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The development of imaging, diagnosis, prognosis and early detection of diseases has been greatly impacted by nanotechnology by enhancing already existing clinically applicable technologies. With the help of their capacity to alter nanoparticles for molecular‐level specificity, tissue‐specific diagnosis is made possible gratitude to the unique biophysical features of the nanoparticles that enable contrast augmentation will boost biomedical imaging. The unique prospect of multiplexing is possible by the fact that minute changes in the nanoparticles' size or composition can have significant effects on their optical, magnetic, or electrical capabilities. This article examines nanotechnology's function in biomedical imaging. In this article, the fundamentals and applications of biomedical imaging, pharmaceutical application of microbial surfactants, intelligent drug delivery systems, and green metallic nanoparticle manufacturing are all examined. The biomedical applications comprising organic (carbon nanotubes, metal oxide and liposomes) and inorganic (metal oxide, metal) nanoparticles, and materials with nanopatterns in diagnostics, biosensing, and bioimaging devices, along with drug delivery systems, are the main topics of discussion. studies conducted in vitro and in vivo have demonstrated that different nanoparticles can be utilized to detect seizures early and precisely along with to treat them successfully. To decrease the possible toxicity and enable improved target specificity, respectively, more development in the synthesis and functionalization of adaptable nanotechnologies is required. [ABSTRACT FROM AUTHOR]

To evaluate the ability of tracheal sound analysis (TSA) to detect airflow obstruction, particularly in patients with acromegaly. A simulated analysis compared free airflow conditions with airflow through orifice plates 6, 8, 10 and 12 mm in diameter. Based on these results, TSA and spirometry examinations were performed on controls (n = 17) and patients with acromegaly (n = 17). The simulated study showed that airway obstruction and airflow values increased the values of power and a progressive displacement of the spectral distribution towards higher frequencies. In agreement with the simulation, airway obstruction in patients with acromegaly also resulted in increased values of power (p < 0.002) and displacement of the spectral distribution (p < 0.01). Significant associations were observed between the TSA parameters and the spirometry indices of obstruction (p < 0.02). In addition, the TSA parameters achieved adequate diagnostic accuracy (AUC ≥ 0.887). The present study provides evidence that TSA during resting breathing would provide adequate biomarkers of early upper airway changes in patients with acromegaly. TSA is carried out during spontaneous ventilation, requires little from the patient, and is fast and inexpensive. Taken together, these practical considerations and the results of the present study suggest that TSA may improve lung function tests for patients with acromegaly. Summary of the study, overall design flow and the main results obtained. [ABSTRACT FROM AUTHOR]

A multi-channel measurement system used to measure electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG) biosignals has been designed and prototyped. The designed system has 16 configurable measurement channels. Of the 16 channels the developed system has, 8 have been designed for EEG, 2 for EMG, 2 for EOG, 1 for ECG measurements, the remaining 3 have been reserved as backup channels. In circuit design, biosignal amplifier design principles have been applied by taking into account the characteristics of the biosignal to be measured for each channel, such as bandwidth, frequency, amplitude, noise level. Modules such as instrumentation amplifier, filter, DC suppression unit, amplifier, DC level determination unit, analogdigital converter, optical isolation unit, power supply have been designed to perform biosignal measurements through these channels. Biosignals measured by the developed system can be shifted to the desired threshold level with the help of the analog output reference voltage, converted to digital data 10-bit resolution and transferred to the computer environment in real time. The data transferred to the computer can be used in C#, Excel, MATLAB, and LabVIEW platforms. The novelty of the developed system is that any of the four desired biosignal types can be measured from any channel. In addition, another feature of the system is that it can work with real-time data without being dependent on the databases serving for human-computer interface applications. In experimental studies with some researchers for the performance tests of the system, ECG, EEG, EMG and EOG signals have been recorded with different module configurations, and signal processing stages were carried out to be used for human-computer applications. [ABSTRACT FROM AUTHOR]

