J Clin Psychopharmacol. 2025 Apr 18. doi: 10.1097/JCP.0000000000001999. Online ahead of print.

ABSTRACT

BACKGROUND: Pharmacogenomic (PGx) testing can help improve response and remission rates for patients with major depressive disorder (MDD) and at least one treatment failure. To investigate real-world outcomes, we examined 1) significant gene-drug interactions (GDIs) and 2) healthcare resource utilization (HRU) in a large US insurance claims dataset.

METHODS: Weighted multigene PGx testing results in adult patients with MDD were linked with deidentified US claims data. The PGx test report organized medications as congruent (no known or moderate GDI) or incongruent (significant GDI). Medication claims data before and after PGx testing was used to categorize patients as no change in congruency, incongruent-to-congruent, or congruent-to-incongruent. HRU (hospitalizations and emergency department visits) was compared in the 180 days before and after PGx testing.

RESULTS: A total of 20,933 patients met inclusion criteria; 16,965 of whom filled medication prescriptions before and after PGx testing. After PGx testing, the proportion of patients filling prescriptions with significant GDIs was reduced (26.1% pretesting vs 15.9% posttesting). All HRU was significantly reduced (P < 0.001) after PGx testing except for nonpsychiatric hospitalizations (P > 0.05). Psychiatric hospitalizations were significantly reduced after PGx testing in the incongruent-to-congruent and no change in congruency categories (P < 0.001), but not in the congruent-to-incongruent category. Conversely, emergency department visits were significantly reduced after PGx testing in all congruency categories (P < 0.005) and did not differ when compared across congruency categories.

CONCLUSIONS: After PGx testing, patients with MDD had decreased prescribing of medications with significant GDI and reduced HRU. PGx testing may have influenced these outcomes, but the retrospective study design limits clarity on its impact.

PMID:40245843 | DOI:10.1097/JCP.0000000000001999

J Appl Microbiol. 2025 Apr 17:lxaf093. doi: 10.1093/jambio/lxaf093. Online ahead of print.

ABSTRACT

AIMS: We aimed to investigate phenotypic and genomic traits of three Cupriavidus spp. isolates recovered from people with cystic fibrosis (PWCF). These bacteria are recognised as emerging pathogens in PWCF.

METHODS AND RESULTS: Using short and long sequencing reads, we assembled three hybrid complete genomes for the genus Cupriavidus, adding to the 45 published currently, describing multipartite genomes and plasmids. The isolates likely represent three different species, and they carry a cumulative total of 30 ARGs with high homology to well-characterised resistance determinants from other bacteria. Multidrug resistance to antibiotics used in CF management was observed in all three isolates. However, two treatments were active across all isolates: cefotaxime and piperacillin/tazobactam. Biofilm formation was only seen at physiological temperatures (37°C) and lost at 20°C and all isolates had low lethality in Galleria mellonella larvae. Isolates demonstrated variable motility, with one non-motile isolate carrying a disrupted flhD transcriptional regulator, abolishing flagella expression.

CONCLUSIONS: Our Cupriavidus spp. isolates showed considerable genomic and phenotypic variability that may impact their virulence and treatment in PWCF, where multidrug resistance will negate treatments and biofilm formation and motility play key roles in infection establishment, as seen in CF pathogens like P. aeruginosa. More detailed investigation of clinical Cupriavidus isolates is needed for full understanding of the risk they pose to PWCF.

PMID:40246707 | DOI:10.1093/jambio/lxaf093

J Cyst Fibros. 2025 Apr 16:S1569-1993(25)00764-7. doi: 10.1016/j.jcf.2025.03.668. Online ahead of print.

ABSTRACT

Portal hypertension (pH) secondary to cystic fibrosis liver disease (CFLD) is the fourth most common cause for mortality (after respiratory/cardiorespiratory, transplant-related, and cancer-related) in adults with cystic fibrosis (CF) and more often occurs in the absence of cirrhosis (i.e. non-cirrhotic pH, NCPH). Here, we describe a patient with NCPH secondary to CFLD, with resolution of pH after starting a cystic fibrosis transmembrane conductance regulator (CFTR) modulator. As demonstrated in this patient, CFTR modulators may provide extra-pulmonary benefits including reversal of NCPH. Long-term use of CFTR modulators could potentially result in reductions in mortality from pH and need for future liver or combined lung-liver transplantation in patients with CFLD.

