Detection of Propionic Acids Trapped in Thin Zeolite Layer Using Thermal Desorption Analysis.

Volatile organic compounds (VOCs) have recently received considerable attention for the analysis and monitoring of different biochemical processes in biological systems such as humans, plants, and microorganisms. The advantage of using VOCs to gather information about a specific process is that they can be extracted using different types of samples, even at low concentrations. Therefore, VOC levels represent the fingerprints of specific biochemical processes. The aim of this work was to develop a sensor based on a photoionization detector (PID) and a zeolite layer, used as an alternative analytic separation technique for the analysis of VOCs. The identification of VOCs occurred through the evaluation of the emissive profile during the thermal desorption phase, using a stainless-steel chamber for analysis. Emission profiles were evaluated using a double exponential mathematical model, which fit well if compared with the physical system, describing both the evaporation and diffusion processes. The results showed that the zeolite layer was selective for propionic acid molecules if compared to succinic acid molecules, showing linear behavior even at low concentrations. The process to define the optimal adsorption time between the propionic acid molecules was performed in the range of 5 to 60 min, followed by a thermal desorption process at 100 °C. An investigation of the relationship between the evaporation and diffusion rates showed that the maximum concentration of detected propionic acid molecules occurred in 15 min. Other analyses were performed to study how the concentration of VOCs depended on the desorption temperature and the volume of the analysis chamber. For this purpose, tests were performed using three analysis chambers with volumes of 25 × 10-6, 50 × 10-6, and 150 × 10-6 m3 at three different desorption temperatures of 20 °C, 50 °C, and 100 °C, respectively. The results demonstrated that the evaporation rate of the VOCs increased rapidly with an increasing temperature, while the diffusion rate remained almost constant and was characterized by a slow decay time. The diffusion ratio increased when using a chamber with a larger volume. These results highlight the capabilities of this alternative technique for VOC analysis, even for samples with low concentrations. The coupling of a zeolite layer and a PID improves the detection selectivity in portable devices, demonstrating the feasibility of extending its use to a wide range of new applications.

Implantable Aptamer-Graphene Microtransistors for Real-Time Monitoring of Neurochemical Release in Vivo.

The real-time monitoring of neurochemical release in vivo plays a critical role in understanding the biochemical process of the complex nervous system. Current technologies for such applications, including microdialysis and fast-scan cyclic voltammetry, suffer from limited spatiotemporal resolution or poor selectivity. Here, we report a soft implantable aptamer-graphene microtransistor probe for real-time monitoring of neurochemical release. As a demonstration, we show the monitoring of dopamine with nearly cellular-scale spatial resolution, high selectivity (dopamine sensor >19-fold over norepinephrine), and picomolar sensitivity, simultaneously. Systematic benchtop evaluations, ex vivo experiments, and in vivo studies in mice models highlight the key features and demonstrate the capability of capturing the dopamine release dynamics evoked by pharmacological stimulation, suggesting the potential applications in basic neuroscience studies and studying neurological disease-related processes. The developed system can be easily adapted for monitoring other neurochemicals and drugs by simply replacing the aptamers functionalized on the graphene microtransistors.

EGFET-Based Sensors for Bioanalytical Applications: A Review.

Since the 1970s, a great deal of attention has been paid to the development of semiconductor-based biosensors because of the numerous advantages they offer, including high sensitivity, faster response time, miniaturization, and low-cost manufacturing for quick biospecific analysis with reusable features. Commercial biosensors have become highly desirable in the fields of medicine, food, and environmental monitoring as well as military applications, whereas increasing concerns about food safety and health issues have resulted in the introduction of novel legislative standards for these sensors. Numerous devices have been developed for monitoring biological processes such as nucleic acid hybridization, protein⁻protein interaction, antigen⁻antibody bonds, and substrate⁻enzyme reactions, just to name a few. Since the 1980s, scientific interest moved to the development of semiconductor-based devices, which also include integrated front-end electronics, such as the extended-gate field-effect transistor (EGFET) biosensor, one of the first miniaturized chemical sensors. This work is intended to be a review of the state of the art focused on the development of biosensors and chemosensors based on extended-gate field-effect transistor within the field of bioanalytical applications, which will highlight the most recent research reported in the literature. Moreover, a comparison among the diverse EGFET devices will be presented, giving particular attention to the materials and technologies.

Deep Multitask Learning by Stacked Long Short-Term Memory for Predicting Personalized Blood Glucose Concentration.

