J Thorac Imaging. 2024 Sep 20. doi: 10.1097/RTI.0000000000000808. Online ahead of print.

ABSTRACT

PURPOSE: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.

MATERIALS AND METHODS: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient. It is part of a fully automatic pipeline for the detection, segmentation, matching, and classification of metastatic lung lesions in longitudinal chest CT scans. A data set of 5040 metastatic lung lesions in 344 pairs of 208 prior and current chest CT scans from 79 patients was used for training/validation (173 scans, 65 patients) and testing (35 scans, 14 patients) of a standalone 3D U-Net models and 3 simultaneous SimU-Net models. Outcome measures were the lesion detection and segmentation precision, recall, Dice score, average symmetric surface distance (ASSD), lesion matching, and classification of lesion changes from computed versus manual ground-truth annotations by an expert radiologist.

RESULTS: SimU-Net achieved a mean lesion detection recall and precision of 0.93±0.13 and 0.79±0.24 and a mean lesion segmentation Dice and ASSD of 0.84±0.09 and 0.33±0.22 mm. These results outperformed the standalone 3D U-Net model by 9.4% in the recall, 2.4% in Dice, and 15.4% in ASSD, with a minor 3.6% decrease in precision. The SimU-Net pipeline achieved perfect precision and recall (1.0±0.0) for lesion matching and classification of lesion changes.

CONCLUSIONS: Simultaneous deep learning analysis of metastatic lung lesions in prior and current chest CT scans with SimU-Net yields superior accuracy compared with individual analysis of each scan. Implementation of SimU-Net in the radiological workflow may enhance efficiency by automatically computing key metrics used to evaluate metastatic lung lesions and their temporal changes.

PMID:39808543 | DOI:10.1097/RTI.0000000000000808

J Neuroophthalmol. 2024 Dec 1;44(4):462-468. doi: 10.1097/WNO.0000000000002229. Epub 2024 Aug 1.

ABSTRACT

BACKGROUND: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss. It is important to distinguish MS ON from other ON subtypes early, to guide appropriate management. Yet, identifying ON and differentiating subtypes can be challenging as MRI and serological antibody test results are not always readily available in the acute setting. The purpose of this study is to develop a deep learning artificial intelligence (AI) algorithm to predict subtype based on fundus photographs, to aid the diagnostic evaluation of patients with suspected ON.

METHODS: This was a retrospective study of patients with ON seen at our institution between 2007 and 2022. Fundus photographs (1,599) were retrospectively collected from a total of 321 patients classified into 2 groups: MS ON (262 patients; 1,114 photographs) and non-MS ON (59 patients; 485 photographs). The dataset was divided into training and holdout test sets with an 80%/20% ratio, using stratified sampling to ensure equal representation of MS ON and non-MS ON patients in both sets. Model hyperparameters were tuned using 5-fold cross-validation on the training dataset. The overall performance and generalizability of the model was subsequently evaluated on the holdout test set.

RESULTS: The receiver operating characteristic (ROC) curve for the developed model, evaluated on the holdout test dataset, yielded an area under the ROC curve of 0.83 (95% confidence interval [CI], 0.72-0.92). The model attained an accuracy of 76.2% (95% CI, 68.4-83.1), a sensitivity of 74.2% (95% CI, 55.9-87.4) and a specificity of 76.9% (95% CI, 67.6-85.0) in classifying images as non-MS-related ON.

CONCLUSIONS: This study provides preliminary evidence supporting a role for AI in differentiating non-MS ON subtypes from MS ON. Future work will aim to increase the size of the dataset and explore the role of combining clinical and paraclinical measures to refine deep learning models over time.

PMID:39808513 | DOI:10.1097/WNO.0000000000002229

JCO Clin Cancer Inform. 2025 Jan;9:e2300248. doi: 10.1200/CCI.23.00248. Epub 2025 Jan 14.

ABSTRACT

PURPOSE: Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and uncertainty in the interpretability of results. We aimed to design a perfusion model applicable to breast cancer SoC dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series with results stable to low temporal resolution imaging, comparable with published results using full-resolution DCE-MRI, and correlative with orthogonal imaging modalities indicative of biophysical markers.

