A compact pulsatile simulator based on cam-follower mechanism for generating radial pulse waveforms

Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning

Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.

Quantitative assessment of corneal elasticity distribution after FS-LASIK using optical coherence elastography

Quantifying corneal elasticity after femtosecond laser-assisted in situ keratomileusis (FS-LASIK) procedure plays an important role in improving surgical safety and quality, since some latent complications may occur ascribing to changes in postoperative corneal biomechanics. Nevertheless, it is suggested that current research has been severely constrained due to the lack of an accurate quantification method to obtain postoperative corneal elasticity distribution. In this paper, an acoustic radiation force optical coherence elastography system combined with the improved phase velocity algorithm was utilized to realize elasticity distribution images of the in vivo rabbit cornea after FS-LASIK under various intraocular pressure levels. As a result, elasticity variations within and between the regions of interest could be identified precisely. This is the first time that elasticity imaging of in vivo cornea after FS-LASIK surgery was demonstrated, and the results suggested that this technology may hold promise in further exploring corneal biomechanical properties after refractive surgery.

Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

This research presents a novel approach for cervical cancer detection and segmentation using tissue images with multiple cells. The study employs a novel deep learning architecture based on Mask Region-Based Convolutional Neural Network (RCNN) and statistical analysis. This new architecture enables us to achieve a high percentage of detection and pix-to-pix area segmentation. A mean Average Precision (mAP) higher than 60% for 3-class and 5-class was achieved. In addition, higher F1-scores of 70% for 3-class and 5-class were obtained. This investigation is a collaborative work, where a medical consultant collected the samples from the Papanicolaou (Pap) Smear examination and labeled the cells presented to the liquid-based cytology (LBC). Furthermore, the online available benchmark data set, SIPaKMeD, was also utilized. Additionally, sample images from the Mendeley data set were also labeled by the trained medical consultant for comparison. The proposed scheme automatically generates a full report for a medical consultant to identify the location of the malicious cells in the given images and expedite the diagnosis and treatment process.

Perfused and Nonperfused Microaneurysms Identified and Characterized by Structural and Angiographic OCT

Purpose

Microaneurysms (MAs) have distinct, oval-shaped, hyperreflective walls on structural OCT, and inconsistent flow signal in the lumen with OCT angiography (OCTA). Their relationship to regional macular edema in diabetic retinopathy (DR) has not been quantitatively explored.

Design

Retrospective, cross-sectional study.

Participants

A total of 99 participants, including 23 with mild, nonproliferative DR (NPDR), 25 with moderate NPDR, 34 with severe NPDR, and 17 with proliferative DR.

Methods

We obtained 3 × 3-mm scans with a commercial device (Solix, Visionix/Optovue) in 99 patients with DR. Trained graders manually identified MAs and their location relative to the anatomic layers from cross-sectional OCT. Microaneurysms were first classified as perfused if flow signal was present in the OCTA channel. Then, perfused MAs were further classified into fully and partially perfused MAs based on the flow characteristics in en face OCTA. The presence of retinal fluid based on OCT near MAs was compared between perfused and nonperfused types. We also compared OCT-based MA detection to fundus photography (FP)- and fluorescein angiography (FA)-based detection.

Main Outcome Measures

OCT-identified MAs can be classified according to colocalized OCTA flow signal into fully perfused, partially perfused, and nonperfused types. Fully perfused MAs may be more likely to be associated with diabetic macular edema (DME) than those without flow.

Results

We identified 308 MAs (166 fully perfused, 88 partially perfused, 54 nonperfused) in 42 eyes using OCT and OCTA. Nearly half of the MAs identified in this study straddle the inner nuclear layer and outer plexiform layer. Compared with partially perfused and nonperfused MAs, fully perfused MAs were more likely to be associated with local retinal fluid. The associated fluid volumes were larger with fully perfused MAs compared with other types. OCT/OCTA detected all MAs found on FP. Although not all MAs seen with FA were identified with OCT, some MAs seen with OCT were not visible with FA or FP.

