Genome-wide QTL and eQTL mapping reveal genes associated with growth rate trait of the Pacific white shrimp (Litopenaeus vannamei).

BACKGROUND: Growth rate is a crucial economic trait for farmed animals, but the genetic regulation of this trait is largely unknown in non-model organisms such as shrimp. RESULTS: In this study, we performed genome-wide phenotypic quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) mapping analyses to identify genes affecting the growth rate of Pacific white shrimp (Litopenaeus vannamei), which is the most commercially-farmed crustacean worldwide. We used RNA-sequencing of 268 individuals in a mapping population, and subsequently validated our findings through gene silencing and shrimp growth experiments. We constructed a high-density genetic linkage map comprising 5533 markers spanning 44 linkage groups, with a total distance of 6205.75 cM and an average marker interval of 1.12 cM. Our analyses identified 11 QTLs significantly correlated with growth rate, and 117,525 eQTLs. By integrating QTL and eQTL data, we identified a gene (metalloreductase STEAP4) highly associated with shrimp growth rate. RNA interference (RNAi) analysis and growth experiments confirmed that STEAP4 was significantly correlated with growth rate in L. vannamei. CONCLUSIONS: Our results indicate that the comprehensive analysis of QTL and eQTL can effectively identify genes involved in complex animal traits. This is important for marker-assisted selection (MAS) of animals. Our work contributes to the development of shrimp breeding and available genetic resources.

A Novel Angle Segmentation Method for Magnetic Encoders Based on Filtering Window Adaptive Adjustment Using Improved Particle Swarm Optimization.

In this paper we outline newly-developed control algorithms, designed to achieve high-precision feedback for a motor control system using a magnetic encoder. The magnetic encoder, combing single-pole and multi-pole magnetic steels, was adopted to extend the resolution of the magnetic encoder. First, with a view to settling the issue of the jump points of the multi-pole angle value at the convergence of two adjacent magnetic poles, the angle segmentation method, which uses the window filter discrimination method, is employed to determine the actual angle value. The appropriate filter window width is selected via the improved particle swarm optimization (IPSO) algorithm, and an expanded resolution is achieved. Second, a compensation table is completed via a linear compensation algorithm based on virtual cutting to enhance the accuracy of the combined magnetic encoder. On this basis, a linear difference algorithm is used to achieve deviation correction of the angle. Finally, the jump points can be restrained effectively via the angle segmentation method. The resolution reaches 0.05°, and the accuracy is 0.045°.

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion.

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

Establishment, FEM analysis and experimental validation of tooth movement prediction model of orthodontic archwire T-loop.

BACKGROUND: The T-loop has been used clinically to close gap between teeth. And it is a typical orthodontic archwire bending method. However, the design of the T-loop parameters for different patients is based on the clinical experience of the dentists. The variation in dentists' clinical experience is the main reason for inadequate orthodontic treatment, even high incidence of postoperative complications. METHODS: Firstly, the tooth movement prediction model is established based on the analysis of the T-loop structure and the waxy model dynamic resistance. As well as the reverse reconstruction of the complete maxillary 3D model based on the patient CBCT images, the oral biomechanical FEM analysis is completed. A maxillary waxy dental model is manufactured to realize the water-bath measurement experiment in vitro mimicking the oral bio-environment. Thus, the calculated, simulation and experimental data are obtained, as well as obtaining a cloud of total deformation from the simulation analysis. RESULTS: The growth trend of the 11 sets of simulation data is the same as that of the experimental data. And all of them show that the tooth displacement is positively correlated with the cross-sectional size of the archwire, and the clearance distance. As well as the higher Young's modulus of the archwire material, the greater the tooth displacement. And the effect of archwire parameters on tooth displacement derived from simulation and experimental data is consistent with the prediction model. The experimental and calculated data are also compared and analyzed, and the two kinds of data are basically consistent in terms of growth trends and fluctuations, with deviation rates ranging from 2.17 to 10.00%. CONCLUSIONS: This study shows that the accuracy and reliability of the tooth movement prediction model can be verified through the comparative analysis and deviation calculation of the obtained calculated, simulation and experimental data, which can assist dentists to safely and efficiently perform orthodontic treatment on patients. And the FEM analysis can achieve predictability of orthodontic treatment results.

