Pharmacogenomics. 2025 May 27:1-24. doi: 10.1080/14622416.2025.2504865. Online ahead of print.

ABSTRACT

Pharmacogenomics (PGx) has the potential to revolutionize hypertension management by tailoring antihypertensive therapy based on genetic profiles. Despite significant advances in genomic research, the clinical translation of PGx in hypertension remains challenging due to genetic complexity, variability in drug response, and implementation barriers. This review explores the genetic basis of hypertension, highlighting key pharmacogenomic markers that influence antihypertensive metabolism and efficacy, including CYP2D6, CYP3A4, UMOD, and ACE polymorphisms. We also examine the role of Mendelian randomization, polygenic risk scores in drug development and stratifying hypertension treatment response. While PGx offers opportunities for personalized medicine - such as reducing trial-and-error prescribing and improving adherence - several obstacles hinder its widespread adoption. These include limited clinical actionability, lack of large-scale randomized controlled trials, cost constraints, and concerns about equity and accessibility. Furthermore, drug-gene interactions and phenoconversion add complexity to implementation. Emerging technologies, including artificial intelligence-driven prescribing, microbiome integration, and pharmacoepigenomics, may enhance PGx precision in hypertension management. However, further research, clinical validation, and policy frameworks are necessary before PGx can be routinely incorporated into hypertension care. This review critically evaluates both the promise and limitations of PGx in hypertension, offering insights into the future of precision medicine in cardiovascular health.

PMID:40421951 | DOI:10.1080/14622416.2025.2504865

Pharmacogenet Genomics. 2025 Jul 1;35(5):170-171. doi: 10.1097/FPC.0000000000000565. Epub 2025 May 27.

NO ABSTRACT

PMID:40421730 | DOI:10.1097/FPC.0000000000000565

Clin Transl Gastroenterol. 2025 May 27. doi: 10.14309/ctg.0000000000000857. Online ahead of print.

ABSTRACT

PURPOSE: Screening guidelines for pancreatic cancer (PC) based on genetic risk do not include patients with CF or CFTR gene variants. The objective of this study was to determine risk of PC in patients with CF or CFTR pathogenic/likely pathogenic (PLPV) gene variants.

METHODS: We conducted a retrospective cohort study of CF/CFTR PLPV patients in an integrated healthcare system from 2008-2023. Index date was the initial encounter within the health system, with censoring at loss of membership, death, or study completion. PC incidence rate (IR) was based on person-time at risk. Age- and sex-adjusted standardized incidence rate ratio (SIR) for PC was calculated for CF/CFTR compared to the non-CFTR reference population. We further stratified PC risk by age and family history of PC.

RESULTS: 12,682 patients with CF/CFTR were included with a median follow-up of 8.3 years (IQR 4.3-13.1). The cohort was 88% female, had median age at index of 25.8 (IQR 19.1-31.1) years, and was majority White and Hispanic. 8 total PC events occurred in the CF/CFTR group (IR 7.3 per 100,000 person-years). The adjusted SIR for PC was 2.3 (95% CL 1.2-4.7) for CF/CFTR variant patients. There was effect modification by age, with SIR (age≥50 years) of 2.87 (95% CL 1.37-6.01). Among CF/CFTR patients with family history of PC, 1 PC case was observed with SIR (age≥50 years) of 13.

CONCLUSION: Patients with CF or CFTR gene variants had an almost 3-fold higher adjusted risk of PC than the general population after age 50 years. The risk may be further increased with a family history of PC.

PMID:40423702 | DOI:10.14309/ctg.0000000000000857

mBio. 2025 May 27:e0077525. doi: 10.1128/mbio.00775-25. Online ahead of print.

