Node Sections Research Articles

Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images

IntroductionColorectal cancer (CRC) constitutes around 10% of global cancer diagnoses and death due to cancer. Treatment involves the surgical resection of the tumor and regional lymph nodes. Assessment of multiple lymph node demands meticulous examination by skilled pathologists, which can be arduous, prompting consideration for an artificial intelligence (AI)-supported workflow due to the growing number of slides to be examined, demanding heightened precision and the global shortage of pathologists.MethodThis was a retrospective cross-sectional study including digital images of glass slides containing sections of positive and negative lymph nodes obtained from radical resection of primary CRC. Lymph nodes from 165 previously diagnosed cases were selected from Agha Khan University Hospital, from Jan 2021 to Jan 2022. The images were prepared at 10X and uploaded into an open source software, Q path and deep learning model Ensemble was applied for the identification of tumor deposits in lymph node.ResultsOut of the 87 positive lymph nodes detected by AI, 73(84%) were true positive and 14(16%) were false positive. The total number of negative lymph nodes detected by AI was 78. Out of these, 69(88.5%) were true negative and 9 (11.5%) were false negative. The sensitivity was 89% and specificity 83.1%. The odds ratio was 40 with a confidence interval of 16.26–98.3. P-value was < 0.05 (< 0.0001).ConclusionThough it was a small study but its results were really appreciating and we encourage more such studies with big sample data in future.

Prostate-Specific Membrane Antigen-Targeted Imaging and Its Correlation with HOXB13 Expression.

Homeobox 13 (HOXB13) is an oncogenic transcription factor that directly regulates expression of folate hydrolase 1, which encodes prostate-specific membrane antigen (PSMA). HOXB13 is expressed in primary and metastatic prostate cancers (PCs) and promotes androgen-independent PC growth. Since HOXB13 promotes resistance to androgen receptor (AR)-targeted therapies and regulates the expression of folate hydrolase 1, we investigated whether SUVs on PSMA PET would correlate with HOXB13 expression. Methods: We analyzed 2 independent PC patient cohorts who underwent PSMA PET/CT for initial staging or for biochemical recurrence. In the discovery cohort, we examined the relationship between HOXB13, PSMA, and AR messenger RNA (mRNA) expression in prostate biopsy specimens from 179 patients who underwent PSMA PET/CT with 18F-piflufolastat. In the validation cohort, we confirmed the relationship between HOXB13, PSMA, and AR by comparing protein expression in prostatectomy and lymph node (LN) sections from 19 patients enrolled in 18F-rhPSMA-7.3 PET clinical trials. Correlation and association analyses were also used to confirm the relationship between the markers, LN positivity, and PSMA PET SUVs. Results: We observed a significant correlation between PSMA and HOXB13 mRNA (P < 0.01). The association between HOXB13 and 18F-piflufolastat SUVs was also significant (SUVmax, P = 0.0005; SUVpeak, P = 0.0006). Likewise, the PSMA SUVmax was significantly associated with the expression of HOXB13 protein in the 18F-rhPSMA-7.3 PET cohort (P = 0.008). Treatment-naïve patients with LN metastases demonstrated elevated HOXB13 and PSMA levels in their tumors as well as higher PSMA tracer uptake and low AR expression. Conclusion: Our findings demonstrate that HOXB13 correlates with PSMA expression and PSMA PET SUVs at the mRNA and protein levels. Our study suggests that the PSMA PET findings may reflect oncogenic HOXB13 transcriptional activity in PC, thus potentially serving as an imaging biomarker for more aggressive disease.

A multi-agent learning framework for mixed-integer linear programming

Mixed integer linear programming (MILP) is an important problem in the combinatorial optimization domain, which has wide applications in practical optimization scenarios. Given that most MILP problems fall into the NP-hard category, which the traditional methods may fail to solve, recent research has tried to derive MILP solutions using machine learning techniques. The whole MILP-solving procedure involves lots of modules, such as pre-solving, cut selection, node section, etc., and these modules are closely related and influence each other. However, the previous machine learning-based approaches neglect the connections between these modules, and focus on single-module learning techniques. To address this, we propose an initial step towards a more comprehensive multi-agent learning framework that allows different modules to interact and collaborate. Specifically, our current implementation involves two key modules: HEM for cut selection applied at the root node and GCNN for variable selection. By employing HEM to influence the training of GCNN, these two agents thus work in unison. Through extensive experiments on four MILP datasets in diverse scenarios, we observe significant improvements in solving time and PD integral metrics compared with the state-of-the-art learning-based MILP solving methods. This work lays the groundwork for future development of a fully integrated multi-agent framework.