[Classification Model of Corneal Opacity Based on Digital Image Features].

OBJECTIVE: According to the digital image features of corneal opacity, a multi classification model of support vector machine (SVM) was established to explore the objective quantification method of corneal opacity. METHODS: The cornea digital images of dead pigs were collected, part of the color features and texture features were extracted according to the previous experience, and the SVM multi classification model was established. The test results of the model were evaluated by precision, sensitivity and F1 scores. The optimal feature subset was found by SVM-RFE combined with cross validation to optimize the model. RESULTS: In the classification of corneal opacity, the highest F1 score was 0.974 4, and the number of features in the optimal feature subset was 126. CONCLUSIONS: The SVM multi classification model can classify the degree of corneal opacity.

Single molecule real-time sequencing and RNA-seq unravel the role of long non-coding and circular RNA in the regulatory network during Nile tilapia (Oreochromis niloticus) infection with Streptococcus agalactiae.

BACKGROUND: The tilapia aquaculture industry is facing heavy economic losses due to Streptococcus agalactiae (S. agalactiae) infections. While progress has been made in past years, the lack of a high-quality tilapia genome and transcript annotations makes systematic and comprehensive exploration for a non-coding RNA regulatory network associated with the infection process unfeasible, and it stunts further research focused on disease defense and treatment. Herein, single molecular real time sequencing (SMRT-Seq) and RNA-seq data were utilized to generate a high-quality transcript annotation. In addition, Changes in mRNA and non-coding RNA expression were also analyzed during a S. agalactiae infection in tilapia. FINDINGS: In total, 16.79 Gb of clean data were obtained by sequencing on six SMRT cells, with 712,294 inserts (326,645 full-length non-chimeric reads and 354,188 non-full-length reads). A total of 197,952 consensus transcripts were obtained. Additionally, 55,857 transcript sequences were acquired, with 12,297 previously annotated and 43,560 newly identified transcripts. To further examine the immune response in Oreochromis niloticus following a S. agalactiae infection, a total of 470.62 Gb of clean data was generated by sequencing a library containing 18 S. agalactiae infected tilapia samples. Of the identified genes, 9911 were newly exploited, of which 7102 were functional annotated. Furthermore, 7874 mRNAs, 1281 long non-coding RNAs (out of 21,860 long non-coding RNAs), and 61 circular RNAs (out of 1026 circular RNAs) were found to be differentially expressed during infection, with the 1026 circRNAs not previously identified in tilapia. Moreover, k-means clustering and WGCNA analyses revealed that the immune response of tilapia to a S. agalactiae infection can be divided into three stages: cytokines driven rapid immune response, energy metabolism promotion, and the production of lysosomes and phagosomes. During this response, the head kidney and spleen have synergistic effects, while maintaining independent characteristics. Finally, lncRNA-mRNA (trans and cis), lncRNA-miRNA-mRNA, and circRNA-miRNA-mRNA regulatory networks were constructed and revealed that non-coding RNA is involved in the regulation of immune-related genes. CONCLUSIONS: This study generated a greatly-improved transcript annotation for tilapia using long-read PacBio sequencing technology, and revealed the presence of a regulatory network comprised of non-coding RNAs in Nile tilapia infected with S. agalactiae.

[Retinal Vessel Segmentation Based on Multiscale Matched Filtering].

Retinal vascular function is complex, morphological structure varies from person to person, and is susceptible to vascular diseases and systemic vascular diseases. Its accurate segmentation is of great significance for disease diagnosis and identification. In this paper, a multi-scale matching filtering algorithm is proposed for the uneven size of retinal blood vessels. On the basis of the traditional singlescale Gaussian matching filter, multiscale Gaussian matched filters with two sizes are used to enhance grayscale images. Enhancement is performed, and the superimposed image is binarized using a twodimensional maximum entropy threshold segmentation algorithm. The algorithm is tested in the DRIVE database with sensitivity, specificity and accuracy of 0.803, 0.959, 0.981, respectively. Comparing with the traditional algorithm, the algorithm has high sensitivity, fast running speed and rich details of segmentation results.

