Int J Comput Assist Radiol Surg. 2025 Feb 11. doi: 10.1007/s11548-024-03281-1. Online ahead of print.

ABSTRACT

PURPOSE: Deep-learning-based supervised CT segmentation relies on fully and densely labeled data, the labeling process of which is time-consuming. In this study, our proposed method aims to improve segmentation performance on CT volumes with limited annotated data by considering category-wise difficulties and distribution.

METHODS: We propose a novel confidence-difficulty weight (CDifW) allocation method that considers confidence levels, balancing the training across different categories, influencing the loss function and volume-mixing process for pseudo-label generation. Additionally, we introduce a novel Double-Mix Pseudo-label Framework (DMPF), which strategically selects categories for image blending based on the distribution of voxel-counts per category and the weight of segmentation difficulty. DMPF is designed to enhance the segmentation performance of categories that are challenging to segment.

RESULT: Our approach was tested on two commonly used datasets: a Congenital Heart Disease (CHD) dataset and a Beyond-the-Cranial-Vault (BTCV) Abdomen dataset. Compared to the SOTA methods, our approach achieved an improvement of 5.1% and 7.0% in Dice score for the segmentation of difficult-to-segment categories on 5% of the labeled data in CHD and 40% of the labeled data in BTCV, respectively.

CONCLUSION: Our method improves segmentation performance in difficult categories within CT volumes by category-wise weights and weight-based mixture augmentation. Our method was validated across multiple datasets and is significant for advancing semi-supervised segmentation tasks in health care. The code is available at https://github.com/MoriLabNU/Double-Mix .

PMID:39932621 | DOI:10.1007/s11548-024-03281-1

Eur J Nucl Med Mol Imaging. 2025 Feb 11. doi: 10.1007/s00259-025-07113-5. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to develop and validate a deep learning framework designed to eliminate the second CT scan of dual-tracer total-body PET/CT imaging.

METHODS: We retrospectively included three cohorts of 247 patients who underwent dual-tracer total-body PET/CT imaging on two separate days (time interval:1-11 days). Out of these, 167 underwent [68Ga]Ga-DOTATATE/[18F]FDG, 50 underwent [68Ga]Ga-PSMA-11/[18F]FDG, and 30 underwent [68Ga]Ga-FAPI-04/[18F]FDG. A deep learning framework was developed that integrates a registration generative adversarial network (RegGAN) with non-rigid registration techniques. This approach allows for the transformation of attenuation-correction CT (ACCT) images from the first scan into pseudo-ACCT images for the second scan, which are then used for attenuation and scatter correction (ASC) of the second tracer PET images. Additionally, the derived registration transform facilitates dual-tracer image fusion and analysis. The deep learning-based ASC PET images were evaluated using quantitative metrics, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) across the whole body and specific regions. Furthermore, the quantitative accuracy of PET images was assessed by calculating standardized uptake value (SUV) bias in normal organs and lesions.

RESULTS: The MAE for whole-body pseudo-ACCT images ranged from 97.64 to 112.59 HU across four tracers. The deep learning-based ASC PET images demonstrated high similarity to the ground-truth PET images. The MAE of SUV for whole-body PET images was 0.06 for [68Ga]Ga-DOTATATE, 0.08 for [68Ga]Ga-PSMA-11, 0.06 for [68Ga]Ga-FAPI-04, and 0.05 for [18F]FDG, respectively. Additionally, the median absolute percent deviation of SUV was less than 2.6% for all normal organs, while the mean absolute percent deviation of SUV was less than 3.6% for lesions across four tracers.

CONCLUSION: The proposed deep learning framework, combining RegGAN and non-rigid registration, shows promise in reducing CT radiation dose for dual-tracer total-body PET/CT imaging, with successful validation across multiple tracers.

PMID:39932542 | DOI:10.1007/s00259-025-07113-5

Adv Sci (Weinh). 2025 Feb 11:e2412533. doi: 10.1002/advs.202412533. Online ahead of print.

