 21.3%). Stratification according to Peritoneal Surface Disease Severity Score (PSDSS) showed improved OS and RFS for patients with lower PSDSS (I-II) in the HIPEC-treated group (p = 0.033 and p = 0.042, respectively). The Clavien-Dindo classification of adverse event grades revealed no significant differences between HIPEC and controls (p = 0.482). While overall results were not statistically significant, our long-term follow-up emphasized the potential benefit of HIPEC-associated cytoreduction with paclitaxel, particularly in selected ovarian cancer patients with lower PSDSS indices.

Synergistic impact of N-antigenemia profiled by a rapid antigen test and low anti-S1 antibodies on the risk of hospitalization in COVID-19.

OBJECTIVES: Identifying patients with COVID-19 who are at risk of poor evolution is key to early decide on their hospitalization. We evaluated the combined impact of nucleocapsid (N)-antigenemia profiled by a rapid test and antibodies against the S1 subunit of the SARS-CoV S protein (S1) on the hospitalization risk of patients with COVID-19. METHODS: N-antigenemia and anti-S1 antibodies were profiled at admission to the emergency department in 146 patients with COVID-19 using the Panbio® antigen Rapid Test and the SARS-CoV-2 immunoglobulin G II Quant/SARS-CoV-2 immunoglobulin G assay from Abbott. A multivariable analysis was used to evaluate the impact of these factors on hospitalization. RESULTS: Patients with a positive N-antigen test in plasma and anti-S1 levels <2821 arbitrary units/mL needed hospitalization more frequently (20 of 23, 87%). A total of 20 of 71 (28.2%) of those showing a negative N-antigen test and anti-S1 ≥2821 arbitrary units/mL were hospitalized for 18 of 52 (34.6%) of the patients with only one of these conditions. Patients with a positive N-antigen test and low antibody levels showed an odds ratio, 95% confidence interval, and P-value for hospitalization of 18.21, 2.74-121.18, and 0.003, respectively, and exhibited the highest mortality (30.4%). CONCLUSIONS: Simultaneous profiling of a rapid N-antigen test in plasma and anti-S1 levels could help to early identify patients with COVID-19 needing hospitalization.

Dilarmycins A-C, Calcium-Dependent Lipopeptide Antibiotics with a Non-canonical Ca2+-Binding Motif.

Genome analysis of strain Streptomyces sp. CA-278952 revealed a biosynthetic gene cluster encoding a putative lipopeptide with a sequence containing an Asp-Gly-Glu-Ala motif. We envisioned that this motif could mimic the canonical Asp-X-Asp-Gly sequence found in previously reported calcium-dependent lipopeptide antibiotics. Chemical investigation of the producing strain led to the discovery of three novel lipodepsipeptides, dilarmycins A-C. The calcium-dependent antibacterial activity of the new compounds was confirmed against the Gram-positive pathogens methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study.

BACKGROUND: Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge. OBJECTIVES: To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management. METHODS: In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM. RESULTS: We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample. CONCLUSIONS: These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

Melanoma is a type of skin cancer that can be deadly if it is not detected at an early stage. Unfortunately, the early detection of melanoma is challenging. Our team has developed a model that could be used to predict whether a person has stage I malignant melanoma based on blood serum analysis. The model was trained on data from a group of people with melanoma and it was found to be accurate in predicting melanoma at an early stage. This means that the model could be used to identify people who have skin cancer before it progresses and becomes more complicated to treat. Although the researchers recommend that further studies are conducted to validate the model in a larger population of people, this research could help with the early diagnosis of melanoma and work toward improving survival rates.

Unveiling the bioactive potential of Actinomycetota from the Tagus River estuary.

The increase in global travel and the incorrect and excessive use of antibiotics has led to an unprecedented rise in antibiotic resistance in bacterial and fungal populations. To overcome these problems, novel bioactive natural products must be discovered, which may be found in underexplored environments, such as estuarine habitats. In the present work, estuarine actinomycetotal strains were isolated with conventional and iChip techniques from the Tagus estuary in Alcochete, Portugal, and analysed for different antimicrobial bioactivities. Extracts were produced from the isolated cultures and tested for bioactivity against Staphylococcus aureus ATCC 29213, Escherichia coli ATCC 25922, Aspergillus fumigatus ATCC 240305, Candida albicans ATCC 10231 and Trichophyton rubrum FF5. Furthermore, bioactive extracts were subjected to dereplication by high-performance liquid chromatography (HPLC) and high-resolution mass spectrometry (HRMS) to putatively identify their chemical components. In total, 105 isolates belonging to 3 genera were obtained. One which was isolated, MTZ3.1 T, represents a described novel taxon for which the name Streptomyces meridianus was proposed. Regarding the bioactivity testing, extracts from 12 strains proved to be active against S. aureus, 2 against E. coli, 4 against A. fumigatus, 3 against C. albicans and 10 against T. rubrum. Dereplication of bioactive extracts showed the presence of 28 known bioactive molecules, 35 hits have one or more possible matches in the DNP and 18 undescribed ones. These results showed that the isolated bacteria might be the source of new bioactive natural products.

