Innovation (Camb). 2025 Jan 18;6(2):100773. doi: 10.1016/j.xinn.2024.100773. eCollection 2025 Feb 3.

ABSTRACT

Pharmacogenomic landscapes and related databases are important for identifying the biomarkers of drug response and toxicity. However, these data are still lacking for the Chinese population. In this study, we constructed a pharmacogenomic landscape and an associated database using whole-genome sequencing data generated by non-invasive prenatal testing in 206,640 Chinese individuals. In total, 1,577,513 variants (including 331,610 novel variants) were identified among 3,538 pharmacogenes related to 2,086 drugs. We found that the variant spectrum in the Chinese population differed among the seven major regions. Regional differences also exist among provinces in China. The average numbers of drug enzyme, transporter, and receptor variants were 258, 557, and 632, respectively. Subsequent correlation analysis indicated that the pharmacogenes affecting multiple drugs had fewer variants. Among the 16 categories of drugs, we found that nervous system, cardiovascular system, and genitourinary system/sex hormone drugs were more likely to be affected by variants of pharmacogenes. Characteristics of the variants in the enzyme, transporter, and receptor subfamilies showed specificity. To explore the clinical utility of these data, a genetic association study was conducted on 1,019 lung cancer patients. Two novel variants, AKT2 chr19:40770621 C>G and SLC19A1 chr21:46934171 A>C, were identified as novel platinum response biomarkers. Finally, a pharmacogenomic database, named the Chinese Pharmacogenomic Knowledge Base (CNPKB: http://www.cnpkb.com.cn/), was constructed to collect all the data. In summary, a pharmacogenomic landscape and database for the Chinese population were constructed in this study, which could support personalized Chinese medicine in the future.

PMID:39991480 | PMC:PMC11846038 | DOI:10.1016/j.xinn.2024.100773

Pan Afr Med J. 2024 Nov 14;49:79. doi: 10.11604/pamj.2024.49.79.42550. eCollection 2024.

ABSTRACT

The thirteenth conference of the African Society of Human Genetics with the theme "harnessing genomics and translational research to improve health in Africa" was held in Dar es Salaam, Tanzania, in August 2021, using a hybrid in-person and virtual model for participation in the wake of COVID-19 pandemic. During the meeting, African research across various human genetics disciplines was presented, including talks on the genetics of infectious and non-communicable diseases, population genetics, and translational research. The meeting also featured presentations on pharmacogenomics, genetics of developmental disorders, cancer genetics and genetics of rare diseases. In-depth discussions on ethical legal and social issues in genomics research and community and patient engagement were also key sessions of this meeting. The primary focus of the conference and the discussions was how to translate the wealth of genomic research in the continent into improved health outcomes in the continent. In this report, we summarize the key scientific research relevant to Africa presented and discussed during the meeting providing an overview of the progress of human genetics in the continent. We also discuss opportunities and challenges of harnessing genomics for health improvement in Africa.

PMID:39989936 | PMC:PMC11845995 | DOI:10.11604/pamj.2024.49.79.42550

Med J Armed Forces India. 2024 Nov-Dec;80(6):731-734. doi: 10.1016/j.mjafi.2022.11.006. Epub 2023 Jan 4.

ABSTRACT

Infantile refractory diarrhea presents after first few days of life leading to intestinal insufficiency. It is a diagnostic challenge due to varied etiologies like food senstive enteropathy, anatomical defects and dysmotility disorders, transport and enzymatic defects, pancreatic malabsorption - cystic fibrosis (CF), primary epithelial causes like microvillus inclusion disease (MVID), tufting enteropathy and heparan sulfate deficiency, immunodeficiencies, metabolic diseases and autoimmune enteropathy. It is refractory to treatment making the patient dependent on total parenteral nutrition. Immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX), is one of the rarest causes of intractable diarrhea. It occurs due to mutations in the FOXP3 gene, leading to dysfunction of T-regulatory cells and is characterised by diarrhoea, diabetes, and dermatitis. We aim to evaluate various causes of infantile refractory chronic diarrhea, and to present one such case of IPEX syndrome from this part of the world due to mutation not been reported in literature so far.

