Sociodemographic and clinical risk factors associated with in-hospital tuberculosis mortality in Türkiye, 2008-2018.

Introduction: Tuberculosis (TB) is an infectious disease that can be fatal if left untreated or poorly treated, and it is associated with many morbidities. Deaths may provide better understanding of the associated factors and help guide interventions to reduce mortality. In this study, it was aimed to reveal some of the features that predict hospital mortality in patients with TB and to present some alarming findings for clinicians. Materials and Methods: Patients who had been hospitalized with the diagnosis of TB between January 2008 and December 2018 were included and analyzed retrospectively. In-hospital mortality because of any TB disease after the initiation of treatment in patients admitted to the TB Ward and the primary cause of mortality were taken as endpoint. Result: A total of 1321 patients with a mean age of 50.1 years were examined. Total mortality was 39.4% (521 deaths) and 13.1% were in-hospital deaths (173 deaths). Of the deaths, 61.8% (n= 107) occurred during the first month after TB treatment were started. On univariate analysis, age over 48.5 years, Charlson comorbidity index, extension of radiological involvement, hypoalbuminemia and lymphopenia were most predictive variables with higher odds ratios (respectively, p<0.001 for all). Conclusions: In-hospital tuberculosis disease mortality is related with older age, cavitary or extensive pulmonary involvement, low albumin levels, unemployment, cigarette smoking and especially those with concomitant malignancy and chronic pulmonary disease.

Advancements in wearable technology for monitoring lactate levels using lactate oxidase enzyme and free enzyme as analytical approaches: A review.

Lactate is a metabolite that holds significant importance in human healthcare, biotechnology, and the food industry. The need for lactate monitoring has led to the development of various devices for measuring lactate concentration. Traditional laboratory methods, which involve extracting blood samples through invasive techniques such as needles, are costly, time-consuming, and require in-person sampling. To overcome these limitations, new technologies for lactate monitoring have emerged. Wearable biosensors are a promising approach that offers non-invasiveness, low cost, and short response times. They can be easily attached to the skin and provide continuous monitoring. In this review, we evaluate different types of wearable biosensors for lactate monitoring using lactate oxidase enzyme as biological recognition element and free enzyme systems.

Treatment Results of Multidrug-Resistant Tuberculosis Patients in the Aegean Region.

OBJECTIVE: We aimed to evaluate 109 rifampicin-resistant or multidrug-resistant tuberculosis patients who are treated in Izmir Chest Diseases MDR Tuberculosis Centre. MATERIAL AND METHODS: The patient profile, side effects, treatment success, and mortality of rifampicin-resistant or multidrugresistant tuberculosis patients who were followed up and treated in our hospital's tuberculosis service between 2010 and 2018 were analyzed retrospectively. RESULTS: Of the rifampicin-resistant or multidrug-resistant tuberculosis patients, 83 (76.1%) were male and the mean age was 46.3 ± 16.3 years. Of the cases 13 (11.9%) had rifampicin resistance without isoniazid. Since 5 out of 109 patients diagnosed with multidrugresistant tuberculosis emigrated to other countries, the treatment results of 104 patients were evaluated. As a result of the treatment, the cure was achieved in 81 (77.9%) patients and treatment was completed in 13 (12.5%). Treatment success was found as 90.4%. No patient experienced recurrence. The mortality rate was determined as 9.6%. The cure rate of patients treated with ≥6 drugs (90.9%) was statistically significantly (P = .029) higher than the group treated with ≤5 drugs (71.8%). CONCLUSIONS: Multidrug-resistant tuberculosis treatment is a long-term, financially burdensome practice that may cause serious side effects and complications, and it requires strict discipline. The fight against tuberculosis can be successful with tuberculosis control programs that are pursued with determination.

Review of functionalized nano porous membranes for desalination and water purification: MD simulations perspective.

