Viruses. 2024 Nov 29;16(12):1864. doi: 10.3390/v16121864.

ABSTRACT

In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights. We used a dataset consisting of individuals who were tested for COVID-19 during the early stages of the pandemic in 2020. The dataset included self-reported symptoms and test results from a wide demographic, and our goal was to identify the most important symptoms that could help predict COVID-19 infection accurately. By applying interpretability techniques to both machine learning and deep learning models, we aimed to improve understanding of symptomatology and enhance early detection of COVID-19 cases. Notably, even though less than 1% of our cohort reported having a sore throat, this symptom emerged as a significant indicator of active COVID-19 infection, appearing 7 out of 9 times in the top four most important features across all methodologies. This suggests its potential as an early symptom marker. Studies have shown that individuals reporting sore throat may have a compromised immune system, where antibody generation is not functioning correctly. This aligns with our data, which indicates that 5% of patients with sore throats required hospitalization. Our analysis also revealed a concerning trend of diminished immune response post-COVID infection, increasing the likelihood of severe cases requiring hospitalization. This finding underscores the importance of monitoring patients post-recovery for potential complications and tailoring medical interventions accordingly. Our study also raises critical questions about the efficacy of COVID-19 vaccines in individuals presenting with sore throat as a symptom. The results suggest that booster shots might be necessary for this population to ensure adequate immunity, given the observed immune response patterns. The proposed method not only enhances our understanding of COVID-19 symptomatology but also demonstrates its broader utility in medical outlier detection. This research contributes valuable insights to ongoing efforts in creating interpretable models for COVID-19 management and vaccine optimization strategies. By leveraging feature importance and interpretability, these models empower physicians, healthcare workers, and researchers to understand complex relationships within medical data, facilitating more informed decision-making for patient care and public health initiatives.

PMID:39772174 | DOI:10.3390/v16121864

Sensors (Basel). 2024 Dec 23;24(24):8220. doi: 10.3390/s24248220.

ABSTRACT

To address the limitations of existing deep learning-based algorithms in detecting surface defects on brake pipe ends, a novel lightweight detection algorithm, FP-YOLOv8, is proposed. This algorithm is developed based on the YOLOv8n framework with the aim of improving accuracy and model lightweight design. First, the C2f_GhostV2 module has been designed to replace the original C2f module. It reduces the model's parameter count through its unique design. It achieves improved feature representation by adopting specific technique within its structure. Additionally, it incorporates the decoupled fully connected (DFC) attention mechanism, which minimizes information loss during long-range feature transmission by separately capturing pixel information along horizontal and vertical axes via convolution. Second, the Dynamic ATSS label allocation strategy is applied, which dynamically adjusts label assignments by integrating Anchor IoUs and predicted IoUs, effectively reducing the misclassification of high-quality prediction samples as negative samples. Thus, it improves the detection accuracy of the model. Lastly, an asymmetric small-target detection head, FADH, is proposed to utilize depth-separable convolution to accomplish classification and regression tasks, enabling more precise capture of detailed information across scales and improving the detection of small-target defects. The experimental results show that FP-YOLOv8 achieves a mAP50 of 89.5% and an F1-score of 87% on the ends surface defects dataset, representing improvements of 3.3% and 6.0%, respectively, over the YOLOv8n algorithm, Meanwhile, it reduces model parameters and computational costs by 14.3% and 21.0%. Additionally, compared to the baseline model, the AP50 values for cracks, scratches, and flash defects rise by 5.5%, 5.6%, and 2.3%, respectively. These results validate the efficacy of FP-YOLOv8 in enhancing defect detection accuracy, reducing missed detection rates, and decreasing model parameter counts and computational demands, thus meeting the requirements of online defect detection for brake pipe ends surfaces.

PMID:39771953 | DOI:10.3390/s24248220

Sensors (Basel). 2024 Dec 23;24(24):8217. doi: 10.3390/s24248217.

