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[Display omitted] • BM pretreatment changed physicochemical properties of digestive slurry and promoted release of VFAs, COD, and free water. • Excessive BM times (>60 min) and TS (>8%) are not recommended. • BM pretreatment shortened lag phase time (37.41–56.12%) and increased methane production (45.63–104.11%). • BM pretreatment improved rheological properties of digestive slurry and reduced stirring consumption during AD. • Energy analysis and energy conversion evaluation illustrates that BM pretreatment is a viable strategy. In this study, the effects of ball-milling (BM) pretreatment on the anaerobic digestion (AD) performance and energy conversion efficiency of corn straw (CS) were explored. The AD testing was conducted by varying BM times (0–120 min) and TS (4–10 %). The results showed that BM pretreatment increased microbial and enzyme accessibility to the CS and facilitated the hydrolytic acidification process of AD. The peak concentration of volatile fatty acids (VFAs) (5325.69 mg/L) and chemical oxygen demand (COD) (14524 mg/L) were increased, resulting in more substrates available for methanogenic bacteria. The lag phase time was shortened (37.41–56.12 %) and the cumulative methane production was increased (45.63–104.11 %). Accelerated rate of total ammonia nitrogen (TAN) release resulted in increased buffering capacity of the digestion system. Reduced the initial density of the digestive slurry (6.91–9.58 %) and altered the water distribution (maximum increase in free water content to 70.4 %), which facilitated the agitation of the AD process. Excessive BM times (>60 min) and TS (>8%) are not recommended. The BM60-6 presented the highest volatile solids (VS) removal (78.79 %), cellulose degradation (92.07 %) and biochemical methane potential (BMP) (309.69 mL/g VS). The BM pretreatment also improved the rheological characteristics of digestive slurry and reduced the stirring power consumption of the AD process. The BM60-6 showed the largest net energy benefit (28.2101 KJ/g VS) and net residual value (0.5000), and the high energy input of the pretreatment was balanced. This discovery breaks the conventional wisdom that mechanical pretreatment is meaningless because of higher power consumption. [ABSTRACT FROM AUTHOR]

Herein, we fabricated a label-free ECL immunosensor for aflatoxin B1 (AFB1) detection. In this system, a small organic aggregation-induced electrochemiluminescence luminophore, 2,5-di-tetraphenylethylene-ylthiazolo [5,4-d] thiazole, was designed, named TPETTZ. Polyaniline-wrapped TiO2 nanoparticles (PANI/TiO2 NPs) complex was synthesized through one-step in situ oxidation polymerization of aniline, and performed excellent electrical conductivity and abundant amino groups. As an ECL accelerator, TiO2 nanoparticles (TiO2 NPs) promoted the oxidation of tri-n-propylamine (TPA) to generate more TPA•; in addition, it also acted as a donor to improve the ECL intensity of TPETTZ (acceptor) through electrochemiluminescence resonance energy transfer (ECL-RET). Encouraged by the above, under the existence of TPA, TPETTZ displayed a strong and continuously stable ECLanode signal due to the introduction of PANI/TiO2 NPs. Therefore, the immunosensor was constructed for AFB1 detection based on the quenching effect of target on the ECL signal, and a linearly decreasing ECL signal was obtained as the increasement of AFB1 in the range of 75 fg/mL to 100 ng/mL, with a lower detection limit of 27.5 fg/mL. Moreover, the as-prepared sensing platform performed a satisfactory anti-interference, stability, and reproducibility, and appeared a good accuracy in walnut sample analysis, presenting a promising application in the future. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of electrochemiluminescence (ECL) sensors, more luminophores have been designed to achieve high-throughput and reliable analysis. Impressively, after the proposed fantastic concept of "aggregation-induced electrochemiluminescence (AIECL)" by Cola, the application of AIECL emitters provides more abundant choices for the further improvement of ECL sensors. In this review, we briefly report the phenomenon, principle and representative applications of aggregation-induced emission (AIE) and AIECL emitters. Moreover, it is noteworthy that the cases of AIECL sensors for bioanalytical detection are summarized in detail, from 2017 to now. Finally, inspired by the applications of AIECL emitters, relevant prospects and challenges for AIECL sensors are proposed, which is of great significance for exploring more advanced bioanalytical detection technology. [ABSTRACT FROM AUTHOR]

In this contribution, a uniformly distributed CuNi active nanoalloys was obtained on the electrode substrate by one-step facile electrodeposition method, and a simple non-enzymatic electrochemical glucose sensor was designed based on the CuNi NAs. The CuNi NAs with high specific surface area was investigated through a series of relevant characterizations, and the nanobimetal substrate electrode performed a satisfactory electrocatalytic activity toward glucose in alkaline solution compared with the pure Cu or Ni nanoparticles modified electrode. Chronoamperometry test showed an acceptable linearity in the range of 0.05–35 μM with the detection limit as low as 0.02 μM. In addition, this sensor demonstrated high sensitivity, stability and selectivity, and showed promising performance in the application of glucose measurement in human blood serum samples. Therefore, the constructed non-enzymatic glucose sensor based on CuNi nanoalloys exhibits a good prospect in food industry in the future. [ABSTRACT FROM AUTHOR]

Aggregation-induced electrochemiluminescence reagent, a distyrylbenzene derivative with donor–acceptor conjugated nanosheet structure, namely TPAPCN, was used as a trace label and modified on the electrode through the formation of classical sandwich complex of antibody-antigen–antibody in this work. In aggregate state, TPAPCN with twisted structure was limited in nanometer space through intermolecular π − π stacking interactions, which not only restricts the intramolecular motions but also combines a large number of singlet excitons to greatly trigger electrochemiluminescence (ECL). The ECL signal of this system enhanced with more captured cytokeratin 19 fragment 21–1 (CYFRA21-1) on the modified electrode. Three-dimensional graphene/platinum nanoparticles with large specific surface, and excellent electroconductivity and biocompatibility were prepared and acted as excellent carriers for thionine handling (3D-GN/PtNPs/Th), which was employed for improving the loading of antibodies and generating internal electrochemical signal. Consequently, a novel ratiometric sandwich immunosensor for CYFRA21-1 detection was fabricated based on TPAPCN and 3D-GN/PtNPs/Th, that is, a rapid and reliable detection was achieved through the ratio between ECL and electrochemical signals. The prepared sensor performed good linearity in the range of 50 fg/mL to 1 ng/mL with a detection limit as low as 16 fg/mL. Moreover, the detection results revealed well in the analysis of human serum samples, demonstrating a significant application for clinical monitoring and biomolecules detection. [ABSTRACT FROM AUTHOR]

