Hydrothermally Synthesized Z-Scheme Nanocomposite of ZIF-9 Modified MXene for Photocatalytic Degradation of 4-Chlorophenol.

4-Chlorophenol (4CP) is a well-known environmental contaminant often detected in wastewater, generally arising from industrial processes such as chemical manufacture, pharmaceutical production, and pesticide formulation. 4CP is a matter of great concern since it is persistent and has the potential to have harmful impacts on both aquatic ecosystems and human health, owing to its hazardous and mutagenic properties. Hence, degradation of 4CP is of utmost significance. This research investigates the photocatalytic degradation of 4CP using a novel Z-scheme heterojunction nanocomposite composed of MXene and ZIF-9. The nanocomposite is synthesized through a two-step hydrothermal method and thoroughly characterized by using XRD, SEM, UV-visible spectroscopy, zeta potential, and electrochemical impedance spectroscopy studies, confirming successful fabrication with improved surface properties. The comparative photocatalytic degradation studies between pristine materials and the nanocomposite were performed, and significant enhancement in performance was observed. The effect of pH on the degradation efficiency is also explored and correlated with the surface charge. The Z-scheme photocatalysis mechanism is proposed, which is supported by time-resolved photoluminescence studies and scavenger experiments. The reusability of the nanocomposite is also evaluated. The study contributes to the development of efficient and sustainable photocatalysts for wastewater treatment.

Electronic Tongue Based on ZnO/ITO@glass for Electrochemical Monitoring of Spiciness Levels.

Capsaicin, a chemical compound present in chili peppers, is widely acknowledged as the main contributor to the spicy and hot sensations encountered during consumption. Elevated levels of capsaicin can result in meals being excessively spicy, potentially leading to health issues, such as skin burning, irritation, increased heart rate and circulation, and discomfort in the gastrointestinal system and even inducing nausea or diarrhea. The level of spiciness that individuals can tolerate may vary, so what may be considered incredibly hot for one person could be mild for another. To ensure food safety, human healthcare, regulatory compliance, and quality control in spicy food products, capsaicin levels must be measured. For these purposes, a reliable and stable sensor is required to quantify the capsaicin level. To leverage the effect of zinc oxide (ZnO), herein, we demonstrated the one-step fabrication process of an electronic tongue (E-Tongue) based on an electrochemical biosensor for the determination of capsaicin. ZnO was electrodeposited on the indium tin oxide (ITO) surface. The biosensor demonstrated the two notable linear ranges from 0.01 to 50 µM and from 50 to 500 µM with a limit of detection (LOD) of 2.1 nM. The present study also included the analysis of real samples, such as green chilis, red chili powder, and dried red chilis, to evaluate their spiciness levels. Furthermore, the E-Tongue exhibited notable degrees of sensitivity, selectivity, and long-term stability for a duration of more than a month. The development of an E-Tongue for capsaicin real-time monitoring as a point-of-care (POC) device has the potential to impact various industries and improve safety, product quality, and healthcare outcomes.

A public dataset of dogs vital signs recorded with ultra wideband radar and reference sensors.

Recently, radar sensors have been extensively used for vital sign monitoring in dogs, owing to their noncontact and noninvasive nature. However, a public dataset on dog vital signs has yet to be proposed since capturing data from dogs requires special training and approval. This work presents the first ever ultra wideband radar-based dog vital sign (UWB-DVS) dataset, which was captured in two independent scenarios. In the first scenario, clinical reference sensors are attached to the fainted dogs, and data from UWB radar and reference sensors are captured synchronously. In the second scenario, the dogs can move freely, and video recordings are provided as a reference for movement detection and breathing extraction. For technical validation, a high correlation, above 0.9, is found between the radar and clinical reference sensors for both the heart rate and breathing rate measurements in scenario 1. In scenario 2, the vital signs and movement of the dogs are shown in the form of dashboards, demonstrating the long-term monitoring capability of the radar sensor.

Infective endocarditis secondary to Peptoniphilus Indolicus and Corynebacterium: an amalgamation.

Corynebacterium or diphtheroid's are gram-positive aerobic, pleomorphic skin and mucosal membrane components that are not pathogenic in nature. Peptostreptococcus indolicus belongs to the Peptostreptococcus genus and is a Gram-Positive Anaerobic Cocci (GPAC). Less than one percent of endocarditis is caused by gram-positive anaerobic bacteria. We report the first case of Peptoniphilus indolicus and Corynebacterium endocarditis in a patient with native valves and a pacemaker. In time, diagnosis of a Peptoniphilus indolicus infection can lead to early management of the infection and a decreased incidence of serious complications such as embolization or abscess formation. The combination of aggressive antibiotic administration and surgical intervention can significantly decrease morbidity and mortality. This case report will highlight the importance of Peptoniphilus infective endocarditis, ultimately leading to better diagnostic strategies and management.

