Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam’s decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from tissue taken from biopsy. This is a time-consuming process. A reliable decision support system would assist healthcare systems that often suffer from a shortage of pathologists. Recent advances in digital pathology allow for high-resolution digitalization of pathological slides. Digital slide scanners combined with modern computer vision models, such as convolutional neural networks, can help pathologists in their everyday work, resulting in shortened diagnosis times. In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. Alongside the whole-slide images, detailed region level annotations are also provided for ten relevant pathological classes. The 200 digital slides, after pre-processing, resulted in 101,389 patches. A single patch is a 512 × 512 pixel image, covering 248 × 248 μm² tissue area. Versions at higher resolution are available as well. Hopefully, HunCRC, this widely accessible dataset will aid future colorectal cancer computer-aided diagnosis and research.

From March through December 2020, 100 autopsies were performed (Semmelweis University, Budapest, Hungary), with chart review, of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection demonstrated by real-time reverse-transcription polymerase chain reaction testing (mean age, 74.73 years, range 40–102 years; 50 males, mean age 71.96 years, and 50 females, mean age 77.5 years). Classified by the date of death, 21 cases were from the pandemic’s “first wave” (March through July) and 79 from the “second wave” (August through December). Three mortality categories were defined by relevance of SARS-CoV-2 infection: (1) “strong” association (n=57), in which COVID-19 was primary responsible for death; (2) “contributive” association (n=27), in which a pre-existing condition independent of COVID-19 was primary responsible for death, albeit with substantial COVID-19 co-morbidity; (3) “weak” association (n=16), in which COVID-19 was minimally or not at all responsible for death. Distributions among categories differed between the first wave, in which the “contributive” association cases dominated (strong: 24%, contributive: 48%, weak: 28%), and the second wave, in which the “strong” association cases dominated (strong: 66%, contributive: 21%, weak: 13%). Charted co-morbidities included hypertension (85 %), cardiovascular diseases (71 %), diabetes (40 %), cerebrovascular diseases (31 %), chronic respiratory diseases (30 %), malignant tumors (20 %), renal diseases (19 %), diseases of the central nervous system (15 %), and liver diseases (6 %). Autopsy evaluation analyzed alterations on macroscopy as well as findings on microscopy of scanned and scored sections of formalin-fixed, paraffin-embedded tissue samples (50–80 blocks/case). Severity of histological abnormalities in the lung differed significantly between “strong” and “contributive” (p<0.0001) and between “strong” and “weak” categories (p<0.0001). Abnormalities included diffuse alveolar damage, macrophage infiltration, and vascular and alveolar fibrin aggregates (lung), with macro- and microvascular thrombi and thromboemboli (lung, kidney, liver). In conclusion, autopsies clarified in what extent COVID-19 was responsible for death, demonstrated the pathological background of clinical signs and symptoms, and identified organ alterations that led to the death. Clinicopathologic correlation, with conference discussions of severity of co-morbidities and of direct pathological signs of disease, permitted accurate categorization of cause of death and COVID-19 association as “strong,” “contributive,” or “weak.” Lung involvement, with reduced ventilatory capacity, was the primary cause of death in the “strong” and “contributive” categories. Shifts in distribution among categories, with “strong” association between COVID-19 and death dominating in the second wave, may reflect improved clinical management of COVID-19 as expertise grew.

The most severe alterations in Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) infection are seen in the lung. However, other organs also are affected. Here, we report histopathologic findings in the liver and detection of viral proteins and RNA in COVID-19 autopsies performed at the Semmelweis University (Budapest, Hungary). Between March 2020 through March 2022, 150 autopsies on patients who died of COVID-19 were analyzed. Cause-of-death categories were formed based on the association with SARS-CoV-2 as strong, contributive, or weak. Samples for histopathologic study were obtained from all organs, fixed in formalin, and embedded in paraffin (FFPE). Immunohistochemical study (IHC) to detect SARS-CoV-2 spike protein and nucleocapsid protein (NP), CD31, claudin-5, factor VIII, macrosialin (CD68), and cytokeratin 7, with reverse transcriptase polymerase chain reaction (RT-PCR), and in situ hybridization (ISH, RNAscope®) for SARS-CoV-2 RNA were conducted using FFPE samples of livers taken from 20 autopsies performed ≤ 2 days postmortem. All glass slides were scanned; the digital images were evaluated by semiquantitative scoring and scores were analyzed statistically. Steatosis, single-cell and focal/zonal hepatocyte necrosis, portal fibrosis, and chronic inflammation were found in varying percentages. Sinusoidal ectasia, endothelial cell disruption, and fibrin-filled sinusoids were seen in all cases; these were assessed semiquantitatively for severity (SEF scored). SEF scores did not correlate with cause-of-death categories (p = 0.92) or with severity of lung alterations (p = 0.96). SARS-CoV-2 RNA was detected in 13/20 cases by PCR and in 9/20 by ISH, with IHC demonstration of spike protein in 4/20 cases and NP in 15/20. Viral RNA and proteins were located in endothelial and Kupffer cells, and in portal macrophages, but not in hepatocytes and cholangiocytes. In conclusion, endothelial damage (SEF scores) was the most common alteration in the liver and was a characteristic, but not specific alteration in COVID-19, suggesting an important role in the pathogenesis of COVID-19-associated liver disease. Detection of SARS-CoV-2 RNA and viral proteins in liver non-parenchymal cells suggests that while the most extended primary viral cytotoxic effect occurs in the lung, viral components are present in other organs too, as in the liver. The necrosis/apoptosis and endothelial damage associated with viral infection in COVID-19 suggest that those patients who survive more severe COVID-19 may face prolonged liver repair and accordingly should be followed regularly in the post-COVID period.

Malignancies are still responsible for a large share of lethalities. Macroscopical evaluation of the surgical resection margins is uncertain. Big data based imaging approaches have emerged in the recent decade (mass spectrometry, two-photon microscopy, infrared and Raman spectroscopy). Indocianine green labelled MS is the most common approach, however, label free mid-infrared imaging is more promising for future practical application. We aimed to identify and separate different transformed (A-375, HT-29) and non-transformed (CCD986SK) cell lines by a label-free infrared spectroscopy method. Our approach applied a novel set-up for label-free mid-infrared range classification method. Transflection spectroscopy was used on aluminium coated glass slides. Both whole range spectra (4000–648 cm⁻¹) and hypersensitive fingerprint regions (1800–648 cm⁻¹) were tested on the imaged areas of cell lines fixed in ethanol. Non-cell spectra were possible to be excluded based on mean transmission values being above 90%. Feasibility of a mean transmission based spectra filtering method with principal component analysis and linear discriminant analysis was shown to separate cell lines representing different tissue types. Fingerprint region resulted the best separation of cell lines spectra with accuracy of 99.84% at 70–75 mean transmittance range. Our approach in vitro was able to separate unique cell lines representing different tissues of origin. Proper data handling and spectra processing are key steps to achieve the adaptation of this dye-free technique for intraoperative surgery. Further studies are urgently needed to test this novel, marker-free approach.

Tumors are intricate ecosystems where cancer cells and non-malignant stromal cells, including cancer-associated fibroblasts (CAFs), engage in complex communication. In this study, we investigated the interaction between poorly (HLE) and well-differentiated (HuH7) hepatoma cells and LX2 fibroblasts. We explored various communication channels, including soluble factors, metabolites, extracellular vesicles (EVs), and miRNAs. Co-culture with HLE cells induced LX2 to produce higher levels of laminin β1, type IV collagen, and CD44, with pronounced syndecan-1 shedding. Conversely, in HuH7/LX2 co-culture, fibronectin, thrombospondin-1, type IV collagen, and cell surface syndecan-1 were dominant matrix components. Integrins α6β4 and α6β1 were upregulated in HLE, while α5β1 and αVβ1 were increased in HuH7. HLE-stimulated LX2 produced excess MMP-2 and 9, whereas HuH7-stimulated LX2 produced excess MMP-1. LX2 activated MAPK and Wnt signaling in hepatoma cells, and conversely, hepatoma-derived EVs upregulated MAPK and Wnt in LX2 cells. LX2-derived EVs induced over tenfold upregulation of SPOCK1/testican-1 in hepatoma EV cargo. We also identified liver cancer-specific miRNAs in hepatoma EVs, with potential implications for early diagnosis. In summary, our study reveals tumor type-dependent communication between hepatoma cells and fibroblasts, shedding light on potential implications for tumor progression. However, the clinical relevance of liver cancer-specific miRNAs requires further investigation.