Detection Capability Research Articles

Towards an integrated imaging for melanoma diagnosis: A review of multispectral, hyperspectral, and thermal technologies with preliminary system development.

The diagnosis of melanoma traditionally relies on visual inspection or on the use of the dermoscope, which do not have capabilities for early and precise detection. In this review, we aimed to explore other imaging technologies that can provide non-invasive and detailed information on skin lesions, such as multispectral, hyperspectral and thermal imaging. In this regard, the systems were evaluated in terms of hardware, performance and clinical applications. Since there is currently a very big interest in developing artificial intelligence (AI) applications in dermatology, the review also focuses on analysing studies that integrated this technology with newer imaging systems. To obtain clinical validation for such systems, there is an extensive need for publicly available datasets, as the current ones are limited. Expanding and obtaining new datasets is crucial in advancing research for a more accurate melanoma diagnosis. Taking into consideration the benefits that these imaging modalities can provide if they are combined with AI, we propose a prototype that can distinguish between melanoma and its precursor, the nevus, for which the set-up, components, imaging processing pipeline and classification techniques are described. The final system benefits of the advantages provided by near infrared, thermal and visible cameras, that allow a more in-depth characterizations of melanoma for a better understanding of its behaviour, an early detection improvement and diagnostic precision.

Enhancing the detection efficiency of the 3D positive ion detector

Enhancing the 3D positive ion detector's efficiency is the objective of this study. The detector finds numerous applications in the fields of radiation biology, radiation dosimetry, radiation protection, radiation measurement etc. Generally, in 3D positive ion detector the cathode used is either high resistive glass or gold coated over ceramic glass. The detector efficiency has been reported as 2%max with high resistive glass cathode material and 9.2%max with gold coated ceramic glass. In the present study, we have significantly improved the detection efficiency by replacing these materials with an oxygen-free high conductivity (OFHC) copper cathode. This modification resulted in a substantial increase in the detector efficiency, reaching a maximum of 31.3%max. The study also presents a comparison between this enhanced performance and the previously reported efficiencies demonstrating the effectiveness of using high conductivity materials to improve ion detection capabilities.

EpiDCA: Adaptation and implementation of a danger theory algorithm for event-based epidemiological surveillance

Amidst the overwhelming volume of health-related data available, diverse epidemiological surveillance strategies have been adopted to swiftly detect outbreak events. These strategies differ in terms of structure, type, and sources used. When combined, they offer a more comprehensive understanding of epidemiological events then when used alone. In this paper, we propose an unsupervised approach that allows epidemiological data to be combined with risk factors related to disease onset. We applied this method, named EpiDCA, to enhance the classification and early detection capabilities of Event-Based Surveillance (EBS) systems.EpiDCA is an adaptation of the Dendritic Cells Algorithm (DCA) inspired by the danger theory. The DCA has been applied in various studies and has shown promising results in real-time and binary classification problems. However, some stochastic elements in the algorithm, such as the random sampling and the migration threshold in the detection phase, have been criticized. To overcome these limitations, we integrated spatio-temporal information into the method. We then applied EpiDCA to an avian influenza case study and evaluated the results. These were very promising, and comparable to well-known, supervised baseline methods.

A sensing system and solving method for dynamic detection of relative pose of hydraulic support group

The precise detection of the pose of hydraulic support groups is crucial for the construction of intelligent coal mines. Addressing current challenges in comprehensive position sensing of support groups, such as one-sided descriptions, missing information, and limited detection capabilities, this paper, based on the concept of digital twin, proposes a sensing system and solving method for dynamic detection of the relative pose of hydraulic support group. Initially, a relative pose detection model, based on the six-point localization principle, is proposed by combining existing sensor information. Then, a sensing system is designed, incorporating a photoelectric detection device for monitoring key position parameters and virtual sensors for determining pose information. Consequently, a method for dynamically deriving relative positions, driven by the fusion of real-virtual iterative solving, real-virtual error feedback, and iterative condition optimization, is established. Finally, a relative pose dynamic detection system for a hydraulic support prototype is constructed in a laboratory environment. Experimental results demonstrate that the sensor system and the relative position detection method designed in this paper achieve a relative position accuracy of over 95.32 % for the hydraulic support, with a dynamic reconstruction time of less than 0.2 s. This verifies the rationality of the sensor system and the feasibility and accuracy of the dynamic deduction method. To sum up, this work provides a theoretical foundation and technical support for the autonomous perception of hydraulic support group, autonomous frame adjustment between frames, and overall straightness adjustment. It also lays the groundwork for further research and the advancement of information-physical systems in the fully-mechanized mining face.

