Monitoring Disease Progression Research Articles

Abstract B010: Improving pancreatic cancer image classification with transfer learning: CD45 vs. PANCK

Abstract Pancreatic ductal adenocarcinoma (PDAC) is known for its high aggressiveness and is the fourth most common cause of death of cancer-related death in the United States. It is highly malignant and often goes undiagnosed until advanced stages due to its late-onset symptoms, resulting in poor prognosis. Using quantitative analyses of histological features, such as the expression of prognostic biomarker, can provide valuable insight to guide treatment decisions, monitor disease progression and predict outcomes. Previous researchers have found that CD45 and PANCK play a huge role in characterizing the tumor microenvironment of PDAC. One study using single-cell RNA sequencing compared the microenvironments of healthy pancreata and pancreatic tumors, revealing differences in cellular composition. Tumor samples showed a higher proportion of immune cells, including CD45+ cells, compared to heathy samples and utilized PANCK to differentiate between epithelial and non-epithelial cells. This study aims to enhance the accuracy of classifying pancreatic cancer pathological images based on prognostic markers using transfer learning techniques. We curated and pre-processed a dataset of pancreatic cancer pathological images with different prognostic markers. Using the pre-trained convolutional neural network (CNN) model VGG16, we fine-tuned it with our dataset to better differentiate between two prognostic markers, CD45 and PANCK. The model's performance was evaluated using a similarity matrix to illustrate distinctions among the two prognostic markers. Our results demonstrate that the transfer learning approach significantly improves classification accuracy, achieving confusion matrix validation results of 90% between CD45 and PANCK prognostic markers. SSIM values were relatively low, highlighting the ability of our model to differentiate between the two prognostic markers. The low SSIM value also shows variability within the two prognostic markers, indicating heterogeneity in structural changes due to pancreatic cancer. These findings indicate that transfer learning can be a powerful tool for improving the classification of pathological images and that there is notable variability in the structural features associated with different prognostic markers in pancreatic cancer. Citation Format: Miracle Thomas. Improving pancreatic cancer image classification with transfer learning: CD45 vs. PANCK [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B010.

Abstract A046: Development of a scoring system to classify HER2 protein expression in circulating tumor cells through immunofluorescence following isolation using Parsortix® instruments

Abstract Background. HER2 testing through immunohistochemistry (IHC) is routinely performed on tissue biopsy samples from metastatic breast cancer (MBC) patients, resulting in a HER2 score that is used in patient stratification and treatment decisions. However, tissue biopsy is invasive, with little opportunity for re-examination while liquid biopsy is minimally invasive and allows monitoring of disease progression through recurrent sample collection. This study aimed to establish an assay (Research Use Only) allowing scoring of HER2 protein expression in circulating tumor cells (CTCs), by immunofluorescence (IF) staining, using cancer cell lines of known HER2 expression. Method. Cultured cancer cell lines with HER2 expression comparable to tissue samples (i.e. HER2 0, 1+, 2+ and 3+ by IHC score) were used in this study (SKBR3 [3+], HCC1954 [3+], SKOV-3 [2+/3+], ZR-75-1 [2+], MCF-7 [0 – 1+] and Hs 578T [0]). Cultured cancer cells were spun onto CellKeep™ slides and stained with ANGLE’s Portrait HER2 IF panel, comprising a nuclear dye, an anti-HER2 antibody, epithelial, mesenchymal and leukocyte markers. Slides were imaged on a BioView Allegro Plus, a platform equipped with artificial intelligence for automated imaging, CTC candidate identification and reporting. The workflow was implemented using samples from 5 MBC patients with known HER2 IHC scores, whose blood was drawn into Streck Cell-Free DNA tubes and processed on ANGLE's Parsortix® instrument, a marker-independent microfluidic device which isolates CTCs based on size and deformability, before being stained with the Portrait HER2 assay. The cohort is being expanded and currently includes three HER2 0 and two HER2 3+ patients as diagnosed by tissue IHC. Results. By observing mean fluorescence intensity (MFI) of each cancer cell line reported to be reflective of each IHC score, thresholds were set to establish a protein scoring system. 95%, 95% and 91% of SKBR3, HCC1954 and SKOV-3, respectively, fell in the 3+ class, 83% of ZR-75-1 fell in the 2+ class, 69% of MCF-7 fell in the 1+ class and 25% and 89% of MCF-7 and Hs 578T, respectively, fell in the 0 class. ≥1 CTC were found in all 5 subjects (mean=405, median=146), with 60% donors presenting mesenchymal CTCs, 20% epithelial CTCs and 20% epithelial and mesenchymal CTCs. HER2+ CTCs were found in both HER2 3+ donors, but not in the HER2 0 donors. HER2+ CTCs from both donors fell in the 3+ class based on MFI, despite one donor receiving Herceptin treatment (1 CTC) and the other being treatment naïve (435 CTCs). Conclusion. This study showed it is possible to establish a HER2 protein scoring system using cultured cancer cell lines stained with the Portrait HER2 assay and demonstrates the potential utility of liquid biopsy in tracking HER2 status. This scoring system will be applied to CTCs isolated from an extended cohort of MBC patients with known tissue biopsy HER2 IHC results to test the robustness of the system and assess concordance of CTC and tissue biopsy scores. FISH will be performed on the samples from MBC patients to confirm results. Citation Format: Alex Young, Laila Vlietinck, Bethan Edwards, Liam Cook, Chassidy Johnson, Mariacristina Ciccioli, Anne-Sophie Pailhes-Jimenez. Development of a scoring system to classify HER2 protein expression in circulating tumor cells through immunofluorescence following isolation using Parsortix® instruments [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A046.

