Non-invasive System Research Articles

Deep Learning-Based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli.

Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a continuous decline in cognitive functions and eventually results in death. An early AD diagnosis is important for taking active measures to slow its deterioration. Traditional diagnoses are usually based on clinical experience, which is limited by several realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as a typical eye-tracking behavior, is of great clinical value in detecting cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conducted a 3D comprehensive visual task on a noninvasive eye-tracking system to collect visual attention heatmaps. Then a multilayered comparison convolutional neural network (MC-CNN) is proposed to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multilayered feature representations of heatmaps were obtained by hierarchical residual blocks to better encode eye movement behaviors, which were further integrated into a distance vector to benefit the comprehensive visual task. From evaluation, MC-CNN can distinguish AD patients from normals with 0.84 accuracy, 0.86 recall, 0.82 precision, 0.83 F1-score and 0.90 area under the curve (AUC). The above results demonstrate the effectiveness of the proposed MC-CNN in AD diagnosis based on the comprehensive 3D visual task.

Design of a novel ECR ion trap facility for nuclear physics and fundamental plasma processes studies

An innovative ECR ion trap facility, called PANDORA (Plasma for Astrophysics, Nuclear Decay Observation and Radiation for Archaeometry), was designed for fundamental plasma processes and nuclear physics investigations. The overall structure consists of three subsystems: a) a large (70 cm in length, 28 cm in inner diameter) ECR plasma trap with a fully superconducting B-minimum magnetic system (Bmax = 3.0 T) and an innovative design to host detectors and diagnostic tools; b) an advanced non-invasive plasma multidiagnostics system to locally characterize the plasma thermodynamic properties; c) an array of 14 HPGe detectors. The PANDORA facility is conceived to measure, for the first time, in-plasma β-decaying isotope rates under stellar-like conditions. The experimental approach consists in a direct correlation of plasma parameters and nuclear activity by disentangling - by means of the multidiagnostic system that will work in synergy with the γ-ray array - the photons emitted by the plasma (from microwave to hard X-ray) and γ-rays emitted after the isotope β-decay. In addition to nuclear physics research, fundamental plasma physics studies can be conducted in this unconventional ion source equipped with tens of detection and diagnostic devices (RF polarimeter, optical emission spectroscopy (OES), X-ray imaging, space and time-resolved spectroscopy, RF probes, scope), with relevant implications for R&D of ion sources for accelerator physics and technology. Several studies have already been performed in downsized nowadays operating ECRIS. Stable and turbulent plasma regimes have been described quantitatively, studying the change of plasma morphology, confinement, and dynamics of losses using space resolved X-ray spectroscopy.

Non-invasive Blood Glucose Monitoring System

The project presents a novel approach to blood glucose monitoring, offering a non-invasive and pain-free method for individuals with diabetes to track their glucose levels. The system utilizes advanced sensor technology coupled with signal processing techniques to detect glucose levels in the body without the need for traditional needle pricking. The key components of the system include a power supply, voltage regulator, ESP32 microcontroller, LCD display, photo sensor, amplifier circuit, and linearization circuit, all seamlessly integrated to provide accurate and real-time glucose readings. This breakthrough technology holds great promise in revolutionizing diabetes management, enhancing the quality of life for millions of affected individuals.By placing the sensor on the individual’s hand, the Glucometer system employs a combination of photonic and electronic principles to non-invasively measure glucose levels. The photo sensor captures specific wavelengths of light that interact with glucose molecules within the tissue, generating a signal proportional to the glucose concentration. This signal is then amplified and processed through the integrated circuits, ensuring accurate linearization and calibration. The ESP32 microcontroller orchestrates these operations, facilitating seamless communication with the LCD display to present the glucose reading in a clear and user-friendly format.The Glucometer’s innovative design eliminates the discomfort associated with traditional blood sampling methods, making it a highly accessible and convenient tool for diabetes management. The system’s efficiency in providing accurate glucose readings without the need for invasive procedures represents a significant advancement in healthcare technology. With further development and refinement, the Glucometer holds the potential to significantly enhance the daily lives of individuals living with diabetes, offering a reliable and painless means of monitoring blood glucose levels.