ABSTRACT

Staphylococcus aureus infections remain an ongoing challenge for people with cystic fibrosis (PwCF), with the increased global prevalence of multidrug-resistant strains requiring new therapeutic approaches. Our previous studies demonstrated anti-inflammatory effects of several MEK1/2 inhibitor compounds, including PD0325901, CI-1040, and trametinib, in human phagocytes from PwCF and a murine S. aureus pulmonary infection model (M. De, G. Serpa, E. Zuiker, K. B. Hisert, et al., Front Cell Infect Microbiol 14:1275940, 2024, https://doi.org/10.3389/fcimb.2024.1275940). A recently developed MEK1/2 inhibitor compound, ATR-002, has been recognized for its ability to exert direct antibacterial effects on gram-positive bacterial species, including S. aureus (C. Bruchhagen, M. Jarick, C. Mewis, T. Hertlein, et al., Sci Rep 8:9114, 2018, https://doi.org/10.1038/s41598-018-27445-7). However, whether ATR-002 elicits antibacterial effects on clinically relevant strains of S. aureus or anti-inflammatory effects is unknown. In this study, the effects of ATR-002 on human CF macrophage TLR2-induced pro-inflammatory cytokine secretion were evaluated, demonstrating that ATR-002 reduced TNF-α and IL-8 secretion induced by the TLR2 agonists FSL-1 or Pam3CSK4. The antibacterial effects of ATR-002 were evaluated by minimum inhibitory concentration testing using S. aureus clinical isolates obtained from PwCF. Utilization of a murine methicillin-resistant S. aureus (MRSA) pulmonary infection model further confirmed the in vivo anti-inflammatory and antibacterial effects of ATR-002. Finally, infection of wild-type and Mek2KO mice revealed that loss of MEK2 was host-protective during MRSA pulmonary infection by reducing neutrophil-mediated inflammation without altering bacterial clearance. In summary, this study highlights the therapeutic potential of targeting the MEK1/2 pathway to combat MRSA pulmonary infections.IMPORTANCEStaphylococcus aureus infections pose a significant burden on global healthcare systems. Community-associated transmission of methicillin-resistant S. aureus (MRSA) and the increasing prevalence of other drug-resistant S. aureus isolates limit therapeutic options to combat this opportunistic pathogen. Infection-induced inflammation is a significant driver of tissue damage, especially in cystic fibrosis pulmonary infections. However, therapeutic strategies that can reduce inflammation without compromising host defense and bacterial clearance mechanisms are lacking. This study investigates the dual anti-inflammatory and antibacterial effects of a MEK1/2 inhibitor as a therapeutic strategy to target both host and pathogen with a single compound. This work also identifies host MEK2 as a specific target that can be modulated to reduce inflammation without impairing host defense against MRSA pulmonary infection. Results from this study can inform future human clinical trials to evaluate the ability of the MEK1/2 inhibitor compound ATR-002 to both combat S. aureus infections and reduce inflammation that accompanies these infections.

PMID:40422262 | DOI:10.1128/mbio.00775-25

Med Biol Eng Comput. 2025 May 27. doi: 10.1007/s11517-025-03381-3. Online ahead of print.

ABSTRACT

Traditional computed tomography (CT) methods for 3D reconstruction face resolution limitations and require time-consuming post-processing workflows. While deep learning techniques improve the accuracy of segmentation, traditional voxel-based segmentation and surface reconstruction pipelines tend to introduce artifacts such as disconnected regions, topological inconsistencies, and stepped distortions. To overcome these challenges, we propose ToPoMesh, an end-to-end 3D mesh reconstruction deep learning framework for direct reconstruction of high-fidelity surface meshes from CT volume data. To address the existing problems, our approach introduces three core innovations: (1) accurate local and global shape modeling by preserving and enhancing local feature information through residual connectivity and self-attention mechanisms in graph convolutional networks; (2) an adaptive variant density (Avd) mesh de-pooling strategy, which dynamically optimizes the vertex distribution; (3) a topology modification module that iteratively prunes the error surfaces and boundary smoothing via variable regularity terms to obtain finer mesh surfaces. Experiments on the LiTS, MSD pancreas tumor, MSD hippocampus, and MSD spleen datasets demonstrate that ToPoMesh outperforms state-of-the-art methods. Quantitative evaluations demonstrate a 57.4% reduction in Chamfer distance (liver) and a 0.47% improvement in F-score compared to end-to-end 3D reconstruction methods, while qualitative results confirm enhanced fidelity for thin structures and complex anatomical topologies versus segmentation frameworks. Importantly, our method eliminates the need for manual post-processing, realizes the ability to reconstruct 3D meshes from images, and can provide precise guidance for surgical planning and diagnosis.

