Hasil untuk "Cytology"

W. Penfield

W. Bernhard

D. Harriman

Francesca Barberini, Riccardo Pietroni, Simone Ielpo et al.

Philip Groult, Julia D. Sistermanns, Ellen Emken et al.

The last decade has seen significant advances in computer-aided diagnostics for cytological screening, mainly through the improvement and integration of scanning techniques such as whole slide imaging (WSI) and the combination with deep learning. Simultaneously, new imaging techniques such as quantitative phase imaging (QPI) are being developed to capture richer cell information with less sample preparation. So far, the two worlds of WSI and QPI have not been combined. In this work, we present a reconstruction algorithm which makes whole slide imaging of cervical smears possible by using a self-referencing three-wave digital holographic microscope. Since a WSI is constructed by combining multiple patches, the algorithm is adaptive and can be used on partial holograms and patched holograms. We present the algorithm for a single shot hologram, the adaptations to make it flexible to various inputs and show that the algorithm performs well for the tested epithelial cells. This is a preprint of our paper, which has been accepted for publication in 2026 IEEE International Symposium on Biomedical Imaging (ISBI).

Ahmed Rahu, Brian Shula, Brandon Combs et al.

Colorectal cancer (CRC) remains a significant cause of cancer-related mortality, despite the widespread implementation of prophylactic initiatives aimed at detecting and removing precancerous polyps. Although screening effectively reduces incidence, a notable portion of patients initially diagnosed with low-grade adenomatous polyps will still develop CRC later in life, even without the presence of known high-risk syndromes. Identifying which low-risk patients are at higher risk of progression is a critical unmet need for tailored surveillance and preventative therapeutic strategies. Traditional histological assessment of adenomas, while fundamental, may not fully capture subtle architectural or cytological features indicative of malignant potential. Advancements in digital pathology and machine learning provide an opportunity to analyze whole-slide images (WSIs) comprehensively and objectively. This study investigates whether machine learning algorithms, specifically convolutional neural networks (CNNs), can detect subtle histological features in WSIs of low-grade tubular adenomas that are predictive of a patient's long-term risk of developing colorectal cancer.

Xinyue Gao, Feichang Liu, Bo Zhang et al.

Abstract Cell division cycle 25 (CDC25) phosphatases serve as crucial regulators of cell cycle phase transitions and essential components of the checkpoint machinery involved in DNA damage response. Emerging evidence indicates the oncogenic potential of CDC25 family members across various cancers. However, comprehensive insights into the expression pattern and function of the CDC25 family in diverse cancers remain unexplored. In our study, we investigated CDC25 family using multiple databases, including gene expression levels, molecular signatures, diagnosis value, and prognostic value in pan-cancer. Furthermore, we focused on melanoma and systematically explored CDC25A expression and its clinical correlations. As a result, the expression of CDC25 family members is significantly abnormal in most cancers, correlating with poorer prognosis. CDC25 family members are differently regulated by DNA methylation and genetic alterations across various cancers. In addition, CDC25 family plays a critical role in the malignant progression of melanoma. Functional investigation reveals that CDC25A inhibition suppresses the proliferation of melanoma cells and sensitizes melanoma cells to chemotherapy and NK cell therapy. In conclusion, our study suggests that CDC25 family may serve as a significant biomarker for diagnosis and prognosis across multiple cancers, with CDC25A as a promising therapeutic target for melanoma.

Decheng Jiang, Ruiling Xiao, Jialu Bai et al.

Abstract The advent of the omics era has facilitated the identification of precise biomarkers for cancer progression, revealing a broader diversity of macrophage phenotypes beyond the traditional M1/M2 classification. Folate receptor 2 (FOLR2)-positive macrophages, co-expressing markers such as mannose receptor C-Type 1 (MRC1/CD206) and lymphatic vessel endothelial hyaluronan receptor 1(LYVE1), are an embryonically derived subset typically found around blood vessels in both tumor stroma and normal tissues. Despite FOLR2’s longstanding association with anti-inflammatory, immunosuppressive macrophages in tumors, its precise role in cancer progression remains unclear. Recent studies suggest that FOLR2+ macrophages can either promote or inhibit cancer progression, depending on their multifaceted roles in the tumor microenvironment. This review provides a comprehensive overview of the biological features, functional roles, molecular mechanisms, and therapeutic potential of FOLR2+ macrophages in cancer.

Jincheng Li, Danyang Dong, Menglin Zheng et al.