[Algorithm for Estimating Tissue Strain Based on Optical Flow].

Because the translation hypothesis of optical flow method can not accurately describe the form of motion after tissue compression, so we proposed a new ultrasonic elastic imaging algorithm. It was assumed that the deformation of the tissue was affine transformation when the probe was pressed to the tissue, and the displacement and strain distribution were estimated simultaneously by the optical flow method combined with the prior estimation. In order to verify the effectiveness of the algorithm, the imaging quality of the algorithm and the other imaging algorithm were compared with the simulated radio frequency echo signal. The results show that the new algorithm is higher in signal to noise ratio (SNRe), contrast to noise ratio (CNRe) and running speed than the contrast algorithm under 8% compression. The results show that the new proposed algorithm can effectively estimate axial displacement and axial strain in the case of large compression.

Design and experimentation with sandwich microstructure for catalytic combustion-type gas sensors.

The traditional handmade catalytic combustion gas sensor has some problems such as a pairing difficulty, poor consistency, high power consumption, and not being interchangeable. To address these issues, integrated double catalytic combustion of alcohol gas sensor was designed and manufactured using silicon micro-electro-mechanical systems (MEMS) technology. The temperature field of the sensor is analyzed using the ANSYS finite element analysis method. In this work, the silicon oxide-PECVD-oxidation technique is used to manufacture a SiO2-Si3N2-SiO2 microstructure carrier with a sandwich structure, while wet etching silicon is used to form a beam structure to reduce the heat consumption. Thin-film technology is adopted to manufacture the platinum-film sensitive resistance. Nano Al2O3-ZrO-ThO is coated to format the sensor carrier, and the sensitive unit is dipped in a Pt-Pd catalyst solution to form the catalytic sensitive bridge arm. Meanwhile the uncoated catalyst carrier is considered as the reference unit, realizing an integrated chip based on a micro double bridge and forming sensors. The lines of the Pt thin-film resistance have been observed with an electronic microscope. The compensation of the sensitive material carriers and compensation materials have been analyzed using an energy spectrum. The results show that the alcohol sensor can detect a volume fraction between 0 and 4,500 × 10(-6) and has good linear output characteristic. The temperature ranges from -20 to +40 °C. The humidity ranges from 30% to 85% RH. The zero output of the sensor is less than ±2.0% FS. The power consumption is ≤0.2 W, and both the response and recovery time are approximately 20 s.

A prostate seed implantation robot system based on human-computer interactions: Augmented reality and voice control.

The technology of robot-assisted prostate seed implantation has developed rapidly. However, during the process, there are some problems to be solved, such as non-intuitive visualization effects and complicated robot control. To improve the intelligence and visualization of the operation process, a voice control technology of prostate seed implantation robot in augmented reality environment was proposed. Initially, the MRI image of the prostate was denoised and segmented. The three-dimensional model of prostate and its surrounding tissues was reconstructed by surface rendering technology. Combined with holographic application program, the augmented reality system of prostate seed implantation was built. An improved singular value decomposition three-dimensional registration algorithm based on iterative closest point was proposed, and the results of three-dimensional registration experiments verified that the algorithm could effectively improve the three-dimensional registration accuracy. A fusion algorithm based on spectral subtraction and BP neural network was proposed. The experimental results showed that the average delay of the fusion algorithm was 1.314 s, and the overall response time of the integrated system was 1.5 s. The fusion algorithm could effectively improve the reliability of the voice control system, and the integrated system could meet the responsiveness requirements of prostate seed implantation.

Orthodontic force/torque modeling and experiment of Kitchon root-controlled auxiliary archwire.