ABSTRACT

Identifying interactions between candidate antibodies and target antigens is a key step in developing effective human therapeutics. The antigen-antibody interaction (AAI) occurs at the structural level, but the limited structure data poses a significant challenge. However, recent studies revealed that structural information can be learned from the vast amount of sequence data, indicating that the interaction prediction can benefit from the abundance of antigen and antibody sequences. In this study, DeepInterAware (deep interaction interface-aware network) is proposed, a framework dynamically incorporating interaction interface information directly learned from sequence data, along with the inherent specificity information of the sequences. Experimental results in interaction prediction demonstrate that DeepInterAware outperforms existing methods and exhibits promising inductive capabilities for predicting interactions involving unseen antigens or antibodies, and transfer capabilities for similar tasks. More notably, DeepInterAware has unique advantages that existing methods lack. First, DeepInterAware can dive into the underlying mechanisms of AAIs, offering the ability to identify potential binding sites. Second, it is proficient in detecting mutations within antigens or antibodies, and can be extended for precise predictions of the binding free energy changes upon mutations. The HER2-targeting antibody screening experiment further underscores DeepInterAware's exceptional capability in identifying binding antibodies for target antigens, establishing it as an important tool for antibody screening.

PMID:39932383 | DOI:10.1002/advs.202412533

Anal Chem. 2025 Feb 11. doi: 10.1021/acs.analchem.4c06677. Online ahead of print.

ABSTRACT

Large language models (LLMs) hold significant promise in the field of medical diagnosis. There are still many challenges in the direct diagnosis of hepatocellular carcinoma (HCC). α-Fetoprotein (AFP) is a commonly used tumor marker for liver cancer. However, relying on AFP can result in missed diagnoses of HCC. We developed an artificial intelligence (AI) agent centered on LLMs, named ChatExosome, which created an interactive and convenient system for clinical spectroscopic analysis and diagnosis. ChatExosome consists of two main components: the first is the deep learning of the Raman fingerprinting of exosomes derived from HCC. Based on a patch-based 1D self-attention mechanism and downsampling, the feature fusion transformer (FFT) was designed to process the Raman spectra of exosomes. It achieved accuracies of 95.8% for cell-derived exosomes and 94.1% for 165 clinical samples, respectively. The second component is the interactive chat agent based on LLM. The retrieval-augmented generation (RAG) method was utilized to enhance the knowledge related to exosomes. Overall, LLM serves as the core of this interactive system, which is capable of identifying users' intentions and invoking the appropriate plugins to process the Raman data of exosomes. This is the first AI agent focusing on exosome spectroscopy and diagnosis, enhancing the interpretability of classification results, enabling physicians to leverage cutting-edge medical research and artificial intelligence techniques to optimize medical decision-making processes, and it shows great potential in intelligent diagnosis.

PMID:39932366 | DOI:10.1021/acs.analchem.4c06677

Stat Med. 2025 Feb 28;44(5):e10349. doi: 10.1002/sim.10349.

NO ABSTRACT

PMID:39932330 | DOI:10.1002/sim.10349

Laryngoscope. 2025 Feb 11. doi: 10.1002/lary.32043. Online ahead of print.

ABSTRACT

OBJECTIVE: The study aims to establish a pre-academic diagnostic tool based on deep learning and conventional radiomics features to guide the clinical decision-making of parapharyngeal space (PPS) tumors.

METHODS: This retrospective study included 217 patients with PPS tumors, from two medical centers in China from March 1, 2011, to October 1, 2023. The study cohort was divided into a training set (n = 145) and a test set (n = 72). A deep learning (DL) model and conventional radiomics (Rad) model based on neck MRI were constructed to distinguish malignant tumors (MTs) and benign tumors (BTs) of PPS tumors. The deep learning radiomics (DLR) model which integrates deep learning and radiomics features was further developed. The area under the receiver operating characteristic curve (AUC), specificity, and sensitivity were used to evaluate model performance. Decision curve analysis (DCA) was applied to assess the clinical utility.

RESULTS: Compared with the Rad and DL models, the DLR model showed excellent performance in this study, with the highest AUC of 0.899 and 0.821 in the training set and test set, respectively. The DCA curve confirmed the clinical utility of the DLR model in distinguishing the pathological types of PPS tumors.

CONCLUSION: The DLR model demonstrated a high predictive ability in diagnosing MTs and BTs of PPS and could serve as a powerful tool to aid clinical decision-making in the preoperative diagnosis of PPS tumors.

LEVEL OF EVIDENCE: III Laryngoscope, 2025.

PMID:39932109 | DOI:10.1002/lary.32043

Curr Drug Discov Technol. 2025 Feb 10. doi: 10.2174/0115701638364199250123062248. Online ahead of print.

ABSTRACT

Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the in-creasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polyphar-macology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based vir-tual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided as-sistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effec-tive drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the de-scriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.

PMID:39931986 | DOI:10.2174/0115701638364199250123062248

Discov Nano. 2025 Feb 11;20(1):26. doi: 10.1186/s11671-025-04208-8.

ABSTRACT

BACKGROUND: Infected wounds are a major health problem as infection can delay wound healing. Wound dressings play an important part in wound care by maintaining a suitable environment that promotes healing. Silver sulfadiazine dressings have been used to prevent infection in burn wounds. Presently, many commercial silver dressings have obtained FDA clearance.

RESULTS: In this study, we report on a novel silver dressing using microporous aluminosilicate zeolites, termed ABF-XenoMEM. Silver and zinc ions are encapsulated in the zeolite supercages. We show that the silver-zinc zeolite (AM30) alone is effective at inhibiting biofilm formation. The encapsulation protects the silver from rapidly precipitating in biological fluids. We exploit the negatively charged zeolite surface to associate positively charged quaternary ammonium ions (quat) with the zeolite. The combination of the AM30 with the quat enhances the antimicrobial activity. The colloidal nature of the zeolite materials makes it possible to make uniform deposits on a commercial extracellular matrix membrane to develop the final dressing (ABF-XenoMEM). The optimum loading of silver, zinc, and quat on the dressing was found to be 30, 3.7, and 221 µg/cm2. Using a colony biofilm model, the activity of ABF-XenoMEM is compared with four well-studied silver-based commercial dressings towards mature biofilms of Pseudomonas aeruginosa PAO1 (ATCC 4708) and methicillin-resistant Staphylococcus aureus (ATCC 33592). Cytotoxicity of the dressings was examined in HepG2 cells using the MTT assay.

CONCLUSION: This study shows that the ABF-XenoMEM is competitive with extensively used commercial wound dressings in a colony biofilm model. Nanozeolite-entrapped silver/zinc antimicrobials in association with quat have the potential for application in biofilm-infected wounds and require animal and clinical studies for definitive proof.

PMID:39932517 | DOI:10.1186/s11671-025-04208-8

J Gerontol A Biol Sci Med Sci. 2025 Feb 11:glaf026. doi: 10.1093/gerona/glaf026. Online ahead of print.

ABSTRACT

The aging process is universal, and it is characterized by a progressive deterioration and decrease in physiological function leading to decline on the organismal level. Nevertheless, a number of genetic and non-genetic interventions have been described, which successfully extend healthspan and lifespan in different species. Furthermore, a number of clinical trials have been evaluating the feasibility of different interventions to promote human health. The goal of the annual Biological Sciences Section of the Gerontological Society of America meeting was to share current knowledge of different topics in aging research and provide a vision of the future of aging research. The meeting gathered international experts in diverse areas of aging research including basic biology, demography, and clinical and translational studies. Specific topics included metabolism, inflammaging, epigenetic clocks, frailty, senescence, neuroscience, stem cells, reproductive aging, inter-organelle crosstalk, comparative transcriptomics of longevity, circadian clock, metabolomics, and biodemography.

PMID:39932386 | DOI:10.1093/gerona/glaf026

Elife. 2025 Feb 11;13:RP98427. doi: 10.7554/eLife.98427.

ABSTRACT

Excessive consumption of sucrose, in the form of sugar-sweetened beverages, has been implicated in the pathogenesis of metabolic dysfunction-associated fatty liver disease (MAFLD) and other related metabolic syndromes. The c-Jun N-terminal kinase (JNK) pathway plays a crucial role in response to dietary stressors, and it was demonstrated that the inhibition of the JNK pathway could potentially be used in the treatment of MAFLD. However, the intricate mechanisms underlying these interventions remain incompletely understood given their multifaceted effects across multiple tissues. In this study, we challenged rats with sucrose-sweetened water and investigated the potential effects of JNK inhibition by employing network analysis based on the transcriptome profiling obtained from hepatic and extrahepatic tissues, including visceral white adipose tissue, skeletal muscle, and brain. Our data demonstrate that JNK inhibition by JNK-IN-5A effectively reduces the circulating triglyceride accumulation and inflammation in rats subjected to sucrose consumption. Coexpression analysis and genome-scale metabolic modeling reveal that sucrose overconsumption primarily induces transcriptional dysfunction related to fatty acid and oxidative metabolism in the liver and adipose tissues, which are largely rectified after JNK inhibition at a clinically relevant dose. Skeletal muscle exhibited minimal transcriptional changes to sucrose overconsumption but underwent substantial metabolic adaptation following the JNK inhibition. Overall, our data provides novel insights into the molecular basis by which JNK inhibition exerts its metabolic effect in the metabolically active tissues. Furthermore, our findings underpin the critical role of extrahepatic metabolism in the development of diet-induced steatosis, offering valuable guidance for future studies focused on JNK-targeting for effective treatment of MAFLD.

