Pharmacol Res Perspect. 2025 Feb;13(1):e70075. doi: 10.1002/prp2.70075.

ABSTRACT

Aromatase inhibitors are used for patients with hormone-receptor positive breast cancer. Alzheimer's disease is the most prevalent cause of dementia. Several studies have suggested an association between the use of aromatase inhibitors and the development of Alzheimer's disease. The objective of this study was to identify potential pharmacovigilance signals associated with dementia and Alzheimer's disease and third-generation aromatase inhibitors in menopausal and postmenopausal women. VigiBase, the global database of individual case safety reports of the World Health Organization, was used to investigate this possible association. A disproportionality analysis was performed for women aged 45 years and older. The reporting odds ratio (ROR) and its 95% CI for reporting dementia are exemestane, 2.08 (1.35-3.19); anastrozole, 1.59 (1.09-2.32); and letrozole, 1.43 (1.05-1.95) and for Alzheimer's disease are exemestane, 0.94 (0.30-2.92); anastrozole: 2.63 (1.55-4.45); and letrozole, 1.33 (0.76-2.35). For senile dementia, only letrozole has cases, with an ROR of 6.77 (2.51-18.31). Signals of disproportionate reporting have been observed between the occurrence of dementia, dementia Alzheimer's type, and senile dementia with aromatase inhibitors, which is in line with estrogen functions and aromatase activity, as well as the findings from preclinical studies. Additional research is required to elucidate this intricate matter.

PMID:39917951 | DOI:10.1002/prp2.70075

Pharmacogenomics. 2025 Feb 7:1-12. doi: 10.1080/14622416.2025.2456449. Online ahead of print.

ABSTRACT

AIM: This study is designed to address the connection between antidepressant and antipsychotic-induced hepatotoxicity with pharmacogenetic and epigenetic indicators, using a novel combined approach of CYP450 polymorphism determination and early liver injury detection via microRNA testing.

METHODS: The multi-centric retrospective case-control study in Slovakia involves 151 cases with signs of hepatotoxicity and 604 controls without. Participants will be tested for selected CYP450, UGT1A1 polymorphisms, and microRNAs.

RESULTS: Anticipated findings will test if patients with specific CYP450 and UGT1A1 polymorphisms are at higher risk for drug-induced hepatotoxicity and if plasma microRNAs hsa-miR-122-5p and hsa-miR-192-5p, alone or combined, can differentiate patients with abnormal liver function.

CONCLUSION: The findings could contribute to personalized treatment approach by combining genetic and epigenetic biomarkers.

PMID:39916529 | DOI:10.1080/14622416.2025.2456449

Am J Respir Crit Care Med. 2025 Feb 7. doi: 10.1164/rccm.202407-1474OC. Online ahead of print.

ABSTRACT

RATIONALE: The mucoid phenotype of Staphylococcus aureus is caused by adaptation. Excessive biofilm formation associated with a protective effect for mucoid S. aureus was observed in isolates from respiratory samples of people with cystic fibrosis (pwCF). However, there is little knowledge about the prevalence of mucoid S. aureus in pwCF and a potential association with CF lung disease.

METHODS: A prospective multicenter study was conducted (cross-sectional and longitudinal). Specimens and case report forms were sent to the central study laboratory for characterization of S. aureus and analysis of clinical parameters.

MEASUREMENTS AND MAIN RESULTS: Cross-sectional study: In 41 of 451 S. aureus-positive pwCF (9.1%) from 13 CF-centers, mucoid S. aureus was cultured. Longitudinal study: The distribution of CFTR genotypes, the number of pwCF with highly effective modulator therapy and co-infection with Pseudomonas aeruginosa were equivalent in the mucoid (35 pwCF) versus the control group (only non-mucoid S. aureus, 36 pwCF). While lung function did not differ between groups as a whole, a subgroup analysis revealed significantly worse lung function for female pwCF with mucoid S. aureus as well as for pwCF if P. aeruginosa co-infection was excluded.

CONCLUSIONS: In the era of highly effective modulator therapy, worse lung function was associated with female and P. aeruginosa-negative pwCF with mucoid S. aureus compared to pwCF with only non-mucoid S. aureus. Therefore, appropriate culture conditions should be established to detect mucoid S. aureus. Further investigations are needed to elucidate the relationship between mucoid S. aureus and CF lung disease.