PMID:40423893 | DOI:10.1007/s11517-025-03381-3

Epilepsia. 2025 May 27. doi: 10.1111/epi.18478. Online ahead of print.

ABSTRACT

OBJECTIVE: Epilepsy is a debilitating disorder affecting more than 50 million people worldwide, and one third of patients continue to have seizures despite maximal medical management. If patients' seizures localize to a discrete brain region, termed a seizure onset zone, resection may be curative. Localization is often confirmed with stereotactic electroencephalography; however, this may require patients to stay in the hospital for weeks to capture spontaneous seizures. Automated localization of seizure onset zones could therefore improve presurgical evaluation and decrease morbidity.

METHODS: Using more than 1 000 000 interictal stereotactic electroencephalography segments collected from 78 patients, we performed five-fold cross-validation and testing on a multichannel, multiscale, one-dimensional convolutional neural network to classify seizure onset zones.

RESULTS: Across held-out test sets, our models achieved a seizure onset zone classification sensitivity of .702 (95% confidence interval [CI] = .549-.805), specificity of .741 (95% CI = .652-.835), and accuracy of .738 (95% CI = .687-.795), which was significantly better than models trained on random labels. The models performed well across the entire brain, with top five region performance demonstrating accuracies between 70.0% and 88.4%. When split by outcomes, the models performed significantly better on patients with favorable Engel outcomes after resection or who were responsive neurostimulation responders. Finally, SHAP (Shapley Additive Explanation) value analysis on median-normalized input data assigned consistently high feature importance to interictal spikes and large deflections, whereas similar analyses on histogram-equalized data revealed differences in feature importance assignments to low-amplitude segments.

SIGNIFICANCE: This work serves as evidence that deep learning on brief interictal intracranial data can classify seizure onset zones across the brain. Furthermore, our findings corroborate current understandings of interictal epileptiform discharges and may help uncover novel interictal morphologies. Clinical application of our models may reduce dependence on recorded seizures for localization and shorten presurgical evaluation time for drug-resistant epilepsy patients, reducing patient morbidity and hospital costs.

PMID:40423629 | DOI:10.1111/epi.18478

Radiology. 2025 May;315(2):e259008. doi: 10.1148/radiol.259008.

NO ABSTRACT

PMID:40423544 | DOI:10.1148/radiol.259008

Toxics. 2025 Apr 27;13(5):349. doi: 10.3390/toxics13050349.

ABSTRACT

The operational complexity of wastewater treatment systems mainly stems from the diversity of influent characteristics and the nonlinear nature of the treatment process. Together, these factors make the control of effluent quality in wastewater treatment plants (WWTPs) difficult to manage effectively. To address this challenge, constructing accurate effluent quality models for WWTPs can not only mitigate these complexities, but also provide critical decision support for operational management. In this research, we introduce a deep learning method that fuses multi-source data. This method utilises various indicators to comprehensively analyse and predict the quality of effluent water: water quantity data, process data, energy consumption data, and water quality data. To assess the efficacy of this method, a case study was carried out at an industrial effluent treatment plant (IETP) in Anhui Province, China. Deep learning algorithms including long short-term memory (LSTM) and gated recurrent unit (GRU) were found to have a favourable prediction performance by comparing with traditional machine learning algorithms (random forest, RF) and multi-layer perceptron (MLP). The results show that the R2 of LSTM and GRU is 1.36%~31.82% higher than that of MLP and 9.10%~47.75% higher than that of traditional machine learning algorithms. Finally, the RReliefF approach was used to identify the key parameters affecting the water quality behaviour of IETP effluent, and it was found that, by optimising the multi-source feature structure, not only the monitoring and management strategies can be optimised, but also the modelling efficiency of the model can be further improved.

