RESUMO

Glucose is the primary source of energy for many organisms and is efficiently taken up by bacteria through a dedicated transport system that exhibits high specificity. In Escherichia coli, the glucose-specific transporter IICBGlc serves as the major glucose transporter and functions as a component of the phosphoenolpyruvate-dependent phosphotransferase system. Here, we report cryo-electron microscopy (cryo-EM) structures of the glucose-bound IICBGlc protein. The dimeric transporter embedded in lipid nanodiscs was captured in the occluded, inward- and occluded, outward-facing conformations. Together with biochemical and biophysical analyses, and molecular dynamics (MD) simulations, we provide insights into the molecular basis and dynamics for substrate recognition and binding, including the gates regulating the binding sites and their accessibility. By combination of these findings, we present a mechanism for glucose transport across the plasma membrane. Overall, this work provides molecular insights into the structure, dynamics, and mechanism of the IICBGlc transporter in a native-like lipid environment.

Assuntos

Microscopia Crioeletrônica , Proteínas de Escherichia coli , Escherichia coli , Glucose , Simulação de Dinâmica Molecular , Escherichia coli/metabolismo , Escherichia coli/genética , Glucose/metabolismo , Proteínas de Escherichia coli/metabolismo , Proteínas de Escherichia coli/química , Proteínas de Escherichia coli/ultraestrutura , Sistema Fosfotransferase de Açúcar do Fosfoenolpiruvato/metabolismo , Sistema Fosfotransferase de Açúcar do Fosfoenolpiruvato/química , Sítios de Ligação , Proteínas Facilitadoras de Transporte de Glucose/metabolismo , Proteínas Facilitadoras de Transporte de Glucose/química , Proteínas Facilitadoras de Transporte de Glucose/genética , Conformação Proteica , Transporte Biológico , Ligação Proteica

RESUMO

Aims: Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes. This study aimed to identify early-stage CAD using ML in conjunction with a panel of clinical and laboratory tests. Methods and results: The study sample included 3316 patients enrolled in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. A comprehensive array of attributes was considered, and an ML pipeline was developed. Subsequently, we utilized five approaches to generating high-quality virtual patient data to improve the performance of the artificial intelligence models. An extension study was carried out using data from the Young Finns Study (YFS) to assess the results' generalizability. Upon applying virtual augmented data, accuracy increased by approximately 5%, from 0.75 to -0.79 for random forests (RFs), and from 0.76 to -0.80 for Gradient Boosting (GB). Sensitivity showed a significant boost for RFs, rising by about 9.4% (0.81-0.89), while GB exhibited a 4.8% increase (0.83-0.87). Specificity showed a significant boost for RFs, rising by ∼24% (from 0.55 to 0.70), while GB exhibited a 37% increase (from 0.51 to 0.74). The extension analysis aligned with the initial study. Conclusion: Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular disease.

RESUMO

Background/Objectives: Obesity and overweight have become increasingly prevalent in different populations of people with type 1 diabetes (PwT1D). This study aimed to assess the effect of body weight on glycaemic indices in PwT1D. Methods: Adult PwT1D using continuous glucose monitoring (CGM) and followed up at a regional academic diabetes centre were included. Body weight, body mass index (BMI), waist circumference, glycated haemoglobin (HbA1c), and standard CGM glycaemic indices were recorded. Glycaemic indices were compared according to BMI, and correlation and linear regression analysis were performed to estimate the association between measures of adiposity and glycaemic indices. Results: A total of 73 PwT1D were included (48% normal weight, 33% overweight, and 19% obese). HbA1c was 7.2% (5.6-10), glucose management indicator (GMI) 6.9% (5.7-8.9), coefficient of variation (CV) for glucose 39.5% ± 6.4, mean glucose 148 (101-235) mg/dL, TIR (time in range, glucose 70-180 mg/dL) 66% (25-94), TBR70 (time below range, 54-69 mg/dL) 4% (0-16), TBR54 (<54 mg/dL) 1% (0-11), TAR180 (time above range, 181-250 mg/dL) 20% ± 7, and TAR250 (>250 mg/dL) 6% (0-40). Glycaemic indices and achievement (%) of optimal glycaemic targets were similar between normal weight, overweight, and obese patients. BMI was associated negatively with GMI, mean glucose, TAR180, and TAR250 and positively with TIR; waist circumference was negatively associated with TAR250. Conclusions: CGM-derived glycaemic indices were similar in overweight/obese and normal weight PwT1D. Body weight and BMI were positively associated with better glycaemic control. PwT1D should receive appropriate ongoing support to achieve optimal glycaemic targets whilst maintaining a healthy body weight.

