Identification Of Tissue Research Articles

In Vivo Formation of Adrenal Organoids in a Novel Porcine Model of Adrenocortical Cell Transplantation.

Recognizing the limitations of current therapies for Addison's disease, novel treatments that replicate dynamic physiologic corticosteroid secretion, under control of ACTH, are required. The aim of these experiments was to evaluate the feasibility of adrenocortical cell transplantation (ACT) in a large animal model, adapting methods successfully used for intracutaneous pancreatic islet cell transplantation, using a fully biodegradable temporizing matrix. Autologous porcine ACT was undertaken by bilateral adrenalectomy, cell isolation, culture, and intracutaneous injection into a skin site preprepared using a biodegradable temporizing matrix (BTM) foam. Hydrocortisone support was provided during adrenocortical cell engraftment and weaned as tolerated. Blood adrenocortical hormone concentrations were monitored, and the transplant site was examined at endpoint. Outcome measures included cellular histochemistry, systemic hormone production, and hydrocortisone independence. Transplanted adrenocortical cells showed a capability to survive and proliferate within the intracutaneous site and an ability to self-organize into discrete tissue organoids with features of the normal adrenal histologic architecture. Interpretation of systemic hormone levels was confounded by the identification of accessory adrenals and regenerative cortical tissue within the adrenal bed postmortem. Corticosteroids were unable to be completely ceased. ACT in a large animal model has not previously been attempted, yet it is an important step toward clinical translation. These results demonstrate rhe potential for ACT based on the development of adrenal organoids at the BTM site. However, the inability to achieve clinically relevant systemic hormone production suggests insufficient function, likely attributable to insufficient cells through delivered dose and subsequent proliferation.

Enhancing Intraoperative Tissue Identification: Investigating a Smart Electrosurgical Knife's Functionality During Electrosurgery.

Detecting the cancerous growth margin and achieving a negative margin is one of the challenges that surgeons face during cancer procedures. A smart electrosurgical knife with integrated optical fibers has been designed previously to enable real-time use of diffuse reflectance spectroscopy for intraoperative margin assessment. In this paper, the thermal effect of the electrosurgical knife on tissue sensing is investigated. Porcine tissues and phantoms were used to investigate the performance of the smart electrosurgical knife after electrosurgery. The fat-to-water content ratio (F/W-ratio) served as the discriminative parameter for distinguishing tissues and tissue mimicking phantoms with varying fat content. The F/W-ratio of tissues and phantoms was measured with the smart electrosurgical knife before and after 14 minutes of electrosurgery. Additionally, a layered porcine tissue and phantom were sliced and measured from top to bottom with the smart electrosurgical knife. Mapping the thermal activity of the electrosurgical knife's electrode during animal tissue electrosurgery revealed temperatures exceeding 400 °C. Electrosurgery for 14 minutes had no impact on the device's accurate detection of the F/W-ratio. The smart electrosurgical knife enables real-time tissue detection and predicts the fat content of the next layer from 4 mm ahead. The design of the smart electrosurgical knife outlined in this paper demonstrates its potential utility for tissue detection during electrosurgery. In the future, the smart electrosurgical knife could be a valuable intraoperative margin assessment tool, aiding surgeons in detecting tumor borders and achieving negative margins.

Correcting common OCT artifacts enhances plaque classification and identification of higher-risk plaque features

BackgroundOptical coherence tomography (OCT) is used widely to guide stent placement, identify higher-risk plaques, and assess mechanisms of drug efficacy. However, a range of common artifacts can prevent accurate plaque classification and measurements, and limit usable frames in research studies. We determined whether pre-processing OCT images corrects artifacts and improves plaque classification. MethodsWe examined both ex-vivo and clinical trial OCT pullbacks for artifacts that prevented accurate tissue identification and/or plaque measurements. We developed Fourier transform-based software that reconstructed images free of common OCT artifacts, and compared corrected and uncorrected images. Results48 % of OCT frames contained image artifacts, with 62 % of artifacts over or within lesions, preventing accurate measurement in 12 % frames. Pre-processing corrected >70 % of all artifacts, including thrombus, macrophage shadows, inadequate flushing, and gas bubbles. True tissue reconstruction was achieved in 63 % frames that would otherwise prevent accurate clinical measurements. Artifact correction was non-destructive and retained anatomical lumen and plaque parameters. Correction improved accuracy of plaque classification compared against histology and retained accurate assessment of higher-risk features. Correction also changed plaque classification and prevented artifact-related measurement errors in a clinical study, and reduced unmeasurable frames to <5 % ex-vivo and ~1 % in-vivo. ConclusionsFourier transform-based pre-processing corrects a wide range of common OCT artifacts, improving identification of higher-risk features and plaque classification, and allowing more of the whole dataset to be used for clinical decision-making and in research. Pre-processing can augment OCT image analysis systems both for stent optimization and in natural history or drug studies.

Improving Intraoperative Parathyroid Hormone Specimen Handling and Processing.

Surgeons request intraoperative parathyroid hormone (PTH) monitoring during parathyroidectomy procedures to confirm identification of abnormal gland tissue. Generally, a 50% decrease in the baseline PTH level indicates the abnormal tissue has been removed. A delay in collecting and processing PTH blood samples can complicate intraoperative decision making and prolong the procedure. The purpose of this quality improvement project was to develop tools to facilitate the specimen management process (eg, requesting, transporting, analyzing) for PTH blood samples and decrease the average total time required for transit and assay. We implemented a two-pronged initiative that involved improving the laboratory requisition form and creating a parathyroid tote box to contain all the needed information and supplies. The average total time for transit and assay decreased from 31.36 minutes before implementation to 22.06 minutes after implementation. Perioperative nurses expressed satisfaction with the changes and continue to use the revised process.

Wavelength-swept spontaneous Raman spectroscopy system improves fiber-based collection efficiency for whole brain tissue classification.

Raman spectroscopy is a valuable technique for tissue identification, but its conventional implementation is hindered by low efficiency due to scattering. Addressing this limitation, we are further developing the wavelength-swept Raman spectroscopy approach. We aim to enhance Raman signal detection by employing a laser capable of sweeping over a wide wavelength range to sequentially excite tissue with different wavelengths, paired with a photodetector featuring a fixed narrow-bandpass filter for collecting the Raman signal at a specific wavelength. We experimentally validate our technique using a fiber-based swept-source Raman spectroscopy setup. In addition, simulations are conducted to assess the efficacy of our approach in comparison with conventional spectrometer-based Raman spectroscopy. Our simulations reveal that the wavelength-swept configuration leads to a significantly stronger signal compared with conventional spectrometer-based Raman spectroscopy. Experimentally, our setup demonstrates an improvement of at least 200× in photon detection compared with the spectrometer-based setup. Furthermore, data acquired from different regions of a fixed monkey brain using our technique achieves 99% accuracy in classification via -nearest neighbor analysis. Our study showcases the potential of wavelength-swept Raman spectroscopy for tissue identification, particularly in highly scattering media, such as the brain. The developed technique offers enhanced signal detection capabilities, paving the way for future in vivo applications in tissue characterization.

Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks.

Hyperspectral imaging has demonstrated its potential to provide correlated spatial and spectral information of a sample by a non-contact and non-invasive technology. In the medical field, especially in histopathology, HSI has been applied for the classification and identification of diseased tissue and for the characterization of its morphological properties. In this work, we propose a hybrid scheme to classify non-tumor and tumor histological brain samples by hyperspectral imaging. The proposed approach is based on the identification of characteristic components in a hyperspectral image by linear unmixing, as a features engineering step, and the subsequent classification by a deep learning approach. For this last step, an ensemble of deep neural networks is evaluated by a cross-validation scheme on an augmented dataset and a transfer learning scheme. The proposed method can classify histological brain samples with an average accuracy of 88%, and reduced variability, computational cost, and inference times, which presents an advantage over methods in the state-of-the-art. Hence, the work demonstrates the potential of hybrid classification methodologies to achieve robust and reliable results by combining linear unmixing for features extraction and deep learning forclassification.