PMID:39918841 | DOI:10.1164/rccm.202407-1474OC

Med Biol Eng Comput. 2025 Feb 7. doi: 10.1007/s11517-025-03314-0. Online ahead of print.

ABSTRACT

Retinal diseases are a serious global threat to human vision, and early identification is essential for effective prevention and treatment. However, current diagnostic methods rely on manual analysis of fundus images, which heavily depends on the expertise of ophthalmologists. This manual process is time-consuming and labor-intensive and can sometimes lead to missed diagnoses. With advancements in computer vision technology, several automated models have been proposed to improve diagnostic accuracy for retinal diseases and medical imaging in general. However, these methods face challenges in accurately detecting specific diseases within images due to inherent issues associated with fundus images, including inter-class similarities, intra-class variations, limited local information, insufficient contextual understanding, and class imbalances within datasets. To address these challenges, we propose a novel deep learning framework for accurate retinal disease classification. This framework is designed to achieve high accuracy in identifying various retinal diseases while overcoming inherent challenges associated with fundus images. Generally, the framework consists of three main modules. The first module is Densely Connected Multidilated Convolution Neural Network (DCM-CNN) that extracts global contextual information by effectively integrating novel Casual Dilated Dense Convolutional Blocks (CDDCBs). The second module of the framework, namely, Local-Patch-based Convolution Neural Network (LP-CNN), utilizes Class Activation Map (CAM) (obtained from DCM-CNN) to extract local and fine-grained information. To identify the correct class and minimize the error, a synergic network is utilized that takes the feature maps of both DCM-CNN and LP-CNN and connects both maps in a fully connected fashion to identify the correct class and minimize the errors. The framework is evaluated through a comprehensive set of experiments, both quantitatively and qualitatively, using two publicly available benchmark datasets: RFMiD and ODIR-5K. Our experimental results demonstrate the effectiveness of the proposed framework and achieves higher performance on RFMiD and ODIR-5K datasets compared to reference methods.

PMID:39918766 | DOI:10.1007/s11517-025-03314-0

Eur J Nucl Med Mol Imaging. 2025 Feb 7. doi: 10.1007/s00259-025-07120-6. Online ahead of print.

ABSTRACT

OBJECTIVE: Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a larger angle of acceptance. However this extended angle increases the number of multiple scatters and the scatter contribution within oblique planes. As scattering affects both quality and quantification of the reconstructed image, it is crucial to correct this effect with more accurate methods than the state-of-the-art single scatter simulation (SSS) that can reach its limits with such an extended field-of-view (FOV). In this work, which is an extension of our previous assessment of deep learning-based scatter estimation (DLSE) carried out on a conventional PET system, we aim to evaluate the DLSE method performance on LAFOV total-body PET.

APPROACH: The proposed DLSE method based on an convolutional neural network (CNN) U-Net architecture uses emission and attenuation sinograms to estimate scatter sinogram. The network was trained from Monte-Carlo (MC) simulations of XCAT phantoms [ 18 F]-FDG PET acquisitions using a Siemens Biograph Vision Quadra scanner model, with multiple morphologies and dose distributions. We firstly evaluated the method performance on simulated data in both sinogram and image domain by comparing it to the MC ground truth and SSS scatter sinograms. We then tested the method on seven [ 18 F]-FDG and [ 18 F]-PSMA clinical datasets, and compare it to SSS estimations.

RESULTS: DLSE showed superior accuracy on phantom data, greater robustness to patient size and dose variations compared to SSS, and better lesion contrast recovery. It also yielded promising clinical results, improving lesion contrasts in [ 18 F]-FDG datasets and performing consistently with [ 18 F]-PSMA datasets despite no training with [ 18 F]-PSMA.

SIGNIFICANCE: LAFOV PET scatter can be accurately estimated from raw data using the proposed DLSE method.

PMID:39918764 | DOI:10.1007/s00259-025-07120-6

J Acoust Soc Am. 2025 Feb 1;157(2):947-961. doi: 10.1121/10.0035787.