Abstract Background Fractional-order (FrOr) models have a high potential to improve pulmonary science. These models could be useful for biomechanical studies and diagnostic purposes, offering accurate models with an improved ability to describe nature. This paper evaluates the performance of the Forced Oscillation (FO) associated with integer (InOr) and FrOr models in the analysis of respiratory alterations in work-related asthma (WRA). Methods Sixty-two individuals were evaluated: 31 healthy and 31 with WRA with mild obstruction. Patients were analyzed pre- and post-bronchodilation. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). To evaluate how well do the studied models correspond to observed data, we analyzed the mean square root of the sum (MSEt) and the relative distance (R d) of the estimated model values to the measured resistance and reactance measured values. Results and discussion Initially, the use of InOr and FrOr models increased our understanding of the WRA physiopathology, showing increased peripheral resistance, damping, and hysteresivity. The FrOr model (AUC = 0.970) outperformed standard FO (AUC = 0.929), as well as InOr modeling (AUC = 0.838) in the diagnosis of respiratory changes, achieving high accuracy. FrOr improved the curve fitting (MSEt = 0.156 ± 0.340; R d = 3.026 ± 1.072) in comparison with the InOr model (MSEt = 0.367 ± 0.991; R d = 3.363 ± 1.098). Finally, we demonstrated that bronchodilator use increased dynamic compliance, as well as reduced damping and peripheral resistance. Conclusions Taken together, these results show clear evidence of the utility of FO associated with fractional-order modeling in patients with WRA, improving our knowledge of the biomechanical abnormalities and the diagnostic accuracy in this disease.

Patient monitoring is an important issue in the medical field, requiring planning, effort, and time. One of the challenges that physicians, nurses, paramedics, and emergency medical technicians face is the shortage of specialized medical equipment and staff with the ability to measure, display, store, and transmit the vital signs of patients for remote diagnosis under critical conditions. Therefore, and based on our evaluation for such needs, our main research objective was to develop a new system to enable these professionals to perform such monitoring functions with a higher level of safety, performance, and time effectiveness with reduced cost. An advanced, integrated, effortless, and portable system was developed, which is capable to measure, monitor, display, store, and transmit data related to the Electrocardiography (ECG) signal along with its derivatives, such as the heart rate using a hybrid system that combines hardware and software.

Additive manufacturing's attributes include print customization, low per-unit cost for small- to mid-batch production, seamless interfacing with mainstream medical 3D imaging techniques, and feasibility to create free-form objects in materials that are biocompatible and biodegradable. Consequently, additive manufacturing is apposite for a wide range of biomedical applications including custom biocompatible implants that mimic the mechanical response of bone, biodegradable scaffolds with engineered degradation rate, medical surgical tools, and biomedical instrumentation. This review surveys the materials, 3D printing methods and technologies, and biomedical applications of metal 3D printing, providing a historical perspective while focusing on the state of the art. It then identifies a number of exciting directions of future growth: (a) the improvement of mainstream additive manufacturing methods and associated feedstock; (b) the exploration of mature, less utilized metal 3D printing techniques; (c) the optimization of additively manufactured load-bearing structures via artificial intelligence; and (d) the creation of monolithic, multimaterial, finely featured, multifunctional implants. [ABSTRACT FROM AUTHOR]

Patient monitoring is an important issue in the medical field, requiring planning, effort, and time. One of the challenges that physicians, nurses, paramedics, and emergency medical technicians face is the shortage of specialized medical equipment and staff with the ability to measure, display, store, and transmit the vital signs of patients for remote diagnosis under critical conditions. Therefore, and based on our evaluation for such needs, our main research objective was to develop a new system to enable these professionals to perform such monitoring functions with a higher level of safety, performance, and time effectiveness with reduced cost. An advanced, integrated, effortless, and portable system was developed, which is capable to measure, monitor, display, store, and transmit data related to the Electrocardiography (ECG) signal along with its derivatives, such as the heart rate using a hybrid system that combines hardware and software. [ABSTRACT FROM AUTHOR]

