Hasil untuk "Cytology"

R. Kasimanickam, T. Duffield, R. Foster et al.

Ruheng Hua, Jiawei Yu, Yuanjie Niu et al.

Abstract Objective Gastric cancer (GC) is a highly invasive malignancy with a propensity for lymph node metastasis. This study investigated how lactylation of TRIM29 contributes to the invasive behavior of GC and lymph node metastasis and the efficacy of chemotherapy for the disease. Methods We examined the expression levels of TRIM29 and its lactylation status in GC tissues and cell lines using quantitative reverse-transcription polymerase chain reaction, immunohistochemistry based on tissue microarrays and western blotting. Functional transwell migration, three-dimensional invasion assay and tube formation assays were performed to assess the role of TRIM29 in GC. The interaction between TRIM29 and heteronuclear ribonucleoprotein A1(hnRNPA1) was explored by co-immunoprecipitation and mass spectrometry. Results Expression of TRIM29 was significantly upregulated in GC tissues in comparison with adjacent non-tumor tissues. This upregulation was associated with lymph node metastasis, vascular tumors and a worse prognosis. Lactylation of TRIM29 in GC cells enhanced the migratory ability and invasiveness of these cells and lymph node metastasis. Mechanistically, TRIM29 formed a complex with hnRNPA1, which in turn activated the Wnt/β-catenin signaling pathway by stabilizing β-catenin in a ubiquitination-dependent manner. Targeting TRIM29 and lymphangiogenesis augmented the efficacy of 5-fluorouracil-based chemotherapy. Conclusion Lactylation of TRIM29 promotes invasive behavior and lymph node metastasis in GC cells by engaging the hnRNPA1-mediated Wnt/β-catenin pathway. Targeting TRIM29 and lymphangiogenesis may be a promising therapeutic strategy for patients with advanced GC.

Vladimir Vinarsky, Stefania Pagliari, Bacel Aldabash et al.

Abstract Cardiac diseases are fueled by extracellular matrix (ECM) remodelling. Together with the altered ECM chemical composition, the mechanical turmoil associated with ECM maladaptive remodelling in the pathological heart drives the shuttling of Yes Associated Protein 1 (YAP1) into cardiomyocyte (CM) nuclei that results either in cell cycle re-entry or cardiomyocyte hypertrophy. The mechanism of YAP1 reactivation and factors driving qualitatively different cellular outcomes is not well understood. Here we employed mechanical actuation as a proxy reproducing ECM remodelling in vitro to trigger YAP1 nuclear shuttling in contractile cardiomyocytes derived from human embryonic and induced pluripotent stem cells (hPSCs). By using hPSC lines in which YAP1 expression has been genetically depleted, super-resolution microscopy and electrophysiological measurements, we show that ECM-triggered nuclear presence of endogenous YAP1 contributes to cardiomyocyte maturation, participates in the formation and alignment of myofibrils, as well as in the maturation of their electrophysiological properties and calcium dynamics. We eventually exploit engineered heart tissues (EHTs) to demonstrate that the net effect of YAP1 deficiency in cardiomyocytes is the inability to respond to physiological stimuli by compensatory growth that results in reduced force development. These results suggest that the re-activation of endogenous YAP1 following ECM maladaptive remodelling promotes cardiomyocyte contractility by restructuring the sarcomere apparatus and the maturation of electrophysiological properties via transcriptionally dependent and independent mechanisms.

Adeline M. Luperchio, Daniel J. Salamango

The family of forkhead box O (FoxO) transcription factors regulate cellular processes involved in glucose metabolism, stress resistance, DNA damage repair, and tumor suppression. FoxO transactivation activity is tightly regulated by a complex network of signaling pathways and post-translational modifications. While it has been well established that phosphorylation promotes FoxO cytoplasmic retention and inactivation, the mechanism underlying dephosphorylation and nuclear translocation is less clear. Here, we investigate the role of protein phosphatase 2A (PP2A) in regulating this process. We demonstrate that PP2A and AMP-activated protein kinase (AMPK) combine to regulate nuclear translocation of multiple FoxO family members following inhibition of metabolic signaling or induction of oxidative stress. Moreover, chemical inhibitor studies indicate that nuclear accumulation of FoxO proteins occurs through inhibition of nuclear export as opposed to promoting nuclear import as previously speculated. Functional, genetic, and biochemical studies combine to identify the PP2A complexes that regulate FoxO nuclear translocation, and the binding motif required. Mutating the FoxO-PP2A interface to enhance or diminish PP2A binding alters nuclear translocation kinetics accordingly. Together, these studies shed light on the molecular mechanisms regulating FoxO nuclear translocation and provide insights into how FoxO regulation is integrated with metabolic and stress-related stimuli.