PMID:40246668 | DOI:10.1016/j.jcf.2025.03.668

Respiration. 2025 Apr 17:1-18. doi: 10.1159/000545645. Online ahead of print.

ABSTRACT

Ensifentrine is a novel, low molecular weight molecule that is a selective, dual inhibitor of phosphodiesterase (PDE)3 and PDE4. Inhibition of PDE3 has been shown to relax airway smooth muscle and inhibition of PDE4 to inhibit inflammatory responses and to stimulate the cystic fibrosis transmembrane conductance regulator in human airway epithelial cells through accumulation of intracellular cyclic adenosine monophosphate. Additionally, the dual inhibition of PDE3 and PDE4 demonstrates enhanced or synergistic effects compared with inhibition of either PDE3 or PDE4 alone on contraction of airway smooth muscle and suppression of inflammatory responses. Ensifentrine inhalation suspension 3 mg was recently approved in the United States for the maintenance treatment of chronic obstructive pulmonary disease in adult patients and is marketed under the tradename OHTUVAYRE™. This manuscript describes further evidence that ensifentrine is a selective dual inhibitor of both human PDE3 and PDE4 enzymes and that this drug has significant anti-inflammatory activity in vivo in both allergic guinea pigs and non-human primates. This dual bronchodilator and anti-inflammatory activity of ensifentrine makes it a promising strategy as a novel inhaled "bifunctional" drug for the treatment of obstructive and inflammatory diseases of the respiratory tract.

PMID:40245851 | DOI:10.1159/000545645

J Chromatogr B Analyt Technol Biomed Life Sci. 2025 Apr 14;1258:124604. doi: 10.1016/j.jchromb.2025.124604. Online ahead of print.

ABSTRACT

An ultra-high performance liquid chromatography-tandem mass spectrometry method was developed to quantify the cystic fibrosis transmembrane conductance regulator (CFTR) modulators ivacaftor, tezacaftor, elexacaftor, and lumacaftor and their active metabolites hydroxymethyl ivacaftor, tezacaftor M1, and N-desmethylelexacaftor in human EDTA plasma. The analytical method utilized protein precipitation with stable isotope dilution for sample preparation, facilitating a simple and rapid assay, with a total runtime of only 2.1 min. Separation of the seven components and stable isotope-labeled internal standards was achieved on a C18 column, followed by detection using a tandem quadrupole mass spectrometer. Validation of the method was conducted in accordance with the "Bioanalytical Method Validation Guidance for Industry," of the Food and Drug Administration and with European Medicines Agency's "Guidance on bioanalytical method validation". The assay covers concentrations ranging from 0.010 to 10 mg/L for ivacaftor, hydroxymethyl ivacaftor and N-desmethylelexacaftor, from 0.025 to 25 mg/L for elexacaftor and tezacaftor, from 0.050 to 50 mg/L for tezacaftor M1 and from 0.100 to 100 mg/L for lumacaftor, using a sample volume of 10 μL. Matrix comparison confirmed the applicability of the assay to human serum and heparin plasma. Stability experiments indicated stability of the CFTR modulators in EDTA plasma over ten days under different conditions. At room temperature, all seven components remained stable for eight days and for ten days in the refrigerator in EDTA plasma and in EDTA whole blood. All seven components were stable in EDTA plasma for ten days in the autosampler after sample preparation and through four freeze-thaw cycles. The developed assay was applied in routine TDM analysis to investigate exposure to elexacaftor, tezacaftor, ivacaftor and their metabolites in people with CF undergoing treatment with Kaftrio®.

PMID:40245791 | DOI:10.1016/j.jchromb.2025.124604

Eur J Med Res. 2025 Apr 17;30(1):297. doi: 10.1186/s40001-025-02331-x.

ABSTRACT

BACKGROUND: Deep-learning networks are promising techniques in dentistry. This study developed and validated a deep-learning network, You Only Look Once (YOLO) v5, for the automatic evaluation of root-canal filling quality on periapical radiographs.