Background: Fractional-order (FrOr) models have a high potential to improve pulmonary science. These models could be useful for biomechanical studies and diagnostic purposes, offering accurate models with an improved ability to describe nature. This paper evaluates the performance of the Forced Oscillation (FO) associated with integer (InOr) and FrOr models in the analysis of respiratory alterations in work-related asthma (WRA).Methods: Sixty-two individuals were evaluated: 31 healthy and 31 with WRA with mild obstruction. Patients were analyzed pre- and post-bronchodilation. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). To evaluate how well do the studied models correspond to observed data, we analyzed the mean square root of the sum (MSEt) and the relative distance (Rd) of the estimated model values to the measured resistance and reactance measured values.Results and Discussion: Initially, the use of InOr and FrOr models increased our understanding of the WRA physiopathology, showing increased peripheral resistance, damping, and hysteresivity. The FrOr model (AUC = 0.970) outperformed standard FO (AUC = 0.929), as well as InOr modeling (AUC = 0.838) in the diagnosis of respiratory changes, achieving high accuracy. FrOr improved the curve fitting (MSEt = 0.156 ± 0.340; Rd = 3.026 ± 1.072) in comparison with the InOr model (MSEt = 0.367 ± 0.991; Rd = 3.363 ± 1.098). Finally, we demonstrated that bronchodilator use increased dynamic compliance, as well as reduced damping and peripheral resistance.Conclusions: Taken together, these results show clear evidence of the utility of FO associated with fractional-order modeling in patients with WRA, improving our knowledge of the biomechanical abnormalities and the diagnostic accuracy in this disease. [ABSTRACT FROM AUTHOR]

Contribution: This article illustrates how to design and implement a Design Thinking project in a biomedical instrumentation course as a bridge between education and industry. It also presents the effect of the project on student perceptions of their learning gains and their postgraduate preparedness. Background: Biomedical engineering continues to experience strong growth, with instrumentation being one of the top three segments of the industry. Design Thinking is a desirable skill in the industry, is highly applicable to the human-centered outcomes of biomedical instrumentation, and has documented a positive impact on student learning. Therefore, Design Thinking implementation in a biomedical instrumentation course would be valuable, but has yet to be accomplished. Intended Outcomes: The implementation of a Design Thinking project is expected to increase student learning and postgraduate preparedness. Application Design: A Design Thinking project was designed and implemented in an upper level biomedical instrumentation course. This article describes the structure of the project, for example, student outcomes and its assessment. The “Student Assessment of Learning Gains” instrument and a postgraduate survey were used to evaluate student perception of their learning and postgraduate preparedness. Findings: The critical elements of Design Thinking were successfully implemented into a four-week biomedical instrumentation project experience within the instrumentation course. Students perception of their learning was significantly increased by the Design Thinking project. A postgraduate survey indicated a positive influence of the project on student preparedness for industry and graduate studies. [ABSTRACT FROM AUTHOR]

Evaluation of arterial carbon dioxide pressure (PaCO2) and dead space to tidal volume ratio (VD/VT) during exercise is important for the identification of exercise limitation causes in heart failure (HF). However, repeated sampling of arterial or arterialized ear lobe capillary blood may be clumsy. The aim of our study was to estimate PaCO2 by means of a non-invasive technique, transcutaneous PCO2 (PtCO2), and to verify the correlation between PtCO2 and PaCO2 and between their derived parameters, such as VD/VT, during exercise in HF patients. 29 cardiopulmonary exercise tests (CPET) performed on a bike with a ramp protocol aimed at achieving maximal effort in ≈10 min were analyzed. PaCO2 and PtCO2 values were collected at rest and every 2 min during active pedaling. The uncertainty of PCO2 and VD/VT measurements were determined by analyzing the error between the two methods. The accuracy of PtCO2 measurements vs. PaCO2 decreases towards the end of exercise. Therefore, a correction to PtCO2 that keeps into account the time of the measurement was implemented with a multiple regression model. PtCO2 and VD/VT changes at 6, 8 and 10 min vs. 2 min data were evaluated before and after PtCO2 correction. PtCO2 overestimates PaCO2 for high timestamps (median error 2.45, IQR −0.635–5.405, at 10 min vs. 2 min, p-value = 0.011), while the error is negligible after correction (median error 0.50, IQR = −2.21–3.19, p-value > 0.05). The correction allows removing differences also in PCO2 and VD/VT changes. In HF patients PtCO2 is a reliable PaCO2 estimation at rest and at low exercise intensity. At high exercise intensity the overall response appears delayed but reproducible and the error can be overcome by mathematical modeling allowing an accurate estimation by PtCO2 of PaCO2 and VD/VT.