Deep learning-based hyperspectral technique identifies metastatic lymph nodes in oral squamous cell carcinoma-A pilot study.

To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes. The continuous sections of metastatic lymph nodes from 45 oral squamous cell carcinoma (OSCC) patients were collected. An improved ResUNet algorithm was established for deep learning to analyze the spectral curve differences between cancer cells and lymphocytes, and that between tumor tissue and normal tissue. It was found that cancer cells, lymphocytes, and erythrocytes in the metastatic lymph nodes could be distinguished basing hyperspectral image, with overall accuracy (OA) as 87.30% and average accuracy (AA) as 85.46%. Cancerous area could be recognized by hyperspectral image and deep learning, and the average intersection over union (IOU) and accuracy were 0.6253 and 0.7692, respectively. This study indicated that deep learning-based hyperspectral techniques can identify tumor tissue in OSCC metastatic lymph nodes, achieving high accuracy of pathological diagnosis, high work efficiency, and reducing work burden. But these are preliminary results limited to a small sample.

Ti–6Al–4V hybrid-strut lattice metamaterials: A design strategy for improved performance

Metal hollow-strut lattices manipulate the strut and node sections to achieve higher strength. Although these metamaterials have recently surpassed the structural efficiency of conventional solid-strut lattices of comparable density and topology, they often observe localised yielding and fragmentation through the nodes that limit their structural efficiency. To quantify the effect of this localised failure and to develop mitigating strategies, we have integrated solid and hollow strut and node sections in singular Ti–6Al–4V simple cubic hybrid-strut lattices for a deployable relative density range of 15–30%. Fabricated through laser powder bed fusion these hybrid-strut lattices are stronger (32%), stiffer (15%), and tougher (65%) than solid-strut lattices of equivalent density. Interestingly, the metamaterials with hollow beams can transition from a bending-dominated to a stretch-dominated deformation response with increasing density, while the lattices with solid beams only observed a uniformly stretch-dominated deformation response. The combination of solid and hollow strut and node sections in the hybrid-strut lattice establishes a simple yet effective strategy to enhance structural efficiency and control of failure response without increasing density or manipulating unit cell topology.

Dynamic Cutting Properties of Miscanthus (giganteus) Stems Using an Impact Tester

Miscanthus (giganteus) is a relatively new energy crop. Its mechanical properties are important for the design and modification of harvesting and processing machines or equipment. The cutting strength is critical to improve the field performance of mowing and other cutting mechanisms. The effects of the cutting blade type, sample supporting method, and sample locations (upper or lower) where it was taken from a single plant stem on the cutting force and energy were studied. A serrated cutting blade and a flat blade were selected to cut Miscanthus samples at the node and internode using a high-speed impact tester. The cutting force, sample diameter, and cutting speed were recorded. The specific cutting force and energy were then calculated based on the cross-sectional area of the stem sample. The average diameter of the Miscanthus samples used for this study was 9.5 mm. The blade cutting speeds for all treatments were ranged from 8.2 ms−1 to 11.3 ms−1. Overall, the maximum specific cutting force and energy of the flat blade were found at the upper portion of a plant stem, which was 441 N cm−2 and 8.3 J cm−2 at the node when one side of the stem sample was fixed, and 469 N cm−2 and 12.1 J cm−2 at the internode if both sides of the sample were fixed. The cutting strengths at the node and internode were significantly different no matter at the upper portion or lower portion of the plant. When using the serrated blade, the maximum specific cutting force was also found at the upper node section with a value of 511 N cm−2 and 437 N cm−2 for one-side fixed and two-side fixed, respectively. Meanwhile, the corresponding maximum specific cutting energies for these two cases were 10.5 J cm−2 and 10.3 J cm−2, respectively. Statistical analysis showed that the blade types and stem sample locations significantly affected the specific cutting force and energy with a 95% confidence level. The sample support methods did not make significant differences when comparing the specific cutting force and energy; but the actual cutting force and energy were significantly different.

