Made available in DSpace on 2016-07-18T17:53:13Z (GMT). No. of bitstreams: 1 Reinaldo Camacho Bezerra.pdf: 607647 bytes, checksum: 1745657e41ebeb1e290325ce90721b08 (MD5) Previous issue date: 2014-03-21 / This work aims to evaluate the alterations, depending on the dose of potassium dichromate (0, 12, 24 and 36 mg.km-¹), in liver tissue after supplementation with probiotic at dosages 0 or 0.2% in 90 male rats. Oral ingestion done for 90 days of increasing amounts of potassium dichromate produced clinical signs of toxicity compared to histopathological analysis (p&#706;0.05), and seric enzymatic activities (p&#706;0.05), markers of hepatic function. The inclusion of probiotics to the diet reduced the effects on the studied parameters, indicating that it can be used to detoxify and/or prevent the action of the mineral, however other studies should be conducted to determine the most appropriate microorganisms and their dosages to minimize and/or to prevent the action of these xenobiotics in humans and animals. / O objetivo do presente trabalho foi o de avaliar as alterações dependentes da dose de dicromato de potássio (0, 12, 24 e 36 mg.kg-1) no tecido hepático, após suplementação com probiótico em dosagens 0 ou 0,2%, em 90 ratos machos. A ingestão oral por 90 dias de doses crescentes de dicromato de potássio produziu sinais clínicos de toxicidade frente a análise histopatológica (p<0,05) e atividades séricas enzimáticas (p<0,05), dos marcadores de função hepática. A inclusão do probiótico na dieta reduziu os efeitos nos parâmetros estudados, indicando que ele pode ser utilizado para desintoxicar e/ou impedir a ação desse mineral, porém outros estudos deverão ser realizados para determinar os microorganismos mais apropriados e suas dosagens, para minimizar e/ou impedir a ação desses xenobióticos nos homens e animais.

Submitted by Ricardo Cedraz Duque Moliterno (ricardo.moliterno@uefs.br) on 2016-09-13T21:44:53Z No. of bitstreams: 1 Disserta??o_George.pdf: 4996097 bytes, checksum: ece2301b72ccb1d9d33a2e2837531079 (MD5) / Made available in DSpace on 2016-09-13T21:44:53Z (GMT). No. of bitstreams: 1 Disserta??o_George.pdf: 4996097 bytes, checksum: ece2301b72ccb1d9d33a2e2837531079 (MD5) Previous issue date: 2016-02-29 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / The realization of an accurate diagnosis from histological images requires pathologists with practical experience because the characteristics of these images lead to a subjective analysis, which often hamper the accuracy of diagnosis. Systems that help to achieve better diagnoses can minimize doubts and improve the quality of diagnosis, influencing on increasing the effectiveness of medical treatments. This paper describes the research and development of PathoSpotter, a computer system to aid in the identification of diseases from histological images. The PathoSpotter proposes to reduce the lack of support work to histopathological diagnosis of renal diseases since much has been done in the area of cancer, but there is few published material in relation to the Digital Pathology applied to nephrology and hepatology. Our goal in this study was to apply the PathoSpotter the classification of proliferative glomerulopathy, which is a family of primary diseases affecting the kidneys. The work was based on a data set consisting of 811 histological pictures glomeruli and classical techniques of processing digital images and histopathology were used. The PathoSpotter presented a performance of 88.4% accuracy, which was similar to other Digital Pathology jobs that can be found in the literature. / A realiza??o do diagn?stico preciso a partir de imagens histol?gicas requer m?dicos patologistas com vasta experi?ncia pr?tica, pois as caracter?sticas dessas imagens conduzem a uma an?lise subjetiva que muitas vezes dificultam a exatid?o do diagn?stico. Sistemas que auxiliam a obten??o de melhores diagn?sticos podem minimizar d?vidas e melhorar a qualidade dos diagn?sticos, influenciando no aumento da efic?cia dos tratamentos m?dicos. Este trabalho descreve a pesquisa e o desenvolvimento do PathoSpotter, um sistema computacional para aux?lio na identifica??o de patologias a partir de imagens histol?gicas. O PathoSpotter se prop?e a reduzir a car?ncia de trabalhos de apoio ao diagn?stico histopatol?gico das doen?as renais, j? que muito tem sido feito na ?rea de neoplasias, mas h? pouco material publicado em rela??o ? Patologia Digital aplicada ? nefrologia ou hepatologia. Nosso objetivo neste trabalho foi aplicar o PathoSpotter na classifica??o das glomerulopatias proliferativas, que ? uma fam?lia de doen?as prim?rias que afetam os rins. O trabalho se baseou em um conjunto de dados composto por 811 imagens histol?gicas de glom?rulos, e foram utilizadas t?cnicas cl?ssicas de processamento de imagens e histopatologia digital. O PathoSpotter apresentou um desempenho de 88,4% de acur?cia, resultado similar ao de outros trabalhos de Patologia Digital que podem ser encontrados na literatura especializada.