METHODS: YOLOv5 was developed using 1,008 periapical radiographs (training set: 806, validation set: 101, testing set: 101) from one center and validated on an external data set of 500 periapical radiographs from another center. We compared the network's performance with that of inexperienced endodontist in terms of recall, precision, F1 scores, and Kappa values, using the results from specialists as the gold standard. We also compared the evaluation durations between the manual method and the network.

RESULTS: On the external test data set, the YOLOv5 network performed better than inexperienced endodontist in terms of overall comprehensive performance. The F1 index values of the network for correct and incorrect filling were 92.05% and 82.93%, respectively. The network outperformed the inexperienced endodontist in all tooth regions, especially in the more difficult-to-assess upper molar regions. Notably, the YOLOv5 network evaluated images 150-220 times faster than manual evaluation.

CONCLUSIONS: The YOLOv5 deep learning network provided clinicians with a new, relatively accurate and efficient auxiliary tool for assessing the radiological quality of root canal fillings, enhancing work efficiency with large sample sizes. However, its use should be complemented by clinical expertise for accurate evaluations.

PMID:40247407 | DOI:10.1186/s40001-025-02331-x

BMC Psychol. 2025 Apr 17;13(1):395. doi: 10.1186/s40359-025-02691-3.

ABSTRACT

BACKGROUND: Aging has become a global trend, and depression, as an accompanying issue, poses a significant threat to the health of middle-aged and older adults. Existing studies primarily rely on statistical methods such as logistic regression for small-scale data analysis, while research on the application of machine learning in large-scale data remains limited. Therefore, this study employs machine learning methods to explore the risk factors for depression among middle-aged and older adults in China.

METHODS: Using a two-step hybrid model combining long short-term memory (LSTM) and machine learning (ML), we compared 20 depression risk/protective factors in a balanced panel dataset of middle-aged and elderly Chinese adults (N = 3706; aged 45-94; 64.65% female; 41.20% middle-aged) from the China Health and Retirement Longitudinal Study (CHARLS). Data were collected across five waves (2011, 2013, 2015, 2018, and 2020). The LSTM model predicted risk factors for the fifth wave via data from the preceding four waves. Five ML models were then used to classify depression (yes/no) based on these factors, which included demographic, lifestyle, health, and socioeconomic variables.

RESULTS: The LSTM model effectively predicted depression-related variables (mean square error = 0.067). The average AUC of the five ML models ranged from 0.78 to 0.82. The key predictive factors were disability, life satisfaction, activities of daily living (ADL) impairment, chronic diseases, and self-reported memory. For the middle-aged group, the top three factors were disability, life satisfaction, and chronic diseases; for the Older people group, they were life satisfaction, chronic diseases, and ADL impairment.

CONCLUSION: The two-step hybrid model ("LSTM + ML") effectively predicted depression over 2 years via demographic and health data, aiding early diagnosis and intervention.

PMID:40247342 | DOI:10.1186/s40359-025-02691-3

Orphanet J Rare Dis. 2025 Apr 17;20(1):186. doi: 10.1186/s13023-025-03655-x.

ABSTRACT

BACKGROUND: Use of artificial intelligence (AI) in rare diseases has grown rapidly in recent years. In this review we have outlined the most common machine-learning and deep-learning methods currently being used to classify and analyse large amounts of data, such as standardized images or specific text in electronic health records. To illustrate how these methods have been adapted or developed for use with rare diseases, we have focused on Fabry disease, an X-linked genetic disorder caused by lysosomal α-galactosidase. A deficiency that can result in multiple organ damage.

METHODS: We searched PubMed for articles focusing on AI, rare diseases, and Fabry disease published anytime up to 08 January 2025. Further searches, limited to articles published between 01 January 2021 and 31 December 2023, were also performed using double combinations of keywords related to AI and each organ affected in Fabry disease, and AI and rare diseases.

RESULTS: In total, 20 articles on AI and Fabry disease were included. In the rare disease field, AI methods may be applied prospectively to large populations to identify specific patients, or retrospectively to large data sets to diagnose a previously overlooked rare disease. Different AI methods may facilitate Fabry disease diagnosis, help monitor progression in affected organs, and potentially contribute to personalized therapy development. The implementation of AI methods in general healthcare and medical imaging centres may help raise awareness of rare diseases and prompt general practitioners to consider these conditions earlier in the diagnostic pathway, while chatbots and telemedicine may accelerate patient referral to rare disease experts. The use of AI technologies in healthcare may generate specific ethical risks, prompting new AI regulatory frameworks aimed at addressing these issues to be established in Europe and the United States.

