Novel non-invasive method for urine mapping: Deep-learning-enabled SERS spectroscopy for the rapid differential detection of kidney allograft injury.

The kidney allograft has been under continuous attack from diverse injuries since the very beginning of organ procurement, leading to a gradual decline in function, chronic fibrosis, and allograft loss. It is vital to routinely and precisely monitor the risk of injuries after renal transplantation, which is difficult to achieve because the traditional laboratory tests lack sensitivity and specificity, and graft biopsies are invasive with the risk of many complications and time-consuming. Herein, a novel method for the diagnosis of graft injury is demonstrated, using deep learning-assisted surface-enhanced Raman spectroscopy (SERS) of the urine analysis. Specifically, we developed a hybrid SERS substrate composed of gold and silver with high sensitivity to the urine composition under test, eliminating the need for labels, which makes measurements easy to perform and meanwhile results in extremely abundant and complex Raman vibrational bands. Deep learning algorithms were then developed to improve the interpretation of the SERS spectral fingerprints. The deep learning model was trained with SERS signals of urine samples of recipients with different injury types including delayed graft function (DGF), calcineurin-inhibitor toxicity (CNIT), T cell-mediated rejection (TCMR), antibody-mediated rejection (AMR), and BK virus nephropathy (BKVN), which explored the features of these types and achieved the injury differentiation with an overall accuracy of 93.03%. The results highlight the potential of combining label-free SERS spectroscopy with deep learning as a method for liquid biopsy of kidney allograft injuries, which can provide great potential to diagnose and evaluate allograft injuries, and thus extend the life of kidney allografts.

CoVi-Net: A hybrid convolutional and vision transformer neural network for retinal vessel segmentation.

Retinal vessel segmentation plays a crucial role in the diagnosis and treatment of ocular pathologies. Current methods have limitations in feature fusion and face challenges in simultaneously capturing global and local features from fundus images. To address these issues, this study introduces a hybrid network named CoVi-Net, which combines convolutional neural networks and vision transformer. In our proposed model, we have integrated a novel module for local and global feature aggregation (LGFA). This module facilitates remote information interaction while retaining the capability to effectively gather local information. In addition, we introduce a bidirectional weighted feature fusion module (BWF). Recognizing the variations in semantic information across layers, we allocate adjustable weights to different feature layers for adaptive feature fusion. BWF employs a bidirectional fusion strategy to mitigate the decay of effective information. We also incorporate horizontal and vertical connections to enhance feature fusion and utilization across various scales, thereby improving the segmentation of multiscale vessel images. Furthermore, we introduce an adaptive lateral feature fusion (ALFF) module that refines the final vessel segmentation map by enriching it with more semantic information from the network. In the evaluation of our model, we employed three well-established retinal image databases (DRIVE, CHASEDB1, and STARE). Our experimental results demonstrate that CoVi-Net outperforms other state-of-the-art techniques, achieving a global accuracy of 0.9698, 0.9756, and 0.9761 and an area under the curve of 0.9880, 0.9903, and 0.9915 on DRIVE, CHASEDB1, and STARE, respectively. We conducted ablation studies to assess the individual effectiveness of the three modules. In addition, we examined the adaptability of our CoVi-Net model for segmenting lesion images. Our experiments indicate that our proposed model holds promise in aiding the diagnosis of retinal vascular disorders.

Self-assembly of Au nanocubes for ultrasensitive detection of Alzheimer's disease biomarkers by SERS.

Since presently Alzheimer's disease (AD) is incurable, early diagnosis of AD is crucial. Aß 1-42 and tau-441 proteins are promising core biomarkers for early diagnosis and early therapeutic intervention in AD. Here we constructed a surface-enhanced Raman spectroscopy (SERS) biosensor for highly sensitive quantitative detection of Aß 1-42 and tau proteins by preparing gold nanocube (AuNC) superlattices through evaporation self-assembly. The results showed that the method has a wide response range (0.1-10 000 ng mL-1 and 0.01-1000 ng mL-1, respectively) and high sensitivity. The detection limits of Aß1-42 and tau protein were 0.0416 ng mL-1 and 0.0087 ng mL-1, respectively. In addition, the method was able to rapidly and simultaneously detect the two biomarkers in serum, which showed the feasibility of the method in complex biological environments. The detection of Aß 1-42 and tau protein has great potential for the accurate prediction and early diagnosis of Alzheimer's disease.