METHODS: Subsampled high-temporal-resolution DCE-MRI series were run through our perfusion model and resulting fits were compared for consistency. The fits were also compared against previously published results from institutions using the full resolution series. The model was then evaluated on a separate cohort for validity of biomarker indications. Finally, the model was used as a fundamental part of predicting response to neoadjuvant chemotherapy (NACT).

RESULTS: Temporally subsampled DCE-MRI series yield perfusion fit variations on the scale of 1% of the tumor median value when input frames are varied. Fits generated from pseudoclinical series are within the variation range seen between imaging sites (ρ = 0.55), voxel-wise. The model also demonstrates significant correlations with orthogonal positron emission tomography imaging, indicating potential for use as a biomarker proxy. Specifically, using the perfusion fits as the grounding for a biophysical simulation of response, we correctly predict the pathologic complete response status after NACT in 15 of 18 patients, for an accuracy of 0.83, with a specificity and sensitivity of 0.83 as well.

CONCLUSION: Clinical DCE-MRI data may be leveraged to provide stable perfusion fit results and indirectly interrogate the tumor microenvironment. These fits can then be used downstream for prediction of response to NACT with high accuracy.

PMID:39808751 | DOI:10.1200/CCI.23.00248

ACS Synth Biol. 2025 Jan 14. doi: 10.1021/acssynbio.4c00290. Online ahead of print.

ABSTRACT

Naturally evolved and synthetically designed forms of compartmentalization benefit encapsulated function by increasing local concentrations of substrates and protecting cargo from destabilizing environments and inhibitors. Crucial to understanding the fundamental principles of compartmentalization are experimental systems enabling the measurement of the permeability rates of small molecules. Here, we report the experimental measurement of the small-molecule permeability of a 40 nm icosahedral bacterial microcompartment shell. This was accomplished by heterologous loading of light-producing luciferase enzymes and kinetic measurement of luminescence using stopped-flow spectrophotometry. Compared to free enzyme, the luminescence signal kinetics was slower when the luciferase was encapsulated in bacterial microcompartment shells. The results indicate that substrates and products can still exchange across the shell, and modeling of the experimental data suggest that a 50× permeability rate increase occurs when shell vertices were vacant. Overall, our results suggest design considerations for the construction of heterologous bacterial microcompartment shell systems and compartmentalized function at the nanoscale.

PMID:39808735 | DOI:10.1021/acssynbio.4c00290

Proc Natl Acad Sci U S A. 2025 Jan 21;122(3):e2409724122. doi: 10.1073/pnas.2409724122. Epub 2025 Jan 14.

ABSTRACT

ADAR is highly expressed and correlated with poor prognosis in hepatocellular carcinoma (HCC), yet the role of its constitutive isoform ADARp110 in tumorigenesis remains elusive. We investigated the role of ADARp110 in HCC and underlying mechanisms using clinical samples, a hepatocyte-specific Adarp110 knock-in mouse model, and engineered cell lines. ADARp110 is overexpressed and associated with poor survival in both human and mouse HCC. It creates an immunosuppressive microenvironment by inhibiting total immune cells, particularly cytotoxic GZMB+CD8+ T cells infiltration, while augmenting Treg cells, MDSCs, and exhausted CD8+ T cells ratios. Mechanistically, ADARp110 interacts with SNRPD3 and RNPS1 to stabilize CD24 mRNA by inhibiting STAU1-mediated mRNA decay. CD24 protects HCC cells from two indispensable mechanisms: macrophage phagocytosis and oxidative stress. Genetic knockdown or monoclonal antibody treatment of CD24 inhibits ADARp110-overexpressing tumor growth. Our findings unveil different mechanisms for ADARp110 modulation of tumor immune microenvironment and identify CD24 as a promising therapeutic target for HCCs.

PMID:39808660 | DOI:10.1073/pnas.2409724122

Discov Oncol. 2025 Jan 14;16(1):44. doi: 10.1007/s12672-025-01779-x.