An ASIC for a high frequency electrical impedance tomography (EIT) imaging system for prostate cancer screening is presented. The ASIC enables a small form-factor architecture, which ensures high signal-to-noise ratio (SNR) at MHz frequencies. The 4-channel ASIC was designed and fabricated in a standard CMOS 0.18- $\mu$ m technology and integrates a novel current driver for current stimulus, instrumentation amplifier to interface with the tissue, VGA to provide variable gain and ADC with SPI interface for digitization. A prototype miniaturized EIT system was built and it was evaluated using a model transrectal imaging probe immersed into a tank filled with saline and a metal inclusion that demonstrated the open-domain problem of imaging prostate cancer lesion. The system maintained an SNR between 66 and 76 dB over the frequency range of 500 Hz to 1 MHz. Also, it produced reconstructed EIT images that depicted the presence of the small metal inclusion that modeled a prostate cancer imaging application. [ABSTRACT FROM AUTHOR]

In recent years, steerable catheters have been developed to combat the effects of the dynamic cardiac environment. Mechanically actuated steerable catheters appear the most in the clinical setting; however, they are bound to a number of mechanical limitations. The aim of this research is to gain insight in these limitations and use this information to develop a new prototype of a catheter with increased steerability. The main limitations in mechanically steerable catheters are identified and analysed, after which requirements and solutions are defined to design a multi-steerable catheter. Finally, a prototype is built and a proof-of-concept test is carried out to analyse the steering functions. The mechanical analysis results in the identification of five limitations: (1) low torsion, (2) shaft shortening, (3) high unpredictable friction, (4) coupled tip-shaft movements, and (5) complex cardiac environment. Solutions are found to each of the limitations and result in the design of a novel multi-steerable catheter with four degrees of freedom. A prototype is developed which allows the dual-segmented tip to be steered over multiple planes and in multiple directions, allowing a range of complex motions including S-shaped curves and circular movements. A detailed analysis of limitations underlying mechanically steerable catheters has led to a new design for a multi-steerable catheter for complex cardiac interventions. The four integrated degrees of freedom provide a high variability of tip directions, and repetition of the bending angle is relatively simple and reliable. The ability to steer inside the heart with a variety of complex shaped curves may potentially change conventional approaches in interventional cardiology towards more patient-specific and lower complexity procedures. Future directions are headed towards further design optimizations and the experimental validation of the prototype. [ABSTRACT FROM AUTHOR]

