Int J Mol Sci. 2025 Jan 8;26(2):471. doi: 10.3390/ijms26020471.

ABSTRACT

The cystic fibrosis transmembrane conductance regulator (CFTR) is an anion channel that is dysfunctional in individuals with cystic fibrosis (CF). The permeability of CFTR can be experimentally manipulated though different mechanisms, including activation via inducing the phosphorylation of residues in the regulatory domain as well as altering the gating/open probability of the channel. Phosphorylation/activation of the channel is achieved by exposure to compounds that increase intracellular cAMP, with forskolin and IBMX commonly used for this purpose. Cact-A1 is a unique CFTR activator that does not increase intracellular cAMP, and VX-770 (ivacaftor) is a CFTR potentiator that is used experimentally and therapeutically to increase the open probability of the channel. Using primary human nasal epithelial cell (HNEC) cultures and Fischer rat thyroid (FRT) epithelial cells exogenously expressing functional CFTR, we examined the impact of VX-770, Cact-A1, and forskolin/IBMX on CFTR activity during analysis in an Ussing chamber. Relative contributions of these compounds to maximal CFTR activity were dependent on order of exposure, the presence of chemical and electrical gradients, the level of constitutive CFTR function, and the cell model tested. Increasing intracellular cAMP appeared to change cellular functions outside of CFTR activity that resulted in alterations in the drive for chloride through CFTR. These results demonstrate that one can utilize combinations of small-molecule CFTR activators and potentiators to provide detailed characterization of CFTR-mediated ion transport in primary HNECs and properties of these modulators in both primary HNECs and FRT cells. Future studies using these approaches may assist in the identification of novel defects in CFTR function and the identification of modulators with unique impacts on CFTR-mediated ion transport.

PMID:39859187 | DOI:10.3390/ijms26020471

Int J Mol Sci. 2025 Jan 7;26(2):437. doi: 10.3390/ijms26020437.

ABSTRACT

Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CFTR gene. Currently, CFTR modulators are the most effective treatment for CF; however, they may not be suitable for all patients. A representative and convenient in vitro model is needed to screen therapeutic agents under development. This study, on the most common mutation, F508del, investigates the efficacy of human induced pluripotent stem cell-derived lung organoids (hiLOs) from NKX2.1+ lung progenitors and airway basal cells (hiBCs) as a 3D model for CFTR modulator response assessment by a forskolin-induced swelling assay. Weak swelling was observed for hiLOs from NKX2.1+ lung progenitors and hiBCs in response to modulators VX-770/VX-809 and VX-770/VX-661, whereas the VX-770/VX-661/VX-445 combination resulted in the highest swelling response, indicating superior CFTR function restoration. The ROC analysis of the FIS assay results revealed an optimal cutoff of 1.21, with 65.9% sensitivity and 71.8% specificity, and the predictive accuracy of the model was 76.4%. In addition, this study compared the response of hiLOs with the clinical response of patients to therapy and showed similar drug response dynamics. Thus, hiLOs can effectively model the CF pathology and predict patients' specific response to modulators.

PMID:39859153 | DOI:10.3390/ijms26020437

Biomedicines. 2025 Jan 1;13(1):82. doi: 10.3390/biomedicines13010082.