The incidence of oral malocclusion is increasing and is seriously damaging the oral health of human beings. The Kitchon root-controlled auxiliary archwire is an individualized orthodontic arch. It is used clinically for the treatment of tooth-lingual tilt/root-lip tilt phenomenon of the central incisors. However, the bending parameters of the Kitchon root-controlled auxiliary archwire used in different patients are based on the clinical experience of the dentists. Therefore, this orthodontic treatment has a high risk and unpredictability. In this paper, the loading performance and orthodontic process of Kitchon root-controlled auxiliary archwire are analyzed. And the prediction model of support resistance and correction torque are established. The bending parameters of the Kitchon root-controlled auxiliary archwire, as well as the effect of the bending parameters on the support resistance and the correction torque, are all quantified. And the prediction models for the support resistance and the correction torque are calculated separately. The correlation coefficients of calculated data and experimental data are ξT1 > .97 and ξA1 > .96, respectively; the correlation coefficients of simulated data and experimental data are ξT2 > .96 and ξA2 > .96, respectively. The accuracy and reliability of the established prediction models are verified. It provides an effective theoretical guide for dentists to safely and efficiently perform root-controlled rotation orthodontic treatment on patients' central incisors.

A review on tissue-needle interaction and path planning models for bevel tip type flexible needle minimal intervention.

A flexible needle has emerged as a crucial clinical technique in contemporary medical practices, particularly for minimally invasive interventions. Its applicability spans diverse surgical domains such as brachytherapy, cardiovascular surgery, neurosurgery and others. Notably, flexible needles find utility in biopsies requiring deep skin penetration to access infected areas. Despite its minimally invasive advantages, the precise guidance of the needle to its intended target, while avoiding damage to bones, blood vessels, organs and tissues, remains a significant challenge for researchers. Consequently, extensive research has been dedicated to enhancing the steering and accuracy of flexible needles. Here, we aim to elucidate the recent advancements, trends and perspectives in flexible needle steering models and path planning over the last 15 years. The discussed models encompass various types, including symmetric-tip needles, curved-tip needles, tendon-actuated needles, programmable needles and the innovative fracture-directed waterjet needles. Moreover, the paper offers a comprehensive analysis, comparing the trajectories followed by these needle models to attain the desired target with minimal tissue damage. By delving into these aspects, the paper contributes to a deeper understanding of the current landscape of flexible needle technology and guides future research directions in this dynamic field.

A review of robot-assisted ultrasound examination: Systems and technology.

BACKGROUND: At present, the number and overall level of ultrasound (US) doctors cannot meet the medical needs, and the medical ultrasound robots will largely solve the shortage of medical resources. METHODS: According to the degree of automation, the handheld, semi-automatic and automatic ultrasound examination robot systems are summarised. Ultrasound scanning path planning and robot control are the keys to ensure that the robot systems can obtain high-quality images. Therefore, the ultrasound scanning path planning and control methods are summarised. The research progress and future trends are discussed. RESULTS: A variety of ultrasound robot systems have been applied to various medical works. With the continuous improvement of automation, the systems provide high-quality ultrasound images and image guidance for clinicians. CONCLUSION: Although the development of medical ultrasound robot still faces challenges, with the continuous progress of robot technology and communication technology, medical ultrasound robot will have great development potential and broad application space.

A mechanics-based model for predicting flexible needle bending with large curvature in soft tissue.

Percutaneous insertion is one of the most common minimally invasive procedures. Compared with traditional straight rigid needles, bevel-tipped flexible needle can generate curved trajectories to avoid obstacles and sensitive organs. However, the nonlinear large deflection problem challenges the bending prediction of the needle, which dramatically influences the surgical success rate. This paper analyzed the mechanism of needle-tissue interaction, and established a mechanics-based model of the needle bending during an insertion. And then, a discretization of the bending model was adopted to accurately predict the large bending of the needle in soft tissue. Insertion experiments were conducted to validate the bending prediction model. The results showed that the large needle bending was predicted with the mean/RMSE/maximumu error of 0.42 mm / 0.26 mm / 1.08 mm, which was clinically acceptable. This proved the rationality and accuracy of the proposed model.