RESUMO

BACKGROUND: Non-Hodgkin lymphomas have a substantial impact on individuals with Sjögren's disease. This study focuses on mucosal-associated lymphoid tissue (MALT) lymphomas, which constitute the majority of Sjögren's disease-associated non-Hodgkin lymphomas. We aimed to identify reliable lymphoma predictors in patients with Sjögren's disease and study their progression over time. METHODS: In this case-control study, patients diagnosed with Sjögren's disease-associated MALT lymphoma, with a minimum of 3 years between Sjögren's disease diagnosis and MALT lymphoma diagnosis, were included from three centres specialising in Sjögren's disease (University of Athens, Athens, Greece; University of Pisa, Pisa, Italy; and University of Udine, Udine, Italy) and matched 1:1 with control participants with Sjögren's disease who did not have lymphoma according to age, sex, disease duration at last follow up, and treatment modality. Three harmonised datasets were constructed, curated, and analysed to identify MALT lymphoma predictors, representing three distinct timepoints in lymphomagenesis progression: V1 at Sjögren's disease diagnosis, V2 3-4 years before lymphoma diagnosis, and V3 0·5-1·5 years before lymphoma diagnosis. All recruited patients fulfilled the 2016 American College of Rheumatology-European League Against Rheumatism criteria for Sjögren's disease. The primary outcome was to identify MALT lymphoma predictors in Sjögren's disease, present at the timepoint of Sjögren's disease diagnosis and 3-4 years before the diagnosis of MALT lymphoma. A fast correlation-based feature selection and logistic regression model was used at V1 and V2 to identify MALT lymphoma predictors. The progression of potential predictors was studied across V1, V2, and V3. Histological parameters were not included in the analysis. An individual with lived experience of Sjögren's disease was involved in the study design. FINDINGS: 80 patients with Sjögren's disease-associated MALT lymphoma were included in the V1 dataset, 68 in the V2 dataset, and 80 in the V3 dataset, and matched to control participants with Sjögren's disease who did not have lymphoma. In both groups, 72 (90%) of 80 participants were women and eight (10%) were men. The mean age at Sjögren's disease diagnosis was 48·6 years (SD 11·6) in the lymphoma group and 48·7 years (11·5) in the control group. All patients were White, with 88 (55%) of 160 individuals of Greek nationality and 72 (45%) of Italian nationality. At the V1 timepoint, rheumatoid factor was the only independent lymphoma predictor (odds ratio 3·33 [95% CI 1·96-5·64]). At the V2 timepoint, rheumatoid factor (3·66 [95% CI 2·08-6·42]) and European League Against Rheumatism Sjögren's Syndrome Disease Activity Index ≥5 (3·88 [1·69-8·90]) were identified as independent lymphoma risk factors. The high disease activity during the transition from the V1 to V2 timepoint was attributed to specific B-cell-derived manifestations, including cryoglobulinaemia and glandular, cutaneous, and hematological manifestations. INTERPRETATION: Following up patients with high-risk of Sjögren's disease-associated MALT lymphoma based on the temporal progression of predictors presents an opportunity for early diagnosis and potential therapeutic interventions. Rheumatoid factor was the earliest and most persistent independent predictor of lymphoma. Specific B-cell manifestations in combination with rheumatoid factor indicate a more advanced stage of the lymphomagenesis process. FUNDING: European Commission-Horizon 2020.