The study aims to develop the failure analysis predictive models, which prognosticate first failure event (FFE), failure-to-year ratio (FYR), and failure rectification group (FRG). The construction of predictive models involved nineteen categories of 13,350 units of medical equipment. We proposed thirteen novel features in assessing medical equipment failures. The failure analysis predictive models were categorised into several classes for training and testing the developed models. There was seven supervised machine learning classifiers and performance metrics applied in the experiment. The experiment demonstrates that Support Vector Machine is the best classifier for the FFE predictive model, which achieves an accuracy of 96.9% after hyperparameter optimisation. Furthermore, Decision Tree is the best classifier for FYR, with an accuracy of 83.9%. Meanwhile, the comparative analysis for FRG discovered that Artificial Neural Network achieved the highest accuracy among others with 76.7% accuracy after the hyperparameter optimisation process. Findings from this study indicate that this failure analysis predictive model functions as a main instrument for conducting predictive maintenance in the direction of smart maintenance practices. Through the developed predictive systems, timely maintenance of medical equipment can be performed. This will also assist healthcare service providers in initiating the remanufacturing and refurbishment programme, ensuring efficient medical care delivery. The suggested framework of machine learning-assisted failure analysis for medical equipment maintenance management may provide clinical engineers with guidance for managing the strategic maintenance management for medical equipment. • The predictive models may help clinical engineers in identifying the actual problem of failures and provide indication on appropriate course of action for better equipment reliability. • The predictive models assist clinical engineers in making a right decision of the implementation of strategic maintenance management for myriad medical equipment. • The failure analysis predictive model functions as a supplementary instrument for conducting predictive maintenance in the direction of sustainable maintenance practices. • The developed failure analysis predictive model may reveal actual deficiency of equipment component and initiate a remanufacturing and refurbishment programme for the manufacturers. • The predictive models provide quick and automatic analysis without complex calculating procedure. [ABSTRACT FROM AUTHOR]

This article focuses on the functionality verification of a novel non-invasive fibre-optic sensor monitoring basic vital signs such as Respiratory Rate (RR), Heart Rate (HR) and Body Temperature (BT). The integration of three sensors in one unit is a unique solution patented by our research team. The integrated sensor is based on two Fiber Bragg Gratings (FBGs) encapsulated inside an inert polymer (non-reactive to human skin) called PolyDiMethylSiloxane (PDMS). The PDMS is beginning to find widespread applications in the biomedical field due to its desirable properties, especially its immunity to ElectroMagnetic Interference (EMI). The integrated sensor's functionality was verified by carrying out a series of laboratory experiments in 10 volunteer subjects after giving them a written informed consent. The Bland-Altman statistical analysis produced satisfactory accuracy for the respiratory and heart rate measurements and their respective reference signals in all test subjects. A total relative error of 0.31% was determined for body temperature measurements. The main contribution of this article is a proof-of-concept of a novel noninvasive fiber-optic sensor which could be used for basic vital sign monitoring. This sensor offers a potential to enhance and improve the comfort level of patients in hospitals and clinics and can even be considered for use in Magnetic Resonance Imaging (MRI) environments.

Single-cell analysis serves as an important approach to study cell functions and interactions. Catering to the demand of Big Data Era, fast reactions for single cells and paralleled high-throughput analysis have become an urgent need. Microdroplet in microfluidics has advantages of modularity and integrity, as well as high throughput and sensitivity, which present great potential in the field of single-cell analysis. This review is carried out on three aspects to introduce microdroplet chips for single-cell analysis: droplet formation, droplet detection and practical functions. Structures of droplet formation are categorized into three types, including T-shaped channel, flow-involved channel and three-dimensional micro-vortice. The detection methods, including fluorescence, Raman spectroscopy, mass spectroscopy and electrochemical detection, are summarized from applications. Both pros and cons for existing techniques are reviewed and discussed. The functions of microdroplets-on-chip cover cell culture, nucleic acid test and cell identification. For each field, principles/mechanisms and/or schematic images are laconically introduced. Microdroplet in microfluidics has become a major research direction in single-cell analysis. With updated methods of droplet formation such as inertial ordering and micro-vortice, microdroplets-based biochips will expect high throughput detection and high-accuracy trace detection for clinical diagnosis in the near future. [ABSTRACT FROM AUTHOR]

Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-It-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPI.com , for open access under a Non Commercial-Share A like 4.0 International License. [ABSTRACT FROM AUTHOR]

