Antibiotics (Basel). 2025 Feb 7;14(2):165. doi: 10.3390/antibiotics14020165.

ABSTRACT

The lack of available treatments in pediatric non-cystic fibrosis (non-CF) bronchiectasis is a major concern, especially in the context of the increasing disease burden due to better detection rates with advanced imaging techniques. Recurrent infections in these patients are the main cause of deterioration, leading to impaired lung function and increasing the risk of morbidity and mortality. Since pediatric non-CF bronchiectasis with early recognition and appropriate treatment can be reversible, optimal management is an issue of growing significance. The use of inhaled antibiotics as a treatment option, although a standard of care for CF patients, has been poorly studied in patients with non-CF bronchiectasis, especially in children. In this review, we present the current data on the potential use of inhaled antibiotics in the treatment of non-CF bronchiectasis and assess their safety and efficacy profile, focusing mainly on children. We conclude that inhaled antibiotics as an adjuvant maintenance treatment option could be tried in a subgroup of patients with frequent exacerbations and recent or chronic Pseudomonas aeruginosa infection as they appear to have beneficial effects on exacerbation rate and bacterial load with minimal safety concerns. However, the level of evidence in children is extremely low; therefore, further research is needed on the validity of this recommendation.

PMID:40001409 | DOI:10.3390/antibiotics14020165

Antimicrob Resist Infect Control. 2025 Feb 25;14(1):17. doi: 10.1186/s13756-025-01527-4.

ABSTRACT

BACKGROUND/INTRODUCTION: Antipseudomonal antibiotics are frequently used in patients admitted to hospitals. Many of these substances are classified as a reserve or watch status by the WHO. Inappropriate risk assessment of invasive detection of P. aeruginosa (PAE) can be a reason for overuse of antipseudomonal antibiotics. Therefore it is important to define relevant and specific risk factors for invasive PAE detection.

OBJECTIVE: The objective of this study was to identify risk factors for invasive detection of PAE in patients upon hospital admission.

METHODS: All patients 18 years of age and older with a detection of PAE and/or Enterobacterales in clinical samples taken within 48 h of admission to one of the hospitals of Charité Universitätsmedizin Berlin between 2015 and 2020 were included into this retrospective cohort study.

RESULTS: Overall, we included a total of 27,710 patients. In 3,764 (13.6%) patients PAE was detected in clinical samples taken within 48 h after admission. The most frequently detected Enterobacterales was E. coli in 14.142 (51%) patients followed by Klebsiella spp. in 4.432 (16%) patients. Multivariable regression analysis identified that prior colonisation with a multi drug resistant PAE or detection of a PAE in clinical samples during a previous hospitalisation increased the risk for invasive detection of PAE (OR 39.41; 95% CI 28.54-54.39) and OR 7.87 (95% CI 6.60-9.38) respectively. Admission to a specialised ward for patients with cystic fibrosis was associated with an increased risk (OR 26.99; 95% CI 20.48-35.54). Presence of chronic pulmonary disease (OR 2.05; 95% CI 1.85-2.26), hemiplegia (OR 2.16; 95% CI 1.90-2.45) and male gender (OR 1.60; 95% CI 1.46-1.75) were associated with a modest increase in risk for presence of PAE.

CONCLUSION: Patients with a prior detection of P. aeruginosa or admission to a cystic fibrosis ward had the highest risk for invasive detection of P. aeruginosa. Adherence to specific risk scores based on local risk factors could help to optimize prescription of anti-pseudomonal antibiotics that categorized as reserve and watch.

PMID:40001254 | DOI:10.1186/s13756-025-01527-4

Nat Mach Intell. 2025;7(2):232-243. doi: 10.1038/s42256-024-00971-y. Epub 2025 Jan 28.

ABSTRACT

Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8+ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein-Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8+ T cell epitopes for rapid T cell vaccine development.

PMID:40008296 | PMC:PMC11847706 | DOI:10.1038/s42256-024-00971-y

Phys Imaging Radiat Oncol. 2025 Feb 1;33:100719. doi: 10.1016/j.phro.2025.100719. eCollection 2025 Jan.