CONCLUSION: AI-based methods will lead to substantial improvements in the diagnosis and management of rare diseases. The need for a human guarantee of AI is a key issue in pursuing innovation while ensuring that human involvement remains at the centre of patient care during this technological revolution.

PMID:40247315 | DOI:10.1186/s13023-025-03655-x

BMC Med Inform Decis Mak. 2025 Apr 17;25(1):167. doi: 10.1186/s12911-025-02997-7.

ABSTRACT

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with increased mortality. This study aims to leverage automated machine learning (AutoML) algorithms to create a model for the early and precise prediction of SIRS in AP.

METHODS: This study retrospectively analyzed patients diagnosed with AP across multiple centers from January 2017 to December 2021. Data from the First Affiliated Hospital of Soochow University and Changshu Hospital were used for training and internal validation, while testing was conducted with data from the Second Affiliated Hospital. Predictive models were constructed and validated using the least absolute shrinkage and selection operator (LASSO) and AutoML. A nomogram was developed based on multivariable logistic regression (LR) analysis, and the performance of the models was assessed through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Additionally, the AutoML model's effectiveness and interpretability were assessed through DCA, feature importance, SHapley Additive exPlanation (SHAP) plots, and locally interpretable model-agnostic explanations (LIME).

RESULTS: A total of 1,224 patients were included, with 812 in the training cohort, 200 in validation, and 212 in testing. SIRS occurred in 33.7% of the training cohort, 34.0% in validation, and 22.2% in testing. AutoML models outperformed traditional LR, with the deep learning (DL) model achieving an area under the ROC curve of 0.843 in the training set, and 0.848 and 0.867 in validation and testing, respectively.

CONCLUSION: The AutoML model using the DL algorithm is clinically significant for the early prediction of SIRS in AP.

PMID:40247291 | DOI:10.1186/s12911-025-02997-7

Harm Reduct J. 2025 Apr 18;22(1):56. doi: 10.1186/s12954-025-01169-0.

ABSTRACT

BACKGROUND: The stigmatisation of gamblers, particularly those with a gambling disorder, and self-stigmatisation are considered substantial barriers to seeking help and treatment. To develop effective strategies to reduce the stigma associated with gambling disorder, it is essential to understand the prevailing stereotypes. This study examines the stigma surrounding gambling disorder in Germany, with a particular focus on user comments on the video platform YouTube.

METHODS: The study employed a deep learning approach, combining guided topic modelling and qualitative summative content analysis, to analyse comments on YouTube videos. Initially, 84,024 comments were collected from 34 videos. After review, two videos featuring a person who had overcome gambling addiction were selected. These videos received significant user engagement in the comment section. An extended stigma dictionary was created based on existing literature and embeddings from the collected data.

RESULTS: The results of the study indicate that there is substantial amount of stigmatisation of gambling disorder in the selected comments. Gamblers suffering from gambling disorder are blamed for their distress and accused of irresponsibility. Gambling disorder is seen as a consequence of moral failure. In addition to stigmatising statements, the comments suggest the interpretation that many users are unaware that addiction develops over a period of time and may require professional treatment. In particular, adolescents and young adults, a group with a high prevalence of gambling-related disorders and active engagement with social media, represent a key target for destigmatisation efforts.

CONCLUSIONS: It is essential to address the stigmatisation of gambling disorder, particularly among younger populations, in order to develop effective strategies to support treatment and help-seeking. The use of social media offers a comprehensive platform for the dissemination of information and the reduction of the stigmatisation of gambling disorder, for example by strengthening certain models of addiction.

PMID:40247272 | DOI:10.1186/s12954-025-01169-0

Sci Rep. 2025 Apr 17;15(1):13281. doi: 10.1038/s41598-025-98098-6.