Today, it is known that most of the water sources in the world are either drying out or contaminated. With the increasing population, the water demand is increasing drastically almost in every sector each year, which makes processes like water treatment and desalination one of the most critical environmental subjects of the future. Therefore, developing energy-efficient and faster methods are a must for the industry. Using functional groups on the membranes is known to be an effective way to develop shorter routes for water treatment. Accordingly, a review of nano-porous structures having functional groups used or designed for desalination and water treatment is presented in this study. A systematic scan has been conducted in the literature for the studies performed by molecular dynamics simulations. The selected studies have been classified according to membrane geometry, actuation mechanism, functionalized groups, and contaminant materials. Permeability, rejection rate, pressure, and temperature ranges are compiled for all of the studies examined. It has been observed that the pore size of a well-designed membrane should be small enough to reject contaminant molecules, atoms, or ions but wide enough to allow high water permeation. Adding functional groups to membranes is observed to affect the permeability and the rejection rate. In general, hydrophilic functional groups around the pores increase membrane permeability. In contrast, hydrophobic ones decrease the permeability. Besides affecting water permeation, the usage of charged functional groups mainly affects the rejection rate of ions and charged molecules.

Investigation of miRNA and cytokine expressions in latent tuberculosis infection and active tuberculosis.

BACKGROUND: In tuberculsosis (TB), miRNA has been used as a biomarker to distinguish between healthy individuals and TB patients. The aim of this study was to investigate (i) the association of the miRNA and cytokine expression levels, the course of tuberculosis infection, clinical forms and response to treatment, and (ii) the effects of genotypic features of bacteria on the course of tuberculosis and the relationship between miRNA and cytokine expressions and bacterial genotypes. METHODS: A total of 200 cases (100: culture positive active tuberculosis, 50: quantiferon positive latent tuberculosis infection and 50: quantiferon negative healthy controls) were included in the study. For the tuberculosis group at the time of admission and after treatment, for the latent tuberculosis infection and healthy control groups at the time of admission, miRNA and cytokine expressions were determined. Genotyping of M.tuberculosis isolates was performed by spoligotyping method. RESULTS: While, in the comparison of miRNA expressions between the pretreatment patient group and the healthy control group, there was a statistically significant decrease in the expression of miR-454-3p, miR-15a-5p, miR-590-5p, miR-381, and miR-449a in the Pulmonary TB group, there was no significant change in miRNA expression in extrapulmonary TB patients. When the cytokine expressions of the patient group and the healthy control group were compared before treatment, the expressions of all cytokines in the patient group decreased. However, the only cytokine that showed a significantly lower expression was IL12A in PTB patients. DISCUSSION: There is no significant relationship between the clinical course of the disease, cytokine and miRNA expression, and the genotype of the bacteria.

MWCNT/Ruthenium hydroxide aerogel supercapacitor production and investigation of electrochemical performances.

In this study, the material obtained from the sonication of the double-walled carbon nanotube and ruthenium chloride was produced as an aerogel. Then, symmetrical supercapacitor devices were made using them, and their electrochemical properties were investigated. XRD and FTIR were used in the structural analysis of the aerogel, STEM in surface images, and elemental analyses in EDX. Electrochemical analysis was performed by galvanostat/potentiostat. From the cyclic voltammetry analysis, the highest specific capacitance for MWCNT/Ruthenium hydroxide aerogels was achieved as 423 F/g at 5 mV/s. On the other hand, the corresponding values calculated from the charge-discharge curves were found to be 420.3 F/g and 319.9 F/g at the current densities of 0.5 A/g and 10.0 A/g, respectively. The capacitance retention of as-synthesized aerogel was 96.38% at the end of the 5000 consecutive consecutive cyclic voltammetry cycles.

Fabrication of sensor based on polyvinyl alcohol functionalized tungsten oxide/reduced graphene oxide nanocomposite for electrochemical monitoring of 4-aminophenol.

4-aminophenol (4-AP) is one of the major environmental pollutants which is broadly exploited as drug intermediate in the pharmaceutical formulations. The extensive release of 4-AP in the environment without treatment has become a serious issue that has led several health effects on humans. This work describe the determination of 4-AP through a new chemically modified sensor based on polyvinyl alcohol functionalized tungsten oxide/reduced graphene oxide (PVA/WO3/rGO) nanocomposite. The fabricated nanocomposite was characterized through XRD and HR-TEM to confirm the crystalline structure with average size of 35.9 nm and 2D texture with ultra-fine sheets. The electrochemical characterization of fabricated sensor was carried out by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) to ensure the charge transfer kinetics of modified sensor that revealed high conductivity of PVA/WO3/rGO/GCE. Under optimized conditions e.g. scan rate 80 mV/s, phosphate buffer (pH 6) as supporting electrolyte and potential window from -0.2 to 0.8 V, the prepared sensor showed excellent response for 4-AP. The linear dynamic range of developed method was optimized as 0.003-70 µM. The LOD of fabricated sensor based on PVA/WO3/rGO/GCE for 4-AP was calculated as 0.51 nM. The practical application of PVA/WO3/rGO/GCE was tested in real water and pharmaceutical samples. The fabricated sensor presented here, exhibited exceptional stability and sensitivity than the reported sensors and could be effectively used for the monitoring 4-AP without interferences.

Electrochemical neuron-specific enolase (NSE) immunosensor based on CoFe2O4@Ag nanocomposite and AuNPs@MoS2/rGO.

Small cell lung cancer (SCLC) is highly associated with the risk of early metastasis. Neuron-specific enolase (NSE), a biomarker of SCLC, is directly related to tumor burden and early diagnosis. This biomarker exists in nerve tissue and neuroendocrine tissue. In this study, an electrochemical NSE immunosensor based on gold nanoparticles modified molybdenum disulfide and reduced graphene oxide (AuNPs@MoS2/rGO) as the electrode platform and CoFe2O4@Ag nanocomposite as the signal amplification was developed. The immobilization of anti-NSE capture antibody was successfully performed on AuNPs@MoS2/rGO modified electrode surface by amino-gold affinity and the conjugation of anti-NSE secondary antibody on CoFe2O4@Ag nanocomposite was successfully completed by the strong esterification reaction. The final immunosensor was designed by the specific interactions of electrode platform and signal amplification. The fabricated nanocomposites and electrochemical immunosensor were characterized by both physicochemical characterization techniques including transmission electron microscopy (TEM), scanning electron microscopy (SEM), x-ray diffraction (XRD), x-ray photoelectron spectroscopy (XPS), fourier transform infrared spectroscopy (FTIR), and electrochemical methods such as cyclic voltammetry (CV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS). The quantification limit (LOQ) and the determination limit (LOD) were computed to be 0.01 pg mL-1 and 3.00 fg mL-1, respectively. In brief, it can be speculated that the constructed electrochemical NSE immunosensor can be successfully utilized in the early diagnosis for lung cancer.

Determination of D&C Red 33 and Patent Blue V Azo dyes using an impressive electrochemical sensor based on carbon paste electrode modified with ZIF-8/g-C3N4/Co and ionic liquid in mouthwash and toothpaste as real samples.

Synthetic azo dyes are widely used in a variety of industries, but many of them pose a risk to human health, particularly when consumed in large quantities. As a result, their existence in products should be closely monitored. D&C red 33 and Patent Blue V are mostly used in cosmetics, especially in toothpaste and mouthwashes. A novel carbon paste electrode modified with ZIF-8/g-C3N4/Co nanocomposite and 1-methyl-3-butylimidazolium bromide as an ionic liquid was employed as a highly sensitive reproducible electrochemical sensor for the simultaneous determination of these common dyes. ZIF structure has unique properties such as high surface area, suitable conductivity, and excellent porosity. The electrochemical behavior of the suggested electrode was investigated by cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). To characterize the synthesized nanocomposites, scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) were applied to investigate the structure of nanocomposites. Under the optimized conditions, the modified sensor offered a wide linear concentration range 0.08-10 µM (R2 = 0.9906) and 10-900 µM (R2 = 0.9932) with a low limit of detection of 0.034 µM. The value of diffusion coefficient (D), and the electron transfer coefficient (α) was calculated to be 310 × 10-5, and 0.9 respectively. This technique offered a successful performance for the determination of target analyte in the real samples with acceptable results between 96% and 107%.