ABSTRACT

BACKGROUND: Thyroid carcinoma (THCA) is the most common cancer of the endocrine system. Natural killer (NK) cell play an important role in tumor immune surveillance. The aim of this study was to explore the possible molecular mechanisms involved in NK cell in THCA to help the management and treatment of the disease.

METHODS: All data were downloaded from public databases. Candidate hub genes associated with NK cell in THCA were identified by limma, WGCNA and singleR packages. Functional enrichment analysis was performed on the candidate hub genes. Hub genes associated with NK cell were identified by Pearson correlation analysis. The mRNA-miRNA-lncRNA and transcription factors (TF) networks were constructed and the drug was predicted.

RESULTS: The infiltration level of NK cell in THCA tissues was higher than that in paracancerous tissues. KEGG functional enrichment analysis only obtained two signaling pathways, thyroid hormone synthesis and mineral absorption. CTSC, FN1, SLC34A2 and TMSB4X identified by Pearson correlation analysis were considered as the hub genes. Receiver operating characteristic analysis suggested that hub genes may be potential diagnostic biomarkers. In mRNA-miRNA-lncRNA network, FN1 had the highest correlation with IQCH-AS1, and IQCH-AS1 was also correlated with hsa-miR-543. In addition, FN1 and RUNX1 were also found to have the highest correlation in TF network. Finally, NK cell-related drugs belinostat and vorinostat were identified based on ASGARD.

CONCLUSION: The identification of important signaling pathways, molecules and drugs provides potential research directions for further research in THCA and contributes to the development of diagnostic and therapeutic approaches for this disease.

PMID:39808350 | DOI:10.1007/s12672-025-01779-x

Clin Transl Sci. 2025 Jan;18(1):e70125. doi: 10.1111/cts.70125.

ABSTRACT

Monoclonal antibodies (mAbs) are critical components in the therapeutic landscape, but their dosing strategies often evolve post-approval as new data emerge. This review evaluates post-marketing label changes in dosing information for FDA-approved mAbs from January 2015 to September 2024, with a focus on both initial and extended indications. We systematically analyzed dosing modifications, categorizing them into six predefined groups: Dose increases or decreases, inclusion of new patient populations by body weight or age, shifts from body weight-based dosing to fixed regimens, and adjustments in infusion rates. Among the 86 mAbs evaluated, 21% (n = 18) exhibited changes in dosing information for the initial indication, with a median time to modification of 37.5 months (range: 5-76 months). Furthermore, for mAbs with extended indications (n = 26), 19.2% (n = 5) underwent dosing changes in their first extensions, with a median time to adjustment of 31 months (range: 8-71 months). Key drivers for these adjustments included optimizing therapeutic efficacy, addressing safety concerns, accommodating special populations, and enhancing patient convenience. We also discuss the role of model-informed drug development, real-world evidence, and pharmacogenomics in refining mAb dosing strategies. These insights underscore the importance of ongoing monitoring and data integration in the post-marketing phase, providing a foundation for future precision medicine approaches in mAb therapy.

PMID:39807701 | DOI:10.1111/cts.70125

Insights Imaging. 2025 Jan 14;16(1):17. doi: 10.1186/s13244-025-01898-7.

ABSTRACT

OBJECTIVES: To evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal.

METHODS: A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. The other cohort, consisting of 959 patients with pathologically confirmed adrenal lesions (external validation dataset 1), was included for validation of the classification performance of this model. Then, another consecutive cohort of patients with a history of malignancy (N = 479) was used for validation in the screening population (external validation dataset 2). Parameters of sensitivity, accuracy, etc., were used, and the performance of the model was compared to the radiology report in these validation scenes.

RESULTS: The DSC of the test set of the segmentation model was 0.900 (0.810-0.965) (median (interquartile range)). The model showed sensitivities and accuracies of 99.7%, 98.3% and 87.2%, 62.2% in external validation datasets 1 and 2, respectively. It showed no significant difference comparing to radiology reports in external validation datasets 1 and lesion-containing groups of external validation datasets 2 (p = 1.000 and p > 0.05, respectively).

CONCLUSION: The 3D V-Net-based segmentation model of adrenal lesions can be used for the binary classification of adrenal glands.