ABSTRACT

Background: The potentiator VX-770 (ivacaftor) has been approved as a monotherapy for over 95 cystic fibrosis (CF)-causing variants associated with gating/conductance defects of the CF transmembrane conductance regulator (CFTR) channel. However, despite its therapeutic success, VX-770 only partially restores CFTR activity for many of these variants, indicating they may benefit from the combination of potentiators exhibiting distinct mechanisms of action (i.e., co-potentiators). We previously identified LSO-24, a hydroxy-1,2,3-triazole-based compound, as a modest potentiator of p.Arg334Trp-CFTR, a variant with a conductance defect for which no modulator therapy is currently approved. Objective/Methods: We synthesized a new set of LSO-24 structure-based compounds, screened their effects on p.Arg334Trp-CFTR activity, and assessed the additivity of hit compounds to VX-770, ABBV-974, ABBV-3067, and apigenin. After validation by electrophysiological assays, the most promising hits were also assessed in cells expressing other variants with defective gating/conductance, namely p.Pro205Ser, p.Ser549Arg, p.Gly551Asp, p.Ser945Leu, and p.Gly1349Asp. Results: We found that five compounds were able to increase p.Arg334Trp-CFTR activity with similar efficacy, but slightly greater potency promoted by LSO-150 and LSO-153 (EC50: 1.01 and 1.26 μM, respectively). These two compounds also displayed a higher rescue of p.Arg334Trp-CFTR activity in combination with VX-770, ABBV-974, and ABBV-3067, but not with apigenin. When tested in cells expressing other CFTR variants, LSO-24 and its derivative LSO-150 increased CFTR activity for the variants p.Ser549Arg, p.Gly551Asp, and p.Ser945Leu with a further effect in combination with VX-770 or ABBV-3067. No potentiator was able to rescue CFTR activity in p.Pro205Ser-expressing cells, while p.Gly1349Asp-CFTR responded to VX-770 and ABBV-3067 but not to LSO-24 or LSO-150. Conclusions: Our data suggest that these new potentiators might share a common mechanism with apigenin, which is conceivably distinct from that of VX-770 and ABBV-3067. The additive rescue of p.Arg334Trp-, p.Ser549Arg-, p.Gly551Asp-, and p.Ser945Leu-CFTR also indicates that these variants could benefit from the development of a co-potentiator therapy.

PMID:39857666 | DOI:10.3390/biomedicines13010082

Health Informatics J. 2025 Jan-Mar;31(1):14604582251315594. doi: 10.1177/14604582251315594.

ABSTRACT

Purpose: In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. Methods: We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance. Results: Based on the annotated data, the BERT model pre-trained with the medical corpus showed a significant performance improvement to the BiLSTM-CRF model with a micro-recall of 0.979 and an F1 value of 0.976, which indicates that the model has promising performance in identifying private information in Chinese clinical texts. Conclusions: The BERT-based BiLSTM-CRF model excels in identifying privacy information in Chinese clinical texts, and the application of this model is very effective in protecting patient privacy and facilitating data sharing.

PMID:39862116 | DOI:10.1177/14604582251315594

Viruses. 2024 Dec 25;17(1):14. doi: 10.3390/v17010014.

ABSTRACT

Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides. Trained on a significantly expanded dataset, AVP-GPT2 employs a transformer-based architecture to generate diverse peptide sequences. A multi-modal screening approach, incorporating Star-Transformer and Vision Transformer, enables accurate prediction of antiviral activity and toxicity, leading to the identification of potent and safe candidates. SHAP analysis further enhances interpretability by explaining the underlying mechanisms of peptide activity. Our in vitro experiments confirmed the antiviral efficacy of peptides generated by AVP-GPT2, with some exhibiting EC50 values as low as 0.01 μM and CC50 values > 30 μM. This represents a substantial improvement over AVP-GPT and traditional methods. AVP-GPT2 has the potential to significantly impact antiviral drug discovery by accelerating the identification of novel therapeutic agents. Future research will explore its application to other viral targets and its integration into existing drug development pipelines.

PMID:39861804 | DOI:10.3390/v17010014

Sensors (Basel). 2025 Jan 20;25(2):589. doi: 10.3390/s25020589.

ABSTRACT

With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult. To address these issues, we use YOLOv8s as the basic framework and introduce a multi-level feature fusion algorithm. Additionally, we design an attention mechanism that links distant pixels to improve small object feature extraction. To address missed detections and inaccurate localization, we replace the detection head with a dynamic head, allowing the model to route objects to the appropriate head for final output. We also introduce Slideloss to improve the model's learning of difficult samples and ShapeIoU to better account for the shape and scale of bounding boxes. Experiments on datasets like VisDrone2019 show that our method improves accuracy by nearly 10% and recall by about 11% compared to the baseline. Additionally, on the AI-TODv1.5 dataset, our method improves the mAP50 from 38.8 to 45.2.