Imaging in Diagnosis and Active Surveillance for Prostate Cancer: A Review.

Active surveillance (AS) has become an increasingly important option for managing low-risk and select intermediate-risk prostate cancer. Although imaging, particularly multiparametric magnetic resonance imaging (mpMRI), has emerged in the prebiopsy pathway for the diagnosis of prostate cancer, the role of mpMRI in patient selection for AS and the necessity of prostate biopsies during AS remain poorly defined. Despite well-founded biopsy schedules, there has been substantial investigation into whether imaging may supplant the need for prostate biopsies during AS. This review aimed to summarize the contemporary role of imaging in the diagnosis and surveillance of prostate cancer. Multiparametric MRI is the most established form of imaging in prostate cancer, with routine prebiopsy use being shown to help urologists distinguish between clinically significant and clinically insignificant disease. The visibility of these lesions on mpMRI closely correlates with their behavior, with visible disease portending a worse prognosis. Combined with other clinical data, risk calculators may better delineate patients with higher-risk disease and exclude them from undergoing AS. While current evidence suggests that mpMRI cannot replace the need for prostate biopsy during AS due to the possibility of missing higher-risk disease, the addition of prostate biomarkers may help to reduce the frequency of these biopsies. The role of prostate-specific antigen positron emission tomography/computed tomography is still emerging but has shown promising early results as an adjunct to mpMRI in initial diagnosis. Imaging in prostate cancer helps to better select patients appropriate for AS, and future studies may strengthen the predictive capabilities of risk calculators. Multiparametric MRI has been shown to be imperative to rationalizing biopsies for patients enrolled in AS. However, heterogeneity in the evidence of mpMRI during AS has suggested that further prospective studies and randomized clinical trials, particularly in homogenizing reporting standards, may reveal a more defined role in monitoring disease progression.

Abstract PR014: Robust disease monitoring using CSF liquid biopsies collected from children undergoing cellular therapy for malignant central nervous system tumors

Abstract Background: Central nervous system (CNS) tumors remain the leading cause of cancer-related mortality in children, necessitating more effective treatment options. Chimeric Antigen Receptor T-cells (CAR-T) have emerged as a promising strategy to treat CNS tumors, with B7H3 (CD276) representing an attractive target due to its high expression across a broad range of entities. B7H3-CAR-T therapy is currently being evaluated in a St. Jude Children’s Research Hospital Phase I clinical trial (NCT05835687) for the treatment of relapse/refractory pediatric CNS tumors. Monitoring disease progression after CAR-T infusion by magnetic resonance imaging (MRI) remains challenging, emphasizing the need for more sensitive biomarkers of treatment response. Detection of circulating tumor DNA (ctDNA) in cerebrospinal fluid (CSF) liquid biopsies holds tremendous potential for minimal residual disease (MRD) monitoring in the clinical setting. Here, we leverage CSF liquid biopsies for longitudinal disease profiling in pediatric CNS tumor patients receiving B7H3-CAR-T immunotherapy. Methods: CSF samples were collected from Ommaya reservoirs of 9 pediatric patients with relapse/refractory CNS tumors undergoing B7H3-CAR-T therapy. CSF was sampled at study enrollment, and weekly intervals pre- and post-infusion. Cell-free DNA (cfDNA) was extracted from 115 longitudinally collected CSF samples and subjected to enzymatic methylation sequencing (EM-seq). Implementation of a custom computational workflow enabled detection of tumor-derived copy number variations (CNVs), estimation of malignant vs. non-malignant fractions, and molecular classification of tumor entity. Results: Tumor-derived CNVs were detected in 7/9 (78%) CSF samples collected at enrollment, prior to immunotherapy. Liquid biopsy-inferred CNV profiles and entity classifications were highly concordant with matched tumor tissue profiles. Chromosomal alterations that were restricted to CSF profiles indicated clonal evolution. Increased ctDNA burden was repeatedly observed after B7H3-CAR-T infusions, suggesting subclinical antitumor activity. Transient subclonal alterations emerging in response to treatment were also identified in a subset of patients. Conclusions: EM-seq is a feasible method for serial tracking of ctDNA burden in patients receiving cellular immunotherapy. CSF liquid biopsies capture the global landscape of tumor heterogeneity and genetic alterations that arise during therapy, providing a sensitive platform for detecting clinical response and disclosing potential mechanisms of treatment resistance. Citation Format: Anna Kostecka, Kyle S Smith, Katie Han, Hong Lin, Deanna Langfitt, Tara Walhart, Stephen Gottschalk, Giedre Krenciute, Christopher DeRenzo, Kelsey Bertrand, Paul A Northcott. Robust disease monitoring using CSF liquid biopsies collected from children undergoing cellular therapy for malignant central nervous system tumors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR014.

