This work proposes a distributed zonotope set-membership filtering (DZSMF) to mitigate non-line-of-sight (NLOS)-induced uncertainties while ensuring the computational efficiency for robust real-time localization. First, a zonotope set-membership filtering is designed for received signal strength (RSSI)-based localization, in which process noise, measurement noise, and higher order Taylor-series residuals are jointly confined within a zonotope ensemble. The filter gain is analytically obtained by minimizing the Fradius of the posterior error zonotope. Second, a compact zonotope refinement mechanism is established by intersecting three zonotopes generated from each anchor-node group's iterative updates. These locally optimized zonotopes are subsequently integrated through a fully distributed, hierarchical information fusion strategy to produce a global state estimate accompanied by rigorous, easy-to-interpret uncertainty intervals. DZSMF uses observable sensor signals and the deployment and selection of multianchor node groups to alleviate NLOS faced in positioning. Finally, experimental results validate the accuracy and effectiveness of the proposed method. The present study represents a substantial improvement over initial estimates, with a 90.38% enhancement in performance. In comparison to existing methods, it demonstrates a 17.93%, 48.95%, 41.82%, and 1.99% improvement in terms of accuracy when evaluated against the extended kalman filtering (EKF), classical ellipsoidal set membership filtering (CESMF), interval ellipsoidal set membership filtering (IESMF), nonlinear dual set membership filtering (NDSMF) algorithms, respectively.

Polymerase chain reaction (PCR) is widely regarded as the gold standard for nucleic acid analysis; however, conventional thermal cycling limits its applicability in rapid and compact analytical systems. Here, we report an oscillating-flow microfluidic PCR method that enables rapid and flexible amplification by repeatedly shuttling the reaction mixture between two fixed-temperature zones. Unlike continuous-flow PCR, the proposed approach decouples PCR cycle number from microchannel geometry, allowing programmable cycling while reducing chip footprint. To enhance analytical reliability, polymer-assisted surface passivation using polyvinylpyrrolidone was employed to suppress nonspecific adsorption in polydimethylsiloxane (PDMS) microchannels, significantly improving amplification efficiency. Using Porphyromonas gingivalis and Treponema denticola as representative periodontal pathogens, 35-cycle amplification was completed within 20 min with reliable product yield. The proposed method advances oscillating-flow PCR toward a robust analytical strategy for rapid pathogen detection and related microfluidic nucleic acid analysis.

Accurate detection of blood cells in microscopic images plays a crucial role in automated hematological analysis and clinical diagnosis. Herein, we proposed an improved YOLOv8n-based model for efficient and precise detection of red blood cells (RBCs), white blood cells (WBCs), and platelets in the BCCD dataset. The baseline YOLOv8n framework was enhanced by integrating GhostConv and C3Ghost modules to reduce model complexity while maintaining high detection performance. A series of ablation experiments were conducted to evaluate the individual and combined effects of these modules on model accuracy and computational efficiency. Experimental results demonstrated that the baseline model achieved an mAP@0.5 of 0.9043 with 3.01 M parameters. After incorporating GhostConv, the model maintained comparable accuracy (mAP@0.5 = 0.9040) with a reduction in parameters to 2.73 M. The C3Ghost integration further decreased parameters to 1.99 M with an mAP@0.5 of 0.8973. The combined model achieved an optimal balance between accuracy (mAP@0.5 = 0.9001) and compactness (1.71 M parameters). Results indicate that the improved YOLOv8n can effectively enhance detection efficiency without sacrificing precision. The proposed lightweight detection framework provides a promising solution for real-time blood cell analysis. Its high accuracy, reduced computational load, and strong generalization ability make it suitable for integration into automated laboratory systems, facilitating rapid and intelligent medical diagnostics in hematology and related biomedical applications.

The rapid expansion of RNA therapeutics requires analytical methods that resolve molecular integrity, heterogeneity, and process-related impurities. Capillary electrophoresis (CE) provides direct RNA separation with low sample consumption and can be coupled with laser-induced fluorescence (LIF) or mass spectrometry (MS). This review assesses CE across RNA therapeutic development, release testing, and process analytics. We examine CE separation methods and platform formats for mRNA integrity and poly(A) tail analysis, size- and structure-related impurity profiling, circular RNA purity evaluation, RNA modification analysis by CE-MS, and aptamer discovery. We also discuss multi-capillary systems, microchip electrophoresis (MCE), automation, artificial intelligence (AI)-assisted data analysis, and bioprocess integration. CE is already highly useful for selected quality control tasks, especially mRNA integrity, poly(A) tail profiling, and circular RNA purity, but broader routine adoption requires improved sensitivity, standardization, method transfer, and regulatory acceptance.