PMID:39860960 | DOI:10.3390/s25020589

Sensors (Basel). 2025 Jan 20;25(2):580. doi: 10.3390/s25020580.

ABSTRACT

The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges. This study centers on employing deep learning methodologies to detect attacks. In general, this research aims to improve the performance of existing deep learning models. To mitigate data imbalances and enhance learning outcomes, the synthetic minority over-sampling technique (SMOTE) is employed. Our approach contributes to a multistage feature extraction process where autoencoders (AEs) are used initially to extract robust features from unstructured data on the model architecture's left side. Following this, long short-term memory (LSTM) networks on the right analyze these features to recognize temporal patterns indicative of abnormal behavior. The extracted and temporally refined features are inputted into convolutional neural networks (CNNs) for final classification. This structured arrangement harnesses the distinct capabilities of each model to process and classify IoT security data effectively. Our framework is specifically designed to address various attacks, including denial of service (DoS) and Mirai attacks, which are particularly harmful to IoT systems. Unlike conventional intrusion detection systems (IDSs) that may employ a singular model or simple feature extraction methods, our multistage approach provides more comprehensive analysis and utilization of data, enhancing detection capabilities and accuracy in identifying complex cyber threats in IoT environments. This research highlights the potential benefits that can be gained by applying deep learning methods to improve the effectiveness of IDSs in IoT security. The results obtained indicate a potential improvement for enhancing security measures and mitigating emerging threats.

PMID:39860948 | DOI:10.3390/s25020580

Sensors (Basel). 2025 Jan 19;25(2):561. doi: 10.3390/s25020561.

ABSTRACT

Real-time and accurate traffic forecasting aids in traffic planning and design and helps to alleviate congestion. Addressing the negative impacts of partial data loss in traffic forecasting, and the challenge of simultaneously capturing short-term fluctuations and long-term trends, this paper presents a traffic forecasting model, D-MGDCN-CLSTM, based on Multi-Graph Gated Dilated Convolution and Conv-LSTM. The model uses the DTWN algorithm to fill in missing data. To better capture the dual characteristics of short-term fluctuations and long-term trends in traffic, the model employs the DWT for multi-scale decomposition to obtain approximation and detail coefficients. The Conv-LSTM processes the approximation coefficients to capture the long-term characteristics of the time series, while the multiple layers of the MGDCN process the detail coefficients to capture short-term fluctuations. The outputs of the two branches are then merged to produce the forecast results. The model is tested against 10 algorithms using the PeMSD7(M) and PeMSD7(L) datasets, improving MAE, RMSE, and ACC by an average of 1.38% and 13.89%, 1% and 1.24%, and 5.92% and 1%, respectively. Ablation experiments, parameter impact analysis, and visual analysis all demonstrate the superiority of our decompositional approach in handling the dual characteristics of traffic data.

PMID:39860932 | DOI:10.3390/s25020561

Sensors (Basel). 2025 Jan 19;25(2):555. doi: 10.3390/s25020555.

ABSTRACT

This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.29% in identifying fatigue from original ECG data, 2D spectral characteristics and physiological information of subjects. In comparison to traditional methods, such as Support Vector Machines (SVMs) and Random Forests (RFs), as well as other deep learning methods, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), the proposed approach demonstrates significantly improved experimental outcomes. Overall, this study offers a promising solution for accurately recognizing fatigue through the analysis of physiological signals, with potential applications in sports and physical fitness training contexts.

PMID:39860925 | DOI:10.3390/s25020555

Sensors (Basel). 2025 Jan 19;25(2):554. doi: 10.3390/s25020554.