Abstract A020: Enhanced cell-free RNA (cfRNA) recovery for liquid biopsy applications: Comparative analysis using controlled RNA samples for early and advanced cancer detection

Abstract Introduction: Extracting cfRNA from biological samples is crucial in liquid biopsy applications, particularly for detecting low-frequency mutations like KRAS G12V, essential for early cancer diagnosis and minimal residual disease (MRD) monitoring. Traditional cfRNA extraction methods involve multiple transfer steps, aliquoting, and pooling, leading to cfRNA loss and affecting mutation detection sensitivity and accuracy. The nRichDX Revolution cfTNA Max 20 kit, with its innovative nRicher cartridge, simplifies the extraction process by eliminating these extra steps, potentially enhancing yield and efficiency. This study evaluates the cfRNA recovery efficiency of the nRichDX kit compared to the Qiagen Circulating Total Nucleic Acid kit. Methods: Plasma samples (5 mL each) were spiked with RNA containing KRAS G12V mutation at concentrations ranging from 100 to 2 million copies, mimicking early-stage cancer detection and MRD monitoring, where low copy numbers represent early disease and high copy numbers reflect advanced cancer stages. cfRNA was extracted using the nRichDX and Qiagen kits, with each condition tested in triplicate. cfRNA recovery efficiency was assessed using quantitative reverse transcription PCR (qRT-PCR) to quantify KRAS G12V RNA copies recovered. High-sensitivity RNA ScreenTape Analysis was also used to evaluate cfRNA profile quality. Results: The nRichDX kit recovered significantly higher cfRNA yields than Qiagen across all concentrations. Recovery rates were consistent and reproducible, with over 70% of input RNA recovered, even at lower inputs (100 to 100,000 copies), where recovery rates were approximately 70-85%. High Sensitivity RNA ScreenTape Analysis showed a linear increase in 18S and 28S ribosomal peaks and high RNA Integrity Number (RIN) scores in nRichDX profiles. The ability to robustly recover low copy numbers is significant for early cancer detection and MRD monitoring. In contrast, the kit’s effectiveness in high copy number samples reflects its ability to monitor advanced cancer stages accurately. Conclusion: The nRichDX cfTNA kit demonstrates exceptional efficiency in cfRNA extraction from plasma samples across a broad dynamic range, from as few as 100 copies to as many as 2 million copies of the KRAS G12V mutation. Its versatility makes it suitable for various cancer detection and monitoring applications. The streamlined process offered by the nRicher cartridge minimizes sample handling and reduces cfRNA loss, enhancing performance. This translates to more accurate and sensitive detection of early-stage and high-burden mutations associated with advanced cancer. The high-quality profiles indicated by RNA ScreenTape Analysis further validate the reliability of the nRichDX kit, making it a valuable tool for liquid biopsy applications, with significant advantages in early cancer detection and monitoring disease progression. Citation Format: Nafiseh Jafari, Mayer Saidian, Jason Saenz, Andrew Dunnigan, Carlos Hernandez, Leila Aghili. Enhanced cell-free RNA (cfRNA) recovery for liquid biopsy applications: Comparative analysis using controlled RNA samples for early and advanced cancer detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A020.

Peak width of skeletonized mean diffusivity: a novel biomarker for white matter damage in spinocerebellar ataxia type 2.