PMID:39990538 | PMC:PMC11842919 | DOI:10.1016/j.mjafi.2022.11.006

Comput Assist Surg (Abingdon). 2025 Dec;30(1):2466426. doi: 10.1080/24699322.2025.2466426. Epub 2025 Feb 24.

ABSTRACT

This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). We employed only the clinical phases derived from video analysis as input to the algorithms. Our results show that InceptionTime and LSTM-FCN yielded the most accurate predictions. InceptionTime achieves Mean Absolute Error (MAE) values below 5 min and Symmetric Mean Absolute Percentage Error (SMAPE) under 6% at 60-s sampling intervals. In contrast, LSTM with attention mechanism and standard LSTM models have higher error rates, indicating challenges in handling both long-term and short-term dependencies. CNN-based models, especially InceptionTime, excel at feature extraction across different scales, making them effective for time-series predictions. We also analyzed training and testing times. CNN models, despite higher computational costs, significantly reduce prediction errors. The Transformer model has the fastest inference time, making it ideal for real-time applications. An ensemble model derived by averaging the two best performing algorithms reported low MAE and SMAPE, although needing longer training. Future research should validate these findings across different procedural contexts and explore ways to optimize training times without losing accuracy. Integrating these models into clinical scheduling systems could improve efficiency in cath labs. Our research demonstrates that the models we implemented can form the basis of an automated tool, which predicts the optimal time to call the next patient with an average error of approximately 30 s. These findings show the effectiveness of deep learning models, especially CNN-based architectures, in accurately predicting procedure end times.

PMID:39992712 | DOI:10.1080/24699322.2025.2466426

Primates. 2025 Feb 24. doi: 10.1007/s10329-025-01185-5. Online ahead of print.

ABSTRACT

Drawings serve as a profound medium of expression for both humans and apes, offering unique insights into the cognitive and emotional landscapes of the artists, regardless of their species. This study employs artificial intelligence (AI), specifically Convolutional Neural Networks (CNNs) and the interpretability tool Captum, to analyse non-figurative drawings by Molly, an orangutan. The research utilizes VGG19 and ResNet18 models to decode seasonal nuances in the drawings, achieving notable accuracy in seasonal classification and revealing complex influences beyond human-centric methods. Techniques, such as occlusion, integrated gradients, PCA, t-SNE, and Louvain clustering, highlight critical areas and elements influencing seasonal recognition, providing deeper insights into the drawings. This approach not only advances the analysis of non-human art but also demonstrates the potential of AI to enrich our understanding of non-human cognitive and emotional expressions, with significant implications for fields like evolutionary anthropology and comparative psychology.

PMID:39992583 | DOI:10.1007/s10329-025-01185-5

Phys Eng Sci Med. 2025 Feb 24. doi: 10.1007/s13246-025-01526-0. Online ahead of print.

ABSTRACT

Subclinical amplitudes complicate the differentiation between essential tremor (ET) and Parkinson's disease (PD) tremor, which is uncertain even when the tremors are apparent. Despite their prevalence-up to 30% of PD cases exhibit subclinical tremors-these tremors remain inadequately studied. Therefore, this study explores the potential of artificial intelligence (AI) to address this differentiation uncertainty. Our objective is to develop a deep learning model that can differentiate among subclinical tremors due to PD, ET, and normal physiological tremors. Subclinical tremor data were obtained from inertial sensors placed on the hands and arms of 51 PD, 15 ET, and 58 normal subjects. The AI architecture used was designed using a long short-term memory network (LSTM) and was trained on the short-time Fourier transformed subclinical tremor data as the input features. The network was trained separately to differentiate firstly between PD and ET tremors and then between PD, ET, and physiological tremors and yielded accuracies of 95% and 93%, respectively. Comparative analysis with existing convolutional LSTM demonstrated the superior performance of our work. The proposed method has 30-50% better accuracies when classifying low amplitude tremors as compared to the reference method. Future enhancements aim to enhance model interpretability and validate on larger, more diverse datasets, including action tremors. The proposed work can potentially serve as a valuable tool for clinicians, aiding in the differentiation of subclinical tremors common in Parkinson's disease, which in turn enhances diagnostic accuracy and informs treatment decisions.

PMID:39992543 | DOI:10.1007/s13246-025-01526-0

Brain Topogr. 2025 Feb 24;38(3):33. doi: 10.1007/s10548-025-01106-1.