CRITICAL RELEVANCE STATEMENT: A 3D V-Net-based segmentation model of adrenal lesions can be used for the detection of abnormalities of adrenal glands, with a high accuracy in the pre-surgical scene as well as a high sensitivity in the screening scene.

KEY POINTS: Adrenal lesions may be prone to inter-observer variability in routine diagnostic workflow. The study developed a 3D V-Net-based segmentation model of adrenal lesions with DSC 0.900 in the test set. The model showed high sensitivity and accuracy of abnormalities detection in different scenes.

PMID:39808346 | DOI:10.1186/s13244-025-01898-7

Brief Bioinform. 2024 Nov 22;26(1):bbaf001. doi: 10.1093/bib/bbaf001.

ABSTRACT

This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus identification often face limitations due to the diversity and rapid evolution of viral genomes. In response, recent efforts have focused on leveraging artificial intelligence (AI) techniques to enhance accuracy and efficiency in virus detection. However, existing AI-based approaches are primarily binary classifiers, lacking specificity in identifying viral types and reliant on nucleotide sequences. To address these limitations, VirDetect-AI, a novel tool specifically designed for the identification of eukaryotic viruses within metagenomic datasets, is introduced. The VirDetect-AI model employs a combination of convolutional neural networks and residual neural networks to effectively extract hierarchical features and detailed patterns from complex amino acid genomic data. The results demonstrated that the model has outstanding results in all metrics, with a sensitivity of 0.97, a precision of 0.98, and an F1-score of 0.98. VirDetect-AI improves our comprehension of viral ecology and can accurately classify metagenomic sequences into 980 viral protein classes, hence enabling the identification of new viruses. These classes encompass an extensive array of viral genera and families, as well as protein functions and hosts.

PMID:39808116 | DOI:10.1093/bib/bbaf001

Radiology. 2025 Jan;314(1):e240238. doi: 10.1148/radiol.240238.

ABSTRACT

Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Materials and Methods Male patients with suspected prostate cancer who underwent multiparametric MRI were retrospectively included from three centers from April 2020 to April 2023. A deep learning model (pix2pix algorithm) was trained to synthesize contrast-enhanced MRI scans from four noncontrast MRI sequences (T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps) and then tested on an internal and two external datasets. The reference standard for model training was the second postcontrast phase of the dynamic contrast-enhanced sequence. Similarity between simulated and acquired contrast-enhanced images was evaluated using the multiscale structural similarity index. Three radiologists independently scored T2-weighted and diffusion-weighted MRI with either simulated or acquired contrast-enhanced images using PI-RADS, version 2.1; agreement was assessed with Cohen κ. Results A total of 567 male patients (mean age, 66 years ± 11 [SD]) were divided into a training test set (n = 244), internal test set (n = 104), external test set 1 (n = 143), and external test set 2 (n = 76). Simulated and acquired contrast-enhanced images demonstrated high similarity (multiscale structural similarity index: 0.82, 0.71, and 0.69 for internal test set, external test set 1, and external test set 2, respectively) with excellent reader agreement of PI-RADS scores (Cohen κ, 0.96; 95% CI: 0.94, 0.98). When simulated contrast-enhanced imaging was added to biparametric MRI, 34 of 323 (10.5%) patients were upgraded to PI-RADS 4 from PI-RADS 3. Conclusion It was feasible to generate simulated contrast-enhanced prostate MRI using deep learning. The simulated and acquired contrast-enhanced MRI scans exhibited high similarity and demonstrated excellent agreement in assessing clinically significant prostate cancer based on PI-RADS, version 2.1. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Neji and Goh in this issue.

PMID:39807983 | DOI:10.1148/radiol.240238

JCO Clin Cancer Inform. 2025 Jan;9:e2400325. doi: 10.1200/CCI-24-00325. Epub 2025 Jan 14.

NO ABSTRACT

PMID:39807853 | DOI:10.1200/CCI-24-00325

Appl Biochem Biotechnol. 2025 Jan 14. doi: 10.1007/s12010-024-05168-y. Online ahead of print.

