Neurol Ther. 2025 Apr 21. doi: 10.1007/s40120-025-00748-4. Online ahead of print.

ABSTRACT

INTRODUCTION: The neonatal Fc receptor (FcRn) inhibitors efgartigimod and rozanolixizumab have not long been introduced for treating generalized myasthenia gravis (MG); hence, real-world evidence for their administration in multiple cycles and switching from intravenous to subcutaneous formulation remains insufficient.

METHODS: We retrospectively assessed 17 consecutive patients with generalized MG and diverse backgrounds who were treated with FcRn inhibitors.

RESULTS: All patients initially received an intravenous efgartigimod formulation. Of 17 patients, 10 (59%) were considered responders, defined as a persistent improvement of at least two points for a minimum of four consecutive weeks in the MG activities of daily living score during the first treatment cycle. Four of the non-responders in the first cycle demonstrated an improvement in fulfilling the criteria for responders in the second cycle. One of these patients, who had thymoma metastatic lesions, experienced a significant worsening of MG symptoms during the first treatment cycle. Five patients switched from intravenous to subcutaneous formulations, which was successful in all patients. The efficacy of the subcutaneous formulations was similar to that of the intravenous formulation, even in patients who switched from efgartigimod to rozanolixizumab. The drugs were well tolerated without any drug-related serious adverse events irrespective of the formulation type.

CONCLUSION: FcRn inhibitors were effective and safe in patients with generalized MG, but their efficacy may depend on the disease activity during treatment. The transition from the intravenous formulation to more convenient subcutaneous formulations was successful, indicating the likely growth of future demand for subcutaneous formulations.

PMID:40257679 | DOI:10.1007/s40120-025-00748-4

Hum Vaccin Immunother. 2025 Dec;21(1):2489900. doi: 10.1080/21645515.2025.2489900. Epub 2025 Apr 21.

ABSTRACT

VN-0200 is an investigational β-glucan-CpG-adjuvanted respiratory syncytial virus (RSV) vaccine (antigen: VAGA-9001a [RSV F glycoprotein], adjuvant: MABH-9002b). This multicenter, randomized, double-blind, dose-finding phase 2 study explored the optimal VN-0200 dose and confirmed its humoral and cellular immunity and safety. In total, 342 healthy Japanese participants aged 60 to 80 years were randomized to one of 10 vaccination groups, each receiving a different combination of VAGA-9001a and MABH-9002a. VN-0200 was administered intramuscularly on Day 1 and Day 29. Geometric mean titer (GMT) and geometric mean fold rise (GMFR) of neutralization activity for anti-RSV subgroups A (RSV/A) and B (RSV/B), anti-VAGA-9001a antibody titer, and VAGA-9001a-specific interferon (IFN)-γ response were evaluated. Safety was monitored throughout the study. GMTs of serum anti-RSV/A neutralization activity increased from baseline to Day 57 and lower limits of the 95% confidence intervals of the corresponding GMFRs were >1.0 relative to baseline in all treatment groups (primary endpoint). Findings were similar for anti-RSV/A (Day 29) and anti-RSV/B (Day 29 and Day 57) neutralization activity, anti-VAGA-9001a antibody titer (Day 29 and Day 57), and VAGA-9001a-specific IFN-γ response (Day 29 and Day 57) (secondary endpoints). There was no clear influence of adjuvant or dose - response relationship of the antigen or adjuvant for any of the study endpoints. There were no serious vaccine-related treatment-emergent adverse events (TEAEs) or vaccine-related TEAEs leading to death. All antigen/adjuvant dose combinations of VN-0200 were well tolerated and elicited an increase in anti-RSV/A and anti-RSV/B neutralization activity from baseline to Day 29 and Day 57.

PMID:40257186 | DOI:10.1080/21645515.2025.2489900

In Silico Pharmacol. 2025 Apr 17;13(2):67. doi: 10.1007/s40203-025-00347-z. eCollection 2025.