ABSTRACT

Synthetic Computed Tomography (sCT) is required to provide electron density information for MR-only radiotherapy. Deep-learning (DL) methods for sCT generation show improved dose congruence over other sCT generation methods (e.g. bulk density). Using 30 female pelvis datasets to train a cycleGAN-inspired DL model, this study found mean dose differences between a deformed planning CT (dCT) and sCT were 0.2 % (D98 %). Three Dimensional Gamma analysis showed a mean of 90.4 % at 1 %/1mm. This study showed accurate sCTs (dose) can be generated from routinely available T2 spin echo sequences without the need for additional specialist sequences.

PMID:40008279 | PMC:PMC11851199 | DOI:10.1016/j.phro.2025.100719

Ther Adv Drug Saf. 2025 Feb 24;16:20420986251321704. doi: 10.1177/20420986251321704. eCollection 2025.

ABSTRACT

Artificial intelligence (AI) is transforming medication research and development, giving clinicians new treatment options. Over the past 30 years, machine learning, deep learning, and neural networks have revolutionized drug design, target identification, and clinical trial predictions. AI has boosted pharmaceutical R&D (research and development) by identifying new therapeutic targets, improving chemical designs, and predicting complicated protein structures. Furthermore, generative AI is accelerating the development and re-engineering of medicinal molecules to cater to both common and rare diseases. Although, to date, no AI-generated medicinal drug has been FDA-approved, HLX-0201 for fragile X syndrome and new molecules for idiopathic pulmonary fibrosis have entered clinical trials. However, AI models are generally considered "black boxes," making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. Despite these obstacles, AI-driven drug discovery has substantially reduced development times and costs, expediting the process and financial risks of bringing new medicines to market. In the future, AI is expected to continue to impact pharmaceutical innovation positively, making life-saving drug discoveries faster, more efficient, and more widespread.

PMID:40008227 | PMC:PMC11851753 | DOI:10.1177/20420986251321704

JAMIA Open. 2025 Jan 31;8(1):ooaf007. doi: 10.1093/jamiaopen/ooaf007. eCollection 2025 Feb.

ABSTRACT

OBJECTIVES: The automatic detection of stance on social media is an important task for public health applications, especially in the context of health crises. Unfortunately, existing models are typically trained on English corpora. Considering the benefits of extending research to other widely spoken languages, the goal of this study is to develop stance detection models for social media posts in Spanish.

MATERIALS AND METHODS: A corpus of 6170 tweets about COVID-19 vaccination, posted between March 1, 2020 and January 4, 2022, was manually annotated by native speakers. Traditional predictive models were compared with deep learning models to ascertain a baseline performance for the detection of stance in Spanish tweets. The evaluation focused on the ability of multilingual and language-specific embeddings to contextualize the topic of those short texts adequately.

RESULTS: The BERT-Multi+BiLSTM combination yielded the best results (macroaveraged F1 and Matthews correlation coefficient scores of 0.86 and 0.79, respectively; interpolated area under the receiver operating curve [AUC] of 0.95 for tweets against vaccination and 0.85 in favor of vaccination and a score of 0.97 for tweets containing no stance information), closely followed by the BETO+BiLSTM and RoBERTa BNE-LSTM Spanish models and the term frequency-inverse document frequency+SVM model (average AUC decrease of 0.01). The main differentiating factor among these models was the ability to predict tweets against vaccination.

DISCUSSION: The BERT Multi+BILSTM model outperformed the other models in terms of per class prediction capacity. The main assumption is that language-specific embeddings do not outperform multilingual embeddings or TF-IDF features because of the context of the topic. The inherent context of BERT or RoBERTa embeddings is general. So, these embeddings are not familiar with the slang commonly used on Twitter and, more specifically, during the pandemic.

CONCLUSION: The best performing model detects tweet stance with performance high enough to ensure its usefulness for public health applications, namely awareness campaigns, misinformation detection and other early intervention and prevention actions seeking to improve an individual's well-being based on autoreported experiences and opinions. The dataset and code of the study are available on GitHub.

PMID:40008184 | PMC:PMC11854073 | DOI:10.1093/jamiaopen/ooaf007

Front Public Health. 2025 Feb 11;13:1505825. doi: 10.3389/fpubh.2025.1505825. eCollection 2025.