ABSTRACT

Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications. Prior to image fusion, the image pairs have to be properly registered and mapped to a common resolution palette. However, due to differences in the device physics of image capture, information from VIS-IR sensors cannot be directly correlated, which is a major bottleneck for this area of research. In the absence of camera metadata, image registration is performed manually, which is not practical for large datasets. Most of the work published in this area assumes calibrated sensors and the availability of camera metadata providing registered image pairs, which limits the generalization capability of these systems. In this work, we propose a novel end-to-end pipeline termed DeepFusion for image registration and fusion. Firstly, we design a recursive crop and scale wavelet spectral decomposition (WSD) algorithm for automatically extracting the patch of visible data representing the thermal information. After data extraction, both the images are registered to a common resolution palette and forwarded to the DNN for image fusion. The fusion performance of the proposed pipeline is compared and quantified with state-of-the-art classical and DNN architectures for open-source and custom datasets demonstrating the efficacy of the pipeline. Furthermore, we also propose a novel keypoint-based metric for quantifying the quality of fused output.

PMID:39771950 | DOI:10.3390/s24248217

Sensors (Basel). 2024 Dec 23;24(24):8207. doi: 10.3390/s24248207.

ABSTRACT

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings. So, the scene knowledge will include global context information, fitting fine-grained visual information and scene structure information. Then, a scene filter module is designed to learn the global context information and fitting fine-grained visual information, and a scene structure module is designed to learn the scene structure information. Finally, the scene semantic features are used as the carrier to integrate three categories of information into the relative scene features, which can assist in the recognition of the occluded fittings and the tiny-scale fittings after feature mining and feature integration. The experiments show that the proposed network can effectively improve the performance of the multi-fitting detection task compared with the Faster R-CNN and other state-of-the-art models. In particular, the detection performances of the occluded and tiny-scale fittings are significantly improved.

PMID:39771941 | DOI:10.3390/s24248207

Sensors (Basel). 2024 Dec 21;24(24):8172. doi: 10.3390/s24248172.

ABSTRACT

Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dorée, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.

PMID:39771913 | DOI:10.3390/s24248172

Sensors (Basel). 2024 Dec 21;24(24):8168. doi: 10.3390/s24248168.

ABSTRACT

This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance. These include deep learning, adaptive optimization, and frequency-specific decomposition, which enhance accuracy and robustness. Classification methods were explored by comparing traditional machine learning with deep learning and emphasizing the role of brain lateralization in imagined speech for effective recognition and classification. This study discusses the challenges of generalizability and scalability in imagined speech recognition, focusing on subject-independent approaches and multiclass scalability. Performance benchmarking across various datasets and methodologies revealed varied classification accuracies, reflecting the complexity and variability of EEG signals. This review concludes that challenges remain despite progress, particularly in classifying directional words. Future research directions include improved signal processing techniques, advanced neural network architectures, and more personalized, adaptive BCI systems. This review is critical for future efforts to develop practical communication tools for individuals with speech and motor impairments using EEG-based BCIs.

PMID:39771903 | DOI:10.3390/s24248168

Sensors (Basel). 2024 Dec 21;24(24):8167. doi: 10.3390/s24248167.

ABSTRACT

Freezing of wind turbines causes loss of wind-generated power. Forecasting or prediction of icing on wind turbine blades based on SCADA sensor data allows taking appropriate actions before icing occurs. This paper presents a newly developed deep learning network model named PCTG (Parallel CNN-TCN GRU) for the purpose of high-accuracy and long-term prediction of icing on wind turbine blades. This model combines three networks, the CNN, TCN, and GRU, in order to incorporate both the temporal aspect of SCADA time-series data as well as the dependencies of SCADA variables. The experimentations conducted by using this model and SCADA data from three wind turbines in a wind farm have generated average prediction accuracies of about 97% for prediction horizons of up to 2 days ahead. The developed model is shown to maintain at least 95% prediction accuracy for long prediction horizons of up to 22 days ahead. Furthermore, for another wind farm SCADA dataset, it is shown that the developed PCTG model achieves over 99% icing prediction accuracy 10 days ahead.

