Collinear double-pulse laser-induced breakdown spectroscopy based Cd profiling in the soil.

Cadmium (Cd) can migrate in the soil and is readily absorbed by crops. High Cd accumulated in grains poses a huge threat to human health by inhibiting the function of the kidney system. Thus, it is crucial to reveal the content of soil Cd in vertical-depth series using a fast, real-time, and reliable method. For this purpose, laser-induced breakdown spectroscopy (LIBS) combined with multivariate chemometrics was developed to analyze Cd content in the soil with vertical-depth series. Soil samples spiked with different levels of Cd were prepared, and LIBS spectra were obtained by single-pulse LIBS (SP-LIBS) and collinear double-pulse LIBS (CDP-LIBS) with wavelengths of 532 nm and 1064 nm. With appropriate parameters, CDP-LIBS showed better performance in detecting Cd than SP-LIBS. Partial least squares regression (PLSR), genetic algorithm (GA)-optimized back propagation artificial neural network (BP-ANN), and particle swarm optimization (PSO)-optimized least squares-support vector machine (LS-SVM) were tested for quantitative analysis of the spectra after median absolute deviation (MAD), multiple scattering correction (MSC), wavelet transform (WT), spectral averaging, and normalization. PSO-optimized LS-SVM yielded an ideal result, with a coefficient of determination (R2, 0.999) and root mean square error (RMSE, 0.359 mg/Kg) in the prediction dataset. Finally, CDP-LIBS coupled with PSO-optimized LS-SVM was employed to analyze soil Cd content in vertical-depth series to reveal the migration pattern of Cd. Our results indicated that soil Cd had a significant positive relationship with the inverse of soil depth. However, Cd was mainly concentrated in 0-20 cm and rarely leached below 45 cm in the soil. This study suggests that LIBS and its enhancement techniques provide a reliable method for revealing the content of soil Cd in vertical-depth series.

Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review.

Heavy metal pollution in agriculture is a significant problem that endangers human health. Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for material and elemental analysis, especially heavy metals, based on atomic emission spectroscopy. The LIBS technique has been widely used for rapid detection of heavy metals with its advantages of convenient operation, simultaneous detection of multi-elements, wide range of elements, and no requirement for the state and quantity of samples. However, the development of LIBS is limited by its detection sensitivity and limit of detection (LOD). Therefore, in order to improve the detection sensitivity and LOD of LIBS, it is necessary to enhance the LIBS signal to achieve the purpose of detecting heavy metal elements in agriculture. This review mainly introduces the basic instruments and principles of LIBS and summarizes the methods of enhanced LIBS signal detection of heavy metal elements in agriculture over the past 10 years. The three main approaches to enhancing LIBS are sample pretreatment, adding laser pulses, and using auxiliary devices. An enhanced LIBS signal may improve the LOD of heavy metal elements in agriculture and the sensitivity and stability of the LIBS technique. The enhanced LIBS technique will have a broader prospect in agricultural heavy metal monitoring and can provide technical support for developing heavy metal detection instruments.

Clinical study of external application of liuhe dan in the treatment of anal edge edema after mixed hemorrhoid operation.

To observe the therapeutic efficacy of external application of Liuhe Dan in the treatment of anal edge edema after mixed hemorrhoid operation. A total of 160 patients who had been treated for lotion anal edge edema from January 2016 to August 2018 were enrolled research ojbects. They were divided into control group accepting routine nursing methods (80 cases) and research group accepting external application of Liuhe Dan (80 cases). The therapeutic efficacies of two groups were compared. The anal edge edema score and wound pain score of the research group were significantly lower than those of the control group (p<0.05). Meanwhile, the length of hospital stay and wound healing time of the research group were significantly shorter than those of the control group (p<0.05). External application of Liuhe Dan in the treatment of anal edge edema after mixed hemorrhoid operation could significantly improve the therapeutic efficacy and alleviate the patient's pain, which is of great application value.

Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging.

This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874⁻1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans.