Molecular characterization of a rare case of high-grade B-cell lymphoma with MYC, BCL2, BCL6, and CCND1 rearrangements

Quadruple-hit lymphomas are extremely rare non-Hodgkin lymphomas with a reported dismal prognosis in the few reported cases. A “quadruple hit” has been defined by the presence of concurrent MYC, BCL2, BCL6, and CCND1 chromosomal rearrangements. We report a new case of a quadruple hit lymphoma in a 73-year-old Hispanic man who presented with an enlarging left-sided neck mass. Computed tomography showed a 1.9-cm mass in left the tonsil with bulky cervical lymphadenopathy. The presence of all four chromosomal rearrangements can reportedly occur with disease progression in both diffuse large B-cell lymphomas and mantle cell lymphomas. Further characterization of the tumor by next-generation sequencing may be of benefit to delineate between these two possibilities. Immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and next-generation sequencing were used to confirm and classify the diagnosis. Histologic sections of the cervical lymph node demonstrated an atypical lymphoid infiltrate with large and pleomorphic cells, which were positive for CD20, CD10, BCL1 (Cyclin D1), BCL2, BCL6, and cMYC and negative for CD5 and SOX11 on immunohistochemistry with a Ki-67 proliferative index of 70%. FISH demonstrated MYC, BCL2, BCL6, and CCND1 rearrangements and the diagnosis of high-grade B-cell lymphoma with MYC, BCL2, BCL6, and CCND1 was rendered. Our patient was treated with dose adjusted etoposide, doxorubicin, cyclophosphamide, prednisone, and rituximab chemotherapy and has been in remission for 20 months.

Геоінформаційна система інженерних мереж та комунікацій морського торгового порту

The work is devoted to researching the process of development, implementation and operation of the geoinformation system of engineering networks and communications (GISIS) in the Odesa Sea Trade Port. The beginning of the development of the system began more than twenty years ago. The beginning of the works was preceded by the stage of pre-project studies, the result of which were the technical tasks for the implementation of the works and the project of the structure of information support. The work was carried out in stages, which corresponded to a separate territory and type of work. The purpose of this work is to research the processes of performing field and camera geodetic, photogrammetric, cartographic works, designing and creating a geoinformation database, selecting and adapting specialized geoinformation software, operating and supporting the system during the full life cycle of the geoinformation system. The methodology mainly corresponds to the following algorithm of work performance. At the stage of preliminary design studies: – collection and analysis of data on the exchange of cartographic and analytical information between external organizations, – collection and analysis of data on the exchange of cartographic and analytical information within the enterprise, primarily between engineering services, – description of the existing document circulation system with cartographic and analytical information, – a description of the shortcomings of the existing document management system, – determination of requirements for geo-information software and justification of the configuration of the geo-information software package and its architecture. At the stage of field work: – construction of a shooting justification and definition of data for communication with other coordinate systems, – reconnaissance on the territory of performance of works. Planning field work. – execution of topographical and geodetic works for the purpose of building or updating 1:500, 1:2000 scale plans. Construction of outlines. – performance of engineering and geodetic works for the study of underground communications in order to design the sections of wells and other engineering nodes. At the stage of design (camera works): – based on outlines and electronic survey protocols, development or correction of 1:500, 1:2000 scale plans using geoinformation software. – based on outlines, photofixation materials, construction of diagrams of sections of wells and other engineering nodes in electronic form. – formation of a geo-informational database of the object on the territory of execution of works. – debugging the project in the environment of geoinformation software. – coordination of the project with the engineering services of the enterprise and external organizations. – commissioning the developed project to the customer. Scientific novelty and practical significance – for the first time, research and development of the methodology of creation and support at all stages of the life cycle of the geoinformation system of engineering networks and communications of a large seaport was carried out. The methodology was developed for a long time during the entire life cycle of the system. The methodology was used by other ports and large industrial enterprises.

Attacking CNNs in Histopathology with SNAP: Sporadic and Naturalistic Adversarial Patches (Student Abstract)

Convolutional neural networks (CNNs) are being increasingly adopted in medical imaging. However, in the race for developing accurate models, their robustness is often overlooked. This elicits a significant concern given the safety-critical nature of the healthcare system. Here, we highlight the vulnerability of CNNs against a sporadic and naturalistic adversarial patch attack (SNAP). We train SNAP to mislead the ResNet50 model predicting metastasis in histopathological scans of lymph node sections, lowering the accuracy by 27%. This work emphasizes the need for defense strategies before deploying CNNs in critical healthcare settings.

