Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson's Patients Based on Vacancy-Engineered Processing of g-C3N4

Exploring scanning electrochemical probe microscopy in single-entity analysis in biology: Past, present, and future

Scanning Electrochemical Probe Microscopy (SEPM) shows significant potential promise for analyzing localized electrochemical activity at biological interfaces of single entities. Utilizing various SEPM probe manipulations allows real-time monitoring of the morphology and physiological activities of single biological entities, offering vital electrochemical insights into biological processes. This review focuses on the application of five SEPM techniques in imaging single biological entities, highlighting their unique advantages in the observation and quantitative evaluation of biological morphology. Specifically, these techniques not only enable high-resolution imaging of single biological structures but also allow for quantitative analysis of their response behavior. Additionally, the integration of Artificial Intelligence (AI) is discussed to improve data processing and image analysis, potentially advancing SEPM technology towards automation. Although still in an early stage, AI integration opens new avenues for deeper single-entity analysis. This review aims to offer an interdisciplinary perspective and encourage advancements in SEPM-based imaging and analytical techniques, contributing to the bioanalytical field.

Integrating transfer learning and spectroscopy for enhanced pork spoilage assessment using correlation analysis

Accurate Total Viable Count (TVC) detection is vital for food quality monitoring. In this study, we investigated the feasibility of using visible near-infrared (VNIR) spectroscopy (400-1000 nm) combined with transfer learning (TL) to track the chemical spoilage of pork. The base models developed using the full band for pork TVC, total volatile basic nitrogen, pH, and color showed predictability; the correlation coefficient of prediction set (RP) for all models ranged from 0.821 to 0.916; and the root mean square error of prediction set (RMSEP) of the TVC model was 0.617 (lg CFU/g). A correlation analysis of the different indexes of pork was carried out to optimize the TVC calibration model. Different TL methods for TVC optimization were designed. The results showed that multiple correlation chain stacking-partial least squares performed best with RP, RMSEP, and the relative percent deviation of 0.947, 0.425 lg CFU/g, and 2.355, respectively, the RMSEP of TVC was reduced by 31.12 % as compared to the base model. This study demonstrated the possibility of combining the VNIR spectroscopy system with TL to monitor the degree of meat's chemical spoilage.

Miniature dual-channel electrochemical 3D-printed sensing platform for enzyme-free screening of L-lactic acid in foodstuffs accompanying pH recognition

Accurate and rapid identification of expired and spoiled food is crucial for food conservation, reducing resource wastage, and preventing food poisoning. This paper presents a portable electrochemical sensing platform supported by a miniature electrochemical workstation and an intelligent pH recognition system, enabling detection of L-lactic acid (L-LA) in food products without enzyme involvement. The system was based on a Cu2O@CuO multifaceted extended spatial hexapod structure. The synthesized Cu2O@CuO was characterized by a well-defined multifaceted structure. Significant enzyme-free catalysis was exhibited, and pH responses dominated by anthocyanins were identified through an intelligent image acquisition system. Additionally, we developed an electrochemical detection device for pH assistance during target testing, addressing the limitations of current electrochemical sensors' complex signal acquisition components using 3D-printed fabrication techniques and smartphones. The proposed multifunctional electrochemical workbench based on Cu2O@CuO was found to offer a preferable linear detection range of 0.1-1000 μM for L-LA, with a low detection limit of 0.027 μM. The visualization of pH determination was introduced as a novel approach for developing advanced electrochemical workbenches. In conclusion, pH-assisted portable electrochemical detection systems hold great potential for immediate food safety identification, particularly in resource-limited areas, facilitating prompt diagnosis and ensuring food safety.

Enhanced photocurrents for photoelectrochemical immunoassay of alpha-fetoprotein with Pt-functionalized Bi2O2S nanoflowers

BACKGROUND

Designing heterojunctions with efficient electron-hole separation holds great promise for improving photoelectric response.

RESULTS

Herein, we reported a multifunctional Pt co-catalyst-modified Bi2O2S nanoflowers (BOS NFs) photocatalytic component for achieving an efficient photoelectric chemistry (PEC) immunosensor for alpha-fetoprotein (AFP). Briefly, the Pt co-catalyst improved the intrinsic band gap structure of BOS on the one hand, and on the other hand, it was able to achieve a rapid decomposition of hydrogen peroxide to hydroxyl radicals, which led to the improvement of electrochemical half-responses during the amplification of target immunosignals. In addition, Pt-functionalized BOS NFs (BOS-Pt) exhibited peroxidase-like enzymatic reaction activity and related properties. By enzyme-linked immunosorbent assay, a sandwich immuno-model in the presence of AFP catalyzed the production of hydrogen peroxide from the substrate glucose and the conversion of a sizable photoelectrochemical signal catalyzed by BOS-Pt. Following condition optimization, it was determined that the developed sensor exhibited a specific response to AFP over a wide linear range of 0.05-50 ng mL-1.

SIGNIFICANCE

This work provides a new strategy for developing efficient immunosensors from the perspective of modulating photoelectrochemical half-reactions.

Wearable Patch Biosensor through Electrothermal Film-Stimulated Sweat Secretion for Continuous Sweat Glucose Analysis at Rest

Wearable patch biosensors for noninvasive and continuous diabetes management through sweat glucose analysis present a promising prospect. However, how to obtain sweat samples safely and effectively remains a huge challenge, especially in a resting state. In this work, we propose an innovative wearable patch biosensor through a heat-stimulated approach for sweat collection. A silver nanowire-loaded electrothermal film was designed as the heat source to stimulate sweat glands for sweat secretion. Subsequently, the secreted sweat sample was transported and enriched through microfluidic channels, which was continuously and sensitively analyzed by a Prussian blue and glucose oxidase comodified glucose electrochemical biosensor. Under optimal conditions, its sensitivity could achieve 14 μM sweat glucose within 15 min, which was 17 min shorter than that without heating. The specificity, reproducibility, and accuracy were also adequate. To achieve on-body perspiration monitoring of human subjects, the wearable patch biosensor was integrated with a portable electrochemical workstation, a temperature controller, and a power source. The glucose concentration was presented on a smartphone. Results showed that the glucose concentration in sweat detected by the wearable biosensor presented a highly consistent trend with the blood glucose measured by a blood glucose meter throughout the day with normal meals. Compared with other conventional sweat stimulation strategies, the simple device and safe principle made it more suitable for individuals who were sedentary or at rest. This work provides a new approach to realizing wearable patch biosensors for personalized health monitoring.

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device

Detecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overall health. In this endeavor, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate glucose estimation. Our proposed microfluidic device comprises layers of adhesive poly-vinyl films stacked on a poly methyl methacrylate (PMMA) base sheet, with micro-channel contours precision-cut using a cutting printer. Employing the gold standard glucose-oxidase/peroxidase reaction on this microfluidic platform, we achieve enzymatic glucose determination. The resulting colored complex, formed by phenol and 4-aminoantipyrine in the presence of hydrogen peroxide generated during glucose oxidation, is captured at various glucose concentrations using a smartphone camera. Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms. Furthermore, the classifier exhibits outstanding precision, recall, and F1 score of 94%, 93%, and 93%, respectively, as validated through our study, showcasing its exceptional predictive capability. Next, a user-friendly smartphone application named "GLUCOLENS AI" was developed to capture images, perform image processing, and communicate with cloud server containing the CNN classifier. The developed CNN model can be successfully used as a pre-trained model for future glucose concentration predictions.

Simultaneous detection of multiple mycotoxins in agricultural products: Recent advances in optical and electrochemical sensing methods

Mycotoxin contamination poses serious threats to human and animal health. Food and environmental systems are often simultaneously contaminated with multiple mycotoxins, a problem that is further exacerbated by the synergistic toxicological effects of these co-occurring mycotoxins. Consequently, the development of rapid detection methods capable of simultaneously identifying multiple mycotoxins in agricultural products is essential to prevent their entry into the food chain. Compared to standard detection methods, optical and electrochemical (EC) sensing methods have distinct advantages for the rapid detection of mycotoxins. This review comprehensively summarizes the latest advancements in the field of simultaneous detection of multiple mycotoxins using optical and EC sensing methods over the last 6 years (2018-2024). First, the review introduces the classification and relevant principles of optical and EC sensing methods. Thereafter, it emphasizes innovative simultaneous detection strategies within these approaches. Finally, it discusses current challenges and offers a reference for further research. Currently, the main challenge lies in the mutual interference among targets, making the development of an interference-free detection platform essential. Furthermore, the ongoing development of integrated technology is expected to aid regulatory authorities in improving the quality of agricultural products for field applications.

Deep learning assisted quantitative detection of cardiac troponin I in hierarchical dendritic copper-nickel nanostructure lateral flow immunoassay

The rising demand for point-of-care testing (POCT) in disease diagnosis has made LFIA sensors based on dendritic metal thin film (HD-nanometal) and background fluorescence technology essential for rapid and accurate disease marker detection, thanks to their integrated design, high sensitivity, and cost-effectiveness. However, their unique 3D nanostructures cause significant fluorescence variation, challenging traditional image processing methods in segmenting weak fluorescence regions. This paper develops a deep learning method to efficiently segment target regions in HD-nanometal LFIA sensor images, improving quantitative detection accuracy. We propose an improved UNet++ network with attention and residual modules, accurately segmenting varying fluorescence intensities, especially weak ones. We evaluated the method using IoU and Dice coefficients, comparing it with UNet, Deeplabv3, and UNet++. We used an HD-nanoCu-Ni LFIA sensor for cardiac troponin I (cTnI) as a case study to validate the method's practicality. The proposed method achieved a 96.3% IoU, outperforming other networks. The R2 between characteristic quantity and cTnI concentration reached 0.994, confirming the method's accuracy and reliability. This enhances POCT accuracy and provides a reference for future fluorescence immunochromatography expansion.

Predicting anion diffusion in bentonite using hybrid machine learning model and correlation of physical quantities

Radionuclide diffusion will be influenced by numerous factors. Establishing a model that can elucidate the internal correlation between mesoscopic diffusion and the microscopic structure of bentonite can enhance the comprehension of radionuclide diffusion mechanisms. In this study, a light gradient boosting machine (LightGBM) was employed to predict the effective diffusion coefficients of HCrO4-, I-, and CoEDTA2- in bentonite. The model's hyperparameters were optimized using the particle swarm optimization (PSO) algorithm. Several correlated physical quantities, such as mesoscopic parameters (total porosity, rock capacity factor, and ion molar conductivity) and microscopic parameters (ionic radius and montmorillonite stacking number) were incorporated to develop a machine learning model that incorporated micro- and meso-scale features. The predictive performance of PSO-LightGBM was verified using diffusion experiments, which investigated the diffusion of HCrO4-, I-, and CoEDTA2- at compacted dry densities of 1200-1800 kg/m3 using a through-diffusion method. Spearman correlation and Shapley additive explanation analyses revealed that the compacted dry density, ionic diffusion coefficient in water, ionic radius, and total porosity were the top-four influencing factors among the 16 input features. Partial dependence plot analysis elucidated the relationship between the effective diffusion coefficient and each input feature. The analysis results were consistent with the experimental findings, demonstrating the reliability of machine learning. Due to the incorporation of multi-scale features, the PSO-LightGBM model demonstrated enhanced predictive accuracy, linking the microstructure of bentonite to radionuclide diffusion, and providing a comprehensive interpretation of the diffusion mechanism.

Multiwalled Carbon Nanotube-Templated Nickel Porphyrin Covalent Organic Framework for Pencil-Drawn Noninvasive Respiration Sensors

Paper-integrated configuration with miniaturized functionality represents one of the future main green electronics. In this study, a paper-based respiration sensor was prepared using a multiwalled carbon nanotube-templated nickel porphyrin covalent organic framework (MWCNTs@COFNiP-Ph) as an electrical identification component and pencil-drawn graphite electric circuits as interdigitated electrodes (IDEs). The MWCNTs@COFNiP-Ph not only inherited the high gas sensing performance of porphyrin and the aperture induction effect of COFs but also overcame the shielding effect between phases through the MWCNT template. Furthermore, it possessed highly exposed M-N4 metallic active sites and unique periodic porosity, thereby effectively addressing the key technical issue of room-temperature sensing for the respiration sensor. Meanwhile, the introduction of a pencil-drawing approach on common printing papers facilitates the inexpensive and simple manufacturing of the as-fabricated graphite IDE. Based on the above advantages, the MWCNTs@COFNiP-Ph respiration sensor had the characteristics of wide detection range (1-500 ppm), low detection limit (30 ppb), acceptable flexibility for toluene, and rapid response/recovery time (32 s/116 s). These advancements facilitated the integration of the respiration sensor into surgical masks and clothes with maximum functionality at a minimized size and weight. Moreover, the primary internal mechanism of COFNiP-Ph for this efficient toluene detection was investigated through in situ FTIR spectra, thereby directly elucidating that the chemisorption interaction of oxygen modulated the depletion layers, resulting in alterations in sensor resistance upon exposure to the target gas. The encouraging results revealed the feasibility of employing a paper-sensing system as a wearable platform in green electronics.

Emerging Roles of Graphitic Carbon Nitride-based Materials in Biomedical Applications

Graphite carbon nitride (g-C3N4) is a two-dimensional conjugated polymer with a unique energy band structure similar to graphene. Due to its outstanding analytical advantages, such as relatively small band gap (2.7 eV), low-cost synthesis, high thermal stability, excellent photocatalytic ability, and good biocompatibility, g-C3N4 has attracted the interest of researchers and industry, especially in the medical field. This paper summarizes the latest research on g-C3N4-based composites in various biomedical applications, including therapy, diagnostic imaging, biosensors, antibacterial, and wearable devices. In addition, the application prospects and possible challenges of g-C3N4 in nanomedicine are also discussed in detail. This review is expected to inspire emerging biomedical applications based on g-C3N4.

An antifouling electrochemical biosensor based on oxidized bacterial cellulose and quaternized chitosan for reliable detection of involucrin in wound exudate

The monitoring of biomarkers in wound exudate is of great importance for wound care and treatment, and electrochemical biosensors with high sensitivity are potentially useful for this purpose. However, conventional electrochemical biosensors always suffer from severe biofouling when performed in the complex wound exudate. Herein, an antifouling electrochemical biosensor for the detection of involucrin in wound exudate was developed based on a wound dressing, oxidized bacterial cellulose (OxBC) and quaternized chitosan (QCS) composite hydrogel. The OxBC/QCS hydrogel was prepared using an in-situ chemical oxidation and physical blending method, and the proportion of OxBC and QCS was optimized to achieve electrical neutrality and enhanced hydrophilicity, therefore endowing the hydrogel with exceptional antifouling and antimicrobial properties. The involucrin antibody SY5 was covalently bound to the OxBC/QCS hydrogel to construct the biosensor, and it demonstrated a low limit of detection down to 0.45 pg mL-1 and a linear detection range from 1.0 pg mL-1 to 1.0 μg mL-1, and it was capable of detecting targets in wound exudate. Crucially, the unique antifouling and antimicrobial capability of the OxBC/QCS hydrogel not only extends its effective lifespan but also guarantees the sensing performance of the biosensor. The successful application of this wound dressing, OxBC/QCS hydrogel for involucrin detection in wound exudate demonstrates its promising potential in wound healing monitoring.

Multi-Enzyme Cascade Nanoreactors for High-Throughput Immunoassay: Transitioning Concept in Lab to Application in Community

In this work, we reported a cholesterol oxidase (Chox)-loaded platinum (Pt) nanozyme with the collaborative cascade nanoreactor for the construction of nanozyme-enzyme-linked immunosorbent assay (N-ELSA) models to realize high-throughput rapid evaluation of cancer markers. Considering the high specific surface area and manipulable surface sites, ZIF-8 was used as a substrate for natural enzyme and nanozyme loading. The constructed ZIF-8-Pt nanozyme platform exhibited efficient enzyme-like catalytic efficiency with a standard corrected activity of 60.59 U mg-1, which was 12 times higher than that of the ZIF-8 precursor, and highly efficient photothermal conversion efficiency (∼35.49%). In N-ELISA testing, developed multienzyme photothermal probes were immobilized in microplates based on antigen-antibody-specific reactions. Cholesterol was reacted in a cascade to reactive oxygen radicals, which attacked 3,3',5,5'-tetramethylbenzidine, causing it to oxidize and color change, thus exhibiting highly enhanced efficient photothermal properties. Systematic temperature evaluations were performed by a hand-held microelectromechanical system thermal imager under the excitation of an 808 nm surface light source to determine the cancer antigen 15-3 (CA15-3) profiles in the samples. Encouragingly, the temperature signal from the microwells increased with increasing CA15-3, with a linear range of 2 mU mL-1 to 100 U mL-1, considering it to be the sensor with the widest working range for visualization and portability available. This work provides new horizons for the development of efficient multienzyme portable colorimetric-photothermal platforms to help advance the community-based process of early cancer detection.

Advances in pH Sensing: From Traditional Approaches to Next-Generation Sensors in Biological Contexts

pH has been considered one of the paramount factors in bodily functions because most cellular tasks exclusively rely on precise pH values. In this context, the current techniques for pH sensing provide us with the futuristic insight to further design therapeutic and diagnostic tools. Thus, pH-sensing (electrochemically and optically) is rapidly evolving toward exciting new applications and expanding researchers' interests in many chemical contexts, especially in biomedical applications. The adaptation of cutting-edge technology is subsequently producing the modest form of these biosensors as wearable devices, which are providing us the opportunity to target the real-time collection of vital parameters, including pH for improved healthcare systems. The motif of this review is to provide insight into trending tech-based systems employed in real-time or in-vivo pH-responsive monitoring. Herein, we briefly go through the pH regulation in the human body to help the beginners and scientific community with quick background knowledge, recent advances in the field, and pH detection in real-time biological applications. In the end, we summarize our review by providing an outlook; challenges that need to be addressed, and prospective integration of various pH in vivo platforms with modern electronics that can open new avenues of cutting-edge techniques for disease diagnostics and prevention.

Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand

Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use.

Shaping the Future of the Neurotransmitter Sensor: Tailored CdS Nanostructures for State-of-the-Art Self-Powered Photoelectrochemical Devices

Semiconductor-based photoelectrochemical (PEC) test protocols offer a viable solution for developing efficient individual health monitoring by converting light and chemical energy into electrical signals. However, slow reaction kinetics and electron-hole complexation at the interface limit their practical application. Here, we reported a triple-engineered CdS nanohierarchical structures (CdS NHs) modification scheme including morphology, defective states, and heterogeneous structure to achieve precise monitoring of the neurotransmitter dopamine (DA) in plasma and noninvasive body fluids. By precisely manipulating the Cd-S precursor, we achieved precise control over ternary CdS NHs and obtained well-defined layered self-assembled CdS NHs through a surface carbon treatment. The integration of defect states and the thin carbon layer effectively established carrier directional transfer pathways, thereby enhancing interface reaction sites and improving the conversion efficiency. The CdS NHs microelectrode fabricated demonstrated a remarkable negative response toward DA, thereby enabling the development of a miniature self-powered PEC device for precise quantification in human saliva. Additionally, the utilization of density functional theory calculations elucidated the structural characteristics of DA and the defect state of CdS, thus establishing crucial theoretical groundwork for optimizing the polymerization process of DA. The present study offers a potential engineering approach for developing high energy conversion efficiency PEC semiconductors as well as proposing a novel concept for designing sensitive testing strategies.

Portable photoelectrochemical immunoassay with micro-electro-mechanical-system for alpha-fetoprotein in hepatocellular carcinoma

Early detection of cancer has a profound impact on patient survival and treatment outcomes considering high treatment success rates and reduced treatment complexity. Here, we developed a portable photoelectrochemical (PEC) immune platform for sensitive testing of alpha-fetoprotein (AFP) based on Pt nanocluster (Pt NCs) loaded defective-state g-C3N4 photon-electron transducers. The broad forbidden band structure of g-C3N4 was optimized by the nitrogen doping strategy and additional homogeneous porous structure was introduced to further enhance the photon utilization. In addition, the in-situ growth of Pt NCs provided efficient electron transfer catalytic sites for sacrificial agents, which were used to further improve the sensitivity of the sensor. Efficient photoelectric conversion under a hand-held flashlight was determined by the geometry of the transducer and the energy band design, and the portable design of the PEC sensor was realized. The developed sensing platform exhibited a wide linear response range (0.1-50 ng mL-1) and low limit of detection (0.043 ng mL-1) for AFP under optimum conditions. This work provides a new idea for designing portable PEC biosensing platforms to meet the current mainstream POC testing needs.

Domain-Limited Sub-Nanometer Co Nanoclusters in Defective Nitrogen Doped Carbon Structures for Non-Invasive Drug Monitoring

In this work, sub-nanometer Co clusters anchored on porous nitrogen-doped carbon (C─N─Co NCs) are successfully prepared by high-temperature annealing and pre-fabricated template strategies for non-invasive sensing of clozapine (CLZ) as an efficient substrate adsorption and electrocatalyst. The introduction of Co sub-nanoclusters (Co NCs) provides enhanced electrochemical performance and better substrate adsorption potential compared to porous and nitrogen-doped carbon structures. Combined with ab initio calculations, it is found that the favorable CLZ catalytic performance with C─N─Co NCs is mainly attributed to possessing a more stable CLZ adsorption structure and lower conversion barriers of CLZ to oxidized state CLZ. An electrochemical sensor for CLZ detection is conceptualized with a wide operating range and high sensitivity, with monitoring capabilities validated in a variety of body fluid environments. Based on the developed CLZ sensing system, the CLZ correlation between blood and saliva and the accuracy of the sensor are investigated by the gold standard method and the rat model of drug administration, paving the way for non-invasive drug monitoring. This work provides new insights into the development of efficient electrocatalysts to enable drug therapy and administration monitoring in personalized healthcare systems.

Multifunctional Siloxene-Decorated Laser-Inscribed Graphene Patch for Sweat Ion Analysis and Electrocardiogram Monitoring

Potentiometric detection in complex biological fluids enables continuous electrolyte monitoring for personal healthcare; however, the commercialization of ion-selective electrode-based devices has been limited by the rapid loss of potential stability caused by electrode surface inactivation and biofouling. Here, we describe a simple multifunctional hybrid patch incorporating an Au nanoparticle/siloxene-based solid contact (SC) supported by a substrate made of laser-inscribed graphene on poly(dimethylsiloxane) for the noninvasive detection of sweat Na+ and K+. These SC nanocomposites prevent the formation of a water layer during ion-to-electron transfer, preserving 3 and 5 μV/h potential drift for the Na+ and K+ ion-selective electrodes, respectively, after 13 h of exposure. The lamellar structure of the siloxene sheets increases the SC area. In addition, the electroplated Au nanoparticles, which have a large surface area and excellent conductivity, further increased the electric double-layer capacitance at the interface between the ion-selective membranes and solid-state contacts, thus facilitating ion-to-electron transduction and ultimately improving the detection stability of Na+ and K+. Furthermore, the integrated temperature and electrocardiogram sensors in the flexible patch assist in monitoring body temperature and electrocardiogram signals, respectively. Featuring both electrochemical ion-selective and physical sensors, this patch offers immense potential for the self-monitoring of health.

The prognosis, chemotherapy and immunotherapy efficacy of the SUMOylation pathway signature and the role of UBA2 in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors worldwide. Small Ubiquitin-like Modifier (SUMO)-ylation plays a crucial role in tumorigenesis. However, the SUMOylation pathway landscape and its clinical implications in LUAD remain unclear. Here, we analyzed genes involved in the SUMOylation pathway in LUAD and constructed a SUMOylation pathway signature (SUMOPS) using the LASSO-Cox regression model, validated in independent cohorts. Our analysis revealed significant dysregulation of SUMOylation-related genes in LUAD, comprising of favorable or unfavorable prognostic factors. The SUMOPS model was associated with established molecular and histological subtypes of LUAD, highlighting its clinical relevance. The SUMOPS stratified LUAD patients into SUMOPS-high and SUMOPS-low subtypes with distinct survival outcomes and adjuvant chemotherapy responses. The SUMOPS-low subtype showed favorable responses to adjuvant chemotherapy. The correlations between SUMOPS scores and immune cell infiltration suggested that patients with the SUMOPS-high subtype exhibited favorable immune profiles for immune checkpoint inhibitor (ICI) treatment. Additionally, we identified UBA2 as a key SUMOylation-related gene with an increased expression and a poor prognosis in LUAD. Cell function experiment confirmed the role of UBA2 in promoting LUAD cell proliferation, invasion, and migration. These findings provide valuable insights into the SUMOylation pathway and its prognostic implications in LUAD, paving the way for personalized treatment strategies and the development of novel therapeutic targets.

A stretchable wearable sensor with dual working electrodes for reliable detection of uric acid in sweat

Wearable sweat sensors with stretch capabilities and robust performances are desired for continuous monitoring of human health, and it remains a challenge for sweat sensors to detect targets reliably in both static and dynamic states. Herein, a flexible sweat sensor was created using a cost-effective approach involving the utilization of three-dimensional graphene foam and polydimethylsiloxane (PDMS). The flexible electrochemical sensor was fabricated based on PDMS and Pt/Pd nanoparticles modified 3D graphene foam for the detection of uric acid in sweat. Pt/Pd nanoparticles were electrodeposited on the graphene foam to markedly enhance the electrocatalytic activity for uric acid detection. The graphene foam with excellent electrical property and high porosity, and PDMS with an ideal mechanical property endow the sensing device with high stretchability (tolerable strain up to 110 %), high sensitivity (0.87 μA μM-1 cm-2), and stability (remaining unchanged for more than 5000 cycles) for daily wear. To eliminate possible interferences, the wearable sensor was designed with dual working electrodes, and their response difference ensured reliable and accurate detection of targets. This strategy of constructing sweat sensors with dual working electrodes based on the flexible composite material represents a promising way for the development of robust wearable sensing devices.

Advice to People with Parkinson's in My Clinic: Exercise

There is compelling evidence that exercise must be part of main line therapy for people with Parkinson's disease. In this viewpoint, we outline the four key components of exercise: aerobic exercise, resistance exercise, flexibility exercise, and neuromotor exercises (posture, gait, balance, and agility) that can improve both motor and non-motor symptoms of the disease and, in the case of aerobic exercise, may delay the disease. We outline guidelines on how to change and optimize the exercise prescription at different stages of the disease.

Inspired by game theory: Multi-signal output photoelectrochemical point-of-care immunoassay based on target-triggered organic electronic barriers

This work presents an integrated photoelectrochemical, impedance and colorimetric biosensing platform for flexible detection of cancer markers based on the targeted response by combining liposome amplification strategies and target-induced non-in situ formation of electronic barriers as the signal transduction modality on carbon-modified CdS photoanodes. Inspired by game theory, the carbon layer modified CdS hyperbranched structure with low impedance and high photocurrent response was firstly obtained by surface modification of CdS nanomaterials. Through a liposome-mediated enzymatic reaction amplification strategy, a large number of organic electron barriers were formed by a biocatalytic precipitation (BCP) reaction triggered by horseradish peroxidase released from cleaved liposomes after the introduction of the target molecule, thereby increasing the impedance characteristics of the photoanode as well as attenuating the photocurrent. The BCP reaction in the microplate was accompanied by a significant color change, which opened up a new window for point-of-care testing. Taking carcinoembryonic antigen (CEA) as a proof of concept, the multi-signal output sensing platform showed a satisfactory sensitive response to CEA with an optimal linear range of 20 pg mL^-1-100 ng mL^-1. The detection limit was as low as 8.4 pg mL^-1. Meanwhile, with the assistance of a portable smartphone and a miniature electrochemical workstation, the electrical signal obtained was synchronized with the colorimetric signal to correct the actual target concentration in the sample, further reducing the occurrence of false reports. Importantly, this protocol provides a new idea for the sensitive detection of cancer markers and the construction of a multi-signal output platform.

