Eur J Dermatol. 2024 Dec 1;34(6):643-650. doi: 10.1684/ejd.2024.4785.

ABSTRACT

Generalized pustular psoriasis (GPP) is a potentially life-threatening orphan disease. Interleukin (IL)-36 is a known pathogenetic key driver of GPP. The IL-36 receptor inhibitor spesolimab has shown efficacy and safety in clinical trials. However, evidence for spesolimab outside of clinical trials is limited. To provide additional evidence for the use of spesolimab beyond clinical trials, we evaluated individual patient data as part of the spesolimab Compassionate Use Program (CUP) for GPP patients in Germany. Adult patients with an acute GPP flare received 900 mg spesolimab intravenously at baseline and received a second dose on day 8. Data on demographics, efficacy and adverse events were collected from participating sites at baseline, on day 8 and at four weeks. The database included datasets from 12 GPP patients. At baseline, 72% of patients with complete data regarding efficacy (n=7) had a GPPGA (Generalized Pustular Psoriasis Physician Global Assessment) of ≥3, and all patients a PS (pustulation subscore) of ≥3. On day 8, 43% of patients had a GPPGA ≤1 and 72% a PS ≤1. After four weeks, all patients had a GPPGA ≤1 and 86% a PS ≤1. No drug-related adverse events were reported. These findings confirm the results of international, randomized clinical trials in a real-world setting. As spesolimab is no longer available in Germany, this study provides important information that cannot be replicated in this country.

PMID:39912471 | DOI:10.1684/ejd.2024.4785

Med Oncol. 2025 Feb 6;42(3):67. doi: 10.1007/s12032-025-02611-w.

ABSTRACT

Currently, there is a growing concern surrounding the treatment of cancer, a formidable disease. Pharmacogenomics and personalized medicine have emerged as significant areas of interest in cancer management. The efficacy of many cancer drugs is hindered by resistance mechanisms, particularly P-glycoprotein (P-gp) efflux, leading to reduced therapeutic outcomes. Efforts have intensified to inhibit P-gp efflux, thereby enhancing the effectiveness of resistant drugs. P-gp, a member of the ATP-binding cassette (ABC) superfamily, specifically the multidrug resistance (MDR)/transporter associated with antigen processing (TAP) sub-family B, member 1, utilizes energy derived from ATP hydrolysis to drive efflux. This review focuses on genetic polymorphisms associated with P-gp efflux and explores various novel pharmaceutical strategies to address this challenge. These strategies encompass SEDDS/SNEDDS, liposomes, immunoliposomes, solid lipid nanoparticles, lipid core nanocapsules, microemulsions, dendrimers, hydrogels, polymer-drug conjugates, and polymeric nanoparticles. The article aims to elucidate the interplay between pharmacogenomics, P-gp-mediated drug resistance in cancer, and formulation strategies to improve cancer therapy by tailoring formulations to genetically susceptible patients.

PMID:39913003 | DOI:10.1007/s12032-025-02611-w

Chronic Obstr Pulm Dis. 2025 Feb 5. doi: 10.15326/jcopdf.2024.0582. Online ahead of print.

ABSTRACT

BACKGROUND: Pseudomonas aeruginosa is an important pathogen in patients with chronic respiratory diseases. It can colonize the airway and could have prognostic value in bronchiectasis and cystic fibrosis. Its role in chronic obstructive pulmonary disease (COPD) is less well defined.

METHODS: A prospective study was conducted in Hong Kong to investigate the possible association between Pseudomonas aeruginosa colonization and acute exacerbation of COPD (AECOPD) risks.

RESULTS: Among 327 Chinese patients with COPD included, 33 (10.1%) of the patients had Pseudomonas aeruginosa colonization. Patients with or without Pseudomonas aeruginosa colonization had similar background characteristics. Patients with Pseudomonas aeruginosa colonization had increased risks of moderate to severe AECOPD, severe AECOPD and pneumonia with adjusted odds ratio (aOR) of 3.15 (95% CI 1.05 - 9.48, p = 0.042), 2.59 (95% CI 1.01 - 6.64, p = 0.048) and 4.19 (95% CI 1.40 - 12.54, p = 0.011) respectively. Patients with Pseudomonas aeruginosa colonization also had increased annual frequency of moderate to severe AECOPD, median 0 [0 - 0.93] in the non-Pseudomonas aeruginosa colonization group and 1.35 [0 - 3.39] in the Pseudomonas aeruginosa colonization group, with a p-value of 0.005 in multi-variate linear regression.