ABSTRACT

INTRODUCTION: Parents' negative parenting style is an important cause of anxiety, depression, and suicide among university students. Given the widespread use of social media, microblogs offer a new and promising way for non-invasive, large-scale assessment of parenting styles of students' parents.

METHODS: In this study, we have two main objectives: (1) investigating the correlation between students' microblog discourses and parents' parenting styles and (2) devising a method to predict students' parenting styles from their microblog discourses. We analyzed 111,258 posts from 575 university students using frequency analysis to examine differences in the usage of topical and emotional word across different parenting styles. Informed by these insights, we developed an effective parenting style assessment method, including a correlation injection module.

RESULTS: Experimental results on the 575 students show that our method outperforms all the baseline NLP methods (including ChatGPT-4), achieving good assessment performance by reducing MSE by 14% to 0.12.

DISCUSSION: Our study provides a pioneering microblog-based parenting style assessment tool and constructs a dataset, merging insights from psychology and computational science. On the one hand, our study advances the understanding of how parenting styles are reflected in the linguistic and emotional expressions of students on microblogs. On the other hand, our study provides an assisting tool that could be used by healthcare institutions to identify students' parenting styles. It facilitates the identification of suicide risk factors among microblog student users, and enables timely interventions to prevent suicides, which enhances human wellbeing and saves lives.

PMID:40008146 | PMC:PMC11850274 | DOI:10.3389/fpubh.2025.1505825

Front Pharmacol. 2025 Feb 11;16:1525029. doi: 10.3389/fphar.2025.1525029. eCollection 2025.

ABSTRACT

Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related and disease-related interactions. Next, the similarity features and topological features are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions.

PMID:40008124 | PMC:PMC11850324 | DOI:10.3389/fphar.2025.1525029

J Oral Biol Craniofac Res. 2025 Mar-Apr;15(2):215-220. doi: 10.1016/j.jobcr.2025.01.016. Epub 2025 Feb 8.

ABSTRACT

BACKGROUND: Dental implants are the most accepted prosthetic alternative for missing teeth. With growing demands, several manufacturers have entered the market and produce a variety of implant brands creating a challenge for clinicians to identify the implant when the necessity arises. Currently, radiographs are the only tools for implant identification which is inherently a complex process, hence the need for implant identification technique. Artificial intelligence capable of analysing images in a radiograph and predicting implant type is an efficient tool. The study evaluated an advanced deep learning technique, DEtection TRanformer for implant identification.

METHODS: A transformer-based deep learning technique, DEtection TRanformer was trained to identify implants in radiographs. A dataset of 1138 images consisting of five implant types captured from periapical and panoramic radiographs was chosen for the study. After augmentation, a dataset of 1744 images was secured and then split into training, validation and test datasets for the model. The model was trained and evaluated for its performance.

RESULTS: The model achieved an overall precision of 0.83 and a recall score of 0.89. The model achieved an F1-score of 0.82 indicating a strong balance between recall and precision. The Precision-Recall Curve, with an AUC of 0.96, showed that the model performed well across various thresholds. The training and validation graphs showed a consistent decrease in the loss functions across classes.

CONCLUSION: The model showed high performance on the training data, though it faced challenges with unseen validation data. High precision, recall and F1 score indicate the model's potential for implant identification. Optimizing this model for a balance between accuracy and efficiency will be necessary for real-time medical imaging applications.

PMID:40008072 | PMC:PMC11849603 | DOI:10.1016/j.jobcr.2025.01.016

Front Hum Neurosci. 2025 Feb 11;19:1498297. doi: 10.3389/fnhum.2025.1498297. eCollection 2025.