An electrochemical molecularly imprinted sensor based on CuBi2O4/rGO@MoS2 nanocomposite and its utilization for highly selective and sensitive for linagliptin assay.

The molecularly imprinted polymers (MIP) is an outstanding electrochemical tool that demonstrates good chemical sensitivity and stability. These main advantages, coupled with the material's vast microfabrication flexibility, make molecularly imprinted sensors an attractive sensing device. Herein, it was aimed to develop a state-of-art molecularly imprinted sensor based on CuBi2O4/rGO@MoS2 nanocomposite to be utilized for the detection of linagliptin (LNG), a novel hypoglycemic drug. The electrochemical characterizations of linagliptin on the surface of the modified electrode was examined via cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Several characterization methods including transmission electron microscope (TEM), X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), and Energy-dispersive X-ray spectroscopy(EDX), were utilized for electrode characterization. The LNG imprinted voltammetric sensor was developed in 80.0 mM phenol containing 20.0 mM LNG. CuBi2O4/rGO@MoS2 nanocomposite on LNG imprinted screen-printed carbon electrode (SPCE) (MIP/CuBi2O4/rGO@MoS2 nanocomposite/SCPE) exhibited a linear relationship between peak current and LNG concentration in the range 0.07-0.5 nM with a detection limit of 0.057 nM. In the existence of interfering substances, an LNG imprinted electrode was utilized to analyze urine, human plasma, and tablet samples with adequate selectivity. The developed sensor was also illustrated for stability, repeatability, reproducibility, and reusability.

A green and sensitive guanine-based DNA biosensor for idarubicin anticancer monitoring in biological samples: A simple and fast strategy for control of health quality in chemotherapy procedure confirmed by docking investigation.

Drug efficiency can be considerably boosted while adverse effects can be reduced by precisely monitoring the concentration of anti-cancer drugs. Thus, one of the most important parameters for human health is the monitoring and detection of anticancer drugs during chemotherapy treatment. Herein, a glassy carbon electrode (GCE) was modified by Pt- and Pd-incorporated ZnO nanoparticles-decorated single-wall carbon nanotubes (Pt-Pd-ZnO/SWCNTs) nanocomposites, and ds-DNA (Calf Thymus) that was a biological recognition element, and it was aimed to be utilized as an ultrasensitive and effective electroanalytical biosensor for idarubicin (IDR) monitoring. Various physicochemical characterization techniques including transmission electron microscopy (TEM), field-emission scanning electron microscopy (FE-SEM) with energy-dispersive X-ray spectroscopy (EDS) were used to investigate the morphology and structure of the Pt-Pd-ZnO/SWCNTs nanocomposite, which was produced via straightforward chemical precipitation combined with the one-pot method. The layer-by-layer modification technique was implemented to fabricate the ds-DNA/Pt-Pd-ZnO/SWCNTs/GCE to be further utilized as a voltammetric sensor for sensitive monitoring of idarubicin in biological fluids and pharmaceutical substances. The electroanalytical method implemented to detect idarubicin was based to detect the ds-DNA's guanine base signal on the surface of the modified electrode in the absence and presence of the anticancer drug. The results explicated that the developed biosensor performed well in determining idarubicin in concentrations ranging from 1.0 nM to 65 µM, with a detection limit of 0.8 nM. The idarubicin detection ability of the modified electrode in real samples was evaluated, and the recovery data was acquired in the range of 98.0% and 104.75%. In the final step, the preferential intercalative binding mode of idarubicin drug with ds-DNA was approved by molecular docking study. This study paves the way for engineering highly sensitive DNA biosensors to be employed in the monitoring of anticancer drugs by combining the benefits of nanocomposites and valuable information of a molecular docking study.