CCS-UNet: a cross-channel spatial attention model for accurate retinal vessel segmentation.

Precise segmentation of retinal vessels plays an important role in computer-assisted diagnosis. Deep learning models have been applied to retinal vessel segmentation, but the efficacy is limited by the significant scale variation of vascular structures and the intricate background of retinal images. This paper supposes a cross-channel spatial attention U-Net (CCS-UNet) for accurate retinal vessel segmentation. In comparison to other models based on U-Net, our model employes a ResNeSt block for the encoder-decoder architecture. The block has a multi-branch structure that enables the model to extract more diverse vascular features. It facilitates weight distribution across channels through the incorporation of soft attention, which effectively aggregates contextual information in vascular images. Furthermore, we suppose an attention mechanism within the skip connection. This mechanism serves to enhance feature integration across various layers, thereby mitigating the degradation of effective information. It helps acquire cross-channel information and enhance the localization of regions of interest, ultimately leading to improved recognition of vascular structures. In addition, the feature fusion module (FFM) module is used to provide semantic information for a more refined vascular segmentation map. We evaluated CCS-UNet based on five benchmark retinal image datasets, DRIVE, CHASEDB1, STARE, IOSTAR and HRF. Our proposed method exhibits superior segmentation efficacy compared to other state-of-the-art techniques with a global accuracy of 0.9617/0.9806/0.9766/0.9786/0.9834 and AUC of 0.9863/0.9894/0.9938/0.9902/0.9855 on DRIVE, CHASEDB1, STARE, IOSTAR and HRF respectively. Ablation studies are also performed to evaluate the the relative contributions of different architectural components. Our proposed model is potential for diagnostic aid of retinal diseases.

Orientation of the NV centers are determined using the cylindrical vector beam array.

The determination of nitrogen-vacancy centers plays an important role in quantum information sensing. Efficiently and rapidly determining the orientation of multiple nitrogen-vacancy center s in a low-concentration diamond is challenging due to its size. Here, we solve this scientific problem by using an azimuthally polarized beam array as the incident beam. In this paper, the optical pen is used to modulate the position of beam array to excite distinctive fluorescence characterizing multiple and different orientations of nitrogen-vacancy centers. The important result is that in a low concentration diamond layer, the orientation of multiple NV centers can be judged except when they are too close within the diffraction limit. Hence, this efficient and rapid method has a good application prospect in quantum information sensing.

Rapid and ultrasensitive detection of acute kidney injury biomarkers CH3L1 and L-FABP using surface-enhanced Raman spectroscopy.

Chitinase 3-like 1 (CH3L1) and liver fatty acid binding protein (L-FABP) are promising biomarkers for the early diagnosis of acute kidney injury (AKI). Here, a highly sensitive method for the quantitative detection of CH3L1 and L-FABP by surface-enhanced Raman spectroscopy (SERS) based on graphene oxide/gold and silver core-shell nanoparticles (GO/Au@Ag NPs) was proposed. The results showed that such GO/Au@Ag substrate can achieve rapid sensing of CH3L1 and L-FABP with a wide response range (2 × 10-1 to 2 × 10-8 mg/mL and 1.2 × 10-1 to 1.2 × 10-8 mg/mL, respectively) and high sensitivity. The detection limits of CH3L1 and L-FABP were 1.21 × 10-8 mg/mL and 0.62 × 10-8 mg/mL, respectively. In addition, the simultaneous detection of the two biomarkers in serum was demonstrated, showing the feasibility of this method in the complex biological environment. The detection of CH3L1 and L-FABP will greatly improve the early diagnosis and intervention of AKI.

Design and Simulation of a Ratiometric SPR Sensor Based on a 2D van der Waals Heterojunction for Refractive Index Measurement.

