Mesenchymal Stem/Stromal Cells Derived from Dental Tissues Mediate the Immunoregulation of T Cells through the Purinergic Pathway.

Human dental tissue mesenchymal stem cells (DT-MSCs) constitute an attractive alternative to bone marrow-derived mesenchymal stem cells (BM-MSCs) for potential clinical applications because of their accessibility and anti-inflammatory capacity. We previously demonstrated that DT-MSCs from dental pulp (DP-MSCs), periodontal ligaments (PDL-MSCs), and gingival tissue (G-MSCs) show immunosuppressive effects similar to those of BM, but to date, the DT-MSC-mediated immunoregulation of T lymphocytes through the purinergic pathway remains unknown. In the present study, we compared DP-MSCs, PDL-MSCs, and G-MSCs in terms of CD26, CD39, and CD73 expression; their ability to generate adenosine (ADO) from ATP and AMP; and whether the concentrations of ADO that they generate induce an immunomodulatory effect on T lymphocytes. BM-MSCs were included as the gold standard. Our results show that DT-MSCs present similar characteristics among the different sources analyzed in terms of the properties evaluated; however, interestingly, they express more CD39 than BM-MSCs; therefore, they generate more ADO from ATP. In contrast to those produced by BM-MSCs, the concentrations of ADO produced by DT-MSCs from ATP inhibited the proliferation of CD3+ T cells and promoted the generation of CD4+CD25+FoxP3+CD39+CD73+ Tregs and Th17+CD39+ lymphocytes. Our data suggest that DT-MSCs utilize the adenosinergic pathway as an immunomodulatory mechanism and that this mechanism is more efficient than that of BM-MSCs.

Detection of SARS-CoV-2 variants in hospital wastewater in Peru, 2022. / Detección de variantes del SARS-CoV-2 en aguas residuales de hospitales en Perú, 2022.

OBJECTIVE.: To identify the presence of the SARS-CoV-2 virus in wastewater from hospitals in Peru. MATERIALS AND METHODS.: Water samples were collected from the effluents of nine hospitals in Peru during March and September 2022. SARS-CoV-2 was identified by using Illumina sequencing. Variant, lineage and clade assignments were carried out using the Illumina and Nextclado tools. We verified whether the SARS-CoV-2 variants obtained from wastewater were similar to those reported by the National Institute of Health of Peru from patients during the same period and region. RESULTS.: Eighteen of the 20 hospital wastewater samples (90%) provided sequences of sufficient quality to be classified as the Omicron variant according to the WHO classification. Among them, six (30%) were assigned by Nextclade to clades 21K lineage BA.1.1 (n=1), 21L lineage BA.2 (n=2), and 22B lineages BA.5.1 (n=2) and BA .5.5 (n=1). CONCLUSIONS.: SARS-CoV-2 variants were found in hospital wastewater samples and were similar to those reported by the surveillance system in patients during the same weeks and geographic areas. Wastewater monitoring could provide information on the environmental and temporal variation of viruses such as SARS-CoV-2.Motivation for the study. To contribute to the surveillance of environmental samples from hospital effluents in order to achieve early warning of possible infectious disease outbreaks. Main findings. The Omicron variant of the COVID-19 virus was detected in wastewater from hospitals in Puno, Cuzco and Cajamarca; these results are similar to the reports by the Peruvian National Institute of Health based on nasopharyngeal swab samples. Implications. The presence of the Omicron variant in hospital wastewater during the third wave of the pandemic should raise awareness of the treatment system before wastewater is discharged into the public sewer system.

Classification of Premature Ventricular Contractions in Athletes During Routine Preparticipation Exams.