Rift Valley fever (RVF) is a mosquito-borne zoonotic disease caused by a virus of the family Bunyaviridae, genus Phlebovirus. It is responsible for extensive outbreaks of disease in livestock in Africa with significant mortality and economic impact. Virus neutralization is considered the gold standard for confirming Rift Valley fever virus (RVFV) infection but the procedure is time consuming and expensive. Real-time reverse transcription-polymerase chain reaction (rRT-PCR), histopathology, and immunohistochemistry (IHC) are the diagnostic methods most often used in South Africa to confirm or exclude a diagnosis of RVF in necropsied animals. Validated estimates of diagnostic accuracy of these tests, in naturally infected livestock, however, have not been published. The objective of this study was to estimate the diagnostic sensitivity and specificity of rRT-PCR, histopathology, and IHC using Bayesian latent class methods in the absence of a gold standard. A secondary objective was to estimate stratum-specific values based on species, age, degree of specimen autolysis, and the presence/absence of tissue pigments. The Sensitivity (Se) and Specificity (Sp) of qRT-PCR were 97.4% (95% credibility interval (CI): 95.2% - 98.8%) and 71.7% (95% CI: 65% - 77.9%) respectively. The extraordinary analytical sensitivity of PCR makes this test very susceptible to false positive reactions, and thus reduced specificity. This is more likely during large-scale epidemics due to crosscontamination of specimens at necropsy facilities or testing laboratories. The Se and Sp of histopathology were 94.6% (95% CI: 91% - 97.2%) and 92.3% (95% CI: 87.6% - 95.8%) respectively. Single cases of RVF could be confused with acute poisoning with plants, bacterial septicaemias, and viral diseases such as infectious bovine rhinotracheitis and Wesselsbron disease. Most of these conditions, however, can be excluded using histological examination of the liver, special stains, bacterial culture, and toxicological or serological investigations. The Se and Sp of IHC were 97.6% (95% CI: 93.9% - 99.8%) and 99.4% (95% CI: 96.9% - 100%) respectively. Immunohistochemistry is highly specific because characteristic positive immunolabelling of the cytoplasm of hepatocytes can be correlated with the presence of hepatocellular injury typical for RVFV infection. False negative results are sometimes obtained with IHC because of reader error or loss of the antigenic epitopes due to advanced autolysis. Scant positive immunolabelling might be missed or viral proteins might be absent from sections of liver with advanced hepatocellular damage. The stratified analysis suggested differences in test accuracy in foetuses and severely autolysed specimens. The Sp of histopathology in foetuses (83.0%) was 9.3% lower than the value obtained for the sample population (92.3%). Lesions in some foetuses are more subtle and the typical eosinophilic intranuclear inclusions are often difficult to detect. In severely autolysed specimens, the Se of IHC decreased by 16.1% and the Sp of rRT-PCR by 17.4%. There is no plausible biological explanation for this decrease in the Sp of rRTPCR since the RNA of RVFV is resistant to degradation in autolysed tissues. Conversely, the antibody used to detect RVFV using IHC detects epitopes raised against nucleoproteins of the virus and it is possible that viral proteins become too widely dispersed and/or degraded in autolysed tissues to detect by light microscopy. It is possible that the marked decrease in Se of histopathology and IHC in severely autolysed