Aggregation-induced electrochemiluminescence reagent, a distyrylbenzene derivative with donor–acceptor conjugated nanosheet structure, namely TPAPCN, was used as a trace label and modified on the electrode through the formation of classical sandwich complex of antibody-antigen–antibody in this work. In aggregate state, TPAPCN with twisted structure was limited in nanometer space through intermolecular π − π stacking interactions, which not only restricts the intramolecular motions but also combines a large number of singlet excitons to greatly trigger electrochemiluminescence (ECL). The ECL signal of this system enhanced with more captured cytokeratin 19 fragment 21–1 (CYFRA21-1) on the modified electrode. Three-dimensional graphene/platinum nanoparticles with large specific surface, and excellent electroconductivity and biocompatibility were prepared and acted as excellent carriers for thionine handling (3D-GN/PtNPs/Th), which was employed for improving the loading of antibodies and generating internal electrochemical signal. Consequently, a novel ratiometric sandwich immunosensor for CYFRA21-1 detection was fabricated based on TPAPCN and 3D-GN/PtNPs/Th, that is, a rapid and reliable detection was achieved through the ratio between ECL and electrochemical signals. The prepared sensor performed good linearity in the range of 50 fg/mL to 1 ng/mL with a detection limit as low as 16 fg/mL. Moreover, the detection results revealed well in the analysis of human serum samples, demonstrating a significant application for clinical monitoring and biomolecules detection. [ABSTRACT FROM AUTHOR]

The development of advanced adsorbents with fast adsorption rate, simple preparation, low cost, and high adsorption capacity is one of the most important topics for water purification. Herein, a novel and pollution-free adsorbent, three-dimensional nanoporous starch-based nanomaterial (3D-PSN), was prepared via sacrifice template method and functionalized for the first time in this work. Relevant characterization was performed through XRD, SEM, TGA, zeta potential analysis, FTIR, and XPS to confirm the formation of nanomaterials. Owing to its unique three-dimensional network nanostructure and abundant active sites, this adsorbent displayed outstanding adsorption properties for heavy metal ions removal, as high as 532.28 mg/g for Cd (II), 381.47 mg/g for Hg(II), 354.15 mg/g for Cu(II), 238.39 mg/g for Pb(II), completed within 30 min. In this process, the pseudo-second-order kinetic model appeared more consistent with the adsorption kinetic data, and the adsorption behavior complied with the Langmuir adsorption model. The adsorption mechanism mainly replied on the ion-exchange reaction, as well as chemical complexation formation. This adsorbent has remarkable recyclability, exhibiting strong application prospects for water purification and environmental remediation. • 3D-PSN was firstly synthesized via sacrifice template method based on native starch. • EDTA/3D-PSN showed high efficient adsorption for Cd(II), Hg(II), Pb(II), and Cu(II). • The adsorption process was exceptionally rapid, within 30 min. • Excellent reusability of this adsorbent [ABSTRACT FROM AUTHOR]

This study presents a novel method for the diagnosis of hepatitis B virus infection using human blood serum Raman spectroscopy combined with a deep learning model. The sera of 499 people infected with the hepatitis B virus and 435 healthy controls were measured in this experiment. The data were subjected to a dimensionality reduction by principal component analysis. Then, the features of multiple scales were preserved and fused by a multiscale fusion convolution operation. The gated recurrent unit network was added to extract time series features and finally output the result of the classification through a softmax layer. A diagnostic model based on a gated recurrent unit and multiscale fusion convolutional neural network was constructed and evaluated by a 10-fold cross-validation method. Compared to existing analysis methods for serum Raman spectroscopy, the proposed model achieved the best performance. The combination of Raman spectroscopy and deep learning models is expected to be applied well in the early screening of hepatitis B and is a promising screening method. [ABSTRACT FROM AUTHOR]

• An ECL immunosensor for AFB 1 based on illuminant COP T 4 VTP 6 and quencher Fc-CHO/Phe. • COP T 4 VTP 6 acted as a nanoreactor to catalyze and protect the formation of PtNPs. • The in situ generated PtNPs served as coreaction accelerator. • ECL response increased with increasing AFB 1 via competition quenching mechanism. • The proposed system provides a great application platform. Herein, a competitive quenching electrochemiluminescence immunosensor towards aflatoxin B 1 (AFB 1) detection was constructed by in-situ forming platinum nanoparticles (PtNPs) on ECL emitter COP T 4 VTP 6 and effective ECL signal quencher Fc-CHO/Phe. In this system, cationic covalent organic polymer COP T 4 VTP 6 emitted stronger cathode ECL signal at 765 nm, it acted as an interesting nanoreactor to immobilize PtCl 6 2− through electrostatic adsorption, and directly in situ catalyzed the redox reaction to produce PtNPs without adding any external reducing agent, where PtNPs not only served as the substrate for antibody immobilization, but also played the role of coreaction accelerator to catalyze the production of SO 4 −, significantly improving more stable ECL signal. Moreover, the Fc-CHO/Phe labeled BSA-AFB 1 was used for competitive reaction. Based on the efficient sensing strategy, ECL signal increased accordingly and exhibited linear signal responses with increasing AFB 1 , which realized a detection limit of 4.56 fg/mL, providing a promising potential on food analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • We designed a potential-resolved differential ECL immunosensor based on IBPHF and 3D-PSC NPs for AFB 1 detection. • IBPHF with PRECL property generated two independent ECL signals in the presence of TPA/K 2 S 2 O 8. • ECL signal decreased with the increment of target via the classical competitive mechanism of "antibody-antigen-antigen". • The quantification of the target was achieved based on the difference between the ECL anode and ECL cathode of IBPHF. • The proposed system can provide a further platform for other immunoassays. Electrochemiluminophore system with potential-resolved has exhibited giant potentiality for the application of bioassays in recent years, however, rarely studies toward the novel luminophore with potential-resolved electrochemiluminescence (PRECL) have been reported. In this work, a novel organoboron complex microrod (IBPHF) with PRECL property was synthesized, which emitted two strongest electrochemiluminescence (ECL) signals in the anode region at around 1.5 V and cathode region at around −1.5 V by using tri- n -propylamine (TPA)/K 2 S 2 O 8 as coreactant. The relevant ECL properties and luminescence mechanisms have been discussed in detail. Moreover, electroactive substrate of three-dimensional porous starch carbon nanomaterials (3D-PSC NPs) were designed to capture antibodies. As a result, the competitive potential-resolved differential electrochemiluminescence immunosensor was fabricated, and greatly enhanced sensitivity was achieved by using IBPHF by using IBPHF as a carrier to label competing antigens. In the presence of aflatoxin B 1 (AFB 1), due to the competitive mechanism and the specific recognition of antigen–antibody, the well-prepared immunosensor performed a quantitative decreasement in the range from 50 fg/mL to 100 ng/mL with a minimum detectable limit of 16.44 fg/mL by using the intensity difference (△ECL) (ECL anode -ECL cathode). The self-correction of △ECL extremely enhanced the accuracy and reliability of the sensor. Furthermore, the designed sensor achieved excellent detection towards AFB 1 in walnut samples, and performed a bright application for trace target detection in future. [ABSTRACT FROM AUTHOR]