ABSTRACT

Morphine and heroin dependence are growing concerns worldwide. Drug dependence is one of the greatest challenges, and developing alternative therapeutic strategies is essential. Due to few treatment options in pain management, morphine, a potent analgesic, is widely prescribed, but it carries a high risk of abuse. For the management of drug dependence, we have limited treatment options available, therefore, strategies should be developed to manage drug-seeking behaviors in clinical settings. We tried to find any FDA-approved drug targeting µ-opioid receptors through the in-silico approach. We screened around 186 FDA-approved drugs; we observed several drugs showing better docking scores with good affinity. We found vilazodone, indinavir, and lorazepam as potential drugs based on their affinity and mechanism of action. Later, these drugs were screened against human µ-opioid (PDB ID:8EF6) and other novel drug targets (5HT1 and TLR-4) that are associated with morphine dependence. Following docking, density functional theory (DFT), molecular dynamics (MD), molecular mechanics, and general born surface area (MM-GBSA) were performed to calculate the stability and ligand-protein binding free energies. Vilazodone, indinavir and lorazepam showed promising docking, MD, the energy gap between the HOMO and LUMO chemical reactivity, and MM-GBSA results compared to morphine and naloxone. We propose that these three drugs have huge potential to reverse the morphine and heroin dependence in diseased subjects near future.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00347-z.

PMID:40255259 | PMC:PMC12006581 | DOI:10.1007/s40203-025-00347-z

J Alzheimers Dis. 2025 Apr 21:13872877251331222. doi: 10.1177/13872877251331222. Online ahead of print.

ABSTRACT

BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.

PMID:40255031 | DOI:10.1177/13872877251331222

PeerJ. 2025 Apr 15;13:e19201. doi: 10.7717/peerj.19201. eCollection 2025.

ABSTRACT

Programmed death-ligand 1 (PD-L1) is an important biomarker increasingly used as a predictive marker in breast cancer immunotherapy. Immunohistochemical quantification remains the standard method for assessment. However, it presents challenges related to time, cost, and reliability. Hematoxylin and eosin (H&E) staining is a routine method in cancer pathology, known for its accessibility and consistently reliability. Deep learning has shown the potential in predicting biomarkers in cancer histopathology. This study employs a weakly supervised multiple instance learning (MIL) approach to predict PD-L1 expression from H&E-stained images using deep learning techniques. In the internal test set, the TransMIL method achieved an area under the curve (AUC) of 0.833, and in an independent external test set, it achieved an AUC of 0.799. Additionally, since RNA sequencing results indicate a threshold that allows for the separation of H&E pathology images, we further validated our approach using the public TCGA-TNBC dataset, achieving an AUC of 0.721. These findings demonstrates that the Transformer-based TransMIL model can effectively capture highly heterogeneous features within the MIL framework, exhibiting strong cross-center generalization capabilities. Our study highlights that appropriate deep learning techniques can enable effective PD-L1 prediction even with limited data, and across diverse regions and centers. This not only underscores the significant potential of deep learning in pathological artificial intelligence (AI) but also provides valuable insights for the rational and efficient allocation of medical resources.

PMID:40256728 | PMC:PMC12007500 | DOI:10.7717/peerj.19201

Cogn Neurodyn. 2025 Dec;19(1):62. doi: 10.1007/s11571-025-10248-8. Epub 2025 Apr 17.

ABSTRACT

Millions of people worldwide are afflicted with the psychological disease Schizophrenia (SZ). Symptoms of SZ include delusions, hallucinations, disoriented speech, and confused thinking. This disorder is manually diagnosed by a skilled medical practitioner. Nowadays, machine learning and deep learning techniques based on electroencephalogram (EEG) signals have been proposed to support medical practitioners. This paper proposes a deep learning system and a wavelet transform-based computer-aided detection method for detecting SZ disorder. The proposed technique aims to present a highly accurate EEG signal-based SZ detection technique. In this work, we first separate the EEG signal into sub-bands and extract the features for each sub-band using the Discrete Wavelet Transform (DWT). We have explored different mother wavelets and decomposition levels for the DWT setting; it is found that the Daubechies (db4) wavelet with 7-level decomposition performs the best for SZ detection. After obtaining the gathered features, the multilayer perceptron neural network (MLP) applies them to differentiate between SZ patients and healthy controls (HC). We validate our proposed automated SZ detection method using two publicly available datasets, Dataset-1 (DS1) with 81 records (32-HC and 49-SZ) and Dataset-2 (DS2) with 28 records (14-HC and 14-SZ), respectively. Compared with previous work, our proposed model surpasses the state-of-the-art technique for SZ detection. Our classification accuracy has increased, achieving an accuracy of 99.61% and 99.12% for DS1 and DS2. Our proposed method for identifying SZ using EEG signals is more reliable and accurate and is ready to support physicians in diagnosing SZ.