CONCLUSION: Pseudomonas aeruginosa colonization is a potential independent risk factor for moderate to severe AECOPD and pneumonia among patients with COPD without co-existing bronchiectasis.

PMID:39912873 | DOI:10.15326/jcopdf.2024.0582

J Med Internet Res. 2025 Feb 6;27:e66072. doi: 10.2196/66072.

ABSTRACT

BACKGROUND: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availability of an effective vaccine, mumps outbreaks have reemerged globally, including in China, where it remains a significant public health issue. In Yunnan province, China, the incidence of mumps has fluctuated markedly and is higher than that in mainland China, underscoring the need for improved outbreak prediction methods. Traditional surveillance methods, however, may not be sufficient for timely and accurate outbreak prediction.

OBJECTIVE: Our study aims to leverage the Baidu search index, representing search volumes from China's most popular search engine, along with environmental data to develop a predictive model for mumps incidence in Yunnan province.

METHODS: We analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). We constructed four models with different combinations of predictors: (1) model BE, combining the Baidu index and environmental factors data; (2) model IB, combining mumps incidence and Baidu index data; (3) model IE, combining mumps incidence and environmental factors; and (4) model IBE, integrating all 3 data sources.

RESULTS: The incidence of mumps in Yunnan showed significant variability, peaking at 37.5 per 100,000 population in 2019. From 2014 to 2023, the proportion of female patients ranged from 41.3% in 2015 to 45.7% in 2020, consistently lower than that of male patients. After excluding variables with a Pearson correlation coefficient of <0.10 or P values of <.05, we included 3 Baidu index search term groups (disease name, symptoms, and treatment) and 6 environmental factors (maximum temperature, minimum temperature, sulfur dioxide, carbon monoxide, particulate matter with a diameter of 2.5 µm or less, and particulate matter with a diameter of 10 µm or less) for model development. DNLM analysis revealed that the relative risks consistently increased with rising Baidu index values, while nonlinear associations between temperature and mumps incidence were observed. Among the 4 models, model IBE exhibited the best performance, achieving the coefficient of determination of 0.72, with mean absolute error, mean absolute percentage error, and root-mean-square error values of 0.33, 15.9%, and 0.43, respectively, in the test set.

CONCLUSIONS: Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks.

PMID:39913179 | DOI:10.2196/66072

Transl Vis Sci Technol. 2025 Feb 3;14(2):11. doi: 10.1167/tvst.14.2.11.

ABSTRACT

PURPOSE: To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by age-related macular degeneration (AMD).

METHODS: The study used OCT volumes of GA patients/eyes from the lampalizumab clinical trials (NCT02247479, NCT02247531, NCT02479386); 1219 and 442 study eyes for model development and holdout performance evaluation, respectively. Four approaches were evaluated: (1) en-face intensity maps; (2) SLIVER-net; (3) a 3D convolutional neural network (CNN); and (4) en-face layer thickness and between-layer intensity maps from a segmentation model. The processed OCT images and maps served as input for CNN models to predict baseline GA lesion area size and annualized growth rate.

RESULTS: For the holdout dataset, the Pearson correlation coefficient squared (r2) in the GA growth rate prediction was comparable for all the evaluated approaches (0.33∼0.35). In baseline lesion size prediction, prediction performance was comparable (0.9∼0.91) except for the SLIVER-net (0.83). Prediction performance with only the thickness map of the ellipsoid zone (EZ) or retinal pigment epithelium (RPE) layer individually was inferior to using both. Addition of other layer thickness or intensity maps did not improve the prediction performance.

CONCLUSIONS: All explored approaches had comparable performance, which might have reached a plateau to predict GA growth rate. EZ and RPE layers appear to contain the majority of information related to the prediction.

TRANSLATIONAL RELEVANCE: Our study provides important insights on the utility of 3D OCT images for GA disease progression predictions.

PMID:39913124 | DOI:10.1167/tvst.14.2.11

Eur Radiol Exp. 2025 Feb 6;9(1):15. doi: 10.1186/s41747-025-00554-5.