Assuntos

Progressão da Doença , Linfoma de Zona Marginal Tipo Células B , Síndrome de Sjogren , Humanos , Linfoma de Zona Marginal Tipo Células B/diagnóstico , Linfoma de Zona Marginal Tipo Células B/patologia , Feminino , Estudos de Casos e Controles , Síndrome de Sjogren/complicações , Síndrome de Sjogren/epidemiologia , Pessoa de Meia-Idade , Masculino , Idoso , Adulto , Itália/epidemiologia

RESUMO

Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data. Studies involving multi-modal synthetic data generation were also explored. The type of method used for the synthetic data generation process was identified in each study and was categorized into statistical, probabilistic, machine learning, and deep learning. Emphasis was given to the programming languages used for the implementation of each method. Our evaluation revealed that the majority of the studies utilize synthetic data generators to: (i) reduce the cost and time required for clinical trials for rare diseases and conditions, (ii) enhance the predictive power of AI models in personalized medicine, (iii) ensure the delivery of fair treatment recommendations across diverse patient populations, and (iv) enable researchers to access high-quality, representative multimodal datasets without exposing sensitive patient information, among others. We underline the wide use of deep learning based synthetic data generators in 72.6 % of the included studies, with 75.3 % of the generators being implemented in Python. A thorough documentation of open-source repositories is finally provided to accelerate research in the field.

RESUMO

Microbial ion-pumping rhodopsins (MRs) are extensively studied retinal-binding membrane proteins. However, their biogenesis, including oligomerisation and retinal incorporation, remains poorly understood. The bacterial green-light absorbing proton pump proteorhodopsin (GPR) has emerged as a model protein for MRs and is used here to address these open questions using cryo-electron microscopy (cryo-EM) and molecular dynamics (MD) simulations. Specifically, conflicting studies regarding GPR stoichiometry reported pentamer and hexamer mixtures without providing possible assembly mechanisms. We report the pentameric and hexameric cryo-EM structures of a GPR mutant, uncovering the role of the unprocessed N-terminal signal peptide in the assembly of hexameric GPR. Furthermore, certain proteorhodopsin-expressing bacteria lack retinal biosynthesis pathways, suggesting that they scavenge the cofactor from their environment. We shed light on this hypothesis by solving the cryo-EM structure of retinal-free proteoopsin, which together with mass spectrometry and MD simulations suggests that decanoate serves as a temporary placeholder for retinal in the chromophore binding pocket. Further MD simulations elucidate possible pathways for the exchange of decanoate and retinal, offering a mechanism for retinal scavenging. Collectively, our findings provide insights into the biogenesis of MRs, including their oligomeric assembly, variations in protomer stoichiometry and retinal incorporation through a potential cofactor scavenging mechanism.

Assuntos

Microscopia Crioeletrônica , Simulação de Dinâmica Molecular , Retinaldeído , Rodopsinas Microbianas , Rodopsinas Microbianas/metabolismo , Rodopsinas Microbianas/química , Rodopsinas Microbianas/genética , Retinaldeído/metabolismo , Retinaldeído/química , Multimerização Proteica , Proteínas de Bactérias/metabolismo , Proteínas de Bactérias/química , Proteínas de Bactérias/genética , Conformação Proteica