Conclusions

OCT-identified MAs with colocalized flow on OCTA are more likely to be associated with DME than those without flow.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. Ophthalmology Retina 2023;■:1-8 © 2023 by the American Academy of Ophthalmology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Biomaterials / bioinks and extrusion bioprinting

Bioinks are formulations of biomaterials and living cells, sometimes with growth factors or other biomolecules, while extrusion bioprinting is an emerging technique to apply or deposit these bioinks or biomaterial solutions to create three-dimensional (3D) constructs with architectures and mechanical/biological properties that mimic those of native human tissue or organs. Printed constructs have found wide applications in tissue engineering for repairing or treating tissue/organ injuries, as well as in vitro tissue modelling for testing or validating newly developed therapeutics and vaccines prior to their use in humans. Successful printing of constructs and their subsequent applications rely on the properties of the formulated bioinks, including the rheological, mechanical, and biological properties, as well as the printing process. This article critically reviews the latest developments in bioinks and biomaterial solutions for extrusion bioprinting, focusing on bioink synthesis and characterization, as well as the influence of bioink properties on the printing process. Key issues and challenges are also discussed along with recommendations for future research.

Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study

Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise therapy for patients with painful DPN. The aim of this study was to use deep learning to predict treatment response in patients with pDPN using resting state functional imaging (rs-fMRI). We divided 43 painful pDPN patients into responders and non-responders to lidocaine treatment (responders n = 29 and non-responders n = 14). We used rs-fMRI to extract functional connectivity features, using group independent component analysis (gICA), and performed automated treatment response deep learning classification with three-dimensional convolutional neural networks (3D-CNN). Using gICA we achieved an area under the receiver operating characteristic curve (AUC) of 96.60% and F1-Score of 95% in a ten-fold cross validation (CV) experiment using our described 3D-CNN algorithm. To our knowledge, this is the first study utilising deep learning methods to classify treatment response in pDPN.

Formulation of a thermo-sensitive hydro-gel for ulcerative colitis treatment

Ulcerative colitis (UC) is a type of inflammatory bowel disease (IBD) that causes chronic intestinal inflammation in gastrointestinal (GI) tract, mainly in innermost lining of colonic mucosa. In any of the UC drug therapy regimens, maintaining remission is challenging and about 20-40% of patients don't respond to conventional UC medications, namely, amino salicylates, steroids and immunosuppressive drugs. These agents can weaken the patient's immune system thus enhancing the risk of infectious diseases. Therefore, in our exploration we probed to test marine-derived anti-inflammatory compounds as potential agents to treat UC. Fucoidan, a complex fucose-rich sulphated polysaccharide originated in edible brown algae with known anti-inflammatory properties was isolated from Turbinaria ornate. Collagen (Achillis tendon) is another agent that may provide a beneficial effect in wound healing and tissue regeneration. Collagen was also reported to possess anti-UC properties. Collagen has a limitation of being in solution form even at high concentrations. We therefore formulated fucoidan with collagen that underwent a sol-gel transition and yielded a gel like consistency in situ. This formulation showed sustained release of fucoidan for about 12 hours. The fucoidan, collagen and the fucoidan-collagen formulation were tested in the dextran sodium sulfate (DSS) induced colitis model in mice. In comparison to the vehicle treated group, fucoidan-collagen hydrogel formulation led to significant reduction in the clinical scores and rectal bleeding, which was higher than the reference standard, mesalamine and those seen with fucoidan and collagen given alone.

Controlled release of vascular endothelial growth factor (VEGF) in alginate and hyaluronic acid (ALG–HA) bead system to promote wound healing in punch-induced wound rat model

For wound healing, angiogenesis is one of the main therapeutic factors for recovering the injured tissue. To address this issue, a combination of two different polymers, alginate (ALG) and hyaluronic acid (HA) in an 80:20 ratio composition is used to optimize the bead system along with the 5 IU heparin (Hep) by crosslinking into calcium chloride (CaCl₂). Encapsulation of Vascular endothelial growth factor (VEGF) in the bead system shows delayed cumulative release in phosphate buffer saline (PBS). For in vitro studies, calf pulmonary artery endothelial (CPAE) cells showed biocompatibility. ALG-HA/VEGF150 improves endothelial Vascular cell adhesion protein 1 (VCAM1) and endothelial nitric oxide synthase (eNOS) expression markers in CPAE cells. In vivo evaluation of the bead system shows around 68% of wound closure 2 weeks post-implantation in 8 mm punch wound models. The treatment group shows decreased epithelial gap between the ends of the wound and neo-epidermal regeneration. ALG-HA/VEGF150 induced significant vascularization, collagen type-1 (Col-1) and fibronectin (FN) development in the in vivo models after 2 weeks of the implantation. Hence, ALG-HA/VEGF150 beads can be used to promote wound healing.