ABSTRACT

BACKGROUND: Gadolinium-enhanced "sampling perfection with application-optimized contrasts using different flip angle evolution" (SPACE) sequence allows better visualization of brain metastases (BMs) compared to "magnetization-prepared rapid acquisition gradient echo" (MPRAGE). We hypothesize that this better conspicuity leads to high-quality annotation (HAQ), enhancing deep learning (DL) algorithm detection of BMs on MPRAGE images.

METHODS: Retrospective contrast-enhanced (gadobutrol 0.1 mmol/kg) SPACE and MPRAGE data of 157 patients with BM were used, either annotated on MPRAGE resulting in normal annotation quality (NAQ) or on coregistered SPACE resulting in HAQ. Multiple DL methods were developed with NAQ or HAQ using either SPACE or MRPAGE images and evaluated on their detection performance using positive predictive value (PPV), sensitivity, and F1 score and on their delineation performance using volumetric Dice similarity coefficient, PPV, and sensitivity on one internal and four additional test datasets (660 patients).

RESULTS: The SPACE-HAQ model reached 0.978 PPV, 0.882 sensitivity, and 0.916 F1-score. The MPRAGE-HAQ reached 0.867, 0.839, and 0.840, the MPRAGE NAQ 0.964, 0.667, and 0.798, respectively (p ≥ 0.157). Relative to MPRAGE-NAQ, the MPRAGE-HAQ F1-score detection increased on all additional test datasets by 2.5-9.6 points (p < 0.016) and sensitivity improved on three datasets by 4.6-8.5 points (p < 0.001). Moreover, volumetric instance sensitivity improved by 3.6-7.6 points (p < 0.001).

CONCLUSION: HAQ improves DL methods without specialized imaging during application time. HAQ alone achieves about 40% of the performance improvements seen with SPACE images as input, allowing for fast and accurate, fully automated detection of small (< 1 cm) BMs.

RELEVANCE STATEMENT: Training with higher-quality annotations, created using the SPACE sequence, improves the detection and delineation sensitivity of DL methods for the detection of brain metastases (BMs)on MPRAGE images. This MRI cross-technique transfer learning is a promising way to increase diagnostic performance.

KEY POINTS: Delineating small BMs on SPACE MRI sequence results in higher quality annotations than on MPRAGE sequence due to enhanced conspicuity. Leveraging cross-technique ground truth annotations during training improved the accuracy of DL models in detecting and segmenting BMs. Cross-technique annotation may enhance DL models by integrating benefits from specialized, time-intensive MRI sequences while not relying on them. Further validation in prospective studies is needed.

PMID:39913077 | DOI:10.1186/s41747-025-00554-5

J Imaging Inform Med. 2025 Feb 6. doi: 10.1007/s10278-025-01429-2. Online ahead of print.

ABSTRACT

Prostate Cancer (PCa) is the second most common cancer in men and affects more than a million people each year. Grading prostate cancer is based on the Gleason grading system, a subjective and labor-intensive method for evaluating prostate tissue samples. The variability in diagnostic approaches underscores the urgent need for more reliable methods. By integrating deep learning technologies and developing automated systems, diagnostic precision can be improved, and human error minimized. The present work introduces a three-stage framework-based innovative deep-learning system for assessing PCa severity using the PANDA challenge dataset. After a meticulous selection process, 2699 usable cases were narrowed down from the initial 5160 cases after extensive data cleaning. There are three stages in the proposed framework: classification of PCa grades using deep neural networks (DNNs), segmentation of PCa grades, and computation of International Society for Urological Pathology (ISUP) grades using machine learning classifiers. Four classes of patches were classified and segmented (benign, Gleason 3, Gleason 4, and Gleason 5). Patch sampling at different sizes (500 × 500 and 1000 × 1000 pixels) was used to optimize the classification and segmentation processes. The segmentation performance of the proposed network is enhanced by a Self-organized operational neural network (Self-ONN) based DeepLabV3 architecture. Based on these predictions, the distribution percentages of each cancer grade within the whole slide images (WSI) were calculated. These features were then concatenated into machine learning classifiers to predict the final ISUP PCa grade. EfficientNet_b0 achieved the highest F1-score of 83.83% for classification, while DeepLabV3 + architecture based on self-ONN and EfficientNet encoder achieved the highest Dice Similarity Coefficient (DSC) score of 84.9% for segmentation. Using the RandomForest (RF) classifier, the proposed framework achieved a quadratic weighted kappa (QWK) score of 0.9215. Deep learning frameworks are being developed to grade PCa automatically and have shown promising results. In addition, it provides a prospective approach to a prognostic tool that can produce clinically significant results efficiently and reliably. Further investigations are needed to evaluate the framework's adaptability and effectiveness across various clinical scenarios.