ABSTRACT

The rapid expansion of dialectally unique Arabic material on social media and the internet highlights how important it is to categorize dialects accurately to maximize a variety of Natural Language Processing (NLP) applications. The improvement in classification performance highlights the wider variety of linguistic variables that the model can capture, providing a reliable solution for precise Arabic dialect recognition and improving the efficacy of NLP applications. Recent advances in deep learning (DL) models have shown promise in overcoming potential challenges in identifying Arabic dialects. In this paper, we propose a novel stacking model based on two transformer models, i.e., Bert-Base-Arabertv02 and Dialectal-Arabic-XLM-R-Base, to enhance the classification of dialectal Arabic. The proposed model consists of two levels, including base models and meta-learners. In the proposed model, Level 1 generates class probabilities from two transformer models for training and testing sets, which are then used in Level 2 to train and evaluate a meta-learner. The stacking model compares various models, including long-short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network (CNN), and two transformer models using different word embedding. The results show that the stacking model combination of two models archives outperformance over single-model approaches due to capturing a broader range of linguistic features, which leads to better generalization across different forms of Arabic. The proposed model is evaluated based on the performance of IADD and Shami. For Shami, the Stacking-Transformer achieves the highest performance in all rates compared to other models with 89.73 accuracy, 89.596 precision, 89.73 recall, and 89.574 F1-score. For IADD, the Stacking-Transformer achieves the highest performance in all rates compared to other models with 93.062 accuracy, 93.368 precision, 93.062 recall, and 93.184 F1 score. The improvement in classification performance highlights the wider variety of linguistic variables that the model can capture, providing a reliable solution for precise Arabic dialect recognition and improving the efficacy of NLP applications.

PMID:40007884 | PMC:PMC11850318 | DOI:10.3389/fnhum.2025.1498297

Front Hum Neurosci. 2025 Feb 11;19:1558584. doi: 10.3389/fnhum.2025.1558584. eCollection 2025.

NO ABSTRACT

PMID:40007883 | PMC:PMC11850378 | DOI:10.3389/fnhum.2025.1558584

Heliyon. 2025 Feb 5;11(3):e42476. doi: 10.1016/j.heliyon.2025.e42476. eCollection 2025 Feb 15.

ABSTRACT

Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. In contrast, deep learning excels at extracting features from raw sequences but often overlooks essential biological context features, hindering effective binding prediction. Additionally, these models struggle to capture global and local feature distributions efficiently in protein sequences and drug SMILES. Previous state-of-the-art models, like transformers and graph-based approaches, face scalability and resource efficiency challenges. Transformers struggle with scalability, while graph-based methods have difficulty handling large datasets and complex molecular structures. In this paper, we introduce InceptionDTA, a novel drug-target binding affinity prediction model that leverages CharVec, an enhanced variant of Prot2Vec, to incorporate both biological context and categorical features into protein sequence encoding. InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. We evaluate InceptionDTA across a range of benchmark datasets commonly used in drug-target binding affinity prediction. Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. In addition to using CharVec, which demonstrates greater accuracy in absolute predictions, InceptionDTA also includes a version that employs simple label encoding and excels in ranking and predicting relative binding affinities. This versatility highlights how InceptionDTA can effectively adapt to various predictive requirements. These results emphasize the promise of our approach in expediting drug repurposing initiatives, enabling the discovery of new drugs, and contributing to advancements in disease treatment.

PMID:40007773 | PMC:PMC11850134 | DOI:10.1016/j.heliyon.2025.e42476

Ther Adv Drug Saf. 2025 Feb 24;16:20420986251321704. doi: 10.1177/20420986251321704. eCollection 2025.

ABSTRACT

Artificial intelligence (AI) is transforming medication research and development, giving clinicians new treatment options. Over the past 30 years, machine learning, deep learning, and neural networks have revolutionized drug design, target identification, and clinical trial predictions. AI has boosted pharmaceutical R&D (research and development) by identifying new therapeutic targets, improving chemical designs, and predicting complicated protein structures. Furthermore, generative AI is accelerating the development and re-engineering of medicinal molecules to cater to both common and rare diseases. Although, to date, no AI-generated medicinal drug has been FDA-approved, HLX-0201 for fragile X syndrome and new molecules for idiopathic pulmonary fibrosis have entered clinical trials. However, AI models are generally considered "black boxes," making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. Despite these obstacles, AI-driven drug discovery has substantially reduced development times and costs, expediting the process and financial risks of bringing new medicines to market. In the future, AI is expected to continue to impact pharmaceutical innovation positively, making life-saving drug discoveries faster, more efficient, and more widespread.

PMID:40008227 | PMC:PMC11851753 | DOI:10.1177/20420986251321704

Front Pharmacol. 2025 Feb 11;16:1509665. doi: 10.3389/fphar.2025.1509665. eCollection 2025.