Practical SERS Substrates by Spray Coating of Silver Solutions for Deep Learning-Assisted Sensitive Antigen Identification

Surface-enhanced Raman spectroscopy (SERS) has long been recognized for its rapid and sensitive detection capabilities; however, challenges persist in practical fabrication of the substrates and interpreting complex data. Herein, we propose a deep learning (DL) assisted SERS approach to enable rapid and sensitive detection of analytes on a practical yet highly effective substrates prepared by direct spray-coating of a nanoparticle-free true solution of a reactive Ag ink and on-site thermal annealing mediated generation of nanostructures. This design ensured homogeneous distribution of Ag nanostructures throughout the entire substrate, significantly increasing the number of hotspots and enhancing the Raman signals, thereby achieving an impressive analytical enhancement factor of ~1010 in a reproducible and consistent manner. The diagnostic utility of this platform was demonstrated by detecting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein in both buffer and saliva, with detection limits of 74.3pg/mL and 7.43ng/mL, respectively. The DL-assisted SERS not only accurately identified the presence or absence of viral antigen, but also automatically quantified the viral load. This automatic identification achieved an outstanding accuracy of ~99.9%, highlighting the exceptional performance of the proposed platform. This simple, cost-effective, scalable, and ultra-sensitive DL-assisted SERS platform offers significant opportunities for early and precise detection in a range of analytical scenarios.

Ultrasensitive and label-free electrochemical immunosensor using gold nanoparticles deposited on a carbon electrode for the quantification of osteopontin: A serum-based oncomarker.

Early detection of cancer biomarkers is crucial for effective diagnosis and treatment, prompting the development of an ultrasensitive label-free electrochemical immunosensor. In this study, we fabricated an ultrasensitive label-free electrochemical immunosensor using a glassy carbon electrode/gold nanoparticles (GCE/AuNPs) modification for quantification of osteopontin (OPN), an oncomarker. The surface features of the modified electrodes were confirmed using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) methods. The electrochemical behavior of the bare and modified electrode was characterized using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The quantification of the OPN antigen was achieved through the differential pulse voltammetry (DPV) method. The fabricated immunosensor demonstrated excellent detection capabilities in both commercial serum samples and phosphate-buffered saline (PBS). It showed sensitive quantification of OPN in the range of 0.001 to 1000ng/mL with a limit of detection (LOD) of 0.005ng/mL in PBS. Furthermore, the immunosensor retained approximately 89.3% of its initial signal after storage for up to 8weeks. The results were validated by detecting OPN-spiked commercial serum samples with a satisfactory recovery rate. The potential of this immunosensor makes it suitable for assaying OPN in real cancer patient's serum samples with minimal interference from complex sample matrices.

Advanced cloud-based real-time credit scoring models: Leveraging big data and AI

Cloud-based credit scoring systems represent a transformative shift in financial technology, leveraging artificial intelligence and machine learning to revolutionize creditworthiness assessment. These platforms integrate traditional and alternative data sources, enabling real-time processing and comprehensive risk evaluation. The evolution from conventional methods has enhanced accessibility for underserved populations while maintaining robust security measures and regulatory compliance. Through advanced technical architecture incorporating data collection, processing, and analysis layers, these systems deliver faster, more accurate credit decisions. The implementation of sophisticated fraud detection mechanisms, coupled with machine learning algorithms, has significantly reduced false positives and improved risk assessment capabilities. Industry leaders like TransUnion demonstrate the practical success of these innovations through improved operational efficiency and expanded market reach. Despite challenges in data quality management and regulatory compliance, the future of credit scoring continues to advance with blockchain integration, enhanced real-time processing, and innovative fraud detection capabilities.