[Hyperspectral Technique for Estimating the Shelf-Lives of Fresh Spinach Leaves Covered with Film].

In order to prolong the shelf-life of fruits and vegetables, plastic films have been covered on them to improve water retention and keep external bacteria away. It is of great significance to estimate the quality of packaged fruits and vegetables accurately by predicting the shelf-life of them. In this research, hyperspectral technology combined with chemometric methods were employed to estimate the shelf-life of fresh spinach leaves in the same environment. Hyperspectral data covering the range of Vis-NIR (380～1 030 nm) and NIR (874～1 734 nm) were acquired from 300 spinach leaves (75 dishs) which were stored in 4 ℃ among 5 periods (0 d, 2 d, 4 d, 6 d, 8 d). Meanwhile, the chlorophyll contents of all spinach leaves were determined. The mean spectra of 300 spinach leaves (200 leaves in training set and 100 leaves in prediction set) were extracted. And then, principal component analysis (PCA) on the training set of 200 spectra from 5 periods of shelf-life displayed apparent cluster. Partial least-squares discriminant analysis (PLS-DA) models were established according to spectral datas and the virtual levels that we ascribed to the different storage periods previously. The total discriminant accuracy rates of prediction set were 83% (VIS-NIR) and 81% (NIR), respectively. The result indicated that the classification and prediction on the shelf-life of fresh spinach can be realized with hyperspectral technology combined with chemometric methods, which offered a theoretical guidance to evaluate the quality of packaged spinach for consumers, and provided technical supports for the development of instruments used for testing the shelf-life of fruits and vegetables in further study.

[Discriminate the Rape Sclerotinia at Early Stage Based on Confocal Raman Spectroscopy].

Oilseed rape(Brassica napus L. ) is a principal source of edible oil for human consumption and it feeds livestock as a by product with high energy and protein. However, oilseed plants often suffer from the invasion of various diseases, which could affect the yield and quality of the rapeseeds. Rape sclerotinia rot caused by the fungus sclerotinia sclerotiorum (Lib. ) de Bary may severely affect the growth of oilseed rape. Therefore, searching non-invasive detection methods of detection plant disease at early stage is crucial for monitoring growing conditions of crops. Confocal Raman spectroscopy in the region of 500～2 000 cm(-1) coupled with chemometrics methods were employed to discriminate the rape sclerotinia disease at early stage on the oilseed rape leaves. A total of 60 samples(30 healthy plant leaves and 30 infected leaves) were used to acquire the Raman spectra and wavelet transform was applied to remove the fluorescence background. Regression coefficients of the partial least squares-discriminant analysis(PLS-DA) were used to select the 8 characteristic peaks based on the whole Raman spectra. 983，1 001, 1 205, 1 521, 1 527, 1 658, 1 670 and 1 758 cm(-1) were employed to establish PLS-DA discriminate models and recognition accuracy was 100%. The results showed Raman spectra combined with chemometrics method is promising for detecting rape sclerotinia infection in the oilseed rape leaves at early stage. This study provided a theoretical reference for researching the interaction between the fungus and plants and early detecting of disease infection.

[Study on Soil Elements Detection with Laser-Induced Breakdown Spectroscopy: A Review].

Laser-induced breakdown spectroscopy (LIBS), as a kind of atomic emission spectroscopy, has been considered to be a future new tool for chemical analysis due to its unique features, such as no need of sample preparation, stand-off or remote analysis. What's more it can achieve fast and multi-element analysis. Therefore, LIBS technology is regarded as a future "SurperStar" in the field of chemical analysis and green analytical techniques. At present, rapid and accurate detection and prevention of soil contamination (mainly in pollutants of heavy metals and organic matter) is deemed to be a concerned and serious central issue in modern agriculture and agricultural sustainable development. In this paper, the reseach achievements and trends of soil elements detection based on LIBS technology were being reviewed. The structural composition and foundmental of LIBS system is first briefly introduced. And the paper offers a review of on LIBS applications and fruits including the detection and analysis of major element, nutrient element and heavy metal element. Simultaneously, some studies on soil related metials and fields are briefly stated. The research tendency and developing prospects of LIBS in soil detection are presented at last.

[Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect feature of 86 Hawthorn could be recognized. Lastly, the detect precision of bruised, insect damage and two-defect samples is 95.65%, 86.67% and 100%, respectively. This investigation demonstrated that hyperspectral imaging technology could detect the defects of bruise, insect damage, calyx, and stem-end in hawthorn fruit in qualitative analysis and feature detection which provided a theoretical reference for the defects nondestructive detection of hawthorn fruit.

[Application of Hyperspectral Imaging for Visualization of Nitrogen Content in Pepper Leaf with Different Positions].

In order to estimate pepper plant growth rapidly and accurately, hyperspectral imaging technology combined with chemometrics methods were employed to realize visualization of nitrogen content (NC) distribution. First, pepper leaves were picked up with the leaf number based on different leaf positions, and hyperspectral data of these leaves were acquired. Then, SPAD and NC value of leaves were measured, respectively. After acquirement of pepper leaves' spectral information, random-frog (RF) algorithm was chosen to extract characteristic wavelengths. Finally, five characteristic wavelengths were selected respectively, and then those characteristic wavelengths and full spectra were used to establish partial least squares regression (PLSR) models, respectively. As a result, SPAD predicted model had an excellent performance of R(C) = 0.970, R(CV) = 0.965, R(P) = 0.934, meanwhile evaluation parameters of NC predicted model were R(C) = 0.857, R(CV) = 0.806, R(P) = 0.839. Lastly, according to the optimal models, SPAD and NC of each pixel in hyperspectral images of pepper leaves were calculated and their distribution was mapped. In fact, SPAD in plant can reflect the NC. In this research, the change trend of both was similar, so the conclusions of this research were proved to be corrected. The results revealed that it was feasible to apply hyperspectral imaging technology for mapping SPAD and NC in pepper leaf, which provided a theoretical foundation for monitoring plant growth and distribution of nutrients.

Bu-zhong-yi-qi pill alleviate the chemotherapy-related fatigue in 4 T1 murine breast cancer model.

BACKGROUND: Paclitaxel induced fatigue still remains underrecognized and undertreated, partly because of limited understanding of its pathophysiology and lack of effective treatments. This study is aim to evaluate the anti-fatigue effects and mechanism of Bu-Zhong-Yi-Qi pill in murine 4 T1 breast cancer mice were treated with paclitaxel. METHODS: Breast cancer mice established with murine 4 T1 cells were randomly and repectively divided into five groups: negative control group (NC), tumor control group (TC), paclitaxel group (PTX), Bu-Zhong-Yi-Qi pill group (BZYQ) and Bu-Zhong-Yi-Qi pill plus paclitaxel group (BZYQ + PTX). The mice were administered for 21 days. During this period, the tumor volume, body weight and the weight-loaded swimming time were measured. After the last administration, all mice were sacrificed, weighted the tumor, measured immune cell cytokines and oxidative stress indicator. The remaining 10 mice in each group were observed for survival analysis. RESULTS: Treatments with BZYQ + PTX and PTX significantly reduced the rates of tumor volume in comparison with TC starting on the 9th day and the 18th day respectively (P < 0.05-0.01), and presented decreased tumor weight compared to TC (P < 0.05-0.01). Compared with mice in TC group, the median survival time and the average survival time in BZYQ + PTX group, BZYQ group and PTX group were significantly prolonged (P < 0.05-0.01). The swimming time of the BZYQ + PTX group gradually increased, which is longer than the PTX group on Day 14 and Day 21 (P < 0.01). The level of TNF-α was lower in BZYQ + PTX group than PTX group (P < 0.01). The level of SOD activity in BZYQ + PTX group was lower than the NC group (P <0.01), but much higher than the PTX group (P < 0.01). The level of MDA of BZYQ + PTX group was higher than the NC group (P < 0.01), but significant lower than the PTX group (P < 0.01). CONCLUSIONS: BZYQ has the potential of alleviating paclitaxel chemotherapy-related fatigue in 4 T1 breast cancer mice by reducing the serum levels of TNF-α and modulating the level of MDA and the SOD activity.