BACKGROUND: Large-scale data on incidental premature ventricular contraction (PVC) prevalence and morphologies have been lacking, leaving many providers without guidance on further cardiac testing for patients with incidental PVCs on ECG. Athletes offer an intriguing cohort to understand the clinical significance, prevalence, and common morphologies of incidental PVCs because they often undergo ECG screening during preparticipation exams. METHODS: Digital ECGs were obtained from 10 728 screened athletes aged 14 to 35 years during mass screenings in schools and professional sports teams between 2014 and 2021. A retrospective analysis of ECGs with PVCs was performed using the simultaneous display of frontal (limb) and horizontal (precordial) plane leads. PVCs were coded for morphology and categorized as benign or nonbenign using recommended criteria. RESULTS: Twenty-six athletes (0.24%) were found to have at least 1 PVC. Among these, 50% were female, 65% were White, 8% were Asian, 4% were Hispanic, and 23% were Black. Nineteen of the 26 (73%) ECGs had PVCs with a left bundle branch block pattern compared with 7 (27%) with a right bundle branch block pattern. Twenty-four ECGs (96%) had PVCs with benign patterns, including 18 with right ventricular outflow tract, 5 with left anterior fascicle, and 2 with left posterior fascicle morphology. CONCLUSIONS: There is a low prevalence of PVCs on routine ECG screening of young athletes, and most PVCs are of benign morphology in this population. This study highlights the value of using digital ECG recorders with simultaneous lead display to guide decision-making about further cardiac testing and referrals in young athletes with PVCs. Using our results and review of the literature, we propose methods and algorithms of PVC evaluation on screening ECGs to help guide many providers with risk stratification and decision-making about further cardiac testing and electrophysiology referrals in young athletes with PVCs.

Optimization of Diclofenac-Loaded Bicomponent Nanofibers: Effect of Gelatin on In Vitro and In Vivo Response.

The use of electrospun fibers as anti-inflammatory drug carriers is currently one of the most interesting approaches for the design of drug delivery systems. In recent years, biodegradable polymers blended with naturally derived ones have been extensively studied to fabricate bioinspired platforms capable of driving biological responses by releasing selected molecular/pharmaceutical signals. Here, sodium diclofenac (DicNa)-loaded electrospun fibers, consisting of polycaprolactone (PCL) or gelatin-functionalized PCL, were studied to evaluate fibroblasts' in vitro and in vivo response. In vitro studies demonstrated that cell adhesion of L929 cells (≈70%) was not affected by the presence of DicNa after 4 h. Moreover, the initial burst release of the drug from PD and PGD fibers, e.g., 80 and 48%, respectively, after 5 h-combined with its sustained release-did not produce any cytotoxic effect and did not negatively influence the biological activity of the cells. In particular, it was demonstrated that the addition of gelatin concurred to slow down the release mechanism, thus limiting the antiproliferative effect of DicNa, as confirmed by the significant increase in cell viability and collagen deposition after 7 days, with respect to PCL alone. In vivo studies in a rat subcutaneous model also confirmed the ability of DicNa-loaded fibers to moderate the inflammatory/foreign body response independently through the presence of gelatin that played a significant role in supporting the formation of small-caliber vessels after 10 days of implantation. All of these results suggest using bicomponent fibers loaded with DicNa as a valid therapeutic tool capable of supporting the wound healing process and limiting in vivo inflammation and rejection phenomena.

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications.

Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram.

Aims: Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models. Methods and results: Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites. Conclusion: Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.

Inequities in atrial fibrillation trials: An analysis of participant race, ethnicity, and sex over time.

BACKGROUND: Despite the importance of racial and ethnic representation in clinical trials, limited data exist about the enrollment trends of these groups in atrial fibrillation (AF) trials over time. OBJECTIVE: The purpose of this study was to examine the characteristics of contemporary AF clinical trials and to evaluate their association with race and ethnicity over time. METHODS: We performed a systematic search of all completed AF trials registered in ClinicalTrials.gov from conception to December 31, 2023, and manually extracted composition of race/ethnicity. We stratified trials by study characteristics, including impact factor, publication status, funding source, and location. We calculated the participation to prevalence ratio (PPR) by dividing the percentage of non-White participants by the percentage of non-White participants in the disease population (PPR of 0.8-1.2 suggests proportional representation) over time. RESULTS: We identified 277 completed AF trials encompassing a total of 1,933,441 adults, with a median proportion of non-White at 12% (interquartile range, 6%-27%), 121 (43.7%) device focused, and 184 (66.4%) funded by industry. Only 36.1% of trials reported comprehensive race information. Overall, non-White participants were underrepresented (PPR = 0.511; P < .001), including Black (PPR = 0.263) and Hispanic (PPR = 0.337) participants. The proportion of non-White participants did not change significantly between 2000 and 2023 (11% vs 9%; P = .343). CONCLUSION: Despite greater awareness, race/ethnicity reporting and representation of non-White groups in AF clinical trials are poor and have not improved significantly over time. These findings demand additional recruitment efforts and novel recruitment policies to ensure adequate representation of these demographic subgroups in future AF clinical trials.