Current trends in the use of human serum albumin for drug delivery in cancer

INTRODUCTION

Human serum albumin is the most abundant transport protein in plasma, which has recently been extensively utilized to form nanoparticles for drug delivery in cancer. The primary reason for selecting albumin protein as drug delivery cargo is its excellent biocompatibility, biodegradability, and non-immunogenicity. Moreover, the albumin structure containing three homologous domains constituted of a single polypeptide (585 amino acid) incorporates various hydrophobic drugs by non-covalent interactions. Albumin shows active tumor targeting via their interaction with gp60 and SPARC proteins abundant in the tumor-associated endothelial cells and the tumor microenvironment.

AREAS COVERED

The review discusses the importance of albumin as a drug-carrier system, general procedures to prepare albumin NPs, and the current trends in using albumin-based nanomedicines to deliver various chemotherapeutic agents. The various applications of albumin in the nanomedicines, such as NPs surface modifier and fabrication of hybrid/active-tumor targeted NPs, are delineated based on current trends.

EXPERT OPINION

Nanomedicines have the potential to revolutionize cancer treatment. However, clinical translation is limited majorly due to the lack of suitable nanomaterials offering systemic stability, optimum drug encapsulation, tumor-targeted delivery, sustained drug release, and biocompatibility. The potential of albumin could be explored in nanomedicines fabrication for superior treatment outcomes in cancer.

A lateral electric field inhibits gel-to-fluid transition in lipid bilayers

We report evidence of lateral electric field-induced changes in the phase transition temperatures of lipid bilayers. Our atomic scale molecular dynamics simulations show that a lateral electric field increases the melting temperatures of DPPC, POPC and POPE bilayers. Remarkably, these shifts in the melting temperatures are only induced by lateral electric fields, and not normal electric fields. This mechanism could provide new mechanistic insights into lipid-lipid and lipid-protein interactions in the presence of endogenous and exogenous electric fields.

Real-time denoising of ultrasound images based on deep learning

Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, which corrupts the resulting image and affects further processing steps, e.g. segmentation and quantitative analysis. We define a novel deep learning framework for the real-time denoising of ultrasound images. Firstly, we compare state-of-the-art methods for denoising (e.g. spectral, low-rank methods) and select WNNM (Weighted Nuclear Norm Minimisation) as the best denoising in terms of accuracy, preservation of anatomical features, and edge enhancement. Then, we propose a tuned version of WNNM (tuned-WNNM) that improves the quality of the denoised images and extends its applicability to ultrasound images. Through a deep learning framework, the tuned-WNNM qualitatively and quantitatively replicates WNNM results in real-time. Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and deep learning architectures.

CT-Derived 3D Printing for Coronary Artery Cannulation Simulator Design Manufacturing

Mastering coronary angiography requires practice. Cadavers and animals do not accurately represent the human anatomical body, and practicing with actual patients has medical safety issues. Simulation offers safe and realistic conditions for cardiology intervention training. In this study, we propose a novel 3D printed simulator that contains physically realistic anatomy and has four access points. It increases safety for patients and students, and production is low-cost. We aimed to make and validate this simulator design as a prototype for coronary cannulation training. It was designed using computed tomography (CT) scan data of aorta, coronary, and heart models, and was printed by 3D printing with resin materials consisting of 75% or 85% clear resin and 25% or 15% flexible resin additive. The simulator was constructed with a camera above the simulator with a degree of LAO of 30°/0°, a display table, and an acrylic box. Twelve validators were interviewed for their expert opinions and analyzed by a qualitative method. They scored the simulator’s suitability on a four-point Likert scale questionnaire. They described the simulator as having admirable values for all aspects (85.8%), curriculum suitability (92%), educational importance (94%), accuracy (83%), efficiency (78%), safety (87.5%), endurance (81.2%), aesthetics (80.7%), storage (85.4%), and affordability (85.8%).

Is the SARS CoV-2 Omicron Variant Deadlier and More Transmissible Than Delta Variant?