Accurate DNA concentration measurement is essential for molecular biology, enabling precise quantification and optimization of various experimental protocols. While UV absorbance at 260 nm is common, fluorometric methods using fluorescent dyes such as SYBR Green I, GelRed, and SYBR Gold offer superior sensitivity and specificity, particularly for low-concentration samples. However, traditional single-channel designs suffer from cross-talk and low throughput. To address this, we developed a high-throughput, low-cost, and compact DNA concentration measurement system. The system integrates an LED array, optical filters, and eight independent light pipes for simultaneous measurement of eight samples, effectively eliminating cross-talk and enhancing both accuracy and throughput. Experimental results demonstrated that DNA/dye mixtures in 0.5× TBE buffer emitted significantly stronger fluorescence under specific LED excitations (480 nm for SYBR Green I and SYBR Gold; 520 nm for GelRed) compared to mixtures in 1× TAE or ultrapure water. For all dyes, fluorescence intensity increased with dye concentration up to an optimal point before declining. The optimal dye concentrations for DNA quantification were determined to be 3× for GelRed, 8× for SYBR Gold, and 25× for SYBR Green I. The system achieved a high correlation coefficient (R) of above 0.92 for all dyes, with SYBR Green I demonstrating the best performance (R = 0.972-0.996) and superior fluorescence stability (relative standard deviation < 2.55%), outperforming GelRed and SYBR Gold. This easy-to-assemble system, built at a cost of under $100, provides a reliable and efficient platform for parallel DNA concentration analysis. Its high throughput, excellent stability, and minimized cross-talk significantly reduce measurement time and cost, making it highly suitable for routine molecular biology laboratories and large-scale studies, ultimately improving experimental accuracy and workflow efficiency.

Parasitic infections in veterinary medicine are commonly diagnosed through microscopic examination of fecal samples, yet traditional manual methods are labor-intensive and subject to diagnostic variability. This study investigates YOLOv8 for automated identification of parasitic elements in fecal microscopy images. Six parasitic taxa were analyzed at 1000×, 2500×, and 10,000× magnifications: Spirometra eggs, Dipylidium egg packets, hookworm eggs, Ascaris eggs, Giardia cysts, and Trichomonas trophozoites. The dataset comprised 326 images with 3710 annotated objects, split at the sample level into training (70%), validation (15%), and testing (15%) sets. The YOLOv8n model achieved mean average precision (mAP@0.5) of 0.982 ± 0.015 across 5-fold cross-validation. Per-class AP exceeded 0.97 for five taxa, with Trichomonas achieving 0.952. Inference time averaged under 60 ms per image on a standard CPU. These results demonstrate that YOLOv8 provides accurate and efficient detection of diverse parasitic elements, supporting its potential as a clinical screening tool.

Capillary electrophoresis (CE) is widely used for DNA fragment analysis, but its separation performance depends strongly on the composition of the polymer sieving matrix, especially in multicapillary systems where matrix operability and reproducibility are critical. Herein, a laboratory-built multicapillary electrophoresis system was used to optimize the sieving matrix for high-throughput DNA fragment separation. The system integrated twelve fused-silica capillaries, high-voltage electrokinetic injection, fluorescence detection, and digital electropherogram acquisition. Using a 100 bp DNA ladder as the model sample, the effects of poly(ethylene oxide) (PEO), Tween 20, and glycerol on electropherogram quality were systematically investigated. PEO concentration was the dominant factor controlling the dynamic sieving network: 0.1% PEO provided insufficient separation, whereas 0.8–1.0% PEO produced clearly resolved DNA peaks. Tween 20 improved peak regularity and electropherogram quality under the tested conditions, with 0.05% providing sufficient improvement without prolonging migration time. Glycerol affected peak distribution by increasing apparent migration resistance in the polymer matrix; however, excessive glycerol slowed DNA migration and markedly extended the separation window. Considering separation quality, matrix operability, and analytical efficiency, 1.0% PEO, 0.05% Tween 20, 2.5% glycerol, 1× SYBR Gold, and 0.5× TBE were selected as the optimized sieving matrix. Under the optimized matrix, inter-capillary migration-time alignment improved the consistency of parallel capillary outputs, with corrected migration-time RSD values generally below 0.5%. The optimized formulation provides a practical basis for high-throughput CE-based DNA fragment analysis.