Diagnosis of Abdominal Diseases Affecting Major Organs Using CT Image and YOLOV8

This study investigates the YOLOv8 method, a popular object detection model, to detect abnormalities in abdominal CT scans. Our study leverages the sophisticated architecture and point-of- care detection capabilities of YOLOv8 to show that the model improves diagnostic accuracy and helps radiologists quickly identify potential panic cases.

Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation.

Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address manual QCA's limitations in reproducibility and correction process. The present study aimed to assess the performance of an updated AI-QCA solution (MPXA-2000) in lesion detection and quantification using manual QCA as the reference standard, and to demonstrate its superiority over visual estimation. This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard. A lesion was classified as 'detected' when the minimum lumen diameter (MLD) identified by manual QCA fell within the boundaries of the lesion delineated by AI-QCA or visual estimation. The detected lesions were evaluated in terms of diameter stenosis (DS), MLD, and lesion length (LL). AI-QCA accurately detected lesions with a sensitivity of 93% (1705/1828) and showed strong correlations with manual QCA for DS, MLD, and LL (R² = 0.65, 0.83 and 0.71, respectively). In views targeting the major vessels, the proportion of undetected lesions by AI-QCA was less than 4% (56/1492). For lesions in the side branches, AI-QCA also demonstrated high sensitivity (> 92%) in detecting them. Compared to visual estimation, AI-QCA showed significantly better lesion detection capability (93% vs. 69%, p < 0.001), and had a higher probability of detecting all lesions in images with multiple lesions (86% vs. 33%, p < 0.001). The updated AI-QCA demonstrated robust performance in lesion detection and quantification without operator intervention, enabling reproducible vessel analysis. The automated process of AI-QCA has the potential to optimize angiography-guided interventions by providing quantitative metrics.

Bioinspired Conductivity-Enhanced, Self-Healing, and Renewable Silk Fibroin Hydrogel for Wearable Sensors with High Sensitivity.

The development of silk fibroin-based hydrogels with excellent biocompatibility, aqueous processability, and facile controllability in structure is indeed an exciting advancement for biological research and strain sensor applications. However, silk fibroin-based hydrogel strain sensors that combine high conductivity, high stretchability, reusability, and high selectivity are still desired. Herein, we report a simple method for preparing double-network hydrogels including silk fibroin and poly(acrylic acid) sodium-polyacrylate (PAA-PAAS) networks. The conformation and aggregate of silk fibroin could be facilely tuned by both ions and pH resulting from the PAA-PAAS network. The optimized hydrogel exhibits intriguing properties, such as high conductivity (3.67 S/m) and transparency, high stretchability (1186%) with a tensile strength of 110 kPa, good adhesion properties, reversible compression, self-healing, and high sensitivity (GF = 10.71). This hydrogel strain sensor can detect large-scale and small human movements in real time, such as limb movements, heartbeats, and pulse. Additionally, its ability to adsorb water and recover effectiveness after losing water from air with 90% humidity along with the capability for low-temperature motion detection facilitated by ethylene glycol further enhance its practical utility. This work offers a novel and simple approach to design flexible bionic strain sensors.

Research on new energy power plant network traffic anomaly detection method based on EMD

OverviewAs Photovoltaic (PV) systems connect into the grid and depend on digital technology, risks develop from obsolete components, insufficient security measures, and insecure access points.ObjectivesImprove the safety and dependability of the main data communication network by conducting research on Proactive Coordinated Fault-Tolerant Federation (PCFT) security structure, building an early warning simulation to handle power data interactions network abnormalities, studying algorithms and technologies for abnormal traffic control, and effectively managing abnormal network traffic like DDoS, network scanning, along with surge traffic; Improve the capability of the SLA hierarchical service, enhance the service quality of the core services executed through the backbone network, as well as strengthen the security capability of the access system for the new energy power plant’s communication network by combing and analyzing the traffic of the main network of the current information communication network.MethodologyThis research propose Network Quality Assessment (NQA) traffic management algorithms to prevent illegal access and data breaches, this involves strong security measures such as encryption, firewalls, and encrypted communication methods. To maximize the efficiency of solar energy systems and allow for prompt maintenance, our suggested framework provides a practical and dependable method for detecting anomalies in PV cells in real-time. The incorporation of these state-of-the-art convolutional methods into the CNN-GRU model enhances detection capabilities and opens up new avenues for exploration in the realm of anomaly detection based on deep learning.ResultsThe grid deployment of large-scale PV power facilities relies heavily on dependable communication networks. The efficiency of PV power plants and their ability to meet application requirements are both impacted by the communication infrastructures that are responsible for real-time monitoring. Presenting simulations of the communication networks of the PV power system, this research sought to evaluate possible futures of PV power plants. We test our model on a massive PV cell dataset and show that it outperforms state-of-the-art approaches in terms of resilience, speed, and accuracy of identification.