PMID:40256687 | PMC:PMC12006578 | DOI:10.1007/s11571-025-10248-8

Rheumatol Adv Pract. 2025 Apr 18;9(2):rkae118. doi: 10.1093/rap/rkae118. eCollection 2025.

ABSTRACT

OBJECTIVE: Diagnosis of axial spondyloarthritis (axSpA) is primarily established through the identification of the presence or absence of radiographic sacroiliitis. However, the reliability of conventional radiographs (X-rays) is undermined by significant interreader variability. A machine learning tool could reduce diagnosis time, thereby minimising interreader variability. The present study aimed to evaluate the performance of a deep learning model for detecting radiographic sacroiliitis in axSpA patients from the RAPID-axSpA (NCT01087762) and C-OPTIMISE (NCT02505542) trials.

METHODS: Radiographs from the RAPID-axSpA and C-OPTIMISE cohorts were retrospectively used. The deep learning model was previously trained by using a transfer learning approach on non-medical data. The model's agreement with expert readers was tested on baseline X-rays using central reader data. Sensitivity, specificity, Cohen's κ, positive and negative predictive values and the area under the receiver operating characteristics curve were calculated.

RESULTS: The model's performance was evaluated in the RAPID-axSpA (n = 277) and C-OPTIMISE (n = 739) cohorts. In RAPID-axSpA, the model achieved 82% sensitivity, 81% specificity and a Cohen's κ of 0.61, closely matching central reader performance. In C-OPTIMISE, the model demonstrated 90% sensitivity, 56% specificity and a Cohen's κ of 0.48. The agreement between the model and central readers was 82% (RAPID-axSpA) and 75% (C-OPTIMISE).

CONCLUSIONS: The tested deep learning model exhibited accurate radiographic sacroiliitis detection in axSpA patients from diverse clinical trials. The proposed deep learning model could expedite diagnosis, reduce healthcare resource usage and improve patient care pathways.

PMID:40256636 | PMC:PMC12007599 | DOI:10.1093/rap/rkae118

Gland Surg. 2025 Mar 31;14(3):391-404. doi: 10.21037/gs-24-440. Epub 2025 Mar 26.

ABSTRACT

BACKGROUND: Accurate preoperative differentiation of breast masses is critical for guiding individualized treatment strategies in Mammotome-assisted minimally invasive resection. While radiomics shows promise, existing methods rely on manual delineation, which is time-consuming and subjective. This study developed an ultrasound-based semi-automatic segmentation ensemble model to improve preoperative assessment.

METHODS: We retrospectively analyzed preoperative ultrasound images from 773 patients (543 tumors, 230 non-tumors). Semi-automatic segmentation was performed using DeepLabv3_ResNet50 and fully convolutional network (FCN)_ResNet50. Radiomic and deep transfer learning (DTL) features were extracted to construct radiomic, deep learning, and combined models. An ensemble strategy integrated these with clinical models. Performance was evaluated via receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

RESULTS: The cohort included 543 tumor patients and 230 non-tumor patients (95 adenosis, 135 other benign lesions). The semi-automatic segmentation model, DeepLabv3_ResNet50, achieved a peak global accuracy of 99.4% and an average Dice coefficient of 92.0% at its best epoch. On the other hand, the FCN_ResNet50 model exhibited a peak global accuracy of 99.5% and an average Dice coefficient of 93.7% at its best epoch. In the task of predicting tumor and non-tumor patients, age, maximum diameter, and BI-RADS (Breast Imaging Reporting and Data System) classification were ultimately identified as key indicators, and the stacking model ultimately demonstrated an area under the curve (AUC) of 0.890 in the training cohort (with a sensitivity of 0.844 and a specificity of 0.815) and an AUC of 0.780 in the testing cohort (with a sensitivity of 0.713 and a specificity of 0.739). In the task of predicting adenosis and other lesion types, focus emerged as a crucial factor, and the stacking model achieved an AUC of 0.813 in the training cohort (with a sensitivity of 0.613 and a specificity of 0.859) and an AUC of 0.771 in the testing cohort (with a sensitivity of 0.759 and a specificity of 0.765).

