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Various foot complications can be easily avoided by continuously monitoring plantar (foot sole) pressure and temperature at home. Systems which can simultaneously measure plantar pressure and temperature in real time are still scarce. In this work, the design, characterization, and implementation of a Fiber Bragg Gratings (FBG) based smart insole capable of simultaneously measuring plantar pressure and temperature has been reported. The instrumented insole was tested and verified during static and gait exercises. The paper also provides a comparison of the developed optoelectronic-based solution with a commercially available and widely used plantar pressure measurement and analysis system, and a lab-made electronic sensor-based solution for simultaneously recording plantar pressure and temperature. It was shown that even though the commercial plantar pressure acquisition system is very robust and highly precise due to many sensing units on the insole, the developed insole with a much smaller number of sensors can simultaneously acquire both plantar temperature and pressure with reasonable precision while displaying both foot pressure and temperature maps, and gait cycle plots in real-time with a development cost more than eight times lower than the manufacturing cost of the commercial solution. Our proposed optoelectronic-based solution is lightweight, uncomplicated but robust, and electronically safer than the commercial system. While the proposed system is far from its optimized form, we expect that our developed prototype will instigate other researchers in this domain to further explore optoelectronic-based solutions in real-time plantar pressure and temperature monitoring. 2022 Elsevier B.V. This work was made possible by Qatar National Research Fund (QNRF) NPRP12S-0227-190164 and International Research Collaboration Co-Fund (IRCC) grant: IRCC-2021-001 and Universiti Kebangsaan Malaysia under Grant GUP-2021-019 and DPK-2021-001 . The statements made herein are solely the responsibility of the authors. The open-access publication is supported by Qatar National Library (QNL). Scopus

External tension in membranes plays a vital role in numerous physiological and physicochemical phenomena. In this review, recent developments in the constant electric- and mechanical-tension-induced rupture of giant unilamellar vesicles (GUVs) are considered. We summarize the results relating to the kinetics of GUV rupture as a function of membrane surface charge, ions in the bathing solution, lipid composition, cholesterol content in the membrane, and osmotic pressure. The mechanical stability and line tension of the membrane under these conditions are discussed. The membrane tension due to osmotic pressure and the critical tension of rupture for various membrane compositions are also discussed. The results and their analysis provide a biophysical description of the kinetics of rupture, along with insight into biological processes. Future directions and possible developments in this research area are included.

A new approach to non-invasively uncover the pattern of myelinated Aα motor nerve fiber conduction velocity distribution (CVD) from the corresponding compound muscle action potential (CMAP), also known as the inverse problem of motor nerve conduction study, has been explored in this work. The previous works to solve this type of problem were mostly in the discrete manner. We leveraged a continuous approach to exploit the gradient optimization technique to solve this problem. A diphasic sinusoidal function was taken to model the motor unit action potential (MUAP) signal and a 5th order polynomial function was taken to model the assumed continuous CVD curve. The continuous CVD instead of a discrete CVD helps us to perform more computation. The inverse results derived using the proposed methodology closely matched (almost 100%) the predicted CVD with the original CVD of different shapes from the corresponding simulated CMAP data using the forward solution of nerve conduction.

Molecular Dynamics (MD) is extensively used for analyzing the physical movements of atoms and molecules. It gives the dynamic ’evolution’ of the system for a fixed period of time. In simulations, GUV (Giant Unilamellar Vesicle) is used as a model of biological cell. The dependence of saturation inside the GUV on size, pore width etc. has been investigated in this work. Fluorescent chemicals are used as probing agents to observe the diffusion in GUV. As a completely different approach, we introduced diffusion weighted (DW) magnetic resonance imaging (MRI) in MD. When a nanopore forms on the membrane of a GUV, molecular diffusion between the intra and extra cellular regions takes place. The type and nature of diffusion were approximated from diffusion coefficient measurement using model MRI sequences and compared with the recognized values. These investigations may contribute to direct estimation of structural and biophysical parameters within cellular domain and help to understand underlying physiology.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes., Accepted for publication in Sensor, 14 Figures, 14 Tables

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients., Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.13145

Cell penetrating peptide transportan 10 and antimicrobial peptide melittin formed submicron pores in the lipid membranes of vesicles which are explained by the leakage of water-soluble fluorescent probes from the inside of vesicles to the outside. It is hypothesized that these submicron pores induce submicron discontinuities in the membranes. Considering this hypothesis, a technique has developed to locate the submicron discontinuities in the membranes of giant unilamellar vesicles (GUVs) using ImageJ. In this technique, at first the edges of membrane of a 'single GUV' are detected and then these edges are used to locate the submicron discontinuities. Two continuous rings are observed after applying the ImageJ in GUVs which indicated the edges of membrane. In contrast, the submicron discontinuations are detected at the edges of transportan 10 and melittin induced pore formed membranes. This investigation might be helpful for the elucidation of mechanism of the peptide-induced pore formation in the membranes of vesicles.

