Hardware implementation of memristor-based artificial neural networks.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Porous Laser-Scribed Graphene Electrodes Modified with Zwitterionic Moieties: A Strategy for Antibiofouling and Low-Impedance Interfaces.

Laser-scribed graphene electrodes (LSGEs) are promising platforms for the development of electrochemical biosensors for point-of-care settings and continuous monitoring and wearable applications. However, the frequent occurrence of biofouling drastically reduces the sensitivity and selectivity of these devices, hampering their sensing performance. Herein, we describe a versatile, low-impedance, and robust antibiofouling interface based on sulfobetaine-zwitterionic moieties. The interface induces the formation of a hydration layer and exerts electrostatic repulsion, protecting the electrode surface from the nonspecific adsorption of various biofouling agents. We demonstrate through electrochemical and microscopy techniques that the modified electrode exhibits outstanding antifouling properties, preserving more than 90% of the original signal after 24 h of exposure to bovine serum albumin protein, HeLa cells, and Escherichia coli bacteria. The promising performance of this antifouling strategy suggests that it is a viable option for prolonging the lifetime of LSGEs-based sensors when operating on complex biological systems.

Signal Processing Circuits and Systems for Smart Sensing Applications.

The rising demand for reliable, real-time, low-maintenance, cost-efficient monitoring systems with a high accuracy is becoming increasingly more notable in everyday life [...].

Smart Multiplex Point-of-Care Platform for Simultaneous Drug Monitoring.

Recently, illicit drug use has become more widespread and is linked to problems with crime and public health. These drugs disrupt consciousness, affecting perceptions and feelings. Combining stimulants and depressants to suppress the effect of drugs has become the most common reason for drug overdose deaths. On-site platforms for illicit-drug detection have gained an important role in dealing, without any excess equipment, long process, and training, with drug abuse and drug trafficking. Consequently, the development of rapid, sensitive, noninvasive, and reliable multiplex drug-detecting platforms has become a major necessity. In this study, a multiplex laser-scribed graphene (LSG) sensing platform with one counter, one reference, and three working electrodes was developed for rapid and sensitive electrochemical detection of amphetamine (AMP), cocaine (COC), and benzodiazepine (BZD) simultaneously in saliva samples. The multidetection sensing system was combined with a custom-made potentiostat to achieve a complete point-of-care (POC) platform. Smartphone integration was achieved by a customized application to operate, display, and send data. To the best of our knowledge, this is the first multiplex LSG-based electrochemical platform designed for illicit-drug detection with a custom-made potentiostat device to build a complete POC platform. Each working electrode was optimized with standard solutions of AMP, COC, and BZD in the concentration range of 1.0 pg/mL-500 ng/mL. The detection limit of each illicit drug was calculated as 4.3 ng/mL for AMP, 9.7 ng/mL for BZD, and 9.0 ng/mL for COC. Healthy and MET (methamphetamine) patient saliva samples were used for the clinical study. The multiplex LSG sensor was able to detect target analytes in real saliva samples successfully. This multiplex detection device serves the role of a practical and affordable alternative to conventional drug-detection methods by combining multiple drug detections in one portable platform.

CD34+ HSPCs-derived exosomes contain dynamic cargo and promote their migration through functional binding with the homing receptor E-selectin.

Exosomes are tiny vesicles released by cells that carry communications to local and distant locations. Emerging research has revealed the role played by integrins found on the surface of exosomes in delivering information once they reach their destination. But until now, little has been known on the initial upstream steps of the migration process. Using biochemical and imaging approaches, we show here that exosomes isolated from both leukemic and healthy hematopoietic stem/progenitor cells can navigate their way from the cell of origin due to the presence of sialyl Lewis X modifications surface glycoproteins. This, in turn, allows binding to E-selectin at distant sites so the exosomes can deliver their messages. We show that when leukemic exosomes were injected into NSG mice, they traveled to the spleen and spine, sites typical of leukemic cell engraftment. This process, however, was inhibited in mice pre-treated with blocking E-selectin antibodies. Significantly, our proteomic analysis found that among the proteins contained within exosomes are signaling proteins, suggesting that exosomes are trying to deliver active cues to recipient cells that potentially alter their physiology. Intriguingly, the work outlined here also suggests that protein cargo can dynamically change upon exosome binding to receptors such as E-selectin, which thereby could alter the impact it has to regulate the physiology of the recipient cells. Furthermore, as an example of how miRNAs contained in exosomes can influence RNA expression in recipient cells, our analysis showed that miRNAs found in KG1a-derived exosomes target tumor suppressing proteins such as PTEN.