Application of Digital Correlation-Spectral Analysis for Identification, Classifiaction and Separation of Plant Roots and Plant Root Tissues: Simple Low-Cost Alternatives for Laser Diffractometer Based 2D FFT Pattern Recognition Techniques

Application of Digital Correlation-Spectral Analysis for Identification, Classifiaction and Separation of Plant Roots and Plant Root Tissues: Simple Low-Cost Alternatives for Laser Diffractometer Based 2D FFT Pattern Recognition Techniques

A Minimally Invasive Robotic Tissue Palpation Device.

Robot-assisted minimally invasive surgery remains limited by the absence of haptic feedback, which surgeons routinely rely on to assess tissue stiffness. This limitation hinders surgeons' ability to identify and treat abnormal tissues, such as tumors, during robotic surgery. To address this challenge, we developed a robotic tissue palpation device capable of rapidly and non-invasively quantifying the stiffness of soft tissues, allowing surgeons to make objective and data-driven decisions during minimally invasive procedures. We evaluated the effectiveness of our device by measuring the stiffness of phantoms as well as lung, heart, liver, and skin tissues obtained from both rats and swine. Results demonstrated that our device can accurately determine tissue stiffness and identify tumor mimics. Specifically, in swine lung, we determined elastic modulus (E) values of 9.1 ± 2.3, 16.8 ± 1.8, and 26.0 ± 3.6 kPa under different internal pressure of the lungs (PIP) of 2, 25, and 45 cmH2O, respectively. Using our device, we successfully located a 2-cm tumor mimic embedded at a depth of 5 mm in the lung subpleural region. Additionally, we measured E values of 33.0 ± 5.4, 19.2 ± 2.2, 33.5 ± 8.2, and 22.6 ± 6.0 kPa for swine heart, liver, abdominal skin, and muscle, respectively, which closely matched existing literature data. Results suggest that our robotic palpation device can be utilized during surgery, either as a stand-alone or additional tool integrated into existing robotic surgical systems, to enhance treatment outcomes by enabling accurate intraoperative identification of abnormal tissue.

EFFECTS OF RIBOSE-INDUCED GLYCATION ON THE ELASTIC MODULUS OF COLLAGEN FIBRILS OBSERVED BY ATOMIC FORCE MICROSCOPY

The interplay between diabetes mellitus and the structural integrity of collagen has significant implications for tissue functionality and disease progression. The aim of this study was to empirically investigate the effects of ribose-induced glycation on the biomechanical properties of collagen fibrils, using atomic force microscopy for precise measurements. Methodology. We used collagen fibrils from the common digital extensor (CDE) and superficial digital flexor (SDF) tendons of an adult bovine model to mimic the glycation processes that occur in diabetic pathology. The samples underwent controlled glycation by incubation with ribose for 24 hours and 14 days compared to phosphate buffered saline treated controls. A Bioscope Catalyst atomic force microscope (Bruker, USA) was used for all atomic force microscopy imaging in this study. Scientific novelty. Our results show a marked increase in the elastic modulus of collagen fibrils after ribose treatment, indicating stiffening with glycation. Notably, SDF fibrils showed a greater increase in stiffness after 24 hours of ribose exposure compared to CDE fibrils, suggesting variations in glycation rates relative to fibril anatomy. Statistical analyses confirmed the significance of these findings and provided a model for understanding similar processes in human diabetes. Conclusions. The different response to glycation observed between CDE and SDF fibrils prompts further investigation into the role of anatomical and structural factors in glycation susceptibility. Identification of tissues at higher risk of glycation-induced damage could lead to the development of targeted prevention strategies for diabetic complications. In addition, the potential for pharmacological intervention to inhibit glycation processes or enhance advanced glycation end products (AGEs) degradation offers a promising avenue for mitigating the progression of diabetes-related complications. The results of this study highlight the potential of ribose-induced changes in collagen as a model for diabetes-related tissue changes and propose a mechanistic framework that could guide the development of interventions aimed at mitigating the effects of collagen-related diabetic complications.