CONCLUSIONS: Our study has established an ensemble learning model grounded in semi-automatic segmentation techniques. This model accurately distinguishes between tumor and non-tumor patients preoperatively, as well as discriminating adenosis from other lesion types among the non-tumor cohort, thus providing valuable insights for individualized treatment planning. The proposed stacking model demonstrates significant clinical utility by reducing unnecessary biopsies and saving diagnostic time compared to manual review. These improvements directly address the challenges of overtreatment and diagnostic delays in breast lesion management. By enhancing preoperative accuracy, our model supports tailored surgical planning and alleviates patient anxiety associated with indeterminate diagnoses.

PMID:40256481 | PMC:PMC12004299 | DOI:10.21037/gs-24-440

IEEE Access. 2025;13:63129-63142. doi: 10.1109/access.2025.3558706. Epub 2025 Apr 8.

ABSTRACT

Given GPS points on a transportation network, the goal of the Quad-tree Based Driver Classification (QBDC) problem is to identify whether drivers have Mild Cognitive Impairment (MCI). The QBDC problem is challenging due to the large volume and complexity of the data. This paper proposes a quad-tree based approach to the QBDC problem by analyzing driving patterns using a real-world dataset. We propose a geo-regional quad-tree structure to capture the spatial hierarchy of driving trajectories and introduce new driving features representation for input into a convolutional neural network (CNN) for driver classification. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an F1 score of 95% that significantly outperforms the baseline models. These results highlight the potential of geo-regional quad-tree structures to extract interpretable features and describe complex driving patterns. This approach offers significant implications for driver classification, with the potential to improve road safety and cognitive health monitoring.

PMID:40256415 | PMC:PMC12007693 | DOI:10.1109/access.2025.3558706

Front Artif Intell. 2025 Apr 4;8:1551959. doi: 10.3389/frai.2025.1551959. eCollection 2025.

ABSTRACT

INTRODUCTION: Machine learning (ML) models have been increasingly applied to predict post-heart transplantation (HT) mortality, aiming to improve decision-making and optimize outcomes. This systematic review and meta-analysis evaluates the performance of ML algorithms in predicting mortality and explores factors contributing to model accuracy.

METHOD: A systematic search of PubMed, Scopus, Web of Science, and Embase identified relevant studies, with 17 studies included in the review and 12 in the meta-analysis. The algorithms assessed included random forests, CatBoost, neural networks, and others. Model performance was evaluated using pooled area under the curve (AUC) values, with subgroup analyses for algorithm type, validation methods, and prediction timeframes. The risk of bias was assessed using the QUADAS-2 tool.

RESULTS: The pooled AUC of all ML algorithms was 0.65 (95% CI: 0.64, 0.67), with no significant difference between machine learning and deep learning models (p = 0.67). Among the algorithms, CatBoost demonstrated the highest accuracy (AUC 0.80, 95% CI: 0.74, 0.86), while K-nearest neighbor had the lowest accuracy (AUC 0.53, 95% CI: 0.50, 0.55). A meta-regression indicated improved model performance with longer post-transplant periods (p = 0.008). When pooling only the best-performing models, the AUC improved to 0.73 (95% CI: 0.68, 0.78). The risk of bias was high in eight studies, with the flow and timing domains most commonly contributing to bias.

CONCLUSION: ML models demonstrate moderate accuracy in predicting post-HT mortality, with CatBoost achieving the best performance. While ML shows potential for improving predictive precision, significant heterogeneity and biases highlight the need for standardized methods and further external validations to enhance clinical applicability.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024509630, CRD42024509630.

PMID:40256322 | PMC:PMC12006172 | DOI:10.3389/frai.2025.1551959

Front Artif Intell. 2025 Apr 4;8:1458707. doi: 10.3389/frai.2025.1458707. eCollection 2025.