Evaluating Pipeline Inspection Technologies for Enhanced Corrosion Detection in Mining Water Transport Systems

Water transport pipelines in the mining industry face significant corrosion challenges due to extreme environmental conditions, such as arid climates, temperature fluctuations, and abrasive soils. This study evaluates the effectiveness of three advanced inspection technologies—Guided Wave Ultrasonic Testing (GWUT), Metal Magnetic Memory (MMM), and In-Line Inspection (ILI)—in maintaining pipeline integrity under such conditions. A structured methodology combining diagnostic assessment, technology research, and comparative evaluation was applied, using key performance indicators like detection capability, operational impact, and feasibility. The results show that GWUT effectively identifies surface anomalies and wall thinning over long pipeline sections but faces depth and diameter limitations. MMM excels at detecting early-stage stress and corrosion in inaccessible locations, benefiting from minimal preparation and strong market availability. ILI provides comprehensive internal and external assessments but requires piggable pipelines and operational adjustments, limiting its use in certain systems. A case study of critical aqueducts of mining site water supply illustrates real-world technology selection challenges. The findings underscore the importance of an integrated inspection approach, leveraging the complementary strengths of these technologies to ensure reliable pipeline integrity management. Future research should focus on quantitative performance metrics and cost-effectiveness analyses to optimize inspection strategies for mining infrastructure.

A Hybrid Framework for Securing 5G-Enabled Healthcare Systems

The rapid adoption of 5G technology in healthcare introduces significant challenges regarding data privacy and security. This paper proposes a hybrid framework integrating blockchain, zero-trust architecture (ZTA), and AI-driven threat detection to address these challenges. Blockchain ensures secure, tamper-proof data storage, while ZTA strengthens access control by continuously verifying users and devices. AI contributes by providing real-time threat detection and dynamic response capabilities, making the system more resilient to evolving cyber risks. A systematic literature review was conducted to analyze existing frameworks and identify gaps in 5G healthcare security. The findings reveal that while individual technologies such as blockchain and ZTA are well-established, their integration into a cohesive framework remains underexplored. The proposed hybrid solution effectively mitigates the risks associated with 5G networks by offering a multi-layered security approach. This research contributes to the field by proposing a scalable, adaptable security model suitable for 5G-enabled healthcare systems. Future research should focus on empirical validation, scalability testing, and exploring lightweight alternatives to blockchain and AI for resource-constrained environments. Additionally, investigating the integration of emerging technologies like quantum computing and 6G networks will further enhance the framework’s security capabilities. This study provides a foundation for developing secure, privacy-preserving systems for healthcare in the 5G era.

Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis.

Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation. This study aimed to evaluate the impact of an AI-assisted diagnostic system on the diagnostic efficiency of radiologists. It specifically examined the report modification rates and missed and misdiagnosed rates of junior radiologists with and without AI assistance. We obtained effective data from 12,889 patients in 2 tertiary hospitals in Beijing before and after the implementation of the AI system, covering the period from April 2018 to March 2022. Diagnostic reports written by both junior and senior radiologists were included in each case. Using reports by senior radiologists as a reference, we compared the modification rates of reports written by junior radiologists with and without AI assistance. We further evaluated alterations in lung nodule detection capability over 3 years after the integration of the AI system. Evaluation metrics of this study include lung nodule detection rate, accuracy, false negative rate, false positive rate, and positive predictive value. The statistical analyses included descriptive statistics and chi-square, Cochran-Armitage, and Mann-Kendall tests. The AI system was implemented in Beijing Anzhen Hospital (Hospital A) in January 2019 and Tsinghua Changgung Hospital (Hospital C) in June 2021. The modification rate of diagnostic reports in the detection of lung nodules increased from 4.73% to 7.23% (χ21=12.15; P<.001) at Hospital A. In terms of lung nodule detection rates postimplementation, Hospital C increased from 46.19% to 53.45% (χ21=25.48; P<.001) and Hospital A increased from 39.29% to 55.22% (χ21=122.55; P<.001). At Hospital A, the false negative rate decreased from 8.4% to 5.16% (χ21=9.85; P=.002), while the false positive rate increased from 2.36% to 9.77% (χ21=53.48; P<.001). The detection accuracy demonstrated a decrease from 93.33% to 92.23% for Hospital A and from 95.27% to 92.77% for Hospital C. Regarding the changes in lung nodule detection capability over a 3-year period following the integration of the AI system, the detection rates for lung nodules exhibited a modest increase from 54.6% to 55.84%, while the overall accuracy demonstrated a slight improvement from 92.79% to 93.92%. The AI system enhanced lung nodule detection, offering the possibility of earlier disease identification and timely intervention. Nevertheless, the initial reduction in accuracy underscores the need for standardized diagnostic criteria and comprehensive training for radiologists to maximize the effectiveness of AI-enabled diagnostic systems.

Highly scalable solid-core inhibited-coupling fiber-based plasmonic refractive index sensor.

Advancements in plasmonic sensing require simultaneous detection capability that ensures large-scale detection with reduced losses. In this work, we propose a new solid-core fiber-based refractive index (RI) sensor with an ultra-broad detection range. The proposed fiber consists of a relatively simple single-ring cladding with six circular tubes in which the light is guided in the core based on the inhibited-coupling (IC) mechanism. The sensing performance is investigated using extensive finite-element modeling (FEM) through the combination of IC and surface plasmon resonance (SPR) sensing technology. Our results show a low loss of <3 dB/cm across the RI detection range from 1 to 1.60, peaking the wavelength sensitivity (WS) of 3000 nm/RIU and figure of merit (FOM) of 120 RIU-1. Our study also includes the investigation of the fabrication tolerance, fiber bending, and the use of alternative plasmonic materials, providing insights into the practical implementation capability of the proposed sensor. Our findings highlight the potential of the proposed sensor in emerging applications such as detecting air pollutants, biochemical substances, and DNA, paving the way towards bio-sensing within a lab-on-a-chip platform.

Investigating Raman peak enhancement in carboxyl-rich molecules: insights from Au@Ag core-shell nanoparticles in colloids

Surface-enhanced Raman scattering (SERS) is a promising analytical technique with applications in environmental monitoring, healthcare, and the biopharmaceutical industry. While SERS has been successfully applied to molecules such as 4-mercaptobenzoic acid and other thiol- and amine-containing compounds, there is limited research on its detection capabilities for molecules rich in carboxyl groups or unsaturated bonds, such as citric acid. This study investigates the SERS enhancement of Au@Ag core-shell nanoparticles in response to citric acid and other molecules with carboxyl and unsaturated bonds. We compare the SERS behavior of nanoparticles in freshly prepared and aged sodium citrate solutions to identify differences in Raman peak enhancement. Our findings show that the Au@Ag core-shell nanoparticles exhibit significant SERS enhancement when exposed to citric acid and other related compounds. The enhancement varies based on the age of the sodium citrate solution, which influences the structural properties of the nanoparticles. This work opens avenues for further research and applications in biological monitoring, environmental testing, and the pharmaceutical industry.