specimens caused an apparent decrease in Sp of rRT-PCR, due to the latent class method. In conclusion, the high estimated Sp (99.4%) of IHC and the low Sp of rRT-PCR (71.3%) suggests that the definitive diagnosis or exclusion of RVF should not rely on a single PCR test and that IHC would be an effective confirmatory test for rRT-PCR positive field cases necropsied during an epidemic. Immunohistochemistry results from severely autolysed specimens, however, should be interpreted with caution and aborted foetuses in areas endemic for RVF should be screened using a variety of tests. The diagnostic Se and Sp of histopathology was much higher than expected confirming the value of routine post mortem examinations and histopathology of liver specimens. The most feasible RVF testing option in areas that do not have suitably equipped PCR laboratories, and where disease is often not detected in livestock until after human cases have been diagnosed, would be routine histopathology screening with IHC confirmation. Key Words: Rift Valley fever; Rift Valley fever virus; Bayesian; latent-class model; real-time reverse transcription-polymerase chain reaction; immunohistochemistry; histopathology; diagnosis; sensitivity; specificity. / Dissertation (MSc)--University of Pretoria, 2014. / gm2014 / Paraclinical Sciences / unrestricted

Unter Praxisbedingungen gilt die Erstbelegung einer Sportstute nach Beendigung der sportlichen Karriere als problematisch, viele Sportpferde konzipieren erst nach einer mehrjährigen Pause. Bisher existieren nur wenige wissenschaftliche Studien zu diesem Thema, in diesen ist die Problematik nur teilweise und ausschließlich unter klinischen Gesichtspunkten analysiert worden. Gegenstand dieser Arbeit ist die histopathologische Untersuchung der Endometriumbioptate von Sportstuten im Hinblick auf Befunde, die eine Erklärung für die bei Sportstuten häufig beobachtete Subfertilität darstellen können. Bei den Sportstuten dieser Studie handelte es sich um Stuten mit einer regelmäßigen Partizipation an Turnieren der Klasse M oder S, sowie um internationale Sportpferde, die in 3 Gruppen aufgeteilt wurden (Gruppe 1: seit dem Einsatz im Sport noch nicht besamte Stuten, n= 68; Gruppe 2: seit dem Einsatz im Sport erfolglos besamte Stuten: n= 84; Gruppe 3: Stuten, die seit dem Einsatz im Sport ein oder mehrmals abgefohlt haben, n= 37). Die an den Endometriumbioptaten der Sportstuten erhobenen Daten wurden überdies mit denen einer vierten Gruppe, bestehend aus Freizeitpferden (n= 31) verglichen. Auffällig häufig konnten in Gruppen 1 und 2 Differenzierungsstörungen in Bioptaten, die während der Decksaison entnommen wurden, beobachtet werden; dabei handelt es sich um Endometriopathien, deren Auftreten in der equinen Reproduktionsmedizin häufig auf hormonelle Imbalancen zurückgeführt wird. Von den Sportstuten der Gruppen 1 und 2, welche weniger als 1 Jahr vor Bioptatentnahme noch im Sport eingesetzt wurden, zeigten 16% (5/31) eine glanduläre Inaktivität. Dabei handelt es sich um einen histopathologischen Befund, der auf eine stark eingeschränkte Ovarfunktion schließen lässt. Eine irregulär proliferative Differenzierung des Endometriums wiesen 16% (5/32) der Stuten auf, die circa ein Jahr zuvor ihre Karriere im Sport beendet hatten. Solche, die bereits längere Zeit (≥2 Jahre) pausierten, zeigten vermehrt eine irregulär sekretorische Differenzierung des Endometriums (37%; 7/19). Die diagnostizierten Formen endometrialer Fehldifferenzierung, insbesondere in der zeitlichen Reihenfolge ihres Vorkommens, sprechen dafür, dass