Efficient training of spiking neural networks with temporally-truncated local backpropagation through time.

Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on event-based datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs' models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT. Thus, the proposed method has shown high potential to enable fast and energy-efficient on-chip training for real-time learning at the edge.

An interconnect-free micro-electromechanical 7-bit arithmetic device for multi-operand programmable computing.

Computational power density and interconnection between transistors have grown to be the dominant challenges for the continued scaling of complementary metal-oxide-semiconductor (CMOS) technology due to limited integration density and computing power. Herein, we designed a novel, hardware-efficient, interconnect-free microelectromechanical 7:3 compressor using three microbeam resonators. Each resonator is configured with seven equal-weighted inputs and multiple driven frequencies, thus defining the transformation rules for transmitting resonance frequency to binary outputs, performing summation operations, and displaying outputs in compact binary format. The device achieves low power consumption and excellent switching reliability even after 3 × 103 repeated cycles. These performance improvements, including enhanced computational power capacity and hardware efficiency, are paramount for moderately downscaling devices. Finally, our proposed paradigm shift for circuit design provides an attractive alternative to traditional electronic digital computing and paves the way for multioperand programmable computing based on electromechanical systems.

Polyoxometalate-cyclodextrin supramolecular entities for real-time in situ monitoring of dopamine released from neuroblastoma cells.

Optimized and sensitive biomarker detection has recently been shown to have a critical impact on quality of diagnosis and medical care options. In this research study, polyoxometalate-γ-cyclodextrin metal-organic framework (POM-γCD MOF) was utilized as an electrocatalyst to fabricate highly selective sensors to detect in-situ released dopamine. The POM-γCD MOF produced multiple modes of signals for dopamine including electrochemical, colorimetric, and smartphone read-outs. Real-time quantitative monitoring of SH-SY5Y neuroblastoma cellular dopamine production was successfully demonstrated under various stimuli at different time intervals. The POM-CD MOF sensor and linear regression model were used to develop a smartphone read-out platform, which converts dopamine visual signals to digital signals within a few seconds. Ultimately, POM-γCD MOFs can play a significant role in the diagnosis and treatment of various diseases that involve dopamine as a significant biomarker.

Three siblings with variable degrees of neuromuscular involvement and congenital sideroblastic anemia: A peculiar phenotype and a surprise genotypic explanation.

INTRODUCTION: Congenital sideroblastic anemias (CSAs) are a group of inherited bone-marrow disorders manifesting with erythroid hyperplasia and ineffective erythropoiesis. METHODS: We describe a detailed clinical and genetic characterization of three siblings with CSA. RESULTS: Two of them had limb-girdle myopathy and global developmental delay. The two elder siblings performed allogenic hematopoietic stem-cell transplantation 5 and 3 years prior with stabilization of the hematological features. Exome sequencing in the non-transplanted sibling revealed a novel homozygous nonsense variant in SLC25A38 gene NM_017875.2:c.559C > T; p.(Arg187*) causing autosomal-recessive sideroblastic anemia type-2, and a second homozygous pathogenic previously reported variant in GMPPB gene NM_013334.3:c.458C > T; p.(Thr153Ile) causing autosomal-recessive muscular dystrophy-dystroglycanopathy type B14. With the established diagnosis, hematopoietic stem cell transplantation is now being scheduled for the youngest sibling, and a trial therapy with acetylcholine esterase inhibitors was started for the two neurologically affected patients with partial clinical improvement. CONCLUSION: This family emphasizes the importance of whole-exome sequencing for familial cases with complex phenotypes and vague neurological manifestations.

Effect of combining UV-C irradiation and vacuum sealing on the shelf life of fresh strawberries and tomatoes.

This research presents the effect of combining UV-C irradiation and vacuum sealing on the shelf life of strawberries and quartered tomatoes and compares it with the effect of the sole use of UV-C irradiation or vacuum sealing. A constant UV-C dose of 360 J/m2 was used for the samples' irradiation, and all the vacuum-sealed samples were stored at a reduced pressure of 40 kPa. Organoleptic analysis, microbial population quantification of yeast and mold, Pseudomonas sp., weight loss, and pH measurements were obtained to identify the spoilage occurrence, monitor the samples' quality, and quantify the shelf life. Sensory evaluation was conducted by 12 consumer panelists to evaluate the aroma, taste, color, texture, and the overall acceptance of the samples. The results revealed that the combination of UV-C irradiation and vacuum sealing prolongs the shelf life of perishables more than the sole use of UV-C irradiation or vacuum sealing. The achieved shelf-life increase using this combination was 124.41% and 54.41% for strawberries and quartered tomatoes, respectively, while acceptable sensory characteristics were maintained throughout the storage period. Hence, this food preservation method can be further improved and integrated in the daily life of modern consumers and the operations of fresh produce retailers, as it could effectively reduce the spoilage rates of fresh produce and help achieve the UN SDG 12.3, which aims to reduce food loss and waste by 50% by 2030 at the consumer and retail levels. PRACTICAL APPLICATION: The system can be further developed and introduced to the market as a kitchen appliance for households or as a predistribution step for fresh produce distribution centers. The shelf-life extension capability of this system, which does not involve any use of chemical substances, would make it an attractive solution for households and food retailers.