ABSTRACT

Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain. The resulting frequency domain data is then used as input to the convolutional neural network for feature extraction; Then, the weights of channel features and spatial features are assigned to the extracted features by CBAM, and the weighted features are then input into the Long Short-Term Memory (LSTM) network to learn temporal features. Finally, the effectiveness of the proposed model is verified using the PHM2012 bearing dataset. Compared to several existing RUL prediction methods, the mean squared error, mean absolute error, and root mean squared error of the proposed method in this paper are reduced by 53%, 16.87%, and 31.68%, respectively, which verifies the superiority of the method. Meanwhile, the experimental results demonstrate that the proposed method achieves good RUL prediction accuracy across various failure modes.

PMID:39860924 | DOI:10.3390/s25020554

Sensors (Basel). 2025 Jan 18;25(2):547. doi: 10.3390/s25020547.

ABSTRACT

Tea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. Improvement of the Cross Stage Partial Bottleneck Module with Two Convolutions(C2f) module via efficient Distributed Shift Convolution (DSConv) yields the C2f module with DSConv(DSCf)module, which reduces the model's size. Additionally, the coordinate attention (CA) mechanism is incorporated to mitigate interference from irrelevant factors, thereby improving mean accuracy. Furthermore, the SIOU_Loss (SCYLLA-IOU_Loss) function and the Dynamic Sample(DySample)up-sampling operator are implemented to accelerate convergence and enhance both average precision and detection accuracy. The experimental results show that compared to the YOLOv8n model, the TBF-YOLOv8n model has a 3.7% increase in accuracy, a 1.1% increase in average accuracy, a 44.4% reduction in gigabit floating point operations (GFLOPs), and a 13.4% reduction in the total number of parameters included in the model. In comparison experiments with a variety of lightweight detection models, the TBF-YOLOv8n still performs well in terms of detection accuracy while remaining more lightweight. In conclusion, the TBF-YOLOv8n model achieves a commendable balance between efficiency and precision, offering valuable insights for advancing intelligent tea bud harvesting technologies.

PMID:39860916 | DOI:10.3390/s25020547

Sensors (Basel). 2025 Jan 18;25(2):545. doi: 10.3390/s25020545.

ABSTRACT

With the proliferation of mobile terminals and the rapid growth of network applications, fine-grained traffic identification has become increasingly challenging. Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. In this paper, we propose AG-ZSL, a zero-shot learning framework based on traffic behavior and attribute representations for general encrypted traffic classification. AG-ZSL primarily learns two mapping functions: one that captures traffic behavior embeddings from burst-based traffic interaction graphs, and the other that learns attribute embeddings from traffic attribute descriptions. Then, the framework minimizes the distance between these embeddings within the shared feature space. The gradient rejection algorithm and K-Nearest Neighbors are introduced to implement a two-stage method for general traffic classification. Experimental results on IoT datasets demonstrate that AG-ZSL achieves exceptional performance in classifying both known and unknown traffic, highlighting its potential for enhancing secure and efficient traffic management at the network edge.

PMID:39860913 | DOI:10.3390/s25020545

Sensors (Basel). 2025 Jan 17;25(2):534. doi: 10.3390/s25020534.

ABSTRACT

The issue of obstacle avoidance and safety for visually impaired individuals has been a major topic of research. However, complex street environments still pose significant challenges for blind obstacle detection systems. Existing solutions often fail to provide real-time, accurate obstacle avoidance decisions. In this study, we propose a blind obstacle detection system based on the PC-CS-YOLO model. The system improves the backbone network by adopting the partial convolutional feed-forward network (PCFN) to reduce computational redundancy. Additionally, to enhance the network's robustness in multi-scale feature fusion, we introduce the Cross-Scale Attention Fusion (CSAF) mechanism, which integrates features from different sensory domains to achieve superior performance. Compared to state-of-the-art networks, our system shows improvements of 2.0%, 3.9%, and 1.5% in precision, recall, and mAP50, respectively. When evaluated on a GPU, the inference speed is 20.6 ms, which is 15.3 ms faster than YOLO11, meeting the real-time requirements for blind obstacle avoidance systems.