Shisen Li, Mianjiao Xie, Yongtao Du et al.

Abstract Chemo-resistance is one of the main obstacles in the treatment of colorectal cancer. Many studies have been performed to identify the mechanisms associated with chemo-resistance in colorectal cancer cells, and it has been found that increasing the activity of ABC family transporters, enhancing DNA repair, weakening apoptosis, strengthening stemness, and EMT are among the most important of these mechanisms. Inflammation and cytokines have been linked to colorectal cancer, and there is even a type of colorectal cancer that is caused by chronic inflammation in patients with inflammatory bowel disease. However, the association between cytokines and chemo-resistance in colorectal cancer cells is not yet clear. Various studies have shown that chemotherapy drugs, by affecting the tumor microenvironment, can enhance the recruitment of some immune cells and the production of some cytokines. These cytokines have a variety of effects on various chemo-resistance mechanisms in colorectal cancer cells. Some of them can strengthen chemo-resistance and others weaken chemo-resistance. IL-6, TNFα, IFN, IL1, IL8, IL-17, IL-10, and IL-22 are among the most important cytokines whose effects on chemo-resistance mechanisms in colorectal cancer cells are known. In this article, we will have a comprehensive overview of the effects of these cytokines on chemo-resistance mechanisms in colorectal cancer cells.

Ryan Erik Landvater, Ulysses Balis

Digital pathology is a tool of rapidly evolving importance within the discipline of pathology. Whole slide imaging promises numerous advantages; however, adoption is limited by challenges in ease of use and speed of high-quality image rendering relative to the simplicity and visual quality of glass slides. We introduce Iris, a new high-performance digital pathology rendering system. Specifically, we outline and detail the performance metrics of Iris Core, the core rendering engine technology. Iris Core comprises machine code modules written from the ground up in C++ and using Vulkan, a low-level and low-overhead cross-platform graphical processing unit application program interface, and our novel rapid tile buffering algorithms. We provide a detailed explanation of Iris Core's system architecture, including the stateless isolation of core processes, interprocess communication paradigms, and explicit synchronization paradigms that provide powerful control over the graphical processing unit. Iris Core achieves slide rendering at the sustained maximum frame rate on all tested platforms and buffers an entire new slide field of, view without overlapping pixels, in 10 ms with enhanced detail in 30 ms. It is able to buffer and compute high-fidelity reduction-enhancements for viewing low-power cytology with increased visual quality at a rate of 100-160 us per slide tile, and with a cumulative median buffering rate of 1.36 GB of decompressed image data per second. This buffering rate allows for an entirely new field of view to be fully buffered and rendered in less than a single monitor refresh on a standard display, and high detail features within 2-3 monitor refresh frames. These metrics far exceed previously published specifications, beyond an order of magnitude in some contexts. The system shows no slowing with high use loads, but rather increases performance due to cache mechanisms.

Mert Ikinci, Luna Toma, Karin U. Loeffler et al.

Accurate grading of Conjunctival Melanocytic Intraepithelial Lesions (CMIL) is essential for treatment and melanoma prediction but remains difficult due to subtle morphological cues and interrelated diagnostic criteria. We introduce INTERACT-CMIL, a multi-head deep learning framework that jointly predicts five histopathological axes; WHO4, WHO5, horizontal spread, vertical spread, and cytologic atypia, through Shared Feature Learning with Combinatorial Partial Supervision and an Inter-Dependence Loss enforcing cross-task consistency. Trained and evaluated on a newly curated, multi-center dataset of 486 expert-annotated conjunctival biopsy patches from three university hospitals, INTERACT-CMIL achieves consistent improvements over CNN and foundation-model (FM) baselines, with relative macro F1 gains up to 55.1% (WHO4) and 25.0% (vertical spread). The framework provides coherent, interpretable multi-criteria predictions aligned with expert grading, offering a reproducible computational benchmark for CMIL diagnosis and a step toward standardized digital ocular pathology.

