Ulcerative colitis (UC) is a common inflammatory bowel disease, during which cell necroptosis plays key roles in driving inflammation initiation and aggravation. Previous studies reported Receptor ...Interacting Protein Kinase 3 (RIP3)-mediated necroptosis in multiple diseases, and RIP3 protein in Paneth cells significantly enriched in the intestines of both humans and mice. Therefore, we hypothesized targeting RIP3 to inhibit necroptosis may depress UC.
We classified clinical UC samples according to the modified Truelove & Witts criterion. The expression of RIP3 was measured by western blot and immunohistochemistry. Cell proliferation and apoptosis were analyzed by MTT assay and flow cytometry. ROS production and the secretion of inflammatory cytokines were measured by DCFH-DA probe and ELISA assay. TLR4/MyD88/NF-κB signaling pathway was analyzed by western blot. We established experimental colitis model in RIP3 knockout and wild-type mice and disease activity index (DAI) score was calculated. The expression and distribution of tight junction protein were analyzed by immunofluorescence. The ratio of CD4
Foxp3
T cells in the spleen was detected by flow cytometry. Oxidative damage of mouse colon was assessed by detecting the levels of SOD, MDA and MPO. Data were analyzed by one-way ANOVA or student's t test.
The expression of RIP3 in human colon is positively associated with the severity of UC. RIP3 inhibitor GSK872 or RIP3 knockdown reverses the inhibitory effect of TNF-α on proliferation and the promoting effect of TNF-α on apoptosis and necrosis in human intestinal epithelial cells. In addition, RIP3 deficiency inhibits the secretion of inflammatory cytokines (IL-16, IL-17 and IFN-γ) and ROS production induced by TNF-α. In vivo, RIP3 inhibitor Nec-1 effectively improves DSS-induced colitis in mice. In mechanism, RIP3 depression could upregulate the proportion of CD4
Foxp3
immunosuppressive Treg cells in the spleen while suppressed TLR4/MyD88/NF-κB signaling pathway and ROS generation, and all these anti-inflammation factors together suppress the secretion of inflammatory cytokines and necroptosis of intestinal epithelial cells.
This study preliminarily explored the regulating mechanism of RIP3 on UC, and Nec-1 may be a promising drug to alleviate the inflammation and necroptosis of the colon in UC patients.
Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. ...In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto-infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K-nearest neighbor (KNN), and principal component analysis-linear discriminant analysis (PCA-LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.
Early and accurate diagnosis of cystic echinococcosis (CE) with existing technologies is still challenging. Herein, we proposed a novel strategy based on the combination of label-free serum ...surface-enhanced Raman scattering (SERS) spectroscopy and machine learning for rapid and non-invasive diagnosis of early-stage CE. Specifically, by establishing early- and middle-stage mouse models, the corresponding CE-infected and normal control serum samples were collected, and silver nanoparticles (AgNPs) were utilized as the substrate to obtain SERS spectra. The early- and middle-stage discriminant models were developed using a support vector machine, with diagnostic accuracies of 91.7% and 95.7%, respectively. Furthermore, by analyzing the serum SERS spectra, some biomarkers that may be related to early CE were found, including purine metabolites and protein-related amide bands, which was consistent with other biochemical studies. Thus, our findings indicate that label-free serum SERS analysis is a potential early-stage CE detection method that is promising for clinical translation.
•The detection of echinococcosis and liver cirrhosis by serum Raman spectroscopy was investigated.•The diagnosis model was established by multivariate analysis.•High and satisfactory diagnostic ...results were obtained.