bei diesen Stuten unmittelbar nach dem Sport eine Ovarialinsuffizienz vorliegt, und dass die Rückkehr in ein reguläres Zyklusgeschehen begleitet wird von verschieden Störungen der Ovarfunktion. Dabei scheint der Schweregrad der ovariellen Dysfunktion mit fortschreitender Dauer seit dem Einsatz im Sport abzunehmen. Die irregulär proliferative Differenzierung wurde signifikant häufiger bei Stuten beobachtet, die vorberichtlich im Präöstrus bioptiert worden waren (p= 0,016); die irregulär sekretorische und die vollständig irreguläre Differenzierung traten häufiger bei im Interöstrus bioptierten Stuten auf. Die Ergebnisse lassen darauf schließen, dass ein Zusammenhang zwischen der Art der irregulären Differenzierung und dem Zyklusstand besteht. In Bezug auf weitere anamnestische Daten, wurde die irreguläre endometriale Differenzierung überdies gruppenübergreifend signifikant häufiger bei Stuten älter als 15 Jahre beobachtet; außerdem zeigten alle vorberichtlich azyklischen bzw. unregelmäßig zyklierenden Stuten (n= 5), alle Stuten mit Ovartumoren (n= 2), sowie eine Stute mit vorberichtlich Ovarzysten und eine mit sogenannten „intrauterine devices“ ein irregulär differenziertes Endometrium. In dieser Arbeit konnte gruppenübergreifend ein signifikanter Zusammenhang zwischen dem Auftreten von degenerativen Veränderungen des Endometriums (Endometrose: p= 0,008; Angiosklerose: p= 0,002) und dem Alter einer Stute nachgewiesen werden; die bei den Sportstuten gewonnen Daten unterschieden sich allerdings kaum merklich von den bei Zuchtstuten erhobenen Daten aus der Literatur. Endometritiden konnten vor allem bei seit dem Einsatz im Sport erfolglos besamten Stuten (Gruppe 2; 31%) nachgewiesen werden und bei ehemaligen Sportstuten, die seit ihrem Einsatz bereits erfolgreich konzipiert hatten (Gruppe 3; 41%). Der Prozentsatz an Stuten mit Endometritiden war in diesen Gruppen höher als der in der Literatur beschriebene Anteil bei „Nicht-Sportstuten“. Eine mögliche Erklärung für das gehäufte Auftreten von Endometritiden bei diesen Stuten stellen darum wiederholte Besamungsversuche dar, und -bei Stuten der Gruppe 3- auch postpartale Involutionsstörungen. Nach dem Kategorisierungssystem von KENNEY und DOIG (1986) hatten seit dem Einsatz im Sport noch nicht besamte Sportstuten (Gruppe 1) bessere Kategorie als Hobbystuten (Gruppe 4). Die in der vorliegenden Untersuchung bei Sportstuten diagnostizierten Differenzierungsstörungen fließen jedoch nicht in dieses Kategorisierungssytem ein. Bei Sportstuten reicht somit die alleinige Anwendung des Kategorisierungssystems für die Beurteilung einer möglichen Fertilitätsminderung nicht aus; deshalb wird eine ausführliche histopathologische Beurteilung eines Endometriumbioptats unter Einbeziehung der glandulären Funktionsmorphologie empfohlen. Zusammenfassend kann davon ausgegangen werden, dass die beobachteten degenerativen Veränderungen des Endometriums, die bei Sportstuten hinsichtlich Grad und Ausprägung mit denen von Nichtsportstuten vergleichbar waren, keine mögliche Erklärung für die Reproduktionsstörungen von Sportstuten darstellen. Vielmehr lassen die im Rahmen dieser Arbeit erhobenen histopathologischen Befunde vermuten, dass bei Sportstuten Differenzierungsstörungen des Endometriums während der Decksaison eine wichtige Ursache der klinisch beobachteten Subfertilität darstellen. Der Nachweis dieser Veränderungen deutet auf ein gehäuftes Auftreten von hormonellen Störungen, ausgelöst durch ovarielle Dysfunktionen hin; zum abschließenden Beweis bedarf es allerdings klinischer Studien.

Indiana University-Purdue University Indianapolis (IUPUI) / Sepsis is a clinical syndrome due to a systemic inflammatory response to severe microbial infection. Little is known about the changes in the bone marrow (BM) and how they affect the hematopoietic response to bacterial infection. Using an animal model of severe sepsis induced by Pseudomonas aeruginosa, we have previously reported that hematopoietic stem cells (HSC) undergo a significant expansion in the BM accompanied with myeloid suppression. This bone marrow response was Toll-like Receptor 4 (TLR4)-dependent. TLR4 is activated by bacterial lipopolysaccharide (LPS) and signals through two major independent downstream molecules: TRIF and MyD88. In the present study, I found that the TLR4/TRIF and the TLR4/MyD88 pathways contribute in a distinct manner to the BM response to P. aeruginosa's LPS. TRIF plays a major role in the expansion of the HSC pool, whereas MyD88 is required for myeloid suppression. Following LPS stimulation, HSCs enter in the cell cycle, expand and exhaust when transplanted in healthy mice. Loss of TRIF rescued completely the long-term engraftment and multilineage reconstitution potential of septic HSCs, but did not affect myeloid differentiation. Conversely, MyD88 deficiency prevented completely the myeloid suppression in the myeloid progenitors, but conferred limited protective effects on the HSC function. It is of great therapeutic value to identify the downstream molecules involved in TLR4/MyD88 dependent myeloid suppression. I found miR-21, a microRNA that is involved in inflammation, was up-regulated upon LPS challenge in a MyD88-dependent manner. However, deletion of miR-21 in the BM did not rescue LPS-induced bone marrow dysfunction, demonstrating that miR-21 is not a critical regulator in these processes. Further studies are warranted to determine the precise molecular mechanisms involved in the complex pathogenesis of BM response to sepsis. Taken together, my results show for the first time that the TLR4/TRIF signaling as a key mediator of HSC damage during acute LPS exposure and that activation of the TLR4/MyD88 signaling pathway play a dominant role in myeloid suppression. These results provide novel insights into our understanding of the molecular mechanisms underlying bone marrow injury during severe sepsis and may lead to the development of new therapeutic approaches in this disease.

Histopathology refers to inspecting and analysing tissue samples under a microscope to identify and examine signs of diseases. The manual investigation procedure of histology slides by pathologists is time-consuming and susceptible to misconceptions. Deep learning models have demonstrated outstanding performance in digital histopathology, providing doctors and clinicians with immediate and reliable decision-making assistance in their workflow. In this study, deep learning models, including vision transformers (ViT) and convolutional neural networks (CNN), were employed to compare their performance in patch-level classification task on feature annotations of glioblastoma multiforme in H\&amp;E histology whole slide images (WSI). The dataset utilised in this study was obtained from the Ivy Glioblastoma Atlas Project (IvyGAP). The pre-processing steps included stain normalisation of the images, and patches of size 256x256 pixels were extracted from the WSIs. In addition, the per-subject split method was implemented to prevent data leakage between the training, validation and test sets. Three models were employed to perform the classification task on the IvyGAP data image, two scratch-trained models, a ViT and a CNN (variant of VGG16), and a pre-trained ViT. The models were assessed using various metrics such as accuracy, f1-score, confusion matrices, Matthews correlation coefficient (MCC), area under the curve (AUC) and receiver operating characteristic (ROC) curves. In addition, experiments were conducted to calibrate the models to reflect the ground truth of the task using the temperature scale technique, and their uncertainty was estimated through the Monte Carlo dropout approach. Lastly, the models were statistically compared using the Wilcoxon signed-rank test. Among the evaluated models, the scratch-trained ViT exhibited the best test accuracy of 67%, with an MCC of 0.45. The scratch-trained CNN obtained a test accuracy of 49% and an MCC of 0.15. However, the pre-trained ViT only achieved a test accuracy of 28% and an MCC of 0.034. The reliability diagrams and metrics indicated that the scratch-trained ViT demonstrated better calibration. After applying temperature scaling, only the scratch-trained CNN showed improved calibration. Therefore, the calibrated CNN was used for subsequent experiments. The scratch-trained ViT and calibrated CNN illustrated different uncertainty levels. The scratch-trained ViT had moderate uncertainty, while the calibrated CNN exhibited modest to high uncertainty across classes. The pre-trained ViT had an overall high uncertainty. Finally, the results of the statistical tests reported that the scratch-trained ViT model performed better among the three models at a significant level of approximately 0.0167 after applying the Bonferroni correction.  In conclusion, the scratch-trained ViT model achieved the highest test accuracy and better class discrimination. In contrast, the scratch-trained CNN and pre-trained ViT performed poorly and were comparable to random classifiers. The scratch-trained ViT demonstrated better calibration, while the calibrated CNN showed varying levels of uncertainty. The statistical tests demonstrated no statistical difference among the models.