In this article, a catalyst-free method for the preparation of orientation-ordered single-crystal Cd 1 − x Zn x S nanorod arrays on graphite substrates has been reported. The morphology and crystal structure of the Cd 1 − x Zn x S nanorod arrays were studied using scanning electron microscopy and transmission electron microscopy. The results showed that the nanorods with 40–100 nm in diameter and 2000 nm in length grew along the [0 0 1] direction of the hexagonal crystalline phase. A vapor–solid (VS) growth mechanism was proposed for the formation of the Cd 1 − x Zn x S nanorod arrays. The photoluminescence characterizations showed a strong blue emission at 420 nm for the Cd 1 − x Zn x S nanorod arrays when the value of x is 0.5. Thus, with a simple CVD technique, the high-density and orientation-ordered nanorod arrays would become a promising candidate in many applications such as building blocks for optoelectronics. [ABSTRACT FROM AUTHOR]

Fluorescent dyes have been considered as one of the most potential candidates for electrochemiluminescence (ECL) reagent due to the excellent photophysical and electrochemical characteristics in recent years. However, the severe self-absorption effect caused by narrow stokes shift inevitably limits its application for ECL. In this work, a new type of ECL emitter, 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene derivative, a BF2 complex of hydrazine-schiff base linked bispyrrole, namely BPYHBF, was prepared and employed as a probe to label the competitor of target in the immunosensor, which could emit powerful ECL signal with tri- n -propylamine. The structure and ECL property of BPYHBF were investigated. Additionally, multi-walled carbon nanotube/ferrocenecarboxaldehyde-metal organic framework (MWCNTs/Fc-MOF) as sensing substrate for Ab1 incubation was synthesized, where the large specific surface area and stable internal reference electrochemical signals improved the sensitivity and reliability of the sensor by self-correction. Herein, a novel ratiometric competitive immunosensor based on the ECLBPYHBF/DPVFc was prepared for ultrasensitive detection of aflatoxin B1 (AFB1). The ratio value is linear in the range from 10 fg/mL to 100 ng/mL, and the detection limit is 5.39 fg/mL. The prepared sensor exhibited excellent sensitivity, accuracy and reproducibility, and achieved satisfactory detection towards AFB1 in actual samples, this demonstrates a promising application in biomolecules detection. • We designed a novel ratiometric sandwich immunosensor for aflatoxin B 1 detection. • ECL signal was generated via the classical formation of antibody-antigen-antibody complex. • ECL response of the system increased with the increment of the target with high selectivity. • This immunosensor was established based on the ratio of ECL of BPYHBF and electrochemistry of Fc. • The proposed system can provide a further platform for other immunoassay. [ABSTRACT FROM AUTHOR]

Abstract: A fabrication of a novel simple porous silicon polybasic photonic crystal with symmetrical structure has been reported as a nucleic acid biosensor for detecting antifreeze protein gene in insects (Microdera puntipennis dzhungarica), which would be helpful in the development of some new transgenic plants with tolerance of freezing stress. Compared to various porous silicon-based photonic configurations, porous silicon polytype layered structure is quite easy to prepare and shows more stability; moreover, polybasic photonic crystals with symmetrical structure exhibit interesting optical properties with a sharp resonance in the reflectance spectrum, giving a higher Q factor which causes higher sensitivity for sensing performance. In this experiment, DNA oligonucleotides were immobilized into the porous silicon pores using a standard crosslink chemistry method. The porous silicon polybasic symmetrical structure sensor possesses high specificity in performing controlled experiments with non-complementary DNA. The detection limit was found to be 21.3nM for DNA oligonucleotides. The fabricated multilayered porous silicon-based DNA biosensor has potential commercial applications in clinical chemistry for determination of an antifreeze protein gene or other genes. [Copyright &y& Elsevier]

Abstract: External random factors have a great influence on the fabrication of accurate photonic crystal, especially porous silicon-based photonic crystals. Compared with the binary photonic crystal, polybasic structure photonic crystal shows more stability and smaller effect of the random fluctuation. In this paper, we have fabricated a novel simple porous silicon polybasic Bragg''s mirror combined with excellent specific antigen–antibody inmunoreaction as an immunosensor for determining Hydroxysafflor yellow A (HSYA), which is the main chemical component of Carthamus tinctorius L. The binding of HSYA and the polyclonal anti-HSYA antibodies causes red shifts in the reflection spectrum of the sensor, and the red shift was proportional to the HSYA concentration with linear relationship ranging from 1 to 3μgmL−1 with a detection limit of 0.78ngmL−1. Importantly, this research offers hope for development of a commercial porous silicon-based immunosensor for component determination of C. tinctorius L. or other antigens. [Copyright &y& Elsevier]

As a highly prevalent and recurrent cancer, detecting the degree of differentiation in oral cancer is crucial. Current methods rely on biopsies in the presence of significant lesions, which are time-consuming. This study introduces an efficient and rapid approach for mass determination of oral cancer differentiation levels. By leveraging serum Raman spectroscopy combined with deep neural networks and extreme gradient boosting, we performed feature selection and interaction on oral cancer samples of varying differentiation levels, achieving the most accurate and reliable classification of oral cancer differentiation stages. [ABSTRACT FROM AUTHOR]