Capillary electrophoresis based on laser-induced fluorescence (CE-LIF) plays an important role in the analysis of nucleic acids. However, the commercial CE-LIF is not only quite expensive but also inflexible, thus hindering its widespread use in the lab. Herein, we proposed a compact, low-cost, and flexible CE-LIF system. We also investigated its stability by separating the DNA ladders. Experiments demonstrated that the relative standard error of the relative fluorescence intensity and migration time was lower than 6.2% and 1.1%, respectively. The aperture size of the light source illuminating the capillary can affect the separation performance. Smaller apertures offer higher resolution length for the adjacent DNA fragments but may reduce the number of theoretical plates. Various fluorescent dyes (e.g., SYBR Green I, Gel Green, EvaGreen) can be employed in the self-built system. The limit of detection of dsDNA was as low as 0.05 ng/μL. The working range for DNA was 0.05 ng/μL~10 ng/μL. Finally, we have successfully separated the PCR products of the target gene of Porphyromonas gingivalis and Candida albicans in the home-built CE system. Such a robust CE-LIF system is easy to assemble in the lab. The total cost of the assembled CE system did not exceed 1100 USD. We believe this work can advance the application of CE and hope it will facilitate the easy assembly of flexible CE instruments in labs.

Polymerase chain reaction (PCR) has evolved from a foundational DNA amplification tool to a sophisticated analytical platform driving precision medicine. This review highlights PCR from fundamental principles to advanced techniques and diverse applications. It summarized the development of PCR technologies including nested PCR, quantitative PCR, multiplex PCR, and various kinds of microfluidic PCR (e.g., convective PCR and continuous flow PCR) that collectively enhanced sensitivity from micrograms to single molecules. Advancements in technology have propelled the application of PCR in two key directions: (1) integration with microfluidic chips for point-of-care testing (POCT), and (2) the shift from qualitative to absolute quantification, enabling the detection of single DNA molecules and thereby advancing the field of precision medicine. Furthermore, we discussed the application of machine vision and neural networks in digital PCR systems significantly enhanced the accuracy of positive microchamber identification in chip-based analyses.

Capillary electrophoresis (CE) plays an important role in the quality control of dsDNA. So far, there has been various fluorescent dyes employed for the separation of dsDNA by CE. However, the molecular weight of the dyes may affect the mass to charge ratio of dsDNA-dye complex, consequently the separation performance of dsDNA will be changed. Herein, we systematically compared the fluorescent intensity and migration times when separating the dsDNA fragments labeled or intercalated by different dyes. Results showed that the concentration of SYBR Green I affected the migration times more than Gel Green and EvaGreen, which may be caused by the lower molecular weight of EvaGreen. The optimal concentration for SYBR Green I and Gel Green is 1×, and it is 0.005× for EvaGreen. There is linear relationship between dsDNA concentration (0.1–0.5 ng/µL) and fluorescence intensity when using SYBR Green I or Gel Green for separation. Finally, we have resolved the фX174-Hinc II digest in 0.5

Capillary electrophoresis (CE) is an effective tool for the analysis of many biocomponents, such as dsDNA, RNA, amino acids and bacteria, which are extremely important not only in research work but also in numerous practical applications. However, there are many factors that affect the separation performance, including the polymers inside the capillary, the electric field strength, the capillary coating and the effective length of the capillary. So far, various CE techniques have been developed to increase the resolution, sample volume consumption and limit of detection. To better understand the development of techniques for the separation of these biomolecules by CE, this review provides a comprehensive summary of polymers (e.g., polyvinylpyrrolidone, hydroxyethyl cellulose and polyethylene glycol), optimization methods, capillary coating methods, technological advancement of microchips for CE and the limitation of detection proposed by different groups worldwide. We also discuss the challenges and future directions associated with CE technology.