Surface plasmon resonance (SPR) sensors have been widely applied in many fields because of their advantages of working in real time and high sensitivity. However, because the spectrum of an SPR sensor is easily affected by the smoothness of the metal surface, this type of sensor has obvious disadvantages in the application of quantitative detection. We designed an SPR refractive index sensor for molecular detection that has the advantage of quantifiability. A ratio spectral quantitative analysis method was established based on the two coherent dips of the SPR spectrum formed by the strong coupling effect between the surface plasmon polaritons and the excitons of the J-aggregate molecule 5,6-dichloro-2-[3-[5,6-dichloro-1-ethyl-3-(4-sulfobutyl)-2-benzimidazoline subunit] propenyl]-3-ethyl-1-(4-sulfobutyl) benzimidazole hydroxide inner salt (TDBC). The introduced MoS2/graphene van der Waals heterojunction produced an effective charge transfer to the Ag film, resulting in significant electric field enhancement at the sensing interface and further improving the detection sensitivity of the sensor. The simulation results showed that for 43 nm Ag film, for example, the ratiometric SPR sensor with the Ag film structure can obtain 16.12 RIU-1 sensing sensitivity, applied to the detection of gas molecules, while the SPR sensor with single-layer graphene and three layers of MoS2 heterostructures can obtain 50.68 RIU-1 sensing sensitivity. The addition of van der Waals heterostructures can significantly improve sensing performance by 215%.

CNN-assisted SERS enables ultra-sensitive and simultaneous detection of Scr and BUN for rapid kidney function assessment.

Kidney disease is highly prevalent and may result in severe clinical outcomes. Serum creatinine (Scr) and blood urea nitrogen (BUN) are the most widely used biomarkers for kidney function assessment, yet when measured alone, the result can be affected by a variety of parameters such as age, gender, protein consumption, etc. Measuring Scr and BUN simultaneously can eliminate most of the external influences and greatly improve the assessment of kidney function. In this study, a real-time kidney function assessment system based on dual biomarker detection was proposed. Scr and BUN were determined using surface-enhanced Raman scattering (SERS) within the concentration range of 10-1 to 10-6 M and 0.28 to 100 mg dl-1, respectively. A one-dimensional convolutional neural network (1D-CNN) model was employed to quantitatively analyze the concentration of biomarkers from the SERS spectral measurements. Moreover, we simulated a variety of kidney health conditions with 16 groups of mixed Scr and BUN in serum. The proposed CNN-assisted SERS method was used to quantify both biomarkers and provide diagnostic results. The Au core-Ag shell nanoprobes provided ultra-sensitive SERS detection and the CNN model achieved excellent regression results with an R2 of 0.9871 in the testing dataset. The system demonstrated a rapid and robust evaluation for the assessment of kidney function, providing a promising idea for medical diagnosis with the help of spectroscopy and deep learning methods.

Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network.

OBJECTIVES: To develop and validate an end-to-end region-based deep convolutional neural network (R-DCNN) to jointly segment the optic disc (OD) and optic cup (OC) in retinal fundus images for precise cup-to-disc ratio (CDR) measurement and glaucoma screening. METHODS: In total, 2440 retinal fundus images were retrospectively obtained from 2033 participants. An R-DCNN was presented for joint OD and OC segmentation, where the OD and OC segmentation problems were formulated into object detection problems. We compared R-DCNN's segmentation performance on our in-house dataset with that of four ophthalmologists while performing quantitative, qualitative and generalization analyses on the publicly available both DRISHIT-GS and RIM-ONE v3 datasets. The Dice similarity coefficient (DC), Jaccard coefficient (JC), overlapping error (E), sensitivity (SE), specificity (SP) and area under the curve (AUC) were measured. RESULTS: On our in-house dataset, the proposed model achieved a 98.51% DC and a 97.07% JC for OD segmentation, and a 97.63% DC and a 95.39% JC for OC segmentation, achieving a performance level comparable to that of the ophthalmologists. On the DRISHTI-GS dataset, our approach achieved 97.23% and 94.17% results in DC and JC results for OD segmentation, respectively, while it achieved a 94.56% DC and an 89.92% JC for OC segmentation. Additionally, on the RIM-ONE v3 dataset, our model generated DC and JC values of 96.89% and 91.32% on the OD segmentation task, respectively, whereas the DC and JC values acquired for OC segmentation were 88.94% and 78.21%, respectively. CONCLUSION: The proposed approach achieved very encouraging performance on the OD and OC segmentation tasks, as well as in glaucoma screening. It has the potential to serve as a useful tool for computer-assisted glaucoma screening.

Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.