ABSTRACT

BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF), an interstitial lung disease of unknown etiology, remains incurable with current therapies, which fail to halt disease progression or restore lung function. However, Feibi Recipe No. 2 (FBR2), a clinically validated traditional Chinese medicine formula, exhibits potential as an IPF treatment.

OBJECTIVE: This study aimed to investigate the regulatory effect of FBR2 on ferroptosis through the SIRT3/p53 pathway and its therapeutic potential in improving IPF.

METHODS: Pulmonary fibrosis was induced in C57BL/6J mice by intratracheal instillation of Bleomycin (BLM), followed by FBR2 treatment via gavage. Assessments encompassed histopathology, ELISA for cytokine detection, IHC and Western blot for protein expression analysis, and qRT-PCR for gene expression quantification. Transmission electron microscopy (TEM) was used to observe mitochondrial morphology. The roles of Erastin and the SIRT3 inhibitor 3-TYP were also explored to elucidate FBR2's mechanisms of action.

RESULTS: FBR2 treatment significantly mitigated BLM-induced lung injury in mice, as evidenced by improved body weight and survival rates, and reduced levels of inflammatory cytokines, including IL-6 and TNF-α. FBR2 decreased collagen deposition in lung tissue, as shown by Masson's staining and IHC detection of Col-I and α-SMA, confirming its anti-fibrotic effects. It also reduced iron and MDA levels in lung tissue, increased GSH-Px activity, improved mitochondrial morphology, and enhanced the expression of GPX4 and SLC7A11, indicating its ferroptosis-inhibitory capacity. Furthermore, FBR2 increased SIRT3 levels and suppressed p53 and its acetylated forms, promoting the translocation of p53 from the nucleus to the cytoplasm where it co-localized with SIRT3. The protective effects of FBR2 were reversed by Erastin, confirming the central role of ferroptosis in pulmonary fibrosis treatment. The use of 3-TYP further confirmed FBR2's intervention in ferroptosis and cellular senescence through the SIRT3/p53 pathway.

CONCLUSION: FBR2 shows therapeutic potential in a BLM-induced pulmonary fibrosis mouse model, with its effects mediated through modulation of the ferroptosis pathway via the SIRT3/p53 mechanism. This study provides novel evidence for the targeted treatment of IPF and offers further insights into its pathogenesis.

PMID:40008127 | PMC:PMC11850536 | DOI:10.3389/fphar.2025.1509665

Respir Med Case Rep. 2025 Jan 28;54:102168. doi: 10.1016/j.rmcr.2025.102168. eCollection 2025.

ABSTRACT

INTRODUCTION: Nintedanib is a tyrosine kinase inhibitor and has been approved for the treatment of idiopathic pulmonary fibrosis (IPF) since 2020. In Clinical trials, the antifibrotic effect of nintedanib was shown.

CASE: A 60-year-old female medical assistant, infected with COVID-19 in 10/2020, experienced a complicated course of disease leading to tracheal stenosis. Various interventions, including stent placements and tracheal surgeries, were performed. Due to recurrent restenosis, the patient was treated with nintedanib, a tyrosine kinase inhibitor used in idiopathic pulmonary fibrosis. The treatment spanned 306 days, during which the patient showed stability in pulmonary function. Nintedanib demonstrated a potential anti-inflammatory effect, reducing the frequency of interventions and prolonging stent-free intervals. The results suggest possible efficacy of nintedanib in managing scar-related granulation tissue, highlighting its potential in treating tracheal stenosis.

CONCLUSION: This case shows a decreased need for interventions, and the longer duration of stent placement may suggest a potential role for nintedanib in diminishing hypertrophic scarring, possibly through an anti-inflammatory effect. Further exploration of this potential in additional clinical trials would be valuable.

PMID:40007765 | PMC:PMC11849196 | DOI:10.1016/j.rmcr.2025.102168

Bioorg Chem. 2025 Feb 18;157:108294. doi: 10.1016/j.bioorg.2025.108294. Online ahead of print.