PMID:40423427 | DOI:10.3390/toxics13050349

Toxics. 2025 Apr 23;13(5):327. doi: 10.3390/toxics13050327.

ABSTRACT

To address the performance degradation in existing PM2.5 prediction models caused by excessive complexity, poor spatiotemporal efficiency, and suboptimal parameter optimization, we employ stacking ensemble learning for feature weighting analysis and integrate the ant colony optimization (ACO) algorithm for model parameter optimization. Combining meteorological and collaborative pollutant data, a model (namely the stacking-ACO-LSTM model) with a much shorter consuming time than that of only long short-term memory (LSTM) networks suitable for PM2.5 concentration prediction is established. It can effectively filter out feature variables with higher weights, thereby reducing the predictive power of the model. The prediction of hourly PM2.5 concentration of the model is trained and tested using real-time monitoring data in Nanchang City from 2017 to 2019. The results show that the established stacking-ACO-LSTM model has high accuracy in predicting PM2.5 concentration, and compared to the same model without considering time and space efficiency and defective parameter optimization, the mean square error (MSE) decreases by about 99.88%, and the coefficient of determination (R2) increases by about 2.39%. This study provides a new idea for predicting PM2.5 concentration in cities.

PMID:40423406 | DOI:10.3390/toxics13050327

Toxins (Basel). 2025 Apr 27;17(5):219. doi: 10.3390/toxins17050219.

ABSTRACT

Cereal grains and nuts are the world's most produced food and the economic backbone of many countries. Food safety in these commodities is crucial, as they are highly susceptible to mold growth and mycotoxin contamination in warm, humid environments. This review explores hyperspectral imaging (HSI) integrated with machine learning (ML) algorithms as a promising approach for detecting and quantifying mycotoxins in cereal grains and nuts. This study aims to (1) critically evaluate current non-destructive techniques for processing these foods and the applications of ML in identifying mycotoxins through HSI, and (2) highlight challenges and potential future research directions to enhance the reliability and efficiency of these detection systems. The ML algorithms showed effectiveness in classifying and quantifying mycotoxins in grains and nuts, with HSI systems increasingly adopted in industrial settings. Mycotoxins exhibit heightened sensitivity to specific spectral bands within HSI, facilitating accurate detection. Additionally, selecting only relevant spectral features reduces ML model complexity and enhances reliability in the detection process. This review contributes to a deeper understanding of the integration of HSI and ML for food safety applications in cereal grains and nuts. By identifying current challenges and future research directions, it provides valuable insights for advancing non-destructive mycotoxin detection methods in the food industry using HSI.

PMID:40423302 | DOI:10.3390/toxins17050219

Tomography. 2025 May 13;11(5):56. doi: 10.3390/tomography11050056.