ABSTRACT

Dysregulated circular RNAs (circRNAs) has been revealed to be involved in pulmonary fibrosis progression. Herein, this study focused on exploring the function and mechanism of circRNA Zinc Finger MYM-Type Containing 2 (circZMYM2) on idiopathic pulmonary fibrosis (IPF) using transforming growth factor (TGF)-β1-stimulated fibroblasts. Human fibroblast cell lines IMR-90 and HFL1 were stimulated with TGF-β1 to mimic fibrosis condition in vitro. Levels of genes and proteins were detected by qRT-PCR and western blotting. Cell proliferation and migration were analyzed using cell counting kit-8 assay, 5-Ethynyl-2'-deoxyuridine (EdU) and wound healing assays. The fibrosis progression was determined by the change of E-cadherin, α-smooth muscle actin (α-SMA), collagen type I α 1 (COL1A1) and collagen type III α 1 (COL3A1). The interaction between miR-199b-5p and circZMYM2 or KLF13 (Kruppel Like Factor 13) was analyzed using dual-luciferase reporter, RIP and RNA-pull-down assays. CircZMYM2 was decreased in TGF-β1-induced IMR-90 and HFL1 fibroblasts. Functionally, re-expression of circZMYM2 in IMR-90 and HFL1 cells could attenuate TGF-β1-evoked proliferation, migration and fibrosis in cells. Mechanistically, the circZMYM2/miR-199b-5p/KLF13 constituted a competing endogenous RNA (ceRNA). TGF-β1 reduced KLF13 expression and increased miR-199b-5p expression in IMR-90 and HFL1 cells. Further rescue experiments suggested that miR-199b-5p up-regulation or KLF13 knockdown reversed the anti-fibrotic effects of circZMYM2; moreover, silencing of miR-199b-5p exhibited anti-fibrotic effects, which was counteracted by KLF13 knockdown. CircZMYM2 had an anti-fibrotic effect that could suppress fibroblast activation via miR-199b-5p/KLF13 axis, pointing a novel perspective into the potential action pattern of circ_0022383 in IPF.

PMID:39808406 | DOI:10.1007/s12010-024-05168-y

Clin Exp Rheumatol. 2025 Jan 14. doi: 10.55563/clinexprheumatol/tjnyz5. Online ahead of print.

ABSTRACT

OBJECTIVES: The progressive decline in interstitial lung disease associated with non-scleroderma connective tissue disease (ILD-NSCTD) is linked to poor prognosis and frequently results in respiratory failure. Lung transplantation (LTx) offers a viable treatment option, yet its outcomes in ILD-NSCTD remain contentious, particularly across different subtypes.

METHODS: This retrospective cohort study included patients with idiopathic pulmonary fibrosis (IPF) (n=11,610) and ILD-NSCTD (n=610) listed in the United Network for Organ Sharing (UNOS) database who underwent lung transplantation between May 5, 2005, and December 31, 2022. We used the Kaplan-Meier method to evaluate cumulative survival rates and logistic regression to assess the risk of post-operative complications.

RESULTS: Compared to IPF patients, those with ILD-NSCTD are generally younger, with a lower proportion of male and white patients. After propensity matching, overall survival rates remained similar between the groups (log-rank, p=0.953). However, ILD-NSCTD was associated with a significantly higher risk of post-operative stroke (adjusted OR 1.75, 95% CI 1.12-2.74, p=0.015) and longer post-operative hospital stays (p<0.001). Subgroup analyses yielded consistent results. Finally, infection was identified as the leading cause of death.

CONCLUSIONS: Compared to IPF, patients with ILD-NSCTD have a significantly higher risk of post-operative stroke and extended hospital stays, potentially due to complications inherent to ILD-NSCTD. However, the underlying causes of these outcomes remain unclear. Despite these differences, short-term and long-term survival rates are comparable between the two groups, with consistent findings across various ILD-NSCTD subgroups. Therefore, ILD-NSCTD should not be regarded as a relative contraindication for lung transplantation. Nonetheless, the influence of extra-pulmonary complications in ILD-NSCTD patients requires further investigation.

PMID:39808303 | DOI:10.55563/clinexprheumatol/tjnyz5

Immun Inflamm Dis. 2025 Jan;13(1):e70123. doi: 10.1002/iid3.70123.