ABSTRACT

Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a number of diseases Due to inherent data uncertainties and Electroencephalogram (EEG) pattern overlap, conventional EEG diagnosis methods frequently encounter difficulties. This paper proposes a novel framework that integrates FLSNN to enhance the accuracy and robustness of multiple neurological disorder disease detection from EEG signals. In multiple neurological disorders, the primary motivation is to overcome the limitations of existing methods that are unable to handle the complex and overlapping nature of EEG signals. The key aim is to provide a unified, automated solution for detecting multiple neurological disorders such as epilepsy, Parkinson's, Alzheimer's, schizophrenia, and stroke in a single framework. In the Fuzzy Logic and Spiking Neural Networks (FLSNN) framework, EEG data is preprocessed to eliminate noise and artifacts, while a fuzzy logic model is applied to handling uncertainties prior to applying spike neural networking to analyze the temporal and dynamics of the signals. Processes EEG data three times faster than traditional techniques. This framework achieves 97.46% accuracy in binary classification and 98.87% accuracy in multi-class classification, indicating increased efficiency. This research provides a significant advancement in the diagnosis of multiple neurological disorders using EEG and enhances both the quality and speed of diagnostics from the EEG signal and the advancement of AI-based medical diagnostics. at https://github.com/jainshraddha12/FLSNN , the source code will be available to the public.

PMID:39992458 | DOI:10.1007/s10548-025-01106-1

Fa Yi Xue Za Zhi. 2024 Oct 25;40(5):419-429. doi: 10.12116/j.issn.1004-5619.2024.440801.

ABSTRACT

OBJECTIVES: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury (BCI), and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.

METHODS: A total of 5 486 CT images of BCI from living persons were collected as the training set, validation set and test set for model training and performance evaluation. Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to segment the five types of craniocerebral injuries including scalp hematoma, skull fracture, epidural hematoma, subdural hematoma, and brain contusion. Another 340 BCI and 120 normal craniocerebral CT images from cadavers were collected as the new blind test set to explore the application value of the model trained by living CT images in the segmentation of BCI in cadavers. The five types CT images of all BCI except the blind test set were manually labeled; then, each dataset was inputted into the model to train the model. The performance of the model was evaluated and optimized based on the loss function and accuracy curves of the training set and validation set, and the generalization ability was evaluated based on the Dice value of the test set. According to the accuracy, precision and F1 value of the blind test set, the segmentation performance of the model for five types of BCI was evaluated.

RESULTS: After training and optimizing the model, the average Dice values of the final optimal model to scalp hematoma, skull fracture, epidural hematoma, subdural hematoma and brain contusion segmentation were 0.766 4, 0.812 3, 0.938 7, 0.782 7 and 0.858 1, respectively, all greater than 0.75, meeting the expected requirements. External validation showed that the F1 values were 93.02%, 89.80%, 87.80%, 92.93% and 86.57% in living CT images, respectively; 83.92%, 44.90%, 76.47%, 64.29% and 48.89% in cadaveric CT images, respectively. The above suggested that the model was able to accurately segment various types of craniocerebral injury on living CT images, while its segmentation ability was relatively poor on cadaveric CT images, but still able to accurately segment scalp hematoma, epidural hematoma and subdural hematoma.

CONCLUSIONS: Deep learning model trained on CT images can be used for BCI segmentation. However, the direct use of living persons' BCI models for the identification of cadaveric BCI has some limitations. This study provides a new approach for intelligent segmentation of virtual anatomical data for BCI.

PMID:39992333 | DOI:10.12116/j.issn.1004-5619.2024.440801

Radiol Med. 2025 Feb 24. doi: 10.1007/s11547-025-01968-2. Online ahead of print.

ABSTRACT

PURPOSE: Evaluate the classification performance and interpretability of the Vision Transformer (ViT) model on acute and chronic vertebral compression fractures using Shapley significance maps.

MATERIALS AND METHODS: This retrospective study utilized medical imaging data from December 2018 to December 2023 from three hospitals in China. The study included 942 patients, with imaging data comprising X-rays, CTs, and MRIs. Patients were divided into training, validation, and test sets with a ratio of 7:2:1. The ViT model variant, SimpleViT, was fine-tuned on the training dataset. Statistical analyses were performed using the PixelMedAI platform, focusing on metrics such as ROC curves, sensitivity, specificity, and AUC values, with statistical significance assessed using the DeLong test.