Heme oxidation of NO-receptor soluble guanylyl cyclase is an important contributor to cerebrovascular and cognitive dysfunctions

Introduction: Decreased cerebral blood flow (CBF) is one of the major hemodynamic alterations leading to neurodegeneration and age-related cognitive decline. Nitric Oxide (NO)-dependent vasomotor reactivity is central in regulating cerebrovascular hemodynamics and adequate brain blood perfusion. NO receptor Soluble Guanylyl Cyclase (sGC) is the primary mediator of NO-dependent vasodilation. Oxidative stress, often persisting in diseased vasculature, renders sGC insensitive to NO and impairs the NO signaling. The objective of our study was to investigate the role of sGC oxidation in regulation CBF and brain function. Hypothesis: Oxidative stress disrupts NO/cGMP signaling in brain by decreasing sGC activity via oxidation of sGC heme. Approach: To assess causal effect of sGC oxidation on NO signaling function we administered ODQ, a sGC-specific heme oxidizing agent, to simulate long-term effect of sGC oxidation in cerebral vasculature. Two groups of young wild-type male and female mice (4 months old C57BL/6) were treated intraperitoneally (IP) twice a week for one month with ODQ (20 mg/kg) or a solvent as control. Changes in cerebrovascular reactivity and memory function were assessed and compared in treated and control groups. Using a non-invasive pulsed Doppler ultrasound-based system we assessed the changes in blood flow velocity (PVF, % of baseline) in the mouse middle cerebral artery (MCA) of male and female groups. MCA vasodilation was induced by bolus IP injection of NO-donor sodium nitroprusside (SNP) or BAY 58-2667, an allosteric NO-independent sGC activator. Memory function was assessed by the Novel Object Recognition Task (NORT, RI, recognition index) in all experimental groups. Western blotting and densitometry assessed the expression of major sGC subunits (α1, α2 and β1) in cleared brain lysates from ODQ-treated and control groups. Results: We observed a significantly blunted vasodilative response to SNP (1 mg/kg) in ODQ-treated mice vs controls of both genders (PVF, max response at 2 min post-injection, males 73.9 ± 10.4% (n=7/7) vs 58.5 ± 8.6 %, p=0.01; females (n=6/6) 77.4 ± 10.6 % vs 61.9 ± 5.4 %, p=0.01). Changes in response to NO-donor were more pronounced in males than females. The difference in BAY 58-2667 response between ODQ-treated and control groups was not statistically significant. ODQ significantly reduced the long-term memory in males but not female, according to NORT (RI, males (n=7/7) 0.58 ± 0.13 vs 0.69 ± 0.05; p=0.01; females (n=6/6) ODQ 0.59 ± 0.17 vs 0.73 ± 0.1, p=0.13). No statistically significant difference in expression of sGC subunits between control and ODQ-treated (n= 4/4) of male and female groups was determined. Conclusions: ODQ treatment significantly impairs vasodilation response to NO-donor but not to NO-independent sGC activator in mice MCA. ODQ treatment negatively affects long-term memory function in male, but not female mice. ODQ treatment does not significantly affect the expression of sGC subunits. Together our data suggest that the oxidation of sGC heme moiety impairs the regulation of cerebral blood flow of both genders and impairs long-term memory function gender-specifically in young mice. This research was supported by National Institute of Health grants RAG071999A (Iraida Sharina), HL139838, and American Heart Association 23TPA1070711 (Emil Martin). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Engineered lecithin-based proniosomes for enhanced trans-tympanic permeation: In vitro, microbiological, ex vivo and invivo evaluation