PMID:39771901 | DOI:10.3390/s24248167

Sensors (Basel). 2024 Dec 21;24(24):8164. doi: 10.3390/s24248164.

ABSTRACT

Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual-inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments. Recorded using Meta Aria Project glasses, it captures realistic scenarios without environmental control. InCrowd-VI features 58 sequences totaling a 5 km trajectory length and 1.5 h of recording time, including RGB, stereo images, and IMU measurements. The dataset captures important challenges such as pedestrian occlusions, varying crowd densities, complex layouts, and lighting changes. Ground-truth trajectories, accurate to approximately 2 cm, are provided in the dataset, originating from the Meta Aria project machine perception SLAM service. In addition, a semi-dense 3D point cloud of scenes is provided for each sequence. The evaluation of state-of-the-art visual odometry (VO) and SLAM algorithms on InCrowd-VI revealed severe performance limitations in these realistic scenarios. Under challenging conditions, systems exceeded the required localization accuracy of 0.5 m and the 1% drift threshold, with classical methods showing drift up to 5-10%. While deep learning-based approaches maintained high pose estimation coverage (>90%), they failed to achieve real-time processing speeds necessary for walking pace navigation. These results demonstrate the need and value of a new dataset to advance SLAM research for visually impaired navigation in complex indoor environments.

PMID:39771900 | DOI:10.3390/s24248164

Sensors (Basel). 2024 Dec 20;24(24):8157. doi: 10.3390/s24248157.

ABSTRACT

Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to cause a series of serious consequences. Traditional deep learning-based bearing fault diagnosis algorithms rely on large amounts of training data; training and inference processes consume significant computational resources. Thus, developing a lightweight and suitable fault diagnosis algorithm for small samples is particularly crucial. In this paper, we propose a bearing fault diagnosis algorithm based on probabilistic decoupling knowledge distillation and meta-learning (MIX-MPDKD). This algorithm is lightweight and deployable, performing well in small sample scenarios and effectively solving the deployment problem of large networks in resource-constrained environments. Firstly, our model utilizes the Model-Agnostic Meta-Learning algorithm to initialize the parameters of the teacher model and conduct efficient training. Subsequently, by employing the proposed probability-based decoupled knowledge distillation approach, the outstanding performance of the teacher model was imparted to the student model, enabling the student model to converge rapidly in the context of a small sample size. Finally, the Paderborn University dataset was used for meta-training, while the bearing dataset from Case Western Reserve University, along with our laboratory dataset, was used to validate the results. The experimental results demonstrate that the algorithm achieved satisfactory accuracy performance.

PMID:39771892 | DOI:10.3390/s24248157

Sensors (Basel). 2024 Dec 20;24(24):8139. doi: 10.3390/s24248139.

ABSTRACT

We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid imaging of highly sparse scenes at the native digital micromirror device (DMD) resolution of 1024 × 768. The reconstruction algorithm consists of two stages. In the first stage, the vector of SPI measurements is multiplied by the generalized inverse of the measurement matrix. In the second stage, we compare two reconstruction approaches: one based on an iterative algorithm and the other on a trained neural network. The neural network outperforms the iterative method when the object resembles the training set, though it lacks the generality of the iterative approach. For images captured at a compression of 0.41 percent, corresponding to a measurement rate of 6.8 Hz with a DMD operating at 22 kHz, the typical reconstruction time on a desktop with a medium-performance GPU is comparable to the image acquisition rate. This allows the proposed SPI method to support high-resolution dynamic SPI in a variety of applications, using a standard SPI architecture with a DMD modulator operating at its native resolution and bandwidth, and enabling the real-time processing of the measured data with no additional delay on a standard desktop PC.

PMID:39771884 | DOI:10.3390/s24248139

Pharmaceuticals (Basel). 2024 Nov 28;17(12):1605. doi: 10.3390/ph17121605.