Vittal L. Rao

Localisation tasks in biomedical data often require models to learn meaningful spatial or temporal relationships from signals with complex intensity distributions. A common strategy, exemplified by CoordConv layers, is to append coordinate channels to convolutional inputs, enabling networks to learn absolute positions. In this work, we propose a signal intensity-weighted coordinate representation that replaces the pure coordinate channels with channels scaled by local signal intensity. This modification embeds an intensity-position coupling directly in the input representation, introducing a simple and modality-agnostic inductive bias. We evaluate the approach on two distinct localisation problems: (i) predicting the time of morphological transition in 20-second, two-lead ECG signals, and (ii) regressing the coordinates of nuclear centres in cytological images from the SiPaKMeD dataset. In both cases, the proposed representation yields faster convergence and higher generalisation performance relative to conventional coordinate-channel approaches, demonstrating its effectiveness across both one-dimensional and two-dimensional biomedical signals.

Karosham Diren Reddy, Dikaia Xenaki, Ian M. Adcock et al.

<b>Background:</b> Chronic obstructive pulmonary disease (COPD) is characterized by progressive and incurable airflow obstruction and chronic inflammation. Both TGF-β1 and CXCL8 have been well described as fundamental to COPD progression. DNA methylation and histone acetylation, which are well-understood epigenetic mechanisms regulating gene expression, are associated with COPD progression. However, a deeper understanding of the complex mechanisms associated with DNA methylation, histone post-translational changes and RNA methylation in the context of regulatory pathways remains to be elucidated. We here report on how DNA methylation and histone acetylation inhibition differentially affect CXCL8 signaling in primary human non-COPD and COPD airway cells. <b>Methods:</b> Airway smooth muscle (ASM) cells, a pivotal cell type in COPD, were isolated from the small airways of heavy smokers with and without COPD. Histone acetylation and DNA methylation were inhibited before the TGF-β1 stimulation of cells. Subsequently, CXCL8 production and the abundance and activation of pertinent transcription regulatory proteins (NF-κB, p38 MAPK and JNK) were analyzed. <b>Results:</b> TGF-β1-stimulated CXCL8 release from ASM cells from ‘healthy’ smoker subjects was significantly modulated by DNA methylation (56.32 pg/mL and 56.60 pg/mL) and acetylation inhibitors (27.50 pg/mL and 48.85 pg/mL) at 24 and 48 h, respectively. However, modulation via the inhibition of DNA methylation (34.06 pg/mL and 43.18 pg/mL) and acetylation (23.14 pg/mL and 27.18 pg/mL) was observed to a lesser extent in COPD ASM cells. These changes were associated with differences in the TGF-β1 activation of NF-κB and MAPK pathways at 10 and 20 min. <b>Conclusions:</b> Our findings offer insight into differential epigenetics in controlling COPD ASM cells and provide a foundation warranting future studies on epigenetic differences associated with COPD diagnosis. This would provide a scope for developing therapeutic interventions targeting signaling and epigenetic pathways to improve patient outcomes.

Mikyla K. Bowen, Jesse W. Wilson

Purpose: A major barrier to the implementation of artificial intelligence for medical applications is the lack of explainability and high confidence for incorrect decisions, specifically with out-of-domain samples. We propose a generalization of image translation networks for image classification and demonstrate their potential as a more interpretable alternative to conventional black-box classifiers. Approach: We train an image2image network to translate an input image to class-specific hypotheticals, and then compare these with the input, both visually and quantitatively. Translation distances, i.e., the degree of alteration needed to conform to one class or another, are examined for clusters and trends, and used as simple low-dimensional feature vectors for classification. Results: On melanoma/benign dermoscopy images, a translation distance classifier achieved 80% accuracy using only a 2-dimensional feature space (versus 85% for a conventional CNN using a ~62,000-dimensional feature space). Visual inspection of rendered images revealed dataset biases, such as scalebars, vignetting, and pale background pigmentation in melanomas. Image distributions in translation distance space revealed a natural separation along the lines of dermatologist decision to biopsy, rather than between malignant and benign. On bone marrow cytology images, translation distance classifiers outperformed a conventional CNN in both 3-class (92% accuracy vs 89% for CNN) and 6-class (90% vs 86% for CNN) scenarios. Conclusions: This proof-of-concept shows the potential for image2image networks to go beyond artistic/stylistic changes and to expose dataset biases, perform dimension reduction and dataset visualization, and in some cases, potentially outperform conventional end-to-end CNN classifiers.