Otitismedia is an acute illness that specifically targets the middle ear. It affects around one-third of the pediatric population in the US. This study aimed to develop lecithin-based proniosomes to enhance the trans-tympanic permeation of the antimicrobial drug ofloxacin (OFX) and boost its antibacterial properties. Proniosomes were fabricated by the coacervation technique using a Box-Behnken design. The investigated factors were cholesterol amount (A), surfactant-to-drug ratio (B), and Brij percentage (C). The numerical optimization feature of Design-Expert® software was implemented to choose the optimum formula. The criteria for selection involved decreasing the polydispersity index (PDI) and particle size (PS) while maximizing the absolute value of zeta potential (ZP) and the percentage of entrapment efficacy (EE%). The formula with the highest desirability value (0.748), an EE% of 73.38 %, a PS of 625.35 nm, and a ZP of −44.20 mV was selected as the optimum formula. Additional investigations were conducted to illustrate the morphological appearance, release rate, stability, activity, and tolerability of the selected formula, such as invitro,ex vivo, microbiological, and invivo tests. Results demonstrated that, compared to the OFX solution (2118.72 μg/cm2), the optimum formula was found to have better otic tolerance, augmented trans-tympanic permeation (3701.01 μg/cm2), and improved antibacterial activity. The aforementioned outcomes highlighted the capability of the OFX-loaded lecithin-based proniosomes to be used as a promising non-invasive delivery system for the treatment of otitismedia.

Development of a hydroxypropyl methyl cellulose/polyacrylic acid interpolymer complex formulated buccal mucosa adhesive film to facilitate the delivery of insulin for diabetes treatment

Buccal mucosa administration is a promising method for insulin (INS) delivery with good compliance. However, buccal mucosa delivery systems still face challenges of long-term mucosal adhesion, sustained drug release, and mucosal drug penetration. To address these issues, a double-layer film consisting of a hydroxypropyl methylcellulose/polyacrylic acid interpolymer complex (IPC)-formulated mucoadhesive layer and an ethylcellulose (EC)-formulated waterproof backing layer (IPC/EC film) was designed. Protamine (PTM) and INS were co-loaded in the mucoadhesive layer of the IPC/EC film (PTM-INS-IPC/EC film). In ex vivo studies with porcine buccal mucosa, this film exhibited robust adhesion, with an adhesion force of 120.2 ± 20.3 N/m2 and an adhesion duration of 491 ± 45 min. PTM has been shown to facilitate INS mucosal transfer. Pharmacokinetic studies indicated that the PTM-INS-IPC/EC film significantly improved the absorption of INS, exhibiting a 1.45 and 2.24-fold increase in the area under the concentration-time curve (AUC0-∞) compared to the INS-IPC/EC film and free INS, respectively. Moreover, the PTM-INS-IPC/EC film effectively stabilized the blood glucose levels of type 1 diabetes mellitus (T1DM) rats with post oral glucose administration, maintaining lower glucose levels for approximately 8 h. Hence, the PTM-INS-IPC/EC film provides a promising noninvasive INS delivery system for diabetes treatment.

Design and analysis of ultra-wideband microstrip patch antenna with various conductive materials for terahertz gap

The paper explores the design and analysis of a wideband microstrip patch antenna with a metallic patch and a 3 × 3 split ring resonator (SRR) array operating in the 0.1–5 THz frequency range. The antenna's structure incorporates different conductive materials such as gold, silver, and graphene as a metallic patch. The dimensions of the metallic patch and SRR are calculated to achieve wideband operation within the desired THz range. The SRR array enhances electromagnetic resonance, thereby improving bandwidth and radiation characteristics for medical imaging. The study discusses the equivalent circuit and design equations for the microstrip patch antenna and SRR unit cell. For designing and analyzing the proposed antenna, CST Microwave Studio 2019 software have been used. Performance parameters such as return loss, bandwidth, gain, efficiency, directivity, VSWR, and radiation pattern have been evaluated. The advantages and limitations of each conductive material are evaluated to determine their suitability for THz-based medical imaging applications. The goal is to maximize the antenna's bandwidth, gain, and image resolution for medical imaging purposes. The findings highlight the performance characteristics of gold, silver, and graphene as conductive materials for medical imaging applications, facilitating the development of high-resolution, non-invasive imaging systems with improved diagnostic capabilities.