ABSTRACT

Background/objectives: Idiopathic pulmonary fibrosis (IPF) is a prevalent interstitial lung disease that typically progresses gradually, leading to respiratory failure and ultimately death. IPF can be treated with the tyrosine kinase inhibitor, nintedanib (NTD), owing to its anti-fibrotic properties, which ameliorate the impairment of lung function. This study aimed to formulate, optimize, and assess NTD-loaded ufasomes (NTD-UFSs) as a nanosystem for its pulmonary targeting to snowball the bioavailability and therapeutic efficacy of the drug. Methods: To investigate the influence of numerous factors on NTD-UFSs assembly and to determine the optimal formulation, Box-Behnken statistical design was implemented with the assistance of Design-Expert® software. The thin-film hydration strategy was employed to fabricate NTD-UFSs. The optimum NTD-UFSs formulation was subsequently selected and subjected to additional evaluations. Also, using a rat model, a comparative pharmacokinetic analysis was scrutinized. Results: The optimal NTD-UFSs elicited an accumulative release of 65.57% after 24 h, an encapsulation efficiency of 62.51%, a zeta potential of -36.07 mV, and a vesicular size of 364.62 nm. In addition, it disclosed remarkable stability and a continuous cumulative release pattern. In vivo histopathological studies ascertained the tolerability of NTD-UFSs administered intratracheally. According to the pharmacokinetic studies, intratracheal NTD-UFSs administration manifested a significantly higher AUC0-∞ value than oral and intratracheal NTD suspensions, by approximately 5.66- and 3.53-fold, respectively. Conclusions: The findings of this study proposed that UFSs might be a promising nanoparadigm for the non-invasive pulmonary delivery of NTD.

PMID:39770447 | DOI:10.3390/ph17121605

Medicina (Kaunas). 2024 Nov 29;60(12):1967. doi: 10.3390/medicina60121967.

ABSTRACT

Recent advances in genetics and epigenetics have provided critical insights into the pathogenesis of both idiopathic and non-idiopathic interstitial lung diseases (ILDs). Mutations in telomere-related genes and surfactant proteins have been linked to familial pulmonary fibrosis, while variants in MUC5B and TOLLIP increase the risk of ILD, including idiopathic pulmonary fibrosis and rheumatoid arthritis-associated ILD. Epigenetic mechanisms, such as DNA methylation, histone modifications, and non-coding RNAs such as miR-21 and miR-29, regulate fibrotic pathways, influencing disease onset and progression. Although no standardized genetic panel for ILD exists, understanding the interplay of genetic mutations and epigenetic alterations could aid in the development of personalized therapeutic approaches. This review highlights the genetic and epigenetic factors driving ILD, emphasizing their potential for refining diagnosis and treatment.

PMID:39768847 | DOI:10.3390/medicina60121967

J Clin Med. 2024 Dec 23;13(24):7865. doi: 10.3390/jcm13247865.

ABSTRACT

Background: The degree of exercise-induced oxygen desaturation and outcomes following antifibrotic drug therapy in asymptomatic patients with fibrosing interstitial lung disease (FILD) remain unclear. Methods: We compared clinical data, incidence of annual FILD progression, overall survival, and tolerability after initiating nintedanib between 58 patients with dyspnea and 18 patients without. Annual FILD progression was defined as >10% decrease in forced vital capacity (FVC), >15% decrease in diffusing capacity of the lungs for carbon monoxide (DLCO), developing acute exacerbations, or FILD-related death within 1 year of starting nintedanib. Outcomes between the two groups were adjusted for covariates, including age, gender, FVC, DLCO, and diagnosis of idiopathic pulmonary fibrosis, all known prognostic factors for FILD. Results: In 6-min walk test, incidence of decrease to <90% of SpO2 was significantly lower in non-dyspnea group than in dyspnea group (24% vs. 55%, p = 0.028), but incidence of >4% decreases showed no significant difference (71% vs. 89%, p = 0.11) The incidence of annual progression was significantly lower in non-dyspnea than in dyspnea group (17% vs. 53%, adjusted p = 0.026). The relative change in DLCO was significantly slower in non-dyspnea group (adjusted p = 0.036), but FVC was not (adjusted p = 0.067). Overall survival was longer in non-dyspnea group (adjusted p = 0.0089). The discontinuation rate and therapeutic period of nintedanib were not significantly different between the groups. Conclusions: Asymptomatic patients with FILD have severe exercise-induced oxygen desaturation and better outcomes after nintedanib therapy than symptomatic patients. Antifibrotic drug therapy should not be avoided solely because of a lack of symptoms.

