Identification Of Tissue Research Articles

Proximity-Enhanced Functional Imaging Analysis of Engineered Tumors.

Functional imaging (FI) techniques have revolutionized tumor imaging by providing information on specific tumor functions, such as glycometabolism. However, tumor cells lack unique molecular characteristics at the molecular level and metabolic pathways, resulting in limited metabolic differences compared to normal cells and increased background signals from FI. To address this limitation, we developed a novel imaging technique termed proximity-enhanced functional imaging (PEFI) for accurate visualization of tumors. By using "two adjacent chemically labeled glycoproteins" as output signals, we significantly enhance the metabolic differences between tumor and normal cells by PEFI, thereby reducing the background signals for analysis and improving the accuracy of tumor functional imaging. Our results demonstrate that PEFI can accurately identify tumors at the cellular, tissue, and animal level, and has potential value in clinical identification and analysis of tumor cells and tissues, as well as in the guidance of clinical tumor resection surgery.

Proximity‐Enhanced Functional Imaging Analysis of Engineered Tumors

AbstractFunctional imaging (FI) techniques have revolutionized tumor imaging by providing information on specific tumor functions, such as glycometabolism. However, tumor cells lack unique molecular characteristics at the molecular level and metabolic pathways, resulting in limited metabolic differences compared to normal cells and increased background signals from FI. To address this limitation, we developed a novel imaging technique termed proximity‐enhanced functional imaging (PEFI) for accurate visualization of tumors. By using “two adjacent chemically labeled glycoproteins” as output signals, we significantly enhance the metabolic differences between tumor and normal cells by PEFI, thereby reducing the background signals for analysis and improving the accuracy of tumor functional imaging. Our results demonstrate that PEFI can accurately identify tumors at the cellular, tissue, and animal level, and has potential value in clinical identification and analysis of tumor cells and tissues, as well as in the guidance of clinical tumor resection surgery.

Accurate Liver Fibrosis Detection Through Hybrid MRMR-BiLSTM-CNN Architecture with Histogram Equalization and Optimization.

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis.

Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue

Breast conserving resection with free margins is the gold standard treatment for early breast cancer recommended by guidelines worldwide. Therefore, reliable discrimination between normal and malignant tissue at the resection margins is essential. In this study, normal and abnormal tissue samples from breast cancer patients were characterized ex vivo by optical emission spectroscopy (OES) based on ionized atoms and molecules generated during electrosurgical treatment. The aim of the study was to determine spectroscopic features which are typical for healthy and neoplastic breast tissue allowing for future real-time tissue differentiation and margin assessment during breast cancer surgery. A total of 972 spectra generated by electrosurgical sparking on normal and abnormal tissue were used for support vector classifier (SVC) training. Specific spectroscopic features were selected for the classification of tissues in the included breast cancer patients. The average classification accuracy for all patients was 96.9%. Normal and abnormal breast tissue could be differentiated with a mean sensitivity of 94.8%, a specificity of 99.0%, a positive predictive value (PPV) of 99.1% and a negative predictive value (NPV) of 96.1%. For 66.6% patients all classifications reached 100%. Based on this convincing data, a future clinical application of OES-based tissue differentiation in breast cancer surgery seems to be feasible.

Identification and postnatal developmental characteristics of brown adipose tissue in yak calves