ABSTRACT

INTRODUCTION: The rapid advancement of AI, particularly artificial neural networks, has led to revolutionary breakthroughs and applications, such as text-generating tools and chatbots. However, this potent technology also introduces potential misuse and societal implications, including privacy violations, misinformation, and challenges to integrity and originality in academia. Several studies have attempted to distinguish and classify AI-generated textual content from human-authored work, but their performance remains questionable, particularly for AI models utilizing large language models like ChatGPT.

METHODS: To address this issue, we compiled a dataset consisting of both human-written and AI-generated (ChatGPT) content. This dataset was then used to train and evaluate a range of machine learning and deep learning models under various training conditions. We assessed the efficacy of different models in detecting and classifying AI-generated content, with a particular focus on transformer-based architectures.

RESULTS: Experimental results demonstrate that the proposed RoBERTa-based custom deep learning model achieved an F1-score of 0.992 and an accuracy of 0.991, followed by DistilBERT, which yielded an F1-score of 0.988 and an accuracy of 0.988. These results indicate exceptional performance in detecting and classifying AI-generated content.

DISCUSSION: Our findings establish a robust baseline for the detection and classification of AI-generated textual content. This work marks a significant step toward mitigating the potential misuse of AI-powered text generation tools by providing a reliable approach for distinguishing between human and AI-generated text. Future research could explore the generalizability of these models across different AI-generated content sources and address evolving challenges in AI text detection.

PMID:40256321 | PMC:PMC12006062 | DOI:10.3389/frai.2025.1458707

Ophthalmol Sci. 2025 Mar 7;5(4):100758. doi: 10.1016/j.xops.2025.100758. eCollection 2025 Jul-Aug.

ABSTRACT

OBJECTIVE: To clinically validate a convolutional neural network (CNN)-based Android smartphone app in the identification of topical glaucoma medications for patients with glaucoma and impaired vision.

DESIGN: Nonrandomized prospective crossover study.

PARTICIPANTS: The study population included a total of 20 non-English-speaking (11 Spanish and 9 Vietnamese) and 21 English-speaking patients who presented to an academic glaucoma clinic from December 2023 through September 2024. Patients with poor vision were selected on the basis of visual acuity (VA) of 20/70 or worse in 1 eye as per the California Department of Motor Vehicles' driver's license screening standard.

INTERVENTION: Enrolled subjects participated in a medication identification activity in which they identified a set of 6 topical glaucoma medications presented in a randomized order. Subjects first identified half of the medications without the CNN-based app. They then identified the remaining half of the medications with the app. Responses to a standardized ease-of-use survey were collected before and after using the app.

MAIN OUTCOME MEASURES: Primary quantitative outcomes from the medication identification activity were accuracy and time. Primary qualitative outcomes from the ease-of-use survey were subjective ratings of ease of smartphone app use.

RESULTS: The CNN-based mobile app achieved a mean average precision of 98.8% and recall of 97.2%. Identification accuracy significantly improved from 27.6% without the app to 99.2% with the app across all participants, with no significant change in identification time. This observed improvement in accuracy was similar among non-English-speaking (71.6%) and English-speaking (71.4%) participants. The odds ratio (OR) for identification accuracy with the app was 319.353 (P < 0.001), with substantial improvement in both non-English-speaking (OR = 162.779, P < 0.001) and English-speaking (no applicable OR given 100% identification accuracy) participants. Survey data indicated that 81% of English speakers and 30% of non-English speakers found the app "very easy" to use, with the overall ease of use strongly associating with improved accuracy.

CONCLUSIONS: The CNN-based mobile app significantly improves medication identification accuracy in patients with glaucomatous vision loss without increasing the time to identification. This tool has the potential to enhance adherence in both English- and non-English-speaking populations and offers a practical adjunct to daily medication management for patients with glaucoma and low VA.

FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PMID:40256318 | PMC:PMC12008510 | DOI:10.1016/j.xops.2025.100758

Curr Urol. 2024 Dec;18(4):291-294. doi: 10.1097/CU9.0000000000000236. Epub 2024 Jan 10.

ABSTRACT

OBJECTIVE: The aim of this study was to develop and evaluate two deep-learning (DL) models for predicting spontaneous ureteral stone passage (SSP).