ABSTRACT

For improving passive detection of underwater broadband sources, a source-detection and direction-of-arrival-estimation method is developed herein based on a deep neural network (DNN) using a spherical array. Spherical Fourier transform is employed to convert the element pressure signals into spherical Fourier coefficients, which are used as inputs of the DNN. A Gaussian distribution with a spatial-spectrum-like form is adopted to design labels for the DNN. A physical model coupling underwater acoustic propagation and the spherical array is established to simulate array signals for DNN training. The introduction of white noise into the training data considerably enhances the detection capability of the DNN and effectively suppresses false estimation. The model's performance is evaluated based on its detection rate at a constant false alarm rate. Notably, the model does not rely on prior knowledge of the source's spectral features. Further, this study demonstrates that a DNN trained by one source can achieve multisource detection to a certain extent. The simulation and experimental processing results validate the broadband detection capability of the proposed method at varying signal-to-noise ratios.

PMID:39918577 | DOI:10.1121/10.0035787

Cytopathology. 2025 Feb 7. doi: 10.1111/cyt.13474. Online ahead of print.

ABSTRACT

OBJECTIVE: Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterisation in the specimen and reporting the corresponding findings, which are extremely burdensome tasks. In this study, we propose a technique to generate cytologic findings from for cytologic images to assist in the reporting of pulmonary cytology.

METHODS: For this study, 801 patch images were retrieved using cytology specimens collected from 206 patients; the findings were assigned to each image as a dataset for generating cytologic findings. The proposed method consists of a vision model and dual text decoders. In the former, a convolutional neural network (CNN) is used to classify a given image as benign or malignant, and the features related to the image are extracted from the intermediate layer. Independent text decoders for benign and malignant cells are prepared for text generation, and the text decoder switches according to the CNN classification results. The text decoder is configured using a transformer that uses the features obtained from the CNN for generating findings.

RESULTS: The sensitivity and specificity were 100% and 96.4%, respectively, for automated benign and malignant case classification, and the saliency map indicated characteristic benign and malignant areas. The grammar and style of the generated texts were confirmed correct, achieving a BLEU-4 score of 0.828, reflecting high degree of agreement with the gold standard, outperforming existing LLM-based image-captioning methods and single-text-decoder ablation model.

CONCLUSION: Experimental results indicate that the proposed method is useful for pulmonary cytology classification and generation of cytologic findings.

PMID:39918342 | DOI:10.1111/cyt.13474

Curr Med Imaging. 2025 Feb 4. doi: 10.2174/0115734056333393250117164020. Online ahead of print.

ABSTRACT

BACKGROUND/OBJECTIVE: Brain tumour is characterized by its aggressive nature and low survival rate and thus regarded as one of the deadliest diseases. Thus, miss-diagnosis or miss-classification of brain tumour can lead to miss treatment or incorrect treatment and reduce survival chances. Therefore, there is need to develop a technique that can identify and detect brain tumour at early stages.

METHODS: Here, we proposed a framework titled I-Brainer which is an Artificial Intelligence/Internet of Things (AI/IoT)-powered classification of MRI. We employed a Br35H+SARTAJ brain MRI dataset which contain 7023 total images which include No tumour, pituitary, meningioma and glioma. In order to accurately classified MRI into 4-class, we developed LeNet model from scratch, implemented 2 pretrained models which include EfficientNet and ResNet-50 as well feature extraction of these models coupled with 2 Machine Learning classifiers k-Nearest Neighbours (KNN) and Support Vector Machines (SVM).

RESULT: Evaluation and comparison of the performance of 3 models has shown that EfficientNet+SVM achieved the best result in terms of AUC (99%) and ResNet-50-KNN ranked higher in terms of accuracy (94%) on testing dataset.

CONCLUSION: This framework can be harness by patients residing in remote areas and as confirmatory approach for medical experts.

PMID:39917913 | DOI:10.2174/0115734056333393250117164020

Synth Syst Biotechnol. 2024 Dec 27;10(2):442-451. doi: 10.1016/j.synbio.2024.12.007. eCollection 2025 Jun.