RESUMO

BACKGROUND: There has been a large discussion in literature regarding the proper management of asymptomatic patients with significant carotid artery stenosis. This study aims to identify potential risk factors associated with high-risk carotid plaques. METHODS: This is a retrospective study based on a prospective database. Eligible patients had medium to severe symptomatic or asymptomatic carotid stenosis (≥50%, North American Symptomatic Carotid Endarterectomy Trial criteria). This study will analyze patients recruited by our institution as part of the multicenter TAXINOMISIS project (NCT03495830). According to protocol, all patients underwent a colored Duplex ultrasound examination and a magnetic resonance angiography at baseline. Carotid plaques were classified according to Gray-Weale ultrasonographic criteria (types I-V). Main outcomes included the occurrence of symptoms, the high/low echogenicity of the plaque, the existence of intraplaque hemorrhage and the existence of lipidic/necrotic core. Secondary, risk factors associated with the aforementioned outcomes were evaluated. RESULTS: A total of 62 patients (mean age: 68.7 ± 9.3 years, 66.1% males, 24.2% symptomatic) were recruited by our department. Mean carotid stenosis was 70.81% ± 13.53%. In multivariate regression analysis, C-reactive protein > 2 mg/l was strongly associated with symptomatic stenosis (odds ratio [OR] = 9.92 [1.12-88.178]; P = 0.039), and low high-density lipoprotein levels (<1200 mmol/l) were associated with lipidic/necrotic plaque core (OR = 16.88 [1.10-259.30]; P = 0.043). Low high-density lipoprotein levels (OR = 7.22 [1.00-51.95], P = 0.049) and HbA1c >7% (OR = 0.08 [0.01-0.93], P = 0.044) were associated with type III/IV plaques whereas HgAbc1 >7% (OR = 14.26 [1.21-168.34], P = 0.035) was associated with type V plaques. CONCLUSIONS: This preliminary study has revealed some potential risk factors associated with unstable carotid plaques. These data could help the future development of prognostic models for early detection patients that could benefit from further intervention.

Assuntos

Estenose das Carótidas , Bases de Dados Factuais , Placa Aterosclerótica , Índice de Gravidade de Doença , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Doenças Assintomáticas , Biomarcadores/sangue , Proteína C-Reativa/análise , Artérias Carótidas/diagnóstico por imagem , Artérias Carótidas/cirurgia , Estenose das Carótidas/diagnóstico por imagem , Estenose das Carótidas/complicações , Hemorragia , Angiografia por Ressonância Magnética , Necrose , Valor Preditivo dos Testes , Prognóstico , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , Ultrassonografia Doppler em Cores

RESUMO

Prostate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.2%, and Hausdorff distance and average surface distance improved by 0.5 mm, with recall and precision also undergoing enhancements. Specifically, for lesions under 15 mm, our model showed a notable increase in five key metrics. In summary, ProLesA-Net consistently ranked at the top, demonstrating enhanced performance and stability. This advancement addresses crucial challenges in prostate lesion segmentation, enhancing clinical decision making and expediting treatment processes.

RESUMO

Background and objective: Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns. Methodology: This paper proposes a novel clustering methodology to efficiently associate three-dimensional data. The algorithm utilizes competitive learning to create a self-organizing neural network and adjust neuron positions in time-dependent and high dimensional feature space in order to assign them as clustering centers. The quantitative evaluation of the clustering was based on well-known clustering indices. Furthermore, a differential expression analysis and classification pipeline was employed to assess the capability of the proposed methodology to extract more accurate pathway-specific genes from its clusters. For that, a comparative analysis was also conducted against a heuristic timeseries clustering method. Results: The proposed methodology achieved better overall clustering indices scores and classification metrics using genes derived from its clusters. Notable cases include a threefold increase in the Calinski-Harabasz clustering index, a twofold improvement in the Davies-Bouldin clustering index and a ∼60% increase in the classification specificity score. Conclusion: A novel clustering methodology was developed and applied on several gene expression timeseries datasets from systemic autoinflammatory diseases, and its ability to efficiently produce well separated clusters compared to existing heuristic methods was demonstrated.

RESUMO

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.