Semantic segmentation of retinal exudates using a residual encoder-decoder architecture in diabetic retinopathy.

Exudates are a common sign of diabetic retinopathy, which is a disease that affects the blood vessels in the retina. Early detection of exudates is critical to avoiding vision problems through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time-consuming and requires intense effort due to the small size of the lesion and the low contrast of the images. Thus, computer-assisted diagnosis of retinal disease based on the detection of red lesions has been actively explored recently. In this paper, we present a comparison of deep convolutional neural network (CNN) architectures and propose a residual CNN with residual skip connections to reduce the parameter for the semantic segmentation of exudates in retinal images. A suitable image augmentation technique is used to improve the performance of network architecture. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. A comparative performance analysis of three benchmark databases: E-ophtha, DIARETDB1, and Hamilton Ophthalmology Institute's Macular Edema, is presented. The proposed method achieves a precision of 0.95, 0.92, 0.97, accuracy of 0.98, 0.98, 0.98, sensitivity of 0.97, 0.95, 0.95, specificity of 0.99, 0.99, 0.99, and area under the curve of 0.97, 0.94, and 0.96, respectively. RESEARCH HIGHLIGHTS: The research focuses on the detection and segmentation of exudates in diabetic retinopathy, a disease affecting the retina. Early detection of exudates is important to avoid vision problems and requires continuous screening and treatment. Currently, manual detection is time-consuming and requires intense effort. The authors compare qualitative results of the state-of-the-art convolutional neural network (CNN) architectures and propose a computer-assisted diagnosis approach based on deep learning, using a residual CNN with residual skip connections to reduce parameters. The proposed method is evaluated on three benchmark databases and demonstrates high accuracy and suitability for diabetic retinopathy screening.

Nanostructured material-based optical and electrochemical detection of amoxicillin antibiotic.

The penicillin derivative amoxicillin (AMX) plays an important role in treating various types of infections caused by bacteria. However, excessive use of AMX may have negative health effects. Therefore, it is of utmost importance to detect and quantify the AMX in pharmaceutical drugs, biological fluids, and environmental samples with high sensitivity. Therefore, this review article provides valuable and up-to-date information on nanostructured material-based optical and electrochemical sensors to detect AMX in various biological and chemical samples. The role of using different nanostructured materials on the performance of important optical sensors such as colorimetric sensors, fluorescence sensors, surface-enhanced Raman scattering sensors, chemiluminescence/electroluminescence sensors, optical immunosensors, optical fibre-based sensors, and several important electrochemical sensors based on different electrode types have been discussed. Moreover, nanocomposites, polymer, and MXenes-based electrochemical sensors have also been discussed, in which such materials are being used to further enhance the sensitivity of these sensors. Furthermore, nanocomposite-based photo-electrochemical sensors and the market availability of biosensors including AMX have also been discussed briefly. Finally, the conclusion, challenges, and future perspectives of the above-mentioned sensing techniques for AMX detection are presented.

Society for Cardiovascular Magnetic Resonance 2022 Cases of SCMR case series.

"Cases of SCMR" is a case series on the SCMR website (https://www.scmr.org) for the purpose of education. The cases reflect the clinical presentation, and the use of cardiovascular magnetic resonance (CMR) in the diagnosis and management of cardiovascular disease. The 2022 digital collection of cases are presented in this manuscript.

Iron Oxide Nanoparticle-Based Ferro-Nanofluids for Advanced Technological Applications.

Iron oxide nanoparticle (ION)-based ferro-nanofluids (FNs) have been used for different technological applications owing to their excellent magneto-rheological properties. A comprehensive overview of the current advancement of FNs based on IONs for various engineering applications is unquestionably necessary. Hence, in this review article, various important advanced technological applications of ION-based FNs concerning different engineering fields are critically summarized. The chemical engineering applications are mainly focused on mass transfer processes. Similarly, the electrical and electronics engineering applications are mainly focused on magnetic field sensors, FN-based temperature sensors and tilt sensors, microelectromechanical systems (MEMS) and on-chip components, actuators, and cooling for electronic devices and photovoltaic thermal systems. On the other hand, environmental engineering applications encompass water and air purification. Moreover, mechanical engineering or magneto-rheological applications include dampers and sealings. This review article provides up-to-date information related to the technological advancements and emerging trends in ION-based FN research concerning various engineering fields, as well as discusses the challenges and future perspectives.

Effects of Receiver Beamforming for Vital Sign Measurements Using FMCW Radar at Various Distances and Angles.