ABSTRACT

Background: According to the World Health Organization (WHO), approximately 5% of children and 2.5% of adults suffer from attention deficit hyperactivity disorder (ADHD). This disorder can have significant negative consequences on people's lives, particularly children. In recent years, methods based on artificial intelligence and neuroimaging techniques, such as MRI, have made significant progress, paving the way for development of more reliable diagnostic tools. In this proof of concept study, our aim was to investigate the potential utility of neuroimaging data and clinical information in combination with a deep learning-based analytical approach, more precisely, a novel feature extraction technique for the diagnosis of ADHD with high accuracy. Methods: Leveraging the ADHD200 dataset, which encompasses demographic information and anatomical MRI scans collected from a diverse ADHD population, our study focused on developing modern deep learning-based diagnostic models. The data preprocessing employed a pre-trained Visual Geometry Group16 (VGG16) network to extract two-dimensional (2D) feature maps from three-dimensional (3D) anatomical MRI data to reduce computational complexity and enhance diagnostic power. The inclusion of personal attributes, such as age, gender, intelligence quotient, and handedness, strengthens the diagnostic models. Four deep-learning architectures-convolutional neural network 2D (CNN2D), CNN1D, long short-term memory (LSTM), and gated recurrent units (GRU)-were employed for analysis of the MRI data, with and without the inclusion of clinical characteristics. Results: A 10-fold cross-validation test revealed that the LSTM model, which incorporated both MRI data and personal attributes, had the best diagnostic performance among all tested models in the diagnosis of ADHD with an accuracy of 0.86 and area under the receiver operating characteristic (ROC) curve (AUC) score of 0.90. Conclusions: Our findings demonstrate that the proposed approach of extracting 2D features from 3D MRI images and integrating these features with clinical characteristics may be useful in the diagnosis of ADHD with high accuracy.

PMID:40423258 | DOI:10.3390/tomography11050056

Tomography. 2025 Apr 30;11(5):52. doi: 10.3390/tomography11050052.

ABSTRACT

Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed.

PMID:40423254 | DOI:10.3390/tomography11050052

Tomography. 2025 Apr 27;11(5):51. doi: 10.3390/tomography11050051.

ABSTRACT

Objectives: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. Methods: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. Results: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. Conclusions: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity.

PMID:40423253 | DOI:10.3390/tomography11050051

Tomography. 2025 Apr 24;11(5):50. doi: 10.3390/tomography11050050.

ABSTRACT

BACKGROUND/OBJECTIVES: Developing a treatment strategy that effectively prolongs the lives of people with brain tumors requires an accurate diagnosis of the condition. Therefore, improving the preoperative classification of meningiomas is a priority. Machine learning (ML) has made great strides thanks to the development of convolutional neural networks (CNNs) and computer-aided tumor detection systems. The deep convolutional layers automatically extract important and dependable information from the input space, in contrast to more traditional neural network layers. One recent and promising advancement in this field is ML. Still, there is a dearth of studies being carried out in this area.

METHODS: Therefore, starting with the analysis of magnetic resonance images, we have suggested in this research work a tried-and-tested and methodical strategy for real-time meningioma diagnosis by image segmentation using a very deep transfer learning CNN model or DNN model (VGG-16) with CUDA. Since the VGGNet CNN model has a greater level of accuracy than other deep CNN models like AlexNet, GoogleNet, etc., we have chosen to employ it. The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. Our VGGNet model takes in an sMRI FLAIR image input. The VGG's convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1 × 1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution. All the hidden layers in our VGG network also use ReLU. A dataset consisting of 264 3D FLAIR sMRI image segments from three different classes (meningioma, tuberculoma, and normal) was employed. The number of epochs in the Sequential Model was set to 10. The Keras layers that we used were Dense, Dropout, Flatten, Batch Normalization, and ReLU.

RESULTS: According to the simulation findings, our suggested model successfully classified all of the data in the dataset used, with a 99.0% overall accuracy. The performance metrics of the implemented model and confusion matrix for tumor classification indicate the model's high accuracy in brain tumor classification.

CONCLUSIONS: The good outcomes demonstrate the possibility of our suggested method as a useful diagnostic tool, promoting better understanding, a prognostic tool for clinical outcomes, and an efficient brain tumor treatment planning tool. It was demonstrated that several performance metrics we computed using the confusion matrix of the previously used model were very good. Consequently, we think that the approach we have suggested is an important way to identify brain tumors.

PMID:40423252 | DOI:10.3390/tomography11050050

Radiology. 2025 May;315(2):e243651. doi: 10.1148/radiol.243651.