BACKGROUND: Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability. RESULTS: In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the topological information in molecular graphs, the interaction information between drugs and the local chemical context in SMILES sequences. In MFFGNN, to fully learn the topological information of drugs, we propose a novel feature extraction module to capture the global features for the molecular graph and the local features for each atom of the molecular graph. In addition, in the multi-type feature fusion module, we use the gating mechanism in each graph convolution layer to solve the over-smoothing problem during information delivery. We perform extensive experiments on multiple real datasets. The results show that MFFGNN outperforms some state-of-the-art models for DDI prediction. Moreover, the cross-dataset experiment results further show that MFFGNN has good generalization performance. CONCLUSIONS: Our proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find that a multi-type feature fusion model can accurately predict DDIs. It may contribute to discovering novel DDIs.

Rapid Determination of Mixed Pesticide Residues on Apple Surfaces by Surface-Enhanced Raman Spectroscopy.

Chlorpyrifos (CPF) and 2,4-dichlorophenoxyacetic acid (2,4-D) are insecticides and herbicides which has been widely used on farms. However, CPF and 2,4-D residues on corps can bring high risks to human health. Accurate detection of pesticide residues is important for controlling health risks caused by CPF and 2,4-D. Therefore, we developed a fast, sensitive, economical, and lossless surface-enhanced Raman spectroscopy (SERS)-based method for pesticide detection. It can rapidly and simultaneously determine the CPF and 2,4-D mixed pesticide residues on an apple surface at a minimum of 0.001 mg L-1 concentration, which is far below the pesticide residue standard in China and the EU. The limits of detection reach down to 1.28 × 10-9 mol L-1 for CPF and 2.47 × 10-10 mol L-1 for 2,4-D. The limits of quantification are 4.27 × 10-9 mol L-1 and 8.23 × 10-10 mol L-1 for CPF and 2,4-D. This method has a great potential for the accurate detection of pesticide residues, and may be applied to other fields of agricultural products and food industry.

Rapid detection of illegally added nifedipine in Chinese traditional patent medicine by surface-enhanced Raman spectroscopy.

Nifedipine is an antihypertensive chemical. The illegal addition of this chemical into Chinese traditional patent medicine (CTPM) is unstandardized and lacks regulation. It could bring serious side effects to patients, causing various symptoms. Therefore, accurate detection of nifedipine is very important for human health and the prevention of illegal additives. Surface-enhanced Raman spectroscopy (SERS) is a fast and sensitive fingerprint spectroscopic technique, which has been shown to be promising in drug detection. In this study, nifedipine in CTPM was determined qualitatively and quantitatively with SERS. Linear relationships between the concentrations of nifedipine and the intensities of the characteristic peaks were established. The results showed a linear relationship within the concentration range of 0.5-10 mg/L, and the lowest detectable concentration of nifedipine in CTPM was 0.1 mg/L (equivalent to 0.03% doping of nifedipine in CTPM). This method has shown a great potential in the detection of drugs illegally added to CTPM.

An efficient self-attention network for skeleton-based action recognition.

There has been significant progress in skeleton-based action recognition. Human skeleton can be naturally structured into graph, so graph convolution networks have become the most popular method in this task. Most of these state-of-the-art methods optimized the structure of human skeleton graph to obtain better performance. Based on these advanced algorithms, a simple but strong network is proposed with three major contributions. Firstly, inspired by some adaptive graph convolution networks and non-local blocks, some kinds of self-attention modules are designed to exploit spatial and temporal dependencies and dynamically optimize the graph structure. Secondly, a light but efficient architecture of network is designed for skeleton-based action recognition. Moreover, a trick is proposed to enrich the skeleton data with bones connection information and make obvious improvement to the performance. The method achieves 90.5% accuracy on cross-subjects setting (NTU60), with 0.89M parameters and 0.32 GMACs of computation cost. This work is expected to inspire new ideas for the field.

Simultaneous and ultra-sensitive SERS detection of SLPI and IL-18 for the assessment of donor kidney quality using black phosphorus/gold nanohybrids.