Short-range millimeter wave radar sensors provide a reliable, continuous and non-contact solution for vital sign extraction. Off-The-Shelf (OTS) radars often have a directional antenna (beam) pattern. The transmitted wave has a conical main lobe, and power of the received target echoes deteriorate as we move away from the center point of the lobe. While measuring vital signs, the human subject is often located at the center of the antenna lobe. Since beamforming can increase signal quality at the side (azimuth) angles, this paper aims to provide an experimental comparison of vital sign extraction with and without beamforming. The experimental confirmation that beamforming can decrease the error in the vital sign extraction through radar has so far not been performed by researchers. A simple, yet effective receiver beamformer was designed and a concurrent measurement with and without beamforming was made for the comparative analysis. Measurements were made at three different distances and five different arrival angles, and the preliminary results suggest that as the observation angle increases, the effectiveness of beamforming increases. At an extreme angle of 40 degrees, the beamforming showed above 20% improvement in heart rate estimation. Heart rate measurement error was reduced significantly in comparison with the breathing rate.

A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology.

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three standard criteria (i.e., mitosis count, necrosis, and nuclear atypia). Among these, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available.

Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities.

Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT) is a mathematical process that generates images at many different angles around the patient. Image reconstruction has a fundamental impact on image quality. In recent years, the literature has focused on deep learning and its applications in medical imaging, particularly image reconstruction. Due to the performance of deep learning models in a wide variety of vision applications, a considerable amount of work has recently been carried out using image reconstruction in medical images. MRI and CT appear as the ultimate scientifically appropriate imaging mode for identifying and diagnosing different diseases in this ascension age of technology. This study demonstrates a number of deep learning image reconstruction approaches and a comprehensive review of the most widely used different databases. We also give the challenges and promising future directions for medical image reconstruction.

Takotsubo Cardiomyopathy: A COVID-19 Complication.

COVID-19 started as an unknown viral illness and has been a challenging pandemic to overcome. The virus has been associated with multiple organ involvement, including the heart. Takotsubo cardiomyopathy (TSCM), a stress cardiomyopathy, is an uncommon complication in patients diagnosed with COVID-19. The pathogenesis is historically a result of stress onto the body that leads to a catecholamine surge. However, COVID-19 may cause direct damage to the cardiac myocytes via spike protein and angiotensin-converting enzyme 2 (ACE2) receptors which can further exacerbate the stressful insult on the patient and lower the threshold for developing TSCM. In this case report, we discuss a 94-year-old female who presented with signs and symptoms of acute coronary syndrome but, upon cardiac catheterization, was found to have basal hypercontraction with apical ballooning, consistent with TSCM.

A Curious Presentation of May-Thurner Syndrome With Isolated Iliofemoral Deep Vein Thrombosis.

May-Thurner syndrome, which has been called by many names, including Cockett syndrome, iliocaval compression syndrome, and iliac vein compression syndrome, is an anatomic variation where there is extrinsic venous compression by the arterial system against the bony structure of the axial skeleton, most commonly right common iliac artery compressing the left iliac vein against the fifth lumbar vertebra. The persistent right common iliac pulsation results in endothelial irritation of the venous system and at the same time reduces venous return, hence satisfying two factors in Virchow's triad for the formation of venous thrombosis. Here we present a rare case of a patient who presented with multiple risk factors that could propagate the formation of deep vein thrombosis with the underlying anatomic variation of May-Thurner syndrome in the setting of dehydration, systemic infection, failure to thrive, and psychiatric decompensation. Treatment required fluid hydration, antibiotic therapy, and mechanical thrombectomy in conjunction with local infusion of thrombolytics. Subsequent stent placement was performed to prevent re-thrombosis and stenosis of the affected area with long-term oral anticoagulation.

Self-healable and anti-freezing ion conducting hydrogel-based artificial bioelectronic tongue sensing toward astringent and bitter tastes.

Numerous efforts have been attempted to mimic human tongue since years. However, they still have limitations because of damages, temperature effects, detection ranges etc. Herein, a self-healable hydrogel-based artificial bioelectronic tongue (E-tongue) containing mucin as a secreted protein, sodium chloride as an ion transporting electrolyte, and chitosan/poly(acrylamide-co-acrylic acid) as the main 3D structure holding hydrogel network is synthesized. This E-tongue is introduced to mimic astringent and bitter mouth feel based on cyclic voltammetry (CV) measurements subjected to target substances, which permits astringent tannic acid (TA) and bitter quinine sulfate (QS) to be detected over wide corresponding ranges of 29.3 mM-0.59 µM and 63.8 mM-6.38 µM with remarkable respective sensitivities of 0.2 and 0.12 wt%-1. Besides, the taste selectivity of this E-tongue is performed in the presence of various mixed-taste chemicals to show its high selective behavior toward bitter and astringent chemicals. The electrical self-healability is shown via CV responses to illustrate electrical recovery within a short time span. In addition, cytotoxicity tests using HeLa cells are performed, where a clear viability of ≥95% verified its biocompatibility. The anti-freezing sensing of E-tongue tastes at -5 °C also makes this work to be useful at sub-zero environments. Real time degrees of tastes are detected using beverages and fruits to confirm future potential applications in food taste detections and humanoid robots.