RESULTS: A total of 942 patients (mean age 69.17 ± 10.61 years) were included, with 1076 vertebral fractures analyzed (705 acute, 371 chronic). In the test set, the ViT model demonstrated superior performance over the ResNet18 model, with an accuracy of 0.880 and an AUC of 0.901 compared to 0.843 and 0.833, respectively. The use of ViT Shapley saliency maps significantly enhanced diagnostic sensitivity and specificity, reaching 0.883 (95% CI: 0.800, 0.963) and 0.950 (95% CI: 0.891, 1.00), respectively.

CONCLUSION: In vertebral compression fractures classification, Vision Transformer outperformed Convolutional Neural Network, providing more effective Shapley-based saliency maps that were favored by radiologists over GradCAM.

PMID:39992331 | DOI:10.1007/s11547-025-01968-2

Radiol Med. 2025 Feb 24. doi: 10.1007/s11547-024-01947-z. Online ahead of print.

ABSTRACT

The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applying and implementing these technologies in daily clinical practice. Radiologists must keep up with this trend and continually update their knowledge. This narrative review discusses the application of artificial intelligence in the field of musculoskeletal imaging. For image generation, we focused on the clinical application of deep learning reconstruction and the recently emerging MRI-based cortical bone imaging. For automated diagnostic support, we provided an overview of qualitative diagnosis, including classifications essential for daily practice, and quantitative diagnosis, which can serve as imaging biomarkers for treatment decision making and prognosis prediction. Finally, we discussed current issues in the use of AI, the application of AI in the diagnosis of rare diseases, and the role of AI-based diagnostic imaging in preventive medicine as part of our outlook for the future.

PMID:39992330 | DOI:10.1007/s11547-024-01947-z

QJM. 2025 Feb 24:hcaf057. doi: 10.1093/qjmed/hcaf057. Online ahead of print.

NO ABSTRACT

PMID:39992255 | DOI:10.1093/qjmed/hcaf057

J Clin Microbiol. 2025 Feb 24:e0151424. doi: 10.1128/jcm.01514-24. Online ahead of print.

ABSTRACT

This study introduces a new framework for the artificial intelligence-based characterization of Gram-stained whole-slide images (WSIs). As a test for the diagnosis of bloodstream infections, Gram stains provide critical early data to inform patient treatment in conjunction with data from rapid molecular tests. In this work, we developed a novel transformer-based model for Gram-stained WSI classification, which is more scalable to large data sets than previous convolutional neural network-based methods as it does not require patch-level manual annotations. We also introduce a large Gram stain data set from Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire, USA) to evaluate our model, exploring the classification of five major categories of Gram-stained WSIs: gram-positive cocci in clusters, gram-positive cocci in pairs/chains, gram-positive rods, gram-negative rods, and slides with no bacteria. Our model achieves a classification accuracy of 0.858 (95% CI: 0.805, 0.905) and an area under the receiver operating characteristic curve (AUC) of 0.952 (95% CI: 0.922, 0.976) using fivefold nested cross-validation on our 475-slide data set, demonstrating the potential of large-scale transformer models for Gram stain classification. Results were measured against the final clinical laboratory Gram stain report after growth of organism in culture. We further demonstrate the generalizability of our trained model by applying it without additional fine-tuning on a second 27-slide external data set from Stanford Health (Palo Alto, California, USA) where it achieves a binary classification accuracy of 0.926 (95% CI: 0.885, 0.960) and an AUC of 0.8651 (95% CI: 0.6337, 0.9917) while distinguishing gram-positive from gram-negative bacteria.

IMPORTANCE: This study introduces a scalable transformer-based deep learning model for automating Gram-stained whole-slide image classification. It surpasses previous methods by eliminating the need for manual annotations and demonstrates high accuracy and generalizability across multiple data sets, enhancing the speed and reliability of Gram stain analysis.

PMID:39992156 | DOI:10.1128/jcm.01514-24

Liver Int. 2025 Mar;45(3):e16205. doi: 10.1111/liv.16205.