Institution of Metal-Organic Frameworks as a Highly Sensitive and Selective Layer In-Field Integrated Soil-Moisture Capacitive Sensor.

The ongoing global industrialization along with the notable world population growth is projected to challenge the global environment as well as pose greater pressure on water and food needs. Foreseeably, an improved irrigation management system is essential and the quest for refined chemical sensors for soil-moisture monitoring is of tremendous importance. Nevertheless, the persisting challenge is to design and construct stable materials with the requisite sensitivity, selectivity, and high performance. Here, we report the introduction of porous metal-organic frameworks (MOFs), as the receptor layer, in capacitive sensors to efficiently sense moisture in two types of soil. Namely, our study unveiled that Cr-soc-MOF-1 offers the best sensitivity (≈24,000 pF) among the other tested MOFs for any given range of soil-moisture content, outperforming several well-known oxide materials. The corresponding increase in the sensitivities for tested MOFs at 500 Hz are ≈450, ≈200, and ≈30% for Cr-soc-MOF-1, Al-ABTC-soc-MOF, and Zr-fum-fcu-MOF, respectively. Markedly, Cr-soc-MOF-1, with its well-known water capacity, manifests an excellent sensitivity of ≈450% in clayey soil, and the analogous response time was 500 s. The noted unique sensing properties of Cr-soc-MOF-1 unveils the great potential of MOFs for soil-moisture sensing application.

Determining the nurses' perception regarding the effectiveness of COVID-19 protocols implemented in Eastern Province: Saudi Arabia.

Background: The global impact of Coronavirus Disease 2019 (COVID-19) has been profound, affecting public health, the global economy, and overall human life. Past experiences with global pandemics underscored the significance of understanding the perception of HCWs and hospital staff in developing and implementing preventive measures. The World Health Organization (WHO) provided protocols to manage the spread of COVID-19 and assist healthcare workers and health systems globally in maintaining high-quality health services. Objective: This study aims to assess nurses' perception, awareness, and compliance regarding the implementation of COVID-19 protocols and explore factors influencing their perception. Methodology: A quantitative cross-sectional survey-based study was conducted, distributing a constructed survey among nurses in the Eastern Province of Saudi Arabia. Results: Out of 141 participants, most adhered to protocols such as hand sanitization, social distancing, and proper personal protective equipment (PPE) usage. The predominant age group among respondents was 31 to 40 years (n = 71, 50%). A significant portion of participants reported holding a bachelor's degree (n = 86, 61%), with only 14% possessing advanced degrees (n = 19). Nearly a third of the nurses in the study had accumulated 6 to 10 years of professional experience (n = 49, 34.8%). A noteworthy percentage of nurses were engaged in daily shifts exceeding 8 h (n = 98, 70%). Gender differences were observed, with females exhibiting a higher tendency to avoid shaking hands and social gatherings. Saudi nationals were more inclined to shake hands and engage in gatherings. Non-Saudi nurses and those aged between <25 to 40 years demonstrated proper donning/doffing practices. Nurses with over 6 years of experience avoided social gatherings, while those working >8 h adhered better to PPE usage, proper donning/doffing, and disposal of PPE in designated bins. Conclusion: Understanding COVID-19 protocols is crucial for tailoring interventions and ensuring effective compliance with COVID-19 preventive measures among nurses. More efforts should be made toward preparing the healthcare nursing to deal with the outbreak. Preparing healthcare nursing with the right knowledge, attitude, and precautionary practices during the COVID-19 outbreak is very essential to patient and public safety.

Experimental Identification of the Second-Order Non-Hermitian Skin Effect with Physics-Graph-Informed Machine Learning.