MATERIALS AND METHODS: A total of 1217 patients with thin-layer computed tomography-confirmed ureteral stones in our hospital from January 2019 to December 2022 were retrospectively examined. These patients were grouped into 3 data sets: the training set (n = 1000), the validation set (n = 100), and the test set (n = 117). Two DL models based on residual neural network (ResNet)-2-dimensional (2D) ResNet29 and 3-dimensional (3D) ResNet29-were separately developed, trained, and assessed. The predictive ability of a conventional approach using a stone diameter of <5 mm on computed tomography was investigated, and the results were compared with those of the two DL models.

RESULTS: Of the 1217 patients, SSP was reported in 446 (36.6%). The total accuracy, sensitivity, and specificity were 76.9%, 56.1%, and 90.8% for the stone diameter approach; 87.1%, 84.2%, and 92.7% for the 2D ResNet29 model; and 90.6%, 88.2%, and 95.1% for the 3D ResNet29 model, respectively. Both the 2D and 3D ResNet29 models showed significantly higher accuracy than the stone diameter approach. Receiver operating characteristic curve analysis showed that both DL models had a significantly higher area under the curve than the stone diameter-based classification.

CONCLUSIONS: The DL models, particularly the 3D model, are novel and effective methods for predicting SSP rates. Using such models may help determine whether a patient should receive surgical intervention or expect a long interval before stone passage.

PMID:40256301 | PMC:PMC12004963 | DOI:10.1097/CU9.0000000000000236

Bioimpacts. 2024 Nov 5;15:30566. doi: 10.34172/bi.30566. eCollection 2025.

ABSTRACT

INTRODUCTION: Colorectal cancer (CRC) is among the lethal cancers, indicating the need for the identification of novel biomarkers for the detection of patients in earlier stages. RNA and microRNA sequencing were analyzed using bioinformatics and machine learning algorithms to identify differentially expressed genes (DEGs), followed by validation in CRC patients.

METHODS: The genome-wide RNA sequencing of 631 samples, comprising 398 patients and 233 normal cases was extracted from the Cancer Genome Atlas (TCGA). The DEGs were identified using DESeq package in R. Survival analysis was evaluated using Kaplan-Meier analysis to identify prognostic biomarkers. Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. The biological pathways, protein-protein interaction (PPI), the co-expression of DEGs, and the correlation between DEGs and clinical data were evaluated. Additionally, the diagnostic markers were assessed with a combioROC package. Finally, the candidate tope score gene was validated by Real-time PCR in CRC patients.

RESULTS: The survival analysis revealed five novel prognostic genes, including KCNK13, C1orf174, CLEC18A, SRRM5, and GPR89A. Thirty-nine upregulated, 40 downregulated genes, and 20 miRNAs were detected by SVM with high accuracy and AUC. The upregulation of KRT20 and FAM118A genes and the downregulation of LRAT and PROZ genes had the highest coefficient in the advanced stage. Furthermore, our findings showed that three miRNAs (mir-19b-1, mir-326, and mir-330) upregulated in the advanced stage. C1orf174, as a novel gene, was validated using RT-PCR in CRC patients. The combineROC curve analysis indicated that the combination of C1orf174-AKAP4-DIRC1-SKIL-Scan29A4 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.90, 0.94, and 0.92, respectively.

CONCLUSION: Machine learning algorithms can be used to Identify key dysregulated genes/miRNAs involved in the pathogenesis of diseases, leading to the detection of patients in earlier stages. Our data also demonstrated the prognostic value of C1orf174 in colorectal cancer.

PMID:40256241 | PMC:PMC12008501 | DOI:10.34172/bi.30566

Breathe (Sheff). 2025 Apr 17;21(2):240260. doi: 10.1183/20734735.0260-2024. eCollection 2025 Apr.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease with limited treatment options. Despite the approval of pirfenidone and nintedanib that slow disease progression, IPF remains a disease with poor survival. Promising therapeutic candidates were tested as potential treatments for IPF and while some drugs were successful in phase II clinical trials, their successful transition to positive phase III was unfortunately disappointing. This highlights the "regression to the truth" concept in drug development, whereby positive phase II trial results may simply be a statistical anomaly rather than the result of true efficacy. We examine three pivotal trials of novel IPF therapies, zinpentraxin alfa, ziritaxestat and pamrevlumab, that failed in late-stage clinical development. These failures underscore common pitfalls in IPF drug development, including inadequate phase II sample sizes, reliance on surrogate endpoints like forced vital capacity, and challenges integrating background antifibrotic therapies. Moving forward, innovative approaches like adaptive trial designs, Bayesian statistics and composite endpoints could improve trial robustness. Moreover, platform trials may accelerate drug development by testing multiple therapies simultaneously. Negative trials are not failures but opportunities for learning. By recognising and addressing these challenges, while also embracing novel trial methodologies, we can enhance drug development and improve IPF outcomes.

