Instantaneous Detection Research Articles

Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms

Near-infrared (NIR) spectroscopy is a non-destructive, efficient and convenient detection technology, with the emergence of portable NIR spectrometers, NIR mobile applications (APPs) come into being. The popularity of intelligent mobile phones provides an impetus to the research and development of NIR APPs, however, the primary functions such as operating the NIR spectrometers and collecting data cannot satisfy NIR users in the field of data processing. Herein, we propose an APP processing NIR data locally at the mobile terminal, by the comprehensive utilization of Principal Component Analysis (PCA) and Cuckoo Search algorithm optimized Support Vector Classifier with radial basis function (RBFSVC) kernel (CS-RBFSVC). 738 NIR samples of four drugs (Cydiodine Buccal Tablets, Sulfasalazine Enteric-coated Tablets, Dexamethasone Acetate Tablets, Vecuronium Bromide for Injection) were used as the validation objects to train and test the data classification model. Firstly, the original data were subjected to dimensional reduction through PCA for the purpose of compressing calculation amount. Secondly, the CS-RBFSVC model was utilized to classify the types of drugs and their manufacturers, moreover, the improved accuracy and efficiency by introducing Cuckoo Search (CS) algorithm into RBFSVC were proven in comparison with the conventional grid optimized RBFSVC (Grid-RBFSVC) and Linear Support Vector Classifier (Linear-SVC). Last but not least, an APP based on the proposed PCA and CS-RBFSVC model is developed and demonstrated to be able to classify the type of drugs with an accuracy of 100%, the accuracies of classifying the drugs’ manufacturers were 100%, 100%, 98.3% and 90.7%, respectively. Conclusively, the proposed PCA and CS-RBFSVC based model can provide a low-consumption, high accuracy and quick strategy for NIR data classification and overcome the limitations of internal storage and operating speed at phone terminals, in conjunction with the portable NIR spectrometer, it is believed to push forward NIR technology into the instant detection and on-site inspection.

Pocket test for instantaneous quantification of starch adulterant in milk using a counterfeit banknote detection pen

This work presents a paper-based analytical device for instantaneous detection of adulterant starch in commercial milk. A highly efficient strategy was based on the well-established starch/iodine complex formation reaction by using a commercial counterfeit detection pen (CDP). Employing the CDP as a user-friendly and reagent-free approach to modify confined regions of a paper substrate readily promotes the reaction with starch in milk. The resulting color change can be analyzed by a digital scanner or via portable smartphone cameras. The proposed device provided a linear behavior in the concentration range between 0.05 and 1 %, with limits of detection and quantification equal to 0.05 % and 0.17 %, respectively. The sensor shows a very stable readout for many interferents, except for sodium hydroxide at higher concentrations (>1.0 %). Recovery tests performed at different concentration levels yielded values from 96.2 to 116.8 %, demonstrating its potential for quantitative analysis.

Smartphone enabled upconversion nanoparticle-based lateral flow strip for ultra-low concentration of methamphetamine detection

The combination of upconversion nanoparticles (UCNPs) and Lateral Flow Assays (LFAs) is becoming one of the most desirable quantitative assays for point-of-care testing (POCT) due to their excellent chemical and optical properties, which make them commercially viable as fast, sensitive and portable. We propose upconversion nanoparticle-based lateral flow assays (UCNP-LFAs) combined with computer vision for the instantaneous detection of small molecule targets in a commercial medical device. The device overcomes the traditional lengthy pre-processing and ambiguous qualitative results, where computer vision algorithms improve the sensitivity and robustness of quantitative results. Compared with the standard quantitative testing process, the accuracy of its quantitative test results was improved by 17.4%. The device detected and reported the quantitative results of Methamphetamine (MET) concentration with the lower limit of detection (LOD) of 0.1 ng/ml in 20 s in field tests. Its success demonstrated good sensitivity, repeatability, and robustness. Most importantly, the entire device measures only 100 mm * 120 mm* 74 mm and weighs 351.2 g, combining portability and immediacy and providing quantitative data for small molecule targets at a lower concentration. This makes our system an extraordinarily creative and attractive approach, especially for applications in complex field environments with chaotic personnel and special emergencies.

Significance of an Electrochemical Sensor and Nanocomposites: Toward the Electrocatalytic Detection of Neurotransmitters and Their Importance within the Physiological System.

A prominent neurotransmitter (NT), dopamine (DA), is a chemical messenger that transmits signals between one neuron to the next to pass on a signal to and from the central nervous system (CNS). The imbalanced concentration of DA may cause numerous neurological sicknesses and syndromes, for example, Parkinson's disease (PD) and schizophrenia. There are many types of NTs in the brain, including epinephrine, norepinephrine (NE), serotonin, and glutamate. Electrochemical sensors have offered a creative direction to biomedical analysis and testing. Researches are in progress to improve the performance of sensors and develop new protocols for sensor design. This review article focuses on the area of sensor growth to discover the applicability of polymers and metallic particles and composite materials as tools in electrochemical sensor surface incorporation. Electrochemical sensors have attracted the attention of researchers as they possess high sensitivity, quick reaction rate, good controllability, and instantaneous detection. Efficient complex materials provide considerable benefits for biological detection as they have exclusive chemical and physical properties. Due to distinctive electrocatalytic characteristics, metallic nanoparticles add fascinating traits to materials that depend on the material's morphology and size. Herein, we have collected much information on NTs and their importance within the physiological system. Furthermore, the electrochemical sensors and corresponding techniques (such as voltammetric, amperometry, impedance, and chronoamperometry) and the different types of electrodes' roles in the analysis of NTs are discussed. Furthermore, other methods for detecting NTs include optical and microdialysis methods. Finally, we show the advantages and disadvantages of different techniques and conclude remarks with future perspectives.

Optimization of the determination method for hypochlorite in natural water

Hypochlorite (ClO−) plays an important source of chlorine contaminant in the environment which may cause various diseases. Therefore, it is imperative to explore a novel method for instant detection of ClO−. In this research, a new fluorescent probe (JSJ-1) has been designed and synthesized for selective response toward ClO−. The response time is less than 30 s while the detection limit can reach as low as 52.3 nM. Further experiments showed that JSJ-1 retains excellent anti-interference features when a variety of possible interferent anions or cations coexist and therefore displayed successful application in ClO− detection in environmental water samples. In addition, the working principle of the probe JSJ-1with high sensitivity and specificity is based on the oxidative cleavage of olefin units by ClO−. The explanation for the chemical processes is supported by fluorescence spectra, UV-vis spectra, 1H NMR, HRMS, and density functional theory (DFT) calculations. A new fluorescent probe JSJ-1 can detect the level of ClO− in aqueous solution quickly and sensitively.

Fluorescence/electrochemiluminescence approach for instant detection of glycated hemoglobin index

The percentage of glycated hemoglobin (HbA1c) in total hemoglobin (Hb) is an important index for the diagnosis of Type II diabetes (T2D) because it reflects the long-term glucose level in blood. Herein, employing a one-pot co-reduction approach using glutathione (GSH) as structure-directing agent, a cluster-like AuAg nanoparticle (AuAg NPs) material was synthesized, therefore an electrochemiluminescence (ECL) aptamer-sensor for HbA1c detection was developed based on functionalized electrode with this material. Meanwhile, the quantitative determination of total Hb was realized based on the quenching effect of Hb on the fluorescence (FL) of luminol. Under compatible conditions, the results of both indexes can be satisfactorily acquired. This multimodal detection system has a good linear response toward Hb from 0.1 to 2.5 μM and HbA1c from 0.005 to 0.5 μM. The blood test proves this strategy is capable of accurate Hb and HbA1c detection, thus to obtain the percentage of HbA1c in total Hb (HbA1c%), which has the potential application for clinical diagnosis of diabetes mellitus.

Enabling effective breathing sound analysis for automated diagnosis of lung diseases.

With the emergence of the COVID-19 pandemic, early diagnosis of lung diseases has attracted growing attention. Generally, monitoring the breathing sound is the traditional means for assessing the status of a patient's respiratory health through auscultation; for that a stethoscope is one of the clinical tools used by physicians for diagnosis of lung disease and anomalies. On the other hand, recent technological advances have made telehealth systems a practical and effective option for health status assessment and remote patient monitoring. The interest in telehealth solutions have further grown with the COVID-19 pandemic. These telehealth systems aim to provide increased safety and help to cope with the massive growth in healthcare demand. Particularly, employing acoustic sensors to collect breathing sound would enable real-time assessment and instantaneous detection of anomalies. However, existing work focuses on autonomous determination of respiratory rate which is not suitable for anomaly detection due to inability to deal with noisy data recording. This paper presents a novel approach for effective breathing sound analysis. We promote a new segmentation mechanism of the captured acoustic signals to identify breathing cycles in recorded sound signals. A scoring scheme is applied to qualify the segment based on the targeted respiratory illness by the overall breathing sound analysis. We demonstrate the effectiveness of our approach via experiments using published COPD datasets.