ABSTRACT

Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i.e., vertebrae and skull). However, sCTA is not considered mainstream because of the high radiation dose generated by the two-scan protocol. We aimed to solve the overexposure problem by training a U-Net-based CA segmentation model using a low-dose computed tomographic angiography (CTA) image-based dataset with various pre-processing methods to achieve a performance similar to that of sCTA. We optimized a non-local means (NLM) algorithm using the coefficient of variation and contrast-to-noise ratio. In addition, datasets were constructed by predicting the CA mask using a semiautomatic thresholding technique based on region growing method. Then, CTA images of 35 (2052 slices), 4 (248 slices), and 5 patients (594 slices) were used, respectively, for the train, validation, and test sets. To evaluate the performance of the U-Net-based CA segmentation model quantitatively according to the constructed dataset, the average precision (AP), intersection over union (IoU), and F1-score were calculated. For the dataset to which both the optimized NLM algorithm and semiautomatic thresholding technique were applied, the segmentation model showed the most improved performance. In particular, the quantitative evaluation of the low-dose CTA image with the NLM algorithm and the semiautomatic thresholding-based U-Net model calculated AP, IoU, and F1-scores of approximately 0.880, 0.955, and 0.809, respectively, which were most similar to the CA segmentation performance of the sCTA technique. The proposed U-Net model provided CA segmentation results without additional radiation exposure. In addition, the selection and optimization of an appropriate pre-processing methods were identified as essential for achieving higher segmentation performance for the U-Net model.

PMID:40247104 | DOI:10.1038/s41598-025-98098-6

Sci Rep. 2025 Apr 17;15(1):13270. doi: 10.1038/s41598-025-97398-1.

ABSTRACT

With the enlarged utilization of computer networks, security has become one of the critical issues. A network intrusion by malicious or unauthorized consumers may cause severe interruption to networks. So, the progress of a strong and dependable network intrusion detection system (IDS) is gradually significant. Intrusion detection relates to a suite of models employed to recognize attacks against network infrastructures and computers. There are dual main intrusion detection models, such as misuse and anomaly detection. Anomaly detection is a central part of intrusion detection in which disruptions of normal behaviour propose the presence of unintentionally or intentionally induced attacks, defects, faults, etc. With the arrival of anomaly-based IDS, many models have progressed in tracking new threats to the systems. Machine learning (ML) and deep learning (DL) models are currently leveraged for anomaly intrusion detection in cybersecurity. This manuscript proposes an Enhanced Anomaly Intrusion Detection using an Optimization Algorithm with Dimensionality Reduction and Hybrid Model (EAID-OADRHM) technique. The proposed EAID-OADRHM technique presents a new approach for perceiving and migrating attacks in cybersecurity. Min-max scaling normalization is primarily employed at the data pre-processing level to clean and transform input data into a consistent range. Furthermore, the proposed EAID-OADRHM technique utilizes the equilibrium optimizer (EO) model for the dimensionality reduction process. Additionally, the classification is performed by employing the long short-term memory and autoencoder (LSTM-AE) model. Finally, the improved Snow Ablation Optimizer (ISAO) model optimally tunes the hyperparameters of the LSTM-AE model, leading to enhanced classification performance. The simulation validation of the EAID-OADRHM approach is examined under the CIC-IDS2017 dataset, and the outcomes are computed using numerous measures. The experimental assessment of the EAID-OADRHM approach portrayed a superior accuracy value of 99.46% over existing methods in the anomaly intrusion detection process.

PMID:40247081 | DOI:10.1038/s41598-025-97398-1

Nat Genet. 2025 Apr 17. doi: 10.1038/s41588-025-02157-7. Online ahead of print.

ABSTRACT

Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disease pathogenesis. However, their low expression levels and high sequence similarity to linear RNAs present substantial challenges for circRNA detection and characterization. Recent advances in long-read and single-cell RNA sequencing technologies, coupled with sophisticated deep learning-based algorithms, have revolutionized the investigation of circRNAs at unprecedented resolution and scale. This Review summarizes recent breakthroughs in circRNA discovery, characterization and functional analysis algorithms. We also discuss the challenges associated with integrating large-scale circRNA sequencing data and explore the potential future development of artificial intelligence (AI)-driven algorithms to unlock the full potential of circRNA research in biomedical applications.

PMID:40247051 | DOI:10.1038/s41588-025-02157-7

Commun Med (Lond). 2025 Apr 17;5(1):124. doi: 10.1038/s43856-025-00835-5.

ABSTRACT

BACKGROUND: Accurate interpretation of optical coherence tomography (OCT) pullbacks is critical for assessing vascular healing after percutaneous coronary intervention (PCI). Manual analysis is time-consuming and subjective, highlighting the need for a fully automated solution.