ABSTRACT

BACKGROUND: Post-coronavirus disease 19 lung fibrosis (PCLF) shares common immunological abnormalities with idiopathic pulmonary fibrosis (IPF), characterized by an unbalanced cytokine profile being associated with the development of lung fibrosis. The aim of the present study was to analyze and compare the different subsets of CD4- and CD8-T cells, along with specific cytokine expression patterns, in peripheral blood (PB) from patients affected by PCLF and IPF and healthy controls (HCs).

METHODS: One-hundred patients followed at the Rare Lung Disease Center of Siena University Hospital were enrolled. Eight HCs were recruited. PB samples were collected, and CD4- and CD8-T subsets were analyzed through flow cytometry. Multiplex bead-based LEGENDplex™ were used for cytokine quantification.

RESULTS: Higher CD8 percentages were observed in IPF than in HCs and PCLF (p = 0.020 and p = 0.007, respectively). PCLF subgroup showed higher Th-naïve, Th-effector, Tc-naïve, and Tc-reg percentages than IPF (p < 0.001; p = 0.018; p = 0.005; p = 0.017, respectively). Th-naïve and Tc-naïve inversely correlated with Tc-reg (p < 0.0001, r = -0.61 and p = 0.005, r = -0.39, respectively). Tc-naïve-PD1 and Tc-effector-PD1 percentages were higher in PCLF than IPF (p < 0.001), while Tfh-reg and Tfc-reg were significantly higher in IPF than PCLF (p < 0.001). IL-4, IL-2, TNF-α, and IL-17A were more expressed in PCLF than IPF (p < 0.001). IL-8 directly correlated with Tc-naïve percentages in PCLF (p = 0.018, r = 0.35).

CONCLUSION: A variety of immune cells is involved in the development and progression of pulmonary fibrosis confirming an immunological similarity between IPF and PCLF. T-reg cells play a key role in the worsening of the disease. High cytokine values showed a pro-fibrotic environment in PCLF patients, suggesting dysregulation of the immune system of these patients. Moreover, the immunological similarity between IPF and PCLF patients suggests that SARS-CoV2 infection may trigger the activation of biological pathways common with IPF.

PMID:39807767 | DOI:10.1002/iid3.70123

J Clin Invest. 2025 Jan 14:e186035. doi: 10.1172/JCI186035. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) remains a leading cause of cancer death due to metastatic spread. LIN28B is overexpressed in 30% of CRCs and promotes metastasis, yet its mechanisms remain unclear. In this study, we genetically modified CRC cell lines to overexpress LIN28B, resulting in enhanced PI3K/AKT pathway activation and liver metastasis in mice. We developed genetically modified mouse models with constitutively active Pik3ca that form intestinal tumors progressing to liver metastases with an intact immune system, addressing the limitations of previous Pik3ca-mutant models, including long tumor latency, mixed histology, and lack of distant metastases. The PI3Kα-specific inhibitor alpelisib reduced migration and invasion in vitro and metastasis in vivo. We present the first comprehensive analysis of vertical inhibition of the PI3K/AKT pathway in CRC using FDA-approved drugs alpelisib and capivasertib (an AKT inhibitor) in combination with LY2584702 (an S6K inhibitor) in CRC cell lines and mouse- and patient-derived organoids (PDOs). Tissue microarrays from CRC patients confirmed that LIN28B and PI3K/AKT pathway activation correlate with CRC progression. These findings highlight the critical role of the LIN28B-mediated PI3K/AKT pathway in CRC metastasis, the therapeutic potential of targeted inhibition, and the promise of PDOs in precision medicine in metastatic CRC.

PMID:39808497 | DOI:10.1172/JCI186035

Appl Microbiol Biotechnol. 2025 Jan 14;109(1):7. doi: 10.1007/s00253-024-13402-0.