Vertical migration behavior simulation and prediction of Pb and Cd in co-contaminated soil around Pb-Zn smelting slag site.

Heavy metal migration in soil poses a serious threat to the soil and groundwater. Understanding the migration pattern of heavy metals (HMs) under different factors could provide a more reasonable position for pollution evaluation and targetoriented treatment of soil heavy metal. In this study, the migration behavior of Pb and Cd in co-contaminated soil under different pH and ionic strength (NaCl concentration) was simulated using convective dispersion equation (CDE). We predicted the migration trends of Pb and Cd in soils after 5, 10, and 20 years via PHREEQC. The results showed that the migration time of Cd in the soil column experiment was about 60 days faster than that of Pb, and the migration trend was much steeper. The CDE was proved to describe the migration behavior of Pb and Cd (R2 > 0.75) in soil. The predicted results showed that Cd migrated to 15-20 cm of soil within 7 years and Pb stayed mainly in the top 0-6 cm of soil within 5 years as the duration of irrigation increased. Overall, our study is expected to provide new insight into the migration of heavy metal in soil ecosystems and guidance for reducing risk of heavy metal in the environment.

DRF-DRC: dynamic receptive field and dense residual connections for model compression.

Deep convolutional neural networks have achived remarkable progress on computer vision tasks over last years. These novel neural architecture are most designed manually by human experts, which is a time-consuming process and not the best solution. Hence neural architecture search (NAS) has become a hot research topic for the design of neural architecture. In this paper, we propose the dynamic receptive field (DRF) operation and measurable dense residual connections (DRC) in search space for designing efficient networks, i.e., DRENet. The search method can be deployed on the MobileNetV2-based search space. The experimental results on CIFAR10/100, SVHN, CUB-200-2011, ImageNet and COCO benchmark datasets and an application example in a railway intelligent surveillance system demonstrate the effectiveness of our scheme, which achieves superior performance.

Flexible Needle Steering with Tethered and Untethered Actuation: Current States, Targeting Errors, Challenges and Opportunities.

Accurate needle targeting is critical for many clinical procedures, such as transcutaneous biopsy or radiofrequency ablation of tumors. However, targeting errors may arise, limiting the widespread adoption of these procedures. Advances in flexible needle (FN) steering are emerging to mitigate these errors. This review summarizes the state-of-the-art developments of FNs and addresses possible targeting errors that can be overcome with steering actuation techniques. FN steering techniques can be classified as passive and active. Passive steering directly results from the needle-tissue interaction forces, whereas active steering requires additional forces to be applied at the needle tip, which enhances needle steerability. Therefore, the corresponding targeting errors of most passive FNs and active FNs are between 1 and 2 mm, and less than 1 mm, respectively. However, the diameters of active FNs range from 1.42 to 12 mm, which is larger than the passive steering needle varying from 0.5 to 1.4 mm. Therefore, the development of active FNs is an area of active research. These active FNs can be steered using tethered internal direct actuation or untethered external actuation. Examples of tethered internal direct actuation include tendon-driven, longitudinal segment transmission and concentric tube transmission. Tendon-driven FNs have various structures, and longitudinal segment transmission needles could be adapted to reduce tissue damage. Additionally, concentric tube needles have immediate advantages and clinical applications in natural orifice surgery. Magnetic actuation enables active FN steering with untethered external actuation and facilitates miniaturization. The challenges faced in the fabrication, sensing, and actuation methods of FN are analyzed. Finally, bio-inspired FNs may offer solutions to address the challenges faced in FN active steering mechanisms.

Image-guided prostate biopsy robots: A review.