PMID:39768788 | DOI:10.3390/jcm13247865

Cells. 2024 Dec 18;13(24):2099. doi: 10.3390/cells13242099.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is associated with a significantly increased risk of thrombotic events and mortality. This review explores the complex bidirectional relationship between pulmonary fibrosis and thrombosis, discussing epidemiological evidence, pathogenetic mechanisms, and therapeutic implications, with a particular focus on the emerging role of extracellular vesicles (EVs) as crucial mediators linking fibrosis and coagulation. Coagulation factors directly promote fibrosis, while fibrosis itself activates thrombotic pathways. Retrospective studies suggest the benefits of anticoagulants in IPF, but prospective trials have faced challenges. Novel anticoagulants, profibrinolytic therapies, and agents targeting protease-activated receptors (PARs) show promise in preclinical studies and early clinical trials. EVs have emerged as key players in the pathogenesis of interstitial lung diseases (ILDs), serving as vehicles for intercellular communication and contributing to both fibrosis and coagulation. EV-based approaches, such as EV modulation, engineered EVs as drug delivery vehicles, and mesenchymal stem cell-derived EVs, represent promising therapeutic strategies. Ongoing research should focus on optimizing risk-benefit profiles, identifying predictive biomarkers, evaluating combination strategies targeting thrombotic, fibrotic, and inflammatory pathways, and advancing the understanding of EVs in ILDs to develop targeted interventions.

PMID:39768190 | DOI:10.3390/cells13242099

Cells. 2024 Dec 12;13(24):2058. doi: 10.3390/cells13242058.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic and lethal interstitial lung disease (ILD) of unknown origin, characterized by limited treatment efficacy and a fibroproliferative nature. It is marked by excessive extracellular matrix deposition in the pulmonary parenchyma, leading to progressive lung volume decline and impaired gas exchange. The chemokine system, a network of proteins involved in cellular communication with diverse biological functions, plays a crucial role in various respiratory diseases. Chemokine receptors trigger the activation, proliferation, and migration of lung-resident cells, including pneumocytes, endothelial cells, alveolar macrophages, and fibroblasts. Around 50 chemokines can potentially interact with 20 receptors, expressed by both leukocytes and non-leukocytes such as tissue parenchyma cells, contributing to processes such as leukocyte mobilization from the bone marrow, recirculation through lymphoid organs, and tissue influx during inflammation or immune response. This narrative review explores the complexity of the chemokine system in the context of IPF and the bleomycin-induced lung fibrosis mouse model. The goal is to identify specific chemokines and receptors as potential therapeutic targets. Recent progress in understanding the role of the chemokine system during IPF, using experimental models and molecular diagnosis, underscores the complex nature of this system in the context of the disease. Despite advances in experimental models and molecular diagnostics, discovering an effective therapy for IPF remains a significant challenge in both medicine and pharmacology. This work delves into microarray results from lung samples of IPF patients and murine samples at different stages of bleomycin-induced pulmonary fibrosis. By discussing common pathways identified in both IPF and the experimental model, we aim to shed light on potential targets for therapeutic intervention. Dysregulation caused by abnormal chemokine levels observed in IPF lungs may activate multiple targets, suggesting that chemokine signaling plays a central role in maintaining or perpetuating lung fibrogenesis. The highlighted chemokine axes (CCL8-CCR2, CCL19/CCL21-CCR7, CXCL9-CXCR3, CCL3/CCL4/CCL5-CCR5, and CCL20-CCR6) present promising opportunities for advancing IPF treatment research and uncovering new pharmacological targets within the chemokine system.