Evaluation of Tre Gai bamboo (Bambusa spinosa) bending strength perpendicular-to-fibre along the culm length

The continued need to decarbonize buildings has led to a deeper exploration of the use of bamboo culms in building systems. For the design and use of full culm bamboo structures, the bending strength perpendicular-to-fibre has received little comprehensive study along the length of commercially produced bamboo poles. We intensively measured the bending strength perpendicular-to-the-fibre for both node and internode sections along the length of commercially purchased Tre Gai (Bambusa spinosa) culms. Eight culms, each four meters long, were sectioned to measure the bending strength terms including tension and compression strengths in both the N-S (top/bottom) and E/W (left/right) directions using ISO 22157 procedures. Digital scanning was used to measure the inner and outer diameter of each section. Node strength was approximately 2.64 times greater than the internode strength. The performances of perpendicular-to-fibre strength terms were related to the primary tissue strength rather than the bamboo fibre strength within the culm. Based upon observations and measurements, the fmT,NS and fmC,EW strengths had greater values with lower variability, and were less sensitive to changes in length. Implications of perpendicular-to-fibre strength design in building elements are discussed.

In vitro propagation and DNA barcoding of the rare near endemic Plantago sinaica (Barnéoud) plant in Saint Katherine, Sinai

Plantago sinaica is a rare perennial shrub near-endemic to Egypt and found in Saint Katherine Protectorate in Sinai. The first successful in vitro propagation protocol was conducted to protect the plant outside its natural reserves. Shoot tip, stem node section, cotyledonary node, and root explants separated from in vitro germinated seedlings were cultured in vitro on Murashige and Skoog (MS) medium enriched with different concentrations and types of cytokinins. It was found that 6-benzyl adenine (BA) is the most efficient cytokinin. MS medium containing 3.33 µM BA and 0.54 µM α-naphthalene acetic acids (NAA) produced 10.25 and 11.30 shoots/explant using shoot tip and stem node section, respectively. Conversely, MS medium + 2.22 µM BA + 0.54 µM NAA produced 13.25 shoots from root explants. Surprisingly, the cotyledonary node explants favored MS medium free from plant growth regulators (PGRs), which produced only 4.25 shoots/explant. The multiplied shoots were rooted successfully with a 100% rooting percentage on half MS medium containing 1.23 or 2.46 µM indole-3-butyric acid (IBA). In vitro, rooted plantlets were efficiently transferred to the greenhouse with a 90% survivability. Finally, the plant was identified using three DNA barcodes; 1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL), plastid photosystem II protein D1 intergenic spacer region (psbA–trnH), and Internal Transcribed Spacer (ITS) barcodes. Additionally, psbA–trnH and ITS were novel and submitted to the GenBank databases for the first time for Plantago sinaica. Our study supports the United Nations Sustainable Development Goal number 15, which is to preserve, restore and reinstate sustainable usage of terrestrial ecosystems and to stop biodiversity loss.

Analysis of false reasons based on the artificial intelligence RRCART model to identify frozen sections of lymph nodes in breast cancer.