ABSTRACT

BACKGROUND & AIMS: Microvascular invasion (MVI) is associated with poor prognosis in hepatocellular carcinoma (HCC). Topology may improve the predictive performance and interpretability of deep learning (DL). We aimed to develop and externally validate an MRI-based topology DL model for preoperative prediction of MVI.

METHODS: This dual-centre retrospective study included consecutive surgically treated HCC patients from two tertiary care hospitals. Automatic liver and tumour segmentations were performed with DL methods. A pure convolutional neural network (CNN) model, a topology-CNN (TopoCNN) model and a topology-CNN-clinical (TopoCNN+Clinic) model were developed and externally validated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Cox regression analyses were conducted to identify risk factors for recurrence-free survival within 2 years (early RFS) and overall survival (OS).

RESULTS: In total, 589 patients were included (292 [49.6%] with pathologically confirmed MVI). The AUCs of the TopoCNN and TopoCNN+Clinic models were 0.890 and 0.895 for the internal test dataset and 0.871 and 0.879 for the external test dataset, respectively. For tumours ≤ 3.0 cm, the AUCs of the TopoCNN and TopoCNN+Clinic models were 0.879 and 0.929 for the internal test dataset, and 0.763 and 0.758 for the external test dataset. The TopoCNN-derived MVI prediction probability was an independent risk factor for early RFS (hazard ratio 6.64) and OS (hazard ratio 13.33).

CONCLUSIONS: The MRI topological DL model based on automatic liver and tumour segmentation could accurately predict MVI and effectively stratify postoperative early RFS and OS, which may assist in personalised treatment decision-making.

PMID:39992060 | DOI:10.1111/liv.16205

Front Immunol. 2025 Feb 7;16:1458341. doi: 10.3389/fimmu.2025.1458341. eCollection 2025.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a degenerative respiratory condition characterized by significant mortality rates and a scarcity of available treatment alternatives. Cuproptosis, a novel form of copper-induced cell death, has garnered attention for its potential implications. The study aimed to explore the diagnostic value of cuproptosis-related hub genes in patients with IPF. Additionally, multiple bioinformatics analyses were employed to identify immune-related biomarkers associated with the diagnosis of IPF, offering valuable insights for future treatment strategies.

METHODS: Four microarray datasets were selected from the Gene Expression Omnibus (GEO) collection for screening. Differentially expressed genes (DEGs) associated with IPF were analyzed. Additionally, weighted gene coexpression network analysis (WGCNA) was employed to identify the DEGs most associated with IPF. Ultimately, we analyzed five cuproptosis-related hub genes and assessed their diagnostic value for IPF in both the training and validation sets. Additionally, four immune-related hub genes were screened using a protein-protein interaction (PPI) network and evaluated through the receiver operating characteristic (ROC) curve. Lastly, single-cell RNA-seq was employed to further investigate differential gene distribution.

RESULTS: We identified a total of 92 DEGs. Bioinformatics analysis highlighted five cuproptosis-related genes as candidate biomarkers, including three upregulated genes (CFH, STEAP1, and HDC) and two downregulated genes (NUDT16 and FMO5). The diagnostic accuracy of these five genes in the cohort was confirmed to be reliable. Additionally, we identified four immune-related hub genes that demonstrated strong diagnostic performance for IPF, with CXCL12 showing an AUROC of 0.90. We also examined the relationship between these four genes and immune cells. CXCL12 was significantly negatively associated with neutrophils, while CXCR2 was associated exclusively with neutrophils, consistent with our single-cell sequencing results. CTSG showed a primarily positive association with follicular helper T, and SPP1 was most strongly associated with macrophages. Finally, our single-cell sequencing data revealed that in patients with IPF, CXCL12 was highly expressed in the endothelial cell subset (ECs), while SPP1 exhibited high expression in multiple cellular populations. The expression of the CTSG showed statistically significant differences in monocyte macrophages.

CONCLUSION: The research methodically depicted the intricate interplay among five cuproptosis-related genes, four immune-related hub genes, and IPF, offering new ideas for diagnosing and treating patients with IPF.