AbstractBackgroundBrown adipose tissue (BAT) provides newborn mammals with a critical capacity for non‐shivering thermogenesis and enables their adaptation to the cold of the extrauterine environment. The domestic yak (Bos grunniens) has evolved on the Qinghai–Tibet Plateau and has adapted well to extremely cold and hypoxic environments. However, the location, characterization and postnatal development of BAT in yak calves remain largely unknown.MethodsWe identified different types of adipocytes and explored the postnatal development of interscapular and subcutaneous adipose tissue in yak calves at 1, 7 and 30 days old using morphological and molecular biology methods.ResultsThe results show that multilocular and unilocular adipocytes were found in two depots. Multilocular adipocytes contained small lipid droplets packed with numerous mitochondria. However, unilocular adipocytes contained a single unilocular lipid droplet and few mitochondria. The number of multilocular adipocytes significantly decreased, whereas the number of unilocular adipocytes significantly increased from day 1 to 30. Furthermore, uncoupling protein 1 (UCP1) expressions in both multilocular and unilocular adipocytes, as well as UCP1 mRNA and protein expression, were significantly decreased from day 1 to 30. In addition, peroxisome proliferator‐activated receptor‐γ coactivator‐1α and peroxisome proliferator‐activated receptor alpha mRNA expression were significantly decreased.ConclusionsThe results of the research indicate that adipocytes in both newborn yak calf interscapular and subcutaneous adipose tissue are multilocular adipocytes. Many multilocular adipocytes were transformed into unilocular adipocytes after birth, but some multilocular adipocytes remained at day 30; this transition occurred at different rates in different depots. These results provide a basis for further study of BAT in yak calves.

Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications

Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355–1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.

Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue.

Inverse mechanical parameter identification enables the characterization of ultrasoft materials, for which it is difficult to achieve homogeneous deformation states. However, this usually involves high computational costs that are mainly determined by the complexity of the forward model. While simulation methods like finite element models can capture nearly arbitrary geometries and implement involved constitutive equations, they are also computationally expensive. Machine learning models, such as neural networks, can help mitigate this problem when they are used as surrogate models replacing the complex high fidelity models. Thereby, they serve as a reduced order model after an initial training phase, where they learn the relation of in- and outputs of the high fidelity model. The generation of the required training data is computationally expensive due to the necessary simulation runs. Here, active learning techniques enable the selection of the "most rewarding" training points in terms of estimated gained accuracy for the trained model. In this work, we present a recurrent neural network that can well approximate the output of a viscoelastic finite element simulation while significantly speeding up the evaluation times. Additionally, we use Monte-Carlo dropout based active learning to identify highly informative training data. Finally, we showcase the potential of the developed pipeline by identifying viscoelastic material parameters for human brain tissue.

Computed Tomography Evaluation of Frozen or Glycerinated Bradypus variegatus Cadavers: A Comprehensive View with Emphasis on Anatomical Aspects

Bradypus variegatus has unique anatomical characteristics, and many of its vascular and digestive tract aspects have yet to be clearly understood. This lack of information makes clinical diagnoses and surgical procedures difficult. The aim of this study was to evaluate the anatomical aspects of frozen and glycerinated corpses of B. variegatus using computed tomography (CT), emphasizing vascular and digestive contrast studies. Nine corpses that died during routine hospital were examined via CT in the supine position with scanning in the craniocaudal direction. In frozen cadavers, the contrast was injected into a cephalic vein after thawing and, subsequently, was administered orally. In addition to bone structures, CT allowed the identification of organs, soft tissues, and vascular structures in specimens. Visualization of soft tissues was better after contrast been administered intravenously and orally, even without active vascularization. Furthermore, the surfaces of the organs were highlighted by the glycerination method. With this technique, it was possible to describe part of the vascularization of the brachial, cervical, thoracic, and abdominal regions, in addition to highlighting the esophagus and part of the stomach. CT can be another tool for the evaluation of B. variegatus cadavers by anatomists or pathologists, contributing to the identification of anatomical structures.