The adverse glycemic events triggered by the inaccurate insulin infusion in Type I diabetes (T1D) can lead to fatal complications. Predicting blood glucose concentration (BGC) based on clinical health records is critical for control algorithms in the artificial pancreas (AP) and aiding in medical decision support. This paper presents a novel deep learning (DL) model incorporating multitask learning (MTL) for personalized blood glucose prediction. The network architecture consists of shared and clustered hidden layers. Two layers of stacked long short-term memory (LSTM) form the shared hidden layers that learn generalized features from all subjects. The clustered hidden layers comprise two dense layers adapting to the gender-specific variability in the data. Finally, the subject-specific dense layers offer additional fine-tuning to personalized glucose dynamics resulting in an accurate BGC prediction at the output. OhioT1DM clinical dataset is used for the training and performance evaluation of the proposed model. A detailed analytical and clinical assessment have been performed using root mean square (RMSE), mean absolute error (MAE), and Clarke error grid analysis (EGA), respectively, which demonstrates the robustness and reliability of the proposed method. Consistently leading performance has been achieved for 30- (RMSE = 16.06 ±2.74, MAE = 10.64 ±1.35), 60- (RMSE = 30.89 ±4.31, MAE = 22.07 ±2.96), 90- (RMSE = 40.51 ±5.16, MAE = 30.16 ±4.10), and 120-minute (RMSE = 47.39 ±5.62, MAE = 36.36 ±4.54) prediction horizon (PH). In addition, the EGA analysis confirms the clinical feasibility by maintaining more than 94 % BGC predictions in the clinically safe zone for up to 120-minute PH. Moreover, the improvement is established by benchmarking against the state-of-the-art statistical, machine learning (ML), and deep learning (DL) methods.

Accountability in the Bangladeshi accountability in healthcare privatized healthcare sector.

PURPOSE: The purpose of this paper is to examine the perceived Bangladesh privatized healthcare sector accountability gap. DESIGN/METHODOLOGY/APPROACH: Data were collected from 533 patients using services in 45 Dhaka city privatized hospitals. A questionnaire was designed based on 60 patient focus study group and the literature. FINDINGS: Structural equation modeling provides a comprehensive picture that allows healthcare constructs and accountability to be tested. The goodness-of-fit statistics supported the four factors of professionals, administration and management, legal enforcement, ethics and government, which were significantly associated with accountability. Despite Bangladeshi privatized healthcare growth, the study revealed that accountability mainly depends on government initiatives and effectively implementing existing laws. RESEARCH LIMITATIONS/IMPLICATIONS: The study covered one Bangladesh city (Dhaka) owing to resource constraints. Qualitative methods may have enriched the findings. PRACTICAL IMPLICATIONS: The accountability dimensions may be applicable to other countries to examine the perceived accountability gap. The study looked at the current Bangladesh privatized healthcare sector. Major issues of Bangladesh privatized healthcare accountability are discussed and recommendations for policymakers are suggested to improve the current circumstances. ORIGINALITY/VALUE: The study is the first of its kind to examine accountability among privatized healthcare providers in developing countries. Patients' accountability views require urgent attention from policy makers.

Medical Devices for Pediatric Apnea Monitoring and Therapy: Past and New Trends.

Apnea in the pediatric population is associated with increased morbidity and mortality in a large number of developed as well as developing countries. It is even more prominent in preterm newborn infants and is commonly referred to as apnea of prematurity. Its current diagnosis and therapy involve the use of traditional technologies, which often result in discomfort to the infants due to the use of invasive devices attached to their sensitive skin, especially in overnight clinical sleep analysis (for over a 12- or 24-h period). Emerging trends for the point-of-care diagnosis of this sleep disorder are focused on the design of integrated devices for less complex and noninvasive monitoring. This paper presents a review of the state of the art of clinical technologies and methodologies for sleep apnea detection and their pros and cons, with particular focus on their working principles and relevance to pediatrics. Moreover, an in-depth discussion on emerging future technologies envisioned to be integral parts of the daily home-based applications is included in the paper.

Direct label-free electrical immunodetection of transplant rejection protein biomarker in physiological buffer using floating gate AlGaN/GaN high electron mobility transistors.

Monokine induced by interferon gamma (MIG/CXCL9) is used as an immune biomarker for early monitoring of transplant or allograft rejection. This paper demonstrates a direct electrical, label-free detection method of recombinant human MIG with anti-MIG IgG molecules in physiologically relevant buffer environment. The sensor platform used is a biologically modified GaN-based high electron mobility transistor (HEMT) device. Biomolecular recognition capability was provided by using high affinity anti-MIG monoclonal antibody to form molecular affinity interface receptors on short N-hydroxysuccinimide-ester functionalized disulphide (DSP) self-assembled monolayers (SAMs) on the gold sensing gate of the HEMT device. A floating gate configuration has been adopted to eliminate the influences of external gate voltage. Preliminary test results with the proposed chemically treated GaN HEMT biosensor show that MIG can be detected for a wide range of concentration varying from 5 ng/mL to 500 ng/mL.