PMID:39860905 | DOI:10.3390/s25020534

Life (Basel). 2025 Jan 16;15(1):106. doi: 10.3390/life15010106.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease with a median survival of 3-5 years. Antifibrotic therapies like pirfenidone and nintedanib slow progression, but the outcomes vary. Gender may influence disease presentation, progression, and response to treatment. This study evaluates the impact of gender on the 5-year survival, pharmacological management, and clinical outcomes of patients with IPF.

METHODS: A retrospective cohort study of 254 IPF patients was conducted, with 164 (131 males:33 females) having complete data. Patients underwent spirometry, DLCO, and 6 min walk tests. Data on comorbidities, smoking, antifibrotic therapy type, dosage adjustments, and adverse events were collected. We used Kaplan-Meier survival curves and logistic regression to assess gender-related differences in outcomes.

RESULTS: Men had worse lung function at diagnosis (FVC 74.9 ± 18.5 vs. 87.2 ± 20.1% of pred.; p < 0.001) and a higher smoking prevalence (74% vs. 30%; p < 0.001). Women had better survival (51.2 vs. 40.8 ± 19.2 months; p = 0.005) despite more frequent biopsy use (36% vs. 17%; p = 0.013). Women tolerated longer therapy better (p = 0.001). No differences were found between patients receiving reduced antifibrotic dosing and those receiving full dosing.

CONCLUSIONS: Gender has a significant impact on IPF outcomes, with women demonstrating better survival and tolerance to long-term therapy. In contrast, reducing antifibrotic treatment does not appear to significantly affect survival outcomes. These findings underscore the need for future research on gender-specific management approaches.

PMID:39860046 | DOI:10.3390/life15010106

Biomolecules. 2025 Jan 14;15(1):122. doi: 10.3390/biom15010122.

ABSTRACT

Diffuse interstitial lung diseases (ILD) include conditions with identifiable causes such as chronic eosinophilic pneumonia (CEP), sarcoidosis (SAR), chronic hypersensitivity pneumonitis (CHP), and connective tissue disease-associated interstitial pneumonia (CTD), as well as idiopathic interstitial pneumonia (IIP) of unknown origin. In non-IIP diffuse lung diseases, bronchoalveolar lavage (BAL) fluid appearance is diagnostic. This study examines lymphocyte subsets in BAL fluid and peripheral blood of 56 patients with diffuse ILD, excluding idiopathic pulmonary fibrosis (IPF), who underwent BAL for diagnostic purposes. Patients were classified into CEP, SAR, CHP, CTD, and IIP groups, and clinical data, BAL cell analysis, and peripheral blood mononuclear cell analysis were compared. Eosinophils and type 3 innate lymphocytes (ILC3s) were significantly increased in the BAL fluid of the CEP group. Receiver operating characteristic curve analysis identified eosinophils ≥ 8% in BAL cells and ILC3s ≥ 0.0176% in the BAL lymphocyte fraction as thresholds distinguishing CEP. SAR patients exhibited significantly elevated CD4/CD8 ratios in the BAL fluid, with a ratio of 3.95 or higher and type 1 innate lymphoid cell frequency ≥ 0.254% as differentiation markers. High Th1 cell frequency (≥17.4%) in BAL lymphocytes in IIP, elevated serum KL-6 (≥2081 U/mL) and SP-D (≥261 ng/mL) in CHP, and increased BAL neutrophils (≥2.0%) or a low CD4/CD8 ratio (≤1.2) in CTD serve as distinguishing markers for each ILD. Excluding CEP and SAR, CD4+ T cell frequencies, including Th1, Th17, and Treg cells in peripheral blood, may differentiate IIP, CHP, and CTD.