• Universal design principles for ratiometric AIEgen probes. • Versatile construction strategies of ratiometric AIEgen probes. • Representative seminal application examples of ratiometric AIEgen probes in biosensing and bioimaging. • Addressing current challenges and exploring future perspectives in the development of ratiometric AIEgen systems. Ratiometric fluorescence probes, with their inherent self-calibration capabilities, have become indispensable tools in the fields of biosensing and bioimaging. These probes can correct for factors that are independent of the target, making the development of innovative ratiometric fluorescent probes a highly sought-after endeavor. Aggregation-induced emission luminogens (AIEgens) do not emit when in solution but show strong emission when aggregated, thus opening up exciting avenues for designing and creating high-performance ratiometric fluorescence probes. This comprehensive review meticulously details the recent advancements in AIEgen-based ratiometric fluorescence probes. The discussion encompasses design principles, construction strategies, and sensing mechanisms. Subsequently, the review explores the diverse applications of ratiometric AIEgen probes, demonstrating their efficacy with seminal examples in biosensing and bioimaging. The concluding section addresses future prospects and current challenges. We believe that this review should inspire researchers to make further contributions to this intriguing field and aid in its progression. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most common malignant tumors worldwide, and early detection is crucial for improving its cure rate. In the field of medical imaging, accurate segmentation of lesion areas within skin images is essential for precise diagnosis and effective treatment. Due to the capacity of deep learning models to conduct adaptive feature learning through end-to-end training, they have been widely applied in medical image segmentation tasks. However, challenges such as boundary ambiguity between normal skin and lesion areas, significant variations in the size and shape of lesion areas, and different types of lesions in different samples pose significant obstacles to skin lesion segmentation. Therefore, this study introduces a novel network model called HDS-Net (Hybrid Dynamic Sparse Network), aiming to address the challenges of boundary ambiguity and variations in lesion areas in skin image segmentation. Specifically, the proposed hybrid encoder can effectively extract local feature information and integrate it with global features. Additionally, a dynamic sparse attention mechanism is introduced, mitigating the impact of irrelevant redundancies on segmentation performance by precisely controlling the sparsity ratio. Experimental results on multiple public datasets demonstrate a significant improvement in Dice coefficients, reaching 0.914, 0.857, and 0.898, respectively. [ABSTRACT FROM AUTHOR]

Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large intra-class differences and small inter-class differences between pathological images of lung adenocarcinoma tissues under different grades. If attention mechanisms such as Coordinate Attention (CA) are directly used for lung adenocarcinoma grading tasks, it is prone to excessive compression of feature information and overlooking the issue of information dependency within the same dimension. Therefore, we propose a Dimension Information Embedding Attention Network (DIEANet) for the task of lung adenocarcinoma grading. Specifically, we combine different pooling methods to automatically select local regions of key growth patterns such as lung adenocarcinoma cells, enhancing the model's focus on local information. Additionally, we employ an interactive fusion approach to concentrate feature information within the same dimension and across dimensions, thereby improving model performance. Extensive experiments have shown that under the condition of maintaining equal computational expenses, the accuracy of DIEANet with ResNet34 as the backbone reaches 88.19%, with an AUC of 96.61%, MCC of 81.71%, and Kappa of 81.16%. Compared to seven other attention mechanisms, it achieves state-of-the-art objective metrics. Additionally, it aligns more closely with the visual attention of pathology experts under subjective visual assessment. [ABSTRACT FROM AUTHOR]

Purpose: The application of deep learning methods to the intelligent diagnosis of diseases has been the focus of intelligent medical research. When dealing with image classification tasks, if the lesion area is small and uneven, the background image involved in the training will affect the ultimate accuracy in determining the extent of the lesion. We did not follow the traditional approach of building an intelligent system to assist physicians in diagnosis from the perspective of CNN models, but instead proposed a pure transformer framework that can be used for diagnostic grading of pathological images. Methods: We propose a Symmetric Mask Pre-Training vision Transformer SMiT model for grading pathological cancer images. SMiT performs a symmetrically identical high probability sparsification of the input image token sequence at the first and last encoder layer positions to pre-train visual transformers, and the parameters of the baseline model are fine-tuned after loading the pre-training weights, allowing the model to concentrate more on extracting detailed features in the lesion region, effectively getting rid of the potential feature dependency problem. Results: SMiT achieved 92.8% classification accuracy on 4500 histopathological images of colorectal cancer processed by Gaussian filter denoising. We validated the effectiveness and generalizability of this study's methodology on the publicly available diabetic retinopathy dataset APTOS2019 from Kaggle and achieved quadratic Cohen Kappa, accuracy and F1-score of 91.9%, 86.91% and 72.85%, respectively, which were 1–2% better than previous studies based on CNN models. Conclusion: SMiT uses a simpler strategy to achieve better results to assist physicians in making accurate clinical decisions, which can be an inspiration for making good use of the visual transformers in the field of medical imaging. [ABSTRACT FROM AUTHOR]

Gold nanoparticles/785 porous silicon photonic crystals (Au NPs/785 PSi PhCs) were used as substrates in combination with a low-concentration serum surface-enhanced Raman spectroscopy (SERS) detection scheme to obtain spectral signals from healthy individuals and cervical cancer patients. The best principal component scores were selected by principal component analysis (PCA) combined with linear discriminant analysis (LDA) and support vector machine (SVM) to analyze the spectral differences distinguishing healthy and cervical cancer patients. The accuracy of the two models was 97.9% and 96.9%, respectively. SERS technique based on Au NPs/785 PSi PhCs has great potential to improve the screening of cervical cancer. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren's syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients. [ABSTRACT FROM AUTHOR]