A blinded study using laser induced endogenous fluorescence spectroscopy to differentiate ex vivo spine tumor, healthy muscle, and healthy bone

Ten patients undergoing surgical resection for spinal tumors were selected. Samples of tumor, muscle, and bone were resected, de-identified by the treating surgeon, and then scanned with the TumorID technology ex vivo. This study investigates whether TumorID technology is able to differentiate three different human clinical fresh tissue specimens: spine tumor, normal muscle, and normal bone. The TumorID technology utilizes a 405 nm excitation laser to target endogenous fluorophores, thereby allowing for the detection of tissue based on emission spectra. Metabolic profiles of tumor and healthy tissue vary, namely NADH (bound and free emission peak, respectively: 487 nm, 501 nm) and FAD (emission peak: 544) are endogenous fluorophores with distinct concentrations in tumor and healthy tissue. Emission spectra analyzed consisted of 74 scans of spine tumor, 150 scans of healthy normal bone, and 111 scans of healthy normal muscle. An excitation wavelength of 405 nm was used to obtain emission spectra from tissue as previously described. Emission spectra consisted of approximately 1400 wavelength intensity pairs between 450 and 750 nm. Kruskal–Wallis tests were conducted comparing AUC distributions for each treatment group, α = 0.05. Spectral signatures varied amongst the three different tissue types. All pairwise comparisons among tissues for Free NADH were statistically significant (Tumor vs. Muscle: p = 0.0006, Tumor vs. Bone: p < 0.0001, Bone vs. Muscle: p = 0.0357). The overall comparison of tissues for FAD (506.5–581.5 nm) was also statistically significant (p < 0.0001), with two pairwise comparisons being statistically significant (Tumor vs. Muscle: p < 0.0001, Tumor vs. Bone: p = 0.0045, Bone vs. Muscle: p = 0.249). These statistically significant differences were maintained when stratifying tumor into metastatic carcinoma (N = 57) and meningioma (N = 17). TumorID differentiates tumor tissue from normal bone and normal muscle providing further clinical evidence of its efficacy as a tissue identification tool. Future studies should evaluate TumorID’s ability to serve as an adjunctive tool for intraoperative assessment of surgical margins and surgical decision-making.

A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning

ObjectiveThis study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT). MethodsCurrently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU). ResultsCropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them. ConclusionThis U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis.

Fiberbots: Robotic fibers for high-precision minimally invasive surgery.

Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber. The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials. This low-profile (below 2 millimeters in diameter) robotic fiber exhibits remarkable motion precision (below 50 micrometers) and repeatability. We developed control algorithms coupling the robot with endoscopic instruments, demonstrating high-resolution in situ molecular and morphological tissue mapping. We assess its practicality and safety during in vivo laparoscopic surgery on a porcine model. High-precision motion of the fiber robot delivered endoscopically facilitates the effective use of cellular-level intraoperative tissue identification and ablation technologies, potentially enabling precise removal of cancer in challenging surgical sites.

Construction of a near infrared fluorescence system for imaging of biological tissues.

Surgical procedures often rely on unaided visual observation or endoscopic assistance, which may pose challenges in cases involving intricate anatomical relationships. Real-time imaging technologies capable of intraoperative visualization of target organs have the potential to enhance the precision of surgical procedures by facilitating accurate identification, separation, and protection of vital tissues or organs. Despite these advantages, the widespread adoption of such technologies has been hindered by factors such as the prohibitive cost of equipment. This study aims to optimize and develop a device based on Indocyanine Green (ICG) for fluorescence imaging. The objective is to monitor changes in the average fluorescence intensity of ICG in the bladder, offering valuable guidance for surgeries involving the bladder. 1. Male rabbits were administered 0.01mg/ml ICG via the renal pelvis and ear vein to obtain fluorescence images of the ureter, bladder, and small intestine. 2. After ligating the bilateral ureters of male rabbits, a retrograde bladder perfusion of 5ml 0.01mg/ml ICG was conducted to capture fluorescence images of the bladder over time. The average fluorescence intensity was computed using Image Pro Plus 6.0, and the corresponding curve was generated using Prism 8.0. Using a similar methodology, the average fluorescence intensity of male rabbits without ureteral ligation was measured and plotted over time. 1. The developed device facilitated imaging of the ureter, bladder, and small intestine. 2. The bladder's average fluorescence intensity exhibited changes over time in response to urine production and ureteral ligation, contrasting with observations without ureteral ligation. We have successfully constructed and optimized a modular fluorescence imaging system for organs and tissues. This system proves effective in imaging experiments involving hollow organs in animals and offers valuable insights for relevant surgical procedures.