ABSTRACT

Polymerase cycling assembly (PCA) stands out as the predominant method in the synthesis of kilobase-length DNA fragments. The design of overlapping regions is the core factor affecting the success rate of synthesis. However, there still exists DNA sequences that are challenging to design and construct in the genome synthesis. Here we proposed a deep learning model based on extensive synthesis data to discern latent sequence representations in overlapping regions with an AUPR of 0.805. Utilizing the model, we developed the SmartCut algorithm aimed at designing oligonucleotides and enhancing the success rate of PCA experiments. This algorithm was successfully applied to sequences with diverse synthesis constraints, 80.4 % of which were synthesized in a single round. We further discovered structure differences represented by major groove width, stagger, slide, and centroid distance between overlapping and non-overlapping regions, which elucidated the model's reasonableness through the lens of physical chemistry. This comprehensive approach facilitates streamlined and efficient investigations into the genome synthesis.

PMID:39917768 | PMC:PMC11799973 | DOI:10.1016/j.synbio.2024.12.007

Front Dent Med. 2025 Jan 6;5:1509361. doi: 10.3389/fdmed.2024.1509361. eCollection 2024.

ABSTRACT

BACKGROUND: Periodontitis is a chronic inflammatory disease affecting the gingival tissues and supporting structures of the teeth, often leading to tooth loss. The condition begins with the accumulation of dental plaque, which initiates an immune response. Current radiographic methods for assessing alveolar bone loss are subjective, time-consuming, and labor-intensive. This study aims to develop an AI-driven model using Convolutional Neural Networks (CNNs) to accurately assess alveolar bone loss and provide individualized periodontal prognoses from panoramic radiographs.

METHODS: A total of 2,000 panoramic radiographs were collected using the same device, based on the periodontal diagnosis codes from the HOSxP Program. Image enhancement techniques were applied, and an AI model based on YOLOv8 was developed to segment teeth, identify the cemento-enamel junction (CEJ), and assess alveolar bone levels. The model quantified bone loss and classified prognoses for each tooth.

RESULTS: The teeth segmentation model achieved 97% accuracy, 90% sensitivity, 96% specificity, and an F1 score of 0.80. The CEJ and bone level segmentation model showed superior results with 98% accuracy, 100% sensitivity, 98% specificity, and an F1 score of 0.90. These findings confirm the models' effectiveness in analyzing panoramic radiographs for periodontal bone loss detection and prognostication.

CONCLUSION: This AI model offers a state-of-the-art approach for assessing alveolar bone loss and predicting individualized periodontal prognoses. It provides a faster, more accurate, and less labor-intensive alternative to current methods, demonstrating its potential for improving periodontal diagnosis and patient outcomes.

PMID:39917716 | PMC:PMC11797906 | DOI:10.3389/fdmed.2024.1509361

Front Dent Med. 2024 Dec 23;5:1525505. doi: 10.3389/fdmed.2024.1525505. eCollection 2024.

ABSTRACT

Artificial intelligence (AI) technology is being used in various fields and its use is increasingly expanding in dentistry. The key aspects of AI include machine learning (ML), deep learning (DL), and neural networks (NNs). The aim of this review is to present an overview of AI, its various aspects, and its application in biomedicine, dentistry, and dental biomaterials focusing on restorative dentistry and prosthodontics. AI-based systems can be a complementary tool in diagnosis and treatment planning, result prediction, and patient-centered care. AI software can be used to detect restorations, prosthetic crowns, periodontal bone loss, and root canal segmentation from the periapical radiographs. The integration of AI, digital imaging, and 3D printing can provide more precise, durable, and patient-oriented outcomes. AI can be also used for the automatic segmentation of panoramic radiographs showing normal anatomy of the oral and maxillofacial area. Recent advancement in AI in medical and dental sciences includes multimodal deep learning fusion, speech data detection, and neuromorphic computing. Hence, AI has helped dentists in diagnosis, planning, and aid in providing high-quality dental treatments in less time.

PMID:39917699 | PMC:PMC11797767 | DOI:10.3389/fdmed.2024.1525505

Front Neurorobot. 2025 Jan 23;19:1527908. doi: 10.3389/fnbot.2025.1527908. eCollection 2025.