Child face detection on front passenger seat through deep learning

Objective One of the main causes of death worldwide among young people are car crashes, and most of these fatalities occur to children who are seated in the front passenger seat and who, at the time of an accident, receive a direct impact from the airbags, which is lethal for children under 13 years of age. The present study seeks to raise awareness of this risk by interior monitoring with a child face detection system that serves to alert the driver that the child should not be sitting in the front passenger seat. Methods The system incorporates processing of data collected, elements of deep learning such as transfer learning, fine-tunning and facial detection to identify the presence of children in a robust way, which was achieved by training with a dataset generated from scratch for this specific purpose. The MobileNetV2 architecture was used based on the good performance shown when compared with the Inception architecture for this task; and its low computational cost, which facilitates implementing the final model on a Raspberry Pi 4B. Results The resulting image dataset consisted of 102 empty seats, 71 children (0-13 years), and 96 adults (14-75 years). From the data augmentation, there were 2,496 images for adults and 2,310 for children. The classification of faces without sliding window gave a result of 98% accuracy and 100% precision. Finally, using the proposed methodology, it was possible to detect children in the front passenger seat in real time, with a delay of 1 s per decision and sliding window criterion, reaching an accuracy of 100%. Conclusions Although our 100% accuracy in an experimental environment is somewhat idealized in that the sensor was not blocked by direct sunlight, nor was it partially or completely covered by dirt or other debris common in vehicles transporting children. The present study showed that is possible the implementation of a robust noninvasive classification system made on Raspberry Pi 4 Model B in any automobile for the detection of a child in the front seat through deep learning methods such as Deep CNN.

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Non-Invasive Hemodynamic Monitoring System Integrating Spectrometry, Photoplethysmography, and Arterial Pressure Measurement Capabilities.

Minimally invasive and non-invasive hemodynamic monitoring technologies have recently gained more attention, driven by technological advances and the inherent risk of complications in invasive techniques. In this article, an experimental non-invasive system is presented that effectively combines the capabilities of spectrometry, photoplethysmography (PPG), and arterial pressure measurement. Both time- and wavelength-resolved optical signals from the fingertip are measured under external pressure, which gradually increased above the level of systolic blood pressure. The optical channels measured at 434-731 nm divided into three groups separated by a group of channels with wavelengths approximately between 590 and 630 nm. This group of channels, labeled transition band, is characterized by abrupt changes resulting from a decrease in the absorption coefficient of whole blood. External pressure levels of maximum pulsation showed that shorter wavelengths (<590 nm) probe superficial low-pressure blood vessels, whereas longer wavelengths (>630 nm) probe high-pressure arteries. The results on perfusion indices and DC component level changes showed clear differences between the optical channels, further highlighting the importance of wavelength selection in optical hemodynamic monitoring systems. Altogether, the results demonstrated that the integrated system presented has the potential to extract new hemodynamic information simultaneously from macrocirculation tomicrocirculation.

Detection of maize stem diameter by using RGB-D cameras' depth information under selected field condition.

Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem's skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera's intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error (MAPE) of 3.01%, a Mean Absolute Error (MAE) of 0.75mm, a Root Mean Square Error (RMSE) of 1.07mm, and a coefficient of determination (R²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.

Noninvasive Injectable Optical Nanosensor-Hydrogel Hybrids Detect Doxorubicin in Living Mice.

While the tissue-transparent fluorescence of single-walled carbon nanotubes (SWCNTs) imparts substantial potential for use in non-invasive biosensors, development of non-invasive systems is yet to be realized. Here, we investigated the functionality of a SWCNT-based nanosensor in several injectable SWCNT-hydrogel systems, ultimately finding SWCNT encapsulation in a sulfonated methylcellulose hydrogel optimal for detection of ions, small molecules, and proteins. We found that the hydrogel system and nanosensor signal were stable for several weeks in live mice. We then found that this system successfully detects local injections of the chemotherapeutic agent doxorubicin in mice. We anticipate future studies to adapt this device for detection of other analytes in animals and, ultimately, patients.