ABSTRACT

Background Limited evidence exists on the prevalence and outcomes of interstitial lung abnormalities (ILAs) in lung cancer screening populations, particularly Asian populations. Purpose To investigate the prevalence of ILAs and the association of ILAs with lung cancer, idiopathic pulmonary fibrosis (IPF), and mortality outcomes in an Asian population. Materials and Methods In this nationwide, population-based retrospective study, baseline screenings from the Korean National Lung Cancer Screening Program performed between August 2019 and December 2020 were analyzed. ILAs were identified from CT structured reports based on program radiologists' visual assessment, and ILA prevalence was analyzed across age groups. Incidence rate ratios were calculated for lung cancer incidence, IPF, and all-cause mortality comparing individuals with ILAs versus individuals without ILAs, and multivariable Cox regression analyses were performed to examine associations between ILAs and these outcomes. Results Among 125 600 individuals (mean age, 62 years ± 5.3 [SD]; 123 331 men), ILA prevalence was 2.65% (3324 of 125 600) and was strongly associated with older age (P < .001). The lung cancer incidence rate was higher in the ILA group (2009 vs 412 per 100 000 person-years, P < .001; incidence rate ratio, 4.88), as was the all-cause mortality rate (2334 vs 712 per 100 000 person-years, P < .001; incidence rate ratio, 3.28). During a median follow-up of 2.9 years, IPF was diagnosed in 3.55% (118 of 3324) of individuals with ILAs (incidence rate, 1344 per 100 000 person-years in group with ILAs vs 18 per 100 000 person-years in group without ILAs, P < .001; incidence rate ratio, 73.24). In multivariable analyses, individuals with ILAs had a threefold higher risk of lung cancer (adjusted hazard ratio, 3.18 [95% CI: 2.71, 3.73]; P < .001) and twofold higher all-cause mortality (adjusted hazard ratio, 2.37 [95% CI: 2.09, 2.69]; P < .001). Individuals with ILAs showed a markedly higher risk of IPF diagnosis, with more than 60-fold higher risk (adjusted hazard ratio, 63.4 [95% CI: 45.9, 87.7]; P < .001). Conclusion The presence of ILAs was associated with higher risks of lung cancer, IPF, and mortality. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Baruah and Kabakus in this issue.

PMID:40423536 | DOI:10.1148/radiol.243651

Toxins (Basel). 2025 Apr 24;17(5):213. doi: 10.3390/toxins17050213.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by the fibrotic thickening of the alveolar walls, resulting in compromised gas exchange, restricted ventilation, and respiratory failure. It has been indicated that elastase inhibitors reduced the severity of IPF by neutralizing excessive elastase levels in the lungs. ShSPI is an elastase inhibitor derived from centipede toxin. The present study evaluates the therapeutic effects of ShSPI in a bleomycin-induced idiopathic pulmonary fibrosis model. According to the results, ShSPI markedly reduced the weight loss, showing the improvement of health status in bleomycin-induced mice. Its robust antifibrotic effects were evidenced by the mitigation of alveolar structural damage, reduction in inflammatory cell infiltration, inhibition of collagen deposition, and suppression of fibrotic nodule formation. ShSPI effectively attenuated inflammatory responses by downregulating pro-inflammatory factors (IL-6, IL-1β, and MCP-1) and upregulating the anti-inflammatory factor interleukin-10 (IL-10). After delivered via inhalation, ShSPI exhibited favorable pharmacokinetic properties. It could be detected at 8 h at doses of 1 mg/kg and achieved maximum plasma concentrations (Cmax) of 188.00 ± 64.40 ng/mL in vivo. At high doses (160 mg/kg), ShSPI maintained a strong safety profile, with no detectable toxicity observed. This feature shows the therapeutic potential of ShSPI in the treatment of idiopathic pulmonary fibrosis and provides valuable evidence for its development as a novel peptide-based therapy.