Breast cancer is the most common malignant tumor in the world. Intraoperative frozen section of sentinel lymph nodes is an important basis for determining whether axillary lymph node dissection is required for breast cancer surgery. We propose an RRCART model based on a deep-learning network to identify metastases in 2362 frozen sections and count the wrongly identified sections and the associated reasons. The purpose is to summarize the factors that affect the accuracy of the artificial intelligence model and propose corresponding solutions. We took the pathological diagnosis of senior pathologists as the gold standard and identified errors. The pathologists and artificial intelligence engineers jointly read the images and heatmaps to determine the locations of the identified errors on sections, and the pathologists found the reasons (false reasons) for the errors. Through NVivo 12 Plus, qualitative analysis of word frequency analysis and nodal analysis was performed on the error reasons, and the top-down error reason framework of "artificial intelligence RRCART model to identify frozen sections of breast cancer lymph nodes" was constructed based on the importance of false reasons. There were 101 incorrectly identified sections in 2362 slides, including 42 false negatives and 59 false positives. Through NVivo 12 Plus software, the error causes were node-coded, and finally, 2 parent nodes (high-frequency error, low-frequency error) and 5 child nodes (section quality, normal lymph node structure, secondary reaction of lymph nodes, micrometastasis, and special growth pattern of tumor) were obtained; among them, the error of highest frequency was that caused by normal lymph node structure, with a total of 45 cases (44.55%), followed by micrometastasis, which occurred in 30 cases (29.70%). The causes of identification errors in examination of sentinel lymph node frozen sections by artificial intelligence are, in descending order of influence, normal lymph node structure, micrometastases, section quality, special tumor growth patterns and secondary lymph node reactions. In this study, by constructing an artificial intelligence model to identify the error causes of frozen sections of lymph nodes in breast cancer and by analyzing the model in detail, we found that poor quality of slices was the preproblem of many identification errors, which can lead to other errors, such as unclear recognition of lymph node structure by computer. Therefore, we believe that the process of artificial intelligence pathological diagnosis should be optimized, and the quality control of the pathological sections included in the artificial intelligence reading should be carried out first to exclude the influence of poor section quality on the computer model. For cases of micrometastasis, we suggest that by differentiating slices into high- and low-confidence groups, low-confidence micrometastatic slices can be separated for manual identification. The normal lymph node structure can be improved by adding samples and training the model in a targeted manner.

МЕТОД ПРОЕКТУВАЛЬНОГО РОЗРАХУНКУ НА МІЦНІСТЬ ПОПЕРЕЧНОГО СТИКУ ПАНЕЛЕЙ ВІД'ЄМНОЇ ЧАСТИНИ КРИЛА ТА ЦЕНТРОПЛАНА ТРАНСПОРТНОГО ЛІТАКА

Due to the limitation in the dimensions of the structural elements of the modeled wing contour and the possibility of increasing their cross-sections in the area of the transverse joint, only on the inside, there is a need to find an effective solution for the design, mass of the joint, as well as its statical strength at the modeling stage. The article presents the method and its application using the example of a preliminary analysis of the design calculation of the design of the flange connection of the center section with the outer wing section (OWS) of a transport aircraft. The method is based on the calculation of stresses caused by the discrete transmission of forces between elements. In the calculation method, a simplified statically indeterminate joint model is obtained according to the geometric characteristics and force transmission characteristics of each node section in the flange joint structure. During the calculations, the following are determined: diagrams of the bending moment and axial forces obtained using the force method, the distribution of force loads in each section of the model for subsequent analysis of the static safety factor. The resulting calculation results are compared with the airworthiness requirements to determine whether they meet the design requirements. For components with greater static strength, or those that do not meet the requirements, it is recommended to further modify the design parameters to ensure an effective design of the center section joint with the outer wing section and subsequent re-calculation. The calculation method used has practical value as a preliminary engineering analysis.The geometric characteristics of the junction of the center section with the negative part of the wing were modeled using the CAD/CAMCAE CATIA system.

OP04 The Efficacy Of Segmentectomy And Lobectomy For Non-Small Cell Lung Cancer: A Systematic Review And Meta-Analysis

IntroductionThe latest clinical practice guidelines for non-small cell lung cancer (NSCLC) published by the National Comprehensive Cancer Network in 2022 recommend that patients with NSCLC (&gt;1 and ≤2 cm) should be diagnosed as T1b. Segmentectomy and lobectomy are equally effective in treating patients with NSCLC no bigger than 2 cm, and especially for tumors no bigger than 1 cm. However, the effectiveness of these treatments for NSCLC tumors between 1 and 2 cm is unknown. We conducted a systemic review and meta- analysis to assess the efficacy of these two surgical treatments in patients with T1b stage NSCLC.MethodsWe searched for randomized controlled trials (RCTs) and cohort studies investigating the efficacy of lobectomy and segmentectomy for patients with T1b stage NSCLC. Study quality was assessed with the Cochrane Quality in Prognosis Studies tool. We used random effects models to analyze overall survival (OS) and lung cancer-specific survival (LCSS), expressed as hazard ratios (HR) and 95% confidence intervals (CIs). The effect of covariates was assessed using subgroup analysis. All procedures were performed according to the PRISMA guidelines.ResultsWe identified ten cohort studies that matched our selection criteria, with general low risk of quality, including a total of 37,691 patients. No publication bias was found. Compared to lobectomy, segmentectomy had lower OS (HR 1.31, 95% CI: 1.16, 1.47; p=0.026) and LCSS (HR 1.21, 95% CI: 1.03, 1.42, p=0.015) before Cox regression. After multivariable Cox regression, adjusted by age, sex, histological type, and lymph node section, segmentectomy had similar OS (HR 1.17, 95% CI: 1.00, 1.37; p=0.2) and LCSS (HR 1.10, 95% CI: 0.89, 1.36; p=0.8).ConclusionsSegmentectomy can be used to treat patients with T1b stage NSCLC. Patients who undergo segmentectomy have survival outcomes that are the same as those of patients who received lobectomy. This evidence-based observation provides a reference for surgical choice in the treatment of patients with T1b stage NSCLC, which should be further confirmed through RCTs.