Seyeon Oh, Do-Young Rhee, Sosorburam Batsukh et al.

Caveolin-1 (Cav-1) induces cellular senescence by reducing extracellular signal-regulated kinase (ERK)1/2 phosphorylation and activating p53 via inhibition of mouse double minute 2 homolog (MDM2) and sirtuin 1 (Sirt1), promoting cell cycle arrest and decreasing fibroblast proliferation and collagen synthesis. High-intensity focused ultrasound (HIFU) treatment increases collagen synthesis, rejuvenating skin. Using H₂O₂-induced senescent fibroblasts and the skin of 12-month-old mice, we tested the hypothesis that HIFU increases collagen production through Cav-1 modulation. HIFU was administered at 0.3, 0.5, or 0.7 J in the LINEAR and DOT modes. In both models, HIFU administration decreased Cav-1 levels, increased ERK1/2 phosphorylation, and decreased the binding of Cav-1 with both MDM2 and Sirt1. HIFU administration decreased p53 activation (acetylated p53) and p21 levels and increased cyclin D1, cyclin-dependent kinase 2, and proliferating cell nuclear antigen levels in both models. HIFU treatment increased collagen and elastin expression, collagen fiber accumulation, and elastin fiber density in aging skin, with 0.5 J in LINEAR mode resulting in the most prominent effects. HIFU treatment increased collagen synthesis to levels similar to those in Cav-1-silenced senescent fibroblasts. Our results suggest that HIFU administration increases dermal collagen and elastin fibers in aging skin via Cav-1 modulation and reduced p53 activity.

Leonora Szabo, Amandine Grimm, Juan Antonio García-León et al.

Pathological abnormalities in the tau protein give rise to a variety of neurodegenerative diseases, conjointly termed tauopathies. Several tau mutations have been identified in the tau-encoding gene <i>MAPT</i>, affecting either the physical properties of tau or resulting in altered tau splicing. At early disease stages, mitochondrial dysfunction was highlighted with mutant tau compromising almost every aspect of mitochondrial function. Additionally, mitochondria have emerged as fundamental regulators of stem cell function. Here, we show that compared to the isogenic wild-type triple <i>MAPT</i>-mutant human-induced pluripotent stem cells, bearing the pathogenic N279K, P301L, and E10+16 mutations, exhibit deficits in mitochondrial bioenergetics and present altered parameters linked to the metabolic regulation of mitochondria. Moreover, we demonstrate that the triple tau mutations disturb the cellular redox homeostasis and modify the mitochondrial network morphology and distribution. This study provides the first characterization of disease-associated tau-mediated mitochondrial impairments in an advanced human cellular tau pathology model at early disease stages, ranging from mitochondrial bioenergetics to dynamics. Consequently, comprehending better the influence of dysfunctional mitochondria on the development and differentiation of stem cells and their contribution to disease progression may thus assist in the potential prevention and treatment of tau-related neurodegeneration.

Yu Ando, Nora Jee-Young Park and, Gun Oh Chong et al.

Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.

Stefan Röhrl, Johannes Groll, Manuel Lengl et al.

Label-free approaches are attractive in cytological imaging due to their flexibility and cost efficiency. They are supported by machine learning methods, which, despite the lack of labeling and the associated lower contrast, can classify cells with high accuracy where the human observer has little chance to discriminate cells. In order to better integrate these workflows into the clinical decision making process, this work investigates the calibration of confidence estimation for the automated classification of leukocytes. In addition, different visual explanation approaches are compared, which should bring machine decision making closer to professional healthcare applications. Furthermore, we were able to identify general detection patterns in neural networks and demonstrate the utility of the presented approaches in different scenarios of blood cell analysis.

M. Arbyn, C. Bergeron, P. Klinkhamer et al.

Olena Maiakovska, Conradin Baumgartl, Dirk Grimm

Lucija Kovacevic, Mislav Cavka, Zlatko Marusic et al.