Peak width of skeletonized mean diffusivity (PSMD) is a robust and fully automated imaging marker employed to detect microstructural damage in white matter. This study aimed to evaluate whether PSMD reflects the severity of white matter damage and tracks disease progression in patients with spinocerebellar ataxia type 2 (SCA2). Nine patients with SCA2 and sixteen age- and gender-matched healthy controls were enrolled. Clinical and imaging data were collected at baseline and after 3.5years. Each participant underwent MRI scans twice to obtain diffusion tensor imaging data, from which PSMD were automatically calculated. Differences in PSMD between SCA2 patients and healthy controls were analyzed using a linear mixed model. Additionally, Spearman's rank correlations were employed to assess associations between PSMD values and clinical variables. Patients with SCA2 exhibited higher PSMD values at baseline and follow-up compared to HCs, indicating more severe white matter damage. Longitudinal data revealed a continual increase in PSMD values in SCA2 patients over time. The mixed-effects model confirmed significant differences in PSMD values between the two groups, as well as an interaction effect suggesting different progression rates. These findings suggest that SCA2 associates with progressive deterioration of white matter. No significant correlations were observed between PSMD values and clinical variables in this study. This study underscores the potential of PSMD as a neuroimaging biomarker for detecting microstructural white matter damage and monitoring disease progression in patients with SCA2.

Non-invasive testing in metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously referred to as non-alcoholic fatty liver disease (NAFLD), is a leading cause of chronic liver disease, affecting up to 30% of the global population. MASLD is strongly associated with metabolic risk factors such as obesity and type 2 diabetes, and can progress to advanced stages including cirrhosis and hepatocellular carcinoma. Early diagnosis and accurate staging of fibrosis are critical in managing the disease and preventing complications. While liver biopsy has long been considered the gold standard for assessing fibrosis, it is invasive and carries associated risks. In response, non-invasive tests (NITs) have emerged as essential alternatives for the diagnosis and monitoring of MASLD. Key methods include blood-based biomarkers such as the Fibrosis-4 (FIB-4) score, NAFLD Fibrosis Score (NFS), and Enhanced Liver Fibrosis (ELF) test, as well as imaging modalities like vibration-controlled transient elastography (VCTE) and magnetic resonance elastography (MRE). These tests provide safer, more accessible methods for identifying liver fibrosis and guiding clinical management. They are integral in assessing disease severity, guiding treatment decisions, and monitoring disease progression, particularly in light of emerging therapies. NITs have become increasingly recommended by clinical guidelines as they reduce the need for invasive procedures like liver biopsy, improving patient care and outcomes. In conclusion, non-invasive testing plays a crucial role in the effective management of MASLD, offering reliable alternatives for diagnosis and monitoring while minimizing risks associated with traditional invasive methods.

Artificial Intelligence-Driven Wearable Electronics and Smart Nanodevices for Continuous Cancer Monitoring and Enhanced Diagnostic Accuracy

Artificial Intelligence (AI)-driven wearable electronics and smart nanodevices are transforming cancer diagnostics by offering continuous monitoring and enhanced diagnostic accuracy. Traditional cancer diagnostic methods often suffer from delays in detection and limited real-time data, which can hinder timely treatment. In contrast, AI integration in wearable technologies and nanodevices allows for the continuous tracking of physiological biomarkers, enabling earlier detection of cancer and more precise monitoring of disease progression. This review explores the advancements in AI-powered wearable electronics and smart nanodevices for cancer diagnostics, focusing on their applications, benefits, and challenges. AI-driven wearable devices, such as smartwatches and biosensors, are equipped with machine learning algorithms capable of real-time data analysis, facilitating personalized and proactive healthcare solutions. Similarly, smart nanodevices, which leverage nanotechnology combined with AI, offer unprecedented precision in identifying and monitoring cancer cells, allowing for more accurate diagnostic outcomes. Case studies are presented to demonstrate the effectiveness of these technologies in improving diagnostic accuracy and patient outcomes. The review also highlights the challenges these technologies face, including data privacy, ethical concerns, and technical limitations. Additionally, the paper discusses future directions in AI-driven cancer diagnostics, emphasizing the potential for these innovations to revolutionize early cancer detection, improve treatment strategies, and enhance long-term patient outcomes. Ultimately, this review provides a comprehensive understanding of how AI and advanced technologies are reshaping the landscape of cancer diagnostics and monitoring, offering significant implications for clinical practice and research in the medical field.

Determination of qPCR reference genes suitable for normalizing gene expression in a novel model of Duchenne muscular dystrophy, the D2-mdx mouse.