PMID:39768150 | DOI:10.3390/cells13242058

Cells. 2024 Dec 12;13(24):2050. doi: 10.3390/cells13242050.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is the most common type of fibrosis in lungs, characterized as a chronic and progressive interstitial lung disease involving pathological findings of fibrosis with a median survival of 3 years. Despite the knowledge accumulated regarding IPF from basic and clinical research, an effective medical therapy for the condition remains to be established. Thus, it is necessary for further research, including stem cell therapy, which will provide new insights into and expectations for IPF treatment. Recently, it has been reported that one of the new therapeutic candidates for IPF is adipose-derived mesenchymal stem cells (ADSCs), which have several benefits, such as easy accessibility and minimal morbidity compared to bone marrow-derived mesenchymal stem cells. Therefore, we investigated the possibility of ADSCs as a therapeutic candidate for IPF. Using human lung fibroblasts (LFs) from IPF patients, we demonstrated that human IPF LFs cocultured with ADSCs led to reduced fibrosis-related genes. Further analysis revealed that ADSCs prevented the activation of the ERK signaling pathway in IPF LFs via the upregulation of protein tyrosine phosphatase receptor-type R (PTPRR), which negatively regulates the ERK signaling pathway. Moreover, we demonstrated that intravascular administration of ADSCs improved the pathogenesis of bleomycin-induced pulmonary fibrosis with reduced collagen deposition in histology and hydroxyproline quantification and collagen markers such as the gene expression of types I and III collagen and α-smooth muscle actin (α-SMA) in a murine model. ADSC transfer was also investigated in a humanized mouse model of lung fibrosis induced via the infusion of human IPF LFs, because the bleomycin installation model does not fully recapitulate the pathogenesis of IPF. Using the humanized mouse model, we found that intravascular administration of ADSCs also improved fibrotic changes in the lungs. These findings suggest that ADSCs are a promising therapeutic candidate for IPF.

PMID:39768142 | DOI:10.3390/cells13242050

Biomedicines. 2024 Dec 19;12(12):2893. doi: 10.3390/biomedicines12122893.

ABSTRACT

Background/Objectives: A novel patient group with chronic pulmonary fibrosis is emerging post COVID-19. To identify patients at risk of developing post-COVID-19 lung fibrosis, we here aimed to identify systemic proteins that overlap with fibrotic markers identified in patients with idiopathic pulmonary fibrosis (IPF) and may predict COVID-19-induced lung fibrosis. Methods: Ninety-two proteins were measured in plasma samples from hospitalized patients with moderate and severe COVID-19 in Sweden, before the introduction of the vaccination program, as well as from healthy individuals. These measurements were conducted using proximity extension assay (PEA) technology with a panel including inflammatory and remodeling proteins. Histopathological alterations were evaluated in explanted lung tissue. Results: Connecting to IPF pathology, several proteins including decorin (DCN), tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) and chemokine (C-X-C motif) ligand 13 (CXCL13) were elevated in COVID-19 patients compared to healthy subjects. Moreover, we found incrementing expression of monocyte chemotactic protein-3 (MCP-3) and hepatocyte growth factor (HGF) when comparing moderate to severe COVID-19. Conclusions: Both extracellular matrix- and inflammation-associated proteins were identified as overlapping with pulmonary fibrosis, where we found DCN, TNFRSF12A, CXCL13, CXCL9, MCP-3 and HGF to be of particular interest to follow up on for the prediction of disease severity.

PMID:39767799 | DOI:10.3390/biomedicines12122893

Biomolecules. 2024 Dec 15;14(12):1605. doi: 10.3390/biom14121605.