Considering radial basis function neural network for effective solution generation in metaheuristic algorithms.

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates.

Image segmentation with Cellular Automata.

Image segmentation is a computer vision technique that involves dividing an image into distinct and meaningful regions or segments. The objective was to partition the image into areas that share similar visual characteristics. Noise and undesirable artifacts introduce inconsistencies and irregularities in image data. These inconsistencies severely affect the ability of most segmentation algorithms to distinguish between true image features, leading to less reliable and lower-quality results. Cellular Automata (CA) is a computational concept that consists of a grid of cells, each of which can be in a finite number of states. These cells evolve over discrete time steps based on a set of predefined rules that dictate how a cell's state changes according to its own state and the states of its neighboring cells. In this paper, a new segmentation approach based on the CA model was introduced. The proposed approach consisted of three phases. In the initial two phases of the process, the primary objective was to eliminate noise and undesirable artifacts that can interfere with the identification of regions exhibiting similar visual characteristics. To achieve this, a set of rules is designed to modify the state value of each cell or pixel based on the states of its neighboring elements. In the third phase, each element is assigned a state that is chosen from a set of predefined states. These states directly represent the final segmentation values for the corresponding elements. The proposed method was evaluated using different images, considering important quality indices. The experimental results indicated that the proposed approach produces better-segmented images in terms of quality and robustness.

Generation of induced pluripotent stem cell lines from patients with LQT1 caused by heterozygous mutations in the KCNQ1 gene.

Long QT Syndrome (LQTS) is a genetic heart disorder that can induce cardiac arrhythmias. The most prevalent subtype, LQT1, stems from rare variants in the KCNQ1 gene. Utilizing induced pluripotent stem cells (iPSCs) enables detailed cellular studies and personalized medicine approaches for this life-threatening condition. We generated two LQT1 iPSC lines with single nucleotide nonsense mutations, c.1031 C > T and c.1121 T > A in KCNQ1. Both lines exhibited typical iPSC morphology, expressed high levels of pluripotent markers, maintained normal karyotype, and possessed the capability to differentiate into three germ layers. These cell lines serve as important tools for investigating the biological mechanisms underlying LQT1 due to mutations in the KCNQ1 gene.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Physical and structural characterization of bis-acryl composite resin.

During the preparation of fixed prosthesis (including individual bridges and crowns) it is important to select the materials that have the best features and properties to predict a successful clinical treatment. The objective of this study was to determine if the chemical and structural characteristics could cause to increase the fracture resistance, we used four bis-acryl resins Luxatemp, Protemp, Structur and Telio. Three-points bending by Flexural test were performed in ten bars and they were carried out to compare with Anova test. In addition, the bis-acryl resins were analyzed by scanning electron microscopy, to analyze microstructure and morphology and the molecular structure were performed by Infrared Spectroscopy through Attenuated Total Reflectance. A higher flexural strength was found in Luxatemp and Structur with, no significant differences between this study groups. Regarding Protemp and Telio, these study groups showed a lower flexural strength when were compared with Luxatemp and Structur. These results corroborate SEM and ATR analysis because Luxatemp sample showed a regular size particle on the surface and chemically presents a long cross-linkage polymer chain. The presence of CO3, SiO2 and N-H groups as a fillers particle interacting with OH groups cause a higher flexural strength compared with another groups.

Biomineralization of Polyelectrolyte-Functionalized Electrospun Fibers: Optimization and In Vitro Validation for Bone Applications.

