Flexible Electrode Arrays Based on a Wide Bandgap Semiconductors for Chronic Implantable Multiplexed Sensing and Heart Pacemakers.

Implantable systems with chronic stability, high sensing performance, and extensive spatial-temporal resolution are a growing focus for monitoring and treating several diseases such as epilepsy, Parkinson's disease, chronic pain, and cardiac arrhythmias. These systems demand exceptional bendability, scalable size, durable electrode materials, and well-encapsulated metal interconnects. However, existing chronic implantable bioelectronic systems largely rely on materials prone to corrosion in biofluids, such as silicon nanomembranes or metals.

Enzyme-free biosensor utilizing chitosan-capped ZnS doped by Mn nanomaterials for tetracycline hydrochloride detection.

Tetracycline hydrochloride is a widely used antibiotic for treating bacterial infections, but its misuse poses serious health risks. Therefore, it is crucial to accurately detect tetracycline in complex matrices. In this study, we propose a simple, enzyme-free absorbance biosensor for tetracycline detection based on the optical properties of chitosan-capped ZnS doped with Mn nanomaterials.

Bioinspired designer DNA NanoGripper for virus sensing and potential inhibition.

DNA has shown great biocompatibility, programmable mechanical properties, and precise structural addressability at the nanometer scale, rendering it a material for constructing versatile nanorobots for biomedical applications. Here, we present the design principle, synthesis, and characterization of a DNA nanorobotic hand, called DNA NanoGripper, that contains a palm and four bendable fingers as inspired by naturally evolved human hands, bird claws, and bacteriophages. Each NanoGripper finger consists of three phalanges connected by three rotatable joints that are bendable in response to the binding of other entities.

Probing the Interplay of Protein Self-Assembly and Covalent Bond Formation in Photo-Crosslinked Silk Fibroin Hydrogels.

Covalent crosslinking of silk fibroin via native tyrosine residues has been extensively explored; however, while these materials are very promising for biomedical, optical, soft robotics, and sensor applications, their structure and mechanical properties are unstable over time. This instability results in spontaneous silk self-assembly and stiffening over time, a process that is poorly understood. This study investigates the interplay between self-assembly and di-tyrosine bond formation in silk hydrogels photo-crosslinked using ruthenium (Ru) and sodium persulfate (SPS) with visible light.

Single-step hydrothermal synthesis of zinc oxide nanorods for potential use as nano-antibiotics without seeding or bases.

The alarming global rise in antibiotic resistance, driven by the widespread overuse of traditional antibiotics, has created an urgent demand for new antimicrobial solutions. This study presents zinc oxide (ZnO) nanorods as a potential nano-antibiotic agent. ZnO nanorods, with a 2:3 aspect ratio, were synthesized using an efficient one-step hydrothermal method at a low temperature of 100°C, reducing the synthesis time to just 5 hours.

A comprehensive review of transduction methods of lectin-based biosensors in biomedical applications.

Biosensors have emerged as a pivotal technology in the biomedical field, significantly enhancing the rapidity and precision of biomolecule detection. These advancements are instrumental in refining diagnostic processes, optimizing treatments, and monitoring diseases more effectively. Central to the development of highly sensitive, selective, and stable biosensors are the bioreceptor and transducer components.

MELEP: A Novel Predictive Measure of Transferability in Multi-label ECG Diagnosis.

In practical electrocardiography (ECG) interpretation, the scarcity of well-annotated data is a common challenge. Transfer learning techniques are valuable in such situations, yet the assessment of transferability has received limited attention. To tackle this issue, we introduce MELEP, which stands for , a measure designed to estimate the effectiveness of knowledge transfer from a pre-trained model to a downstream multi-label ECG diagnosis task.

Ampicillin detection using absorbance biosensors utilizing Mn-doped ZnS capped with chitosan micromaterials.

The detection of ampicillin plays a crucial role in managing and monitoring its usage and resistance. This study introduces a simple and effective biosensor for ampicillin detection, utilizing the unique absorbance features of Mn-doped ZnS capped by chitosan micromaterials in conjunction with β-lactamase activity. The biosensors can detect ampicillin concentrations from 13.