RESUMO

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

Assuntos

Realidade Aumentada , Mãos , Aprendizado de Máquina , Reabilitação do Acidente Vascular Cerebral , Acidente Vascular Cerebral , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Acidente Vascular Cerebral/fisiopatologia , Idoso , Mãos/fisiopatologia , Mãos/fisiologia , Reabilitação do Acidente Vascular Cerebral/métodos , Destreza Motora/fisiologia , Adulto

RESUMO

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification.

RESUMO

Background: Unhealthy behavior increases the risk of dementia. Various socio-cognitive determinants influence whether individuals persist in or alter these unhealthy behaviors. Objective: This study identifies relevant determinants of behavior associated to dementia risk. Methods: 4,104 Dutch individuals (40-79 years) completed a screening questionnaire exploring lifestyle behaviors associated with dementia risk. Subsequently, 3,065 respondents who engaged in one or more unhealthy behaviors completed a follow-up questionnaire investigating socio-cognitive determinants of these behaviors. Cross-tables were used to assess the accuracy of participants' perceptions regarding their behavior compared to recommendations. Confidence Interval-Based Estimation of Relevance (CIBER) was used to identify the most relevant determinants of behavior based on visual inspection and interpretation. Results: Among the respondents, 91.3% reported at least one, while 65% reported two or more unhealthy lifestyle behaviors associated to dementia risk. Many of them were not aware they did not adhere to lifestyle recommendations. The most relevant determinants identified include attitudes (i.e., lacking a passion for cooking and finding pleasure in drinking alcohol or smoking), misperceptions on social comparisons (i.e., overestimating healthy diet intake and underestimating alcohol intake), and low perceived behavioral control (i.e., regarding changing physical inactivity, altering diet patterns, and smoking cessation). Conclusions: Individual-level interventions that encourage lifestyle change should focus on enhancing accurate perceptions of behaviors compared to recommendations, while strengthening perceived control towards behavior change. Given the high prevalence of dementia risk factors, combining interventions at both individual and environmental levels are likely to be the most effective strategy to reduce dementia on a population scale.

Assuntos

Demência , Estilo de Vida , Comportamento de Redução do Risco , Humanos , Demência/epidemiologia , Demência/prevenção & controle , Demência/psicologia , Países Baixos/epidemiologia , Feminino , Masculino , Pessoa de Meia-Idade , Idoso , Adulto , Inquéritos e Questionários , Comportamentos Relacionados com a Saúde , Cognição , Consumo de Bebidas Alcoólicas/psicologia , Consumo de Bebidas Alcoólicas/epidemiologia

RESUMO

The fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive and invasive imaging modalities. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy. METHODS: Search of the bibliographic database for articles published from 2011 to 2023 using the following search terms: invasive FFR and non-invasive FFR. Pooled analysis of the sensitivity and specificity, with the corresponding confidence intervals from 32% to 94%. In addition, the summary processing times were determined. RESULTS: In total, 24 studies published between 2011 and 2023 were included, with a total of 13,591 patients and 3345 vessels. The diagnostic accuracy of the invasive and non-invasive techniques at the per-patient level was 89% (95% CI, 85-92%) and 76% (95% CI, 61-80%), respectively, while on the per-vessel basis, it was 92% (95% CI, 82-88%) and 81% (95% CI, 75-87%), respectively. CONCLUSION: These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice.

RESUMO

Accurate and objective pain evaluation is crucial in developing effective pain management protocols, aiming to alleviate distress and prevent patients from experiencing decreased functionality. A multimodal automatic assessment framework for acute pain utilizing video and heart rate signals is introduced in this study. The proposed framework comprises four pivotal modules: the Spatial Module, responsible for extracting embeddings from videos; the Heart Rate Encoder, tasked with mapping heart rate signals into a higher dimensional space; the AugmNet, designed to create learning-based augmentations in the latent space; and the Temporal Module, which utilizes the extracted video and heart rate embeddings for the final assessment. The Spatial-Module undergoes pre-training on a two-stage strategy: first, with a face recognition objective learning universal facial features, and second, with an emotion recognition objective in a multitask learning approach, enabling the extraction of high-quality embeddings for the automatic pain assessment. Experiments with the facial videos and heart rate extracted from electrocardiograms of the BioVid database, along with a direct comparison to 29 studies, demonstrate state-of-the-art performances in unimodal and multimodal settings, maintaining high efficiency. Within the multimodal context, 82.74% and 39.77% accuracy were achieved for the binary and multi-level pain classification task, respectively, utilizing 9.62 million parameters for the entire framework.