ABSTRACT

PDE4 inhibitors have been developed as anti-inflammatory medications primarily used in the clinical treatment of pulmonary inflammations such as asthma, chronic obstructive pulmonary disease, and idiopathic pulmonary fibrosis. However, the application of these drugs is usually restricted by obvious side effects, such as nausea and vomiting. Our previous study found that several natural PDE4 inhibitors or their modified derivatives showed minimal side effects, particularly reduced incidence of nausea and vomiting, which aroused our interest in searching for natural PDE4 inhibitors. In this study, a chemical investigation of an active fraction of Piper betle L. leaves led to the characterization of 23 hydroxychavicol derivatives, including 18 hydroxychavicol-type lignans. Compounds 1-9 were new lignans, with three of them being racemates that were eventually resolved into isolated (+)- and (-)-enantiomers. Compounds 1-5 and 10, neolignans characterized by a dioxane moiety, were unique to this species within the genus Piper. Compounds 5 and 10 were the sole sesquineolignans found in the genus Piper. Compounds 5, 7-14, 16, 17, and 21 exhibited considerable inhibition towards PDE4 with IC50 values ranging from 1.8 to 10 μM, with hit 7 exhibiting remarkable activity (1.8 μM). Further anti-inflammatory assays revealed that compounds 5, 7, 9, and 16 decreased the expression of several key inflammatory mediators in LPS-stimulated RAW 264.7 cells. Notably, 16 was comparable to the positive control rolipram at the same concentration of 10 μM. A primary study of the mechanism of action revealed that 16 may exert anti-inflammatory effect by inhibiting the NF-κB signaling pathway, displaying significant inhibition of the phosphorylation of IκB-α and p65 at concentrations of 5 and 10 μM. These findings suggest that hydroxychavicol derivatives from P. betle L. leaves may serve as new PDE4 inhibitors, offering promising leads for the development of anti-inflammatory medications.

PMID:40007350 | DOI:10.1016/j.bioorg.2025.108294

Pharmaceutics. 2025 Feb 14;17(2):253. doi: 10.3390/pharmaceutics17020253.

ABSTRACT

Background/Objectives: The identification of inflammatory mediators and the involvement of CD206 macrophages in anti-inflammatory responses, along with the synthesis of fibrotic mediators, are crucial for the diagnosis and treatment of Idiopathic Pulmonary Fibrosis (IPF). Methods: In this study, the assessment of 68Ga-labeled linear and cyclic forms of the RP832c peptide, which demonstrate a specific affinity for CD206 macrophages, was performed to evaluate efficacy for CD206 imaging through PET/CT, biodistribution studies, and CD206 staining in a bleomycin-induced lung injury mouse model (BLM). This model serves as a representative framework for inflammation and fibrosis. Results: The findings reveal significant peak PET/CT signals (SUV means), ID/gram values, and CD206 staining scores in lung tissues at one week post bleomycin instillation, likely due to the heightened expression of CD206 in the bleomycin-induced lung injury model. In contrast, the healthy mice exhibited no detectable CD206 staining, lower PET signals, and reduced radiopharmaceutical accumulation in lung tissues at the same timepoint. Conclusions: These findings suggest that both linear and cyclic [68Ga]Ga-RP832c may function as promising PET imaging agents for CD206 macrophages, and thereby a strategy to non-invasively explore the role of macrophages during fibrogenesis.

PMID:40006620 | DOI:10.3390/pharmaceutics17020253

Medicina (Kaunas). 2025 Feb 6;61(2):283. doi: 10.3390/medicina61020283.

ABSTRACT

Background and Objectives: The aim of our study is to compare the effects of pirfenidone and nintedanib on lung function and radiologic findings in Idiopathic Pulmonary Fibrosis and to identify which drug is more appropriate for which patient group. Materials and Methods: The data of patients who were treated in our department for at least one year between 1 January 2010 and 31 December 2022 and who were started on pirfenidone or nintedanib treatment with the diagnosis of Idiopathic Pulmonary Fibrosis were retrospectively reviewed. The patients were divided into two groups-the nintedanib and pirfenidone groups-and both groups were compared in terms of progression in lung function tests (changes in FEV1, FVC, 6 MWT and DLCO values at the 3rd, 6th, 9th and 12th months compared to baseline values) and radiological findings (the presence of progression in findings such as ground-glass opacity, reticulation, honeycomb and traction bronchiectasis) within 1 year after diagnosis. Results: The study included 109 patients. The number of patients treated with pirfenidone (IPF patients) was 82 (75.2%) and the number of patients treated with nintedanib was 27 (24.8%). When the PFT values at 3, 6, 9 and 12 months were compared with the baseline values in both groups, there was no statistically significant difference in any parameter between the two groups. No significant difference was found in terms of radiological progression at the end of 1 year in both groups. Conclusions: The results of our study show that pirfenidone and nintedanib are equivalent in their effectiveness in preventing disease progression in patients with IPF.