PMID:40423296 | DOI:10.3390/toxins17050213

J Pers Med. 2025 May 21;15(5):213. doi: 10.3390/jpm15050213.

ABSTRACT

Interstitial lung disease (ILD) prevalence and survival are increasing due to improvement in scientific research together with clinical complications typical of advanced disease. Lung cancer (LC) is described as a possible event occurring in lung parenchyma in the context of fibrotic abnormalities that worsen patients' prognosis. This growth of malignant cells on a fibrotic background has also been called scar-cinoma. For this reason, not only an early diagnosis but also personalized decisions on the best treatment approach should be considered for each patient in a multidisciplinary discussion, since in some cases chemotherapy or surgery could be detrimental for patients with pulmonary fibrosis. LC and lung fibrosis may share common pathogenetic mechanisms like an altered healing process in response to repeated tissue damage from environmental exposure in genetically susceptible individuals. Smoking history and air pollution together with mutations in telomere and surfactant protein genes lead to the production of cytokines and nitro derivatives in the microenvironment that facilitate the carcinomatous transformation during fibrogenesis. The evolution of LC therapy and the implementation of immunotherapy acting on targetable immune checkpoints have raised interest in evaluating ILD-LC actionable mutations. The main pathogenetic mechanisms, clinical presentations and treatment implications are presented in this review.

PMID:40423084 | DOI:10.3390/jpm15050213

J Chem Inf Model. 2025 May 27. doi: 10.1021/acs.jcim.4c02363. Online ahead of print.

ABSTRACT

Effective drug safety assessment, guided by the 3R principle (Replacement, Reduction, Refinement) to minimize animal testing, is critical in early drug development. Drug-induced liver injury (DILI), particularly drug-induced cholestasis (DIC), remains a major challenge. This study introduces a computational method for predicting DIC by integrating PubChem substructure fingerprints with biological data from liver-expressed targets and pathways, alongside nine hepatic transporter inhibition models. To address class imbalance in the public cholestasis data set, we employed undersampling, a technique that constructs a small and robust consensus model by evaluating distinct subsets. The most effective baseline model, which combined PubChem substructure fingerprints, pathway data and hepatic transporter inhibition predictions, achieved a Matthews correlation coefficient (MCC) of 0.29 and a sensitivity of 0.79, as validated through 10-fold cross-validation. Subsequently, target prediction using four publicly available tools was employed to enrich the sparse compound-target interaction matrix. Although this approach showed lower sensitivity compared to experimentally derived targets and pathways, it highlighted the value of incorporating specific systems biology related information. Feature importance analysis identified albumin as a potential target linked to cholestasis within our predictive model, suggesting a connection worth further investigation. By employing an expanded consensus model and applying probability range filtering, the refined method achieved an MCC of 0.38 and a sensitivity of 0.80, thereby enhancing decision-making confidence. This approach advances DIC prediction by integrating biological and chemical descriptors, offering a reliable and explainable model.

PMID:40421892 | DOI:10.1021/acs.jcim.4c02363

JAMA Netw Open. 2025 May 1;8(5):e2512455. doi: 10.1001/jamanetworkopen.2025.12455.

ABSTRACT

IMPORTANCE: Accurately quantifying adverse event (AE) costs is essential for cost-effectiveness analyses (CEAs) of anticancer drugs. Misestimates in AE costs may significantly affect cost-effectiveness conclusions.

OBJECTIVE: To assess whether AE cost quantification in anticancer drug CEAs accurately reflects the true cost of AEs and to evaluate whether replacing AE costs with actual values affects cost-effectiveness conclusions.