In recent years, polyelectrolytes have been successfully used as an alternative to non-collagenous proteins to promote interfibrillar biomineralization, to reproduce the spatial intercalation of mineral phases among collagen fibrils, and to design bioinspired scaffolds for hard tissue regeneration. Herein, hybrid nanofibers were fabricated via electrospinning, by using a mixture of Poly ɛ-caprolactone (PCL) and cationic cellulose derivatives, i.e., cellulose-bearing imidazolium tosylate (CIMD). The obtained fibers were self-assembled with Sodium Alginate (SA) by polyelectrolyte interactions with CIMD onto the fiber surface and, then, treated with simulated body fluid (SBF) to promote the precipitation of calcium phosphate (CaP) deposits. FTIR analysis confirmed the presence of SA and CaP, while SEM equipped with EDX analysis mapped the calcium phosphate constituent elements, estimating an average Ca/P ratio of about 1.33-falling in the range of biological apatites. Moreover, in vitro studies have confirmed the good response of mesenchymal cells (hMSCs) on biomineralized samples, since day 3, with a significant improvement in the presence of SA, due to the interaction of SA with CaP deposits. More interestingly, after a decay of metabolic activity on day 7, a relevant increase in cell proliferation can be recognized, in agreement with the beginning of the differentiation phase, confirmed by ALP results. Antibacterial tests performed by using different bacteria populations confirmed that nanofibers with an SA-CIMD complex show an optimal inhibitory response against S. mutans, S. aureus, and E. coli, with no significant decay due to the effect of CaP, in comparison with non-biomineralized controls. All these data suggest a promising use of these biomineralized fibers as bioinspired membranes with efficient antimicrobial and osteoconductive cues suitable to support bone healing/regeneration.

Characteristics of contemporary atrial fibrillation clinical trials and their association with industry sponsorship.

BACKGROUND: Industry sponsorship is an important source of funding for atrial fibrillation (AF) clinical trials, the implications of which have not been analyzed. OBJECTIVE: The purpose of this study was to determine the characteristics of contemporary AF clinical trials and to evaluate their association with funding source. METHODS: We systematically assessed all completed AF trials registered in the ClinicalTrials.gov database between conception to October 31, 2023, and extracted publicly available information including funding source, trial size, demographic distribution, intervention, location, and publication status. Trial characteristics were compared using the Wilcoxon rank-sum test and Fisher exact test for continuous and categorical variables, respectively. RESULTS: Of the 253 clinical trials assessed, 171 (68%) reported industry funding. Industry funding was associated with a greater median number of patients enrolled (172 vs 80; P <.001), publication rate (56.7% vs 42.7%; P = .04), probability of being product-focused (48.0% vs 24.4%; P <.001), and multicontinental recruitment location (25.2% vs 2.4%; P <.001) when compared to nonindustry-funded trials. However, industry funding was not associated with a significant difference in median impact factor (7.7 vs 7.7; P = .723). The overall proportion of industry-funded trials did not change over time (P = 1). CONCLUSION: Industry-funded clinical trials in AF often are larger, more frequently published, multicontinental, and product-focused. Industry funding was found to be associated with significant differences in study enrollment and publication metrics.

Prevalence of frequent premature ventricular contractions and nonsustained ventricular tachycardia in older women screened for atrial fibrillation in the Women's Health Initiative.

BACKGROUND: Frequent premature ventricular contractions (PVCs) and nonsustained ventricular tachycardia (NSVT) have been associated with cardiovascular disease and mortality. Their prevalence, especially in ambulatory populations, is understudied and limited by few female participants and the use of short-duration (24- to 48-hour) monitoring. OBJECTIVE: The objective of this study was to report the prevalence of frequent PVCs and NSVT in a community-based population of women likely to undergo electrocardiogram (ECG) screening by sequential patch monitoring. METHODS: Participants from the Women's Health Initiative Strong and Healthy (WHISH) trial with no history of atrial fibrillation (AF) but 5-year predicted risk of incident AF ≥5% by CHARGE-AF score were randomly selected to undergo screening with 7-day ECG patch monitors at baseline, 6 months, and 12 months. Recordings were reviewed for PVCs and NSVT (>5 beats); data were analyzed with multivariate regression models. RESULTS: There were 1067 participants who underwent ECG screening at baseline, 866 at 6 months, and 777 at 12 months. Frequent PVCs were found on at least 1 patch from 4.3% of participants, and 1 or more episodes of NSVT were found in 12 (1.1%) women. PVC frequency directly correlated with CHARGE-AF score and NSVT on any patch. Detection of frequent PVCs increased with sequential monitoring. CONCLUSION: In postmenopausal women at high risk for AF, frequent PVCs were relatively common (4.3%) and correlated with higher CHARGE-AF score. As strategies for AF screening continue to evolve, particularly in those individuals at high risk of AF, the prevalence of incidental ventricular arrhythmias is an important benchmark to guide clinical decision-making.