ABSTRACT

Process intensification and simplification in biopharmaceutical manufacturing have driven the exploration of advanced feeding strategies to improve culture performance and process consistency. Conventional media design strategies, however, are often constrained by the stability and solubility challenges of amino acids, particularly in large-scale applications. As a result, dipeptides have emerged as promising alternatives. Despite extensive research on amino acids, dipeptide supplementation in Chinese hamster ovary (CHO) cell-based manufacturing has received comparatively less attention. In this review, we critically analyze challenges associated with amino acids prone to instability and poor solubility (e.g., glutamine, cysteine, and tyrosine), and explore the potential of dipeptides to address these limitations. We explore the intricate mechanisms of dipeptide transport and enzymatic cleavage, highlighting how chemical properties, stereoisomerism, and competitive metabolites influence their utilization. Notably, while most dipeptides exhibit enhanced solubility, their stabilization effects and culture performance remain variable, underlining the need for rational design. To guide future innovations, we propose tailored dipeptide strategies derived for specific biomanufacturing needs by integrating multi-omics analysis, metabolic flux modeling, and artificial intelligence (AI) modeling. KEY POINTS : •Explored dipeptides as a solution to amino acid instability and poor solubility, enhancing cell culture performance. •Discussed transporter kinetics and cleavage enzymes influencing dipeptide utilization in biomanufacturing. •Suggested various design strategies for identifying appropriate dipeptide pairs to improve bioprocess efficiency.

PMID:39808320 | DOI:10.1007/s00253-024-13402-0

Clin Exp Rheumatol. 2025 Jan 3. doi: 10.55563/clinexprheumatol/fok820. Online ahead of print.

ABSTRACT

OBJECTIVES: Dermatomyositis (DM) is frequently associated with interstitial lung disease (ILD); however, the molecular mechanisms underlying this association remain unclear. This study aimed to employ bioinformatics approaches to identify potential molecular mechanisms linking DM and ILD.

METHODS: GSE46239 and GSE47162 were analysed to identify common differentially expressed genes (DEGs). These DEGs underwent Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed to identify hub genes and transcriptional regulators. Potential therapeutic drugs were predicted using the Drug-Gene Interaction Database (DGIDB).

RESULTS: A total of 122 common DEGs were identified between the DM and ILD datasets. These DEGs were significantly enriched in signal transduction, transcriptional regulation, inflammation, and cell proliferation. Key pathways included the NOD-like receptor signalling pathway, cytokine-cytokine receptor interaction, and TNF signalling pathway. PPI network analysis revealed the top 10 hub genes: CD163, GZMB, IRF4, CCR7, MMP9, AIF1, CXCL10, CCL5, IRF8, and NLRP3. Additionally, interactions between hub genes and transcription factors/miRNAs were constructed. Eleven drugs targeting four hub genes (CXCL10, MMP9, GZMB, and NLRP3) were predicted using the DGIDB.

CONCLUSIONS: In summary, the study identified 10 key genes involved in the molecular pathogenesis of DM and ILD. Moreover, 11 potential drugs were identified that may offer viable therapeutic options for treating DM and ILD in the future.

PMID:39808289 | DOI:10.55563/clinexprheumatol/fok820

Analyst. 2025 Jan 14. doi: 10.1039/d4an01323a. Online ahead of print.

ABSTRACT

Ergothioneine (ERG) is a natural sulfur-containing amino acid found in many organisms, including humans. It accumulates at high concentrations in red blood cells and is distributed to various organs, including the brain. ERG has numerous health benefits and antioxidant capabilities, and it has been linked to various human physiological processes, such as anti-inflammatory, neuroprotective, and anti-aging effects. Accurate, rapid, and cost-effective quantification of ERG levels in human biofluids is crucial for understanding its role in oxidative stress-related diseases. Surface-enhanced Raman scattering (SERS) is an effective approach for measuring compounds at concentrations similar to those at which ERG is present in serum. However, while SERS has been used to characterize or detect ERG, quantification has not yet been achieved due to the variability in the signal enhancement that can arise during sample preparation and analysis. This study introduces a highly efficient and reliable technique for quickly (20 min is typical per sample) measuring ERG levels in human serum using SERS. This employs an internal standard highly specific for ERG which resulted in limit of quantification values of 0.71 μM. To validate this approach, we analysed real human serum with unknown ERG levels as a blind test set and primary reference levels of ERG were produced using a targeted UHPLC-MS/MS reference method.