Naphthalene-grafted MOF as a unique fluorescent sensor for “turn-off” detection for Fe3+ and “turn-on” detection for ClO4- in different solvents with high selectivity and sensitivity

Naphthalene-grafted zirconium-organic framework UiO-66 (HNA@UiO-66-NH2) was synthesized via the Schiff base reaction. HNA@UiO-66-NH2 retained the original crystal structure of UiO-66 and high thermal stability. HNA@UiO-66-NH2 exhibited fluorescence sensing ability in two modes, “turn-off” response to Fe3+ in methanol and “turn-on” response to ClO4- in ethanol with high selectivity and sensitivity accompanied by obvious changes of fluorescence color. Its fluorescence intensity presented linear changes with the concentrations of Fe3+ and ClO4- with low detection limits (LOD) were 0.911 μM for Fe3+ and 1.44 μM for ClO4-. In addition, HNA@UiO-66-NH2 can simply identified Fe3+ and ClO4- in the test paper mode with naked eyes. The photoinduced electron transfer (PET) from excited HNA@UiO-66-NH2 to Fe3+ ion and the absorption competition quenching (ACQ) were responsible for the fluorescence quenching, the fluorescence enhancement of HNA@UiO-66-NH2 induced by ClO4- was attributed to the hydrogen bond formation between HNA@UiO-66-NH2 and ClO4-. The satisfying accuracy in detection of Fe3+ in serum and the good recovery of HNA@UiO-66-NH2 in detection of Fe3+ and ClO4- in water samples confirmed the practical application capability. This work paved a way to fabricate the fluorescent sensing materials for the simple, accurate and instantaneous detection and recognition of the different analytes.

Deep Learning-Based Automatic Detection and Grading of Motion-Related Artifacts on Gadoxetic Acid-Enhanced Liver MRI.

The aim of this study was to develop and validate a deep learning-based algorithm (DLA) for automatic detection and grading of motion-related artifacts on arterial phase liver magnetic resonance imaging (MRI). Multistep DLA for detection and grading of motion-related artifacts, based on the modified ResNet-101 and U-net, were trained using 336 arterial phase images of gadoxetic acid-enhanced liver MRI examinations obtained in 2017 (training dataset; mean age, 68.6 years [range, 18-95]; 254 men). Motion-related artifacts were evaluated in 4 different MRI slices using a 3-tier grading system. In the validation dataset, 313 images from the same institution obtained in 2018 (internal validation dataset; mean age, 67.2 years [range, 21-87]; 228 men) and 329 from 3 different institutions (external validation dataset; mean age, 64.0 years [range, 23-90]; 214 men) were included, and the per-slice and per-examination performances for the detection of motion-related artifacts were evaluated. The per-slice sensitivity and specificity of the DLA for detecting grade 3 motion-related artifacts were 91.5% (97/106) and 96.8% (1134/1172) in the internal validation dataset and 93.3% (265/284) and 91.6% (948/1035) in the external validation dataset. The per-examination sensitivity and specificity were 92.0% (23/25) and 99.7% (287/288) in the internal validation dataset and 90.0% (72/80) and 96.0% (239/249) in the external validation dataset, respectively. The processing time of the DLA for automatic grading of motion-related artifacts was from 4.11 to 4.22 seconds per MRI examination. The DLA enabled automatic and instant detection and grading of motion-related artifacts on arterial phase gadoxetic acid-enhanced liver MRI.

An Efficient, Multiplexed Strategy for Instant Detection of Bacterial Biomarker by a Lanthanide-Organic Material.