ABSTRACT

Traffic forecasting is crucial for a variety of applications, including route optimization, signal management, and travel time estimation. However, many existing prediction models struggle to accurately capture the spatiotemporal patterns in traffic data due to its inherent nonlinearity, high dimensionality, and complex dependencies. To address these challenges, a short-term traffic forecasting model, Trafficformer, is proposed based on the Transformer framework. The model first uses a multilayer perceptron to extract features from historical traffic data, then enhances spatial interactions through Transformer-based encoding. By incorporating road network topology, a spatial mask filters out noise and irrelevant interactions, improving prediction accuracy. Finally, traffic speed is predicted using another multilayer perceptron. In the experiments, Trafficformer is evaluated on the Seattle Loop Detector dataset. It is compared with six baseline methods, with Mean Absolute Error, Mean Absolute Percentage Error, and Root Mean Square Error used as metrics. The results show that Trafficformer not only has higher prediction accuracy, but also can effectively identify key sections, and has great potential in intelligent traffic control optimization and refined traffic resource allocation.

PMID:39917631 | PMC:PMC11799296 | DOI:10.3389/fnbot.2025.1527908

Bioinformation. 2024 Sep 30;20(9):1512-1515. doi: 10.6026/9732063002001512. eCollection 2024.

ABSTRACT

Artificial intelligence (AI) is being used to diagnose deadly diseases such as cancer. The possible decrease in human error, fast diagnosis, and consistency of judgment are the key incentives for implementing these technologies. Therefore, it is of interest to assess the use of artificial intelligence in cancer diagnosis. Total 200 cancer cases were included with 100 cases each of Breast and lung cancer to evaluate with AI and conventional method by the radiologist. The cancer cases were identified with the application of AI-based machine learning techniques. The sensitivity and specificity check-up was used to assess the effectiveness of both approaches. The obtained data was statistically evaluated. AI has shown higher accuracy, sensitivity and specificity in cancer diagnosis compared to manual method of diagnosis by radiologist.

PMID:39917228 | PMC:PMC11795495 | DOI:10.6026/9732063002001512

Expert Rev Respir Med. 2025 Feb 7. doi: 10.1080/17476348.2025.2464886. Online ahead of print.

NO ABSTRACT

PMID:39917880 | DOI:10.1080/17476348.2025.2464886

Epigenetics. 2025 Dec;20(1):2462898. doi: 10.1080/15592294.2025.2462898. Epub 2025 Feb 7.

ABSTRACT

Although N6-methyladenosine (m6A) may be related to the pathogenesis of fibrotic process, the mechanism of m6A modification in aging-related idiopathic pulmonary fibrosis (IPF) remains unclear. Three-milliliter venous blood was collected from IPF patients and healthy controls. MeRIP-seq and RNA-seq were utilized to investigate differential m6A modification. The expressions of identified m6A regulator and target gene were validated using MeRIP-qPCR and real-time PCR. Moreover, we established an animal model and a senescent model of A549 cells to explore the associated molecular mechanism. Our study provided a panorama of m6A methylation in IPF. Increased peaks (3756) and decreased peaks (4712) were observed in the IPF group. The association analysis showed that 749 DEGs were affected by m6A methylation in IPF. Among the m6A regulators, the expression of METTL14 decreased in IPF. The m6A level of our interested gene DDIT4 decreased significantly, but the mRNA level of DDIT4 was higher in IPF. This was further verified in bleomycin-induced pulmonary fibrosis. At the cellular level, it was further confirmed that METTL14 and DDIT4 might participate in the senescence of alveolar epithelial cells. The downregulation of METTL14 might inhibit the decay of DDIT4 mRNA by reducing the m6A modification level of DDIT4 mRNA, leading to high expression of DDIT4 mRNA and protein. Our study provided a panorama of m6A alterations in IPF and discovered METTL14 as a potential intervention target for epigenetic modification in IPF. These results pave the way for future investigations regarding m6A modifications in aging-related IPF.

PMID:39916577 | DOI:10.1080/15592294.2025.2462898

Expert Opin Investig Drugs. 2025 Feb 6. doi: 10.1080/13543784.2025.2462592. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing interstitial lung disease of unknown cause that occurs primarily in older adults and is associated with poor quality of life and substantial healthcare utilization. IPF has a dismal prognosis. Indeed, first-line therapy, which includes nintedanib and pirfenidone, does not stop disease progression and is often associated with tolerability issues. Therefore, there remains a high medical need for more efficacious and better tolerated treatments.