PMID:39932177 | DOI:10.7554/eLife.98427

Inflammopharmacology. 2025 Feb 11. doi: 10.1007/s10787-025-01647-8. Online ahead of print.

ABSTRACT

In the dynamic realm of scientific inquiry, the identification and characterization of biologically active compounds derived from plant extracts have become of utmost significance. A particularly noteworthy flavonoid in this regard is aromadendrin (AMD), which can be found in a diverse range of foods, fruits, plants, and natural sources. The versatility of this compound is evident through its wide array of biological properties, including its well-documented anti-inflammatory, antioxidant, antidiabetic, neuroprotective, immunomodulatory, cardioprotective, and hepatoprotective effects. These diverse actions validate its potential utilization in addressing drug-related side effects, adverse reactions, neoplasms, ulcers, jaundice, diabetes mellitus, dermatitis, neurodegenerative diseases, cognitive disorders, polyploidy, carcinomas, common colds, and cumulative trauma disorders. This review aims to unlock the full potential of AMD and pave the way for groundbreaking advancements in the fields of medicine and nutrition. Prepare to embark on an enthralling journey as we unveil the hidden treasures and extraordinary prospects associated with AMD.

PMID:39932620 | DOI:10.1007/s10787-025-01647-8

Curr Drug Saf. 2025 Feb 7. doi: 10.2174/0115748863356840250112181406. Online ahead of print.

ABSTRACT

Pharmacovigilance is an important subject in medicine and healthcare, which aims to prevent side effects and other drug-related problems by identifying, evaluating, understanding, and avoiding them. Its main objectives are ensuring that a drug's benefits balance its hazards and improving patient safety. Within medicine and healthcare, pharmacovigilance is an essential subject that focuses on identifying, evaluating, comprehending, and preventing side effects or any other issues associated with drugs. Its main objective is to improve patient safety and ensure a drug's advantages exceed its drawbacks. Pharmacovigilance has evolved significantly as a result of technological advancements, enabling more efficient medication, safety monitoring, and management. The combination of machine learning (ML) with artificial intelligence (AI) for data analysis, adverse reaction prediction, and signal detection, electronic health records (EHRs), and mobile health (mHealth) applications have enhanced real-time data collecting and expedited the reporting of adverse drug reactions (ADRs). Pharmacovigilance plays an important role which focuses on detecting, assessing, comprehending, and averting adverse medication reactions. Making sure a drug's advantages outweigh its disadvantages is its main objective to improve patient safety. Pharmacovigilance, which balances patient safety, efficacy, and regulatory compliance in clinical trials, is necessary to promote the safe and effective use of drugs.

PMID:39931995 | DOI:10.2174/0115748863356840250112181406

Mol Pharm. 2025 Feb 11. doi: 10.1021/acs.molpharmaceut.4c01197. Online ahead of print.

ABSTRACT

Alcohol liver disease (ALD) is a chronic liver disorder resulting from long-term heavy alcohol consumption. The pathogenesis of ALD is multifactorial, and existing therapeutic agents primarily target specific aspects of the disease while presenting significant side effects, including drug-induced liver injury and hepatobiliary disease. Silibinin (SLB) has attracted widespread attention for its hepatoprotective effects and favorable safety profile. However, inherent limitations associated with SLB, such as poor solubility and bioavailability, have significantly limited its clinical application. Drug delivery systems, including liposomes, offer promising potential for the delivery of hydrophobic drugs. However, the selection of an appropriate delivery vehicle requires optimization. Ursodeoxycholic acid sodium (UAS) serves as a promising alternative to cholesterol in liposomal formulations, offering a potential strategy to mitigate the health risks associated with cholesterol. In this study, UAS was employed as the liposomal membrane material to prepare a UAS liposome loaded with SLB (SUL), and its efficacy and mechanism of action in alcoholic-induced liver injury were subsequently evaluated. The experimental results demonstrated that SUL exhibited a uniform particle size distribution, good stability, and an effective release profile in vitro. Following oral administration, SUL effectively inhibited alcohol-induced liver damage, oxidative stress, and fat accumulation. In addition, SUL regulated the expression of the kelch-1ike ECH- associated protein l (Keap1), nuclear factor erythroid 2-related factor 2 (Nrf2), and heme oxygenase 1 (HO-1) proteins, thereby exerting antioxidative stress effects. Furthermore, it also modulated apoptosis-related factors, including B-cell lymphoma-2 (Bcl-2), BCL-2-associated X (Bax), cysteinyl aspartate specific proteinase-3 (Caspase-3), and cleaved caspase-3, to mitigate hepatocyte apoptosis. In summary, SUL demonstrates enhanced therapeutic efficacy against ALD, offering a novel approach for the clinical application of SLB in the prevention and treatment of ALD.