PMID:39913023 | DOI:10.1007/s10278-025-01429-2

Eur J Nucl Med Mol Imaging. 2025 Feb 6. doi: 10.1007/s00259-025-07122-4. Online ahead of print.

ABSTRACT

PURPOSE: Whole-body PET imaging plays an essential role in cancer diagnosis and treatment but suffers from low image quality. Traditional deep learning-based denoising methods work well for a specific acquisition but are less effective in handling diverse PET protocols. In this study, we proposed and validated a 3D Denoising Diffusion Probabilistic Model (3D DDPM) as a robust and universal solution for whole-body PET image denoising.

METHODS: The proposed 3D DDPM gradually injected noise into the images during the forward diffusion phase, allowing the model to learn to reconstruct the clean data during the reverse diffusion process. A 3D convolutional network was trained using high-quality data from the Biograph Vision Quadra PET/CT scanner to generate the score function, enabling the model to capture accurate PET distribution information extracted from the total-body datasets. The trained 3D DDPM was evaluated on datasets from four scanners, four tracer types, and six dose levels representing a broad spectrum of clinical scenarios.

RESULTS: The proposed 3D DDPM consistently outperformed 2D DDPM, 3D UNet, and 3D GAN, demonstrating its superior denoising performance across all tested conditions. Additionally, the model's uncertainty maps exhibited lower variance, reflecting its higher confidence in its outputs.

CONCLUSIONS: The proposed 3D DDPM can effectively handle various clinical settings, including variations in dose levels, scanners, and tracers, establishing it as a promising foundational model for PET image denoising. The trained 3D DDPM model of this work can be utilized off the shelf by researchers as a whole-body PET image denoising solution. The code and model are available at https://github.com/Miche11eU/PET-Image-Denoising-Using-3D-Diffusion-Model .

PMID:39912940 | DOI:10.1007/s00259-025-07122-4

Eur J Nucl Med Mol Imaging. 2025 Feb 6. doi: 10.1007/s00259-025-07086-5. Online ahead of print.

ABSTRACT

PURPOSE: To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve precise MR-CT matching for accurate MRAC evaluation.

METHODS: A validation dataset of 21 patients underwent whole-body [18F]FDG PET/CT followed by [18F]FDG PET/MR. A flatbed insert ensured consistent positioning, allowing direct comparison of four MRAC methods-four-tissue and five-tissue models with discrete and continuous μ-maps-against CT-based attenuation correction (CTAC). A deep learning-based framework, trained on a dataset of 300 patients, was used to generate synthesized-CTs from MR images, forming the basis for all MRAC methods. Quantitative analyses were conducted at the whole-body, region of interest, and lesion levels, with lesion-distance analysis evaluating the impact of bone proximity on standardized uptake value (SUV) quantification.

RESULTS: Distinct differences were observed among MRAC methods in spine and femur regions. Joint histogram analysis showed MRAC-4 (continuous μ-map) closely aligned with CTAC. Lesion-distance analysis revealed MRAC-4 minimized bone-induced SUV interference (r = 0.01, p = 0.8643). However, tissues prone to bone segmentation interference, such as the spine and liver, exhibited greater SUV variability and lower reproducibility in MRAC-4 compared to MRAC-2 (2D bone segmentation, discrete μ-map) and MRAC-3 (3D bone segmentation, discrete μ-map).

CONCLUSION: Using a flatbed insert, this study validated MRAC with high precision. Continuous μ-value MRAC method (MRAC-4) demonstrated superior accuracy and minimized bone-related SUV errors but faced challenges in reproducibility, particularly in bone-rich regions.

