A Standardized Protocol for the Safe Retrieval of Infectious Postmortem Human Brain for Studying Whole-Brain Pathology.

ABSTRACT: We describe a safe and standardized perfusion protocol for studying brain pathology in high-risk autopsies using a custom-designed low-cost infection containment chamber and high-resolution histology. The output quality was studied using the histological data from the whole cerebellum and brain stem processed using a high-resolution cryohistology pipeline at 0.5 µm per pixel, in-plane resolution with serial sections at 20-µm thickness. To understand the pathophysiology of highly infectious diseases, it is necessary to have a safe and cost-effective method of performing high-risk autopsies and a standardized perfusion protocol for preparing high-quality tissues. Using the low-cost infection containment chamber, we detail the cranial autopsy protocol and ex situ perfusion-fixation of 4 highly infectious adult human brains. The digitized high-resolution histology images of the Nissl-stained series reveal that most of the sections were free of processing artifacts, such as fixation damage, freezing artifacts, and osmotic shock, at the macrocellular and microcellular level. The quality of our protocol was also tested with the highly sensitive immunohistochemistry staining for specific protein markers. Our protocol provides a safe and effective method in high-risk autopsies that allows for the evaluation of pathogen-host interaction, the underlying pathophysiology, and the extent of the infection across the whole brain at microscopic resolutions.

Methodological Considerations for Assessing Automatic Brightness Control in Endoscopy: Experimental Study.

Endoscopy is a critical application that requires adaptable illumination to adjust to varying imaging conditions. Automatic brightness control (ABC) algorithms ensure optimal brightness throughout the image with rapid but smooth response and render the true colours of the biological tissue under examination. To achieve good image quality, high-quality ABC algorithms are necessary. In this study, we propose a three-assessment method approach for objectively evaluating ABC algorithms based on (1) image brightness and its homogeneity, (2) controller response and response time, and (3) colour rendition. We conducted an experimental study to assess the effectiveness of ABC algorithms in one commercial and two developmental endoscopy systems using the proposed methods. The results showed that the commercial system achieved good, homogeneous brightness within 0.4 s, and its damping ratio was 0.597, indicating a stable system, but its colour rendition was suboptimal. The developmental systems had control parameter values that resulted in either a sluggish response (over 1 s) or a fast (about 0.3 ms) but unstable response with damping ratios above 1, causing flickers. Our findings indicate that the interdependency among the proposed methods can establish tradeoffs in the overall ABC performance better than single-parameter approaches. The study establishes that comprehensive assessments using the proposed methods can contribute to designing new ABC algorithms and optimising already implemented ones for efficient performance in endoscopy systems.

Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models.

Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes' well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model's accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors.

A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation.

Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model's parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices.

Efficacy of an Automated Algorithm for Screening Diabetic Retinopathy in Gradable and Ungradable Images in Real-Time Conditions.

Background: To examine the effectiveness of a computer-assisted device (CAD) for diabetic retinopathy (DR) screening from retinal photographs at a vitreoretinal outpatient department (VR OPD), telecamps, and diabetes outpatient clinic by an ophthalmologist. In particular, the effectiveness of CAD in gradable and ungradable retinal images was examined. Methods: A total of 848 eyes of 485 patients underwent 45° retinal photographs at the VR OPD of a tertiary care hospital in southern India. A total of 939 eyes of 472 patients with diabetes were examined in the telecamps conducted in remote villages in Tamil Nadu, a state in southern India. A total of 2,526 eyes of 1,263 patients were examined in a diabetes clinic using 45° field retinal photographs. The algorithm was validated under physiological dilatation (without pharmacological dilatation) in all three arms. Results: Seventy-one percent of 848 eyes in VR OPD, 13% of 939 eyes in telecamps, and 7% of 2,526 eyes in diabetes clinic were diagnosed to have DR. The algorithm showed 78.3% sensitivity and 55.1% specificity for all images and 78.9% sensitivity and 56.8% specificity for gradable images in the VR OPD; 80.1% sensitivity and 79.2% specificity for all images and 84.8% sensitivity and 80.0% sensitivity for gradable images in telecamps; 63.0% sensitivity and 79.6% specificity for all images and 63.2% sensitivity and 78.1% specificity for gradable images in the diabetes clinic. The algorithm had an overall accuracy of 76.4%. The ungradable rate was variable. Conclusion: The algorithm performs equally well in identifying DR from gradable and ungradable photographs and may be used for DR screening in a rural setting with limited or no access to eye care.