One of the key developments of science is the technology of sensors, which detect a certain event and produce a signal compatible with the event. Sensing technologies have been developed over time and implemented in many fields. Among them, biomedical applications are prominent. In today's world, biomedical sensing and detection of various biomedical phenomena are the cornerstone of many diagnostic processes. They also play a vital role in enhancing the quality of life. Although biomedical sensing has been on the road to improvement for a long time, the leap of the decade in this field has been the incorporation of nanotechnology and nanomaterials. Nanotechnology has greatly improved the performance of biosensors; the inclusion of nanomaterials like nanoparticles, nanowires, carbon nanotubes, and graphene has led to the development of miniature and flexible sensors with dependable and multidimensional characteristics. Ongoing research has produced outstanding applications of nanotechnologically designed biosensors, from diagnostics and nanomedicine to environmental monitoring, for better quality of life. This chapter focuses on biosensors designed through nanotechnology, along with their types and performance parameters. Some of the applications of such sensors are also discussed, providing an overview of nanosensors used in biomedical applications.

A new purification technique is developed for obtaining distribution of giant unilamellar vesicles (GUVs) within a specific range of sizes using dual filtration. The GUVs were prepared using well known natural swelling method. For filtration, different combinations of polycarbonate membranes were implemented in filter holders. In our experiment, the combinations of membranes were selected with corresponding pore sizes–(i) 12 and 10 μm, (ii) 12 and 8 μm, and (iii) 10 and 8 μm. By these filtration arrangements, obtained GUVs size distribution were in the ranges of 6−26 μm, 5–38 μm and 5–30 μm, respectively. In comparison, the size distribution range was much higher for single filtration technique, for example, 6−59 μm GUVs found for a membrane with 12 μm pores. Using this technique, the water-soluble fluorescent probe, calcein, can be removed from the suspension of GUVs successfully. The size distributions were analyzed with lognormal distribution. The skewness became smaller (narrow size distribution) when a dual filtration was used instead of single filtration. The mode of the size distribution obtained in dual filtration was also smaller to that of single filtration. By continuing this process of purification for a second time, the GUVs size distribution became even narrower. After using an extra filtration with dual filtration, two different size distributions of GUVs were obtained at a time. This experimental observation suggests that different size specific distributions of GUVs can be obtained easily, even if GUVs are prepared by different other methods.

Purpose This work describes the development of a method to measure the variation of apparent diffusion coefficient (ADC) with diffusion time (Δ) in the brachial plexus, as a potential method of probing microstructure. Methods Diffusion-weighted MRI with body signal suppression was used to highlight the nerves from surrounding tissues, and sequence parameters were optimized for sensitivity to change with diffusion time. A porous media-restricted diffusion model based on the Latour-Mitra equation was fitted to the diffusion time-dependent ADC data from the brachial plexus nerves and cord. Results The ADC was observed to reduce at long diffusion times, confirming that diffusion was restricted in the nerves and cord in healthy subjects. T2 of the nerves was measured to be 80 ± 5 ms, the diffusion coefficient was found to vary from (1.5 ± 0.1) × 10−3 mm2/s at a diffusion time of 18.3 ms to (1.0 ± 0.2) × 10−3 mm2/s at a diffusion time of 81.3 ms. Conclusion A novel method of probing restricted diffusion in the brachial plexus was developed. Resulting parameters were comparable with values obtained previously on biological systems.

The antimicrobial peptide (AMP) magainin 2 induces nanopores in the lipid membranes of giant unilamellar vesicles (GUVs), as observed by the leakage of water-soluble fluorescent probes from the inside to the outside of GUVs through the pores. However, molecular transport through a single nanopore has not been investigated in detail yet and is studied in the present work by simulation. A single pore was designed in the membrane of a GUV using computer-aided design software. Molecular transport, from the outside to the inside of GUV through the nanopore, of various fluorescent probes such as calcein, Texas-Red Dextran 3000 (TRD-3k), TRD-10k and TRD-40k was then simulated. The effect of variation in GUV size (diameter) was also investigated. A single exponential growth function was fitted to the time course of the fluorescence intensity inside the GUV and the corresponding rate constant of molecular transport was calculated, which decreases with an increase in the size of fluorescent probe and also with an increase in the size of GUV. The rate constant found by simulation agrees reasonably well with reported experimental results for inside-to-outside probe leakage. Based on Fick's law of diffusion an analytical treatment is developed for the rate constant of molecular transport that supports the simulation results. These investigations contribute to a better understanding of the mechanism of pore formation using various membrane-active agents in the lipid membranes of vesicles and the biomembranes of cells.