Genetic variants of severe acute respiratory syndrome coronavirus (SARS-CoV-2) have been globally surging and devastating many countries around the world. There are at least eleven reported variants dedicated with inevitably catastrophic consequences. In 2021, the most dominant Delta and Omicron variants were estimated to lead to more severity and deaths than other variants. Furthermore, these variants have some contagious characteristics involving high transmissibility, more severe illness, and an increased mortality rate. All outbreaks caused by the Delta variant have been rapidly skyrocketing in infection cases in communities despite tough restrictions in 2021. Apart from it, the United States, the United Kingdom and other high-rate vaccination rollout countries are still wrestling with this trend because the Delta variant can result in a significant number of breakthrough infections. However, the pandemic has changed since the latest SARS-CoV-2 variant in late 2021 in South Africa, Omicron. The preliminary data suggest that the Omicron variant possesses 100-fold greater than the Delta variant in transmissibility. Therefore, this paper aims to review these characteristics based on the available meta-data and information from the first emergence to recent days. Australia and the five most affected countries, including the United States, India, Brazil, France, as well as the United Kingdom, are selected in order to review the transmissibility, severity and fatality due to Delta and Omicron variants. Finally, the vaccination programs for each country are also reviewed as the main factor in prevention.

Finite Element Models of Osteocytes and Their Load-Induced Activation

PURPOSE OF REVIEW

Osteocytes are the conductors of bone adaptation and remodelling. Buried inside the calcified matrix, they sense mechanical cues and signal osteoclasts in case of low activity, and osteoblasts when stresses are high. How do osteocytes detect mechanical stress? What physical signal do they perceive? Finite element analysis is a useful tool to address these questions as it allows calculating stresses, strains and fluid flow where they cannot be measured. The purpose of this review is to evaluate the capabilities and challenges of finite element models of bone, in particular the osteocytes and load-induced activation mechanisms.

RECENT FINDINGS

High-resolution imaging and increased computational power allow ever more detailed modelling of osteocytes, either in isolation or embedded within the mineralised matrix. Over the years, homogeneous models of bone and osteocytes got replaced by heterogeneous and microstructural models, including, e.g. the lacuno-canalicular network and the cytoskeleton. The lacuno-canalicular network induces strain amplifications and the osteocyte protrusions seem to be stimulated much more than the cell body, both by strain and fluid flow. More realistic cell geometries, like minute constrictions of the canaliculi, increase this effect. Microstructural osteocyte models describe the transduction of external stimuli to the nucleus. Supracellular multiscale models (e.g. of a tunnelling osteon) allow to study differential loading of osteocytes and to distinguish between strain and fluid flow as the pivotal stimulatory cue. In the future, the finite element models may be enhanced by including chemical transport and intercellular communication between osteocytes, osteoclasts and osteoblasts.

Irrigant flow characteristics in the root canal with internal root resorption: a computational fluid dynamics evaluation

Irrigation dynamics of syringe irrigation with different needle designs (side-vented, double side-vented, notched) and ultrasonic irrigation in the root canal with internal root resorption were evaluated using a computational fluid dynamics model. A micro-CT scanned mandibular premolar was used for modeling internal root resorption. The needles and the ultrasonic tip were positioned at 2, 4, and 5 mm from the working length. The insertion depth and the irrigation model were found influential on the shear stress and the irrigant extension. The extension of the irrigant increased toward 2-5 mm from the working length. Ultrasonic irrigation revealed the highest shear stress values regardless of the insertion depth. The shear stress distribution on the resorption cavity walls gradually increased when the needles were positioned coronally. The residence time of the irrigant in the canal was affected by the needle position relative to the internal root resorption cavity and the needle type.

Measuring Arterial Pulsatility With Dynamic Inflow Magnitude Contrast

The pulsatility of blood flow through cerebral arteries is clinically important, as it is intrinsically associated with cerebrovascular health. In this study we outline a new MRI approach to measuring the real-time pulsatile flow in cerebral arteries, which is based on the inflow phenomenon associated with fast gradient-recalled-echo acquisitions. Unlike traditional phase-contrast techniques, this new method, which we dub dynamic inflow magnitude contrast (DIMAC), does not require velocity-encoding gradients as sensitivity to flow velocity is derived purely from the inflow effect. We achieved this using a highly accelerated single slice EPI acquisition with a very short TR (15 ms) and a 90° flip angle, thus maximizing inflow contrast. We simulate the spoiled GRE signal in the presence of large arteries and perform a sensitivity analysis. The sensitivity analysis demonstrates that in the regime of high inflow contrast, DIMAC shows much greater sensitivity to flow velocity over blood volume changes. We support this theoretical prediction with in-vivo data collected in two separate experiments designed to demonstrate the utility of the DIMAC signal contrast. We perform a hypercapnia challenge experiment in order to experimentally modulate arterial tone within subjects, and thus modulate the arterial pulsatile flow waveform. We also perform a thigh-cuff release challenge, designed to induce a transient drop in blood pressure, and demonstrate that the continuous DIMAC signal captures the complex transient change in the pulsatile and non-pulsatile components of flow. In summary, this study proposes a new role for a well-established source of MR image contrast and demonstrates its potential for measuring both steady-state and dynamic changes in arterial tone.