Parotid tumors are among the most prevalent tumors in otolaryngology, and malignant parotid tumors are one of the main causes of facial paralysis in patients. Currently, the main diagnostic modality for parotid tumors is computed tomography, which relies mainly on the subjective judgment of clinicians and leads to practical problems such as high workloads. Therefore, to assist physicians in solving the preoperative classification problem, a stacked generalization model is proposed for the automated classification of parotid tumor images. A ResNet50 pretrained model is used for feature extraction. The first layer of the adopted stacked generalization model consists of multiple weak learners, and the results of the weak learners are integrated as input data in a meta-classifier in the second layer. The output results of the meta-classifier are the final classification results. The classification accuracy of the stacked generalization model reaches 91%. Comparing the classification results under different classifiers, the stacked generalization model used in this study can identify benign and malignant tumors in the parotid gland effectively, thus relieving physicians of tedious work pressure. [ABSTRACT FROM AUTHOR]

Considering the poor effect of short text classification due to insufficient semantic information mining in the current short text matching methods, a new short text classification method is proposed based on explicit and implicit multiscale weighting semantic information interaction. First, the explicit and implicit representations of short text are obtained by a word vector model (word2vec), convolutional neural networks (CNNs), and long short-term memory (LSTM). Then, a multiscale convolutional neural network obtains the explicit and implicit multiscale weighting semantics information of short text. Finally, the multiscale weighting semantics is fused for more accurate short text classification. The experimental results show that this method is superior to the existing classical short text classification algorithms and two advanced short text classification models on the five short text classification datasets of MR, Subj, TREC, SST1 and SST2 with accuracies of 85.7%, 96.9%, 98.1%, 53.4% and 91.8%, respectively. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren's syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients. [ABSTRACT FROM AUTHOR]

Purpose: Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients. Methods: In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA. Results: Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001). Conclusion: In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD. [ABSTRACT FROM AUTHOR]

Purpose: Skin cancer is one of the ten most common cancer types in the world. Early diagnosis and treatment can effectively reduce the mortality of patients. Therefore, it is of great significance to develop an intelligent diagnosis system for skin cancer. According to the survey, at present, most intelligent diagnosis systems of skin cancer only use skin image data, but the multi-modal cross-fusion analysis using image data and patient clinical data is limited. Therefore, to further explore the complementary relationship between image data and patient clinical data, we propose multimode data fusion diagnosis network (MDFNet), a framework for skin cancer based on data fusion strategy. Methods: MDFNet establishes an effective mapping among heterogeneous data features, effectively fuses clinical skin images and patient clinical data, and effectively solves the problems of feature paucity and insufficient feature richness that only use single-mode data. Results: The experimental results present that our proposed smart skin cancer diagnosis model has an accuracy of 80.42%, which is an improvement of about 9% compared with the model accuracy using only medical images, thus effectively confirming the unique fusion advantages exhibited by MDFNet. Conclusions: This illustrates that MDFNet can not only be applied as an effective auxiliary diagnostic tool for skin cancer diagnosis, help physicians improve clinical decision-making ability and effectively improve the efficiency of clinical medicine diagnosis, but also its proposed data fusion method fully exerts the advantage of information convergence and has a certain reference value for the intelligent diagnosis of numerous clinical diseases. [ABSTRACT FROM AUTHOR]

Food safety problems are becoming increasingly severe in modern society, and establishing an accurate food safety risk warning and analysis model is of positive significance in avoiding food safety accidents. We propose an algorithmic framework that integrates the analytic hierarchy process based on the entropy weight (AHP-EW) and the autoencoder-recurrent neural network (AE-RNN). Specifically, the AHP-EW method is first used to obtain the weight percentages of each detection index. The comprehensive risk value of the product samples is obtained by weighted summation with the detection data, which is used as the expected output of the AE-RNN network. The AE-RNN network is constructed to predict the comprehensive risk value of unknown products. The detailed risk analysis and control measures are taken based on the risk value. We applied this method to the detection data of a dairy product brand in China for example validation. Compared with the performance of 3 models of the back propagation algorithm (BP), the long short-term memory network (LSTM), and the LSTM based on the attention mechanism (LSTM-Attention), the AE-RNN model has a shorter convergence time, predicts data more accurately. The root mean square error (RMSE) of experimental data is only 0.0018, proving that the model is feasible in practice and helps improve the food safety supervision system in China to avoid food safety incidents. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value. [ABSTRACT FROM AUTHOR]

As an emerging technology for artificial intelligence‐aided medical diagnosis, deep learning combined with Raman spectroscopy has great potential. The technology still has some problems in the actual medical diagnosis research process. The differences in spectrometers, experimental conditions, and experimental operations can result in non‐uniform and universally applicable data standards, which in turn lead to low data utilization. At the same time, it is still necessary to retrain the models when building diagnostic models for different diseases, which is time‐consuming and laborious. In this paper, a more complete transfer learning model for multiple types of serum Raman spectra is established for the first time, and a decision fusion strategy is applied to this diagnostic model. The Raman spectral data of serum from hepatitis B patients/control group, serum from abnormal thyroid function patients/control group, and serum from glioma patients/control group were selected as the source domains, and the Raman spectral data of tissue from hepatitis C patients/control group, serum from esophageal cancer patients/control group, and tissue from cervical cancer and cervical inflammation (patients/control) group were selected as the target domains. Three deep neural network models, ResNet, GoogLeNet, and CNN‐LSTM were trained in the source domain data for disease diagnosis, and the trained models were transfer to the target domain. The model is fine‐tuned by freezing different layers and then combined with logistic regression algorithms to construct a decision fusion model, which further improves the model effect. The results show that the proposed method can effectively improve the accuracy of transfer learning models. At the same time, this experiment extends the application of transfer learning in Raman spectroscopy and demonstrates that unrelated and scale‐different Raman datasets are still intrinsically connected, which also lays the foundation for us to build more stable and data‐inclusive spectral transfer learning fusion models in the future. [ABSTRACT FROM AUTHOR]

Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high mortality rate, such as renal failure and uremia. At present, the diagnosis of membranous nephropathy mainly relies on the clinical manifestations of patients and pathological examination of kidney biopsy, which are expensive, time-consuming, and have certain chance and other disadvantages. Therefore, there is an urgent need to find a rapid, accurate and non-invasive diagnostic technique for the diagnosis of membranous nephropathy. In this study, Raman spectra of serum and urine were combined with deep learning methods to diagnose membranous nephropathy. After baseline correction and smoothing of the data, Gaussian white noise of different decibels was added to the training set for data amplification, and the amplified data were imported into ResNet, AlexNet and GoogleNet models to obtain the evaluation results of the models for membranous nephropathy. The experimental results showed that the three deep learning models achieved an accuracy of 1 for the classification of serum data of patients with membranous nephropathy and control group, and the discrimination of urine data was above 0.85, among which AlexNet was the best classification model for both samples. The above experimental results illustrate the great potential of serum- and urine-based Raman spectroscopy combined with deep learning methods for rapid and accurate identification of patients with membranous nephropathy. [ABSTRACT FROM AUTHOR]

Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian. [ABSTRACT FROM AUTHOR]

Simple Summary: Guanine nucleotide-binding protein-like 3-like (GNL3L) is a novel GTP-binding nucleolar protein. In this study, we analyzed the expression, prognosis, and immune roles of GNL3L in pan-cancer from multiple omics analyses. The final results showed that GNL3L is differentially expressed in a variety of cancers, plays a prognostic role, and has good immune value. Moreover, GNL3L may affect the occurrence of cancer through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, we established an esophageal cancer (ESCA) prediction model with strong predictive ability and proved that GNL3L can significantly affect the proliferation ability of esophageal cancer cells through clone formation assays. In conclusion, GNL3L is an important biomarker. Guanine nucleotide-binding protein-like 3-like protein (GNL3L) is a novel, evolutionarily conserved, GTP-binding nucleolar protein. This study aimed to investigate the expression, prognosis, and immune value of GNL3L in pan-cancer from multiple omics analyses. Firstly, the expression and prognostic value of GNL3L in pan-cancer were discussed using the TIMER2 database, the GEPIA database, the cBioportal database, COX regression analysis, and enrichment analysis. The association of GNL3L with tumor mutational burden (TMB), tumor microsatellite instability (MSI), mismatch repair (MMR) genes, and immune cells was then analyzed. Finally, an esophageal cancer (ESCA) prediction model was established, and GNL3L clone formation assays were performed. The final results showed that GNL3L is differentially expressed in the vast majority of cancers, is associated with the prognosis of various cancers, and may affect cancer occurrence through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, it was found that the correlation between GNL3L and TMB, MSI, MMR, and various immune cells is significant. The established ESCA prediction model had a strong predictive ability, and GNL3L could significantly affect the proliferation of esophageal cancer cells. In conclusion, GNL3L may serve as an important prognostic biomarker and play an immunomodulatory role in tumors. [ABSTRACT FROM AUTHOR]

To improve the detection sensitivity of a porous silicon optical biosensor in the real-time detection of biomolecules, a non-spectral porous silicon optical biosensor technology, based on dual-signal light detection, is proposed. Double-light detection is a combination of refractive index change detection and fluorescence change detection. It uses quantum dots to label probe molecules to detect target molecules. In the double-signal-light detection method, the first detection-signal light is the detection light that is reflected from the surface of the porous silicon Bragg mirror. The wavelength of the detection light is the same as the wavelength of the photonic band gap edge of the porous silicon Bragg mirror. CdSe/ZnS quantum dots are used to label the probe DNA and hybridize it with the target DNA molecules in the pores of porous silicon to improve its effective refractive index and enhance the detection-reflection light. The second detection-signal light is fluorescence, which is generated by the quantum dots in the reactant that are excited by light of a certain wavelength. The Bragg mirror structure further enhances the fluorescence signal. A digital microscope is used to simultaneously receive the digital image of two kinds of signal light superimposed on the surface of porous silicon, and the corresponding algorithm is used to calculate the change in the average grey value before and after the hybridization reaction to calculate the concentration of the DNA molecules. The detection limit of the DNA molecules was 0.42 pM. This method can not only detect target DNA by hybridization, but also detect antigen by immune reaction or parallel biochip detection for a porous silicon biosensor. [ABSTRACT FROM AUTHOR]

Histopathological image analysis is the gold standard for pathologists to grade colorectal cancers of different differentiation types. However, the diagnosis by pathologists is highly subjective and prone to misdiagnosis. In this study, we constructed a new attention mechanism named MCCBAM based on channel attention mechanism and spatial attention mechanism, and developed a computer-aided diagnosis (CAD) method based on CNN and MCCBAM, called HCCANet. In this study, 630 histopathology images processed with Gaussian filtering denoising were included and gradient-weighted class activation map (Grad-CAM) was used to visualize regions of interest in HCCANet to improve its interpretability. The experimental results show that the proposed HCCANet model outperforms four advanced deep learning (ResNet50, MobileNetV2, Xception, and DenseNet121) and four classical machine learning (KNN, NB, RF, and SVM) techniques, achieved 90.2%, 85%, and 86.7% classification accuracy for colorectal cancers with high, medium, and low differentiation levels, respectively, with an overall accuracy of 87.3% and an average AUC value of 0.9.In addition, the MCCBAM constructed in this study outperforms several commonly used attention mechanisms SAM, SENet, SKNet, Non_Local, CBAM, and BAM on the backbone network. In conclusion, the HCCANet model proposed in this study is feasible for postoperative adjuvant diagnosis and grading of colorectal cancer. [ABSTRACT FROM AUTHOR]

Fennel contains many antioxidant and antibacterial substances, and it has very important applications in food flavoring and other fields. The kinds and contents of chemical substances in fennel vary from region to region, which can affect the taste and efficacy of the fennel and its derivatives. Therefore, it is of great significance to accurately classify the origin of the fennel. Recently, origin detection methods based on deep networks have shown promising results. However, the existing methods spend a relatively large time cost, a drawback that is fatal for large amounts of data in practical application scenarios. To overcome this limitation, we explore an origin detection method that guarantees faster detection with classification accuracy. This research is the first to use the machine learning algorithm combined with the Fourier transform-near infrared (FT-NIR) spectroscopy to realize the classification and identification of the origin of the fennel. In this experiment, we used Rubberband baseline correction on the FT-NIR spectral data of fennel (Yumen, Gansu and Turpan, Xinjiang), using principal component analysis (PCA) for data dimensionality reduction, and selecting extreme learning machine (ELM), Convolutional Neural Network (CNN), recurrent neural network (RNN), Transformer, generative adversarial networks (GAN) and back propagation neural network (BPNN) classification model of the company realizes the classification of the sample origin. The experimental results show that the classification accuracy of ELM, RNN, Transformer, GAN and BPNN models are above 96%, and the ELM model using the hardlim as the activation function has the best classification effect, with an average accuracy of 100% and a fast classification speed. The average time of 30 experiments is 0.05 s. This research shows the potential of the machine learning algorithm combined with the FT-NIR spectra in the field of food production area classification, and provides an effective means for realizing rapid detection of the food production area, so as to merchants from selling shoddy products as good ones and seeking illegal profits. [ABSTRACT FROM AUTHOR]