At present, the incidence of prostate cancer (PCa) in men is increasing year by year. So, the early diagnosis of PCa is of great significance. Transrectal ultrasonography (TRUS)-guided biopsy is a common method for diagnosing PCa. The biopsy process is performed manually by urologists but the diagnostic rate is only 20%-30% and its reliability and accuracy can no longer meet clinical needs. The image-guided prostate biopsy robot has the advantages of a high degree of automation, does not rely on the skills and experience of operators, reduces the work intensity and operation time of urologists and so on. Capable of delivering biopsy needles to pre-defined biopsy locations with minimal needle placement errors, it makes up for the shortcomings of traditional free-hand biopsy and improves the reliability and accuracy of biopsy. The integration of medical imaging technology and the robotic system is an important means for accurate tumor location, biopsy puncture path planning and visualization. This paper mainly reviews image-guided prostate biopsy robots. According to the existing literature, guidance modalities are divided into magnetic resonance imaging (MRI), ultrasound (US) and fusion image. First, the robot structure research by different guided methods is the main line and the actuators and material research of these guided modalities is the auxiliary line to introduce and compare. Second, the robot image-guided localization technology is discussed. Finally, the image-guided prostate biopsy robot is summarized and suggestions for future development are provided.

Arsenic triggered nano-sized uranyl arsenate precipitation on the surface of Kocuria rosea.

Arsenic (As) and uranium (U) frequently occur together naturally and, in consequence, transform into cocontaminants at sites of uranium mining and processing, yet the simultaneous interaction process of arsenic and uranium has not been well documented. In the present contribution, the influence of arsenate on the removal and reduction of uranyl by the indigenous microorganism Kocuria rosea was characterized using batch experiments combined with species distribution calculation, SEM-EDS, FTIR, XRD and XPS. The results showed that the coexistence of arsenic plays an active role in Kocuria rosea growth and the removal of uranium under neutral and slightly acidic conditions. U-As complex species of UO2HAsO4 (aq) had a positive effect on uranium removal, while Kocuria rosea cells appeared to have a large specific surface area serving as attachment sites. Furthermore, a large number of nano-sized flaky precipitates, constituted by uranium and arsenic, attached to the surface of Kocuria rosea cells at pH 5 through P=O, COO-, and C=O groups in phospholipids, polysaccharides, and proteins. The biological reduction of U(VI) and As(V) took place in a successive way, and the formation of a chadwickite-like uranyl arsenate precipitate further inhibited U(VI) reduction. The results will help to design more effective bioremediation strategies for arsenic-uranium cocontamination.

Enhanced NIR-II Fluorescent Lateral Flow Biosensing Platform Based on Supramolecular Host-Guest Self-Assembly for Point-of-Care Testing of Tumor Biomarkers.

Point-of-care detection of tumor biomarkers with high sensitivity remains an enormous challenge in the early diagnosis and mass screening of cancer. Fluorescent lateral flow immunoassay (LFA) is an attractive platform for point-of-care testing due to its inherent advantages. Particularly, a fluorescent probe is crucial to improving the analytical performance of the LFA platform. Herein, we developed an enhanced second near-infrared (NIR-II) LFA (ENIR-II LFA) platform based on supramolecular host-guest self-assembly for detection of the prostate-specific antigen (PSA) as a model analyte. In this platform, depending on the effective supramolecular surface modification strategy, cucurbit[7]uril (CB[7])-covered rare-earth nanoparticles (RENPs) emitting in the NIR-II (1000-1700 nm) window were prepared and employed as an efficient fluorescent probe (RENPs-CB[7]). Benefiting from its superior optical properties, such as low autofluorescence, excellent photostability, enhanced fluorescence intensity, and increased antibody-conjugation efficiency, the ENIR-II LFA platform displayed a wide linear detection range from 0.65 to 120 ng mL-1, and the limit of detection was down to 0.22 ng mL-1 for PSA, which was 18.2 times lower than the clinical cutoff value. Moreover, the testing time was also shortened to 6 min. Compared with the commercial visible fluorescence LFA kit (VIS LFA) and the previously reported NIR-II LFA based on a RENPs-PAA probe, this ENIR-II LFA demonstrated more competitive advantages in analytical sensitivity, detection range, testing time, and production cost. Overall, the ENIR-II LFA platform offers great potential for the highly sensitive, rapid, and convenient detection of tumor biomarkers and is expected to serve as a useful technique in the general population screening of the high-incidence cancer region.