PMID:40005400 | DOI:10.3390/medicina61020283

Medicina (Kaunas). 2025 Jan 31;61(2):244. doi: 10.3390/medicina61020244.

ABSTRACT

Background and Objectives: Acute exacerbation of idiopathic pulmonary fibrosis (IPF-AE) often results in severe respiratory distress requiring treatment in the intensive care unit and has a high mortality rate. Identifying prognostic markers and assessing disease severity are crucial for clinicians to gain detailed insights. The mean platelet volume-to-platelet count ratio (MPR) is an inflammatory marker commonly used in malignancies. This study aimed to evaluate MPR and other factors affecting mortality in patients with IPF-AE who were monitored in the intensive care unit (ICU). Materials and Methods: This retrospective study was conducted on patients monitored in the ICU for IPF-AE between 2017 and 2023. Demographic characteristics, vital signs, laboratory and imaging findings, and administered treatments were reviewed. MPR was calculated by dividing the mean platelet volume by the platelet count. The primary endpoint was defined as 1-month in-hospital mortality. Results: A total of 59 patients monitored in the ICU for IPF-AE were included in the study. The mean age of the patients was 62.75 years, and 81.4% of the participants were male. During the 30-day follow-up period, 62.7% of the patients died. The need for invasive mechanical ventilation (IMV) was significantly associated with increased mortality (p < 0.001). The optimal cutoff value for MPR was determined to be 0.033, with a sensitivity of 83.7% and specificity of 63.64%, indicating its predictive value for mortality (AUC: 0.764; 95% CI: 0.635-0.864; p < 0.001). Conclusions: In this study, the need for IMV emerged as a critical parameter in predicting mortality in patients with IPF-AE. Additionally, the use of the MPR as a prognostic biomarker may offer a novel approach in the management of IPF patients. These findings could contribute to the development of strategies aimed at early intervention in IPF patients. Further studies with larger sample sizes are needed to validate these results. This study has demonstrated that MPR is a significant prognostic biomarker for predicting mortality in patients with IPF-AE who are managed in the intensive care unit. The potential use of MPR as a biomarker in clinical decision-making may provide new approaches to the management of IPF patients. Additionally, the need for IMV in IPF-AE emerges as a critical parameter for predicting mortality. These findings may contribute to the development of early intervention strategies for IPF patients. Further studies with larger cohorts are needed to validate these results.

PMID:40005361 | DOI:10.3390/medicina61020244

J Clin Med. 2025 Feb 17;14(4):1317. doi: 10.3390/jcm14041317.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a relatively common progressive fibrotic interstitial lung disease associated with significant morbidity and mortality. The available medications for IPF only slow down the disease process, with lung transplantation the only option for a cure. Non-pharmacological therapies are significant adjuncts that can improve symptom burden and quality of life with minimal or no side effects. Supplemental oxygen can improve exercise capacity and the sensation of dyspnea in a significant portion of patients with resting or exertional hypoxemia and has been supported by several professional societies. Pulmonary rehabilitation is a comprehensive program that includes education and therapeutic exercises to improve patient stamina and strength. It is one of the few interventions that have been shown to produce a meaningful increase in a patient's exercise capacity, but its wide adoption is limited by availability, especially in rural areas. Sleep optimization with supplemental oxygen and positive airway pressure therapy should actively be investigated for all patients diagnosed with IPF. Although gastroesophageal reflux control with non-pharmacological means is still controversial as an intervention to reduce the rate of lung function decline, it can help control reflux symptoms and improve cough intensity. IPF patients should be educated on the importance of balanced nutrition and the potential benefits of screening for lung transplantation. Palliative medicine can help with symptom control and should be considered for all patients regardless severity, but especially in those in the later stages of disease.

PMID:40004847 | DOI:10.3390/jcm14041317