ABSTRACT

Histone deacetylases (HDACs) are enzymes that play an essential role in the onset and progression of cancer. As a consequence, a variety of HDAC inhibitors (HDACis) have been developed as potent anticancer agents, several of which have been approved by the FDA for cancer treatment. However, recent accumulated research results have suggested that HDACs are also involved in several other pathophysiological conditions, such as fibrotic, inflammatory, neurodegenerative, and autoimmune diseases. Very recently, the HDAC inhibitor givinostat has been approved by the FDA for an indication beyond cancer: the treatment of Duchenne muscular dystrophy. In recent years, more and more HDACis have been developed as tools to understand the role that HDACs play in various disorders and as a novel therapeutic approach to fight various diseases other than cancer. In the present perspective article, we discuss the development and study of HDACis as anti-fibrotic and anti-inflammatory agents, covering the period from 2020-2024. We envision that the discovery of selective inhibitors targeting specific HDAC isozymes will allow the elucidation of the role of HDACs in various pathological processes and will lead to the development of promising treatments for such diseases.

PMID:39766311 | DOI:10.3390/biom14121605

Antioxidants (Basel). 2024 Dec 2;13(12):1480. doi: 10.3390/antiox13121480.

ABSTRACT

The aging process significantly impacts lung physiology and is a major risk factor for chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), asthma, and non-IPF interstitial lung fibrosis. This narrative clinical review explores the molecular and biochemical hallmarks of aging, such as oxidative stress, telomere attrition, genomic instability, epigenetic modifications, proteostasis loss, and impaired macroautophagy, and their roles in lung senescence. Central to this process are senescent cells, which, through the senescence-associated secretory phenotype (SASP), contribute to chronic inflammation and tissue dysfunction. The review highlights parallels between lung aging and pathophysiological changes in respiratory diseases, emphasizing the role of cellular senescence in disease onset and progression. Despite promising research into modulating aging pathways with interventions like caloric restriction, mTOR inhibitors, and SIRT1 activators, clinical evidence for efficacy in reversing or preventing age-related lung diseases remains limited. Understanding the interplay between aging-related mechanisms and environmental factors, such as smoking and pollution, is critical for developing targeted therapies. This review underscores the need for future studies focusing on therapeutic strategies to mitigate aging's detrimental effects on lung health and improve outcomes for patients with chronic respiratory conditions.

PMID:39765809 | DOI:10.3390/antiox13121480

Viruses. 2024 Dec 17;16(12):1926. doi: 10.3390/v16121926.

ABSTRACT

This study investigated a library of known and novel glycyrrhizic acid (GL) conjugates with amino acids and dipeptide esters, as inhibitors of the DENV NS2B-NS3 protease. We utilized docking algorithms to evaluate the interactions of these GL derivatives with key residues (His51, Asp75, Ser135, and Gly153) within 10 Å of the DENV-2 NS2B-NS3 protease binding pocket (PDB ID: 2FOM). It was found that compounds 11 and 17 exhibited unique binding patterns, forming hydrogen bonds with Asp75, Tyr150, and Gly153. Based on the molecular docking data, conjugates 11 with L-glutamic acid dimethyl ester, 17 with β-alanine ethyl ester, and 19 with aminoethantic acid methyl ester were further demonstrated as potent inhibitors of DENV-2 NS3 protease, with IC50 values below 1 μM, using NS3-mediated cleavage assay. Compound 11 was the most potent, with EC50 values of 0.034 μM for infectivity, 0.042 μM for virus yield, and a selective index over 2000, aligning with its strong NS3 protease inhibition. Compound 17 exhibited better NS3 protease inhibition than compound 19 but showed weaker effects on infectivity and virus yield. While all compounds strongly inhibited viral infectivity post-entry, compound 19 also blocked viral entry. This study provided valuable insights into the interactions between active GL derivatives and DENV-2 NS2B-NS3 protease, offering a comprehensive framework for identifying lead compounds for further drug optimization and design as NS2B-NS3 protease inhibitors against DENV.

PMID:39772233 | DOI:10.3390/v16121926