PMID:39931930 | DOI:10.1021/acs.molpharmaceut.4c01197

J Med Virol. 2025 Feb;97(2):e70229. doi: 10.1002/jmv.70229.

ABSTRACT

The main protease (Mpro) of SARS-CoV-2 is an attractive drug target for antivirals, as this enzyme plays a key role in virus replication. Drug repurposing is a promising option for the treatment of coronavirus disease 2019 (COVID-19). Recently, a number of FDA-approved drugs have been identified as Mpro inhibitors, but stringent hit validation is lacking. In this study, we rigorously reevaluated the in vitro inhibition of the Mpro enzyme by repurposed drugs via combined experiments, including the fluorescence resonance energy transfer (FRET) assay, fluorescence polarization (FP) assay, and protease biosensor cleavage assay. Our results from a set of in vitro assays revealed that boceprevir is a potential Mpro inhibitor, but other repurposed drugs, including atazanavir, dipyridamole, entrectinib, ethacridine, glecaprevir, hydroxychloroquine, ivermectin, meisoindigo, pelitinib, raloxifene, roxatidine acetate, saquinavir, teicoplanin, thonzonium bromide, and valacyclovir, are not. Our research highlights that the use of candidate Mpro inhibitors from primary screening warrants further comprehensive studies before the reporting of new findings.

PMID:39930936 | DOI:10.1002/jmv.70229

ERJ Open Res. 2025 Feb 10;11(1):00442-2024. doi: 10.1183/23120541.00442-2024. eCollection 2025 Jan.

ABSTRACT

BACKGROUND: Pulmonary gas exchange is assessed by the transfer factor of the lungs (T L) for carbon monoxide (T LCO), and can also be measured with inhaled xenon-129 (129Xe) magnetic resonance imaging (MRI). A model has been proposed to estimate T L from 129Xe MRI metrics, but this approach has not been fully validated and does not utilise the spatial information provided by three-dimensional 129Xe MRI.

METHODS: Three models for predicting T L from 129Xe MRI metrics were compared: 1) a previously-published physiology-based model, 2) multivariable linear regression and 3) random forest regression. Models were trained on data from 150 patients with asthma and/or COPD. The random forest model was applied voxel-wise to 129Xe images to yield regional T L maps.

RESULTS: Coefficients of the physiological model were found to differ from previously reported values. All models had good prediction accuracy with small mean absolute error (MAE): 1) 1.24±0.15 mmol·min-1·kPa-1, 2) 1.01±0.06 mmol·min-1·kPa-1, 3) 0.995±0.129 mmol·min-1·kPa-1. The random forest model performed well when applied to a validation group of post-COVID-19 patients and healthy volunteers (MAE=0.840 mmol·min-1·kPa-1), suggesting good generalisability. The feasibility of producing regional maps of predicted T L was demonstrated and the whole-lung sum of the T L maps agreed with measured T LCO (MAE=1.18 mmol·min-1·kPa-1).

CONCLUSION: The best prediction of T LCO from 129Xe MRI metrics was with a random forest regression framework. Applying this model on a voxel-wise level to create parametric T L maps provides a useful tool for regional visualisation and clinical interpretation of 129Xe gas exchange MRI.

PMID:39931664 | PMC:PMC11808933 | DOI:10.1183/23120541.00442-2024

Front Cell Dev Biol. 2025 Jan 27;13:1526306. doi: 10.3389/fcell.2025.1526306. eCollection 2025.