PMID:40255293 | PMC:PMC12004256 | DOI:10.1183/20734735.0260-2024

Breathe (Sheff). 2025 Apr 17;21(2):240176. doi: 10.1183/20734735.0176-2024. eCollection 2025 Apr.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive and fatal interstitial fibrosing disease and, despite some well-known risk factors, its cause is still unknown. Cough is experienced by most patients and is commonly chronic and refractory, having a significant impact on quality of life. Its aetiology is complex, combining factors related to interstitial lung disease (ILD) such as an increased sensitivity of cough-sensitive nerves, structural lung changes and inflammation, genetic factors, several comorbidities and medication-adverse effects. Despite the therapeutic advancements in IPF over the past decade with the introduction of antifibrotic drugs that slow disease progression, effective treatment options for cough in IPF remain unavailable. Cough management often relies on empirical approaches based on studies involving chronic cough patients of unspecified causes and ILD physicians' personal experiences. Different classes of medications have been tried over time and, more recently, the focus has turned to neuromodulators and opioids, but several studies have shown suboptimal efficacy in cough. On the other hand, these drugs are associated with significant physical, psychological and economic burdens. However, the future brings us hope to the extent that most current ongoing clinical trials are using new molecules and some have demonstrated promising antitussive effects. This review aims to provide a practical guide to understanding and managing cough in IPF patients, presenting pharmacological and non-pharmacological approaches over time, as well as those treatments that are currently being investigated in clinical settings.

PMID:40255292 | PMC:PMC12004257 | DOI:10.1183/20734735.0176-2024

Breathe (Sheff). 2025 Apr 17;21(2):240167. doi: 10.1183/20734735.0167-2024. eCollection 2025 Apr.

ABSTRACT

Interstitial lung disease (ILD) is a heterogeneous chronic form of lung disease. The pathogenesis of ILD is poorly understood and a common form of ILD, idiopathic pulmonary fibrosis (IPF) is associated with poor prognosis. There is evidence for substantial dysregulated immune responses in ILD. The microbiome is a key regulator of the immune response, and the lung microbiome correlates with alveolar immunity and clinical outcomes in ILD. Most observational lung microbiome studies have been conducted in patients with IPF. A consistent observation in these studies is that the bacterial burden of the lung is elevated in patients with IPF and predicts mortality. However, our understanding of the mechanism is incomplete and our understanding of the role of the lung microbiome in other forms of ILD is limited. The microbiomes of the oropharynx and gut may have implications for the lung microbiome and pulmonary immunity in ILD but require substantial further research. Here, we discuss the studies supporting a role for the lung microbiome in the pathogenesis of IPF, and briefly describe the putative role of the oral-lung axis and the gut-lung axis in ILD.

PMID:40255291 | PMC:PMC12004254 | DOI:10.1183/20734735.0167-2024

Arch Razi Inst. 2024 Aug 1;79(4):695-700. doi: 10.32592/ARI.2024.79.4.695. eCollection 2024 Aug.

ABSTRACT

Chronic kidney disease (CKD), also called chronic kidney failure, is increasingly recognized as a global public health problem in the entire world. It is characterized by slow, progressive, and irreversible loss in kidney physiology. Today, the prevalence of CKD is increasing dramatically. CKD can affect almost every organ system, including the cardiovascular system. Many treatments have been attempted for CKD, such as renal transplantation, hemodialysis (HD), and peritoneal dialysis (PD). At the end stage of CKD, HD is the most widely used therapy throughout the world. However, these options can decrease volume expansion and uremic solute retention and also increase patient survival. Furthermore, there are certain complications associated with the use of these methods. Previous studies have reported that the main side effects are headaches, muscle cramps, abdominal pain, hypotension, hypertension, vomiting, and constipation. Therefore, the investigation for better and more convenient dialysis techniques should continue, as well as the search for a better material to enhance the clearance of nitrogenous waste products from the body. The intestine has a significant effect on the clearance of nitrogenous waste products from the body, making it a potentially appropriate site for CKD management. The potential mechanism of the intestinal dialysis technique is that it can absorb excess fluids, uremic toxins, and electrolytes within the gastrointestinal (GI) tract and exert them in the feces before they can be absorbed into the blood. In the present review, we will focus on different absorbents as a conservative treatment to remove uremic waste metabolites from the GI tract for the improvement of kidney function in CKD.