Deep Learning-Based Identification of Tissue of Origin for Carcinomas of Unknown Primary Using MicroRNA Expression: Algorithm Development and Validation.

Carcinoma of unknown primary (CUP) is a subset of metastatic cancers in which the primary tissue source of the cancer cells remains unidentified. CUP is the eighth most common malignancy worldwide, accounting for up to 5% of all malignancies. Representing an exceptionally aggressive metastatic cancer, the median survival is approximately 3 to 6 months. The tissue in which cancer arises plays a key role in our understanding of sensitivities to various forms of cell death. Thus, the lack of knowledge on the tissue of origin (TOO) makes it difficult to devise tailored and effective treatments for patients with CUP. Developing quick and clinically implementable methods to identify the TOO of the primary site is crucial in treating patients with CUP. Noncoding RNAs may hold potential for origin identification and provide a robust route to clinical implementation due to their resistance against chemical degradation. This study aims to investigate the potential of microRNAs, a subset of noncoding RNAs, as highly accurate biomarkers for detecting the TOO through data-driven, machine learning approaches for metastatic cancers. We used microRNA expression data from The Cancer Genome Atlas data set and assessed various machine learning approaches, from simple classifiers to deep learning approaches. As a test of our classifiers, we evaluated the accuracy on a separate set of 194 primary tumor samples from the Sequence Read Archive. We used permutation feature importance to determine the potential microRNA biomarkers and assessed them with principal component analysis and t-distributed stochastic neighbor embedding visualizations. Our results show that it is possible to design robust classifiers to detect the TOO for metastatic samples on The Cancer Genome Atlas data set, with an accuracy of up to 97% (351/362), which may be used in situations of CUP. Our findings show that deep learning techniques enhance prediction accuracy. We progressed from an initial accuracy prediction of 62.5% (226/362) with decision trees to 93.2% (337/362) with logistic regression, finally achieving 97% (351/362) accuracy using deep learning on metastatic samples. On the Sequence Read Archive validation set, a lower accuracy of 41.2% (77/188) was achieved by the decision tree, while deep learning achieved a higher accuracy of 80.4% (151/188). Notably, our feature importance analysis showed the top 3 most important features for predicting TOO to be microRNA-10b, microRNA-205, and microRNA-196b, which aligns with previous work. Our findings highlight the potential of using machine learning techniques to devise accurate tests for detecting TOO for CUP. Since microRNAs are carried throughout the body via extracellular vesicles secreted from cells, they may serve as key biomarkers for liquid biopsy due to their presence in blood plasma. Our work serves as a foundation toward developing blood-based cancer detection tests based on the presence of microRNA.

Human robotic surgery with intraoperative tissue identification using rapid evaporation ionisation mass spectrometry

Instantaneous, continuous, and reliable information on the molecular biology of surgical target tissue could significantly contribute to the precision, safety, and speed of the intervention. In this work, we introduced a methodology for chemical tissue identification in robotic surgery using rapid evaporative ionisation mass spectrometry. We developed a surgical aerosol evacuation system that is compatible with a robotic platform enabling consistent intraoperative sample collection and assessed the feasibility of this platform during head and neck surgical cases, using two different surgical energy devices. Our data showed specific, characteristic lipid profiles associated with the tissue type including various ceramides, glycerophospholipids, and glycerolipids, as well as different ion formation mechanisms based on the energy device used. This platform allows continuous and accurate intraoperative mass spectrometry-based identification of ablated/resected tissue and in combination with robotic registration of images, time, and anatomical positions can improve the current robot-assisted surgical platforms and guide surgical strategy.