Water contamination is a highly critical issue owing to its strong relationship to human health. In addition to chemical pollutants, microorganisms such as multiresistant pathogenic bacteria have received significant attention from the World Health Organization. The main problem associated with monitoring pathogenic bacteria in water is the interference from concomitant species and their low concentrations. To address this problem, we synthesized a bilanthanide-organic material as an efficient luminescence sensor for the detection of Pseudomonas aeruginosa, a representative bacterium, via its two unique biomarkers: 1-hydroxyphenazine (1-HX) and 2-aminoacetophenone (2-AA). This multiplexed sensing approach overcomes a common issue encountered by single-marker luminescence sensors that may report false positives due to coexisting species in the complex environment. High sensitivities and low limits of detection for 1-HX and 2-AA were obtained with very fast response time. The key structural factors governing the high-performance sensing function were revealed. This work provides an alternative route for the effortless and instant detection of bacterial biomarkers in water.

Portable food diagnostic devices and methods: A review

AbstractThe instantaneous detection of food quality is of paramount importance as it can help in taking different decisions in real time. These include the decision on consumption, inventory management, maintaining the storage conditions and even to repurpose the food if it cannot be used for the desired application. In this review, different portable food diagnostic devices and methods are studied and compared as consumers are interested in knowing the food's quality before consumption. The quality of food in real‐time will help in avoiding many problems such as the consumption of unsafe food and taking prompt decisions to avoid wastage in the food supply chain. Portable food diagnostic devices provide advantages such as easy to use, compact, handheld, connectivity with smartphone for rapid diagnosis, as well as these devices have the potential to eliminate traditional method problems such as the requirement of a trained user, bulky equipment, and lengthy diagnosis procedure. In this study, different types of portable food diagnostic devices and methods are compared in terms of their working principle, features, applications, and limitations. These devices are classified in three main categories, as composition analysis, safety, and security based on application. Further these devices are subclassified in single/multiple composition analysis devices, toxic, allergen detecting devices, and methods. Portable food diagnostic devices and methods provide a rapid and on‐site diagnosis. However, the success of these devices depends upon their accuracy, simplicity, and cost‐effectiveness.Practical ApplicationsThe study provides comparative analysis of different portable food diagnostic devices, which help in selecting an appropriate device in terms of its accuracy, size and cost suitable under different supply chain scenarios. This will ultimately help in decision making required for minimizing the food losses. These devices will benefit farmers, suppliers, retailers, and operators in food processing industries. The individual users can also use these devices to manage the food quality. The study also provides the limitations of exiting devices and the need of new research for developing more compact devices measuring multiple constituents in the food in a precise and cost‐effective manner.

Pump-probe strategy for instantaneous 2D detection of CH3 in flames using a single laser

Visualization of the reaction zone of flames using CH radicals as markers is restricted by the low concentration of CH in fuel-lean conditions. To address this, methyl radicals (CH3) are employed as a substitution of CH in premixed methane/air flames. A pump-probe method was adopted with the pump laser photolyzing CH3 and the probe laser detecting the photolyzed CH (X2Π) fragments. Laser excitation scans were performed to ensure that the fluorescence detected was from CH only. Visualization of the reaction zone of flames was accomplished by a CH3 photofragmentation laser-induced fluorescence technique in fuel-lean conditions (the equivalence ratio of 0.4), where CH planar laser-induced fluorescence did not work in both laminar and turbulent jet flames. The proposed pump-probe method of CH3 can be used to visualize the reaction zone of hydrocarbon combustion under both fuel-lean and fuel-rich conditions with a superior signal-to-noise ratio.

Multifunctional MnCo@C yolk-shell nanozymes with smartphone platform for rapid colorimetric analysis of total antioxidant capacity and phenolic compounds

Accurate on-site analysis of food quality, environmental pollutants, and disease biomarkers is of great significance for safeguarding public health. In this work, based on the novel nanozymes, MnCo oxides@carbon yolk-shell nanocages (MnCo@C NCs), a portable colorimetric sensor with smartphone platform has been developed for rapid, on-site and quantitative analysis of total antioxidant capacity (TAC) and phenolic compounds. The MnCo@C NCs are synthesized via one-step calcination of polydopamine-coated MnCo Prussian blue analogs (MnCo-PBA@PDA). The PDA-derived carbon shell is found to be able to protect the nanocages from collapsing, thus increasing their specific surface areas and porosity. Benefiting from the unique structure and multivalent MnCo bimetallic oxides, the MnCo@C NCs perform outstanding catalytic performance and multiple enzyme-mimicking activities including oxidase, laccase and catalase. Hence, a multifunctional application platform integrated smartphone has been constructed for rapid and sensitive colorimetric detection of three model analytes (i.e., ascorbic acid (AA), 2,4-dichlorophenol (2,4-DP), and epinephrine) with extremely low detection limits of 0.29 μM, 0.76 μM, and 0.70 μM, respectively. This sensor device is successfully applied in TAC analysis in vegetables, fruits, and beverages, as well as epinephrine determination in human serum samples. This work provides new insights into designing multifunctional nanozymes to advance the instant detection technology in the field of food supervision, environment monitoring, and human health.