A technique is demonstrated to detect DNA hybridization based on surface layer of Au/porous silicon microcavity (Au/PSM) substrate for very small amount of biomolecules. Simulations show that the increase of effective refractive index for the first layer of PSM will cause a blue shift for its reflectance spectrum, and the blue shift becomes less with the increase of refractive index for one more layers. In experiments, such a blue shift of reflectance spectrum of PSM comes from the increase of refractive index by DNA hybridization on the surface. The detection limit of Au/PSM biosensor is 15.15 nM for 19-base pair DNA, which is comparable to that of reported biosensors based on porous silicon (PS). Therefore such an Au/PSM could be very useful to develop simple, rapid and sensitive optical biosensors when the amount of target is very small. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel spectral fusion diagnosis technology is proposed. • A new and efficient DBRAN fusion network is designed to process spectral data. • DBRAN fusion technology has achieved 100% accuracy, sensitivity and specificity in SLE disease classification, achieving significant performance improvement compared to single-modality (ORS or SERS) methods. As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future. [ABSTRACT FROM AUTHOR]

After collecting Raman spectral data of 12 compounds from commonly used Uyghur medicinal materials, the mixture data are synthesized using computer simulation methods. An accurate identification method for Uyghur medicine components based on Raman spectroscopy and multi-label deep learning is constructed, and the performance of the model Mix2Com is evaluated using several metrics such as accuracy, precision, recall value, and F1 score. [Display omitted] • Existing methods identifying Uyghur medicinal components aren't fit for large samples. • A new method based on Raman spectroscopy and multi-label learning is proposed. • Macro-parallel identification of components is achieved. • The model Mix2Com is constructed with LSTM and Attention introduced. • Mix2Com achieves 90.76% accuracy, better than XGBoost and DeepRaman. Uyghur medicine is one of the four major ethnic medicines in China and is a component of traditional Chinese medicine. The intrinsic quality of Uyghur medicinal materials will directly affect the clinical efficacy of Uyghur medicinal preparations. However, in recent years, problems such as adulteration of Uyghur medicinal materials and foreign bodies with the same name still exist, so it is necessary to strengthen the quality control of Uyghur medicines to guarantee Uyghur medicinal efficacy. Identifying the components of Uyghur medicines can clarify the types of medicinal materials used, is a crucial step to realizing the quality control of Uyghur medicines, and is also an important step in screening the effective components of Uyghur medicines. Currently, the method of identifying the components of Uyghur medicines relies on manual detection, which has the problems of high toxicity of the unfolding agent, poor stability, high cost, low efficiency, etc. Therefore, this paper proposes a method based on Raman spectroscopy and multi-label deep learning model to construct a model Mix2Com for accurate identification of Uyghur medicine components. The experiments use computer-simulated mixtures as the dataset, introduce the Long Short-Term Memory Model (LSTM) and Attention mechanism to encode the Raman spectral data, use multiple parallel networks for decoding, and ultimately realize the macro parallel prediction of medicine components. The results show that the model is trained to achieve 90.76% accuracy, 99.41% precision, 95.42% recall value and 97.37% F1 score. Compared to the traditional XGBoost model, the method proposed in the experiment improves the accuracy by 49% and the recall value by 18%; compared with the DeepRaman model, the accuracy is improved by 9% and the recall value is improved by 14%. The method proposed in this paper provides a new solution for the accurate identification of Uyghur medicinal components. It helps to improve the quality standard of Uyghur medicinal materials, advance the research on screening of effective chemical components of Uyghur medicines and their action mechanisms, and then promote the modernization and development of Uyghur medicine. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sensitive and reproducible CuNPs@AgNPs@PSB SERS substrates were prepared using a self-assembly approach. • The CuNPs@AgNPs structure was successfully synthesized on the Porous silicon Bragg reflector (PSB) surface using the reduction of PSB and copper nanoparticles. • Low concentration detection of R6G and CPPU was completed. • The prepared high-performance and high-sensitivity SERS substrate have a very promising application in detection technology. The development of a highly sensitive, synthetically simple and economical SERS substrate is technically very important. A fast, economical, sensitive and reproducible CuNPs@AgNPs@ Porous silicon Bragg reflector (PSB) SERS substrate was prepared by electrochemical etching and in situ reduction method. The developed CuNPs@AgNPs@PSB has a large specific surface area and abundant "hot spot" region, which makes the SERS performance excellent. Meanwhile, the successful synthesis of CuNPs@AgNPs can not only modulate the plasmon resonance properties of nanoparticles, but also effectively prolong the time stability of Cu nanoparticles. The basic performance of the substrate was evaluated using rhodamine 6G (R6G). (Detection limit reached 10−15 M, R2 = 0.9882, RSD = 5.3 %) The detection limit of Forchlorfenuron was 10 μg/L. The standard curve with a regression coefficient of 0.979 was established in the low concentration range of 10 μg/L −100 μg/L. This indicates that the prepared substrates can accomplish the detection of pesticide residues in the low concentration range. The prepared high-performance and high-sensitivity SERS substrate have a very promising application in detection technology. [ABSTRACT FROM AUTHOR]

Researchers continuously modify deep learning network architecture for improved fusion results. However, little attention is given to the influence of noise feature maps generated during the convolution process on the fusion outcomes. Here, we aim to minimize the influence of noisy feature maps on fusion results and propose a fusion model, the infrared and visible image fusion model based on adaptive selection feature maps (ASFFuse). We propose an adaptive selection feature maps module (ASFM). ASFM measures the amount of information contained in each feature map and filters out feature maps that contain more noise information. Additionally, we introduce a feature enhancement module (FEM) to enrich the fusion image with more source image information. For unsupervised training of the proposed model, we propose a texture loss function based on contrast learning. This loss function preserves the texture information of the image in a better way and makes the fusion image have a better visual effect. Our ASFFuse model has been shown to outperform state-of-the-art models in both quantitative and qualitative evaluations in extensive experiments on the TNO and RoadScene datasets. The code is available at https://github.com/LKZ1584905069/ASFFuse. [Display omitted] • This method is the first infrared and visible image fusion method based on adaptive selection of feature maps. • To achieve feature enhancement in fused images, we use a feature enhancement module. • We design a texture loss function based on contrast learning. [ABSTRACT FROM AUTHOR]