Highlights • Novel method for monitoring the ruminants feeding behavior. • Method based on the signal processing of the masseter muscle surface electromyography (sEMG) and Artificial Intelligence. • Reliable method for the recognition of the grazed grass and the identification of rumination and feed chews. • Accuracy results over 70% identifying the rumination. Recognition of four grasses based on the signal of a single chew. • Method with potential as a research tool for ruminant studies regarding the intake and selectivity. Abstract This work presents a method to identify the ingestive behavior in ruminants using Surface Electromyography (sEMG) of the masseter muscle. The main hypothesis tested is whether the rumination and food eaten can be recognized from sEMG signal features using machine learning techniques. Also, a novel segmentation technique was explored and applied to automatically subdivide the chewing movement signal. Seven classifiers were evaluated using eight features extracted from the signal and combined into five sets. The three scenarios investigated were: differentiation between rumination and grazing (IR), food identification for four different foods (FC) and both situations combined (FCR). The segmentation window size effect on the accuracy was also investigated. We found an accuracy over 70% for IR and FC and nearby 60% for FCR using a Multilayer Perceptron Neural Network (MLP-NN). Highlighted features were the Cepstrum coefficients (CEPS) and the signal Wavelength (WL). Segments between 600 and 1000 ms proved to be suitable. The segmentation technique and the proposed scheme are reliable for application although there is room for improvement. [ABSTRACT FROM AUTHOR]

This article illustrates the benefits of using PSoC microcontrollers for biomedical applications such as: electromyography (EMG), electroencephalography (EEG) and electrocardiography (ECG). The article also describes the steps to be followed for the implementation of biomedical equipment capable of capturing the different types of bio-potentials generated from the human body. These steps include: signal conditioning, filter design, signal digitalization, data communication and visualization. Finally, a low-cost electromyography device design is shown. The design easily serves as a device for either electroencephalography or electrocardiography, always using a lower amount of electronic components than regular equipment.

Medical devices have a great impact but rigorous production and quality norms to meet, which pushes manufacturing technology to its limits in several fields, such as electronics, optics, communications, among others. This paper briefly explores how the medical industry is absorbing many of the technological developments from other industries, and making an effort to translate them into the healthcare requirements. An example is discussed in depth: implantable neural microsystems used for brain circuits mapping and modulation. Conventionally, light sources and electrical recording points are placed on silicon neural probes for optogenetic applications. The active sites of the probe must provide enough light power to modulate connectivity between neural networks, and simultaneously ensure reliable recordings of action potentials and local field activity. These devices aim at being a flexible and scalable technology capable of acquiring knowledge about neural mechanisms. Moreover, this paper presents a fabrication method for 2-D LED-based microsystems with high aspect-ratio shafts, capable of reaching up to 20 mm deep neural structures. In addition, PDMS $\mu $ lenses on LEDs top surface are presented for focusing and increasing light intensity on target structures. [ABSTRACT FROM PUBLISHER]

A novel clinically practical upper limb model is introduced that has been developed through clinical use in children and adults with neurological conditions to guide surgery to the elbow and wrist. This model has a minimal marker set, minimal virtual markers, and no functional joint centres to minimise the demands on the patient and duration of data collection. The model calculates forearm supination independently from the humerus segment, eliminating any errors introduced by poor modelling of the shoulder joint centre. Supination is calculated by defining the forearm segment twice, from the distal and proximal ends: first, using the ulna and radial wrist markers as a segment defining line and second using the medial and lateral elbow markers as a segment defining line. This is comparable to the clinical measurement of supination utilising a goniometer and enables a reduced marker set, with only the elbow, wrist, and hand markers to be applied when only the wrist and forearm angles are of interest. A sensitivity analysis of the calculated elbow flexion-extension angles to the position of the glenohumeral joint centre is performed on one healthy female subject, aged 20 years, during elbow flexion and a forward reaching task. A comparison of the supination angles calculated utilising the novel technique compared to the rotation between the humeral and forearm segments is also given. All angles are compared to a published kinematic model that follows the recommendations of the International Society of Biomechanics. [ABSTRACT FROM AUTHOR]