Compared with capillary electrophoresis (CE), gel electrophoresis (GE) is a traditional method for the analysis of nucleic acids because of its low cost, although the operation process is complicated. The electropherogram from CE can offer more information (e.g., DNA size and its concentration) for researchers. Based on the self-built integrated biochip GE system, we proposed a computational method that converts conventional agarose GE images into CE-like fluorescence profiles for enhanced DNA analysis. The gel images were processed using an image-based algorithm involving median filtering to remove background noise and pixel-wise intensity summation along the migration axis to generate one-dimensional records of electrophoretic separations. Each DNA band in the gel was thereby transformed into a distinct fluorescence peak, reflecting its migration distance and relative intensity. To further enhance resolution and peak separation, Gaussian modeling was applied to fit the fluorescence intensity distribution, providing smoother and more distinguishable spectral peaks. To validate the method, three periodontal pathogens—Porphyromonas gingivalis (P.g), Treponema denticola (T.d), and Tannerella forsythia (T.f)—were amplified using PCR and analyzed by gel electrophoresis. The method successfully identified distinct electrophoretic patterns for the three pathogens by using a 50 bp DNA ladder as an internal calibration reference. The results demonstrate that image-based reconstruction of electrophoretic data provides a reliable, quantitative, and visually interpretable representation of DNA migration, comparable to CE output. This approach bridges a gap between traditional GE and modern capillary systems, allowing for the semi-quantitative analysis of DNA fragments without specialized CE instrument. The proposed method offers a valuable analysis method for the separation of DNA, RNA, protein and polypeptides.

Received Signal Strength Indicator (RSSI)-based indoor localization is attractive for its low cost and ease of deployment, yet its accuracy is often compromised by multipath propagation, non-line-of-sight conditions, and environmental noise. Traditional filtering approaches, such as Kalman and particle filters, rely on precise noise models and struggle under sparse sensing or model mismatch. In this study, we recast RSSI localization as a sequential state estimation problem and propose a reinforcement learning-based reduced-order estimator (RL-ROE). By combining model-driven dynamics with data-driven policy correction, the method achieves improved accuracy and robustness under noisy indoor environments, demonstrating its potential as a scalable solution for real-time localization in wireless sensor networks.

In most existing indoor localization techniques based on the Received Signal Strength Indicator (RSSI), location accuracy and the impact of computational complexity on system performance are major concerns. To improve computational efficiency and reduce potential inaccuracies, the Nonlinear Dual Set-Membership Filtering (NDSMF) is proposed. Firstly, the critical parameters of transmit power and path loss exponent are estimated by a multi-objective optimization algorithm in RSSI-based localization. Secondly, to avoid the errors and high computational complexity caused by the direct linearization of the nonlinear system and themultiple solution of the Semi-Definite Program (SDP) during the filtering iteration process, a NDSMF is designed based on the principles of strong duality and set theory to determine the ellipsoid set containing the optimal estimate of the target node. Then, based on the designed set-membership filter, a new ellipsoid-based fusion scheme is developed to prove that there exists a smaller and better performing intersection ellipsoid set than all local ellipsoid sets. Finally, simulations and experiments are presented to validate the accuracy and effectiveness of the proposed algorithms. Under the same experimental scenario, the proposed algorithm achieves 47.6% and 58.9% improvement in localization accuracy compared to the currently mainstream NSMF and ESMF algorithms, respectively.

Polymerase chain reaction (PCR) has been considered as the gold standard for detecting nucleic acids. The simple PCR system is of great significance for medical applications in remote areas, especially for the developing countries. Herein, we proposed a low-cost self-assembled platform for microchamber PCR. The working principle is rotating the chamber PCR microfluidic chip between two heaters with fixed temperature to solve the problem of low temperature variation rate. The system consists of two temperature controllers, a screw slide rail, a chamber array microfluidic chip and a self-built software. Such a system can be constructed at a cost of about US60. The micro chamber PCR can be finished by rotating the microfluidic chip between two heaters with fixed temperature. Results demonstrated that the sensitivity of the temperature controller is 0.1℃. The relative error of the duration for the microfluidic chip was 0.02 s. Finally, we successfully finished amplification of the target gene of Porphyromonas gingivalis in the chamber PCR microfluidic chip within 35 min and on-site detection of its PCR products by fluorescence. The chip consisted of 3200 cylindrical chambers. The volume of reagent in each volume is as low as 0.628 nL. This work provides an effective method to reduce the amplification time required for micro chamber PCR.

Diabetes is a common chronic Metabolic disorder, and its treatment has always been a hot research topic in the medical field. Traditional Chinese medicine has a significant therapeutic effect on it and has become one of the important ways for patients to choose treatment methods. In recent years, with the development and application of deep learning technology, more and more research has begun to attempt to use deep learning models to process traditional Chinese medicine case data, achieving automated information extraction and analysis. This study shows that BERT model and jieba segmentation algorithm have good effect in the study of the relationship between symptoms and Medication in diabetes medical record data, which is expected to provide scientific basis and reference for clinical treatment of traditional Chinese medicine.

Continuous-flow PCR (CF-PCR) can realize rapid DNA amplification because of the high temperature variation rate. However, off-line detection methods for PCR may induce cross contamination. To overcome this problem, we herein fabricated an integrated CF-PCR and electrophoresis microfluidic chip. The optimal voltage applied in the electrophoresis part of the microfluidic chip was achieved by simulation in COMSOL. Coating the inside wall of the microchannel can inhibit electroosmotic flow and improve the resolution for DNA fragments. The temperature distribution of the serpentine part can meet the PCR and has no obvious suppressive effect on sample separation. Finally, we have performed the amplification of target genes for Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola and detected the corresponding PCR products in the microfluidic chip within 11 min. Such work provides a new method for the rapid detection of bacteria.

The indoor moving target localization based on the received signal strength indicator is studied in this paper. By introducing filtering algorithms, a novel dynamic parameters set-membership filtering (DPSMF) is proposed to reduce the interference of the multipath effect and noises. First, the higher-order remainder is included in the bounded range based on the interval mathematics method to improve the linearization accuracy of the Taylor series expansion of the measurement function. Second, the parameters to be determined in the channel model are set to local dynamic parameters, which are obtained by solving a particular semi-definite programming problem. Third, the optimized localization estimations of the target node at each sampling instant and the ellipsoid set containing the real state of the system are obtained based on the DPSMF. Finally, an example is given to verify the effectiveness of the proposed DPSMF algorithm.

With the development of smart hardware devices and the increasing demand for indoor positioning, Wi-Fi Re-ceived Signal Strength Indicator (RSSI)-based fingerprinting has become a simple and low-hardware requirement method for indoor positioning. However, in complex indoor environments, signal attenuation and multipath effects can severely interfere with wireless signal propagation, thereby reducing positioning accuracy. This paper proposes a new positioning scheme that uses an EfficientNetV2 classification model and a Convolutional Neural Network (CNN) regression model. The scheme first converts RSSI values into feature maps, then uses the EfficientNetV2 model to classify the wall where the drone is located, narrowing down the positioning area. Then the feature maps are input into the proposed CNN model to estimate three-dimensional coordinates and improve positioning accuracy. Experimental results show that the proposed method achieves an average error of 2.01 meters in three-dimensional positioning Euclidean distance, compared to 2.14 meters for the traditional method, resulting in a 6.07 % improvement in positioning accuracy. These results demonstrate the advantages of the new method in overcoming signal interference in indoor environments and effectively improving positioning accuracy.

Polymerase chain reaction (PCR) is a traditional method employed for the amplification of a target gene that has played an important role in biomolecular diagnostics. However, traditional PCR is very time-consuming because of the low-temperature variation efficiency. This work proposes a continuous-flow-PCR (CF-PCR) system based on a microfluidic chip. The amplification time can be greatly reduced by running the PCR solution into a microchannel placed on heaters set at different temperatures. Moreover, as capillary electrophoresis (CE) is an ideal way to differentiate positive and false-positive PCR products, a CE system was built to achieve efficient separation of the DNA fragments. This paper describes the process of amplification of Escherichia coli (E. coli) by the CF-PCR system built in-house and the detection of the PCR products by CE. The results demonstrate that the target gene of E. coli was successfully amplified within 10 min, indicating that these two systems can be used for the rapid amplification and detection of nucleic acids.