RESUMO

A combined computational and experimental study of 3D-printed scaffolds made from hybrid nanocomposite materials for potential applications in bone tissue engineering is presented. Polycaprolactone (PCL) and polylactic acid (PLA), enhanced with chitosan (CS) and multiwalled carbon nanotubes (MWCNTs), were investigated in respect of their mechanical characteristics and responses in fluidic environments. A novel scaffold geometry was designed, considering the requirements of cellular proliferation and mechanical properties. Specimens with the same dimensions and porosity of 45% were studied to fully describe and understand the yielding behavior. Mechanical testing indicated higher apparent moduli in the PLA-based scaffolds, while compressive strength decreased with CS/MWCNTs reinforcement due to nanoscale challenges in 3D printing. Mechanical modeling revealed lower stresses in the PLA scaffolds, attributed to the molecular mass of the filler. Despite modeling challenges, adjustments improved simulation accuracy, aligning well with experimental values. Material and reinforcement choices significantly influenced responses to mechanical loads, emphasizing optimal structural robustness. Computational fluid dynamics emphasized the significance of scaffold permeability and wall shear stress in influencing bone tissue growth. For an inlet velocity of 0.1 mm/s, the permeability value was estimated at 4.41 × 10-9 m2, which is in the acceptable range close to human natural bone permeability. The average wall shear stress (WSS) value that indicates the mechanical stimuli produced by cells was calculated to be 2.48 mPa, which is within the range of the reported literature values for promoting a higher proliferation rate and improving osteogenic differentiation. Overall, a holistic approach was utilized to achieve a delicate balance between structural robustness and optimal fluidic conditions, in order to enhance the overall performance of scaffolds in tissue engineering applications.

RESUMO

Controlling the pH at the microliter scale can be useful for applications in research, medicine, and industry, and therefore represents a valuable application for synthetic biology and microfluidics. The presented vesicular system translates light of different colors into specific pH changes in the surrounding solution. It works with the two light-driven proton pumps bacteriorhodopsin and blue light-absorbing proteorhodopsin Med12, that are oriented in opposite directions in the lipid membrane. A computer-controlled measuring device implements a feedback loop for automatic adjustment and maintenance of a selected pH value. A pH range spanning more than two units can be established, providing fine temporal and pH resolution. As an application example, a pH-sensitive enzyme reaction is presented where the light color controls the reaction progress. In summary, light color-controlled pH-adjustment using engineered proteoliposomes opens new possibilities to control processes at the microliter scale in different contexts, such as in synthetic biology applications.

Assuntos

Bacteriorodopsinas , Concentração de Íons de Hidrogênio , Proteolipídeos

RESUMO

Although several studies have deployed gradient boosting trees (GBT) as a robust classifier for federated learning tasks (federated GBT [FGBT]), even with dropout rates (federated gradient boosting trees with dropout rate [FDART]), none of them have investigated the overfitting effects of FGBT across heterogeneous and highly imbalanced datasets within federated environments nor the effect of dropouts in the loss function. In this work, we present the federated hybrid boosted forests (FHBF) algorithm, which incorporates a hybrid weight update approach to overcome ill-posed problems that arise from overfitting effects during the training across highly imbalanced datasets in the cloud. Eight case studies were conducted to stress the performance of FHBF against existing algorithms toward the development of robust AI models for lymphoma development across 18 European federated databases. Our results highlight the robustness of FHBF, yielding an average loss of 0.527 compared with FGBT (0.611) and FDART (0.584) with increased classification performance (0.938 sensitivity, 0.732 specificity).