Scalable Similarity-Aware Test Suite Minimization with Reinforcement Learning

The Multi-Criteria Test Suite Minimization (MCTSM) problem aims to remove redundant test cases, guided by adequacy criteria such as code coverage or fault detection capability. However, current techniques either exhibit a high loss of fault detection ability or face scalability challenges due to the NP-hard nature of the problem, which limits their practical utility. We propose TripRL, a novel technique that integrates traditional criteria such as statement coverage and fault detection ability with test coverage similarity into an Integer Linear Program (ILP), to produce a diverse reduced test suite with high test effectiveness. TripRL leverages bipartite graph representation and its embedding for concise ILP formulation and combines ILP with effective reinforcement learning (RL) training. This combination renders large-scale test suite minimization more scalable and enhances test effectiveness. Our empirical evaluations demonstrate that TripRL’s runtime scales linearly with the magnitude of the MCTSM problem. Notably, for large test suites from the Defects4j dataset where existing approaches fail to provide solutions within a reasonable time frame, our technique consistently delivers solutions in less than 47 minutes. The reduced test suites produced by TripRL also maintain the original statement coverage and fault detection ability while having a higher potential to detect unknown faults.

Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.

The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia--seeing faces in inanimate objects. Despite extensive research, it remains unclear why the visual system employs such broadly tuned face detection capabilities. We hypothesized that face pareidolia results from the visual system's optimization for recognizing both faces and objects. To test this hypothesis, we used task-optimized deep convolutional neural networks (CNNs) and evaluated their alignment with human behavioral signatures and neural responses, measured via magnetoencephalography (MEG), related to pareidolia processing. Specifically, we trained CNNs on tasks involving combinations of face identification, face detection, object categorization, and object detection. Using representational similarity analysis, we found that CNNs that included object categorization in their training tasks represented pareidolia faces, real faces, and matched objects more similarly to neural responses than those that did not. Although these CNNs showed similar overall alignment with neural data, a closer examination of their internal representations revealed that specific training tasks had distinct effects on how pareidolia faces were represented across layers. Finally, interpretability methods revealed that only a CNN trained for both face identification and object categorization relied on face-like features-such as 'eyes'-to classify pareidolia stimuli as faces, mirroring findings in human perception. Our results suggest that human-like face pareidolia may emerge from the visual system's optimization for face identification within the context of generalized object categorization.

A 64 × 1 Multi-Mode Linear Single-Photon Avalanche Detector with Storage and Shift Reuse in Histogram

Single-photon avalanche detectors (SPADs) have significant applications in fields such as autonomous driving. However, processing massive amounts of background data requires substantial storage and computational resources. This paper designs a linear SPAD sensor capable of three detection modes: 2D intensity detection, 3D synchronous detection, and 3D asynchronous detection. A configurable coincidence circuit is used to effectively suppress background light. To overcome the significant resource demands for storage and computation, this paper designs a histogram circuit that simultaneously possesses data storage and shifting capabilities. This circuit can not only perform statistical counting on time data but also shift data to quickly complete computational analysis. The chip is fabricated using a 0.13 μm mixed-signal CMOS process, with a pixel scale of 64 elements, a time resolution of 132 ps, and a power consumption of 12.9 mW. Test results indicate that the chip has good detection capabilities and good background light suppression. When the background light intensity is 6000 lux, the maximum background data are suppressed by 95.4%, and the average suppression rate increases to 86% as the coincidence threshold is raised from 0 to 1.

Hydrogen-Bonded Organic Framework Films Integrated with Wavy Structured Design for Wearable Bioelectronics.

The integration of hydrogen-bonded organic frameworks (HOFs) with flexible electronic technologies offers a promising strategy for monitoring detailed health information, owing to their inherent porosity, excellent biocompatibility, and tunable catalytic capabilities. However, their application in wearable and real-time health monitoring remains largely unexplored, primarily due to the mechanical mismatch between the traditionally fragile HOFs particles and the softness of human skin. Herein, this study demonstrates an epidermal biosensor that maintains reliable sensing capability even under extreme deformation and complex environmental conditions by integrating HOFs films with wavy bioelectrodes. This wearable biosensor demonstrates ultrasensitive detection capabilities, with a limit of detection of 49.64nM, and accurately measures nutritional content in sweat while conforming to curved skin surfaces. The sensor's performance is comparable to those obtained using high-performance liquid chromatography (HPLC). More strikingly, scratched HOFs films can be regenerated through a simple solvent rinsing process, enabling their reuse in the fabrication of new biosensors and offering a significant advantage over conventional sensing materials. This work has the potential to inspire the development of more flexible electronic devices, leveraging the structural adaptability and diversity of HOFs for personalized healthcare applications and real-time health monitoring.