METHODS: In this study, 1148 frames from 92 OCT pullbacks were manually annotated to classify neointima as homogeneous, heterogeneous, neoatherosclerosis, or not analyzable on a quadrant level. Stent and lumen contours were annotated in 305 frames for segmentation of the lumen, stent struts, and neointima. We used these annotations to train a deep learning algorithm called DeepNeo. Performance was further evaluated in an animal model (male New Zealand White Rabbits) of neoatherosclerosis using co-registered histopathology images as the gold standard.

RESULTS: DeepNeo demonstrates a strong classification performance for neointimal tissue, achieving an overall accuracy of 75%, which is comparable to manual classification accuracy by two clinical experts (75% and 71%). In the animal model of neoatherosclerosis, DeepNeo achieves an accuracy of 87% when compared with histopathological findings. For segmentation tasks in human pullbacks, the algorithm shows strong performance with mean Dice overlap scores of 0.99 for the lumen, 0.66 for stent struts, and 0.86 for neointima.

CONCLUSIONS: To the best of our knowledge, DeepNeo is the first deep learning algorithm enabling fully automated segmentation and classification of neointimal tissue with performance comparable to human experts. It could standardize vascular healing assessments after PCI, support therapeutic decisions, and improve risk detection for cardiac events.

PMID:40247001 | DOI:10.1038/s43856-025-00835-5

Sci Rep. 2025 Apr 17;15(1):13223. doi: 10.1038/s41598-025-97065-5.

ABSTRACT

With an increased chronic disease and an ageing population, remote health monitoring is a substantial method to enhance the care of patients and decrease healthcare expenses. The Internet of Things (IoT) presents a promising solution for remote health monitoring by collecting and analyzing vital data like body temperature, ECG, and heart rate, giving real-time insights to medical professionals. However, maintaining effectual monitoring in environments with bandwidth or energy constraints presents crucial threats. While machine analysis and human insight performance must be content, conveying extra data to gratify both would be evaded for efficient resource application. Therefore, this article proposes an Enhanced Security Mechanism for Human-Centered Systems using Deep Learning with Jellyfish Search Optimizer (ESHCS-DLJSO) approach for IoT healthcare applications. The projected ESHCS-DLJSO approach allows IoT devices in the healthcare field to securely convey medical data and early recognition of health problems in the human-machine interface. To achieve this, the ESHCS-DLJSO approach utilizes a min-max normalization technique to transform the input data into a more suitable format. The bacterial foraging optimization algorithm (BFOA) method is used for feature extraction. Moreover, a convolutional neural network with long short-term memory (CNN-LSTM-Attention) technique is used for disease detection and classification. Finally, the ESHCS-DLJSO technique employs the jellyfish search optimizer (JSO) technique for hyperparameter tuning. The simulation of the ESHCS-DLJSO technique is examined on an IoT healthcare security dataset. The performance validation of the ESHCS-DLJSO technique portrayed a superior accuracy value of 99.43% over existing approaches.

PMID:40246970 | DOI:10.1038/s41598-025-97065-5

AAPS PharmSciTech. 2025 Apr 17;26(5):112. doi: 10.1208/s12249-025-03089-5.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease of unknown cause, with few effective therapies available and high mortality rates. Our preceding research indicated that formononetin (FMN) could improve the symptoms of the bleomycin-induced pulmonary fibrosis and be a promising drug against IPF. In this study, an inhalable formononetin-loaded poly(lactic-co-glycolic) acid (PLGA) large porous microspheres (FMN-PLGA-MSs) was prepared by the method of emulsion solvent evaporation. SEM showed that FMN-PLGA-MSs were loose particles existing many pores on the surfaces, and the measured mean geometric diameter was more than 10 µm. The encapsulation efficiency (EE) and drug loading efficiency (DL) were 87.72 ± 6.34% and 4.18 ± 0.30%. FMN in FMN-PLGA-MSs could be rapidly released within 2 h and sustainably released for 21 d. Cell tests and q-RT-PCR tests showed that FMN could inhibit the activation of fibroblasts and the deposition of extracellular matrix (ECM) by acting on the TGF-β1/Smad3 signaling pathway. FMN-PLGA-MSs showed higher antifibrotic effects than free FMN oral administration in the pulmonary fibrosis models of mice, remarkably improving pulmonary function, decreasing hydroxyproline levels, and attenuating lung injuries. By formulating formononetin into microsphere preparations, its solubility can be significantly enhanced, enabling effective pulmonary drug delivery. This approach not only improves lung targeting but also reduces systemic toxicity. Additionally, it facilitates superior lung deposition and extends the retention time of the formononetin within the lungs. Taken together, FMN-PLGA-MSs may be a promising inhaled medication for the treatment of IPF.