Electrical Impedance Spectroscopy Quantifies Skin Barrier Function in Organotypic In Vitro Epidermis Models

3 D human epidermal equivalents (HEEs) are a state-of-the-art organotypic culture model in pre-clinical investigative dermatology and regulatory toxicology. Here, we investigated the utility of electrical impedance spectroscopy (EIS) for non-invasive measurement of HEE epidermal barrier function. Our setup comprised a custom-made lid fit with 12 electrode pairs aligned on the standard 24-transwell cell culture system. Serial EIS measurements for seven consecutive days did not impact epidermal morphology and readouts showed comparable trends to HEEs measured only once. We determined two frequency ranges in the resulting impedance spectra: a lower frequency range termed EISdiff correlated with keratinocyte terminal differentiation independent of epidermal thickness and a higher frequency range termed EISSC correlated with stratum corneum thickness. HEEs generated from CRISPR/Cas9 engineered keratinocytes that lack key differentiation genes FLG, TFAP2A, AHR or CLDN1 confirmed that keratinocyte terminal differentiation is the major parameter defining EISdiff. Exposure to pro-inflammatory psoriasis- or atopic dermatitis-associated cytokine cocktails lowered the expression of keratinocyte differentiation markers and reduced EISdiff. This cytokine-associated decrease in EISdiff was normalized after stimulation with therapeutic molecules. In conclusion, EIS provides a non-invasive system to consecutively and quantitatively assess HEE barrier function and to sensitively and objectively measure barrier development, defects and repair.

NON-INVASIVE BLOOD GLUCOSE AND THYROID MONITORING SYSTEM

Frequent blood tests have historically been necessary to monitor thyroid function and blood glucose levels, which can be painful and inconvenient for patients. In this work, we provide a unique non-invasive blood glucose and thyroid monitoring device. With the use of cutting-edge sensor technology, our system can precisely measure thyroid and glucose via the skin without the need for blood draws or needles. This method provides a simple and easy substitute for conventional techniques, which may increase patient adherence to monitoring schedules. This non-invasive monitoring system's creation marks a substantial leap forward in medical science and could improve the way thyroid and diabetic diseases are managed. Additional investigation and clinical studies are necessary to confirm the efficacy and dependability of this novel technology in real world. Key Words: Non-invasive, Blood glucose, Thyroid, Monitoring system, Sensor technology, Healthcare innovation

Noninvasive Blood Glucose Detection System With Infrared Pulse Sensor and Hybrid Feature Neural Network

Noninvasive Blood Glucose Detection System With Infrared Pulse Sensor and Hybrid Feature Neural Network

Development and characterization of magnetic hydrogels loaded with greenly synthesized iron-oxide nanoparticles conjugated with cisplatin

A novel approach was devised to address the challenges in delivering cisplatin (CIS) for lung cancer treatment. This involved the development of a non-invasive hydrogel delivery system, aiming to minimize side effects associated with its administration. Using carbopol 971 (CP) and chitosan (CH) at varying ratios, the hydrogels were prepared and loaded with eco-friendly iron oxide nanoparticles (IONPs) conjugated to CIS. The physical properties, yield, drug loading, and cytotoxicity against lung cancer cell lines (A549) were assessed, along with hydrogel rheological properties and in vitro drug diffusion. Hydrogel A1 that composed of 1:1 of CP:CH hydrogel loaded with 100 mg IONPs and 250 µg CIS demonstrated distinctive properties that indicate its suitability for potential delivery. The loaded greenly synthesized IONPs@CIS exhibited a particle size of 23.0 nm, polydispersity index of 0.47, yield of 71.6%, with 88.28% drug loading. They displayed significant cytotoxicity (61.7%) against lung cancer cell lines (A549), surpassing free CIS cytotoxicity (28.1%). Moreover, they demonstrated shear-thinning behaviour, viscoelastic properties, and Fickian drug release profile over 24 h (flux 2.34 µg/cm2/h, and permeability 0.31 cm/h).

Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study.

The increasing prevalence of obesity necessitates innovative approaches to better understand this health crisis, particularly given its strong connection to chronic diseases such as diabetes, cancer, and cardiovascular conditions. Monitoring dietary behavior is crucial for designing effective interventions that help decrease obesity prevalence and promote healthy lifestyles. However, traditional dietary tracking methods are limited by participant burden and recall bias. Exploring microlevel eating activities, such as meal duration and chewing frequency, in addition to eating episodes, is crucial due to their substantial relation to obesity and disease risk. The primary objective of the study was to develop an accurate and noninvasive system for automatically monitoring eating and chewing activities using sensor-equipped smart glasses. The system distinguishes chewing from other facial activities, such as speaking and teeth clenching. The secondary objective was to evaluate the system's performance on unseen test users using a combination of laboratory-controlled and real-life user studies. Unlike state-of-the-art studies that focus on detecting full eating episodes, our approach provides a more granular analysis by specifically detecting chewing segments within each eating episode. The study uses OCO optical sensors embedded in smart glasses to monitor facial muscle activations related to eating and chewing activities. The sensors measure relative movements on the skin's surface in 2 dimensions (X and Y). Data from these sensors are analyzed using deep learning (DL) to distinguish chewing from other facial activities. To address the temporal dependence between chewing events in real life, we integrate a hidden Markov model as an additional component that analyzes the output from the DL model. Statistical tests of mean sensor activations revealed statistically significant differences across all 6 comparison pairs (P<.001) involving 2 sensors (cheeks and temple) and 3 facial activities (eating, clenching, and speaking). These results demonstrate the sensitivity of the sensor data. Furthermore, the convolutional long short-term memory model, which is a combination of convolutional and long short-term memory neural networks, emerged as the best-performing DL model for chewing detection. In controlled laboratory settings, the model achieved an F1-score of 0.91, demonstrating robust performance. In real-life scenarios, the system demonstrated high precision (0.95) and recall (0.82) for detecting eating segments. The chewing rates and the number of chews evaluated in the real-life study showed consistency with expected real-life eating behaviors. The study represents a substantial advancement in dietary monitoring and health technology. By providing a reliable and noninvasive method for tracking eating behavior, it has the potential to revolutionize how dietary data are collected and used. This could lead to more effective health interventions and a better understanding of the factors influencing eating habits and their health implications.

Allogeneic bone marrow mesenchymal stem cell-derived exosomes alleviate human hypoxic AKI-on-a-Chip within a tight treatment window

BackgroundAcute hypoxic proximal tubule (PT) injury and subsequent maladaptive repair present high mortality and increased risk of acute kidney injury (AKI) - chronic kidney disease (CKD) transition. Human bone marrow mesenchymal stem cell-derived exosomes (hBMMSC-Exos) as potential cell therapeutics can be translated into clinics if drawbacks on safety and efficacy are clarified. Here, we determined the real-time effective dose and treatment window of allogeneic hBMMSC-Exos, evaluated their performance on the structural and functional integrity of 3D microfluidic acute hypoxic PT injury platform.MethodshBMMSC-Exos were isolated and characterized. Real-time impedance-based cell proliferation analysis (RTCA) determined the effective dose and treatment window for acute hypoxic PT injury. A 2-lane 3D gravity-driven microfluidic platform was set to mimic PT in vitro. ZO-1, acetylated α-tubulin immunolabelling, and permeability index assessed structural; cell proliferation by WST-1 measured functional integrity of PT.ResultshBMMSC-Exos induced PT proliferation with ED50 of 172,582 µg/ml at the 26th hour. Hypoxia significantly decreased ZO-1, increased permeability index, and decreased cell proliferation rate on 24–48 h in the microfluidic platform. hBMMSC-Exos reinforced polarity by a 1.72-fold increase in ZO-1, restored permeability by 20/45-fold against 20/155 kDa dextran and increased epithelial proliferation 3-fold compared to control.ConclusionsThe real-time potency assay and 3D gravity-driven microfluidic acute hypoxic PT injury platform precisely demonstrated the therapeutic performance window of allogeneic hBMMSC-Exos on ischemic AKI based on structural and functional cellular data. The novel standardized, non-invasive two-step system validates the cell-based personalized theragnostic tool in a real-time physiological microenvironment prior to safe and efficient clinical usage in nephrology.