Solving the mystery of Reed Sternberg like cells in a composite Peripheral T Cell Lymphoma with Epstein Barr Virus and Epstein Barr Virus driven Lymphoproliferative Disorder

Abstract Introduction/Objective Nodal PTCL with EBV positivity can occasionally contain Reed Sternberg-like cells, and EBV- LPD can mimic Hodgkin lymphoma. Coexistence of these two unusual lymphomas in the same lymph node is very rare. We present a case of a 56-year-old male with Composite EBV+ PTCL NOS and EBV LPD, with positive B-cell gene rearrangement and negative T-cell gene rearrangement presenting a significant diagnostic challenge. Methods/Case Report A 56-year-old male with a history of abdominal tuberculosis presented with lethargy, fatigue, and poor appetite. Physical examination revealed generalized lymphadenopathy. CT scan confirmed lymphadenopathy in bilateral axilla, supraclavicular lymph nodes along with lytic lesions on illiac crest. Ill defined splenic lesions raised concerns for metastasis. Cervical lymph node and bone marrow biopsies were performed. Flow cytometry on the lymph node was not done due to tuberculosis history. Flow cytometry on the bone marrow showed an inverted CD4:CD8 ratio. H&amp;E sections of the lymph node revealed a polymorphic lymphoid infiltrate with prominent Reed Sternberg-like cells. Immunohistochemical staining showed CD30-positive and CD45-negative large cells. T-cell and B-cell gene rearrangements were performed. Initially, B-cell gene rearrangements were positive, while T-gamma was negative. Further testing by IHC and morphological evaluation revealed two distinct cell populations, one with CD3, CD5, and CD7 positivity with partial weak positivity for PD1, CD43, and CD8 and the other consisting of B-cells expressing CD30, EBV, MUM1, and partial expression of BCL6 rare CD79a, and partial weak Pax5. Outside molecular testing showed positive T-cell gene rearrangements. The diagnosis of Composite EBV+ Peripheral T Cell Lymphoma, not otherwise specified, with cytotoxic profile along with EBV driven LPD was made. Bone marrow studies confirmed involvement by the same lymphoma. Results (if a Case Study enter NA) NA Conclusion Identifying EBV-positive Reed Sternberg-like cells in both EBV LPD and PTCL-NOS in a composite lymphoma can be challenging. In the absence of flow cytometry, a composite lymphoma can potentially be misdiagnosed, leading to under-treatment. Repeat testing can be helpful in case of any doubt. Although EBV-positive PTCL with cytotoxic profile and EBV-positive LPD has been reported, the combination of these two lymphomas together is exceedingly rare.

The presence of Mott cells in the lymph nodes of rats with experimental autoimmune encephalomyelitis.

Mott cells are plasma cells that have multiple spherical Russell bodies packed in their cytoplasm. Russell bodies are dilated endoplasmic reticulum cisternae filled with aggregates of immunoglobulins that are neither secreted nor degraded. Mott cells were observed in our study by light and electron microscope in the lymph nodes of rats with experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis. Mott cells were detected on hematoxylin and eosin (HE)-stained lymph node sections as vacuolated cells with eccentrically positioned nuclei and large number of faint blue spherical inclusions in the cytoplasm. Electron microscopic investigation revealed the presence of Russell bodies of the "medusa" form inside Mott cells in lymph node ultra-thin sections of EAE animals. Mott cells expressed the plasma cell marker CD138 and either kappa or lambda immunoglobulin light chains, indicating their origin from polyclonally activated B cells. Finally, Mott cells were associated with active EAE, as they were not found in the lymph nodes of EAE-resistant Albino Oxford rats. The presence of Russell bodies implies an excessive production of immunoglobulins in EAE, thus further emphasizing the role of B cells, and among them Mott cells, in the pathogenesis of this animal model of multiple sclerosis.