PMID:40246731 | DOI:10.1208/s12249-025-03089-5

Rheum Dis Clin North Am. 2025 May;51(2):201-212. doi: 10.1016/j.rdc.2025.01.003. Epub 2025 Feb 28.

ABSTRACT

The lung microbiome is a diverse mucosal environment that has been shown to be implicated in the pathogenesis of various chronic lung diseases including insterstitial lung diseases (ILD) such as idiopathic pulmonary fibrosis (IPF). ILD is a well-established manifestation of several types of autoimmune diseases. This review will highlight recent work exploring the role of the lung microbiome in the pathogenesis of autoimmune-related ILD.

PMID:40246438 | DOI:10.1016/j.rdc.2025.01.003

Nat Plants. 2025 Apr 17. doi: 10.1038/s41477-025-01986-y. Online ahead of print.

ABSTRACT

Molecular and physiological changes across crop developmental stages shape the plant phenome and render its prediction from genetic markers challenging. Here we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize the temporal changes and to predict genotype-specific dynamics for multiple morphometric, geometric and colourimetric traits scored by high-throughput phenotyping. Using genetic markers and data from high-throughput phenotyping of a maize multiparent advanced generation inter-cross population and an Arabidopsis thaliana diversity panel, we show that dynamicGP outperforms a baseline genomic prediction approach for the multiple traits. We demonstrate that the developmental dynamics of traits whose heritability varies less over time can be predicted with higher accuracy. The approach paves the way for interrogating and integrating the dynamical interactions between genotype and environment over plant development to improve the prediction accuracy of agronomically relevant traits.

PMID:40247143 | DOI:10.1038/s41477-025-01986-y

Nat Genet. 2025 Apr 17. doi: 10.1038/s41588-025-02157-7. Online ahead of print.

ABSTRACT

Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disease pathogenesis. However, their low expression levels and high sequence similarity to linear RNAs present substantial challenges for circRNA detection and characterization. Recent advances in long-read and single-cell RNA sequencing technologies, coupled with sophisticated deep learning-based algorithms, have revolutionized the investigation of circRNAs at unprecedented resolution and scale. This Review summarizes recent breakthroughs in circRNA discovery, characterization and functional analysis algorithms. We also discuss the challenges associated with integrating large-scale circRNA sequencing data and explore the potential future development of artificial intelligence (AI)-driven algorithms to unlock the full potential of circRNA research in biomedical applications.

PMID:40247051 | DOI:10.1038/s41588-025-02157-7

NPJ Syst Biol Appl. 2025 Apr 17;11(1):37. doi: 10.1038/s41540-025-00509-x.

ABSTRACT

MIRO1 is a mitochondrial outer membrane protein important for mitochondrial distribution, dynamics and bioenergetics. Over the last decade, evidence has pointed to a link between MIRO1 and Parkinson's disease (PD) pathogenesis. Moreover, a heterozygous MIRO1 mutation (p.R272Q) was identified in a PD patient, from which an iPSC-derived midbrain organoid model was derived, showing MIRO1 mutant-dependent selective loss of dopaminergic neurons. Herein, we use patient-specific iPSC-derived midbrain organoids carrying the MIRO1 p.R272Q mutation to further explore the cellular and molecular mechanisms involved in dopaminergic neuron degeneration. Using single-cell RNA sequencing (scRNAseq) analysis and metabolic modeling we show that the MIRO1 p.R272Q mutation affects the dopaminergic neuron developmental path leading to metabolic deficits and disrupted neuron-astrocyte metabolic crosstalk, which might represent an important pathogenic mechanism leading to their loss.

PMID:40246848 | DOI:10.1038/s41540-025-00509-x