Duchenne muscular dystrophy (DMD) is a X-linked neuromuscular disorder arising from mutations in the dystrophin gene, leading to a progressive muscle wasting and disability. Currently there is no universal therapy, and there is thus a strong interest in preclinical studies for finding novel treatments. The most widely used and characterized mouse model for DMD is the C57BL/10ScSn-Dmdmdx/J (BL10-mdx), but this model exhibits mild pathology and does not replicate key features of human disease. The D2.B10-Dmdmdx/J (D2-mdx) mouse is a more recent model which seems to better mimics the complex human DMD phenotype. However, the D2-mdx mouse remains less extensively characterised than its BL10-mdx counterpart. Quantitative PCR analysis of gene expression is an important tool to monitor disease progression and evaluate therapeutic efficacy, but measurements must be normalised to stably expressed reference genes, which should ideally be determined and validated empirically. We examined gene expression in the gastrocnemius (GC), diaphragm (DIA) and heart in the D2-mdx mouse, the BL10-mdx mouse, and appropriate strain-matched wild-type controls (D2-wt and BL10-wt), from 4 to 52 weeks of age, using a large panel of candidate references (ACTB, AP3D1, CSNK2A2, GAPDH, HPRT1, PAK1IP1, RPL13A, SDHA, and in the heart, also HTATSF1 and HMBS). Data was analyzed using GeNorm, Bestkeeper, deltaCt and Normfinder algorithms to identify stable references under multiple possible scenarios. We show that CSNK2A2, AP3D1 and ACTB represent strong universal reference genes in both GC and DIA, regardless of age, muscle type, strain and genotype, while HTATSF1 and SDHA are optimal for the heart. GAPDH, HPRT1 and RPL13A were conversely revealed to be poor references, showing tissue-, age- or disease-specific changes in expression. Our results illustrate the importance of determining appropriate reference genes for specific comparative scenarios, but also reconfirm that universal panels can nevertheless be identified for normalising gene expression studies in even complex pathological states.

From immune response to mental health: neutrophils in schizophrenia

Schizophrenia is a multifaceted psychiatric disorder with a complex etiology that includes genetic, environmental, and immunological factors. Emerging research has increasingly focused on the role of the immune system, particularly neutrophils, in the pathogenesis of schizophrenia. Neutrophils, the most abundant type of white blood cells, are crucial in the body’s defense mechanisms against infections and in the regulation of inflammation. This review explores the evolving evidence that implicates neutrophils in schizophrenia, highlighting their potential contribution to the disorder’s onset and progression through mechanisms such as neuroinflammation and oxidative stress. Recent studies have demonstrated elevated neutrophil counts and neutrophil-to-lymphocyte ratios (NLR) in individuals with schizophrenia, correlating with the severity of psychotic symptoms and cognitive impairments. Furthermore, genetic and molecular analyses have revealed abnormalities in neutrophil function and immune-related gene expression in schizophrenia patients. These findings suggest that neutrophil dysfunction and the resulting chronic inflammation could play a significant role in the disorder’s pathophysiology, affecting neuronal function and contributing to the clinical manifestations of schizophrenia. Neutrophil-related biomarkers, such as NLR, could aid in the diagnosis and monitoring of disease progression. Additionally, targeting neutrophil activity and associated inflammatory pathways presents a promising avenue for developing new therapeutic interventions. This review underscores the need for further research to elucidate the precise mechanisms by which neutrophils influence schizophrenia and to explore potential treatments that could improve outcomes for patients.