PMID:39858516 | DOI:10.3390/biom15010122

Pneumonia (Nathan). 2025 Jan 25;17(1):2. doi: 10.1186/s41479-024-00155-7.

ABSTRACT

BACKGROUND: A growing body of evidence suggests that prolonged use of inhaled corticosteroids (ICS) and proton pump inhibitors (PPIs) is associated with increased risks of pneumonia. A substantial proportion of people with idiopathic pulmonary fibrosis (IPF) are prescribed PPIs or ICS to treat common comorbidities, giving rise to concerns that use of these medications may be associated with potential harms in this patient population.

METHODS: We used UK Clinical Practice Research Datalink (CPRD) Aurum primary care data linked to national mortality and hospital admissions data to create a cohort of people diagnosed with IPF on or after 1 January 2010. Patients were assigned to one of three exposure categories according to their prescribing history in the 12 months prior to IPF diagnosis as follows: "regular" users (≥ 4 prescriptions), "irregular" users (1-3 prescriptions) and "non-users" (no prescriptions). We explored the association between PPI/ICS prescription and pneumonia hospitalisation and all-cause mortality using multinomial Cox regression models.

RESULTS: A total of 17,105 people met our study inclusion criteria; 62.6% were male and 15.9% were current smokers. Median age at IPF diagnosis was 76.7 years (IQR: 69.6-82.7). 19.9% were regularly prescribed PPIs, and 16.0% ICS, prior to IPF diagnosis. Regular prescribing of PPIs and ICS was positively associated with hospitalisation for pneumonia; the adjusted HR for pneumonia hospitalisation comparing regular PPI users with non-users was 1.14 (95%CI: 1.04-1.24); for regular ICS users the corresponding HR was 1.40 (95%CI: 1.25-1.55). We also observed a small increased risk for all-cause mortality in the "regular ICS user" group compared with the "non-user" control group (HRadj = 1.19, 1.06-1.33). We found no evidence of an association between PPI prescribing and all-cause mortality.

CONCLUSION: Prolonged prescription of medications used to treat common comorbidities in IPF may be associated with increased risks for severe respiratory infections. These findings point to a need to adopt an adequate risk-benefit balance approach to the prescribing of ICS-containing inhalers and PPIs in people with IPF without evidence of comorbidities, especially older patients and/or those with more advanced disease in whom respiratory infections are more likely to result in poorer outcomes.

PMID:39856755 | DOI:10.1186/s41479-024-00155-7

BMC Pulm Med. 2025 Jan 24;25(1):36. doi: 10.1186/s12890-025-03506-2.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is the most common Interstitial Lung Disease (ILD). It is characterized by dyspnoea and a progressive decline in lung function, which negatively affects life. This study aimed to evaluate Health-Related Quality of Life (HRQoL) in IPF patients in Latin American countries.

METHODS: Six countries (Argentina, Bolivia, Colombia, Chile, Mexico, and the Dominican Republic) enrolled patients with IPF. They answered the Saint George's Respiratory Questionnaire for Idiopathic Pulmonary Fibrosis (SGRQ-I) and the Hospital Anxiety and Depression Scale (HADS). Demographic characteristics, the Torvan index, and a lung function test were also assessed. IPF was diagnosed according to the ATS/ERS/JRS/ALAT 2018 criteria.

RESULTS: We enlisted 75 patients diagnosed with IPF; 81% were male, with an average age of 74 ± 7. The total SGRQ-I score was 49 ± 23, with a higher score in the activity domain of 70 ± 23. Torvan index average was 17 ± 6. We found that 28% presented anxiety and 35% depression. Besides, we observed that patients requiring oxygen had a worse quality of life (total SGRQ-I 62 ± 22 vs. 45 ± 22, p = 0.003) without finding differences in antifibrotic therapy. We did not find differences in HRQoL when dividing groups according to their altitude above sea level, except for a higher frequency of anxiety in patients living at sea level.