High-frame-rate A-mode ultrasound for calibration-free cuffless carotid pressure: feasibility study using lower body negative pressure intervention.

PURPOSE: Existing technologies to measure central blood pressure (CBP) intrinsically depend on peripheral pressure or calibration models derived from it. Pharmacological or physiological interventions yielding different central and peripheral responses compromise the accuracy of such methods. We present a high-frame-rate ultrasound technology for cuffless and calibration-free evaluation of BP from the carotid artery. The system uses a pair of single-element ultrasound transducers to capture the arterial diameter and local pulse wave velocity (PWV) for the evaluation of beat-by-beat BP employing a novel biomechanical model. MATERIALS AND METHODS: System's functionality assessment was conducted on eight male subjects (26 ± 4 years, normotensive and no history of cardiovascular risks) by perturbing pressure via short-term moderate lower body negative pressure (LBNP) intervention (-40 mmHg for 1 min). The ability of the system to capture dynamic responses of carotid pressure to LBNP was investigated and compared against the responses of peripheral pressure measured using a continuous BP monitor. RESULTS: While the carotid pressure manifested trends similar to finger measurements during LBNP, the system also captured the differential carotid-to-peripheral pressure response, which corroborates the literature. The carotid diastolic and mean pressures agreed with the finger pressures (limits-of-agreement within ±7 mmHg) and exhibited acceptable uncertainty (mean absolute errors were 2.4 ± 3.5 and 2.6 ± 4.0 mmHg, respectively). Concurrent to the literature, the carotid systolic and pulse pressures (PPs) were significantly lower than those of the finger pressures by 11.1 ± 9.4 and 11.3 ± 8.2 mmHg, respectively (p < .0001). CONCLUSIONS: The study demonstrated the method's potential for providing cuffless and calibration-free pressure measurements while reliably capturing the physiological aspects, such as PP amplification and dynamic pressure responses to intervention.

Comparative study of silicone membrane simulator and animal eye models for sub-Tenon's block.

To compare and assess silicone membrane-based sub-Tenon's block (STB) simulator and animal eye model (goat's eye) for practicing STB in terms of anatomical similarity and feel of texture of eye layers. The study included 34 participants (26 learners and 8 consultants) from tertiary ophthalmic centres. The participants were divided into groups A and B. Group A performed STB on the goat's eyes before using the silicone membrane simulator. Group B performed STB on the simulator and further proceeded to the goat's eye. Participants had to rate the anatomical similarity and feel of the texture for the simulator model on a scale of 0-10 and share their preference between the two models. In group A, the scores given to the simulator model and the feel of texture of layers were 8.05 ± 0.88 and 7.97 ± 1.07, respectively, and the scores given to the animal model and the feel of texture of layers were 8.11 ± 0.97 and 8.21 ± 0.88, respectively. Group B participants scored the simulator model and feel of texture of layers with 8.13 ± 0.95 and 8.25 ± 0.99, respectively. Overall, 89% participants preferred the simulator; the reasons included ease of usage, helpful warning system, absence of biological waste, and facility for repeatable training. The study validated anatomical accuracy, preference, and ability of usage of the STB simulator. For broader usage, further study involving higher number of participants is recommended.

Systematic development of immunohistochemistry protocol for large cryosections-specific to non-perfused fetal brain.