Automated lung segmentation on chest MRI in children with cystic fibrosis

IntroductionSegmentation of lung structures in medical imaging is crucial for the application of automated post-processing steps on lung diseases like cystic fibrosis (CF). Recently, machine learning methods, particularly neural networks, have demonstrated remarkable improvements, often outperforming conventional segmentation methods. Nonetheless, challenges still remain when attempting to segment various imaging modalities and diseases, especially when the visual characteristics of pathologic findings significantly deviate from healthy tissue.MethodsOur study focuses on imaging of pediatric CF patients [mean age, standard deviation (7.50 ± 4.6)], utilizing deep learning-based methods for automated lung segmentation from chest magnetic resonance imaging (MRI). A total of 165 standardized annual surveillance MRI scans from 84 patients with CF were segmented using the nnU-Net framework. Patient cases represented a range of disease severities and ages. The nnU-Net was trained and evaluated on three MRI sequences (BLADE, VIBE, and HASTE), which are highly relevant for the evaluation of CF induced lung changes. We utilized 40 cases for training per sequence, and tested with 15 cases per sequence, using the Sørensen-Dice-Score, Pearson’s correlation coefficient (r), a segmentation questionnaire, and slice-based analysis.ResultsThe results demonstrated a high level of segmentation performance across all sequences, with only minor differences observed in the mean Dice coefficient: BLADE (0.96 ± 0.05), VIBE (0.96 ± 0.04), and HASTE (0.95 ± 0.05). Additionally, the segmentation quality was consistent across different disease severities, patient ages, and sizes. Manual evaluation identified specific challenges, such as incomplete segmentations near the diaphragm and dorsal regions. Validation on a separate, external dataset of nine toddlers (2–24 months) demonstrated generalizability of the trained model achieving a Dice coefficient of 0.85 ± 0.03.Discussion and conclusionOverall, our study demonstrates the feasibility and effectiveness of using nnU-Net for automated segmentation of lung halves in pediatric CF patients, showing promising directions for advanced image analysis techniques to assist in clinical decision-making and monitoring of CF lung disease progression. Despite these achievements, further improvements are needed to address specific segmentation challenges and enhance generalizability.

Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications

IntroductionColorectal cancer is the third most common malignancy in developed countries. Diagnosis strongly depends on the pathologist’s expertise and laboratory equipment, and patient survival is influenced by the cancer’s stage at detection. Non-invasive spectroscopic techniques can aid early diagnosis, monitor disease progression, and assess changes in physiological parameters in both heterogeneous samples and advanced platforms like Organ-on-Chip (OoC).MethodsIn this study, Raman microspectroscopy combined with Machine Learning was used to analyse structural and biochemical changes in a Caco-2 cell-based intestinal epithelial model before and after treatment with a calcium chelating agent.ResultsThe Machine Learning (ML) algorithm successfully classified different epithelium damage conditions, achieving an accuracy of 91.9% using only 7 features. Two data-splitting approaches, “sample-based” and “spectra-based,” were also compared. Further, Raman microspectroscopy results were confirmed by TEER measurements and immunofluorescence staining.DiscussionExperimental results demonstrate that this approach, combined with supervised Machine Learning, can investigate dynamic biomolecular changes in real-time with high spatial resolution. This represents a promising non-invasive alternative technique for characterizing cells and biological barriers in organoids and OoC platforms, with potential applications in cytology diagnostics, tumor monitoring, and drug efficacy analysis.

Non-invasive assessment of IgA nephropathy severity with [18F] AlF-NOTA-FAPI-04 PET/CT imaging

Abstract Background Renal biopsy plays a crucial role in diagnosing and assessing the severity of IgA nephropathy (IgAN), despite being an invasive procedure with potential risk of failure. Our study focused on evaluating the capability of [18F] AlF-NOTA-FAPI-04 PET/CT in identifying the extent of pathological alterations in IgAN. Methods Twenty patients (13 males and 7 females; mean age, 44 ± 16 years) with newly diagnosed primary IgAN and 10 patients (7 males and 3 females; mean age, 51 ± 4 years) without known renal disease underwent [18F] AlF-NOTA-FAPI-04 PET/CT imaging. Kidney tissues from biopsies were stained with various techniques and examined using immunofluorescence. The Oxford classification was used to evaluate pathological indicators. Immunohistochemical staining was conducted to assess α-smooth muscle actin (αSMA) and FAP expression. Renal FAPI uptake measured by PET/CT (SUVmax and SUVmean) was correlated with histological findings. Results The renal parenchymal FAPI uptake was significantly higher in IgAN patients compared to control patients (SUVmax = 3.9 ± 1.3 vs 1.9 ± 0.4, SUVmean = 3.6 ± 1.2 vs 1.5 ± 0.4; all P < 0.001). We identified a significant difference in renal parenchymal FAPI uptake among the various categories of the Oxford classification. Correlation analysis revealed a positive association between SUVmax and interstitial fibrosis and tubular atrophy (IF/TA), as well as tubulointerstitial inflammation (TII) scores in scarred cortex and non-scarred cortex (r = 0.637, 0.593, and 0.491, all P < 0.05), Similar associations were observed between SUVmean and these scores (r = 0.641, 0.592, and 0.479, all P < 0.05). Furthermore, significant positive correlations were observed between SUVmax or SUVmean and the staining scores for glomerular αSMA and FAP, as well as for tubulointerstitial αSMA and FAP (all P < 0.01). Conclusion [18F] AlF-NOTA-FAPI-04 PET/CT imaging offers IgAN patients a non-invasive and reproducible auxiliary modality to monitor disease progression.