PMID:39912939 | DOI:10.1007/s00259-025-07086-5

Rev Sci Instrum. 2025 Feb 1;96(2):025105. doi: 10.1063/5.0233552.

ABSTRACT

Electromagnetic tomography (EMT) has great application potential in fields such as industrial inspection. However, currently, EMT image reconstruction has problems of being highly nonlinear and ill-posed, resulting in artifacts in reconstructed images and uncertainties in quality, detail accuracy, and robustness. To address these challenges, a deep learning model STDBOGAN (generative adversarial network based on spectral normalization, two timescale update rule, and improved dung beetle optimization algorithm) based on generative adversarial networks is proposed. STDBOGAN introduces spectral normalization and two timescale update rules to stabilize the training process and avoid training instability and gradient problems. The improved dung beetle optimization algorithm automatically adjusts network hyper-parameters to improve image reconstruction accuracy. A dataset is established through simulation software. Ablation studies are conducted on the network before and after improvement, and simulations and metal physical experiments are carried out to compare STDBOGAN to UNet3+, DeepLabv3+, PSPNet, Segmenter, and SegRefiner networks. Experiments show that STDBOGAN has the best performance, anti-noise, and generalization abilities, and has made contributions to improving the quality of electromagnetic tomography image reconstruction.

PMID:39912879 | DOI:10.1063/5.0233552

JMIR AI. 2025 Feb 6;4:e60847. doi: 10.2196/60847.

ABSTRACT

BACKGROUND: The rapid advancement of deep learning in health care presents significant opportunities for automating complex medical tasks and improving clinical workflows. However, widespread adoption is impeded by data privacy concerns and the necessity for large, diverse datasets across multiple institutions. Federated learning (FL) has emerged as a viable solution, enabling collaborative artificial intelligence model development without sharing individual patient data. To effectively implement FL in health care, robust and secure infrastructures are essential. Developing such federated deep learning frameworks is crucial to harnessing the full potential of artificial intelligence while ensuring patient data privacy and regulatory compliance.

OBJECTIVE: The objective is to introduce an innovative FL infrastructure called the Personal Health Train (PHT) that includes the procedural, technical, and governance components needed to implement FL on real-world health care data, including training deep learning neural networks. The study aims to apply this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer and present the results from a proof-of-concept experiment.

METHODS: The PHT framework addresses the challenges of data privacy when sharing data, by keeping data close to the source and instead bringing the analysis to the data. Technologically, PHT requires 3 interdependent components: "tracks" (protected communication channels), "trains" (containerized software apps), and "stations" (institutional data repositories), which are supported by the open source "Vantage6" software. The study applies this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer, with the introduction of an additional component called the secure aggregation server, where the model averaging is done in a trusted and inaccessible environment.

RESULTS: We demonstrated the feasibility of executing deep learning algorithms in a federated manner using PHT and presented the results from a proof-of-concept study. The infrastructure linked 12 hospitals across 8 nations, covering 4 continents, demonstrating the scalability and global reach of the proposed approach. During the execution and training of the deep learning algorithm, no data were shared outside the hospital.

CONCLUSIONS: The findings of the proof-of-concept study, as well as the implications and limitations of the infrastructure and the results, are discussed. The application of federated deep learning to unstructured medical imaging data, facilitated by the PHT framework and Vantage6 platform, represents a significant advancement in the field. The proposed infrastructure addresses the challenges of data privacy and enables collaborative model development, paving the way for the widespread adoption of deep learning-based tools in the medical domain and beyond. The introduction of the secure aggregation server implied that data leakage problems in FL can be prevented by careful design decisions of the infrastructure.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05775068; https://clinicaltrials.gov/study/NCT05775068.

PMID:39912580 | DOI:10.2196/60847

Eur J Dermatol. 2024 Dec 1;34(6):595-603. doi: 10.1684/ejd.2024.4804.