Topological phases of matter are conventionally characterized by the bulk-boundary correspondence in Hermitian systems. The topological invariant of the bulk in d dimensions corresponds to the number of (d - 1)-dimensional boundary states. By extension, higher-order topological insulators reveal a bulk-edge-corner correspondence, such that nth order topological phases feature (d - n)-dimensional boundary states. The advent of non-Hermitian topological systems sheds new light on the emergence of the non-Hermitian skin effect (NHSE) with an extensive number of boundary modes under open boundary conditions. Still, the higher-order NHSE remains largely unexplored, particularly in the experiment. An unsupervised approach-physics-graph-informed machine learning (PGIML)-to enhance the data mining ability of machine learning with limited domain knowledge is introduced. Through PGIML, the second-order NHSE in a 2D non-Hermitian topoelectrical circuit is experimentally demonstrated. The admittance spectra of the circuit exhibit an extensive number of corner skin modes and extreme sensitivity of the spectral flow to the boundary conditions. The violation of the conventional bulk-boundary correspondence in the second-order NHSE implies that modification of the topological band theory is inevitable in higher dimensional non-Hermitian systems.

Metal-Organic Frameworks Meet Molecularly Imprinted Polymers: Insights and Prospects for Sensor Applications.

The use of porous materials as the core for synthesizing molecularly imprinted polymers (MIPs) adds significant value to the resulting sensing system. This review covers in detail the current progress and achievements regarding the synergistic combination of MIPs and porous materials, namely metal/covalent-organic frameworks (MOFs/COFs), including the application of such frameworks in the development of upgraded sensor platforms. The different processes involved in the synthesis of MOF/COF-MIPs are outlined, along with their intrinsic properties. Special attention is paid to debriefing the impact of the morphological changes that occur through the synergistic combination compared to those that occur due to the individual entities. Thereafter, the strategies used for building the sensors, as well as the transduction modes, are overviewed and discussed. This is followed by a full description of research advances for various types of MOF/COF-MIP-based (bio)sensors and their applications in the fields of environmental monitoring, food safety, and pharmaceutical analysis. Finally, the challenges/drawbacks, as well as the prospects of this research field, are discussed in detail.

Ruthenium and Nickel Molybdate-Decorated 2D Porous Graphitic Carbon Nitrides for Highly Sensitive Cardiac Troponin Biosensor.

Two-dimensional (2D) layered materials functionalized with monometallic or bimetallic dopants are excellent materials to fabricate clinically useful biosensors. Herein, we report the synthesis of ruthenium nanoparticles (RuNPs) and nickel molybdate nanorods (NiMoO4 NRs) functionalized porous graphitic carbon nitrides (PCN) for the fabrication of sensitive and selective biosensors for cardiac troponin I (cTn-I). A wet chemical synthesis route was designed to synthesize PCN-RuNPs and PCN-NiMoO4 NRs. Morphological, elemental, spectroscopic, and electrochemical investigations confirmed the successful formation of these materials. PCN-RuNPs and PCN-NiMoO4 NRs interfaces showed significantly enhanced electrochemically active surface areas, abundant sites for immobilizing bioreceptors, porosity, and excellent aptamer capturing capacity. Both PCN-RuNPs and PCN-NiMoO4 NRs materials were used to develop cTn-I sensitive biosensors, which showed a working range of 0.1-10,000 ng/mL and LODs of 70.0 pg/mL and 50.0 pg/mL, respectively. In addition, the biosensors were highly selective and practically applicable. The functionalized 2D PCN materials are thus potential candidates to develop biosensors for detecting acute myocardial infractions.

Graphitic Carbon Nitride and IGZO Bio-FET for Rapid Diagnosis of Myocardial Infarction.

Acute myocardial infarction (AMI), commonly known as a heart attack, is a life-threatening condition that causes millions of deaths every year. In this study, a transistor-based biosensor is developed for rapid and sensitive detection of cardiac troponin-I (cTnI), a diagnostic biomarker of AMI. A biosensing technique based on a field effect transistor (FET), which uses indium gallium zinc oxide (IGZO) as an excellent semiconducting channel, is integrated with nanosheet materials to detect cTnI. Porous carbon nitride (PCN) decorated with gold nanoparticles (Au NPs) is used as a bridge between the solid-state device and the biorecognition element. We demonstrate that this biosensor is highly sensitive and has an experimental limit of detection of 0.0066 ng/mL and a dynamic range of 0.01 ng/mL-1000 ng/mL. This is the first report of a semiconducting metal oxide FET cardiac biomarker sensor combined with PCN for the detection of cTnI. The reported compact microsystem paves the way for rapid and inexpensive detection of cardiac biomarkers.

Multiplexed sensing techniques for cardiovascular disease biomarkers - A review.

