3D particle-based cell modelling for haptic microrobotic cell injection

Introducing haptic interface to conduct microrobotic intracellular injection has many beneficial implications. In particular, the haptic device provides force feedback to the bio-operator\u27s hand. This paper introduces a 3D particle-based model to simulate the deformation of the cell membrane and corresponding cellular forces during microrobotic cell injection. The model is based on the kinematic and dynamic of spring – damper multi particle joints considering visco-elastic fluidic properties. It simulates the indentation force feedback as well as cell visual deformation during the microinjection. The model is verified using experimental data of zebrafish embryo microinjection. The results demonstrate that the developed cell model is capable of estimating zebrafish embryo deformation and force feedback accurately

Haptic guidance for microrobotic intracellular injection

The ability for a bio-operator to utilise a haptic device to manipulate a microrobot for intracellular injection offers immense benefits. One significant benefit is for the bio-operator to receive haptic guidance while performing the injection process. In order to address this, this paper investigates the use of haptic virtual fixtures for cell injection and proposes a novel force field virtual fixture. The guidance force felt by the bio-operator is determined by force field analysis within the virtual fixture. The proposed force field virtual fixture assists the bio-operator when performing intracellular injection by limiting the micropipette tip\u27s motion to a conical volume as well as recommending the desired path for optimal injection. A virtual fixture plane is also introduced to prevent the bio-operator from moving the micropipette tip beyond the deposition target inside the cell. Simulation results demonstrate the operation of the guidance system.<br /

Haptic microrobotic intracellular injection assistance using virtual fixtures

In manual cell injection the operator relies completely on visual information for task feedback and is subject to extended training times as well as poor success rates and repeatability. From this perspective, enhancing human-in-the-loop intracellular injection through haptic interaction offers significant benefits. This paper outlines two haptic virtual fixtures aiming to assist the human operator while performing cell injection. The first haptic virtual fixture is a parabolic force field designed to assist the operator in guiding the micropipette\u27s tip to a desired penetration point on the cell\u27s surface. The second is a planar virtual fixture which attempts to assist the operator from moving the micropipette\u27s tip beyond the deposition target location inside the cell. Preliminary results demonstrate the operation of the haptically assisted microrobotic cell injection system

Virtual haptic cell model for operator training

Microrobotic cell injection is an area of growing research interest. Typically, operators rely on visual feedback to perceive the microscale environment and are subject to lengthy training times and low success rates. Haptic interaction offers the ability to utilise the operator&rsquo;s haptic modality and to enhance operator performance. Our earlier work presented a haptically enabled system for assisting the operator with certain aspects of the cell injection task. The system aimed to enhance the operator&rsquo;s controllability of the micropipette through a logical mapping between the haptic device and microrobot, as well as introducing virtual fixtures for haptic guidance. The system was also designed in such a way that given the availability of appropriate force sensors, haptic display of the cell penetration force is straightforward. This work presents our progress towards a virtual replication of the system, aimed at facilitating offline operator training. It is suggested that operators can use the virtual system to train offline and later transfer their skills to the physical system. In order to achieve the necessary representation of the cell within the virtual system, methods based on a particle-based cell model are utilised. In addition to providing the necessary visual representation, the cell model provides the ability to estimate cell penetration forces and haptically display them to the operator. Two different approaches to achieving the virtual system are discussed

Naltrexone; as an efficient adjuvant in induction of Th1 immunity and protection against Fasciola hepatica infection

Toxic effects of available therapeutics are major drawbacks for conventional management approaches in parasitic infections. Vaccines have provided a promising opportunity to obviate such unwanted complications. In present study, we examined immune augmenting capacities of an emerging adjuvant, Naltrexone, against Fasciola hepatica infection in BALB/c mice. Seventy BALB/c mice were divided into five experimental groups (14 mice per group) including 1- control (received PBS), 2- vaccine (immunized with F. hepatica E/S antigens), 3- Alum-vaccine (immunized with Alum adjuvant and E/S antigens), 4- NLT-vaccine (immunized with NLT adjuvant and E/S antigens), and 5- Alum-NLT-vaccine (immunized with mixed Alum-NLT adjuvant and E/S antigens). Lymphocyte stimulation index was assessed by MTT assay. Production of IFN-γ, IL-4, IgG2a and IgG1 was assessed by ELISA method. Results showed that NLT, either alone or in combination with alum, can induce immune response toward production of IFN-γ and IgG2a as representatives of Th1 immune response. Also, using this adjuvant in immunization experiment was associated with significantly high proliferative response of splenocytes/lymphocytes. Utilization of mixed Alum-NLT adjuvant revealed the highest protection rate (73.8%) in challenge test of mice infected with F. hepatica. These findings suggest the potential role of NLT as an effective adjuvant in induction of protective cellular and Th1 immune responses against fasciolosis. © 2018 Elsevier Inc

PV Maximum Power-Point Tracking by Using Artificial Neural Network

In this paper, using artificial neural network (ANN) for tracking of maximum power point is discussed. Error back propagation method is used in order to train neural network. Neural network has advantages of fast and precisely tracking of maximum power point. In this method neural network is used to specify the reference voltage of maximum power point under different atmospheric conditions. By properly controling of dc-dc boost converter, tracking of maximum power point is feasible. To verify theory analysis, simulation result is obtained by using MATLAB/SIMULINK

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper

In vivo Skin Penetration, Radical Protection, and Structural Changes after Topical Application of a Herbal Oil Cream Compared to Topical Calcipotriol in Mild to Moderate Psoriasis

Background: The chronicity of psoriasis often requires continuous topical treatment. Materials and Methods: Here, the radical protection of a cream containing various herbal oils was evaluated in vivo by electron paramagnetic resonance (EPR) spectroscopy and its skin penetration by Raman microscopy in intact and barrier-disturbed skin. Changes in skin barrier properties were evaluated after 4 weeks of daily topical application using in vivo laser scanning microscopy (LSM) and transepidermal water loss in 26 healthy volunteers. A randomized, controlled, double-blind, three-arm parallel clinical study evaluated the efficacy of the herbal oil cream compared to a 0.05% calcipotriol-containing cream and to a vehicle cream, in 135 patients with mild to moderate plaque psoriasis with the change in Psoriasis Area and Severity Index (PASI) from baseline to week 12 as the primary endpoint. Results: EPR spectroscopy disclosed a significantly higher radical formation in untreated than skin treated with the herbal oil cream (p ≤ 0.05). LSM measurements indicated a protective skin barrier effect in treated compared to untreated skin. In the clinical trial, the topical application of herbal oils showed a significant reduction of the PASI score compared to topical calcipotriol at week 12 (p = 0.016). The mean reduction in PASI was 49% for the herbal oil cream, 38% for calcipotriol, and 55% for the vehicle cream. The percentage of patients, who reached PASI 50 and 75 at any time point, was 55.9% and 29.4% for the herbal oil cream, 47.4% and 15.8% for calcipotriol, and 23 (60.5%) and 13 (34.2%) for the vehicle, respectively (p > 0.05). The vehicle, originally designed as a placebo, contained a main ingredient of the herbal oil cream and therefore showed corresponding results. Conclusion: The herbal oil cream demonstrated effectiveness in the treatment of mild to moderate plaque psoriasis

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed