Periodic magnetic modulation enhanced electrochemical analysis for highly sensitive determination of genomic DNA methylation.

DNA methylation aberrations have a strong correlation with cancer in early detection, diagnosis, and prognosis, which make them possible candidate biomarkers. Electrochemical biosensors offer rapid protocols for detecting DNA methylation status with minimal pretreatment of samples. However, the inevitable presence of background current in the time domain, including electrochemical noise and variations, limits the detection performance of these biosensors, especially for low concentration analytes. Here, we propose an ultrasensitive frequency-domain electrochemical analysis strategy to effectively separate the weak signals from background current. To achieve this, we employed periodic magnetic field modulation of magnetic beads (MBs) on and off the electrode surface to generate a periodic electrochemical signal for subsequent frequency-domain analysis. By capturing labeled MBs with as low as 0.5 pg of DNA, we successfully demonstrated a highly sensitive electrochemical method for determination of genome-wide DNA methylation levels. We also validated the effectiveness of this methodology using DNA samples extracted from three types of hepatocellular carcinoma (HCC) cell lines. The results revealed varying genomic methylation levels among different HCC cell lines, indicating the potential application of this approach for early-stage cancer detection in terms of DNA methylation status.

Stereo matching of binocular laparoscopic images with improved densely connected neural architecture search.

PURPOSE: Stereo matching is a crucial technology in the binocular laparoscopic-based surgical navigation systems. In recent years, neural networks have been widely applied to stereo matching and demonstrated outstanding performance. however, this method heavily relies on manual feature engineering meaning that professionals must be involved in the feature extraction and matching. This process is both time-consuming and demands specific expertise. METHODS: This paper introduces a novel stereo matching framework DCStereo that realizes a fully automatic neural architecture design for the stereo matching of binocular laparoscopic images. The proposed framework utilizes a densely connected search space which enables a more flexible and diverse architecture composition. Furthermore, the proposed algorithm leverages the channel and path sampling strategies to reduce memory consumption during searching. RESULTS: Empirically, our searched DCStereo on the SCARED training dataset achieves a mean absolute error of 3.589 mm on the test dataset, which outperforms hand-crafted stereo matching methods and other approaches. Furthermore, when directly testing on the SERV-CT dataset, our DCStereo demonstrates better generalization ability than other methods. CONCLUSION: Our proposed approach leverages the neural architecture search technique and a densely connected search space for automatic neural architecture design in stereo matching of binocular laparoscopic images. Our method delivers advanced performance on the SCARED dataset and promising results on the SERV-CT dataset. These findings demonstrate the potential of our approach for improving clinical surgical navigation systems.

Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection.

Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated simpler detection of early lesions like polyps during real-time colonoscopy. However, the majority of existing techniques require a large training dataset annotated by experienced experts. To alleviate the laborious task of image annotation and utilize the vast amounts of readily available unlabeled colonoscopy data to further improve the polyp detection ability, this study proposed a novel self-supervised representation learning method called feature pyramid siamese networks (FPSiam). First, a feature pyramid encoder module was proposed to effectively extract and fuse both local and global feature representations among colonoscopic images, which is important for dense prediction tasks like polyp detection. Next, a self-supervised visual feature representation containing the general feature of colonoscopic images is learned by the siamese networks. Finally, the feature representation will be transferred to the downstream colorectal polyp detection task. A total of 103 videos (861,400 frames), 100 videos (24,789 frames), and 60 videos (15,397 frames) in the LDPolypVideo dataset are used to pre-train, train, and test the performance of the proposed FPSiam and its counterparts, respectively. The experimental results have illustrated that our FPSiam approach obtains the optimal capability, which is better than that of other state-of-the-art self-supervised learning methods and is also higher than the method based on transfer learning by 2.3 mAP and 3.6 mAP for two typical detectors. In conclusion, FPSiam provides a cost-efficient solution for developing colorectal polyp detection systems, especially in conditions where only a small fraction of the dataset is labeled while the majority remains unlabeled. Besides, it also brings fresh perspectives into other endoscopic image analysis tasks.

Retrospective Analysis of Death Cases of Oral Diphenidol Hydrochloride Poisoning.

OBJECTIVES: To explore the characteristics of postmortem examination, chemical examination and scene investigation of deaths caused by oral diphenidol hydrochloride poisoning, and so as to provide a reference for proper settlement and prevention of such deaths. METHODS: The data of 22 deaths caused by oral diphenidol hydrochloride poisoning in a city from January 2018 to August 2020 were collected, including case details, scene investigations, autopsies, chemical examinations and digital evidence. Thirty-one cases of deaths caused by oral diphenidol hydrochloride poisoning reported in previous literature were also collected. RESULTS: In the 53 oral diphenidol hydrochloride poisoning death cases, 50 cases were suicide, 2 cases were accidental, while 1 case was undetermined. Fifty-two cases were found in the medical records or crime scene investigation reports with doses ranging from 775 mg to 12 500 mg, and 23 deceased were detected with postmortem blood concentrations ranging from 2.71 mg/L to 83.1 mg/L. Clinical symptoms were recorded in 6 patients, including conscious disturbance and convulsion. Among the 45 cases which were performed with external examination, 23 cases autopsied. CONCLUSIONS: Most of the deceased of oral diphenidol hydrochloride poisoning were suicide. No significant correlation was found between dose and blood concentration through the retrospective analysis of cases.

A novel intrarenal multimodal 2D/3D registration algorithm and preliminary phantom study.

Retrograde intrarenal surgery (RIRS) is a widely utilized diagnostic and therapeutic tool for multiple upper urinary tract pathologies. The image-guided navigation system can assist the surgeon to perform precise surgery by providing the relative position between the lesion and the instrument after the intraoperative image is registered with the preoperative model. However, due to the structural complexity and diversity of multi-branched organs such as kidneys, bronchi, etc., the consistency of the intensity distribution of virtual and real images will be challenged, which makes the classical pure intensity registration method prone to bias and random results in a wide search domain. In this paper, we propose a structural feature similarity-based method combined with a semantic style transfer network, which significantly improves the registration accuracy when the initial state deviation is obvious. Furthermore, multi-view constraints are introduced to compensate for the collapse of spatial depth information and improve the robustness of the algorithm. Experimental studies were conducted on two models generated from patient data to evaluate the performance of the method and competing algorithms. The proposed method obtains mean target error (mTRE) of 0.971 ± 0.585 mm and 1.266 ± 0.416 mm respectively, with better accuracy and robustness overall. Experimental results demonstrate that the proposed method has the potential to be applied to RIRS and extended to other organs with similar structures.

Multi-Parameter Detection of Urine Based on Electropolymerized PANI: PSS/AuNPs/SPCE.

Urine analysis is widely used in clinical practice to indicate human heathy status and is important for diagnosing chronic kidney disease (CKD). Ammonium ions (NH4+), urea, and creatinine metabolites are main clinical indicators in urine analysis of CKD patients. In this paper, NH4+ selective electrodes were prepared using electropolymerized polyaniline-polystyrene sulfonate (PANI: PSS), and urea- and creatinine-sensing electrodes were prepared by modifying urease and creatinine deiminase, respectively. First, PANI: PSS was modified on the surface of an AuNPs-modified screen-printed electrode, as a NH4+-sensitive film. The experimental results showed that the detection range of the NH4+ selective electrode was 0.5~40 mM, and the sensitivity reached 192.6 mA M-1 cm-2 with good selectivity, consistency, and stability. Based on the NH4+-sensitive film, urease and creatinine deaminase were modified by enzyme immobilization technology to achieve urea and creatinine detection, respectively. Finally, we further integrated NH4+, urea, and creatinine electrodes into a paper-based device and tested real human urine samples. In summary, this multi-parameter urine testing device offers the potential for point-of-care testing of urine and benefits the efficient chronic kidney disease management.

Highly selective electrosynthesis of H2O2 by N, O co-doped graphite nanosheets for efficient electro-Fenton degradation of p-nitrophenol.

The activity and selectivity of the cathode towards electrosynthesis of H2O2 are critical for electro-Fenton process. Herein, nickel-foam modified with N, O co-doped graphite nanosheets (NO-GNSs/Ni-F) was developed as a cathode for highly efficient and selective electrosynthesis of H2O2. Expectedly, the accumulation of H2O2 at pH= 3 reached 494.2 mg L-1 h-1, with the selectivity toward H2O2 generation reaching 93.0%. The synergistic effect of different oxygen-containing functional groups and N species on the performance and selectivity of H2O2 electrosynthesis was investigated by density functional theory calculations, and the combination of epoxy and graphitic N (EP + N) was identified as the most favorable configuration with the lowest theoretical overpotential for H2O2 generation. Moreover, NO-GNSs/Ni-F was applied in the electro-Fenton process for p-nitrophenol degradation, resulting in 100% removal within 15 min with the kinetic rate constant of 0.446 min-1 and 97.6% mineralization within 6 h. The efficient removal was mainly attributed to the generation of bulk ·OH. Furthermore, NO-GNSs/Ni-F exhibited excellent stability. This work provides a workable option for the enhancement of H2O2 accumulation and the efficient degradation of pollutants in electro-Fenton system.

Tissue-like Conductive Ti3C2/Sodium Alginate Hybrid Hydrogel for Electrochemical Sensing.

Endowed with a soft and conductive feature, hydrogels have been widely used as interface materials in bioelectronics to fulfill mechanical matching and bidirectional exchange between electronic platforms and living samples. Despite their ionic conductivity, the lack of electron mobility has limited their further applications in biosensing, especially in the field of electrochemical sensing. Here, we propose a Ti3C2/sodium alginate (SA) hybrid hydrogel with not only a tissue-like mechanical strength (down to 80 kPa) but also a combined exchange interface for ions and electrons, realizing both mechanical and electrical coupling toward biological tissues. Due to the shared gelation tendency with cations, the Ti3C2 sheets and SA chains can be easily in situ coassembled through a one-step electrogelation method, making the hybrid hydrogel a well-suited interface layer for device functionalization. In addition, the typical two-dimensional (2D) structure and the abundant active terminals of Ti3C2 have endowed the Ti3C2/SA with a massive loading capacity toward catalytic nanoparticles. For example, the Prussian Blue (PB)-loaded Ti3C2/SA hybrid hydrogel exhibited an excellent electrochemical performance (sensitivity: 600 nA µM-1 cm-2; LOD: 12 nM) toward hydrogen peroxide sensing in tissue fluids, illustrating a promising application potentiality of the hybrid hydrogel in biochemical detection at tissue interfaces.

Research and Development of Ankle-Foot Orthoses: A Review.

The ankle joint is one of the important joints of the human body to maintain the ability to walk. Diseases such as stroke and ankle osteoarthritis could weaken the body's ability to control joints, causing people's gait to be out of balance. Ankle-foot orthoses can assist users with neuro/muscular or ankle injuries to restore their natural gait. Currently, passive ankle-foot orthoses are mostly designed to fix the ankle joint and provide support for walking. With the development of materials, sensing, and control science, semi-active orthoses that release mechanical energy to assist walking when needed and can store the energy generated by body movement in elastic units, as well as active ankle-foot orthoses that use external energy to transmit enhanced torque to the ankle, have received increasing attention. This article reviews the development process of ankle-foot orthoses and proposes that the integration of new ankle-foot orthoses with rehabilitation technologies such as monitoring or myoelectric stimulation will play an important role in reducing the walking energy consumption of patients in the study of human-in-the-loop models and promoting neuro/muscular rehabilitation.

A Power-Harvesting CGM Chiplet Featuring Silicon-Based Enzymatic Glucose Sensor.

Diabetes has become a leading cause of death and disability in the past decades. Continuous glucose monitoring (CGM) is a prevailing technique to determine the glucose level and provide in-time treatment. However, conventional CGM systems combine an electrochemical sensor with a CMOS chip, suffering from bulky size and interface issues. Integrating the CGM sensor on silicon is potential to miniaturize the CGM system and reduce the cost, while the recent silicon-based sensors show limited detection range and sensitivity. In this work, we present a silicon-based CGM chip let with wireless power transfer (WPT) and real-time wireless telemetry. Fabricated on a single silicon substrate, the chiplet consists of a silicon-based CGM sensor, a power-harvesting wireless-telemetry chip, and a silicon-based antenna. Measured results show that the chip let achieves a sensitivity of 4 µA.mM.cm-2 and a linear detection range of 0-10 mM. Based on WPT and backscattering communication, the chip let consumes 18.8 µ W power in glucose telemetry.

A Refined Blood Pressure Estimation Model Based on Single Channel Photoplethysmography.

This study proposed a refined BP prediction strategy that using single-channel photoplethysmography (PPG) signals to stratify populations by cardiovascular status before BP estimation. Combining demographic characteristics (age, gender) and pulse wave morphological features, the random forest was applied to screen two kinds of typical cardiovascular diseases (CVDs), with an accuracy of 92.2%. A deep learning model (BiLSTM-At) was proposed to estimate the long-term BP trend for different CVD groups. Transfer learning technique was used for personalized modeling to reduce computational complexity while improving performance. The method was validated on 255 patients with different CVDs. The mean absolute errors (MAEs) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimation were 2.815 mmHg and 1.876 mmHg for normal subjects, 3.024 mmHg and 1.334 mmHg for AF subjects, and 4.444 mmHg and 2.549 mmHg for CA subjects. The results met the American Association for the Advancement of Medical Instrumentations (AAMI) and British Hypertension Society (BHS) Class A criteria. This indicated that our strategy has good performance and can realize long-term monitoring of BP through a small batch samples, with the potential to implement real-time monitoring in healthy devices.

Shape estimation of the anterior part of a flexible ureteroscope for intraoperative navigation.

PURPOSE: Flexible ureteroscopy (FURS) plays an important role in the diagnosis and treatment of urological diseases. However, manipulating a flexible ureteroscope to the target quickly and safely may be challenging because of the tortuous lumen or poor visibility. Thus, information on the shape of the anterior part of a flexible ureteroscope in addition to the real-time pose is needed to perform accurate maneuvering in the lumen with minimal impingement on the inner renal wall and resulting tissue damage in FURS. METHODS: An adaptive mixed-order Bézier curve fitting algorithm and electromagnetic tracking (EMT) technique were developed for shape estimation utilizing the length of the anterior part, kinematic constraints and the pose information provided by two electromagnetic (EM) sensors mounted at the tip and base of the anterior part. A series of experiments were performed to qualitatively and quantitatively verify the validity of our method. Moreover, algorithm threshold conditions with reference significance under various shape cases were studied. RESULTS: The performance of our method was evaluated based on 19 representative planar bending shapes that often appear in FURS and eight non-planar shapes, yielding an average error (AE) of 1.0 mm. Moreover, the experiments proved the feasibility of applying our method in cases in which large bending angles (near 270 degrees) occur. CONCLUSION: Based on data from two EM sensors mounted at the tip and base of the anterior part of a flexible ureteroscope, the proposed algorithm adaptively selects a cubic or quartic Bézier curve to fit the shape of the anterior part. Experimental results prove the feasibility of our shape estimation method over a broad bending range. The proposed method demonstrates significant potential for use in ureteroscopic navigation systems and robot-assisted surgery.

A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition.

The camera is the main sensor of vison-based human activity recognition, and its high-precision calibration of distortion is an important prerequisite of the task. Current studies have shown that multi-parameter model methods achieve higher accuracy than traditional methods in the process of camera calibration. However, these methods need hundreds or even thousands of images to optimize the camera model, which limits their practical use. Here, we propose a novel point-to-point camera distortion calibration method that requires only dozens of images to get a dense distortion rectification map. We have designed an objective function based on deformation between the original images and the projection of reference images, which can eliminate the effect of distortion when optimizing camera parameters. Dense features between the original images and the projection of the reference images are calculated by digital image correlation (DIC). Experiments indicate that our method obtains a comparable result with the multi-parameter model method using a large number of pictures, and contributes a 28.5% improvement to the reprojection error over the polynomial distortion model.

Improved performance of an opposite-flow low-pressure ultrafiltration membrane system in the treatment of groundwater containing Fe2+, Mn2+, and NH4.

In remote areas, low-pressure ultrafiltration membrane (LPM) systems can be applied in decentralized water supplies for the treatment of groundwater containing Fe2+, Mn2+, and NH4+. However, improving the performance of the LPM systems, such as the stable flux and removal capacity, presents a challenge. In this study, a novel opposite-flow low-pressure ultrafiltration membrane (O-LPM) system was applied, and its performance was evaluated. Experimental results showed that after 46 days of operation, the steady flux of the O-LPM systems were 1.87-fold and 1.74-fold higher than that of the conventional D-LPM systems under Mn2+ concentration of 0.3 mg L-1 and 1.5 mg L-1, respectively. With a mixed pollutant system containing Fe2+ (0.5 mg L-1), Mn2+ (0.3 mg L-1), and NH4+ (1.0 mg L-1), the O-LPM-ripening period for Mn2+ removal was shortened from 16 days to 8 days, and the NH4+ removal efficiency was increased from 61.46% to 80.97%. The bio-cake layer in the O-LPM systems was thinner and had a higher uniformity than in the D-LPM systems, resulting in a larger stable flux range. The relative abundance of functional bacteria (MnOB, IOB, and NOB) was generally higher in O-LPM systems than in the D-LPM systems. Overall, these results are of high relevance for groundwater treatment in remote areas, providing guidance for the widespread application of the O-LPM system in decentralized water supplies.

3D MXene/PEDOT:PSS Composite Aerogel with a Controllable Patterning Property for Highly Sensitive Wearable Physical Monitoring and Robotic Tactile Sensing.

MXene based composite conductive aerogels have been extensively investigated as sensitive materials for wearable pressure sensors owing to their effective 3D network microstructures and the excellent conductivity of MXene. In this work, we fabricated a 3D porous Ti3C2Tx MXene/poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) composite aerogel (MPCA) with a controllable patterning property utilizing the Cu-assisted electrogelation method. The prepared composite aerogel can be assembled into pressure sensors for wearable physical monitoring and high-resolution sensor microarrays for robotic tactile sensing. The multi-interactions between MXene and PEDOT:PSS enable the MPCA to have a stable 3D conductive network, which consequently enhances both the mechanical flexibility and the piezoresistive property of the MPCA. Thus, the fabricated pressure sensor demonstrating high sensitivity (26.65 kPa-1 within 0-2 kPa), fast response ability (106 ms), and excellent stability can be further applied for wearable physical monitoring. Moreover, due to the controllable patterning property of the electrogelation preparation method, a high-resolution pressure sensor microarray was successfully prepared as an artificial tactile interface, which can be attached to a robotic fingertip to directly recognize the tactile stimuli from human fingers and identify braille letters like human fingers. The proposed MPCA, endowed with a remarkable comprehensive property, particularly the highly sensitive sensing performance and controllable patterning property, demonstrates an enormous advantage and a great potentiality toward wearable electronics.

Magnetic Immunosensor Coupled to Enzymatic Signal for Determination of Genomic DNA Methylation.