BACKGROUND: Immunohistochemistry (IHC) is an important technique in understanding the expression of neurochemical molecules in the developing human brain. Despite its routine application in the research and clinical setup, the IHC protocol specific for soft fragile fetal brains that are fixed using the non-perfusion method is still limited in studying the whole brain. NEW METHOD: This study shows that the IHC protocols, using a chromogenic detection system, used in animals and adult humans are not optimal in the fetal brains. We have optimized key steps from Antigen retrieval (AR) to chromogen visualization for formalin-fixed whole-brain cryosections (20 µm) mounted on glass slides. RESULTS: We show the results from six validated, commonly used antibodies to study the fetal brain. We achieved optimal antigen retrieval with 0.1 M Boric Acid, pH 9.0 at 70°C for 20 minutes. We also present the optimal incubation duration and temperature for protein blocking and the primary antibody that results in specific antigen labeling with minimal tissue damage. COMPARISON WITH EXISTING METHODS: The IHC protocol commonly used for adult human and animal brains results in significant tissue damage in the fetal brains with little or suboptimal antigen expression. Our new method with important modifications including the temperature, duration, and choice of the alkaline buffer for AR addresses these pitfalls and provides high-quality results. CONCLUSION: The optimized IHC protocol for the developing human brain (13-22 GW) provides a high-quality, repeatable, and reliable method for studying chemoarchitecture in neurotypical and pathological conditions across different gestational ages.

Histological characterization and development of mesial surface sulci in the human brain at 13-15 gestational weeks through high-resolution histology.

Cellular-level anatomical data from early fetal brain are sparse yet critical to the understanding of neurodevelopmental disorders. We characterize the organization of the human cerebral cortex between 13 and 15 gestational weeks using high-resolution whole-brain histological data sets complimented with multimodal imaging. We observed the heretofore underrecognized, reproducible presence of infolds on the mesial surface of the cerebral hemispheres. Of note at this stage, when most of the cerebrum is occupied by lateral ventricles and the corpus callosum is incompletely developed, we postulate that these mesial infolds represent the primordial stage of cingulate, callosal, and calcarine sulci, features of mesial cortical development. Our observations are based on the multimodal approach and further include histological three-dimensional reconstruction that highlights the importance of the plane of sectioning. We describe the laminar organization of the developing cortical mantle, including these infolds from the marginal to ventricular zone, with Nissl, hematoxylin and eosin, and glial fibrillary acidic protein (GFAP) immunohistochemistry. Despite the absence of major sulci on the dorsal surface, the boundaries among the orbital, frontal, parietal, and occipital cortex were very well demarcated, primarily by the cytoarchitecture differences in the organization of the subplate (SP) and intermediate zone (IZ) in these locations. The parietal region has the thickest cortical plate (CP), SP, and IZ, whereas the orbital region shows the thinnest CP and reveals an extra cell-sparse layer above the bilaminar SP. The subcortical structures show intensely GFAP-immunolabeled soma, absent in the cerebral mantle. Our findings establish a normative neurodevelopment baseline at the early stage.

Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning.

Magnetic Resonance (MR) images suffer from various types of artifacts due to motion, spatial resolution, and under-sampling. Conventional deep learning methods deal with removing a specific type of artifact, leading to separately trained models for each artifact type that lack the shared knowledge generalizable across artifacts. Moreover, training a model for each type and amount of artifact is a tedious process that consumes more training time and storage of models. On the other hand, the shared knowledge learned by jointly training the model on multiple artifacts might be inadequate to generalize under deviations in the types and amounts of artifacts. Model-agnostic meta-learning (MAML), a nested bi-level optimization framework is a promising technique to learn common knowledge across artifacts in the outer level of optimization, and artifact-specific restoration in the inner level. We propose curriculum-MAML (CMAML), a learning process that integrates MAML with curriculum learning to impart the knowledge of variable artifact complexity to adaptively learn restoration of multiple artifacts during training. Comparative studies against Stochastic Gradient Descent and MAML, using two cardiac datasets reveal that CMAML exhibits (i) better generalization with improved PSNR for 83% of unseen types and amounts of artifacts and improved SSIM in all cases, and (ii) better artifact suppression in 4 out of 5 cases of composite artifacts (scans with multiple artifacts).Clinical relevance- Our results show that CMAML has the potential to minimize the number of artifact-specific models; which is essential to deploy deep learning models for clinical use. Furthermore, we have also taken another practical scenario of an image affected by multiple artifacts and show that our method performs better in 80% of cases.