Due to the global challenge of donor kidney shortage, expanding the pool of deceased donors has been proposed to include expanded criteria donors. However, the lack of methods to precisely measure donor kidney injury and predict the outcome still leads to high discard rates and recipient complications. As such, evaluation of deceased donor kidney quality is critical prior to transplantation. Biomarkers from donor urine or serum provide potential advantages for the precise measure of kidney quality. Herein, simultaneous detection of secretory leukocyte peptidase inhibitor (SLPI) and interleukin 18 (IL-18), two important kidney injury biomarkers, has been achieved, for the first time, with an ultra-high sensitivity using surface enhanced Raman scattering (SERS). Specifically, black phosphorus/gold (BP/Au) nanohybrids synthesized by depositing Au nanoparticles (NPs) onto the BP nanosheets serve as SERS-active substrates, which offer a high-density of inherent and accessible hot-spots. Meanwhile, the nanohybrids possess biocompatible surfaces for the enrichment of target biomarkers through the affinity with BP nanosheets. Quantitative detection of SLPI and IL-18 were then achieved by characterizing SERS signals of these two biomarkers. The results indicate high sensitivity and excellent reproducibility of this method. The limits of detection reach down to 1.53×10-8 mg/mL for SLPI and 0.23×10-8 mg/mL for IL-18. The limits of quantification are 5.10×10-8 mg/mL and 7.67×10-9 mg/mL for SLPI and IL-18. In addition, simultaneous detection of these biomarkers in serum was investigated, which proves the feasibility in biologic environment. More importantly, this method is powerful for detecting multiple analytes inheriting from excellent multiplexing ability of SERS. Giving that the combined assessment of SLPI and IL-18 expression level serves as an indicator of donor kidney quality and can be rapidly and reproducibly conducted, this SERS-based method holds great prospective in clinical practice.

Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs.

OBJECTIVES: To present and validate a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fundus images. METHODS: A total of 8739 retinal fundus images were collected from a retrospective cohort of 3285 patients. For detecting DR and DMO, a multiple improved Inception-v4 ensembling approach was developed. We measured the algorithm's performance and made a comparison with that of human experts on our primary dataset, while its generalization was assessed on the publicly available Messidor-2 dataset. Also, we investigated systematically the impact of the size and number of input images used in training on model's performance, respectively. Further, the time budget of training/inference versus model performance was analyzed. RESULTS: On our primary test dataset, the model achieved an 0.992 (95% CI, 0.989-0.995) AUC corresponding to 0.925 (95% CI, 0.916-0.936) sensitivity and 0.961 (95% CI, 0.950-0.972) specificity for referable DR, while the sensitivity and specificity for ophthalmologists ranged from 0.845 to 0.936, and from 0.912 to 0.971, respectively. For referable DMO, our model generated an AUC of 0.994 (95% CI, 0.992-0.996) with a 0.930 (95% CI, 0.919-0.941) sensitivity and 0.971 (95% CI, 0.965-0.978) specificity, whereas ophthalmologists obtained sensitivities ranging between 0.852 and 0.946, and specificities ranging between 0.926 and 0.985. CONCLUSION: This study showed that the deep ensemble model exhibited excellent performance in detecting DR and DMO, and had good robustness and generalization, which could potentially help support and expand DR/DMO screening programs.

Thermally Tunable Orbital Angular Momentum Mode Generator Based on Dual-Core Photonic Crystal Fibers.

The combination of mode division multiplexing (MDM) based on orbital angular momentum (OAM) modes with wavelength division multiplexing (WDM) has attracted considerable attention due to its ability to increase optical transmission capacity. However, the switching of the multi-wavelength and multi-order OAM mode in an all-fiber structure has always been a challenge. As a solution, a thermally tunable dual-core photonic crystal fiber (DC-PCF) is proposed to achieve multi-order and multi-wavelength switching of the OAM mode. The results show that the OAM mode with topological charge m = ±1 can be excited with the linear polarization fundamental mode (LPFM) and circular polarization fundamental mode (CPFM). In addition, the device can effectively excite a high-purity ±1st order OAM mode with wavelengths ranging from 1520 to 1575 nm by thermal tuning. The purity of the mode is in excess of 99%, and the energy conversion efficiency (ECE) is above 95%. The proposed design is expected to be applied in all-fiber communication systems combined with MDM and WDM.

Dual-targeting SERS-encoded graphene oxide nanocarrier for intracellular co-delivery of doxorubicin and 9-aminoacridine with enhanced combination therapy.