Intra-axial Supratentorial Tumours Resection under Fluorescein Guidance with 560 nm Yellow Filter: A Cross-sectional Study

Introduction: Excision of intra-axial tumours with maximal resection and minimal deficits is one of the intricate procedures in neurosurgery. Fluorescence-guided Surgery (FGS) helps in better identification of tumour tissue and allows Maximal Safe Resection (MSR) of high-grade brain tumours. Sodium fluorescein dye, when injected intravenously, gets concentrated in High-grade Glioma (HGG) tissue due to a disrupted BloodBrain Barrier (BBB), thus aiding in MSR, Extent Of Resection (EOR), and Gross Total Resection (GTR). Aim: To assess the effectiveness of intraoperative use of sodium fluorescein in achieving MSR and GTR. Materials and Methods: This was a cross-sectional study conducted on 30 patients with HGG who underwent surgery between January 2021 and May 2023 at SDM College of Medical Sciences, Dharwad, Karnataka, India. Two groups of 15 patients each were divided into those operated with the use of Fluorescein Sodium (FS) and those without FS. FS was administered at a dose of 5 mg/kg at the time of craniotomy. The primary endpoint was the number of patients with Histopathological Examination (HPE)-confirmed HGGs showing no contrast-enhancing tumour in the immediate postoperative Magnetic Resonance Imaging (MRI). Secondary endpoints included residual tumours on postoperative MRI, Focal Neurological Deficits (FND), and FS-related toxicity. The association between FS status and recurrence at different treatment time points was assessed using the Pearson Chisquare test. Recurrence rates were compared in each FS group using the Cochran Q test. Statistical significance was set at a 5% level (p-value&lt;0.05). Results: Homogeneous or heterogeneous yellow-green fluorescence was observed on the tumour tissue in all 15 patients (100%). Of the 15 patients where FS was used, two showed lesion recurrences at six months, and 13 showed recurrences at 12 months. Among the 15 patients where FS was not used, 11 showed recurrences at six months, and four showed recurrences at 12 months. This indicates that MSR, GTR, and EOR can be improved using FS intraoperatively during tumour resection. Conclusion: Based on the results, fluorescein dye with a 560 nm yellow filter under a highly illuminated microscope can help achieve better tumour resection with minimal injury to surrounding eloquent areas. It can be utilised as an ideal adjunct for tumour resection rather than with a microscope alone.

Early Detection of Brain Tumor from MRI Images Using Different Machine Learning Techniques

Effective treatment of brain tumours depends on early identification of tumour tissues. Categorizing tumors is essential for its early identification. In order to split and categorise brain tumours using MRI images, various machine learning algorithms are examined. Preprocessing methods including image augmentation, rotation flipping, and adaptive deformation are used on the BRATs dataset to improve the data quality. Convolutional neural network (CNN) feature extraction techniques like VGG19 and InceptionV3 are applied. K-Nearest Neighbours (KNN) and logistic regression techniques are used to categorise the features. The proposed models’ accuracy for Logistic Regression, KNN, VGG19, Inception V3 and VGG19 with augmentation is 84.66%, 90.68%, 92.17%, 91.56% and 95.43% respectively. These findings imply that CNN-based feature extraction and picture augmentation techniques greatly improves the accuracy of brain tumour identification, Overall thecInception V3 with augmentation produces the best results.

R2D2-GAN: Robust Dual Discriminator Generative Adversarial Network for Microscopy Hyperspectral Image Super-Resolution.