Face-Free Chest Detection Using Convolutional Neural Networks for Non-Contact Respiration Monitoring.

Non-contact methods for monitoring respiration face limitations when it comes to selecting the chest region of interest. The semi-automatic method, which requires the user to select the chest region in the first frame, is not suitable for real-time applications. The automatic method, which tracks the face first and then detects the chest region based on the face's position, can be inaccurate if the face is not visible or is rotated. Moreover, using the face region to track the chest region can under-utilize camera pixels since the face is not essential for monitoring respiration. This approach may adversely affect the quality of the respiration signal being measured. To address these issues, we propose a face-free chest detection model based on Convolutional Neural Networks. Our model enhances the measured non-contact respiration signal quality and utilizes more pixels for the chest region alone. In our quantitative study, we demonstrate that our method outperforms traditional methods that require the presence of the face. This approach offers potential benefits for real-time, non-contact respiration monitoring applicationsClinical relevance- This work enhances the performance of non-contact respiration monitoring techniques by precisely detecting the chest region without the need of face in it through a CNN-based model. The use of the CNN-based chest detection model also enhances the real-time monitoring capabilities of non-contact respiration monitoring techniques.

Improving Endoscopic Image Quality through the Use of High Dynamic Range Imaging-Like Method with Real-Time Performance.

High Dynamic Range (HDR) imaging is a digital image processing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in real time. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.

Multispectral Imaging for Vein Localization and Contrast Enhancement.

Intravenous (IV) catheterization is a common procedure. Still, there is a 26% chance of the first attempt catheterization failure due to the changing visibility of veins because of the patient's skin tone and body fat content. Ultrasound assistive devices help locate deeper veins but are not practical in emergencies, and transillumination assistive devices have a low field of view. Commercial near-infrared (NIR) imaging devices are effective in vein localization but are expensive and are not used in low-cost clinical settings. To overcome this, NIR Multispectral Imaging (MSI) was used to find the optimal wavelength that provides the enhanced visualization of veins for all skin types and Body Mass Index (BMI). The band with the highest vein-to-skin contrast ratio was selected and contrast enhancement was done using our proposed method. The primary blocks of the proposed method are Gamma correction, Contrast Limited Adaptive Histogram Equalization (CLAHE), Adaptive Thresholding, and image Fusion. The optimal spectral range was found to be 814-876 nm and our method increased the contrast by 0.41, 0.375, and 0.39 for fair, brown, and dark brown skin types, respectively, with different BMI.Clinical relevance- From the study, we can develop a potentially low-cost vein localization assistive device for training medical and nursing students and use it in emergencies for venous access to improve confidence in IV catheterization.

Ultrasound for Venous Local Pulse Wave Velocity: Comparison of Pulse Transit Time Methods.

Venous pulse wave velocity (vPWV) is a potential marker for determining the state of venous hemodynamics, venosclerosis, and vascular filling. Although there have been several studies on pulse wave velocity through blood vessels, the majority have focused on arteries, with only limited studies on veins. To our knowledge, this study is the first to compare the local vPWV estimation metrices. An in vivo study was conducted on 10 participants where the jugular venous pulses (JVP) from two proximal sites were simultaneously acquired using a dual-element high frame rate system. The local vPWV was computed using different transit time-based techniques. The study demonstrates the comparison between vPWV ranges computed using thresholding, fiduciary point (c and v) and correlation-based approaches indicated as vPWV|th, vPWV|c, vPWV|v and vPWV|Xcorr respectively. High fidelity echo frames were acquired from the jugular vein (JV) at a temporal resolution of 2 ms and an amplitude resolution of 10 µm. The study findings indicated that the vPWV computed using various transit time metrics were comparable without significant bias (p > 0.05). Among the VPWV metrices, vPWV|th had the lowest beat-to-beat variation (CoV = 18 %). The mean deviations in vPWV|c, vPWV|v and vPWV|Xcorr values from vPWV|th were 0.28, 0.17 and 0.22 m/s respectively, where the average beat-to-beat variation was minimal. The results suggested that the thresholding and cross-correlation metrices offered better performance in comparison with the fiduciary point techniques for vPWV estimation.Clinical Relevance- The study demonstrated the potential of direct transit time methods to reliably estimate the local vPWV on the internal jugular vein.