ABSTRACT

Rare diseases affect a small percentage of an individual country's population; however, with over 7,000 in total, rare diseases represent a significant disease burden impacting up to 10% of the world's population. Despite this, there are no approved treatments for almost 95% of rare diseases, and the existing treatments are cost-intensive for the patients. More than 70% of rare diseases are genetic in nature, with patient-specific mutations. This calls for the need to have personalised and patient-specific preclinical models that can lead to effective, speedy, and affordable therapeutic options. Complex in vitro models (CIVMs), including those using induced pluripotent stem cells (iPSCs), organoids, and organs-on-chips are emerging as powerful human-based pre-clinical systems with the capacity to provide efficacy data enabling drugs to move into clinical trials. In this narrative review, we discuss how CIVMs are providing insights into biomedical research on rare diseases. We also discuss how these systems are being used in clinical trials to develop efficacy models for rare diseases. Finally, we propose recommendations on how human relevant CIVMs could be leveraged to increase translatability of basic, applied and nonclinical research outcomes in the field of rare disease therapeutics in developed as well as middle-and low-income countries.

PMID:39931243 | PMC:PMC11807990 | DOI:10.3389/fcell.2025.1526306

Ther Adv Respir Dis. 2025 Jan-Dec;19:17534666251314706. doi: 10.1177/17534666251314706.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is characterized by chronic neutrophilic inflammation in the airways. Elexacaftor/tezacaftor/ivacaftor (ETI) therapy has demonstrably improved clinical outcomes and quality of life in people with CF (pwCF), but its effects on systemic inflammatory parameters remain unclear.

OBJECTIVE: To evaluate the impact of ETI on systemic inflammation in children and adolescents with CF.

DESIGN: Retrospective, dual-center observational, propensity score-matching study of pediatric pwCF on ETI.

METHODS: PwCF aged ⩽ 18 years treated with ETI at two Italian reference centers were included in this study. Data on immunoglobulins (Ig) (A, G, and M), γ-globulin, leukocyte levels, percent predicted forced expiratory volume in the first second (ppFEV1), sweat chloride (SC) concentration, and sputum cultures were collected at baseline, 12, and 24 months of treatment. Laboratory data of a control group (pwCF, not in ETI therapy, same demographic characteristics as the study group) were also collected.

RESULTS: Sixty-six patients (30 males, median age: 12 years, F508del homozygous: 23) were included. Mean IgG levels (SD) significantly decreased (p = 0.001) from 1168.20 mg/dl (344.41) at baseline to 1093.05 mg/dl (258.73; 12 months) and 1092.87 mg/dl (232.42; 24 months). Similar reductions were observed for IgA and γ-globulin; IgM reduction was not statistically significant. Leukocyte levels also decreased significantly from 8.04 × 103/µl (3.23 × 103) at baseline to 6.61 × 103/µl (1.74 × 103) (12 months) and 6.45 × 103/µl (1.70 × 103; 24 months). As for the control group, no significant changes in the levels of Ig, leukocytes, and γ-globulin were detected throughout the study period (p > 0.05).The mean (SD) ppFEV1 and the overall mean (SD) SC concentration significantly decreased during the follow-up. Regarding cultures, 18 (27%) of the 27 patients positive (41%) for Staphylococcus aureus at baseline became negative during treatment. Three patients (4%) with persistently positive cultures for Pseudomonas aeruginosa during the first 12 months, became negative after 24 months. One patient (1.5%), with a baseline positive culture for Pseudomonas Aeruginosa, showed negative cultures after 12 months.

CONCLUSION: ETI treatment improved respiratory outcomes and significantly reduced values of IgG, IgA, γ-globulin, and leukocytes, suggesting an effect on the systemic inflammatory response. Further research is warranted to elucidate the role of inflammatory parameters in monitoring response to therapy.

PMID:39930791 | DOI:10.1177/17534666251314706

J Pediatr Gastroenterol Nutr. 2025 Feb 10. doi: 10.1002/jpn3.70010. Online ahead of print.

ABSTRACT

OBJECTIVES: Food insecurity (FI), limited or uncertain access to adequate food, impacts every state, county, and community in the United States. The goals of this quality improvement (QI) initiative were to first achieve greater than 90% compliance with FI screening for patients seen at pediatric GI clinics within 1 year and second increase the proportion of families identified as FI connected with resources to 50% at follow-up visits.

METHODS: Using plan-do-study-act cycles, interventions were implemented to (1) educate, (2) create a screening process, (3) optimize communication with EMR utilization, and (4) connect families to resources. Descriptive statistics on all variables collected were performed. Differences between the FI and food secure groups were assessed using the Mann-Whitney test for continuous variables and the Chi-squared test for categorical variables. QIMacros® Quality Improvement/SPC Software for Excel was used to create process control charts to show improvement.