A graphene oxide (GO)-based nanocarrier that imparts tumor-selective delivery of dual-drug with enhanced therapeutic index, is introduced. GO is conjugated with Au@Ag and Fe3O4 nanoparticles, which facilitates it with SERS tracking and magnetic targeting abilities, followed by the covalent binding of the anti-HER2 antibody, thus allowing it to both actively and passively target SKBR3 cells, human breast cancer cells expressed with HER2. Intracellular drug delivery behaviors are probed using SERS spectroscopy in a spatiotemporal manner, which demonstrates that nanocarriers are internalized into the lysosomes and release the drug in response to the acidic microenvironment. The nanocarriers loaded with dual-drug possess increased cancer cytotoxicity in comparison to those loaded with a single drug. Attractively, the enhanced cytotoxicity against cancer cells is achieved with relatively low concentrations of the drug, which is demonstrated to be involved in the drug adsorption status. These results may give us the new prospects to design GO-based delivery systems with rational drug dosages, thus achieving optimal therapeutic response of the multi-drug with increased tumor selectivity and reduced side effects.

Detection of carbamazepine in saliva based on surface-enhanced Raman spectroscopy.

Carbamazepine (CBZ) is a commonly used drug for the treatment of epilepsy. Due to the narrow effective range, CBZ concentration was usually monitored with blood draw from patients. Frequent blood draw is inconvenient and causes physical and psychological pain. Therefore, highly-sensitive, rapid, label-free, and non-invasive drug detection methods can be alternatives to bring a relief. In this work, we have proposed a method for the non-invasive detection of CBZ using surface-enhanced Raman spectroscopy (SERS). Gold-silver core-shell nanomaterial substrates were prepared and optimized. Salivary CBZ concentration was measured with SERS as a non-invasive alternative to blood draw. The results showed that there was a linear relationship between SERS response and CBZ concentration in the entire measured range of 10-1 ∼ 10-8 mol/L. The detection limit of this method was 1.26 × 10-9 mol/L. Satisfactory repeatability and stability were also demonstrated. Due to its high sensitivity and ease of operation, the proposed method can serve as an alternative to blood draw for non-invasively monitoring CBZ concentration. It also has great potentials in many other applications of biomedical sciences.

Numerical Investigation of a Short Polarization Beam Splitter Based on Dual-Core Photonic Crystal Fiber with As2S3 Layer.

A polarization beam splitter is an important component of modern optical system, especially a splitter that combines the structural flexibility of photonic crystal fiber and the optical modulation of functional material. Thus, this paper presents a compact dual-core photonic crystal fiber polarization beam splitter based on thin layer As2S3. The mature finite element method was utilized to simulate the performance of the proposed splitter. Numerical simulation results indicated that at 1.55 µm, when the fiber device length was 1.0 mm, the x- and y-polarized lights could be split out, the extinction ratio could reach -83.6 dB, of which the bandwidth for extinction ratio better than -20 dB was 280 nm. It also had a low insertion loss of 0.18 dB for the x-polarized light. In addition, it can be completely fabricated using existing processes. The proposed compact polarization beam splitter is a promising candidate that can be used in various optical fields.

Broadband Plasmonic Polarization Filter Based on Photonic Crystal Fiber with Dual-Ring Gold Layer.

Polarization filter is a very important optical device with extinction characteristics. Due to the design flexibility of photonic crystal fibers and the high excitation losses of the gold layer, the polarization filter based on the photonic crystal fiber and surface plasmonic resonance effect is widely studied. Considering these, we present a simple and high-performance polarization filter using the finite element method. Numerical simulations show that there is a large difference in energy between the two polarization directions by reasonable adjustment of the structural parameters, the confinement loss in the x-pol direction is less than that in the y-pol direction, which is suitable to realize a broadband polarization filter. When the fiber length is 2 mm, the extinction ratio peak can reach -478 dB, and the bandwidth with the extinction ratio better than -20 dB is 750 nm, which covers communication wavelengths of 1.31 µm and 1.55 µm (1.05-1.8 µm). It also has a low insertion loss of 0.11 dB at 1.31 µm and 0.04 dB at 1.55 µm. In addition, our design has high feasibility in fabrication and better tolerance. The proposed filter with compactness, high extinction ratio, broad bandwidth, and low insertion loss would play an important role in the sensing detection, bio-medical, and telecommunication field.