High-resolution microscopy hyperspectral (HS) images can provide highly detailed spatial and spectral information, enabling the identification and analysis of biological tissues at a microscale level. Recently, significant efforts have been devoted to enhancing the resolution of HS images by leveraging high spatial resolution multispectral (MS) images. However, the inherent hardware constraints lead to a significant distribution gap between HS and MS images, posing challenges for image super-resolution within biomedical domains. This discrepancy may arise from various factors, including variations in camera imaging principles (e.g., snapshot and push-broom imaging), shooting positions, and the presence of noise interference. To address these challenges, we introduced a unique unsupervised super-resolution framework named R2D2-GAN. This framework utilizes a generative adversarial network (GAN) to efficiently merge the two data modalities and improve the resolution of microscopy HS images. Traditionally, supervised approaches have relied on intuitive and sensitive loss functions, such as mean squared error (MSE). Our method, trained in a real-world unsupervised setting, benefits from exploiting consistent information across the two modalities. It employs a game-theoretic strategy and dynamic adversarial loss, rather than relying solely on fixed training strategies for reconstruction loss. Furthermore, we have augmented our proposed model with a central consistency regularization (CCR) module, aiming to further enhance the robustness of the R2D2-GAN. Our experimental results show that the proposed method is accurate and robust for super-resolution images. We specifically tested our proposed method on both a real and a synthetic dataset, obtaining promising results in comparison to other state-of-the-art methods. Our code and datasets are accessible through Multimedia Content.

Characterization of Polarimetric Properties in Various Brain Tumor Types Using Wide-Field Imaging Mueller Polarimetry.

Neuro-oncological surgery is the primary brain cancer treatment, yet it faces challenges with gliomas due to their invasiveness and the need to preserve neurological function. Hence, radical resection is often unfeasible, highlighting the importance of precise tumor margin delineation to prevent neurological deficits and improve prognosis. Imaging Mueller polarimetry, an effective modality in various organ tissues, seems a promising approach for tumor delineation in neurosurgery. To further assess its use, we characterized the polarimetric properties by analysing 45 polarimetric measurements of 27 fresh brain tumor samples, including different tumor types with a strong focus on gliomas. Our study integrates a wide-field imaging Mueller polarimetric system and a novel neuropathology protocol, correlating polarimetric and histological data for accurate tissue identification. An image processing pipeline facilitated the alignment and overlay of polarimetric images and histological masks. Variations in depolarization values were observed for grey and white matter of brain tumor tissue, while differences in linear retardance were seen only within white matter of brain tumor tissue. Notably, we identified pronounced optical axis azimuth randomization within tumor regions. This study lays the foundation for machine learning-based brain tumor segmentation algorithms using polarimetric data, facilitating intraoperative diagnosis and decision making.

Monkeypox Diagnosis in Clinical Settings: A Comprehensive Review of Best Laboratory Practices.

An outbreak of monkeypox (Mpox) was reported in more than 40 countries in early 2022. Accurate diagnosis of Mpox can be challenging, but history, clinical findings, and laboratory diagnosis can establish the diagnosis. The pre-analytic phase of testing includes collecting, storing, and transporting specimens. It is advised to swab the lesion site with virus transport medium (VTM) containing Dacron or polyester flock swabs from two different sites. Blood, urine, and semen samples may also be used. Timely sampling is necessary to obtain a sufficient amount of virus or antibodies. The analytical phase of infectious disease control involves diagnostic tools to determine the presence of the virus. While polymerase chain reaction (PCR) is the gold standard for detecting Mpox, genome sequencing is for identifying new or modified viruses. As a complement to these methods, isothermal amplification methods have been designed. ELISA assays are also available for the determination of antibodies. Electron microscopy is another effective diagnostic method for tissue identification of the virus. Wastewater fingerprinting provides some of the most effective diagnostic methods for virus identification at the community level. The advantages and disadvantages of these methods are further discussed. Post-analytic phase requires proper interpretation of test results and the preparation of accurate patient reports that include relevant medical history, clinical guidelines, and recommendations for follow-up testing or treatment.

Angiosome Origins and Concept

An angiosome is a composite block of tissue fed by a primary source artery. The term angiosome is derived from the Greek words angeion, which means channel, and somite, which means segment or sector of the body derived from soma, body. Angiosomes can be further differentiated into individual skin perforators or into matching venosomes (venous regions) and arteriosomes (arterial territories). The concept of angiosomes plays a pivotal role in surgical procedures involving tissue transplantation and composite flap formation. The secure anatomic borders defined by angiosomes enable the precise identification and isolation of tissues at various layers, facilitating the transplantation of individual components or the creation of composite flaps.