Aberrations of genomic DNA methylation have been confirmed to be involved in the evolution of human cancer and have thus gained the potential to be depicted as biomarkers for cancer diagnostics and prognostic predictions, which implicates an urgent need for detection of total genomic DNA methylation. In this work, we suggested an assay for the quantification of global DNA methylation, utilizing methylation specific antibody (5mC) modified magnetic beads (MBs) for immunorecognition and affinity enrichment. Subsequently, the captured DNA on the surface of MBs interacted with the glucose oxidase-conjugated DNA antibody whose catalytic reaction product was engaged in electrochemical detection of the overall level of DNA methylation on a PB-doped screen-printed electrode. With 15 pg of input DNA, which, to our best knowledge, is the lowest required amount of DNA without sodium bisulfite treatment or amplification, this test strategy was able to perceive as low as 5% methylation level within 70 min including the preparation of anti-5mC-MBs. We believe this detection technique offers a promising option to detect global DNA methylation in both academic and clinical scenarios.

The development and clinical application of microscopic endoscopy for in vivo optical biopsies: Endocytoscopy and confocal laser endomicroscopy.

Endoscopies are crucial for detecting and diagnosing diseases in gastroenterology, pulmonology, urology, and other fields. To accurately diagnose diseases, sample biopsies are indispensable and are currently considered the gold standard. However, random 4-quadrant biopsies have sampling errors and time delays. To provide intraoperative real-time microscopic images of suspicious lesions, microscopic endoscopy for in vivo optical biopsy has been developed, including endocytoscopy and confocal laser endomicroscopy. This article reviews recent advances in technology and clinical applications, as well as their shortcomings and future directions.

Differential Amperometric Microneedle Biosensor for Wearable Levodopa Monitoring of Parkinson's Disease.

Levodopa (L-Dopa) is considered to be one of the most effective therapies available for Parkinson's disease (PD) treatment. The therapeutic window of L-Dopa is narrow due to its short half-life, and long-time L-Dopa treatment will cause some side effects such as dyskinesias, psychosis, and orthostatic hypotension. Therefore, it is of great significance to monitor the dynamic concentration of L-Dopa for PD patients with wearable biosensors to reduce the risk of complications. However, the high concentration of interferents in the body brings great challenges to the in vivo monitoring of L-Dopa. To address this issue, we proposed a minimal-invasive L-Dopa biosensor based on a flexible differential microneedle array (FDMA). One working electrode responded to L-Dopa and interfering substances, while the other working electrode only responded to electroactive interferences. The differential current response of these two electrodes was related to the concentration of L-Dopa by eliminating the common mode interference. The differential structure provided the sensor with excellent anti-interference performance and improved the sensor's accuracy. This novel flexible microneedle sensor exhibited favorable analytical performance of a wide linear dynamic range (0-20 µM), high sensitivity (12.618 nA µM-1 cm-2) as well as long-term stability (two weeks). Ultimately, the L-Dopa sensor displayed a fast response to in vivo L-Dopa dynamically with considerable anti-interference ability. All these attractive performances indicated the feasibility of this FDMA for minimal invasive and continuous monitoring of L-Dopa dynamic concentration for Parkinson's disease.

Deep learning for gastroscopic images: computer-aided techniques for clinicians.

Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.

An integrated and conductive hydrogel-paper patch for simultaneous sensing of Chemical-Electrophysiological signals.

Simultaneous monitoring of electrophysiological and biochemical signals is of great importance in healthcare and fitness management, while the fabrication of highly integrated and flexible devices is crucial to these applications. Herein, we devised a multifunctional and flexible hydrogel-paper patch (HPP) that was capable of simultaneously real-time monitoring of electrocardiogram (ECG) signal and biochemical signal (glucose content) in sweat during exercise. The self-assembly of the highly porous PEDOT:PSS hydrogel on paper fiber provided the HPP with good conductivity and hydrophilic wettability for efficient electron transmission and substance diffusion, thereby enabling it to serve as a low-impedance ECG electrode and a highly sensitive glucose sensor. Additionally, the spontaneous capillary flow effect allows the paper patch to be used as microfluidic channels for the collect and analysis of sweat. Moreover, the HPP is integrated with a flexible printed circuit board (FPCB) and works as a multifunctional wearable device mounted on the chest for real-time monitoring of electrophysiological and biochemical signals during exercise.