PMID:39991151 | PMC:PMC11842377 | DOI:10.3389/fimmu.2025.1458341

Theranostics. 2025 Jan 13;15(6):2092-2103. doi: 10.7150/thno.106367. eCollection 2025.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by an excessive collagen deposition ultimately leading to tissue stiffening and functional decline. Beyond IPF, other progressive pulmonary fibrosis are often associated with connective tissue diseases and may develop in ∼18-32% of patients. Therapeutic options are limited to nintedanib and pirfenidone which are only able to reduce fibrosis progression without curing it. The current lack of biomarker to accurately assess and predict disease progression and therapy efficacy for IPF remains a major clinical concern. Methods: In our study, collagen deposition was monitored in bleomycin-induced lung fibrosis in mice by in vivo molecular imaging using a collagen-targeted radiopharmaceutical, [68Ga]Ga-NODAGA-collagelin. Fibrosis progression was also monitored using computed tomography, the gold standard technique to detect lung fibrosis in patients. Results: We demonstrated that the bleomycin-induced increase in collagen lung content can be accurately quantified by [68Ga]Ga-NODAGA-collagelin PET imaging in correlation with disease stage and severity. The lung uptake of [68Ga]Ga-NODAGA-collagelin was mainly found in fibrotic areas of lungs in bleomycin-receiving mice. Most interestingly, [68Ga]Ga-NODAGA-collagelin PET imaging allowed the in vivo non-invasive monitoring of nintedanib efficacy as well as the anti-fibrotic effect of the JAK inhibitor, tofacitinib. Conclusion: Thus, collagen-targeted PET imaging appears as a promising non-invasive tool for staging, monitoring and prediction of disease progression and therapy efficacy towards personalized medicine in IPF.

PMID:39990206 | PMC:PMC11840721 | DOI:10.7150/thno.106367

Toxicol Rep. 2025 Jan 31;14:101941. doi: 10.1016/j.toxrep.2025.101941. eCollection 2025 Jun.

ABSTRACT

Silicosis is an occupational fibrotic lung disease caused by exposure to respirable crystalline silica dust particles produced during industrial activities. Other crystalline silica-induced pulmonary disorders include a predisposition to mycobacterial infections, obstructive airway diseases, idiopathic pulmonary fibrosis, and lung cancer. This review paper discusses the burden of silicosis and associated co-morbidities in developed as well as developing countries globally using the published data of various government agencies, related organizations, and epidemiological findings. Moreover, it sheds light on diverse mechanisms of silicosis, outlining molecular events and peculiar alterations in lung parenchyma leading to this occupational lung disease. Evaluation of pathophysiological mechanisms could aid in the identification of novel target molecules and treatments; to date, there is no curative treatment for silicosis. In recent periods, a lot of attention has been focused on the development and fabrication of suitable nanocarriers for improved and sustained drug delivery in the pulmonary system. Nanoparticle-based therapeutic modality has been evaluated in in-vitro and ex-vivo silicosis models for prolongation of drug activity and improved therapeutic outcomes. The preclinical findings open the doors to clinical trials for operational and regenerative nanoformulations, which eventually create a positive change in medical practice. The following review summarizes various therapeutic approaches available and in the pipe line for silicosis and also stresses the preventive practices for effectively combating this occupational hazard.

PMID:39989982 | PMC:PMC11847043 | DOI:10.1016/j.toxrep.2025.101941

Ann Hematol. 2025 Feb 24. doi: 10.1007/s00277-025-06274-5. Online ahead of print.

ABSTRACT

In classic Hodgkin-lymphoma (cHL), only a few cases recur, and only a limited fraction of patients is primary-refractory to standard-polychemotherapy. Underlying genomic features of unfavorable clinical courses remain sparsely characterized. Here, we investigated the genomic characteristics of primary-refractory/relapsed cHL in contrast with responders. Therefore, ultra-deep next-generation panel-sequencing was performed on a total of 59 FFPE-samples (20 responders, 26 relapsed (rHL: 11 initial-diagnosis, 15 relapse) and 13 primary-refractory (prHL: 8 initial-diagnosis, 5 progression) from 44 cHL-patients applying a hybrid-capture approach. We compared samples associated with distinct disease courses concerning their oncogenic drivers, mutational signatures, and perturbed pathways. Compared to responders, mutations in genes such as PMS2, PDGFRB, KAT6A, EPHB1, and HGF were detected more frequently in prHL/rHL. Additionally, we observed that in rHL or prHL, BARD1-mutations occur, whereas ETV1, NF1, and MET-mutations were eliminated through clonal selection. A significant enrichment of non-synonymous variants was detected in prHL compared to responders and a significant selection process in favor of NOTCH-pathway mutations driving rHL or prHL was observed. However, our analysis revealed a negative selection process for non-synonymous variants affecting the hippo-pathway. This study delineates distinct mutational signatures between responders and rHL/prHL, whilst illustrating longitudinal dynamics in mutational profiles using paired samples. Further, several exploitable therapeutic vulnerabilities for rHL and prHL were identified.