Analytical Methodologies for the Estimation of Oxazolidinone Antibiotics as Key Members of anti-MRSA Arsenal: A Decade in Review

Gram-positive bacterial infections are among the most serious diseases related with high mortality rates and huge healthcare costs especially with the rise of antibiotic-resistant strains that limits treatment options. Thus, development of new antibiotics combating these multi-drug resistant bacteria is crucial. Oxazolidinone antibiotics are the only totally synthetic group of antibiotics that showed activity against multi-drug resistant Gram positive bacteria including MRSA because of their unique mechanism of action in targeting protein synthesis. This group include approved marketed members (tedizolid, linezolid and contezolid) or those under development (delpazlolid, radezolid and sutezolid). Due to the significant impact of this class, larger number of analytical methods were required to meet the needs of both clinical and industrial studies. Analyzing these drugs either alone or with other antimicrobial agents commonly used in ICU, in the presence of pharmaceutical or endogenous biological interferences, or in the presence of matrix impurities as metabolites and degradation products poses a big analytical challenge. This review highlights current analytical approaches published in the last decade (2012-2022) that dealt with the determination of these drugs in different matrices and discusses their advantages and disadvantages. Various techniques have been described for their determination including chromatographic, spectroscopic, capillary electrophoretic and electroanalytical methods. The review comprises six sections (one for each drug) with their related tables that depict critical figures of merit and some experimental conditions for the reviewed methods. Furthermore, future perspectives about the analytical methodologies that can be developed in the near future for determination of these drugs are suggested.

Enzyme-catalyzed high-performing reaction with in-situ amplified photocurrent on carbon-functionalized inorganic photoanode for immunosensing

An enzyme-catalyzed high-performing reaction with in-situ amplified photocurrent was innovatively designed for the quantitative screening of carcinoembryonic antigen (CEA) in biological fluids by coupling with carbon-functionalized inorganic photoanode. A split-type photoelectrochemical (PEC) immunoassay was initially executed with horseradish peroxidase (HRP)-labeled secondary antibody on the capture antibody-coated microtiter. Then, the photocurrent of carbon-functionalized inorganic photoanode were improved through enzymatic insoluble product. Experimental results revealed that introduction of the outer carbon layer on the inorganic photoactive materials caused the amplifying photocurrent because of the improving light harvesting and separation of photo-generated e^-/h⁺ pairs. Under optimum conditions, the split-type photoelectrochemical immunosensing platform displayed good photocurrent responses within the dynamic range of 0.01 - 80 ng mL^-1 CEA, and allowed the detection of CEA as low as a concentration of 3.6 pg mL^-1 at the 3S_blank level. The strong attachment of antibodies onto nano label and high-performing photoanode resulted in a good repeatability and intermediate precision down to 9.83%. No significant differences at the 0.05 significance level were encountered in the analysis of six human serum specimens between the developed PEC immunoassay and the commercially available CEA ELISA kits.

In-Time Detection of Plant Water Status Change by Self-Adhesive, Water-Proof, and Gas-Permeable Electrodes

Leaf capacitance can reflect plant water content. However, the rigid electrodes used in leaf capacitance monitoring may affect plant health status. Herein, we report a self-adhesive, water-proof, and gas-permeable electrode fabricated by in situ electrospinning of a polylactic acid nanofiber membrane (PLANFM) on a leaf, spraying a layer of the carbon nanotube membrane (CNTM) on PLANFM, and in situ electrospinning of PLANFM on CNTM. The electrodes could be self-adhered to the leaf via electrostatic adhesion due to the charges on PLANFM and the leaf, thus forming a capacitance sensor. Compared with the electrode fabricated by a transferring approach, the in situ fabricated one did not show obvious influence on plant physiological parameters. On that basis, a wireless leaf capacitance sensing system was developed, and the change of plant water status was detected in the first day of drought stress, which was much earlier than direct observation of the plant appearance. This work paved a useful way to realize noninvasive and real-time detection of stress using plant wearable electronics.