A Spatial-Frequency Feature Ensemble for Detecting Cervical Dysplasia from Pap Smear Images

Among women, cervical cancer is the commonest and the most treatable and preventable type of cancer. In most cases, cervical cancer begins as precancerous changes which gradually develop into cancer. Pap smear is widely used for cervical cancer diagnosis. Cell analysis is a time-consuming and cumbersome job; thus, an automatic detecting framework is proposed. Wavelet transforms offer the associated coefficients as the input image data representation, used as feature vectors. Artificial Neural Networks (ANNs) have outstanding attributes such as enhanced input-to-output mapping, non-linearity, fault tolerance, adaptively, and self-learning. Classification of cervical cancers employs neural network systems that have a huge role in most applications related to image processing. For application in diverse fields such as bioinformatics and pattern recognition, most researchers choose ensemble classifiers. A spatial-frequency feature ensemble has been proposed in this work to identify cervical dysplasia from images of Pap smears.

Application of Bayesian network and regression method in treatment cost prediction

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

A Transfer Function Model Development for Reconstructing Radial Pulse Pressure Waveforms Using Non-Invasively Measured Pulses by a Robotic Tonometry System

The primary goal of this study is to develop a mathematical model that can establish a transfer function relationship between the “external” pulse pressures measured by a tonometer and the “internal” pulse pressure in the artery. The purpose of the model is to accurately estimate and rebuild the internal pulse pressure waveforms using arterial tonometry measurements. To develop and validate a model without human subjects and operators for consistency, this study employs a radial pulse generation system, a robotic tonometry system, and a write model with an artificial skin and vessel. A transfer function model is developed using the results of the pulse testing and the mechanical characterization testing of the skin and vessel. To evaluate the model, the pulse waveforms are first reconstructed for various reference pulses using the model with tonometry data. They are then compared with pulse waveforms acquired by internal measurement (by the built-in pressure sensor in the vessel) the external measurement (the on-skin measurement by the robotic tonometry system). The results show that the model-produced pulse waveforms coinciding well with the internal pulse waveforms with small relative errors, indicating the effectiveness of the model in reproducing the actual pulse pressures inside the vessel.

Dynamics of Mechanisms with Superior Couplings

The paper briefly presents the dynamic synthesis of mechanisms with superior couplings, force, and speed distribution, efficiency, loss coefficient, dynamic coefficient or motion transmission function, determination of variable angular input speed from the crank or cam based on solving the equation Lagrange, the determination of the dynamic variation of the follower (adept) based on the integration of Newton’s equation, and the dynamic analysis of several models taken into account. In the end, the original relations for calculating the efficiency of a gear are presented.

Portable Interactive Pulse Tactile Recorder and Player System

Pulse palpation is an effective method for diagnosing arterial diseases. However, most pulse measurement devices use preconfigured pressures to collect pulse signals, and most pulse tactile simulators can only display standard or predefined pulse waveforms. Here, a portable interactive human pulse measurement and reproduction system was developed that allows users to take arbitrary pulses and experience realistic simulated pulse tactile feedback in real time by using their natural pulse-taking behaviors. The system includes a pulse tactile recorder and a pulse tactile player. Pulse palpation forces and vibrations can be recorded and realistically replayed for later tactile exploration and examination. To retain subtle but vital pulse information, empirical mode decomposition was used to decompose pulse waveforms into several intrinsic mode functions. Artificial neural networks were then trained based on intrinsic mode functions to determine the relationship between the driving signals of the pulse tactile player and the resulting vibration waveforms. Experimental results indicate that the average normalized root mean square error and the average R-squared values between the reproduced and original pulses were 0.0654 and 0.958 respectively, which indicate that the system can reproduce high-fidelity pulse tactile vibrations.