PMID:39807959 | DOI:10.1039/d4an01323a

Aging Clin Exp Res. 2025 Jan 14;37(1):23. doi: 10.1007/s40520-024-02921-5.

ABSTRACT

OBJECTIVE: This study aims to analyze adverse drug events (ADE) related to romosozumab from the second quarter of 2019 to the third quarter of 2023 from FAERS database.

METHODS: The ADE data related to romosozumab from 2019 Q2 to 2023 Q3 were collected. After data normalization, four signal strength quantification algorithms were used: ROR (Reporting Odds Ratios), PRR (Proportional Reporting Ratios), BCPNN (Bayesian Confidence Propagation Neural Network), and EBGM (Empirical Bayesian Geometric Mean).

RESULTS: Screening for romosozumab-related AEs (adverse events) included 23 system organ categories (SOCs). PT (preferred terms) levels were screened for adverse drug reaction (ADR) signals. A total of 7055 reports with romosozumab as the primary suspect (PS) and 14,041 PTs induced by romosozumab as PS were identified. Common significant signals of general disorders and administration site conditions, musculoskeletal and connective tissue disorders have emerged. Specifically, unexpected AEs such as gastrointestinal disorder, respiratory, thoracic and mediastinal disorders also occur. Notably, fracture (n = 503, ROR = 107.8, PRR = 103.83, IC = 6.6, EBGM = 97.02) and bone density abnormal (n = 429, ROR = 343.65, PRR = 332.77, IC = 8.08, EBGM = 271.34) exhibited relatively high occurrence rates and signal strengths.

CONCLUSION: Our study identifies potential new AE signals and provides broader data support for the safety of romosozumab. In clinical application, doctors are provided with a warning to closely monitor adverse reactions to support their rational use in diseases such as osteoporosis.

PMID:39808360 | DOI:10.1007/s40520-024-02921-5

J Acquir Immune Defic Syndr. 2024 Dec 1;97(4):379-386. doi: 10.1097/QAI.0000000000003506.

ABSTRACT

BACKGROUND: On demand, topical PrEP is desired by those preferring episodic, nonsystemic PrEP. PC-1005 gel (MIV-150, zinc, and carrageenan) exhibits in vitro antiviral HIV-1, human papillomavirus (HPV), and herpes simplex virus type 2 (HSV-2) activity, attractive for a multipurpose prevention technology candidate. We evaluated the safety, pharmacokinetics, and antiviral effect of rectally applied PC-1005.

METHODS: HIV-uninfected adults received a series of 3 rectal PC-1005 doses-4, 16, and 32 mL separated by 2-week washout periods. Following each dose, plasma, rectal fluid and tissue, and vaginal fluid were collected over 48 hours.

RESULTS: Thirteen adults enrolled; 12 completed all 3 doses. All 13 adverse events reported were grade 1 or 2; 5 were judged study drug related. Plasma MIV-150 peaked 1-2 h after dosing with a median peak concentrations range of 0.07-0.23 ng/mL and median half-life range of 4.9-7.4 hours across dose volumes; median concentrations were below assay quantitation limits (BLQ) 24 hours after dosing. Rectal tissue MIV-150 peaked 0.5-1 hours after dosing at 1.4 ng/g (ng/mL) (0.8, 1.9), 46.0 (30.7, 831.0), and 79.7 (11.9, 116.0), respectively, after each dose volume; median tissue concentrations were BLQ beyond 5 hours for all doses. All vaginal fluid samples were BLQ. Ex vivo antiviral assays showed 5 hours of antiviral HPV and HSV effects but no anti-HIV activity.

CONCLUSIONS: MIV-150 rectal tissue concentrations were below the 100 ng/g target concentration and transient. Ex vivo assays demonstrated antiviral HSV and HPV effects but not against HIV. PC-1005 requires a more potent antiviral and longer-lasting formulation for further consideration as a multipurpose prevention technology candidate.

CLINICAL TRIALS: NCT03408899.

PMID:39808074 | DOI:10.1097/QAI.0000000000003506