Dual-channel fluorescent sensors based on chitosan-coated Mn-doped ZnS micromaterials to detect ampicillin.

The global threat of antibiotic resistance has increased the importance of the detection of antibiotics. Conventional methods to detect antibiotics are time-consuming and require expensive specialized equipment. Here, we present a simple and rapid biosensor for detecting ampicillin, a commonly used antibiotic.

Disposable impedance sensors based on novel hybrid MoS2 nanosheets and microparticles to detect Escherichia Coli DNA.

The rapid and accurate detection of pathogenic bacteria is essential for food safety and public health. Conventional detection techniques, such as nucleic acid sequence-based amplification and polymerase chain reaction, are time-consuming and require specialized equipment and trained personnel. Here, we present quick, disposable impedance sensors based on the novel hybrid MoS2 nanomaterial for detecting Escherichia coli DNA.

Bacillus subtilis DNA fluorescent sensors based on hybrid MoS2 nanosheets.

Although sensor technology has advanced with better materials, biomarkers, and fabrication and detection methods, creating a rapid, accurate, and affordable bacterial detection platform is still a major challenge. In this study, we present a combination of hybrid-MoS2 nanosheets and an amine-customized probe to develop a fast, sensitive biosensor for Bacillus subtilis DNA detection. Based on fluorescence measurements, the biosensor exhibits a detection range of 23.

Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiography.

Background: Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. Existing attempts typically formulate this task as classification and rely on a single segmentation model to estimate myocardial segment displacements.

Healthcare in Vietnam: Harnessing Artificial Intelligence and Robotics to Improve Patient Care Outcomes.

Healthcare in Vietnam is increasingly utilizing artificial intelligence (AI) and robotics to enhance patient care outcomes. The Vietnamese healthcare sector recognizes the potential of AI and is actively exploring its applications in research and clinical practice. AI technologies, such as text mining and machine learning, can be employed to analyze medical data and improve decision-making processes.

High accurate and explainable multi-pill detection framework with graph neural network-assisted multimodal data fusion.

Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identification, thus, is a crucial concern that needs to be investigated thoroughly. Recently, several attempts have been made to exploit deep learning to tackle the pill identification problem.

Absorbance biosensors-based hybrid [Formula: see text] nanosheets for Escherichia coli detection.

Detecting Escherichia coli is essential in biomedical, environmental, and food safety applications. In this paper, we have developed a simple, rapid, sensitive, and selective E. coli DNA sensor based on the novel hybrid-type [Formula: see text] and [Formula: see text] nanosheets.

VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography.

Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology.

PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children.

Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning algorithms. However, the development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021.

Optical Glucose Sensors Based on Chitosan-Capped ZnS-Doped Mn Nanomaterials.

The primary goal of glucose sensing at the point of care is to identify glucose concentrations within the diabetes range. However, lower glucose levels also pose a severe health risk. In this paper, we propose quick, simple, and reliable glucose sensors based on the absorption and photoluminescence spectra of chitosan-capped ZnS-doped Mn nanomaterials in the range of 0.

Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks.

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically.

Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese.

Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence of large medical image datasets. However, it is exceedingly expensive and time-consuming to construct a large and trustworthy medical dataset; hence, there has been multiple research leveraging medical reports to automatically extract labels for data.

Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings.

Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to investigate the performance of the system in real-world clinical settings and compare its effectiveness to the in-lab performance.

Method: The AI system was directly integrated into the Hospital's Picture Archiving and Communication System (PACS) after being trained on a fixed annotated dataset from other sources.

VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations.

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam.

Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling.

Purpose: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires an accurate classification of the phases. Current approaches to classify the CT phases are commonly based on three-dimensional (3D) convolutional neural network (CNN) approaches with high computational complexity and high latency. This work aims at developing and validating a precise, fast multiphase classifier to recognize three main types of contrast phases in abdominal CT scans.