ABSTRACT

Advancements in machine learning (ML) are making artificial intelligence more feasible in dermatology, with promising results for diagnosing skin cancers, though few studies cover common or inflammatory dermatoses. To evaluate the diagnostic accuracy for common non-cancerous skin diseases and the clinical applicability of an ML model in practical telemedicine. A prospective, multi-centre, diagnostic accuracy study including patients with common dermatoses, between October 2022 and July 2023, was performed. The top three diagnoses (Top 1, Top 2 and Top 3) from the AI system, trained to recognize 25 common dermatoses based on skin lesion images and medical data, were compared to diagnoses by two dermatologists (gold standard) to calculate the AI model's diagnostic accuracy, sensitivity, and specificity. Two versions of the AI software were evaluated: version 1 (V1) and version 2 (V2) with and without medical supervision (MS), referring to the use of metadata to control diagnostic predictions. Seventy participants and 195 photographs were included. The sensitivity and specificity of the Top 3 algorithm were 88% and 90%, respectively, for V2, with a significant improvement compared with V1. For V1, diagnostic accuracy was 0.57 (0.46;0.69) for Top 1, 0.70 (0.59;0.81) for Top 2, and 0.81 (0.72;0.91) for Top 3. For V2, diagnostic accuracy was 0.69 (0.58;0.79) and 0.71 (0.61;0.82) without and with MS, respectively, for Top 1; 0.87 (0.79;0.95) for Top 2; and 0.90 (0.83;0.97) for Top 3. Our AI model appears to be a promising tool for triaging and diagnosing skin lesions, especially for non-specialist physicians.

PMID:39912464 | DOI:10.1684/ejd.2024.4804

Expert Rev Respir Med. 2025 Feb 6. doi: 10.1080/17476348.2025.2464035. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by progressive scarring and reduced survival. The development of IPF is influenced by rare and common genetic variants, cigarette smoking, aging, and environmental exposures. Among the two dozen genetic contributors, the MUC5B promoter variant (rs35705950) is the dominant risk factor, increasing the risk of both familial and sporadic IPF and accounting for nearly 50% of the genetic predisposition to the disease.

AREAS COVERED: This review provides an expert perspective on the genetic underpinnings of IPF rather than a systematic analysis, emphasizing key insights into its genetic basis. The articles referenced in this review were identified through targeted searches in PubMed, Scopus, and Web of Science for studies published between 2000 and 2023, prioritizing influential research on the genetic factors contributing to IPF. Search terms included 'idiopathic pulmonary fibrosis,' 'genetics,' 'MUC5B,' 'telomere dysfunction,' and 'surfactant proteins.' The selection of studies was guided by the authors' expertise, focusing on the most relevant publications.

EXPERT OPINION: The identification of genetic variants not only highlights the complexity of IPF but also offers potential for earlier diagnosis and personalized treatment strategies targeting specific genetic pathways, ultimately aiming to improve patient outcomes.

PMID:39912527 | DOI:10.1080/17476348.2025.2464035

Bull Math Biol. 2025 Feb 6;87(3):39. doi: 10.1007/s11538-025-01415-3.

ABSTRACT

The concept of identifiability describes the possibility of inferring the parameters of a dynamic model by observing its output. It is common and useful to distinguish between structural and practical identifiability. The former property is fully determined by the model equations, while the latter is also influenced by the characteristics of the available experimental data. Structural identifiability can be determined by means of symbolic computations, which may be performed before collecting experimental data, and are hence sometimes called a priori analyses. Practical identifiability is typically assessed numerically, with methods that require simulations-and often also optimization-and are applied a posteriori. An approach to study structural local identifiability is to consider it as a particular case of observability, which is the possibility of inferring the internal state of a system from its output. Thus, both properties can be analysed jointly, by building a generalized observability matrix and computing its rank. The aim of this paper is to investigate to which extent such observability-based methods can also inform about practical aspects related with the experimental setup, which are usually not approached in this way. To this end, we explore a number of possible extensions of the rank tests, and discuss the purposes for which they can be informative as well as others for which they cannot.

PMID:39913007 | DOI:10.1007/s11538-025-01415-3

Mar Biotechnol (NY). 2025 Feb 6;27(1):43. doi: 10.1007/s10126-025-10415-2.