Untargeted and Targeted Lipidomics Unveil Dynamic Lipid Metabolism Alterations in Type 2 Diabetes

Background: Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder with a growing body of evidence suggesting the central role of lipid metabolism in its pathogenesis. However, the dynamic changes in lipid metabolism across different stages of T2DM remain understudied. Objective: This study aimed to elucidate the temporal alterations in lipid metabolism in T2DM using an integrated lipidomics approach. Method: Serum samples from 155 subjects were analyzed using LC-MS-based lipidomics, including untargeted and targeted approaches. Results: We identified significant alterations in 44 lipid metabolites in newly diagnosed T2DM patients and 29 in high-risk individuals, compared with healthy controls. Key metabolic pathways such as sphingomyelin, phosphatidylcholine, and sterol ester metabolism were disrupted, highlighting the involvement of insulin resistance and oxidative stress in T2DM progression. Moreover, 13 lipid metabolites exhibited diagnostic potential for T2DN, showing consistent trends of increase or decrease as the disease progressed. Conclusion: Our findings underscore the importance of lipid metabolism in T2D development and identify potential lipid biomarkers for early diagnosis and monitoring of disease progression, which contribute to paving the way for novel therapeutic strategies.

Changes in lipid abundance are associated with disease progression and treatment response in chronic Trypanosoma cruzi infection

BackgroundChagas disease, caused by the parasite Trypanosoma cruzi, is a zoonosis that affects more than seven million people. Current limitations on the diagnosis of the disease hinder the prognosis of patients and the evaluation of treatment efficacy, slowing the development of new therapeutic options. The infection is known to disrupt several host metabolic pathways, providing an opportunity for the identification of biomarkers.MethodsThe metabolomic and lipidomic profiles of a cohort of symptomatic and asymptomatic patients with T. cruzi infection and a group of uninfected controls were analysed using liquid chromatography/mass spectrometry. Differences among all groups and changes before and after receiving anti-parasitic treatment across those with T. cruzi infection were explored.ResultsThree lipids were found to differentiate between symptomatic and asymptomatic participants: 10-hydroxydecanoic acid and phosphatidylethanolamines PE(18:0/20:4) and PE(18:1/20:4). Additionally, sphinganine, 4-hydroxysphinganine, hexadecasphinganine, and other sphingolipids showed post-treatment abundance similar to that in non-infected controls.ConclusionsThese molecules hold promise as potentially useful biomarkers for monitoring disease progression and treatment response in patients with chronic T. cruzi infection.Graphical

The concept of the liquid biopsy in the treatment of malignant eye tumours

The liquid biopsy is acutting-edge technique that involves analysing non-solid biological tissues, primarily blood but also ocular fluids, for the presence of cancer cells or fragments of tumour DNA. Unlike traditional biopsies, liquid biopsies are usually minimally invasive and can be performed more frequently, enabling continuous monitoring of disease progression and treatment efficacy. This article (and the associated series of articles) outlines the key developments in liquid biopsy, which include the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTC) and exosomal RNA and protein biomarkers. Techniques, such as digital droplet PCR and next-generation sequencing (NGS) have made it possible to detect even very low levels of ctDNA, which is crucial for early cancer detection and monitoring minimal residual disease. The detection of rare CTCs is enhanced by techniques, such as microfluidic devices and immunomagnetic separation. Multiomic approaches, whereby exosomal RNA, protein and ctDNA analyses are combined, help to create amore comprehensive picture of tumour biology, including insights into tumour heterogeneity, potentially leading to better diagnostic and prognostic tools and helping to predict treatment response and resistance. The challenges of liquid biopsy application, which will be described in the following article, include (a)standardization, (b)cost and accessibility, (c)validation and clinical utility. However, the liquid biopsy represents apromising frontier in the application of precision ocular oncology, with ongoing research likely to expand its applications and improve its effectiveness in the coming years.

Sensitive Detection of Gynecological Cancer Recurrence Using Circulating Tumor DNA and Digital PCR: A Comparative Study with Serum Biochemical Markers