Acute Coronary Syndrome or Right Atrial Cardiac Myxoma? An Atypical Presentation.

The size and location of cardiac tumors determine how patients present with signs of heart failure due to diminished cardiac output within the circulatory system. Poor cardiac output presents with signs of heart failure, which include pulmonary edema, lower extremity edema, jugular venous distention, dyspnea, orthopnea and can be insidious in onset. Vital signs on presentation can often be abnormal and patients may present hemodynamically unstable. We present a case of a female who presented to the emergency room after experiencing a sudden onset of substernal, pressure-like chest pain while sleeping. Vital signs on presentation were stable with no evidence of heart failure symptoms as listed above. Cardiac catheterization showed patent coronary arteries but was found to have a 5.8 x 4.7 x 3.5 cm hypervascular cardiac myxoma located in the right atrium. Instead of a typical heart failure presentation, as any space-occupying mass would decrease the effective cardiac output, the patient presented with angina. During the procedure, the mass was noted to be perfused by the left circumflex artery, creating coronary steal phenomenon, shifting blood away from the coronary arteries and into the mass, causing ischemic anginal pain. The patient ultimately underwent surgical excision of the lesion and her anginal symptoms resolved.

Clinical Course and Outcome of ESRD Patients on Maintenance Hemodialysis Infected with COVID-19: A Single-Center Study.

BACKGROUND: In an ESRD subset of patients, COVID-19 infection is associated with increased disease burden and higher mortality rates. METHODS: We conducted a retrospective single-center cohort study in which 43 ESRD patients had a diagnosis of COVID-19. Association of risk factors with mortality was assessed by chi-square test and logistic regression analysis. Data were collected on a structured performa which included variables like age, gender, comorbid conditions, drug history, clinical presentation, hemodynamic status and laboratory parameters. Outcome variables were recovery and death. All patients received standard treatment for COVID-19 according to hospital protocols, along with hemodialysis and continuous renal replacement therapy (CRRT) when needed. RESULTS: Those most affected were found to be male, 25 (58.1%), while the number of females affected was 18 (41.9%). The most frequent comorbid condition was hypertension (HTN), seen in 35 (81.4%) patients; however, thromboembolic complications were very few in these patients. The mortality rate in our study was 25.6%, and the population most susceptible to poor outcomes in the ESRD subgroup was elderly people (45.5%), while younger patients recovered the most from COVID-19 (53.1%). Hypoalbuminemia, leukocytosis, lymphopenia and raised LDH were also found to be associated with death in ESRD patients suffering from COVID-19 (81.8, 72.7, 100 and 100%, respectively). In multivariate logistic regression analysis, we found that the odds ratio of dying from COVID-19 was 19.5 times higher in patients aged >65 years as compared to patients aged 18-50 years (p=0.039). Similarly, patients with a high TLC were 24.1 times more likely to die than patients with a normal TLC (p=0.008). CONCLUSION: In our center, the mortality rate of ESRD patients affected with COVID-19 disease was 25.6%, and older age, leukocytosis, lymphopenia, hypoalbuminemia and high LDH were significantly associated with mortality.

Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application.

The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.

UWB-gestures, a public dataset of dynamic hand gestures acquired using impulse radar sensors.

In the past few decades, deep learning algorithms have become more prevalent for signal detection and classification. To design machine learning algorithms, however, an adequate dataset is required. Motivated by the existence of several open-source camera-based hand gesture datasets, this descriptor presents UWB-Gestures, the first public dataset of twelve dynamic hand gestures acquired with ultra-wideband (UWB) impulse radars. The dataset contains a total of 9,600 samples gathered from eight different human volunteers. UWB-Gestures eliminates the need to employ UWB radar hardware to train and test the algorithm. Additionally, the dataset can provide a competitive environment for the research community to compare the accuracy of different hand gesture recognition (HGR) algorithms, enabling the provision of reproducible research results in the field of HGR through UWB radars. Three radars were placed at three different locations to acquire the data, and the respective data were saved independently for flexibility.

Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier.

The emerging integration of technology in daily lives has increased the need for more convenient methods for human-computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man-machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstrate radar signals as three-dimensional image patterns is presented and then, the inception module-based variant of GoogLeNet is used to analyze the pattern within the images for the recognition of different hand gestures. The proposed framework is exploited for eight different hand gestures with a promising classification accuracy of 95%. To verify the robustness of the proposed algorithm, multiple human subjects were involved in data acquisition.