Arterial Wave Separation Analysis and Reflection Wave Transit Time Estimation using a Double Rayleigh Flow Rate Model.

Arterial pulse wave separation analysis (WSA) requires simultaneously measured pressure and flow rate waveform from the same arterial site. Modelling approaches to flow rate waveforms offers a methodological and instrumentational advantage. However, current techniques are limited to the aortic site. For non-aortic sites such as carotid artery, modelling methods that were developed for aortic sites are not likely to capture the intrinsic differences in the carotid flow rate. In this work, a double-Rayleigh flow rate model for the carotid artery is developed to separate the forward and backward pressure waves using WSA (DRMWSA). The model parameters are optimally found based on characteristic features - obtained from the pressure waveform. The DRMWSA was validated using a database of 4374 virtual (healthy) subjects, and its performance was compared with actual flow rate based WSA (REFWSA) at the carotid artery. An RMSE < 2 mmHg were obtained for forward and backward pressure waveforms. The reflection quantification indices (ΔPF, ΔPB), (RM, RI) obtained from DRMWSA demonstrated strong and statistically significant correlation (r > 0.96, p < 0.001) and (r > 0.80, p < 0.001) respectively, with insignificant bias (p > 0.05), upon comparing with counterparts in REFWSA. A moderate correlation (r = 0.64, p < 0.001) was obtained for reflection wave transit time between both methods. The proposed method minimises the measurements required for WSA and has the potential to widen the vascular screening procedures incorporating carotid pulse wave dynamics.Clinical Relevance-This methodology quantifies arterial pressure wave reflections in terms of pressure augmentation and reflection transit time. The methodological advantage of using only a single waveform helps easy translation to technological solutions for clinical research.

Automated Knowledge Modeling for Cancer Clinical Practice Guidelines.

Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation.

Automation of slide staining for large tissue sections.

Staining is a critical step in tissue analysis as it enhances the visibility and contrast of tissue structures for microscopic examination. Large tissue sections such as the human brain, heart, and liver are becoming increasingly important in studying complex tissue structures, providing critical information about the tissue's normal or abnormal development, function, and disease processes. Manual staining methods are still widely used and are prone to inconsistencies and inaccuracies, leading to unreliable results. Commercially available automated staining systems offer a more efficient alternative, but currently, these systems are only available for smaller 1" x 3" slides which are ill-suited for examining larger tissue sections. To address this challenge, we present a custom-designed Large format Automated Slide Stainer that can handle various glass slides, from the standard 1" x 3" slides to the custom-sized 2" x 3", 5" x 7", and 6" x 8" glass slides. The system uses a Cartesian robotic arm to stain the slides and has a user-friendly and intuitive interface for creating and modifying custom staining protocols. Safety features include chemical isolation, a ventilation system, an emergency shutdown, and a protective shield to minimize hazards from handling chemicals and biological materials. The automated stainer showed little variability in positioning with a mean offset error of 1.65 ± 0.65 mm and 1.73 ± 0.76 mm in the X and Y axes, respectively. In addition, the automated staining process showed better uniformity than manual staining. A pairwise distance was used to evaluate how well image histograms matched within a batch. The automated staining had a mean pairwise distance of 0.0070 ± 0.0017 (Nissl) and 0.0060 ± 0.0003 (Hematoxylin and Eosin(H&E)), which were far superior to the manual staining distances (Nissl: 0.0173 ± 0.0107 and H&E: 0.0185 ± 0.0067). This system represents a substantial advancement in tissue staining and has the potential to improve the reliability of tissue analysis significantly.Clinical relevance - Automated system for providing accurate, reproducible, and high-throughput staining of large tissue sections for use in histopathology and research.

Wearable Ambulatory Accelerometer System for Estimating Arterial Stiffness: A Pilot Study.