We aimed to determine diagnostic accuracy of CT-guided bone lesion biopsy for the confirmation of bone metastases in patients with breast cancer and assessment of hormone receptor status in metastatic tissue. A total of 56 female patients with breast cancer that underwent CT-guided biopsy of suspected bone metastasis were enrolled in this retrospective study. Three different techniques were employed to obtain samples from various sites of skeleton. Collectively, 11 true negative and 3 false negative findings were revealed. The sensitivity of CT-guided biopsy for diagnosing bone metastases was 93.6%, specificity was 100% and accuracy was 94.8%. Discordance in progesterone receptor status and complete concordance in estrogen receptor status was observed. Based on our single-center experience, bone metastasis biopsy should be routinely performed in patients with breast cancer and suspicious bone lesions, due to the impact on further treatment.

Nadezhda Koriakina, Nataša Sladoje, Vladimir Bašić et al.

The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample from the oral cavity. This process is time consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Skilled cytotechnologists are able to detect changes due to malignancy, however, to introduce this approach into clinical routine is associated with challenges such as a lack of experts and labour-intensive work. To design a trustworthy OC detection system that would assist cytotechnologists, we are interested in AI-based methods that reliably can detect cancer given only per-patient labels (minimizing annotation bias), and also provide information on which cells are most relevant for the diagnosis (enabling supervision and understanding). We, therefore, perform a comparison of a conventional single instance learning (SIL) approach and a modern multiple instance learning (MIL) method suitable for OC detection and interpretation, utilizing three different neural network architectures. To facilitate systematic evaluation of the considered approaches, we introduce a synthetic PAP-QMNIST dataset, that serves as a model of OC data, while offering access to per-instance ground truth. Our study indicates that on PAP-QMNIST, the SIL performs better, on average, than the MIL approach. Performance at the bag level on real-world cytological data is similar for both methods, yet the single instance approach performs better on average. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source at https://github.com/MIDA-group/OralCancerMILvsSIL

Muhammad Dawood, Raja Muhammad Saad Bashir, Srijay Deshpande et al.

Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computational pathology these tasks are referred to as nuclear segmentation, classification and composition and are used to extract meaningful interpretable cytological and architectural features for downstream analysis. The CoNIC challenge poses the task of automated nuclei segmentation, classification and composition into six different types of nuclei from the largest publicly known nuclei dataset - Lizard. In this regard, we have developed pipelines for the prediction of nuclei segmentation using HoVer-Net and ALBRT for cellular composition. On testing on the preliminary test set, HoVer-Net achieved a PQ of 0.58, a PQ+ of 0.58 and finally a mPQ+ of 0.35. For the prediction of cellular composition with ALBRT on the preliminary test set, we achieved an overall $R^2$ score of 0.53, consisting of 0.84 for lymphocytes, 0.70 for epithelial cells, 0.70 for plasma and .060 for eosinophils.

G. Ellison, G. Zhu, Alexandros Moulis et al.

Aims Activating mutations in the gene encoding epidermal growth factor receptor (EGFR) can confer sensitivity to EGFR tyrosine kinase inhibitors such as gefitinib in patients with advanced non-small-cell lung cancer. Testing for mutations in EGFR is therefore an important step in the treatment-decision pathway. We reviewed reported methods for EGFR mutation testing in patients with lung cancer, initially focusing on studies involving standard tumour tissue samples. We also evaluated data on the use of cytology samples in order to determine their suitability for EGFR mutation analysis. Methods We searched the MEDLINE database for studies reporting on EGFR mutation testing methods in patients with lung cancer. Results Various methods have been investigated as potential alternatives to the historical standard for EGFR mutation testing, direct DNA sequencing. Many of these are targeted methods that specifically detect the most common EGFR mutations. The development of targeted mutation testing methods and commercially available test kits has enabled sensitive, rapid and robust analysis of clinical samples. The use of screening methods, subsequent to sample micro dissection, has also ensured that identification of more rare, uncommon mutations is now feasible. Cytology samples including fine needle aspirate and pleural effusion can be used successfully to determine EGFR mutation status provided that sensitive testing methods are employed. Conclusions Several different testing methods offer a more sensitive alternative to direct sequencing for the detection of common EGFR mutations. Evidence published to date suggests cytology samples are viable alternatives for mutation testing when tumour tissue samples are not available.