DNMT3A/TET2/ASXL1 Mutations are an Age-independent Thrombotic Risk Factor in Polycythemia Vera Patients: An Observational Study.

BACKGROUND: Polycythemia vera (PV) patients are classified as high or low thrombotic risk based on age and prior history of thrombosis. Despite adherence to treatment recommendations, vascular events remain frequent, leading us to question whether thrombotic risk stratification could be improved. We previously reported an association between thrombotic events and mutations in DTA genes (DNMT3A, TET2, and ASXL1). The objective of this study was to confirm this observation in a larger series of PV patients. METHODS: PV patients with a minimum follow-up of 3 years were recruited from 8 European centers. Medical history was searched for thrombotic event recorded at any time and next-generation sequencing carried out with a myeloid panel. Multivariable logistic regression evaluated the impact of variables on thrombotic risk. Kaplan-Meier thrombosis-free survival curves were compared by the log rank test. Associations in the total cohort were confirmed in a case-control study to exclude selection bias. RESULTS: Of the 136 patients recruited, 74 (56.1%) had a thrombotic event, with an incidence density of 2.83/100 person-years. In multivariable analysis, DTA mutation was a risk factor for thrombotic event, being predictive for shorter thrombosis-free survival in the whole cohort (p = 0.007), as well as in low-risk patients (p = 0.039) and older patients (p = 0.009), but not for patients with a prediagnostic event. A gender- and age-matched case-control study confirmed the increased risk of thrombotic event for PV patients with a DTA mutation. CONCLUSION: Our results support the use of molecular testing at diagnosis to help predict which PV patients are at higher risk of developing thrombosis.

Uncovering the biotechnological capacity of marine and brackish water Planctomycetota.

An appealing strategy for finding novel bioactive molecules in Nature consists in exploring underrepresented and -studied microorganisms. Here, we investigated the antimicrobial and tumoral anti-proliferative bioactivities of twenty-three marine and estuarine bacteria of the fascinating phylum Planctomycetota. This was achieved through extraction of compounds produced by the Planctomycetota cultured in oligotrophic medium followed by an antimicrobial screening against ten relevant human pathogens including Gram-positive and Gram-negative bacteria, and fungi. Cytotoxic effects of the extracts were also evaluated against five tumoral cell lines. Moderate to potent activities were obtained against Enterococcus faecalis, methicillin-sensitive and methicillin-resistant Staphylococcus aureus and vancomycin-sensitive and vancomycin-resistant Enterococcus faecium. Anti-fungal effects were observed against Trichophyton rubrum, Candida albicans and Aspergillus fumigatus. The highest cytotoxic effects were observed against human breast, pancreas and melanoma tumoral cell lines. Novipirellula caenicola and Rhodopirellula spp. strains displayed the widest spectrum of bioactivities while Rubinisphaera margarita ICM_H10T affected all Gram-positive bacteria tested. LC-HRMS analysis of the extracts did not reveal the presence of any known bioactive natural product, suggesting that the observed activities are most likely caused by novel molecules, that need identification. In summary, we expanded the scope of planctomycetal species investigated for bioactivities and demonstrated that various strains are promising sources of novel bioactive compounds, which reenforces the potential biotechnological prospects offered by Planctomycetota.

Establishment of a screening platform based on human coronavirus OC43 for the identification of microbial natural products with antiviral activity.

IMPORTANCE: The COVID-19 pandemic has revealed the lack of effective treatments against betacoronaviruses and the urgent need for new broad-spectrum antivirals. Natural products are a valuable source of bioactive compounds with pharmaceutical potential that may lead to the discovery of new antiviral agents. Specifically, compared to conventional synthetic molecules, microbial natural extracts possess a unique and vast chemical diversity and are amenable to large-scale production. The implementation of a high-throughput screening platform using the betacoronavirus OC43 in a human cell line infection model has provided proof of concept of the approach and has allowed for the rapid and efficient evaluation of 1,280 microbial extracts. The identification of several active compounds validates the potential of the platform for the search for new compounds with antiviral capacity.

Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine.

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.

The use of the eye-fixation-related potential to investigate visual perception in professional domains with high attentional demand: a literature review.

INTRODUCTION: Visual attention is a cognitive skill related to visual perception and neural activity, and also moderated by expertise, in time-constrained professional domains (e.g., aviation, driving, sport, surgery). However, the contribution of both perceptual and neural processes on performance has been studied separately in the literature. DEVELOPMENT: We defend an integration of visual and neural signals to offer a more complete picture of the visual attention displayed by professionals of different skill levels when performing free-viewing tasks. Specifically, we propose to zoom the analysis in data related to the quiet eye and P300 component jointly, as a novel signal processing approach to evaluate professionals' visual attention. CONCLUSION: This review highlights the advantages of using portable eye trackers and electroencephalogram systems altogether, as a promising technique for a better understanding of early cognitive components related to attentional processes. Altogether, the eye-fixation-related potentials method may provide a better understanding of the cognitive mechanisms employed by the participants in natural settings, revealing what visual information is of interest for participants and distinguishing the neural bases of visual attention between targets and non-targets whenever they perceive a stimulus during free viewing experiments.