CONCLUSIONS: We found similar data compared to those reported in real-life European populations. We also found that anxiety and depression are prevalent. However, they are often underdiagnosed and, therefore, left untreated.

PMID:39856586 | DOI:10.1186/s12890-025-03506-2

Pharmaceutics. 2025 Jan 3;17(1):52. doi: 10.3390/pharmaceutics17010052.

ABSTRACT

BACKGROUND: The Epstein-Barr virus (EBV) is intricately linked to a range of human malignancies, with EBV latent membrane protein 2A (LMP2A) emerging as a potential target antigen for immunotherapeutic strategies in the treatment of nasopharyngeal carcinoma (NPC).

METHODS: The modified vaccinia virus Ankara (MVA) is universally used in vector vaccine research because of its excellent safety profile and highly efficient recombinant gene expression. Here, we constructed a novel MVA-LMP2A recombinant virus and investigated its specific immune response induction and oncolytic effect.

RESULTS: An immunization dose of 2 × 107 PFU induced the highest specific immune response, which was no longer increased by boost injections after four doses. Three weeks post-final immunization, the specific immune response reached its peak. The MVA-LMP2A vaccine-induced LMP2A-specific cytotoxic T lymphocytes (CTLs), which exhibited substantial efficacy against target cells and effectively inhibited tumor growth.

CONCLUSIONS: Thus, the MVA-LMP2A recombinant virus effectively induces strong LMP2A-specific cellular and humoral immune responses and anti-tumor activity. This work provides a promising therapeutic strategy for developing NPC candidate vaccines, as well as a reference for the treatment of EBV LMP2-associated malignancies.

PMID:39861700 | DOI:10.3390/pharmaceutics17010052

Pharmaceuticals (Basel). 2025 Jan 3;18(1):45. doi: 10.3390/ph18010045.

ABSTRACT

BACKGROUND: Osteoporosis is characterized by the microstructural depletion of bone tissue and decreased bone density, leading to an increased risk of fractures. Cotoneaster wilsonii Nakai, an endemic species of the Korean Peninsula, grows wild in Ulleungdo. In this study, we aimed to investigate the effects of C. wilsonii and its components on osteoporosis.

METHODS AND RESULTS: The alkaline phosphatase (ALP) activity of C. wilsonii extracts and fractions was evaluated in MC3T3-E1 pre-osteoblasts, and the n-hexane fraction (CWH) showed the best properties for ALP activity. The effects of the CWH on bone formation were assessed in MC3T3-E1 cells and ovariectomized mice. Biochemical assays and histological analyses focused on the signaling activation of osteoblast differentiation and osteogenic markers, such as ALP, collagen, and osterix. The CWH significantly activated TGF-β and Wnt signaling, enhancing osteoblast differentiation and bone matrix formation. Notably, CWH treatment improved micro-CT indices, such as femoral bone density, and restored serum osteocalcin levels compared to OVX controls.

CONCLUSIONS: These results highlight the potential of the C. wilsonii Nakai n-hexane fraction as a promising therapeutic agent for managing osteoporosis.

PMID:39861108 | DOI:10.3390/ph18010045

Molecules. 2025 Jan 7;30(2):214. doi: 10.3390/molecules30020214.

ABSTRACT

Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, prediction methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. Deep learning approaches are especially well suited for this task due to the large amounts of protein sequences for training them, the trivial codification of this sequence data to feed into these systems, and the intrinsic sequential nature of the data that makes them suitable for language models. As a consequence, deep learning-based approaches are being applied to the prediction of different types of functional sites and regions in proteins. This review aims to give an overview of the current landscape of methodologies so that interested users can have an idea of which kind of approaches are available for their proteins of interest. We also try to give an idea of how these systems work, as well as explain their limitations and high dependence on the training set so that users are aware of the quality of expected results.

PMID:39860084 | DOI:10.3390/molecules30020214