Сравнение классических подходов машинного обучения с гибридными квантовыми подходами в прикладных задачах

&lt;p&gt;The work is aimed at analyzing the potential advantages of using quantum approaches in applied problems of artificial intelligence. In this paper, the task of classifying medical images extracted from histopathological images of sections of lymph nodes is set. The theoretical basis used for the construction of quantum and hybrid-quantum computing elements used in the article will be given. Quantum analogues of classical machine learning algorithms and neural networks will be considered. The paper will give a step-by-step description of the data transformation, the construction of models and their training, followed by an analysis of the results obtained and the performance of the simulation of quantum computing.&lt;/p&gt;

Necrotic Cell Death and Inflammasome NLRP3 Activity in Mycobacterium bovis-Infected Bovine Macrophages.

Mycobacterium bovis is a facultative intracellular bacterium that produces cellular necrosis in granulomatous lesions in bovines. Although M. bovis-induced inflammation actively participates in granuloma development, its role in necrotic cell death and in bovine macrophages has not been fully explored. In this study, we evaluate the effect of M. bovis AN5 and its culture filtrate protein extract (CFPE) on inflammasome activation in bovine macrophages and its consequences on cell death. Our results show that both stimuli induce necrotic cell death starting 4 h after incubation. CFPE treatment and M. bovis infection also induce the maturation of IL-1β (>3000 pg/mL), oligomerization of ASC (apoptosis-associated speck-like protein containing CARD), and activation of caspase-1, following the canonical activation pathway of the NLRP3 inflammasome. Inhibiting the oligomerization of NLRP3 and caspase-1 decreases necrosis among the infected or CFPE-stimulated macrophages. Furthermore, histological lymph node sections of bovines naturally infected with M. bovis contained cleaved gasdermin D, mainly in macrophages and giant cells within the granulomas. Finally, the induction of cell death (apoptosis and pyroptosis) decreased the intracellular bacteria count in the infected bovine macrophages, suggesting that cell death helps to control the intracellular growth of the mycobacteria. Our results indicate that M. bovis induces pyroptosis-like cell death that is partially related to the NLRP3 inflammasome activation and that the cell death process could control bacterial growth.

Clinical significance of detection of micrometastases in lymph nodes in ovarian cancer

Ovarian cancer of epithelial origin is the most common type of oncological process in this organ and is characterized by a high probability of fatal outcome. It is believed that this is due to insufficiently effective diagnosis of the prevalence and severity of this disease. This review presents data on the development of disease recurrence in a significant number of patients after cytoreductive surgery. According to some authors, the reason for the high risk of recurrence is the insufficient information content of the method of studying histological preparations of regional lymph nodes after staining with hematoxylin and eosin to detect small-sized metastases, including isolated tumor cells and micrometastases. To improve the accuracy of such diagnostics, it is recommended to supplement the study of multilevel sections of lymph nodes using routine staining with the use of immunohistochemistry and other methods using various antibodies to cytokeratins, along with other epithelial markers. The review provides an analysis of the opinions of various researchers on the prognostic significance of the detection of micrometastases in the lymph nodes, which can increase the effectiveness of therapy due to a more accurate assessment of the prevalence of the disease and correction of the tactics of treating patients with various oncological diseases.

RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning

Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification. The limitation is evident in the application stage as the heavy computation for extracting patch-level features is inevitable. In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method for fast observation strategy on WSIs. Imitating the diagnostic logic of human pathologists, our RL agent learns how to find regions of observation value and obtain representative features across multiple resolution levels, without having to analyze each part of the WSI at the high magnification. We benchmark our method on two whole-slide level classification tasks, including detection of metastases in WSIs of lymph node sections, and subtyping of lung cancer. Experimental results demonstrate that RLogist achieves competitive classification performance compared to typical multiple instance learning algorithms, while having a significantly short observation path. In addition, the observation path given by RLogist provides good decision-making interpretability, and its ability of reading path navigation can potentially be used by pathologists for educational/assistive purposes. Our code is available at: https://github.com/tencent-ailab/RLogist.