Exploring the Impact of Nanoparticle Stealth Coatings in Cancer Models: From PEGylation to Cell Membrane-Coating Nanotechnology.

Nanotechnological platforms offer advantages over conventional therapeutic and diagnostic modalities. However, the efficient biointerfacing of nanomaterials for biomedical applications remains challenging. In recent years, nanoparticles (NPs) with different coatings have been developed to reduce nonspecific interactions, prolong circulation time, and improve therapeutic outcomes. This study aims to compare various NP coatings to enhance surface engineering for more effective nanomedicines. We prepared and characterized polystyrene NPs with different coatings of poly(ethylene glycol), bovine serum albumin, chitosan, and cell membranes from a human breast cancer cell line. The coating was found to affect the colloidal stability, adhesion, and elastic modulus of NPs. Protein corona formation and cellular uptake of NPs were also investigated, and a 3D tumor model was employed to provide a more realistic representation of the tumor microenvironment. The prepared NPs were found to reduce protein adsorption, and cell-membrane-coated NPs showed significantly higher cellular uptake. The secretion of proinflammatory cytokines in human monocytes after incubation with the prepared NPs was evaluated. Overall, the study demonstrates the importance of coatings in affecting the behavior and interaction of nanosystems with biological entities. The findings provide insight into bionano interactions and are important for the effective implementation of stealth surface engineering designs.

Quantification of bacterial DNA in blood using droplet digital PCR: a pilot study.

We used droplet digital PCR (ddPCR) assays to detect/quantify DNA from Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Enterococcus spp. in blood samples. Bacterial DNA from clinical strains (4 < n < 12) was extracted, quantified and diluted (10-0.0001 ng/µL) and ddPCR assays were performed in triplicate. These ddPCR assays showed low replication variability, low detection limit (1-0.1 pg/µL), and genus/species specificity. ddPCR assays were also used to quantify bacterial DNA obtained from spiked blood (1 × 104-1 CFU/mL) of each bacterial genus/species. Comparison between ddPCR assays and bacterial culture was performed by Pearson correlation. There was an almost perfect correlation (r ≥ 0.997, P ≤ 0.001) between the number of CFU/mL from bacterial culture and the number of gene copies/mL detected by ddPCR. The time from sample preparation to results was determined to be 3.5 to 4 hours. The results demonstrated the quantification capacity and specificity of the ddPCR assays to detect/quantify 4 of the most important bloodstream infection (BSI) bacterial pathogens directly from blood. SIGNIFICANCE AND IMPACT: This pilot study results support the potential of ddPCR for the diagnosis and/or severity stratification of BSI. Applied to patients' blood samples it can improve diagnosis and diminish sample-to-results time, improving patient care.

Bilateral internal thoracic artery versus single internal thoracic artery plus radial artery: A double meta-analytic approach.

OBJECTIVES: We explored the current evidence on the best second conduit in coronary surgery carrying out a double meta-analysis of propensity score matched or adjusted studies comparing bilateral internal thoracic artery (BITA) versus single internal thoracic artery plus radial artery. METHODS: PubMed, Embase, and Google Scholar were searched for propensity score matched or adjusted studies comparing BITA versus single internal thoracic artery plus radial artery. The end point was long-term mortality. Two statistical approaches were used: the generic inverse variance method and the pooled meta-analysis of Kaplan-Meier-derived individual patient data. RESULTS: Twelve matched populations comparing 6450 patients with BITA versus 9428 patients with single internal thoracic artery plus radial artery were included in our meta-analysis. The generic inverse variance method showed a statistically significant survival benefit of the BITA group (hazard ratio, 0.84; 95% CI, 0.74-0.95; P = .04). The Kaplan-Meier estimates of survival at 1, 5, 10, and 15 years of the BITA group were 97.0%, 91.3%, 80.0%, and 68.0%, respectively. The Kaplan-Meier estimates of survival at 1, 5, 10, and 15 years of the single internal thoracic artery plus radial artery group were 97.3%, 91.5%, 79.9%, and 63.9%, respectively. The Kaplan-Meier-derived individual patient data meta-analysis applied to very long follow-up time data, showed that BITA provided a survival benefit after 10 years from surgery (hazard ratio, 0.77; 95% CI, 0.63-0.94; P = .01). No differences in terms of survival between the 2 groups were detected when the analysis was focused on the first 10 years of follow-up (hazard ratio, 0.99; 95% CI, 0.91-1.09; P = .93). CONCLUSIONS: The present meta-analysis suggests that double internal thoracic artery may provide, compared with single internal thoracic artery plus radial artery, a statistically significant survival advantage after 10 years of follow-up, but not before. VIDEO ABSTRACT.

An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression.

Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier "GSE22309". The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories.