The importance of CXCR4 expression in tumor stroma as a potential biomarker in pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the main causes of cancer mortality in the world. A characteristic feature of this cancer is that a large part of the tumor volume is composed of a stroma with different cells and factors. Among these, we can highlight the cytokines, which perform their function through binding to their receptors. Given the impact of the CXCR4 receptor in the interactions between tumor cells and their microenvironment and its involvement in important signaling pathways in cancer, it is proposed as a very promising prognostic biomarker and as a goal for new targeted therapies. Numerous studies analyze the expression of CXCR4 but we suggest focusing on the expression of CXCR4 in the stroma. METHODS: Expression of CXCR4 in specimens from 33 patients with PDAC was evaluated by immunohistochemistry techniques and matched with clinicopathological parameters, overall and disease-free survival rates. RESULTS: The percentage of stroma was lower in non-tumor tissue (32.4 ± 5.2) than in tumor pancreatic tissue (67.4 ± 4.8), P-value = 0.001. The level of CXCR4 expression in stromal cells was diminished in non-tumor tissue (8.7 ± 4.6) and higher in tumor pancreatic tissue (23.5 ± 6.1), P-value = 0.022. No significant differences were identified in total cell count and inflammatory cells between non-tumor tissue and pancreatic tumor tissue. No association was observed between CXCR4 expression and any of the clinical or pathological data, overall and disease-free survival rates. Analyzing exclusively the stroma of tumor samples, the CXCR4 expression was associated with tumor differentiation, P-value = 0.05. CONCLUSIONS: In this study, we reflect the importance of CXCR4 expression in the stroma of patients diagnosed with PDAC. Our results revealed a high CXCR4 expression in the tumor stroma, which is related to a poor tumor differentiation. On the contrary, we could not find an association between CXCR4 expression and survival and the rest of the clinicopathological variables. Focusing the study on the CXCR4 expression in the tumor stroma could generate more robust results. Therefore, we consider it key to develop more studies to enlighten the role of this receptor in PDAC and its implication as a possible biomarker.

Metastatic Melanoma Negative for 5 Melanocytic Markers, Complete Regressed Primary Cutaneous Melanoma, and Melanoma-Associated Leukoderma in the Same Patient.

Melanomas with complete histological regression have been seen very infrequently. On the other hand, the diagnosis of metastatic melanoma is based on the histopathology and positivity of markers such as S100, Melan-A, and HMB-45 whose sensitivity is 99%, 82%, and 76%, respectively. It is very rare that metastatic melanomas and even more primary melanoma are negative for all of these markers. In these rare cases, there is usually a known primary. We present the case of a 82-year-old woman with a erythematous mass in the left groin and a 1-cm black-bluish irregular nodule on the skin of the ipsilateral foot. This lesion was clinical and dermoscopically compatible with primary melanoma. In the histological evaluation of the skin, a dermis full of melanophages and hemosiderophages were found in a background of fibrosis, scarce lymphocytic infiltrate, and neovascularization. Any cells expressing melanocytic markers were observed. It was diagnosed as tumoral melanosis. Lymph nodes showed a proliferation of atypical epithelioid cells with eosinophilic cytoplasm. Mitosis was conspicuous. Tumoral cells were vimentin and CD99 positive, and S100, CD34, HMB-45, Melan-A, SOX 10, tyrosinase, C-KIT, CD45, and CKAE1/AE3 negative, and BRAF-V600 mutated was detected. During follow-up, atypical vitiligo-like lesions were discovered, suggesting the diagnosis of metastatic melanoma totally regressed in our patient.

Short and long-term outcomes of underwater EMR compared to the traditional procedure in the real clinical practice.

BACKGROUND AND AIMS: underwater endoscopic mucosal resection (U-EMR) has been recently described as an alternative to endoscopic mucosal resection (EMR) for flat colorectal polyps. However, the real applications remain unclear due to the lack of comparative studies. METHODS: a multi-centric prospective study was performed from November 2016 to December 2017. All lesions larger than 15 mm that were resected with both techniques were included in the study. The samples were matched using the size, morphology, site and access (SMSA) score as a reference. The efficacy, efficiency and adverse events rates were compared. RESULTS: a total of 162 resections were collected (112 EMR and 50 U-EMR) with an average size of 25 mm. U-EMR achieved better results for the en bloc resection rate (49 vs 62%; p = 0.08) and there were no cases of an incomplete resection (10.7 vs 0%; p = 0.01). U-EMR was faster than EMR and there were no differences in the adverse events rate. Furthermore, U-EMR tended to achieve better results in terms of recurrence. Performing the resection in emersion appeared to prevent the cautery artefact, especially in sessile serrated adenomas. CONCLUSION: in the real clinical practice, U-EMR and EMR are equivalent in terms of efficacy and safety. Furthermore, U-EMR may be a feasible approach to prevent cautery artefact, allowing an accurate pathologic assessment.

Comparison of deep learning models for digital H&E staining from unpaired label-free multispectral microscopy images.

BACKGROUND AND OBJECTIVE: This paper presents the quantitative comparison of three generative models of digital staining, also known as virtual staining, in H&E modality (i.e., Hematoxylin and Eosin) that are applied to 5 types of breast tissue. Moreover, a qualitative evaluation of the results achieved with the best model was carried out. This process is based on images of samples without staining captured by a multispectral microscope with previous dimensional reduction to three channels in the RGB range. METHODS: The models compared are based on conditional GAN (pix2pix) which uses images aligned with/without staining, and two models that do not require image alignment, Cycle GAN (cycleGAN) and contrastive learning-based model (CUT). These models are compared based on the structural similarity and chromatic discrepancy between samples with chemical staining and their corresponding ones with digital staining. The correspondence between images is achieved after the chemical staining images are subjected to digital unstaining by means of a model obtained to guarantee the cyclic consistency of the generative models. RESULTS: The comparison of the three models corroborates the visual evaluation of the results showing the superiority of cycleGAN both for its larger structural similarity with respect to chemical staining (mean value of SSIM ∼ 0.95) and lower chromatic discrepancy (10%). To this end, quantization and calculation of EMD (Earth Mover's Distance) between clusters is used. In addition, quality evaluation through subjective psychophysical tests with three experts was carried out to evaluate quality of the results with the best model (cycleGAN). CONCLUSIONS: The results can be satisfactorily evaluated by metrics that use as reference image a chemically stained sample and the digital staining images of the reference sample with prior digital unstaining. These metrics demonstrate that generative staining models that guarantee cyclic consistency provide the closest results to chemical H&E staining that also is consistent with the result of qualitative evaluation by experts.

Anti-glomerular basement membrane disease in HLA-identical non-twin siblings.

Anti glomerular basement membrane disease (AGBM) is an autoinmune disorder characterised by the presence of anti-glomerular basement membrane (Anti-GBM) antibodies, alveolar hemorrhage, necrotizing glomerulonephritis, and linear deposition of immunoglobulins through direct inmunofluorescence. Genetic predisposition, among other factors, plays an important role in the development of the disease. Previous studies have shown that HLA-DR15 and HLA-DR4 increase the risk of presenting it, while HLA-DR1 and HLA-DR7 protect against its development. We describe the first case of two non-twin siblings with AGBM and identical HLA, with HLA-DR4 as risk factor and HLA-DR7 as protection factor. We propose the importance of analysing HLA in siblings of patients with AGBM, to determine the degree of genetic susceptibility and to carry out a close follow-up on them, with the aim of achieving an early diagnosis and treatment in case of presenting the disease.

Characterization of Permeability Barrier Dysfunction in a Murine Model of Cutaneous Field Cancerization Following Chronic UV-B Irradiation: Implications for the Pathogenesis of Skin Cancer.

Chronic ultraviolet B (UV-B) irradiation is known to be one of the most important hazards acting on the skin and poses a risk of developing photoaging, skin with cutaneous field cancerization (CFC), actinic keratosis (AKs), and squamous cell carcinomas (SCCs). Most of the UV-B light is absorbed in the epidermis, affecting the outermost cell layers, the stratum corneum, and the stratum granulosum, which protects against this radiation and tries to maintain the permeability barrier. In the present work, we show an impairment in the transepidermal water loss, stratum corneum hydration, and surface pH after chronic UV-B light exposure in an immunologically intact mouse model (SKH1 aged mice) of skin with CFC. Macroscopic lesions of AKs and SCCs may develop synchronically or over time on the same cutaneous surface due to both the presence of subclinical AKs and in situ SCC, but also the accumulation of different mutations in keratinocytes. Focusing on skin with CFC, yet without the pathological criteria of AKs or SCC, the presence of p53 immunopositive patches (PIPs) within the epidermis is associated with these UV-B-induced mutations. Reactive epidermis to chronic UV-B exposure correlated with a marked hyperkeratotic hyperplasia, hypergranulosis, and induction of keratinocyte hyperproliferation, while expressing an upregulation of filaggrin, loricrin, and involucrin immunostaining. However, incidental AKs and in situ SCC might show neither hypergranulosis nor upregulation of differentiation markers in the upper epidermis. Despite the overexpression of filaggrin, loricrin, involucrin, lipid enzymes, and ATP-binding cassette subfamily A member 12 (ABCA12) after chronic UV-B irradiation, the permeability barrier, stratum corneum hydration, and surface pH were severely compromised in the skin with CFC. We interpret these results as an attempt to restore the permeability barrier homeostasis by the reactive epidermis, which fails due to ultrastructural losses in stratum corneum integrity, higher pH on skin surface, abundant mast cells in the dermis, and the common presence of incidental AKs and in situ SCC. As far as we know, this is the first time that the permeability barrier has been studied in the skin with CFC in a murine model of SCC induced after chronic UV-B irradiation at high doses. The impairment in the permeability barrier and the consequent keratinocyte hyperproliferation in the skin of CFC might play a role in the physiopathology of AKs and SCCs.

Anti-glomerular basement membrane disease in HLA-identical non-twin siblings. / Enfermedad antimembrana basal glomerular en hermanos no gemelos con HLA idéntico.

Anti-glomerular basement membrane disease (AGBM) is an autoinmune disorder characterized by the presence of anti-glomerular basement membrane (anti-GBM) antibodies, alveolar hemorrhage, necrotizing glomerulonephritis, and linear deposition of immunoglobulins through direct inmunofluorescence. Genetic predisposition, among other factors, plays an important role in the development of the disease. Previous studies have shown that HLA-DR15 and HLA-DR4 increase the risk of presenting it, while HLA-DR1 and HLA-DR7 protect against its development. We describe the first case of two non-twin siblings with AGBM and identical HLA, with HLA-DR4 as risk factor and HLA-DR7 as protection factor. We propose the importance of analyzing HLA in siblings of patients with AGBM, to determine the degree of genetic susceptibility and to carry out a close follow-up on them, with the aim of achieving an early diagnosis and treatment in case of presenting the disease.

Glomerulosclerosis identification in whole slide images using semantic segmentation.

BACKGROUND AND OBJECTIVE: Glomeruli identification, i.e., detection and characterization, is a key procedure in many nephropathology studies. In this paper, semantic segmentation based on convolutional neural networks (CNN) is proposed to detect glomeruli using Whole Slide Imaging (WSI) follows by a classification CNN to divide the glomeruli into normal and sclerosed. METHODS: Comparison between U-Net and SegNet CNNs is performed for pixel-level segmentation considering both a two and three class problem, that is, a) non-glomerular and glomerular structures and b) non-glomerular normal glomerular and sclerotic structures. The two class semantic segmentation result is then used for a CNN classification where glomerular regions are divided into normal and global sclerosed glomeruli. RESULTS: These methods were tested on a dataset composed of 47 WSIs belonging to human kidney sections stained with Periodic Acid Schiff (PAS). The best approach was the SegNet for two class segmentation follows by a fine-tuned AlexNet network to characterize the glomeruli. 98.16% of accuracy was obtained with this process of consecutive CNNs (SegNet-AlexNet) for segmentation and classification. CONCLUSION: The results obtained demonstrate that the sequential CNN segmentation-classification strategy achieves higher accuracy reducing misclassified cases and therefore being the methodology proposed for glomerulosclerosis detection.

Data for glomeruli characterization in histopathological images.

The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH This data is also related to the research paper entitle "Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation", published in Computer Methods and Programs in Biomedicine Journal [1]. In that article, different methods based on deep learning for glomeruli segmentation and their classification into normal and sclerotic glomerulous are presented and discussed. The raw data used is described and provided here. In addition, the detected glomeruli are also provided as individual image files. These data will encourage research on artificial intelligence (AI) methods, create and compare fresh algorithms, and measure their usability in quantitative nephropathology.

The human olfactory system in two proteinopathies: Alzheimer's and Parkinson's diseases.

Alzheimer's and Parkinson's diseases are the most prevalent neurodegenerative disorders. Their etiologies are idiopathic, and treatments are symptomatic and orientated towards cognitive or motor deficits. Neuropathologically, both are proteinopathies with pathological aggregates (plaques of amyloid-ß peptide and neurofibrillary tangles of tau protein in Alzheimer's disease, and Lewy bodies mostly composed of α-synuclein in Parkinson's disease). These deposits appear in the nervous system in a predictable and accumulative sequence with six neuropathological stages. Both disorders present a long prodromal period, characterized by preclinical signs including hyposmia. Interestingly, the olfactory system, particularly the anterior olfactory nucleus, is initially and preferentially affected by the pathology. Cerebral atrophy revealed by magnetic resonance imaging must be complemented by histological analyses to ascertain whether neuronal and/or glial loss or neuropil remodeling are responsible for volumetric changes. It has been proposed that these proteinopathies could act in a prion-like manner in which a misfolded protein would be able to force native proteins into pathogenic folding (seeding), which then propagates through neurons and glia (spreading). Existing data have been examined to establish why some neuronal populations are vulnerable while others are resistant to pathology and to what extent glia prevent and/or facilitate proteinopathy spreading. Connectomic approaches reveal a number of hubs in the olfactory system (anterior olfactory nucleus, olfactory entorhinal cortex and cortical amygdala) that are key interconnectors with the main hubs (the entorhinal-hippocampal-cortical and amygdala-dorsal motor vagal nucleus) of network dysfunction in Alzheimer's and Parkinson's diseases.

[Prognostic significance of the microscopic invasion of the renal vein wall in renal cell cancer]. / Valor pronóstico de la afectación microscópica de la vena renal en el cáncer de células renales.

OBJECTIVES: International TNM Staging System for renal cell carcinoma (RCC) classifies as T3b when "tumor grossly extends into the renal vein or its segmental branches, or vena cava below the diaphragm". The finding of microscopic invasion of the vein wall is not taken into account for TNM staging. We analyse its prognostic significance in localized and locally advanced RCC. METHODS: From January 1989 to July 2002, 257 RCC were surgically excised. Excluding Von Hippel-Lindau patients and stage IV (TNM 2002), 241 cases were studied in retrospect, with a median follow up of 50.96 months. Histopathological data from the renal vein were available in 216 specimens. There was renal vein wall invasion in 22. We compare the outcomes in this group vs. the group without microscopic involvement of the renal vein wall (n: 194). RESULTS: Mean age for the group with renal vein invasion (RVI) was 65.02 years. Mean tumour size in the same group was 9 cm, larger than in control group (p<0,001). Thrombus was found in 72.7% vs. 6.2% in the control group. Clear cell carcinoma (77.3%) was the predominant histological subtype. Nuclear grade 2 according to Fuhrman's Classification System accounts for 42.9% of the cases. Metastatic progression risk (HR: 4,86) and death risk (HR: 6,49) are significantly higher in RVI group. When renal vein thrombosis is found, progression and death risks are still higher (HR: 7.22 and 8.38, respectively). CONCLUSIONS: Microscopic invasion of the renal vein wall is a dependent prognostic factor for disease progression and death for RCC. Macroscopic renal vein involvement is an independent prognostic factor. When both factors are found together, disease outcome is worse.

Cranial Pair 0: The Nervus Terminalis.

Originally discovered in elasmobranchs by Fritsh in 1878, the nervus terminalis has been found in virtually all species, including humans. After more than one-century debate on its nomenclature, it is nowadays recognized as cranial pair zero. The nerve mostly originates in the olfactory placode, although neural crest contribution has been also proposed. Developmentally, the nervus terminalis is clearly observed in human embryos; subsequently, during the fetal period loses some of its ganglion cells, and it is less recognizable in adults. Fibers originating in the nasal cavity passes into the cranium through the middle area of the cribiform plate of the ethmoid bone. Intracranially, fibers joint the telencephalon at several sites including the olfactory trigone and the primordium of the hippocampus to reach preoptic and precommissural regions. The nervus terminalis shows ganglion cells, that sometimes form clusters, normally one or two located at the base of the crista galli, the so-called ganglion of the nervus terminalis. Its function is uncertain. It has been described that its fibers facilitates migration of luteinizing hormone-releasing hormone cells to the hypothalamus thus participating in the development of the hypothalamic-gonadal axis, which alteration may provoke Kallmann's syndrome in humans. This review summarizes current knowledge on this structure, incorporating original illustrations of the nerve at different developmental stages, and focuses on its anatomical and clinical relevance. Anat Rec, 302:394-404, 2019. © 2018 Wiley Periodicals, Inc.

Automatic quantification of IHC stain in breast TMA using colour analysis.

Immunohistochemical (IHC) biomarkers in breast tissue microarray (TMA) samples are used daily in pathology departments. In recent years, automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis. These errors are mostly due to subjective evaluation. The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA for different biomarkers. To avoid the problem of colour variation and make a robust tool independent of the staining process, several colour standardization methods have been analysed. Four colour standardization methods have been compared against colour model segmentation. The standardization methods have been compared by means of NBS colour distance. The use of colour standardization helps to reduce noise due to stain and histological sample preparation. However, the most reliable and robust results have been obtained by combining the HSV and RGB colour models for segmentation with the HSB channels. The segmentation provides three outputs based on three saturation values for weak, medium and strong staining. Each output image can be combined according to the type of biomarker staining. The results with 12 biomarkers were evaluated and compared to the segmentation and density calculation done by expert pathologists. The Hausdorff distance, sensitivity and specificity have been used to quantitative validate the results. The tests carried out with 8000 TMA images provided an average of 95.94% accuracy applied to the total tissue cylinder area. Colour standardization was used only when the tissue core had blurring and fading stain and the expert could not evaluate them without a pre-processing.

Influence of Texture and Colour in Breast TMA Classification.

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors.

A CAD System for the Acquisition and Classification of Breast TMA in Pathology.

Breast cancer is the most common type of cancer and the fifth leading cause of death in women over 40. Therefore, prompt diagnostic and treatment is essential. In this work a TMA Computer Aided Diagnosis (CAD) system has been implemented to provide support to pathologists in their daily work. For that purpose, the tool covers each and every process from the TMA core image acquisition to their individual classification. The first process includes: tissue core location, segmentation and rigid registration of digital microscopic images acquired at different magnifications (5x, 10x, 20x, 20x and 40x) from different devices. The classification process allows performing the core classification selecting different types of color models, texture descriptors and classifiers. Finally, the cores are classified into three categories: malignant, doubtful and benign.