Identifying Collector Channel Orifices In Vivo with Phase-Sensitive Optical Coherence Tomography: A Preliminary Study

Collector channels are openings located in the trabecular meshwork (TM) of the human eye that function as conduits, connecting the anterior chamber to the episcleral veins. Identifying the positions of collector channel orifices (CCOs) is essential for positioning implants in microinvasive canal-based glaucoma surgery, which is still not possible in vivo currently. Considerable evidence indicates that aqueous outflow becomes more active near the CCOs. Because the TM movement regulates the aqueous outflow, identification of the TM motion signal has the potential to locate the CCOs. Phase-sensitive optical coherence tomography (PhS-OCT) is an effective tool for the instantaneous detection of TM motion in vivo with sensitivity at the nanometer scale. However, the downside of this method is that phase measurement is prone to mix noises that negatively distinguish between biomedical signals. The TM motion was considered initially to be set up by the cardiac pulse. In this paper, a signal quality index related to blood pressure monitoring was applied to assess the validity of the TM motion signal. Measurements were carried out on two pairs of healthy human eyes. Quantitative measurements of the TM motion signal region such as size and frequency were recorded as the judgment indicator for CCOs. These results demonstrate that the PhS-OCT is a valuable tool capable of revealing the aqueous outflow pathway in vivo, offering a novel alternative to optimize glaucoma surgery.

A high throughput dual-signal ultra-sensitive electrochemical and photoelectrochemical microfluidic immunoassay platform with raspberry Au/PANI@CdS for Cyfra 21-1 detection

The excellent integration capability of microfluidic platform provides infinite possibilities for the development of analysis and detection technology. A microfluidic immunoassay platform was designed by combining the screen printing and microfluidic chip manufacturing. Four screen printed electrodes were applied as detection units and were divided into two groups for the generation of both electrochemical and photoelectrochemical signals. The light source for the photoelectrochemical process was achieved by the integration of two LED lamp beads in the 3D printed chip holder. Check valves between the chambers of working units and auxiliary units were exploited for the separated steps of working electrode modification and signal detection. A sandwich-type immunosensing configuration was adoped using raspberry-like Au/PANI@CdS nanocomposites as signal tags. Electrochemical and photoelectrochemical current signals can be detected simultaneously on a multichannel electrochemical workstation. After the optimization of microfluidic parameters and other experimental conditions, the microfluidic immunoassay platform shows good analytical performances for the detection of Cyfra 21-1 in the range of 0.1 pg/mL ∼100 ng/mL, with the PEC detection limit of 0.035 pg/mL and the DPV detection limit of 0.041 pg/mL. This multi-functional high-throughput portable microfluidic immunoassay platform has great application prospect in disease diagnosis and instant detection.

Microseismic event detection in noisy environments with instantaneous spectral Shannon entropy.

The detection of information-bearing signals in a time series is very important for describing and analyzing a wide variety of complex physical systems. However, identifying events in low signal-to-noise ratio circumstances remains a challenge once heavy data preprocessing is usually required. In this work, we propose a robust methodology based on the instantaneous-spectral Shannon entropy for capturing microseismic events in noisy environments without the requirement of data preprocessing. We call our proposal the instantaneous spectral entropy detection (ISED) method. We tested the ISED in a couple of real empirical seismic records to detect microseismic events. Our methodology detects microseismic patterns even without any preprocessing, in contrast to usual methods in the literature which need appreciable data preprocessing.

Predictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification

With the rising demand for integrated and autonomous systems in the field of engineering, efficient frameworks for instant detection of performance anomalies are imperative for improved productivity and cost-effectiveness. This study proposes a systematic predictive maintenance framework based on the hybrid multisensor fusion technique of fuzzy rough set feature selection and stacked ensemble for the efficient classification of fault conditions characterised by uncertainties. First, a feature vector of time-domain features was extracted from 17 multiple sensor signals. Then, a comparative study of six different Fuzzy Rough Set Feature Selection (FRFS) methods was employed to select the various combinations of optimal feature subsets for various faults classification tasks. The determined optimal feature subsets then served as inputs for training the stacked ensemble (ESB(STK)). In the ESB(STK), Support Vector Machine (SVM), Multilayer Perceptron (MLP), k -Nearest Neighbour ( k -NN), C4.5 Decision Tree (C4.5 DT), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) served as the base classifiers while the LR was selected to be the metaclassifier. The proposed hybrid framework (FRFS-ESB(STK)) improved the classification accuracy with the selected combinations of optimal feature subset size whiles reducing the computational cost, overfitting, training runtime, and uncertainty in modelling. Overall analyses showed that the FRFS-ESB(STK) proved to be generalisable and versatile in the classification of all conditions of four monitored hydraulic components (i.e., cooler, valve, accumulator, and internal pump leakage) when compared with the six base classifiers (standalone) and three existing ensemble classifiers (Stochastic Gradient Boosting (SGB), Ada Boost (ADB), and Bagging (BAG)). The proposed FRFS-ESB(STK) showed an average improvement of 11.28% and 0.88% test accuracies when classifying accumulator and pump conditions, respectively, whiles 100% classification rates were obtained for both cooler and valve.

Europium (III) complex-based fluorescent probe for instantaneous, selective, and sensitive detection of phosgene

Phosgene (carbonyl chloride, COCl2) is a widely used colorless gas in organic synthesis. However, its high toxicity sets a severe potential damage of public safety. As the fluorescence method has the advantages of simple operation and real-time detection of phosgene, it is extremely important to develop a fluorescent phosgene probe for public health and safety. This study aimed to present a simple Eu3+ complex (1) with 2-hydroxyl-1H-benzimidazole moiety as a novel phosgene probe. Probe 1 exhibited characteristic emission of Eu3+ in CH3CN solution, which was specifically quenched after encountering phosgene. The change in the solution color from light red to dark could be easily distinguished with the naked eye under a 365 nm ultraviolet lamp. Finally, the test paper with probe 1 was fabricated for effortless, selective, and visual detection of phosgene gas.

Parallel Field-Effect Nanosensors Detect Trace Biomarkers Rapidly at Physiological High-Ionic-Strength Conditions.

Sensitivity and speed of detection are contradicting demands that profoundly impact the electrical sensing of molecular biomarkers. Although single-molecule sensitivity can now be achieved with single-nanotube field-effect transistors, these tiny sensors, with a diameter less than 1 nm, may take hours to days to capture the molecular target at trace concentrations. Here, we show that this sensitivity-speed challenge can be addressed using covalently functionalized double-wall CNTs that form many individualized, parallel pathways between two electrodes. Each carrier that travels across the electrodes is forced to take one of these pathways that are fully gated chemically by the target-probe binding events. This sensor design allows us to electrically detect Lyme disease oligonucleotide biomarkers directly at the physiological high-salt concentrations, simultaneously achieving both ultrahigh sensitivity (as low as 1 fM) and detection speed (<15 s). This unexpectedly simple strategy may open opportunities for sensor designs to broadly achieve instant detection of trace biomarkers and real-time probing of biomolecular functions directly at their physiological states.

Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System

Current medical methods still confront numerous limitations and barriers to detect and fight against illnesses and disorders. The introduction of emerging technologies in the healthcare industry is anticipated to enable novel medical techniques for an efficient and effective smart healthcare system. Internet of Things (IoT), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), and Cloud Computing (CC) can play a vital role in the instant detection of illnesses, diseases, viruses, or disorders. Complicated techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) could provide acceleration in drug and antibiotics discovery. Moreover, the integration of visualization techniques such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) with Tactile Internet (TI), can be applied from the medical staff to provide the most accurate diagnosis and treatment for the patients. A novel system architecture, which combines several future technologies, is proposed in this paper. The objective is to describe the integration of a mixture of emerging technologies in assistance with advanced networks to provide a smart healthcare system that may be established in hospitals or medical centers. Such a system will be able to deliver immediate and accurate data to the medical stuff in order to aim them in order to provide precise patient diagnosis and treatment.