RESUMO

Although the incidence of restenosis and stent thrombosis has substantially declined during the last decades, they still constitute the two major causes of stent failure. These complications are partially attributed to the currently used cytostatic drugs, which can cause local inflammation, delay or prevent re-endothelialization and essentially cause arterial cell toxicity. Retinoic acid (RA), a vitamin A (retinol) derivative, is a naturally occurring substance used for the treatment of cell proliferation disorders. The agent has pleiotropic effects on vascular smooth muscle cells and macrophages: it influences the proliferation, migration, and transition of smooth muscle cells to other cell types and modulates macrophage activation. These observations are supported by accumulated evidence from in vitro and in vivo experiments. In addition, systemic and topical administration of RA can decrease the development of atherosclerotic plaques and reduce or inhibit restenosis after vascular injury (caused by embolectomy, balloon catheters, or ligation of arteries) in various experimental models. Recently, an RA-drug eluting stent (DES) has been tested in an animal model. In this review, we explore the effects of RA in atherosclerosis and the potential of the local delivery of RA through an RA-DES or RA-coated balloon for targeted therapeutic percutaneous vascular interventions. Despite promising published results, further experimental study is warranted to examine the safety and efficacy of RA-eluting devices in vascular artery disease.

Assuntos

Fármacos Cardiovasculares , Reestenose Coronária , Stents Farmacológicos , Animais , Stents Farmacológicos/efeitos adversos , Retinoides , Tretinoína/farmacologia , Tretinoína/uso terapêutico , Reestenose Coronária/prevenção & controle , Reestenose Coronária/etiologia , Stents/efeitos adversos , Resultado do Tratamento , Desenho de Prótese

RESUMO

OBJECTIVES: To evaluate pulmonary and small airway function in patients with idiopathic inflammatory myopathies (IIM) and make comparisons between patients with and without interstitial lung disease (ILD). METHODS: Newly diagnosed IIM patients with and without ILD determined by high resolution computed tomography were included in the study. Pulmonary and small airway function was assessed by spirometry, diffusing capacity for carbon monoxide (DLCO), body plethysmography, single and multiple breath nitrogen washout, impulse oscillometry and measurement of respiratory resistance by the interrupter technique (Rint) using the Q-box system. We used discrepancies between lung volumes measured by multiple breath nitrogen washout and body plethysmography to evaluate for small airway dysfunction. RESULTS: Study cohort comprised of 26 IIM patients, 13 with and 13 without ILD. IIM-ILD patients presented more frequently with dyspnoea, fever, arthralgias and positive anti-synthetase antibodies, compared to IIM patients without ILD. Classic spirometric parameters and most lung physiology parameters assessing small airway function did not differ between the two groups. Predicted total lung capacity and residual volume (TLCN2WO, RVN2WO) measured by multiple breath nitrogen washout and the TLCN2WO/TLCpleth ratio were significantly lower in IIM-ILD patients compared to those without ILD (mean: 111.1% vs. 153.4%, p=0.034, median: 171% vs. 210%, p=0.039 and median: 1.28 vs. 1.45, p=0.039, respectively). Rint tended to be higher among IIM-ILD patients (mean:100.5% vs. 76.6%, p=0.053). CONCLUSIONS: Discrepancies between lung volumes measured by multiple breath nitrogen washout and body plethysmography in IIM-ILD patients indicate an early small airways dysfunction in these patients.

Assuntos

Doenças Pulmonares Intersticiais , Miosite , Humanos , Pulmão/diagnóstico por imagem , Doenças Pulmonares Intersticiais/diagnóstico por imagem , Miosite/complicações , Miosite/diagnóstico , Testes de Função Respiratória , Nitrogênio , Estudos Retrospectivos