Effect of radiation therapy on lymph node fluorescence in head and neck squamous cell carcinoma after intravenous injection of indocyanine green: a prospective evaluation

BackgroundIndocyanine green (ICG)-guided surgery has proven effective in the identification of neoplastic tissues. The effect of radiation therapy (RT) on lymph node fluorescence after intravenous injection of ICG has not been addressed yet. The objective of this study was to evaluate the influence of RT on node fluorescence during neck dissection in head and neck squamous cell carcinoma (HNSCC).ResultsTwenty-four patients with planned neck dissection for HNSCC were prospectively enrolled. Eleven were included without previous radiation therapy and 13 after RT. ICG was intravenously administered in the operating room. The resected specimen was analyzed by the pathology department to determine the status of each resected lymph node (invaded or not). The fluorescence of each resected node was measured in arbitrary units (AU) on paraffin blocs. The surface area (mm2) of all metastatic nodes and of the invaded component were measured. The values of these surface areas were correlated to fluorescence values. A total of 707 nodes were harvested, the mean fluorescence of irradiated nodes (n = 253) was 9.2 AU and of non-irradiated nodes (n = 454) was 9.6 AU (p = 0.63). Fifty nodes were invaded, with a mean fluorescence of 22 AU. The mean fluorescence values in the invaded irradiated nodes (n = 20) and the invaded non-irradiated nodes (n = 30) were 19 AU and 28 AU (p = 0.23), respectively. The surface area of metastatic nodes and of the invaded component were correlated to fluorescence values even after previous RT (p = 0.02).ConclusionNo differences were observed between the fluorescence of irradiated and non-irradiated lymph nodes, including invaded nodes. ICG-guided surgery can be performed after failed RT.Trial registrationEudraCT ref. 2013-004498-29, registered 29 November 2013. https://www.clinicaltrialsregister.eu/ctr-search/search?query=2013-004498-29

SimCol3D — 3D reconstruction during colonoscopy challenge

Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform. However, reconstructing the colon from video footage remains difficult. Learning-based approaches hold promise as robust alternatives, but necessitate extensive datasets. Establishing a benchmark dataset, the 2022 EndoVis sub-challenge SimCol3D aimed to facilitate data-driven depth and pose prediction during colonoscopy. The challenge was hosted as part of MICCAI 2022 in Singapore. Six teams from around the world and representatives from academia and industry participated in the three sub-challenges: synthetic depth prediction, synthetic pose prediction, and real pose prediction. This paper describes the challenge, the submitted methods, and their results. We show that depth prediction from synthetic colonoscopy images is robustly solvable, while pose estimation remains an open research question.

Accuracy Enhancement Of Glioma Boundary Tissue Identification By Polarization-Resolved LIBS Spectral Fusion

Accuracy Enhancement Of Glioma Boundary Tissue Identification By Polarization-Resolved LIBS Spectral Fusion

Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications.

A Self-Optimized Machine Learning Approach for Constitutive Parameters Identification of Aortic Walls