AREAS COVERED: Gene therapy is a relatively unexplored field of research in IPF that has the potential to mitigate a range of profibrotic pathways by introducing genetic material into cells. Here, we summarize and critically discuss publications that have explored the safety and efficacy of gene therapy in experimentally-induced pulmonary fibrosis in animals, as clinical studies in humans have not been published yet.

EXPERT OPINION: The application of gene therapy in pulmonary fibrosis requires further investigation to address several technical and biological hurdles, improve vectors' design, drug delivery, and target selection, mitigate off-target effects and develop markers of gene penetration into target cells. Long-term clinical data are needed to bring gene therapy in IPF one step closer to practice.

PMID:39916340 | DOI:10.1080/13543784.2025.2462592

J Phys Chem B. 2025 Feb 7. doi: 10.1021/acs.jpcb.4c08267. Online ahead of print.

ABSTRACT

We report a computational protocol for simulating electric field gradient dynamics around Na+ cations in mixtures of 1-ethyl-3-methylimidazolium tetrafluoroborate ([Im21][BF4]) and water validated by comparison to measurements of nuclear magnetic resonance (NMR) T1 relaxation times. Our protocol combines classical molecular dynamics simulations of a scaled charge model of [Im21][BF4] and TIP4Pew water to generate the electric field gradient (EFG) correlation function, CEFG(t), with quantum chemical calculations for determining the EFG variance ⟨Vzz2⟩. Although we demonstrate that the Sternheimer approximation is as valid in these mixtures as it is in neat water, we do not recommend using the Sternheimer approximation as it underestimates ⟨Vzz2⟩ by ∼10% compared to a set of computationally efficient density functional theory calculations. Our protocol is capable of reproducing both the composition- and temperature-dependence of T1 over the full range of experimentally accessible [Im21][BF4]/water compositions and a temperature range of 285-350 K. We also show that scaling the [Im21][BF4] charges does not simply speed up the dynamics of the solvent, but has effects on the shape of CEFG(t). Following validation of our protocol, we analyze the shape and relaxation times of CEFG(t) to show that the mechanism by with T1 changes is different when the composition of the mixture varies compared to changes in temperature. As composition changes, the balance between inertial and diffusive relaxation alters, whereas temperature only affects the time scale of the diffusion portion of the relaxation. We also show that solvation shell of Na+ in these mixtures is significantly more labile than in neat [Im21][BF4] and that water and BF4- anions compete to be in the Na+ solvation shell. This validated computational protocol opens the door to more detailed interpretation of NMR T1 relaxation experiments of monatomic ions in complex liquid environments.

PMID:39918115 | DOI:10.1021/acs.jpcb.4c08267

J Clin Endocrinol Metab. 2025 Feb 7:dgaf073. doi: 10.1210/clinem/dgaf073. Online ahead of print.

ABSTRACT

OBJECTIVE: Fetuin B is a steatosis-responsive hepatokine that induces glucose intolerance in mice. Recently, we found that fetuin B in white adipose tissue was positively associated with peripheral insulin resistance in mice and a small study population, possibly through a fetuin B-induced inflammatory response in adipocytes. This translational study aimed to investigate the link between plasma fetuin B and the adipose tissue transcriptome and plasma proteome in a large cohort of humans.

METHODS: Continuous linear regression analysis in R was applied to investigate the link between plasma fetuin B and the adipose tissue transcriptome (n=207) and plasma proteome (n=558) in humans, after adjustment for sex, age and study centre (model 1), model 1 + BMI (model 2) and model 2 + insulin sensitivity (MATSUDA-index) (model 3).

RESULTS: Plasma fetuin B was associated with >100 genes in white adipose tissue, belonging to pathways related to cytokine/chemokine signaling (models 1 and 2) and insulin signaling (all models), and with >146 plasma proteins, involved in pathways related to metabolic processes and insulin signaling (all models).

CONCLUSION: Plasma fetuin B is related to adipose tissue genes and plasma proteins involved in metabolic processes and insulin signaling. Our findings provide evidence for the involvement of white adipose tissue in fetuin B-induced insulin resistance.

PMID:39918061 | DOI:10.1210/clinem/dgaf073

J Physiol Investig. 2025 Feb 7. doi: 10.4103/ejpi.EJPI-D-24-00090. Online ahead of print.