Early detection of recurrences in gynecological cancers is crucial for women’s health. Circulating tumor DNA (ctDNA) analysis through liquid biopsy offers a promising approach for monitoring disease progression and identifying relapses. This study investigated the utility of digital Polymerase Chain Reaction (dPCR) for ctDNA detection in three gynecological cancer patients with clinically confirmed relapses during a two-year post-surgical follow-up. Patient-specific tumor mutations were identified through whole-exome sequencing (WES) and confirmed via Sanger sequencing. dPCR probes targeting these mutations were used to quantify the ctDNA levels in plasma samples collected throughout the follow-up period, and the findings were compared with standard serum biochemical markers. In two patients, persistent positive dPCR signals for the selected mutations were detected after tumor removal, with ctDNA levels progressively increasing even after post-surgical chemotherapy. Notably, dPCR identified elevated ctDNA levels before an increase in the cancer antigen 125 (CA125) biochemical marker was observed. In the third patient, no ctDNA signals from the two selected mutations were detected despite clinical evidence of recurrence, suggesting the emergence of new mutations. While this study highlights the promise of dPCR for early recurrence detection in gynecological cancers, it also underscores the critical need for comprehensive mutation panels to overcome the inherent challenges posed by tumor heterogeneity and the emergence of new mutations during disease progression.

Serum levels of free light chains and syndecan-1 in patients with rheumatoid arthritis and systemic lupus erythematosus.

Systemic autoimmune rheumatic diseases (SARDs) are characterized by chronic inflammation. Reliable biomarkers are crucial for diagnosis, monitoring disease progression and therapeutic responses. This study explores serum Syndecan-1 (SDC-1) as a biomarker for these conditions and its relationship with free light chain (FLC) levels. A retrospective analysis was performed on sera from 60 patients with rheumatoid arthritis (RA) and from 60 with systemic lupus erythematosus (SLE), alongside 50 healthy donors (HD). Κ- and λ- FLCs were determined by turbidimetric assay, while SDC-1 levels by ELISA. Kruskal-Wallis test, Wilcoxon-Mann-Whitney U test, multivariable linear regression and Spearman's correlation were employed to compare biomarker levels across groups and to explore correlations. SDC-1, κ-FLC, and λ-FLC were significantly increased in RA and SLE patients compared with HD (p< 0.001), while no significant differences in the κ/λ ratio were observed among the groups (p= 0.4). A significant difference in subject age was also identified. However, multivariate regression analysis indicated that RA and SLE are significantly associated with the levels of these markers, with minimal confounding by age. A significant correlation was observed separately in all groups between the FLC markers. Conversely, no correlation was detected between SDC-1 and FLCs, nor between these markers and age or disease activity indices. Elevated serum levels of FLCs and SDC-1 in RA and SLE patients compared with HD underscore their potential as biomarkers for SARDs. The findings also suggest sustained plasma cell activation, supporting the multifaceted role of SDC-1 in the pathogenesis of SARDs.

Glial fibrillary acidic protein in Alzheimer’s disease: a narrative review

Abstract Astrocytes are fundamental in neural functioning and homeostasis in the Central Nervous System. These cells respond to injuries and pathological conditions through astrogliosis, a reactive process associated with neurodegenerative diseases such as Alzheimer’s disease. This process is thought to begin in the early stages of these conditions. Glial Fibrillary Acidic Protein (GFAP), a type III intermediate filament protein predominantly expressed in astrocytes, has emerged as a key biomarker for monitoring this response. During astrogliosis, GFAP is released into biofluids, making it a candidate for non-invasive diagnosis and tracking of neurodegenerative diseases. Growing evidence positions GFAP as a biomarker for Alzheimer’s disease with specificity and disease-correlation characteristics comparable to established clinical markers, such as Aβ peptides and phosphorylated tau protein. To improve diagnostic accuracy, particularly in the presence of confounders and comorbidities, incorporating a panel of biomarkers may be advantageous. This review will explore the potential of GFAP within such a panel, examining its role in early diagnosis, disease progression monitoring, and its integration into clinical practice for Alzheimer’s disease management.

112° field of view high-resolution swept-source OCT angiography for rat retinas.

This study introduces an ultra-wide field (UWF) and high-resolution swept-source optical coherence tomographic angiography (OCTA) system for rat retinal imaging. Using an asymmetrical optics design, the system achieves unprecedented details of retinal structures and vascular plexuses over a large field of view (112°) in a single-shot acquisition. Views of single-nerve fiber bundles and single capillary vessels are consistently visible over a 112° field of view. The system has a long imaging range and high penetration and allows a full view of vitreous hyaloid vessels, retina, choroid, sclera, and posterior ciliary arteries, down to sub-sclera connective tissues. In a longitudinal study of oxygen-induced retinopathy (OIR) in rats, the system successfully revealed the progression and regression of OIR-related vascular pathologies, such as ischemia, neovascularization, and tortuosity. To our knowledge, this novel UWF-OCT/OCTA prototype designed for rat retinal imaging will be a vital tool for monitoring disease progression and evaluating therapeutic interventions in preclinical models.