The accurate identification of constitutive parameters is considered the key challenge for the study of mechanical properties of biological soft tissues. The popular machine learning (ML)-based inverse identification frameworks always require large amounts of training datasets. This work proposes an ML framework called self-optimized ML for accurate identifying the constitutive parameters of aortic walls under few training datasets conditions. The self-optimized ML includes three steps: Step 1: the forward physical FEM models first simulate the nonlinear deformation of aortic walls subject to uniaxial tension tests and are used to establish the nonlinear relationship datasets, Step 2: the carefully designed inverse random forest (RF) of the ML model can offer rapid identification by learning the established nonlinear relationship datasets, and Step 3: forward physical FEM models are recalled to evaluate the error between the identification results in Step 2 and real values, and then the accuracy are embedded into RF for guiding optimization directions to ensure that the final identification results are accurately and physically reasonable. The accuracy and robustness validation of proposed framework was conducted by constitutive parameters identification of uniaxial tension experiment samples of bovine aortic walls. The approach proposed achieves the R-squared exceeding 96.90% in longitudinal direction and 98.30% in circumferential direction, which is better than directly ML approach and gradient-based approach under the same amounts of datasets, respectively. The comparison results show that self-optimized ML can not only achieve accurate identification of the constitutive parameters of aortic walls, but also can decrease the identification results dependency of the initial number of sampling data effectively. The identification approach developed herein provides a common and convenient framework for constitutive parameters identification of biological soft tissues.

DNA methylation-based organ tissue identification: Marker identification, SNaPshot multiplex assay development, and interlaboratory comparison

Identifying body fluids and organ tissues is highly significant as they can offer crucial evidence in criminal investigations and aid the court in making informed decisions, primarily through evaluating the biological source and possibly at the activity level up to death or fatal damage. In this study, organ tissue-specific CpG markers were identified from Illumina's methylation EPIC array data of nine organ tissues, including epidermis, dermis, heart, skeletal muscle, blood, kidney, brain, lung, and liver, from autopsies of 10 Koreans. Through the validation test using 43 samples, 18 hypomethylation markers, with two markers for each organ tissue type, were selected to construct a SNaPshot assay. Two multiplex assays involving forward and reverse SBE primers were designed to help investigators accurately determine the organ origin of the analyzed tissue samples through repeated analysis of the same PCR products for markers. The developed multiplex demonstrated high accuracy, achieving 100.0 % correct detection of the presence of nine organ tissue types in 88 samples from autopsies of 10 Asians. However, two lung samples showed additional positive indications of the presence of blood. An interlaboratory comparison using 80 autopsy samples (heart, skeletal muscle, blood, kidney cortex, kidney medulla, brain, lung, and liver) from 10 individuals in Germany revealed overall comparable results with correct detection of the presence of eight organ tissue types in 92.5 % samples (74 of 80 samples). In the case of six samples, it was impossible to determine the correct tissue successfully due to drop-outs of unmethylation signals at target tissue marker loci. One of these lung samples revealed only non-intended off-target signals for blood. The observed differences might be due to differences in sample collection during routine autopsy, technical differences due to the PCR cycler, and the threshold used for signal calling. Indicating the presence of additional tissue type and off-target unmethylation signals seems alleviated by applying more stringent hypomethylation thresholds. Therefore, the developed SNaPshot multiplex assays will be valuable for forensic investigators dealing with organ tissue identification, as well as for prosecutors and defense aiming to establish the circumstances that occurred at the crime scene.

A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images

Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.

Endoscopic-Assisted Microsurgical Meningioma Resection in the Skull Base via Minicraniotomy: Is There a Difference in Radicality and Outcome between Anterior Skull Base and Posterior Fossa?

Background: Keyhole-based approaches are being explored for skull base tumor surgery; aiming for reduced complications while maintaining resection success rates. This study evaluates skull base meningiomas resected using an endoscopic-assisted microsurgical keyhole approach, comparing outcomes with standard procedures. Methods: Between 2013 and 2019; 71 out of 89 patients were treated using an endoscopic-assisted microsurgical procedure. A total of 42 meningiomas were localized at the anterior skull base and 29 in the posterior fossa. The surgical techniques and use of an endoscope were analyzed and compared in terms of complications, surgical radicality, outcome, and recurrences in the patients' follow-up. Results: The two different cohorts yielded similar rates of GTR (anterior skull base: 80% versus posterior fossa: 82%). The complication rate was 31% for the posterior fossa and 16% for the anterior skull base. An endoscope was used in 79% of all cases. Tumor remnants were detected by means of endoscopic visualization in 58.6% of posterior fossa and 33% of anterior skull base meningiomas. The statistical analysis revealed significantly higher benefits from endoscope use in the posterior fossa cohort (p < 0.05). Conclusions: The results revealed that endoscopy was beneficial in both locations. The identification of remnant tumor tissue and the benefit of endoscopy were clearly higher in the posterior fossa. Endoscopic assistance is a very helpful tool for increasing radicality, providing a better anatomical overview during surgery, and better identifying remnant tumor tissue in skull base meningioma surgery.