A Validation Study of Non-invasive Scoring Systems for Assessing Severity of Hepatic Fibrosis in a Cohort of South Indian Patients With Non-alcoholic Fatty Liver Disease

IntroductionLiver biopsy is the gold standard for diagnosing and staging NAFLD. But liver biopsy has its limitations. Non-invasive tests (NITs) eliminate many of the drawbacks of liver biopsy. We did a retrospective observational study to validate the non-alcoholic fatty liver disease Fibrosis Score (NFS score) and Fibrosis Score 4 (FIB-4 index) against the gold standard liver biopsy in a cohort of south Indian patients with NAFLD. Material and MethodsA retrospective observational analytical study of patients who had a liver biopsy with a diagnosis of NAFLD and had all the data for aetiology assessment and NIT calculation within 4 weeks of biopsy were included in the study. On liver biopsy NAFLD was scored as per NIH’s NASH committee (NASH CRN) grading system. NAFLD Fibrosis Score (NFS) and FIB-4 index were calculated and scores more than 0.676 and 2.67 respectively was taken as the cut-off to predict advanced fibrosis. The sensitivity, specificity, positive predictive value, negative predictive value and area under the ROC curve for NFS score and FIB-4 score to diagnose advanced fibrosis were calculated. ResultsA total of 147 patients were included in the study. Of these 56 (38.1%) patients had advanced fibrosis (Stage 3, 4). Patients with advanced fibrosis were more likely to be older and have DM. Patients with advanced fibrosis had lower platelet count, higher AST, lower albumin and higher AST/ALT ratio. The NFS of > 0.676 had a sensitivity of 68% and specificity of 100% and FIB-4 index of >2.67 had a sensitivity of 67% and specificity of 95.6 % in diagnosing advanced fibrosis in our study. ConclusionThe non-invasive scoring systems NAFLD Fibrosis Score (NFS) and FIB-4 index, can be used as a bedside tool for diagnosing Liver fibrosis in NAFLD allowing liver biopsy to be used in a more targeted manner for patients diagnosed with advanced fibrosis on NITs.

Non-invasive glucometer monitoring system through optical based near-infrared sensor method

ABSTRACT Diabetes is a fast-developing medical issue that causes most renal and cardiac illnesses. Thus, diabetes management requires regular glucose monitoring. One potential technology is non-invasive glucometer monitoring. This work aims to develop a user-friendly near-infrared sensor-based non-invasive glucose monitoring system, correlating sensor output voltage variations with glucose levels, to provide accurate and convenient glucose monitoring for diabetes management. The objective is to validate the system’s accuracy against existing fingerpick methods and analyze its performance across different age groups and food intake conditions through experimental testing and Clarke grid analysis. In our research, we propose a near-infrared sensor-based non-invasive-type glucose monitoring technique which is a user-friendly system. The experimental setup and prototype system are designed and implemented for measuring the variation of glucose level with respect to a sensor output voltage. Using Beer Lambert’s law, the established results correlated the absorbance property of light with the sample concentration level. Demonstration of testing for different aged people was done under various food intake conditions. The obtained results are tabulated and validated with the existing fingerpick method and achieved an accuracy of 97.8%. Also, Clarke grid analysis has been done and depicted the pattern obtained.