ABSTRACT

Aurantiochytrium not only excels in producing long-chain polyunsaturated fatty acids such as docosahexaenoic acid for humans, but it is also a source of essential fatty acids with minimal impacts on wild fisheries and is vital in the transfer of atmospheric carbon to oceanic carbon sinks and cycles. This study aims to unveil the systems biology of lipid production in the Australian Aurantiochytrium sp. TC20 by comparing the transcriptomic profiles under optimal growth conditions with increased fatty acid production from the early (Day 1) to late exponential growth phase (Day 3). Particular attention was paid to 227 manually annotated genes involved in lipid metabolism, such as FAS (fatty acid synthetase) and subunits of polyunsaturated fatty acids (PUFA) synthase. PCA analysis showed that differentially expressed genes, related to lipid metabolism, efficiently discriminated Day 3 samples from Day 1, highlighting the key robustness of the developed lipid-biosynthesis signature. Highly significant (pFDR < 0.01) upregulation of polyunsaturated fatty acid synthase subunit B (PFAB) involved in fatty acid synthesis, lipid droplet protein (TLDP) involved in TAG-synthesis, and phosphoglycerate mutase (PGAM-2) involved in glycolysis and gluconeogenesis were observed. KEGG enrichment analysis highlighted significant enrichment of the biosynthesis of unsaturated fatty acids (pFDR < 0.01) and carbon metabolism pathways (pFDR < 0.01). This study provides a comprehensive overview of the transcriptional landscape of Australian Aurantiochytrium sp. TC20 in the process of fatty acid production.

PMID:39912956 | DOI:10.1007/s10126-025-10415-2

ACS Infect Dis. 2025 Feb 6. doi: 10.1021/acsinfecdis.4c00912. Online ahead of print.

ABSTRACT

Tetracyclines (TCs) are an important class of antibiotics threatened by enzymatic inactivation. These tetracycline-inactivating enzymes, also known as tetracycline destructases (TDases), are a subfamily of class A flavin monooxygenases (FMOs) that catalyze hydroxyl group transfer and oxygen insertion (Baeyer-Villiger type) reactions on TC substrate scaffolds. Semisynthetic modification of TCs (e.g., tigecycline, omadacycline, eravacycline, and sarecycline) has proven effective in evading certain resistance mechanisms, such as ribosomal protection and efflux, but does not protect against TDase-mediated resistance. Here, we report the design, synthesis, and evaluation of a new series of 22 semisynthetic TDase inhibitors that explore D-ring substitution of anhydrotetracycline (aTC) including 14 C10-benzoate ester and eight C9-benzamides. Overall, the C10-benzoate esters displayed enhanced bioactivity and water solubility compared to the corresponding C9-benzamides featuring the same heterocyclic aryl side chains. The C10-benzoate ester derivatives of aTC were prepared in a high-yield one-step synthesis without the need for protecting groups. The C10-esters are water-soluble, stable toward hydrolysis, and display dose-dependent rescue of tetracycline antibiotic activity in E. coli expressing two types of tetracycline destructases, represented by TetX7 (Type 1) and Tet50 (Type 2). The best inhibitors recovered tetracycline antibiotic activity at concentrations as low as 2 μM, producing synergistic scores <0.5 in the fractional inhibitory concentration index (FICI) against TDase-expressing strains of E. coli and clinical P. aeruginosa. The C10-benzoate ester derivatives of aTC reported here are promising new leads for the development of tetracycline drug combination therapies to overcome TDase-mediated antibiotic resistance.

PMID:39912785 | DOI:10.1021/acsinfecdis.4c00912

Eur J Dermatol. 2024 Dec 1;34(6):643-650. doi: 10.1684/ejd.2024.4785.

ABSTRACT

Generalized pustular psoriasis (GPP) is a potentially life-threatening orphan disease. Interleukin (IL)-36 is a known pathogenetic key driver of GPP. The IL-36 receptor inhibitor spesolimab has shown efficacy and safety in clinical trials. However, evidence for spesolimab outside of clinical trials is limited. To provide additional evidence for the use of spesolimab beyond clinical trials, we evaluated individual patient data as part of the spesolimab Compassionate Use Program (CUP) for GPP patients in Germany. Adult patients with an acute GPP flare received 900 mg spesolimab intravenously at baseline and received a second dose on day 8. Data on demographics, efficacy and adverse events were collected from participating sites at baseline, on day 8 and at four weeks. The database included datasets from 12 GPP patients. At baseline, 72% of patients with complete data regarding efficacy (n=7) had a GPPGA (Generalized Pustular Psoriasis Physician Global Assessment) of ≥3, and all patients a PS (pustulation subscore) of ≥3. On day 8, 43% of patients had a GPPGA ≤1 and 72% a PS ≤1. After four weeks, all patients had a GPPGA ≤1 and 86% a PS ≤1. No drug-related adverse events were reported. These findings confirm the results of international, randomized clinical trials in a real-world setting. As spesolimab is no longer available in Germany, this study provides important information that cannot be replicated in this country.

PMID:39912471 | DOI:10.1684/ejd.2024.4785

Front Immunol. 2025 Jan 22;16:1539645. doi: 10.3389/fimmu.2025.1539645. eCollection 2025.

ABSTRACT

OBJECTIVE: T cell-mediated rejection (TCMR) remains a significant challenge in organ transplantation. This study aimed to define a TCMR-associated cytokine gene set and identify drugs to prevent TCMR through drug repurposing.

METHODS: Gene expression profiles from kidney, heart, and lung transplant biopsies were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between TCMR and non-TCMR groups were identified, and their intersection with cytokine-related genes yielded an 11-gene TCMR-associated cytokine gene set (TCMR-Cs). To evaluate the effectiveness of this gene set, a diagnostic predictive model was constructed using Lasso regression and multivariate logistic regression, with validation in independent datasets. Connectivity Map (CMap) analysis was employed to screen drugs targeting TCMR-Cs. Experimental validation of the identified drug was performed in vitro using T cell activation and Th1 differentiation assays, and in vivo in a mouse skin transplant model with survival analysis.

RESULTS: The TCMR-Cs exhibited outstanding predictive performance for TCMR, achieving an AUC of 0.99 in the training cohorts and maintaining strong performance in the test cohorts. CMap analysis identified peroxisome proliferator-activated receptor gamma (PPARγ) agonists as potential therapeutic candidates. Experimental validation showed that the PPARγ agonist rosiglitazone significantly suppressed T cell activation and reduced Th1 differentiation in vitro without cytotoxic effects. The combination of rosiglitazone and rapamycin significantly prolonged graft survival.

CONCLUSIONS: This study defined a novel TCMR-associated cytokine gene set that effectively predicts TCMR and identified PPARγ agonists, which prevent TCMR and improve graft survival when combined with rapamycin.

PMID:39911401 | PMC:PMC11794815 | DOI:10.3389/fimmu.2025.1539645

Front Public Health. 2025 Jan 15;12:1501942. doi: 10.3389/fpubh.2024.1501942. eCollection 2024.

ABSTRACT

INTRODUCTION: Diagnosis, treatment and management of rare diseases (RD) pose unique challenges due to their complex nature, significantly impacting the daily experiences of researchers and healthcare professionals working in this field. Despite increasing awareness and progress in the field of RD worldwide in recent years, a significant gap remains in our understanding of the specific barriers that these professionals face in their work with RD. This study provides a detailed survey analysis that sheds light on the challenges that researchers and healthcare professionals face in diagnosing, treating, managing and conducting research on RD.

METHODS: We developed a national online survey with three RD stakeholder groups (Researchers, Healthcare professionals and researcher-healthcare professionals) to identify the main challenges and needs in Türkiye for the diagnosis, treatment and follow-up processes of rare and undiagnosed diseases.

RESULTS: The survey was completed by 363 participants, revealing that participants face key challenges such as the need to refer patients to specialized centers, financial burdens, limited access to necessary tests, inadequate support for rare disease research and a lack of interdisciplinary collaboration. Participants also noted that RD are inherently difficult to conduct research on with small cohorts. Survey results also suggest a number of policy improvements to accelerate research on RD: increased funding, establishment of robust surveillance systems, and development of comprehensive national action plans and guidelines on RD.

DISCUSSION: To the best of our knowledge, this is the first study to be conducted in Türkiye. This study contributes to the understanding of the needs of professionals in rare disease research and highlights the urgent need for system improvements to support them.

PMID:39911789 | PMC:PMC11795313 | DOI:10.3389/fpubh.2024.1501942

J Palliat Med. 2025 Feb 6. doi: 10.1089/jpm.2025.0038. Online ahead of print.

NO ABSTRACT

PMID:39911035 | DOI:10.1089/jpm.2025.0038