Bioinformatic analysis of SMN1-ACE/ACE2 interactions hinted at a potential protective effect of spinal muscular atrophy against COVID-19-induced lung injury.

Patients with spinal muscular atrophy (SMA) are susceptible to the respiratory infections and might be at a heightened risk of poor clinical outcomes upon contracting coronavirus disease 2019 (COVID-19). In the face of the COVID-19 pandemic, the potential associations of SMA with the susceptibility to and prognostication of COVID-19 need to be clarified. We documented an SMA case who contracted COVID-19 but only developed mild-to-moderate clinical and radiological manifestations of pneumonia, which were relieved by a combined antiviral and supportive treatment. We then reviewed a cohort of patients with SMA who had been living in the Hubei province since November 2019, among which the only 1 out of 56 was diagnosed with COVID-19 (1.79%, 1/56). Bioinformatic analysis was carried out to delineate the potential genetic crosstalk between SMN1 (mutation of which leads to SMA) and COVID-19/lung injury-associated pathways. Protein-protein interaction analysis by STRING suggested that loss-of-function of SMN1 might modulate COVID-19 pathogenesis through CFTR, CXCL8, TNF and ACE. Expression quantitative trait loci analysis also revealed a link between SMN1 and ACE2, despite low-confidence protein-protein interactions as suggested by STRING. This bioinformatic analysis could give hint on why SMA might not necessarily lead to poor outcomes in patients with COVID-19.

Efficacy and Safety of Huashi Baidu Granules in the Treatment of Children Suffering from Influenza with Exterior-cold and Interior-heat Syndrome: A Multi-center, Randomized Controlled Trial Protocol.

Background: Influenza is one of the major public health problems worldwide. Children are the high-risk group for influenza and the high-risk population with symptoms. Huashi Baidu(HSBD) Granules have played a great role in fighting against COVID-19. In recent decades, this recipe has also been applied by pediatricians to treat influenza, with remarkable curative effects. However, there is still a lack of high-quality evidence-based medical practice. Methods: This study was designed as a multi-center, randomized, parallel-controlled trial, with an estimated sample size of 520 children suffering from influenza with exterior-cold and interior-heat syndrome. All the enrolled children will be randomly assigned to a test group and a control group. Children in the test group were treated with Huashi Baidu Granules, and those in the control group were provided with Oseltamivir Phosphate Granules for intervention. The primary outcome measure was the time to clinical recovery, with the Chinese version of the Canadian Acute Respiratory Illness and Flu Scale (CARIFS) score measured at baseline and every 24 hours after treatment, which was evaluated at the endpoint of follow-up. The secondary outcome was the time to complete fever remission, scores of CARIFS symptom dimensions and the area under the curve with time, the incidence of complications/severe/critical influenza, the rate of single symptom disappearance, and the therapeutic effect of traditional Chinese medicine syndromes, which were recorded at baseline and after treatment, and evaluated at the end of treatment. Safety and endpoint events were evaluated at the same time. Conclusion: This study is intended to apply Huashi Baidu Granules to treat influenza with exterior-cold and interior-heat syndrome to identify the clinical efficacy and safety of this recipe. Simultaneously, our study will also discuss the characteristics of clinical syndrome in traditional Chinese medicine and syndrome distribution of influenza in the studied children.

Rapid identification of the variety of maize seeds based on near-infrared spectroscopy coupled with locally linear embedding.

Maize is the main cereal crop in China. In the process of maize planting, the selection of suitable maize varieties is an important link to achieving a high yield. Because the appearance of maize seeds is very similar, it is difficult to accurately identify their species with the naked eye. In order to realize the rapid identification of different varieties of maize seeds, this paper proposes a rapid identification method of maize varieties based on near-infrared (NIR) spectroscopy coupled with locally linear embedding (LLE) and a support vector machine (SVM). The NIR data, preprocessed by multiple scattering correction (MSC), were dimensionally reduced by LLE, a principal component analysis (PCA), and isometric mapping (Isomap), and combined with SVM to establish a maize variety identification model. The results show that the LLE-SVM model has the best performance, whose classification accuracy and kappa coefficient of the test set can reach 100% and 1.00. The classification accuracy and kappa coefficient of the LLE-SVM model are better than the PCA-SVM model and Isomap-SVM model. Therefore, LLE can reduce the complexity of the model and improve the accuracy of the model. It can be used for the rapid identification of maize varieties and provide a new idea for the classification and identification of other agricultural products.

Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy.

BACKGROUND: The aim of this study was to investigate the potential use of renal ultrasonography radiomics features in the histologic classification of glomerulopathy. METHODS: A total of 623 renal ultrasound images from 46 membranous nephropathy (MN) and 22 IgA nephropathy patients were collected. The cases and images were divided into a training group (51 cases with 470 images) and a test group (17 cases with 153 images). A total of 180 dimensional features were designed and extracted from the renal parenchyma in the ultrasound images. Least absolute shrinkage and selection operator (LASSO) logistic regression was then applied to these normalized radiomics features to select the features with the highest correlations. Four machine learning classifiers, including logistic regression, a support vector machine (SVM), a random forest, and a K-nearest neighbour classifier, were deployed for the classification of MN and IgA nephropathy. Subsequently, the results were assessed according to accuracy and receiver operating characteristic (ROC) curves. RESULTS: Patients with MN were older than patients with IgA nephropathy. MN primarily manifested in patients as nephrotic syndrome, whereas IgA nephropathy presented mainly as nephritic syndrome. Analysis of the classification performance of the four classifiers for IgA nephropathy and MN revealed that the random forest achieved the highest area under the ROC curve (AUC) (0.7639) and the highest specificity (0.8750). However, logistic regression attained the highest accuracy (0.7647) and the highest sensitivity (0.8889). CONCLUSIONS: Quantitative radiomics imaging features extracted from digital renal ultrasound are fully capable of distinguishing IgA nephropathy from MN. Radiomics analysis, a non-invasive method, is helpful for histological classification of glomerulopathy.