[Study on SPAD visualization of pumpkin leaves based on hyperspectral imaging technology].

Visible/near-infrared (380 approximately 1 030 nm) hyperspectral imaging technique was used to realize SPAD visualization of pumpkin leaves in the present study. Downy mildew could be diagnosed rapidly according to significant positive correlation between downy mildew epidemic and chlorophyll content. Leaves uninfected and infected with different level downy mildew were used to acquire hyperspectral images and extract spectral information. Competitive adaptive reweighted sampling (CARS) was applied to select optimal wavelengths and finally 10 optimal wavelengths were obtained. Partial least squares regression (PLSR) was employed to establish SPAD prediction model. Results showed that, through the analysis of calibration of 48 samples and prediction of 23 samples, CARS-PLSR could obtain good results with Rc= 0. 918, RMSECV= 3. 932; Rcv- 0. 846, RMSECV = 5. 254; Rp = 0. 881, and RMSEP= 3. 714. Regression model was gained based on the relationship between SPAD and spectral of pumpkin leaves. While SPAD of each pixel was calculated with PLSR regression equation, then SPAD distribution map of pumpkin was visualized using imaging processing technology. Final downy mildew infection could be diagnosed based on SPAD distribution map. This study provided a theoretical reference for effective monitoring plant growth and downy mildew epidemic.

[Study on identification the crack feature of fresh jujube using hyperspectral imaging].

Crack is one of the most important indicators to evaluate the quality of fresh jujube. Crack not only accelerates the decay of fresh jujube, but also diminishes the shelf life and reduces the economic value severely. In this study, the potential of hyperspectral imaging covered the range of 380 - 1030 nm was evaluated for discrimination crack feature (location and area) of fresh jujube. Regression coefficients of partial least squares regression (PLSR), successive projection analysis (SPA) and principal component analysis (PCA) based full-bands image were adopted to extract sensitive bands of crack of fresh jujube. Then least-squares support vector machine (LS-SVM) discriminant models using the selected sensitive bands for calibration set (132 samples)" were established for identification the prediction set (44 samples). ROC curve was used to judge the discriminant models of PLSR-LS-SVM, SPA-LS-SVM and PCA-LS-SVM which are established by sensitive bands of crack of fresh jujube. The results demonstrated that PLSR-LS-SVM model had an optimal effect (area=1, std=0) to discriminate crack feature of fresh jujube. Next, images corresponding to five sensitive bands (467, 544, 639, 673 and 682 nm) selected by PLSR were executed to PCA. Finally, the image of PC4 was employed to identify the location and area of crack feature through imaging processing. The results revealed that hyperspectral imaging technique combined with image processing could achieve the qualitative discrimination and quantitative identification of crack feature of fresh jujube, which provided a theoretical reference and basis for develop instrument of discrimination of crack of jujube in further work.

Suspected coexistence of perianal necrotizing sweet syndrome in chronic myelomonocytic leukemia: A case report.

BACKGROUND: Chronic myelomonocytic leukemia (CMML) complicated with Sweet syndrome (SS) is a rare hematological neoplasm. However, cases of concomitant development of perianal necrotizing SS (NSS) have not been reported. CASE SUMMARY: We report a case of a 49-year-old male patient who underwent sequential procedures for hemorrhoids and perianal abscess. He developed postoperative incision infection and was referred to the department where the authors work. Initially, perianal necrotizing fasciitis secondary to incision infection after perianal abscess surgery was suspected. Despite receiving antibiotic therapy and undergoing surgical debridement, deeper necrotic areas formed in the patient's perianal wounds, accompanied by persistent high fever. Blood and fungal cultures yielded negative results. The final diagnosis was corrected to be CMML with suspected concomitant perianal NSS. CONCLUSION: CMML with perianal NSS is a rare condition, often misdiagnosed as perianal abscess or perianal necrotizing fasciitis. Conventional antibiotic therapy and surgical debridement are ineffective in managing this condition.

Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning.

Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.

Terahertz Spectroscopy and Imaging Techniques for Herbal Medicinal Plants Detection: A Comprehensive Review.

Herbal medicine (HM), derived from various therapeutic plants, has garnered considerable attention for its remarkable effectiveness in treating diseases. However, numerous issues including improved varieties selection, hazardous residue detection, and concoction management affect herb quality throughout the manufacturing process. Therefore, a practical, rapid, nondestructive detection technology is necessary. Terahertz (THz) spectroscopy, with low energy, penetration, and fingerprint features, becomes preferable method for herb quality appraisal. There are three parts in our review. THz techniques, data processing, and modeling methods were introduced in Part I. Three primary applications (authenticity, composition and active ingredients, and origin detection) of THz in medicinal plants quality detection in industrial processing and marketing were detailed in Part II. A thorough investigation and outlook on the well-known applications and advancements of this field were presented in Part III. This review aims to bring new enlightenment to the in-depth THz application research in herbal medicinal plants.

Modified ultrasound scalpel haemorrhoidectomy versus conventional haemorrhoidectomy for mixed haemorrhoids: a study protocol for a single-blind randomised controlled trial.

BACKGROUND: Haemorrhoids are common and frequently occurring diseases in the clinical setting, and severe haemorrhoids require surgical treatment. There are various surgical methods to treat haemorrhoids, but each has advantages and disadvantages. In recent years, ultrasonic scalpels have been used in haemorrhoid surgery and have achieved good results. Ultrasonic scalpel haemorrhoidectomy is safer and more effective in the surgical treatment of grade III and IV haemorrhoids, with less intraoperative bleeding, less postoperative pain, and fewer complications than diathermic therapy, electrosurgical haemorrhoidectomy, PROXIMATE® PPH haemorrhoidal circular stapler haemorrhoidopexy (PPH), and traditional haemorrhoidectomy. In previous reports, the majority of ultrasonic scalpel haemorrhoidectomies were performed as open procedures, with only the body of the haemorrhoid removed with the ultrasonic scalpel and the wound left open for drainage and natural healing. However, we performed a preliminary experiment with 12 patients who underwent open ultrasonic scalpel haemorrhoidectomy in the early stage. The results showed that 8 patients had different degrees of postoperative bleeding, and 4 of them required a second haemostatic surgery under anaesthesia. Therefore, we modified the open ultrasonic scalpel haemorrhoidectomy procedure by removing the mucosa of the internal haemorrhoid and closing the base of the incision with figure-eight penetrating sutures and designed this study protocol to evaluate its clinical efficacy and safety. METHODS: A randomised single-blind parallel-controlled trial is proposed for this project, and patients who meet the inclusion criteria will be divided into a test group and a control group, with 39 patients in each group. The experimental group will be treated with modified ultrasonic scalpel haemorrhoidectomy, and the control group will be treated with the Milligan-Morgan operation. The effectiveness of modified ultrasonic scalpel haemorrhoidectomy for haemorrhoids will be objectively evaluated, including the incision healing time and the time for patients to return to normal activities, postoperative complications, evaluations of anal function 3 months and 6 months after surgery, an evaluation of quality of life 6 months after surgery, and an evaluation of the patient satisfaction rate 6 months after surgery. The safety assessment will consider all adverse and serious adverse events associated with the study treatment. DISCUSSION: The study was approved by the ethics committee. The first patient was registered on July 1 2021. The purpose of this trial will be to evaluate the clinical efficacy and safety of the modified ultrasonic scalpel haemorrhoidectomy procedure for the treatment of mixed haemorrhoids and to provide an evidence base for the clinical promotion and application of the procedure. A limitation of this study is that only the patients will be single-blinded because the researchers and the patients cannot be blinded at the same time, which may produce certain bias in the results. In addition, the sample size of this study will be small, and the test results will only represent the findings from this clinical trial. In later stages, the sample size needs to be further expanded to improve the level of evidence. Despite its limitations, we hope the present study will help provide a more optimised surgical approach in the selection of haemorrhoid surgery. TRIAL REGISTRATION: Chinese Clinical Trial Registry (Registration ID: ChiCTR2100047229). Registered on June 11, 2021.