ABSTRACT

Inflammatory bowel disease (IBD) is an autoimmune disorder characterized by chronic inflammation of the gut and compromised intestinal barrier function, resulting from aberrant immune responses targeting the intestinal microbiota. While the involvement of Candida albicans in IBD pathogenesis is well-documented, the role of non-albicans Candida species in IBD remains less understood. Recent studies have identified a correlation between elevated levels of Candida tropicalis, a notable non-albicans opportunistic fungus, and the development of IBD. However, the precise impact of C. tropicalis on intestinal barrier function is not well elucidated. To address this knowledge gap, we utilized a cell model comprising polarized Caco-2 monolayers, which mimic the intestinal epithelium, to investigate the interaction between C. tropicalis and intestinal barrier function. Our results showed that incubation with C. tropicalis influenced transepithelial electrical resistance and increased permeability to the small molecule lucifer yellow, but did not affect permeability to the larger molecule fluorescein isothiocyanate-dextran. In addition, we observed internalization of the tight junction protein claudin-1 in the Caco-2 monolayers. Further experiments using Caco-2 monolayers exposed to the dectin-1 ligand zymosan induced similar changes in the distribution of claudin-1 but did not alter monolayer permeability. These findings suggest that C. tropicalis specifically affects intestinal barrier integrity and permeability to smaller solutes in intestinal epithelial cells.

PMID:39918057 | DOI:10.4103/ejpi.EJPI-D-24-00090

Comb Chem High Throughput Screen. 2025 Feb 4. doi: 10.2174/0113862073335295241216152011. Online ahead of print.

ABSTRACT

AIMS: The primary objective of this study is to explore the impact of Ganfule (GFL), a traditional Chinese medicine, on differentially expressed genes (DEGs) linked to nonalcoholic fatty liver disease (NAFLD). By identifying potential biomarkers, we seek to enhance GFL's clinical efficacy through targeted pharmaceutical design.

BACKGROUND: NAFLD a prevalent liver disorder, is often associated with obesity and metabolic syndrome. While GFL has demonstrated clinical efficacy in treating NAFLD, its precise targets and mechanisms of action remain elusive. Understanding these mechanisms could pave the way for more effective treatments.

OBJECTIVES: GFL, a long-standing traditional Chinese medicine (TCM), has demonstrated clinical effectiveness in treating NAFLD. However, its precise targets and mechanism of action remain elusive. In this study, we aim to explore GFL's impact on differentially expressed genes, which could potentially serve as biomarkers for developing targeted therapies. This approach is intended to enhance GFL's clinical efficacy by identifying key genes that respond to its treatment.

METHODS: To induce NAFLD, 23 Sprague-Dawley rats were fed a high-fat diet. These rats were then categorized into three groups: normal diet (NOR), high-fat diet model (HFD), and those treated with GFL. Highthroughput sequencing was employed to identify DEGs in their livers. Utilizing the STRING and DAVID databases, we analyzed potential protein interactions expressed by these genes. Furthermore, the KEGG, Reactome, and Wiki databases aided in determining their biological roles and signaling pathways. Key DEGs' mRNA expression levels and corresponding proteins were further screened and confirmed through haematoxylin- eosin staining (HE), immunohistochemistry (IHC), Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR), and western blotting.

RESULTS: Significant variations in DEGs were observed across the three groups, with 19 intersecting genes identified within a cluster of 90 NAFLD-related genes. GFL was found to adjust the expression of nine core DEGs, including Abcg1, Igfgb1, Lepr, Pdk4, Socs3, and Stat3. These genes-related proteins are tied to proteins such as FABP4, LEPR, SCD1, SOCS3, and STAT3, which are intimately connected to adipocytokine and adipogenesis pathways. Our study reveals that GFL modifies the expression of IGFBP1, LEPR, PDK4, SCD1, and SOCS3, thereby regulating the adipocytokine, JAK-STAT, leptin-insulin signaling, and adipogenesis metabolic pathways, respectively.

CONCLUSIONS: This study enhances understanding of GFL's efficacy and identifies potential biomarkers for NAFLD treatment. Optimizing GFL's efficacy and elucidating its mechanism provides a methodological reference for traditional Chinese medicine exploration.

PMID:39917919 | DOI:10.2174/0113862073335295241216152011