RESULTS: During the timeframe from August 29, 2022, to February 29, 2024, 2946 visits were completed in the GI clinic, and 58% (1731 patients) were screened for FI. Of the patients that were screened for FI, 13% screened positive. Compliance with FI screening improved to 90%, and connection to resources improved to 75%. Race, ethnicity, preferred language, and insurance were all significantly associated with FI, p < 0.001 CONCLUSIONS: This QI initiative demonstrates standardized FI screening improves FI identification and connection to resources.

PMID:39930736 | DOI:10.1002/jpn3.70010

Thorac Res Pract. 2025 Jan 20. doi: 10.4274/ThoracResPract.2024.24047. Online ahead of print.

ABSTRACT

OBJECTIVE: Composite Model for End-Stage Liver Disease (MELD), an adapted version of the model score excluding international normalised ratio (MELD-XI), was reported to predict outcomes in patients with organ failure. Aim of study was to evaluate the prognostic significance of the MELD-XI score and compare it with the Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation 2 (APACHE 2) scores in patients admitted to the intensive care unit (ICU) for respiratory failure.

MATERIAL AND METHODS: Out of 822 patients with respiratory failure between September 2020 and June 2023, a total of 727 patients with etiologies of chronic obstructive pulmonary disease exacerbation, cardiogenic pulmonary edema, pulmonary thromboembolism, pneumonia, bronchiectasis, kyphoscoliosis, neuromuscular diseases, obesity hypoventilation syndrome, and diffuse parenchymal lung disease were included.

RESULTS: A statistically significant correlation was found between MELD-XI, SOFA, and APACHE 2 scores. The cutoff value of the MELD-XI score was 11 on receiver operating characteristic analysis, indicating a higher risk of mortality in patients with a score of 11 or above. The APACHE 2 and SOFA scores of the MELD-XI ≥11 group were found to be higher and the Glasgow Coma Scale were lower than the MELD-XI <11 group. MELD-XI ≥11 was associated with an increased risk of mortality in overall [Hazard ratio (HR): 4.1, 95% confidence interval (CI): 2-6.4, P < 0.001] and subgroups with different etiologies in Cox regression analysis. In the multivariate analysis, MELD-XI was the most important independent variable indicating an increased risk of mortality, regardless of etiology (HR: 2.4, 95% CI: 2.0-2.5, P < 0.001).

CONCLUSION: MELD-XI is an important marker of ICU mortality in patients with respiratory failure due to different etiologies and is as effective as the SOFA and APACHE 2 in predicting mortality.

PMID:39930732 | DOI:10.4274/ThoracResPract.2024.24047

Drug Deliv. 2025 Dec;32(1):2463428. doi: 10.1080/10717544.2025.2463428. Epub 2025 Feb 10.

ABSTRACT

Nanoparticles-based gene delivery has emerged as a promising approach for the treatment of genetic diseases based on efficient delivery systems for therapeutic nucleic acids (NAs) into the target cells. For pulmonary diseases such as cystic fibrosis (CF), chronic obstructive pulmonary diseases (COPD), infectious disease or lung cancer, aerosol delivery is the best choice to locally deliver NAs into the lungs. It is, therefore, important to investigate the effects of nebulization conditions on the efficiency of delivery. To this purpose, the non-viral vector branched polyethyleneimine (b-PEI, 25 kDa) was investigated for plasmid delivery by aerosol. Two types of nebulizers, jet nebulizer and mesh nebulizer, were compared regarding the properties of the nanoparticles (NPs) formed, the efficiency of NAs delivery in vitro and in vivo models and the pulmonary deposition. The results indicate that the mesh nebulizer has a better gene delivery performance than the jet nebulizer in this application. This superiority was demonstrated in terms of size, concentration, distribution of NPs and efficiency of NAs delivery. However, pulmonary deposition appears to be similar regardless of the nebulizer used, and the difference between the two systems lies in the inhalable dose. These results underline the crucial role of nebulization techniques in optimizing aerosol-mediated gene delivery by b-PEI and highlight the potential of mesh nebulizers as promising tools to improved gene therapy. Therefore, the comparison must be performed for each gene therapy formulation to determine the most suitable nebulizer.

PMID:39930696 | DOI:10.1080/10717544.2025.2463428