In recent years mammary cancer has been increasingly recognized in pet rabbits. In addition to uterine carcinomas—the most common tumor of female rabbits—mammary cancer can also markedly reduce the life expectancy of pet rabbits. The aim of this review is to raise awareness for these tumors and to report recent progress in related research. Their detailed characterization will likely improve medical care for affected rabbits. Moreover, study results will contribute to comparative pathology and may reveal if the rabbit is a suitable model for certain types of breast cancer in humans. Available information suggests that most invasive cancer cases develop through stepwise progression from non-invasive forms. Thus, early recognition will likely improve a complete cancer cure. So far, the only treatment option is surgical excision and prognostic factors are unknown. Recent investigations have identified tumor features with likely prognostic value. They have also revealed differences and similarities to mammary tumors in other species and breast cancer in women. Despite these initial data, continued research is necessary to gain more insights into the development of these tumors and their molecular features.

No description available.

Researchers within digital pathology are endeavouringto develop machine-learning tools to support dentists whenmaking a diagnosis. The purpose of this study was to investigatehow applying colour normalisation (CN) algorithms on an oral,histopathological dataset would impact both machine-learningmodels and ensembles of models when classifying cell types.The dataset was run through four different CN algorithms byusing a stain normalisation toolbox. The now five datasets (1 +4) were then fed separately into a pipeline to create machinelearningmodels, specifically convolutional neural networks withEfficientNet architecture. Two different ensembles were studied,one that used all the models and one that used the three modelswith the highest test accuracy. Each model gave a cell typeprediction of each cell. The ensembles super positioned theirmodels’ predictions of the same cell and used the results as theirown predictions.The models based on datasets created by two of the CNalgorithms had a weighted, average accuracy of ca. four percentagepoints higher than the model based on the unnormaliseddataset. Unexpectedly, the models based on the colour-normaliseddatasets had a larger standard deviation than the model basedon the unnormalised dataset. All the models were generally badat classifying two of the four cell types. Both the ensembleshad a weighted, average accuracy of ca. ten percentage pointshigher than the model based on the unnormalised dataset, aswell as a larger standard deviation. The increase in accuracyis significant and could move forward the timeline for whenmachine-leaning tools can be implemented into dentists’ andpathologists’ workflow. / Forskare inom digital patologi strävar efteratt utveckla maskininlärnings-verktyg som stödjer tandläkarenär de ställer diagnoser. Syftet med denna studie är att utreda hurtillämpning av färgnormaliserande algoritmer (CN algoritmer)på ett oralt, histopatologiskt dataset påverkar hur både maskininlärningsmodeller och ensembler av modeller klassificerarcelltyper.Datasetet kördes igenom fyra olika CN algoritmer med hjälpav en färgnormaliserings-verktygslåda. De nu fem dataseten(1 + 4) matades separat in i en ”pipeline” för att skapamaskininlärningsmodeller, specifikt djupa neurala nätverk medEfficientNet arkitektur. Två olika ensembler skapades, en somanvände alla modeller och en som endast använde de tre somhade högst noggrannhet på testsettet. Varje modell uppskattadecelltypen för varje cell. Ensemblerna superpositionerade derasmodellers uppskattningar för varje cell och använde resultatensom sina egna uppskattningar.Modellerna som tränats på två av de färgnormaliseradedataseten ökade i viktad, snitt-noggrannhet med fyra procentenheteri förhållande till modeller tränade på det ursprungligadatasetet. Förvånansvärt nog så ökade även standardavvikelsenhos modeller tränade på de färgnormaliserade dataseten. Allamodeller var generellt dåliga på att klassificera två av de fyracelltyperna. Ensemblen uppnådde en viktad snitt-noggrannhet på ca. tio procentenheter mer än modeller tränade på detursprungliga datasetet. Noggrannhetens signifikanta ökning kanleda till en tidigare implementering av maskininlärnings-verktygi tandläkares och patologers arbetsflöde. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

yes / Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitotic-cells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.