Purpose: Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious.Methods: Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way.Results: Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images.Conclusions: This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images. [ABSTRACT FROM AUTHOR]

Screening and diagnosis of early‐stage lung cancer is low worldwide, and when lung cancer progresses to late stage, it will greatly affect the treatment and survival time of patients. Therefore, the development of an affordable and accurate diagnostic technique is critical for lung cancer patients. In this study, we analyzed and verified the changes of substance composition in the serum of early‐stage lung cancer patients and proposed an improved ResNeXt model to achieve accurate classification of serum Raman spectra of lung cancer. The robustness of the model is improved by adding different decibels of Gaussian white noise and spectral offset to augment the data, trying to process the augmented data with various pre‐processing methods, and inputting the pre‐processed data into the improved ResNeXt model, the improved ResNeXt model still shows the optimal performance after sufficient comparison experiments with three other more advanced deep learning models. The experimental results show that the improved ResNeXt model is very suitable for the classification of early‐stage lung cancer serum Raman spectra and may provide a reference for future early screening studies of other cancers. [ABSTRACT FROM AUTHOR]

With the development of commodity economy, the emergence of fake and shoddy raisin has seriously harmed the interests of consumers and enterprises. To deal with this problem, a classification method combining near-infrared spectroscopy and pattern recognition algorithms were proposed for adulterated raisins. In this study, the experiment was performed by three kinds of raisins in Xinjiang (Hongxiangfei, Manaiti, Munage). After collecting and normalizing the spectral data, we compared the spectra of three kinds of raisins. Next the principal component analysis (PCA) was preformed to compress the dimension of the spectral data, and then classification models including support vector machine (SVM), multiscale fusion convolutional neural network (MCNN) and improved AlexNet were established to identify raisins. The accuracy of SVM, MCNN, and improved AlexNet is 100%, 92.83%, and 97.78% respectively. This study proves that near-infrared spectroscopy combined with pattern recognition is feasible for the raisin inspection. [ABSTRACT FROM AUTHOR]

The influence of permeable layer number of porous silicon microcavity (PSM) on the reflectance spectrum has been carried out from both simulations and experiments. The increase of the effective refractive index of the first porous silicon (PS) layer of PSM will causes a blue shift for the reflectance spectrum. With one more layer refractive index increasing, the blue shift becomes less, and it will shift to a higher wavelength when the layer number becomes large enough. This study would be helpful for simple fabrication of highly sensitive, fast responsible biosensors based on the blue shift of reflectance spectrum, and such fabricated Au/PSM substrate of multilayer PS might have a potential to detect minor biomolecules by means of surface state effects. [ABSTRACT FROM AUTHOR]

We report on the fluorescence enhancement induced by gold nanoparticles (Au NPs) deposited on porous silicon Bragg reflectors (Bragg PS). The rhodamine 6 G (R6G) fluorescence was studied on single-layer porous silicon films and porous silicon Bragg reflectors. By comparison, R6G fluorescence of porous silicon films with Au NPs infiltrated is about two times that of porous silicon films without Au NPs. The R6G fluorescence of this porous silicon Bragg reflectors with Au NPs infiltrated is also stronger than the single-layered porous silicon films (∼2.3-fold) with Au NPs infiltrated. Such substrates with a multiple enhancement of the fluorescence have great potential for applications in the fields of medical diagnostics and biotechnology. [ABSTRACT FROM AUTHOR]

We fabricated a new porous silicon photonic device which is a special multi-layer porous silicon including two different single layer porous silicon and a porous silicon Bragg mirror, and investigated the influence of porous silicon Bragg mirror's structure on the fluorescence intensity of quantum dots (QDs) which infiltrated into porous silicon device, and CdSe/ZnS QDs we used emit at 605 nm and 625 nm respectively. By immersing porous silicon samples in QDs solution, QDs were successfully infiltrated into porous silicon devices which have high reflection band at or beyond fluorescence peak. Experimental results show that the fluorescence intensity of QDs which infiltrated into the first layer of porous silicon device can be enhanced when fluorescence peak falls into the high reflection band of porous silicon device. [ABSTRACT FROM AUTHOR]

Medical diagnosis technology based on convolutional neural networks (CNNs) has achieved good performance. In this study, we collected serum samples from 38 glioma patients and 45 healthy controls and used partial least squares (PLS) analysis to reduce the dimension of the data. Different levels of noise were added to the reduced data onto data augmentation, and the AlexNet, ResNet, and GoogLeNet fine‐tuning models were applied for classification. To evaluate the performance of the models, we used five‐fold cross‐validation. The accuracy rates of the AlexNet, ResNet, and GoogLeNet fine‐tuning models were 98.50%, 98.24%, and 99.50%, respectively. The model with the best classification effect was GoogLeNet. The specificity and sensitivity of this model were 98.98% and 98.48%, respectively. In addition, the area under the receiver operating characteristic (ROC) curve (AUC) of the established diagnostic model was 0.9949. The results showed that the combination of serum Raman spectroscopy and the PLS‐Gaussian‐GoogLeNet model achieved a good diagnostic effect for glioma. This method has high clinical application value and is worthy of further popularization. [ABSTRACT FROM AUTHOR]

Zhejiang Suichang native honey, which is included in the list of China's National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey. [ABSTRACT FROM AUTHOR]

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value. [ABSTRACT FROM AUTHOR]

In recent years, liver cancer has caused great harm to human health. This study aims to use serum Raman spectroscopy combined with deep learning algorithms to classify liver cancer patients and control groups. For improving the robustness of models, we added equal proportions of Gaussian white noise of 5, 10, 15, 20, 25 dBW to enhance the data, and compared the results of convolutional neural networks and long short-term memory networks which show that the convolutional neural network combined with 10-fold data enhancement was better. The accuracy was 96.95%, indicating the great potential of this experimental model in liver cancer detection. [ABSTRACT FROM AUTHOR]