PMID:39992429 | DOI:10.1007/s00277-025-06274-5

Neurochem Res. 2025 Feb 24;50(2):103. doi: 10.1007/s11064-025-04357-3.

ABSTRACT

Human immunodeficiency virus (HIV) infections remain a significant public health burden globally with infected individuals at high risk for cognitive decline and memory loss even on combination antiretroviral therapy. Almost half of HIV infected individuals smoke, which drives poorer health outcomes including a higher dementia rate. Microglia are the brain's immune cells that serve as a persistent HIV reservoir contributing to neuroinflammatory signaling. We examined interactions between the HIV envelope glycoprotein gp120 and nicotine within human microglia cells (HMC3) that endogenously express chemokine receptor 5 (CCR5) and nicotinic acetylcholine receptors (nAChRs). Liquid chromatography coupled to electrospray ionization mass spectrometry (LC-ESI/MS) shows that gp120 alters mitochondria proteins within HMC3 cells. In the presence of nicotine, gp120 increased the expression of mitochondrial prohibitin 2 (PHB2), cytochrome c (cyt c), and mitofusin 2 (MFN2) but decreased fission 1 (FIS1) levels. An analysis of mito-YFP expression confirms that interaction between nicotine and gp120 increases the size and branching of mitochondrial networks. Interaction between nicotine and gp120 is also surprisingly found to promote the release of amyloid precursor protein (APP) peptides from microglia. This was accompanied by visualization of amyloid containing vesicles that colocalized with the autophagy protein LC3B-II in the cell. Taken together, our findings show that interaction between nicotine and gp120 impact microglia in a manner that regulates mitochondrial proteins and network properties and impacts amyloid protein management and release within microglia. These mechanisms may contribute to understanding neuroinflammatory signaling in smokers with HIV.

PMID:39992414 | DOI:10.1007/s11064-025-04357-3

Front Genet. 2025 Feb 7;16:1558406. doi: 10.3389/fgene.2025.1558406. eCollection 2025.

NO ABSTRACT

PMID:39991320 | PMC:PMC11842322 | DOI:10.3389/fgene.2025.1558406

ISME Commun. 2025 Jan 29;5(1):ycaf006. doi: 10.1093/ismeco/ycaf006. eCollection 2025 Jan.

ABSTRACT

The role of microbial necromass (nonliving microbial biomass), a significant component of belowground organic carbon, in nutrient cycling and its impact on the dynamics of microbial communities in subsurface systems remains poorly understood. It is currently unclear whether necromass metabolites from various microbes are different, whether certain groups of metabolites are preferentially utilized over others, or whether different microbial species respond to various necromass metabolites. In this study, we aimed to fill these knowledge gaps by designing enrichments with necromass as the sole nutrient source for subsurface microbial communities. We used the soluble fraction of necromass from bacterial isolates belonging to Arthrobacter, Agrobacterium, and Pseudomonas genera, and our results indicate that metabolite composition of necromass varied slightly across different strains but generally included amino acids, organic acids, and nucleic acid constituents. Arthrobacter-derived necromass appeared more recalcitrant. Necromass metabolites enriched diverse microbial genera, particularly Massilia sp. responded quickly regardless of the necromass source. Despite differences in necromass utilization, microbial community composition converged rapidly over time across the three different necromass amendments. Uracil, xanthine, valine, and phosphate-containing isomers were generally depleted over time, indicating microbial assimilation for maintenance and growth. However, numerous easily assimilable metabolites were not significantly depleted, suggesting efficient necromass recycling and the potential for necromass stabilization in systems. This study highlights the dynamic interactions between microbial necromass metabolites and subsurface microbial communities, revealing both selective utilization and rapid community and necromass convergence regardless of the necromass source.

PMID:39991274 | PMC:PMC11843093 | DOI:10.1093/ismeco/ycaf006