Given the gap between the crucial role of measuring arterial stiffness in cardiovascular disease prevention and the lack of a technology for frequent/continuous measurement to assess it without an operator, we have developed a wearable accelerometer-based system. It estimates local stiffness metrics (Ep, ß, and AC) by employing a one-point patient-specific calibration on the features of acceleration plethysmogram (APG) signal. An in-vivo study on 12 subjects was conducted (a) to select suitable ones from the host features on which the calibration could be applied and (b) to assess the feasibility of reliably estimating the stiffness metrics post-exercise when calibrated prior. The acquired APG signals were found to be reliable (SNR > 38 dB) and repeatable (CoV < 10 %). By examining a correlation matrix, it was found that (a-b)/(a"-b") is a potential feature of consideration for calibration against the stiffness. Due to exercise intervention, the local stiffness metrics have physiologically perturbed by a significant amount (p < 0.05), as observed from the reference measurements. Estimated Ep was found to have statistically significant and strong correlation (r = 0.761, p < 0.05) with actual Ep value, whereas statistically significant and moderate correlation were found with estimated ß (r = 0.682, p < 0.05) and estimated AC (r = 0.615, p < 0.05) with their respective actual measures. The system demonstrated its ability to estimate post-exercise stiffness metrics using the baseline calibration, even when subject to significant physiological changes.Clinical Relevance- This study reveals the potential of the developed wearable system to be used for continuous stiffness estimation even in the presence of hemodynamic perturbations.

Automation of slide coverslipping for large tissue sections.

Coverslipping is the process of placing a cover glass or coverslip over a glass slide mounted with a stained tissue specimen without forming air bubbles, which can negatively impact the microscopic examination. While manual coverslipping is still widely used, automated systems have made the process easier and more consistent. Commercially available automated cover-slippers are limited to handling only slides that are 1" x 3", suitable for processing smaller tissue specimens. However, for larger tissue specimens sectioned from organs like the brain, liver, etc., slides can reach sizes up to 6" x 8", exceeding the capabilities of these systems. We present SLIDE PROTEKT, a fully automated large format coverslipping system designed to efficiently coverslip large format slides. This system has multiple zones for slide and coverslip transportation, dispensing of mounting medium, and precise placement of the coverslip without air bubbles. The ability of the system to output quality coverslipped slides was validated by processing 50 large-format brain tissue slides. The results were found to be comparable to manual coverslipping. The system achieved a coverslip placement accuracy of 80% with a mean positional offset that was within a tolerance of ±3 millimeters. Additionally, 75% of the slides had no air bubbles, while the remaining slides had air bubbles that were less than 120 micrometers in size. These results demonstrate the potential impact of SLIDE PROTEKT in the field of histology.

Wide field block face imaging using deep ultraviolet induced autofluorescence of the human brain.

BACKGROUND: Imaging large volume human brains at cellular resolution involve histological methods that cause structural changes. A reference point prior to sectioning is needed to quantify these changes and is achieved by serial block face imaging (BFI) methods that have been applied to small volume tissue (∼1 cm3). NEW METHOD: We have developed a BFI uniquely designed for large volume tissues (∼1300 cm3) with a very large field of view (20 × 20 cm) at a resolution of 70 µm/pixel under deep ultraviolet (UV-C) illumination which highlights key features. RESULTS: The UV-C imaging ensures high contrast imaging of the brain tissue and highlights salient features of the brain. The system is designed to provide uniform and stable illumination across the entire surface area of the tissue and to work at low temperatures, which are required during cryosectioning. Most importantly, it has been designed to maintain its optical focus over the large depth of tissue and over long periods of time, without readjustments. The BFI was installed within a cryomacrotome, and was used to image a large cryoblock of an adult human cerebellum and brainstem (∼6 cm depth resulting in 2995 serial images) with precise optical focus and no loss during continuous serial acquisition. COMPARISON WITH EXISTING METHOD(S): The deep UV-C induced BFI highlights several large fibre tracts within the brain including the cerebellar peduncles, and the corticospinal tract providing important advantage over white light BFI. CONCLUSIONS: The 3D reconstructed serial BFI images can assist in the registration and alignment of the microscopic high-resolution histological tissue sections.