Chinese herbal medicine liu jun zi tang and xiang sha liu jun zi tang for functional dyspepsia: meta-analysis of randomized controlled trials.

Objectives. To assess the efficacy and safety of Liu Jun Zi Tang (LJZT) and Xiang Sha Liu Jun Zi Tang (XSLJZT) for treating functional dyspepsia. Methods. Literature searches were carried out on Medline database, Cochrane Library, CNKI database, Chinese Biomedical Literature database, Wanfang database, and VIP database up to July 2012. Hand search for further references was conducted. Study selection, data extraction, quality assessment, and data analyses were performed according to the Cochrane standards. Results. Fifteen publications in total were suitable for inclusion. There was evidence that LJZT compared with prokinetic drugs increased symptom improvement (odds ratio 1.96, 95% CI 1.15 to 3.36). There was also evidence that XSLJZT compared with prokinetic drugs increased symptom improvement (odds ratio 2.63, 95% CI 1.72 to 4.03). No adverse events were reported in LJZT or XSLJZT group in any of these randomized controlled trials. Conclusion. LJZT and XSLJZT might be more effective compared with prokinetic drugs in the treatment of functional dyspepsia, and no side effects are identified in the included trials. However, due to poor methodological quality in the majority of included studies, the potential benefit from LJZT and XSLJZT need to be confirmed in rigorously designed, multicentre, and large-scale trials.

The development and application of advanced analytical methods in microplastics contamination detection: A critical review.

Microplastics have gradually become emerging environmental contaminants for their extensive distribution, small particle size, and harmful effects on organisms. Therefore, finding accurate, efficient, and rapid analytical methods for detecting microplastics pollution has become an urgent problem. We reviewed the derivation, transport, and classification of microplastics and then highlighted the harmfulness of microplastics which would bring microplastics pollution to the environment and potential damage to organisms. Further, various analytical methods were classified into the thermal analytical method, spectral analytical approach, and other analytical methods based on detection principles. In addition, the application of each analytical method in sea and soil was concluded in detail, and the promising development prospect of each analytical method was discussed. In the end, the chemical analytical method was proposed to explore further in the direction of no sample preparation, nondestructive analysis, low detection limit and it is crucial to establish a unified detection and identification method for microplastics in different environments.

Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review.

Heavy metal elements, which inhibit plant development by destroying cell structure and wilting leaves, are easily absorbed by plants and eventually threaten human health via the food chain. Recently, with the increasing precision and refinement of optical instruments, optical imaging spectroscopy has gradually been applied to the detection and reaction of heavy metals in plants due to its in-situ, real-time, and simple operation compared with traditional chemical analysis methods. Moreover, the emergence of machine learning helps improve detection accuracy, making optical imaging spectroscopy comparable to conventional chemical analysis methods in some situations. This review (a): summarizes the progress of advanced optical imaging spectroscopy techniques coupled with artificial neural network algorithms for plant heavy metal detection over ten years from 2012-2022; (b) briefly describes and compares the principles and characteristics of spectroscopy and traditional chemical techniques applied to plants heavy metal detection, and the advantages of artificial neural network techniques including machine learning and deep learning techniques in combination with spectroscopy; (c) proposes the solutions such as coupling with other analytical and detection methods, portability, to address the challenges of unsatisfactory sensitivity of optical imaging spectroscopy and expensive instruments.

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage.

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.