In this paper, we investigated the feasibility of using serum Raman spectroscopy and multivariate analysis method to discriminate echinococcosis and liver cirrhosis from healthy volunteers. Raman spectra of serum samples from echinococcosis, liver cirrhosis, and healthy volunteers were recorded under 532 nm excitation. The normalized mean Raman spectra revealed specific biomolecular differences associated with the disease, mainly manifested as the contents of β carotene in the serum of patients with echinococcosis and liver cirrhosis were lower than those of healthy people. Furthermore, principal components analysis (PCA), combined with linear discriminant analysis (LDA), was adopted to distinguish patients with echinococcosis, liver cirrhosis, and healthy volunteers. The overall diagnostic accuracy based on the PCA-LDA algorithm was 87.7 %. The diagnostic sensitivities to healthy volunteers, patients with echinococcosis, and liver cirrhosis were 92.5 %, 81.5 %, and 89.1 %, and the specificities were 93.2 %, 96.1 %, and 92.4 %, respectively. This exploratory work demonstrated that serum Raman spectroscopy technology combined with PCA-LDA diagnostic algorithm has great potential for the non-invasive identification of echinococcosis and liver cirrhosis.
This paper proposes a label-free and spectrometer-free method for biological detection with high detecting resolution. Taking advantage of the optical properties of porous silicon microcavity, the ...refractive index changes caused by biological reaction can be detected by measuring the incident angle of the minimum reflected light intensity. Based on the above method, label-free eight-base pair DNA detection can be realized with a corresponding detection limit is as low as 87 nM. This method provides high detecting resolution at a low equipment cost, and can be further used to develop an advanced instrument for biological detection.
•In this study, the normalized mean Raman spectra of cervical adenocarcinoma and cervical squamous cell carcinoma tissues were analyzed and compared.•The main Raman characteristic peaks of the ...normalized mean spectrum of two types of cervical cancer tissues were summarized.•The main differences of the normalized mean Raman spectra between two types of cervical cancer tissues were pointed out.•The differences of biochemical components between two types of cervical cancer tissues were analyzed.•PCA-SVM model was established, and the classification accuracy of cervical adenocarcinoma and squamous cell carcinoma was 93.125%.
In this report, we collected the Raman spectrum of cervical adenocarcinoma and cervical squamous cell carcinoma tissues by a micro-Raman spectroscopy system. We analysed, compared and summarized the characteristics and differences of the normalized mean Raman spectra of the two tissues and pointed out the major differences in the biochemical composition between the two tissues. The PCA-SVM model that was used to distinguish the two types of cervical cancer tissues was established. The accuracy of the model in differentiating cervical adenocarcinoma from cervical squamous cell carcinoma was 93.125%.
The results of this study indicate that Raman spectroscopy of cervical adenocarcinoma and cervical squamous cell carcinoma tissue in combination with SVM (support vector analysis) and PCA (principal component analysis) can be useful for the classification of cervical adenocarcinoma and cervical squamous cell carcinoma tissues and for the exploration of the differences in biochemical compositions between the two types of cervical tissue. This study lays a foundation to further study Raman spectroscopy as a clinical diagnostic method for cervical cancer.
Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is ...time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data. Among all the bands, 1500-1700 cm
band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.
A rapid and inexpensive method of screening and diagnosis for echinococcosis is proposed for Raman spectroscopy, together with improved neural networks. We use the adaptive iteratively reweighted ...penalized least squares (airPLS) algorithm to deduct the fluorescence background from the Raman spectra of healthy people and echinococcosis patients. The processed data was compressed into the principal component by the PLS method, and the Kennard-stone (KS) algorithm was used to divide it into a training set and a testing set. Finally, the data was put into the back propagation (BP) neural network for modeling and prediction. The results show that the true positive rate of echinococcosis diagnosis is (94.2857 ± 4.0721)%, the true negative rate is (95.2381 ± 0)% and the overall accuracy rate is (94.6939 ± 2.3269)%. The algorithm is compared with three other algorithms and it is shown that its superiority can be proved. The Raman spectroscopy combined with the airPLS-KS-BP algorithm can achieve fast and accurate diagnosis of echinococcosis.
The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and ...optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or on global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image-processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east, and the Chengdu–Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower, at 80.33%. This study provides an objective and accurate assessment of the distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.
PDF