Purpose: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. Methods: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simultaneous T1 and T2 quantification. The accuracy of parametric map estimations were improved by using an iterative projection algorithm. The results were compared to a conventional spiral MRF implementation. The acquisition time per slice was 8s and this method was validated on a phantom and a healthy volunteer brain in vivo. Results: Joint T1 and T2 estimations using the 16-shot EPI readout are in good agreement with the spiral implementation using the same acquisition parameters (deviation less than 3% for T1 and less than 4% for T2) for the healthy volunteer brain. The T1 and T2 values also agree with the conventional values previously reported in the literature. The visual quality of the multi-parametric maps generated by the multi-shot EPI-MRF and spiral-MRF implementations were comparable. Conclusion: The multi-shot EPI-MRF method generated accurate quantitative multi-parametric maps similar to conventional Spiral - MRF. This multi-shot approach achieved provides an alternative for performing MRF using an accelerated Cartesian readout, thereby increasing the potential usability of MRF., Comment: 12 pages, 11 main figures, 2 supplementry figures, preprint of accepted abstract

The interaction of anionic magnetite nanoparticles (MNPs) of size 18 nm with negatively charged giant unilamellar vesicles (GUVs) formed from a mixture of neutral dioleoylphosphatidylcholine (DOPC) and negatively charged dioleoylphosphatidylglycerol (DOPG) lipids has been investigated. It has been obtained that NPs induces the deformation of spherical GUVs. The reaction of other GUVs on NPs consists in the appearance of pores in their membranes. We focused the effect of electrostatics on the interaction of charged membranes with MNPs. To study the influence of the surface charge of GUVs on the processes under consideration, we varied the fraction of DOPG in the vesicles from 0 to 100%. We examined the influence of salt concentration in the range of 50-300 mM NaCl concentration. To describe the degree of deformation, a special parameter compactness was introduced. The pore formation in the membranes of GUVs was investigated by the leakage of sucrose. The compactness increases with time and also NPs concentration. The fraction of deformed GUVs increases with the increase of surface charge density of membranes as well as the decrease of salt concentration in buffer. The value of compactness for neutral membrane is 1.25 times higher than that of charged ones. The fraction of deformed GUVs become constant after 20 min, however it increases with NPs concentration. The time taken for stochastic pore formation is less for charged membrane than neutral one. The physical mechanism explaining the experimental results obtained in these investigations.

This work is focused on the quantitative study of Magnetic Resonance Imaging (MRI) for the purpose of identification of brain tumor by apparent diffusion coefficient (ADC) calculations of Diffusion-weighted images (DWI). Such diffusion-based measurements of cellular response can provide additional quantitative information for tissue characterization that strengthens the diagnosis carried out by conventional T1 and T2 weighted MRI. Initially, the DWI protocol were implemented on different test subjects with 6 sets of diffusion weighting factor by using a 3T MR scanner at National Institute of Neuroscience, Dhaka, Bangladesh. Afterward, based on the discussion with radiologists and specialists, two subjects (subject number 2 and 5) with suspected brain tumor were selected from the previous pool; ADC calculations were performed on the tumor region and the normal tissues on the symmetric region of the tumor on the other hemisphere. The comparison revealed a significant difference in ADC values of both regions, thus indicating a successful detection of the brain tumor. Such quantitative analysis provides a broader diagnostic scope as an addition with routine anatomical MRI and could play a crucial role in the treatment planning for pre and post-operative condition.

Evoked EMG M-responses obtained from the thenar muscle in the palm by electrical stimulation of the median nerve demonstrate a well-established smooth bipolar shape for normal healthy subjects. Kinks in this curve are observed in certain neurological disorders and preliminary work suggests their relationship to cervical spondylosis. The present work was taken up to develop an objective method for the identification of such neurological disorders for automated diagnosis by analysing the M-responses. A Fourier transform was performed using MATLAB, and features in the frequency domain were studied to distinguish healthy and smooth M-responses from ones with kinks. The features included some basic parameters like peak amplitude, peak frequency, frequency bandwidths, and areas in specified frequency segments. Ratio and deviation parameters from the above basic parameters were also studied to make 39 parameters in all. Out of these 10 came out as ‘highly significant’, 17 as ‘significant’ and the rest as insignificant, in statistical t-tests. A weighted combination of the significant parameters may allow identification of kinks with confidence.