Congo red dye removal from aqueous environment by cationic surfactant modified-biomass derived carbon: Equilibrium, kinetic, and thermodynamic modeling, and forecasting via artificial neural network approach.

Herein, it was aimed to optimize, model, and forecast the biosorption of Congo Red onto biomass-derived biosorbent. Therefore, the waste-orange-peels were processed to fabricate biomass-derived carbon, which was activated by ZnCl2 and modified with cetyltrimethylammonium bromide. The physicochemical properties of the biosorbents were explored by scanning electron microscopy and N2 adsorption/desorption isotherms. The effects of pH, initial dye concentration, temperature, and contact duration on the biosorption capacity were investigated and optimized by batch experimental process, followed by the kinetics, equilibrium, and thermodynamics of biosorption were modeled. Furthermore, various artificial neural network (ANN) architectures were applied to experimental data to optimize the ANN model. The kinetic modeling of the biosorption offered that biosorption was in accordance both with the pseudo-second-order and saturation-type kinetic model, and the monolayer biosorption capacity was calculated as 666.67 mg g-1 at 25 °C according to Langmuir isotherm model. According to equilibrium modeling, the Freundlich isotherm model was better fitted to the experimental data than the Langmuir isotherm model. Moreover, the thermodynamic modeling revealed biosorption took place spontaneously as an exothermic process. The findings revealed that the best ANN architecture trained with trainlm as the backpropagation algorithm, with tansig-purelin transfer functions, and 14 neurons in the single hidden layer with the highest coefficient of determination (R2 = 0.9996) and the lowest mean-squared-error (MSE = 0.0002). The well-agreement between the experimental and ANN-forecasted data demonstrated that the optimized ANN model can predict the behavior of the anionic dye biosorption onto biomass-derived modified carbon materials under various operation conditions.

Utilization of a double-cross-linked amino-functionalized three-dimensional graphene networks as a monolithic adsorbent for methyl orange removal: Equilibrium, kinetics, thermodynamics and artificial neural network modeling.

Herein, it is aimed to develop a high-performance monolithic adsorbent to be utilized in methyl orange (MO) adsorption. Therefore, amino-functionalized three-dimensional graphene networks (3D-GNf) fulfilling the requirements of reusability and high capacity have been fabricated via hydrothermal self-assembly approach followed by a double-crosslinking strategy. The potential utilization of 3D-GNf as an adsorbent for removal MO has been assessed using both batch-adsorption studies and an artificial neural network (ANN) approach. Graphene oxide sheets have been amino-functionalized and cross-linked, by ethylenediamine (EDA) during hydrothermal treatment, following the glutaraldehyde has used as a double-crosslinking agent to facilitate the crosslinking of architecture. The successful fabrication of 3D-GNf has been confirmed by field-emission scanning electron microscopy (FESEM), Fourier transform infrared (FT-IR), Raman and X-ray photoelectron spectroscopy (XPS). Moreover, N2 adsorption/desorption isotherms have revealed the high specific surface area (1015 m2 g-1) with high pore volume (1.054 cm3 g-1) and hierarchical porous structure of 3D-GNf. The effect of initial concentration, contact time, and temperature on adsorption capacity have been thoroughly studied, and the kinetics, isotherms, and thermodynamics of MO adsorption have been modelled. The MO adsorption has been well defined by the pseudo-second-order kinetic model and Langmuir isotherm model with a monolayer adsorption capacity of 270.27 mg g-1 at 25 °C. The thermodynamic findings have revealed MO adsorption has occurred spontaneously with an endothermic process. The Levenberg-Marquardt backpropagation algorithm has been implemented to train the ANN model, which has used the activation functions of tansig and purelin functions at the hidden and output layers, respectively. An optimum ANN model with high-performance metrics (coefficient of determination, R2 = 0.9995; mean squared error, MSE = 0.0008) composed of three hidden layers with 5 neurons in each layer was constructed to forecast MO adsorption. The findings have shown that experimental results are consistent with ANN-based data, implying that the suggested ANN model may be used to forecast cationic dye adsorption.

A molecularly imprinted electrochemical biosensor based on hierarchical Ti2Nb10O29 (TNO) for glucose detection.

A novel molecularly imprinted electrochemical biosensor for glucose detection is reported based on a hierarchical N-rich carbon conductive-coated TNO structure (TNO@NC). Firstly, TNO@NC was fabricated by a novel polypyrrole-chemical vapor deposition (PPy-CVD) method with minimal waste generation. Afterward, the electrode modification with TNO@NC was performed by dropping TNO@NC particles on glassy carbon electrode surfaces by infrared heat lamp. Finally, the glucose-imprinted electrochemical biosensor was developed in presence of 75.0 mM pyrrole and 25.0 mM glucose in a potential range from + 0.20 to + 1.20 V versus Ag/AgCl via cyclic voltammetry (CV). The physicochemical and electrochemical characterizations of the fabricated molecularly imprinted biosensor was conducted by transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD) method, X-ray photoelectron spectroscopy (XPS), electrochemical impedance spectroscopy (EIS), and CV techniques. The findings demonstrated that selective, sensitive, and stable electrochemical signals were proportional to different glucose concentrations, and the sensitivity of molecularly imprinted electrochemical biosensor for glucose detection was estimated to be 18.93 µA µM-1 cm-2 (R2 = 0.99) at + 0.30 V with the limit of detection (LOD) of 1.0 × 10-6 M. Hence, it can be speculated that the fabricated glucose-imprinted biosensor may be used in a multitude of areas, including public health and food quality.

Sensitive sandwich-type electrochemical SARS-CoV­2 nucleocapsid protein immunosensor.

A sensitive and fast sandwich-type electrochemical SARS-CoV­2 (COVID-19) nucleocapsid protein immunosensor was prepared based on bismuth tungstate/bismuth sulfide composite (Bi2WO6/Bi2S3) as electrode platform and graphitic carbon nitride sheet decorated with gold nanoparticles (Au NPs) and tungsten trioxide sphere composite (g-C3N4/Au/WO3) as signal amplification. The electrostatic interactions between capture antibody and Bi2WO6/Bi2S3 led to immobilization of the capture nucleocapsid antibody. The detection antibody was then conjugated to g-C3N4/Au/WO3 via the affinity of amino-gold. After physicochemically characterization via transmission electron microscopy (TEM), scanning electron microscopy (SEM), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS), cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) analysis were implemented to evaluate the electrochemical performance of the prepared immunosensor. The detection of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 NP) in a small saliva sample (100.0 µL) took just 30 min and yielded a detection limit (LOD) of 3.00 fg mL-1, making it an effective tool for point-of-care COVID-19 testing.

Three-dimensional porous reduced graphene oxide decorated with carbon quantum dots and platinum nanoparticles for highly selective determination of azo dye compound tartrazine.

In this work, an electrochemical sensor for the azo dye compound tartrazine (TRT) determination was proposed. A screen-printed carbon electrode (SPCE) was modified by depositing three-dimensional porous reduced graphene oxide decorated with carbon quantum dots and platinum nanoparticles (Pt/CQDs@rGO/SPCE). The resulting amount of TRT was observed by differential pulse voltammetry. Under optimal conditions, the sensor exhibited two wide linearities ranging from 0.01 to 1.57 µM and 1.57-9.3 µM with the reliability coefficient of determination of 0.991 and 0.992, respectively. The detection limit (LOD) was also estimated to be 7.93 nM. Moreover, the Pt/CQDs@rGO/SPCE suggested high selectivity in the presence of several interfering agents and azo dye compounds that have a similar structure. Additionally, the Pt/CQDs@rGO/SPCE revealed superior recovery values of about 96.5-101.6% for candy, 99.7-103.5% for soft drinks, 96.0-101.2% for jelly powder, and 98.0-103.0% for water samples. Furthermore, the fabricated sensor exhibits excellent selectivity, stability, reproducibility, and repeatability, indicating a great perspective in the monitoring of TRT. Therefore, it can be speculated that the proposed electrode could be effectively applied to determine TRT in food samples.