PMID:40256591 | PMC:PMC12004044 | DOI:10.32592/ARI.2024.79.4.695

RSC Med Chem. 2025 Apr 17. doi: 10.1039/d5md00086f. Online ahead of print.

ABSTRACT

Overexpression of histone deacetylase 6 (HDAC6) is implicated in tumorigenesis, invasion, migration, survival, apoptosis, and growth of various malignancies, making it a promising target for cancer treatment. Building on our previous work, we report a novel series of tetrahydro-β-carboline-piperazinedione derivatives as HDAC6 inhibitors. Structural modifications were introduced at the 6-aryl group, with the m-bromophenyl derivative (9c) emerging as the most potent HDAC6 inhibitor, exhibiting an IC50 of 7 nM. Compound 9c demonstrated robust growth inhibitory activity across 60 cancer cell lines from the NCI panel, with a mean GI50 of 2.64 μM and a GI50 below 5 μM for nearly all tested lines, while exhibiting significantly lower cytotoxicity towards non-tumor cell lines. The triple-negative breast cancer cell line MDA-MB-231 was selected for further investigation of 9c's cellular effects. 9c selectively increased the acetylation of non-histone α-tubulin in MDA-MB-231 cells, confirming its HDAC6 selectivity. Furthermore, 9c effectively induced apoptosis, caused apoptotic sub-G1 phase accumulation, upregulated pro-apoptotic caspase-3, and downregulated anti-apoptotic Bcl-2. Notably, 9c reduced the expression of programmed death-ligand 1 (PD-L1), a key immune checkpoint protein that enables tumor cells to evade immune surveillance, highlighting its potential role in enhancing anti-tumor immunity. In addition, 9c inhibited phosphorylated extracellular signal-regulated kinase (ERK)1/2, a central signaling pathway that drives cell proliferation, survival, and migration, further highlighting its significance in suppressing tumor progression and growth. In migration assays, 9c impaired cell motility, achieving 80% gap closure inhibition in a wound-healing assay. Collectively, these findings underline compound 9c as a highly promising candidate for the treatment of triple-negative breast cancer, with the added benefits of PD-L1 and ERK inhibition for potential synergy in enhancing anti-tumor immunity and reducing tumor cell proliferation.

PMID:40256307 | PMC:PMC12004265 | DOI:10.1039/d5md00086f

Bioinform Adv. 2025 Feb 6;5(1):vbaf017. doi: 10.1093/bioadv/vbaf017. eCollection 2025.

ABSTRACT

SUMMARY: Influencing the behavior of a Boolean network involves applying perturbations, which, in standard deterministic Boolean networks, is equivalent to modifying the update rules. Nevertheless, manipulating update functions to make a Boolean network exhibit the desired dynamics is challenging, as it requires extensive knowledge of the rationale behind the logical equations. Moreover, modifying logical rules can inadvertently alter essential functional and behavioral characteristics of the network. An alternative approach is to incorporate a set of parameters into the logical functions of Boolean networks. With such methods, one can alter the behavior of the network without needing detailed knowledge of the logical functions. We developed pastboon, an R package to simulate parameterized stochastic Boolean networks using three parameterization methods. This package enables researchers to study the phenotypic effects of various perturbations on Boolean network models describing cellular processes, which find valuable applications in systems biology.

AVAILABILITY AND IMPLEMENTATION: pastboon is freely available on the R CRAN repository at https://cran.r-project.org/package=pastboon, and its source code can be accessed on GitHub at https://github.com/taherimo/pastboon.

PMID:40255969 | PMC:PMC12007881 | DOI:10.1093/bioadv/vbaf017