EVIDENCE REVIEW: A systematic search of PubMed, Web of Science, and Tufts CEA databases was conducted from October 24 to December 1, 2023, with an additional search from November 4 to 10, 2024, for English-language CEAs and claims-based studies examining AE costs for anticancer drugs published between January 2003 and December 2023. Claims-based AE costs were considered to represent actual values. AE costs were compared in absolute terms and as a proportion of total medical costs. Impact of replacing CEA AE cost estimates with actual values for incremental cost-effectiveness ratios (ICERs) was examined at thresholds of $100 000 and $150 000 per quality-adjusted life year (QALY). AE cost differences between CEA estimates and actual values and their impact on ICERs were the main outcomes.

FINDINGS: The sample included 11 claims-based US studies with 34 022 patients and 102 US payer-perspective CEAs. AE cost estimates in CEAs were consistently lower than actual values, with a median difference of 9.73% (IQR, 5.15%-27.22%; P = .002) in proportion of total medical costs and of $17 201 (IQR, $13 365-$48 970; P = .03) in absolute costs. Adjusting AE costs led to an ICER change of $42 656 per QALY, altering cost-effectiveness conclusions in 8 of 17 cases (47.1%). Among the 102 CEAs, 41 (40.2%) did not report AE types; of the remaining 61 (59.8%), 48 (78.7%) focused on treatment-related AEs instead of all-cause AEs. Of all CEAs, 79 (77.5%) considered grade 3 or higher AEs, ignoring grades 1 and 2. Only 13 studies (12.7%) accounted for AE-related dose reductions or interruptions, 87 (85.3%) did not consider postprogression AE costs, and 77 (82.8%) assumed AEs occurred only in the first treatment cycle. Substantial variability was observed in both drug AE and unit AE costs across studies.

CONCLUSIONS AND RELEVANCE: In this systematic review of AE costs in oncology CEAs, AE costs were frequently underestimated, potentially altering cost-effectiveness conclusions. Key problems included incomplete AE inclusion, inaccurate AE cost estimates, overlooked long-term AEs, and unaccounted dose modifications. Best practices and standardized guidelines should be established to improve AE cost quantification in oncology CEAs.

PMID:40423968 | DOI:10.1001/jamanetworkopen.2025.12455

J Biochem Mol Toxicol. 2025 Jun;39(6):e70316. doi: 10.1002/jbt.70316.

ABSTRACT

Valproic acid (VPA) is a frequently prescribed treatment for many psychiatric disorders, particularly for epilepsy. However, it has been associated with possible side effects including hepatotoxicity and neurotoxicity. The present study investigated the protective effect of vinpocetine (Vinpo) against VPA-induced hepatotoxicity and hippocampal neurotoxicity in rats. Vinpo (5 and 10 mg/kg/day; p.o) was given for 14 days, with/without VPA (500 mg/kg/day; p.o) in adult male Wistar rats. VPA showed marked increase in hepatic and hippocampal MDA levels with increased liver function enzymes as well as a marked decline in serum total antioxidant capacity (TAC). Simultaneously, VPA administration resulted in a significant reduction in cAMP, cAMP response element binding protein (CREB), and PI3K/AKT protein levels in liver tissue and hippocampus. These results were confirmed by histological degenerative changes in both tissues. VPA also associated with increased hepatic and dentate gyrus nuclear factor kappa (NF-κB) immunoexpression with increased Glial fibrillary acidic protein (GFAP) expression in the dentate gyrus. Administration of Vinpo markedly attenuated VPA-induced toxicity in rats by its anti-oxidant effect on MDA and TAC levels. Vinpo resulted in a significant increase in the levels of cAMP/CREB and PI3K/AKT in liver and hippocampus tissues, together with significant decrease in NF-κB nuclear expression. Vinpo ameliorated astrogliosis as indicated by reduction in the expression of GFAP. Vinpo exerted a hepatoprotective and neuroprotective role against VPA-induced toxicity by cAMP and PI3K/AKT dependent activation of CREB and this hold a promise as a safe and effective adjuvant while treating psychiatric patients with VPA.

PMID:40421768 | DOI:10.1002/jbt.70316