Sustainable electrode material for high-energy supercapacitor: biomass-derived graphene-like porous carbon with three-dimensional hierarchically ordered ion highways.

Biomass-derived carbonaceous materials have been deemed to be one of the up-and-coming electrode materials for high-performance energy storage systems due to their cost-neutral abundant resources, sustainable nature, easy synthesis methods, and environmentally benign features. In this work, various graphene-like porous carbon networks (GPCs) with three-dimensional (3D) hierarchically ordered "ion highways" have been synthesized by the carbonization/activation of orange-peel wastes for use as an electrode material in high-energy supercapacitors. The porous structures and surface morphologies of the GPCs were rationally fine-tuned as a function of the activation agent ratio. The prepared GPCs offered superior specific surface area in addition to a 3D porous structure with a fine-tuned pore size distribution. The electrochemical behaviors of all the GPCs were evaluated in 6.0 M KOH aqueous electrolyte via a three-electrode electrochemical setup. Owing to their synergistic characteristics, including superior specific surface area (1150 m2 g-1), large pore volume, and fine-tuned 3D porous architecture, GPC-3.0 (synthesized with a KOH : GPC ratio of 3.0, by wt.) exhibited the best capacitive behavior amongst the studied GPCs. The 3D hierarchically ordered architecture acts like well-designed ion highways that boost electron transportation, thereby enhancing electrochemical energy storage. A coin-cell-type symmetrical supercapacitor based on GPC-3.0 was tested in both 1.0 M Na2SO4 (salt-in-water) and 12.0 m NaNO3 (water-in-salt) electrolytes. The supercapacitor cell based on the water-in-salt electrolyte offered a wide operating voltage of 2.3 V. The obtained energy density and power density values were comparable to those of commercial high-performance electrical double-layer capacitors. Such notable findings will shed light on next-generation high-rate electrochemical energy storage systems based on biomass-derived carbonaceous materials.

Electrochemical immunosensor development based on core-shell high-crystalline graphitic carbon nitride@carbon dots and Cd0.5Zn0.5S/d-Ti3C2Tx MXene composite for heart-type fatty acid-binding protein detection.

Acute myocardial infarction (AMI) is a significant health problem owing to its high mortality rate. Heart-type fatty acid-binding protein (h-FABP) is an important biomarker in the diagnosis of AMI. In this work, an electrochemical h-FABP immunosensor was developed based on Cd0.5Zn0.5S/d-Ti3C2Tx MXene (MXene: Transition metal carbide or nitride) composite as signal amplificator and core-shell high-crystalline graphitic carbon nitride@carbon dots (hc-g-C3N4@CDs) as electrochemical sensor platform. Firstly, a facile calcination technique was applied to the preparation of hc-g-C3N4@CDs and immobilization of primary antibody was performed on hc-g-C3N4@CDs surface. Then, the conjugation of the second antibody to Cd0.5Zn0.5S/d-Ti3C2Tx MXene was carried out by strong π-π and electrostatic interactions. The prepared electrochemical h-FABP immunosensor was characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), x-ray diffraction (XRD) method, Fourier-transform infrared spectroscopy (FTIR), x-ray photoelectron spectroscopy (XPS), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The prepared electrochemical h-FABP immunosensor indicated a good sensitivity with detection limit (LOD) of 3.30 fg mL-1 in the potential range +0.1 to +0.5 V. Lastly, low-cost, satisfactory stable, and environmentally friendly immunosensor was presented for the diagnosis of acute myocardial infarction.

Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images.

X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5-fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.

COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader.

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of "ChestX-ray14", "COVID-19 image data collection", and "Chest X-ray collection from Indiana University," respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.