Automatic detection of abnormal cervical cells from Thinprep Cytologic Test (TCT) images is a critical component in the development of intelligent computer-aided diagnostic systems. However, existing algorithms typically fail to effectively model the correlations of visual features, while these spatial correlation features actually contain critical diagnostic information. Furthermore, no detection algorithm has the ability to integrate inter-correlation features of cells with intra-discriminative features of cells, lacking a fusion strategy for the end-to-end detection model. In this work, we propose a hypergraph-based cell detection network that effectively fuses different types of features, combining spatial correlation features and deep discriminative features. Specifically, we use a Multi-level Fusion Sub-network (MLF-SNet) to enhance feature extractioncapabilities. Then we introduce a Cross-level Feature Fusion Strategy with Hypergraph Computation module (CLFFS-HC), to integrate mixed features. Finally, we conducted experiments on three publicly available datasets, and the results demonstrate that our method significantly improves the performance of cervical abnormal cell detection.

Aleksandra Weronika Nielsen, Hafez Eslami Manoochehri, Hua Zhong et al.

The ability of tumors to evolve and adapt by developing subclones in different genetic and epigenetic states is a major challenge in oncology. Traditional tools like multi-regional sequencing used to study tumor evolution and the resultant intra-tumor heterogeneity (ITH) are often impractical because of their resource-intensiveness and limited scalability. Here, we present MorphoITH, a novel framework that leverages histopathology slides to deconvolve molecular ITH through tissue morphology. MorphoITH integrates a self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) with rigorous methods to eliminate spurious sources of variation. Using a prototype of ITH, clear cell renal cell carcinoma (ccRCC), we show that MorphoITH captures clinically-significant biological features, such as vascular architecture and nuclear grades. Furthermore, we find that MorphoITH recognizes differential biological states corresponding to subclonal changes in key driver genes (BAP1/PBRM1/SETD2). Finally, by applying MorphoITH to a multi-regional sequencing experiment, we postulate evolutionary trajectories that largely recapitulate genetic evolution. In summary, MorphoITH provides a scalable phenotypic lens that bridges the gap between histopathology and genomics, advancing precision oncology.

Lanlan Kang, Jian Wang, Jian QIn et al.

The ThinPrep Cytologic Test (TCT) is the most widely used method for cervical cancer screening, and the sample quality directly impacts the accuracy of the diagnosis. Traditional manual evaluation methods rely on the observation of pathologist under microscopes. These methods exhibit high subjectivity, high cost, long duration, and low reliability. With the development of computer-aided diagnosis (CAD), an automated quality assessment system that performs at the level of a professional pathologist is necessary. To address this need, we propose a fully automated quality assessment method for Cervical Cytopathology Whole Slide Images (WSIs) based on The Bethesda System (TBS) diagnostic standards, artificial intelligence algorithms, and the characteristics of clinical data. The method analysis the context of WSIs to quantify quality evaluation metrics which are focused by TBS such as staining quality, cell counts and cell mass proportion through multiple models including object detection, classification and segmentation. Subsequently, the XGBoost model is used to mine the attention paid by pathologists to different quality evaluation metrics when evaluating samples, thereby obtaining a comprehensive WSI sample score calculation model. Experimental results on 100 WSIs demonstrate that the proposed evaluation method has significant advantages in terms of speed and consistency.

Nanxin Gong, Saori Takeyama, Masahiro Yamaguchi et al.

The Papanicolaou stain, consisting of five dyes, provides extensive color information essential for cervical cancer cytological screening. The visual observation of these colors is subjective and difficult to characterize. Direct RGB quantification is unreliable because RGB intensities vary with staining and imaging conditions. Stain unmixing offers a promising alternative by quantifying dye amounts. In previous work, multispectral imaging was utilized to estimate the dye amounts of Papanicolaou stain. However, its application to RGB images presents a challenge since the number of dyes exceeds the three RGB channels. This paper proposes a novel training-free Papanicolaou stain unmixing method for RGB images. This model enforces (i) nonnegativity, (ii) weighted nucleus sparsity for hematoxylin, and (iii) total variation smoothness, resulting in a convex optimization problem. Our method achieved excellent performance in stain quantification when validated against the results of multispectral imaging. We further used it to distinguish cells in lobular endocervical glandular hyperplasia (LEGH), a precancerous gastric-type adenocarcinoma lesion, from normal endocervical cells. Stain abundance features clearly separated the two groups, and a classifier based on stain abundance achieved 98.0% accuracy. By converting subjective color impressions into numerical markers, this technique highlights the strong promise of RGB-based stain unmixing for quantitative diagnosis.

Maryam Alsadat Hashemipour, Parsa Behnam, Fatemeh Ghasemzadeh

Background: The present work deals with examine the awareness and attitudes of specialists of oral diseases, pathology and maxillofacial surgery in 2022 in oral exfoliative cytology.Methods: This study was an analytical and cross-sectional research. The statistical population of the present study was Iranian oral diseases specialists, pathologists and maxillofacial surgeons. A researcher-made questionnaire was given to the specialists by senior student and then they were asked to complete it . The questionnaire was given to people by the final year student. The obtained results were analyzed using the T-test, Chi-Square and SPSS-18 Software. The significance level in data analysis was considered by P <0.05 level.Results: A total of 210 questionnaires were distributed in the study, of which 192 were examined (Response Rate = 91.4%). The study revealed that only 18 participants used exfoliative cytology technique. Moreover, 62% of people had a positive attitude towards the application and performance of cytology. There was a significant relationship between the positive attitude, field of study, graduation year and age. The mean scores of awareness in men and women were 32.38 ± 4.21 and 34.42 ± 3.89 , respectively. The participants had a high level of awareness and attitude towards this technique.Additionally, the mean score of awareness in oral specialists, surgeons and pathologists was 33.12±4.23, 33.65±5.12 and 33.45±5.34, respectively. The study revealed no significant relationship between awareness score, field of study and gender. Nonetheless, there was a significant relationship between graduation year, age and awareness score.Conclusion: The study revealed that only 9.4 percent of participants used exfoliative cytology technique. However, they had a high level of awareness and attitude towards this technique.Keywords: Awareness, Attitude, Exfoliative Cytology, Dentistry

Jan Lötvall

Shreeram Athreya, Andrew Melehy, Sujit Silas Armstrong Suthahar et al.

Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positive predictive value (PPV), only using molecular profiles, ignoring ultrasound (US) imaging and biopsy. We address this limitation by applying attention multiple instance learning (AMIL) to US images. Methods: We retrospectively reviewed 333 patients with indeterminate thyroid nodules at UCLA medical center (259 benign, 74 malignant). A multi-modal deep learning AMIL model was developed, combining US images and MT to classify the nodules as benign or malignant and enhance the malignancy risk stratification of MT. Results: The final AMIL model matched MT sensitivity (0.946) while significantly improving PPV (0.477 vs 0.448 for MT alone), indicating fewer false positives while maintaining high sensitivity. Conclusion: Our approach reduces false positives compared to MT while maintaining the same ability to identify positive cases, potentially reducing unnecessary benign thyroid resections in patients with indeterminate nodules.

Hyam Omar Ali, Romain Abraham, Guillaume Desoubeaux et al.

Mycetoma is a chronic and neglected inflammatory disease prevalent in tropical and subtropical regions. It can lead to severe disability and social stigma. The disease is classified into two types based on the causative microorganisms: eumycetoma (fungal) and actinomycetoma (bacterial). Effective treatment strategies depend on accurately identifying the causative agents. Current identification methods include molecular, cytological, and histopathological techniques, as well as grain culturing. Among these, histopathological techniques are considered optimal for use in endemic areas, but they require expert pathologists for accurate identification, which can be challenging in rural areas lacking such expertise. The advent of digital pathology and automated image analysis algorithms offers a potential solution. This report introduces a novel dataset designed for the automated detection and classification of mycetoma using histopathological images. It includes the first database of microscopic images of mycetoma tissue, detailing the entire pipeline from species distribution and patient sampling to acquisition protocols through histological procedures. The dataset consists of images from 142 patients, totalling 864 images, each annotated with binary masks indicating the presence of grains, facilitating both detection and segmentation tasks.

Thamara Nishida Xavier da Silva, Clemens Schulte, Ariane Nunes Alves et al.

Abstract Ferroptosis is a form of cell death characterized by phospholipid peroxidation, where numerous studies have suggested that the induction of ferroptosis is a therapeutic strategy to target therapy refractory cancer entities. Ferroptosis suppressor protein 1 (FSP1), an NAD(P)H-ubiquinone reductase, is a key determinant of ferroptosis vulnerability, and its pharmacological inhibition was shown to strongly sensitize cancer cells to ferroptosis. A first generation of FSP1 inhibitors, exemplified by the small molecule iFSP1, has been reported; however, the molecular mechanisms underlying inhibition have not been characterized in detail. In this study, we explore the species-specific inhibition of iFSP1 on the human isoform to gain insights into its mechanism of action. Using a combination of cellular, biochemical, and computational methods, we establish a critical contribution of a species-specific aromatic architecture that is essential for target engagement. The results described here provide valuable insights for the rational development of second-generation FSP1 inhibitors combined with a tracer for screening the druggable pocket. In addition, we pose a cautionary notice for using iFSP1 in animal models, specifically murine models.

Li Li, Tao Xing, Yiran Chen et al.

Abstract Interferon-gamma (IFN-γ) exerts anti-tumor effects by inducing ferroptosis. Based on CRISPR/Cas9 knockout screening targeting genome-wide protein encoding genes in HepG2 and SK-Hep-1 cell lines, we found that cAMP response element-binding protein (CREB) regulated transcription coactivator 3 (CRTC3) protects tumor cells from drug-induced ferroptosis and significantly inhibits the efficacy of IFN-γ treatment in hepatocellular carcinoma (HCC). Mechanistically, CRTC3 knockout altered tumor cell lipid patterns and increased the abundance of polyunsaturated fatty acids (PUFAs), which enables lipid peroxidation and enhances the susceptibility of HCC cells to ferroptosis inducers. To scavenge for accumulated lipid peroxides (LPO) and maintain redox equilibrium, HCC cells up-regulate SLC7A11 and glutathione peroxidase 4 (GPx4) expressions to enhance the activities of glutamate-cystine antiporter (system xc−) and LPO clearance. As IFN-γ inhibiting system xc−, simultaneous treatment with IFN-γ disrupts the compensatory mechanism, and generates a synergistic effect with CRTC3 knockout to facilitate ferroptosis. Sensitizing effects of CRTC3 depletion were confirmed using typical ferroptosis inducers, including RSL3 and erastin. Sorafeinib, a commonly used target drug in HCC, was repeatedly reported as a ferroptosis inducer. We then conducted both in vitro and vivo experiments and demonstrated that CRTC3 depletion sensitized HCC cells to sorafenib treatment. In conclusion, CRTC3 is involved in the regulation of PUFAs metabolism and ferroptosis. Targeting CRTC3 signaling in combination with ferroptosis inducers present a viable approach for HCC treatment and overcoming drug resistance.

Mohammad Nazmul Hasan, Jianglei Chen, Huaiwen Wang et al.

<i>Cyp2c70</i> knockout mice lack the enzyme that produces muricholic acids and show a “human-like” hydrophobic bile acid pool-induced hepatobiliary injury. In this study, we investigated the potential anti-cholestasis effect of glycine-conjugated β muricholic acid (G-β-MCA) in male <i>Cyp2c70</i> KO mice based on its hydrophilic physiochemical property and signaling property as an farnesoid X receptor (FXR) antagonist. Our results showed that G-β-MCA treatment for 5 weeks alleviated ductular reaction and liver fibrosis and improved gut barrier function. Analysis of bile acid metabolism suggested that exogenously administered G-β-MCA was poorly absorbed in the small intestine and mostly deconjugated in the large intestine and converted to taurine-conjugated MCA (T-MCA) in the liver, leading to T-MCA enrichment in the bile and small intestine. These changes decreased the biliary and intestine bile acid hydrophobicity index. Furthermore, G-β-MCA treatment decreased intestine bile acid absorption via unknown mechanisms, resulting in increased fecal bile acid excretion and a reduction in total bile acid pool size. In conclusion, G-β-MCA treatment reduces the bile acid pool size and hydrophobicity and improves liver fibrosis and gut barrier function in <i>Cyp2c70</i> KO mice.

Hiroshi Ueda

Prothymosin alpha (ProTα) was discovered to be a necrosis inhibitor from the conditioned medium of a primary culture of rat cortical neurons under starved conditions. This protein carries out a neuronal cell-death-mode switch from necrosis to apoptosis, which is, in turn, suppressed by a variety of neurotrophic factors (NTFs). This type of NTF-assisted survival action of ProTα is reproduced in cerebral and retinal ischemia–reperfusion models. Further studies that used a retinal ischemia–reperfusion model revealed that ProTα protects retinal cells via ecto-F₁ ATPase coupled with the G_i-coupled P2Y₁₂ receptor and Toll-like receptor 4 (TLR4)/MD2 coupled with a Toll–IL-1 receptor domain-containing adaptor inducing IFN-β (TRIF). In cerebral ischemia–reperfusion models, ProTα has additional survival mechanisms via an inhibition of matrix metalloproteases in microglia and vascular endothelial cells. Heterozygous or conditional ProTα knockout mice show phenotypes of anxiety, memory learning impairment, and a loss of neurogenesis. There are many reports that ProTα has multiple intracellular functions for cell survival and proliferation through a variety of protein–protein interactions. Overall, it is suggested that ProTα plays a key role as a brain guardian against ischemia stress through a cell-death-mode switch assisted by NTFs and a role of neurogenesis.