Cardiovascular diseases (CVDs) are the number one cause of death worldwide, taking 17.9 million lives each year. The rapid, sensitive, and accurate determination of cardiac biomarkers is vital for the timely diagnosis of CVDs. For accurate diagnosis, dependence on a single biomarker is unreliable because each one has also been linked to other diseases. To overcome this problem, the multiplexed determination of two or more markers has emerged as a promising alternative to single-marker analysis. Over the last 5 years, research interest in the development of biosensors for targeting multiple cardiac markers has increased. In this study, we critically reviewed the various multiplexed biosensing approaches reported during the last 5 years, categorizing them by signal readouts. Prospective detection configurations, capture probes, electrode design strategies, electrode types, nanomaterials, reporter tags, and assay types were reviewed, tabulated, and critically discussed. Then, their advantages and limitations were highlighted. For each category, we provided our perspective as well as the overall critical discussion. Lastly, we summarized potential commercial multiplexed cardiac biosensors and commented on the challenges and future prospects for such sensors.

Nano-treatment of HEPA filters in COVID-19 isolation rooms in an academic medical center in Saudi Arabia.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has spread globally. The major reservoir for SARS-CoV-2 transmission remains controversial, with the airborne route remaining a possible transmission vehicle for carrying the virus within indoor environments. This study aimed to detect contamination of SARS-CoV-2 in high-efficiency particulate air (HEPA) filters within hospital isolation rooms of confirmed COVID-19 patients, exploring the role of nano-treatment of these filters with silver and titanium dioxide nanoparticles (Ag/TiO2 NPs). MATERIALS AND METHODS: We investigated the effectiveness of Ag-NPs/TiO2-treated HEPA filters in the air of rooms occupied by patients with confirmed COVID-19 in a university teaching hospital in the Eastern province of Saudi Arabia during the first wave of the pandemic. Ag/TiO2 NPs were designed and coated on HEPA filters to examine the filtration efficiency and antiviral ability in the presence of aerosolized virus particles. A total of 20 viral swab samples were collected from five patients' rooms before and after treatment with nanoparticle-prepared solutions into the sterile virus-transporting media. Samples were evaluated for SARS-CoV-2 with a reverse transcription-polymerase chain reaction. RESULTS: Two samples taken from the HEPA filter air exhaust outlets prior to nano-treatment tested positive for SARS-CoV-2 RNA in the intensive care unit, which has stringent aerosolization control procedures, suggesting that small virus-laden droplets may be displaced by airflow. All air samples collected from the HEPA filters from the rooms of patients with confirmed COVID-19 following nano-treatment were negative. CONCLUSION: We recommend further experimental exploration using a larger number of HEPA filters in areas with aerosol-generating procedures, along with viability studies on the HEPA filters to facilitate decision-making in high-risk facilities regarding the replacement, storage, and disposal of HEPA filters in wards occupied by cases diagnosed with a highly transmissible disease.

Smartphone-Based Multiplexed Biosensing Tools for Health Monitoring.

Many emerging technologies have the potential to improve health care by providing more personalized approaches or early diagnostic methods. In this review, we cover smartphone-based multiplexed sensors as affordable and portable sensing platforms for point-of-care devices. Multiplexing has been gaining attention recently for clinical diagnosis considering certain diseases require analysis of complex biological networks instead of single-marker analysis. Smartphones offer tremendous possibilities for on-site detection analysis due to their portability, high accessibility, fast sample processing, and robust imaging capabilities. Straightforward digital analysis and convenient user interfaces support networked health care systems and individualized health monitoring. Detailed biomarker profiling provides fast and accurate analysis for disease diagnosis for limited sample volume collection. Here, multiplexed smartphone-based assays with optical and electrochemical components are covered. Possible wireless or wired communication actuators and portable and wearable sensing integration for various sensing applications are discussed. The crucial features and the weaknesses of these devices are critically evaluated.

Minimally-invasive, real-time, non-destructive, species-independent phytohormone biosensor for precision farming.

To keep up with population growth, precision farming technologies must be implemented to sustainably increase agricultural output. The impact of such technologies can be expanded by monitoring phytohormones, such as salicylic acid. In this study, we present a plant-wearable electrochemical sensor for in situ detection of salicylic acid. The sensor utilizes microneedle-based electrodes that are functionalized with a layer of salicylic acid selective magnetic molecularly imprinted polymers. The sensor's capability to detect the phytohormone is demonstrated both in vitro and in vivo with a limit of detection of 2.74 µM and a range of detection that can reach as high as 150 µM. Furthermore, the selectivity of the sensor is verified by testing the sensor on commonly occurring phytohormones. Finally, we demonstrate the capability of the sensor to detect the onset of fungal infestation in Tobacco 5 min post-inoculation. This work shows that the sensor could serve as a promising platform for continuous and non-destructive monitoring in the field and as a fundamental research tool when coupled with a portable potentiostat.