A Case in Which Identification of Cardiac Scar Tissue by MDCT Was Effective in Ablation for Ventricular Tachycardia – A Secondary Publication

During catheterization of ventricular tachycardia, it is often found difficult to identify the origin of ventricular tachycardia when tachycardia is not induced during the procedure or hemodynamics are disrupted. Late gadolinium enhancement (LGE) in cardiac MRI and late Iodine enhancement (LIE) in cardiac CT are reportedly performed to identify myocardial scar tissue and estimate the origin of ventricular tachycardia preoperatively. However, although LGE is useful for identifying the origin of tachycardia, the slice thickness is large and imaging takes a long time, and if a premature beat occurs or the device is inserted during imaging, a good image cannot be obtained. On the other hand, LIE also has poor resolution, making it difficult to take clear images. In this case report, the origin of ventricular tachycardia was presumed preoperatively using a new image processing method called Subtraction Myocardial Image for Late Iodine Enhancement (SMILIE) using a 320-row Area Detector CT, which was useful during catheter ablation.

Rational design of a red-emission fluorescent probe for imaging mitochondrial viscosity and fatty liver tissues

Abnormal mitochondrial viscosity is closely associated with various diseases. Thus, it is imperative to design and synthesized fluorescent probes for monitoring changes in mitochondrial viscosity. In this work, two series of fluorescence probes with triphenylamine unit and 2-hydroxyphenylbenzothiazole unit respectively were constructed. The impact of varying chain lengths on the photophysical properties of these probes was investigated. The results indicated that probe TPAC12 with dodecane group exhibited the largest fluorescence enhancement. The results of cell imaging experiments demonstrated that all these probes exhibited specific targeting towards mitochondria and were not influenced by changes in mitochondrial membrane potential. Additionally, probe TPAC12 facilitated the monitoring of viscosity alterations within mitochondria. Importantly, probe TPAC12 has been effectively utilized for the identification of both fatty liver and normal liver tissues. These results suggested that probe TPAC12 holds great potential as a valuable tool for detecting mitochondrial viscosity in biological systems.

A rapid and field-ready snap-chill polymerase spiral reaction (PSR) colorimetric assay for identification of buffalo (Bubalus bubalus) tissue

The present study introduces a novel Point-of-Care (POC) polymerase spiral reaction (PSR) assay for rapid, on-site buffalo tissue identification, particularly in resource-constrained settings. Polymerase spiral reaction addresses the limitations of existing detection methods by utilizing chemical-free snap-chill DNA extraction, highly specific mitochondrial d-loop gene primers for amplification, and a unique chromogenic indicator triad of hydroxy naphthol blue, malachite green, and phenol red for visual detection. The effectiveness of the chromogenic indicators was validated against SYBR Green I and agarose gel electrophoresis methods. The assay exhibits high specificity with no cross-amplification observed in common meats (beef, mutton, chevon, and pork), sensitivity down to 10 fg of buffalo DNA, and the ability to detect adulteration as low as 0.1% buffalo meat content. Comprehensive validation through diverse, blind, and processed samples, along with established compatibility with the commercially available DNA isolation kit and comparison with standard buffalo-specific PCR, confirms its accuracy. To the best of our knowledge, this represents the first report of a PSR colorimetric assay capable of identifying buffalo tissue within 90 min in resource-constrained settings.