Suppression of miR-330-3p alleviates DSS-induced ulcerative colitis and apoptosis by upregulating the endoplasmic reticulum stress components XBP1.

BACKGROUND: This study aimed to explore the biological activities of miR-330-3p in dextan sulphate sodium (DSS)-induced ulcerative colitis and apoptosis and the direct target of miR-330-3p in this process. HT-29 cells and male C57BL/6 mice were used to examine the function of miR-330-3p in vitro and in vivo, respectively. Expression of miRNA and mRNA was measured using quantitative real time PCR (qRT-PCR). Western blotting was used to measure the change of protein expression. Flow cytometry was used to determine cell apoptosis and luciferase assay was used to confirm the direct target of miR-330-3p. RESULTS: miR-330-3p expression was increased by DSS in both HT-29 cells and mice. Upregulation miR-330-3p induced cell apoptosis, mice weight loss and ulcerative colitis in vivo, which could prevent by suppression of miR-330-3p. Cell apoptosis related protein expression, cleaved caspase-3 and cleaved PARP was also inhibited by miR-330-3p overexpression and elevated by miR-330-3p inhibition both in vitro and in vivo. Luciferase assay confirmed that 3' untranslated region (3'-UTR) of XBP1 is the directed target of miR-330-3p and Western blotting results have showed that protein expression of XBP1 was decreased by miR-330-3p mimics and increased by miR-330-3p inhibitor. CONCLUSION: miR-330-3p is upregulated by DSS in both HT-29 cells and mice and promoted ulcerative colitis and cell apoptosis by targeting of 3'-UTR of XBP1, which is a key component of ER stress. Inhibition of miR-330-3p prevent DSS-induced ulcerative colitis and cell apoptosis mediated by upregulation of XBP1 expression.

[Two cases of rare diseases with abnormalities of X chromosome].

OBJECTIVE: To explore the clinical features and genetic diagnosis of two cases with rare diseases and X chromosome abnormalities. METHODS: Multiple ligation-dependent probe amplification (MLPA) and karyotype analysis were carried out on an 8-year-old girl who was diagnosed with Duchenne muscular dystrophy. Karyotype analysis and PCR assay for SRY and AZF genes were carried out for a-2-month-old male infant with short penis. RESULTS: The girl, who featured short stature and cubitus valgus, was diagnosed as Turner syndrome with a karyotype of 46,X,i(Xq). The male infant was detected with a karyotype of 45,X, with presence of SRY gene but absence of AZF gene. CONCLUSION: Both cases may be associated with abnormalities of X chromosome. Genetic testing can facilitate early diagnosis and clinical intervention for such patients.

[Method for the Discrimination of the Variety of Potatoes with Vis/NIR Spectroscopy].

Potato (Solanum tuberosum L.) , as one of the most important carbohydrate food crops in the China ranking thefourth after rice, wheat and maize, plays a significant role in national economy. Since there are many varieties of potato, the quality such as physical sensory property and chemical components, differ drastically with the variety of potato. Different potato varieties are suitable for different utilization. Thus, the rapid and nondestructive identification of potato cultivars plays an important role in the better use of varieties. Near infrared (NIR) spectroscopy has raised a lot of interest in the classification and identification of agricultural products because it is a rapid and non-invasive analytical technique. In this study, a rapid visible (VIS) and near infrared (NIR) spectroscopic system was explored as a tool to measure the diffuse spectroscopy of three different species of potatoes. 352 potato samples (Sample A 142, Sample B 84, Sample C 126) from different sites in Heilongjiang province of China, obtained from peddlers market, were randomly divided into two sets at random: calibration set and prediction set, with 307 samples and 45 samples respectively for each set. The potatoes in the calibration set were tested with visible-near infrared spectroscopy method. The spectral data obtained from this test were analyzed with near infrared spectral technology, along with data processing algorithm, i.e., Savitzky-Golay (S-G) smoothing and multiplicative scatter correction (MSC). The spectra data was firstly transformed by multiplicative scatter correction (MSC) to compensate for additive and/or multiplicative effects. In order to reduce the noise components from a raw spectroscopic data set, Savitzky-Golay smoothing and differentiation filter method were introduced. It was proved that, with the soothing segment size of 9, many high frequency noises components can be eliminated. Based on the following analysis with principal component analysis (PCA), partial least square (PLS) regression and back propagation artificial neural network (BP-ANN), a near infrared discrimination model was established. The results obtained from the partial least squares (PLS) analysis showed a positive cumulate reliability of more than 96% for the first four components. The clustering effect was also getting better. After that, twenty absorption peaks extracted from the first four principal components were applied as BP neural network inputswhile a three layers BP neural network [20(input) - 12(implicit) - 3 (output)] was constructed, upon which the recognition accuracy of potato varieties for those Prediction Set samples reaches 100%. As a result, the model established in this study can rapidly and accurately identify potato varieties without any destruction, which provides a new way for potato quality detection and variety identification.

[Regulation of Jinxin Oral Liquid for the expression of negative regulatory factor of TLR3 signaling pathway SOCS1 in RSV infected BALB/c mice].

OBJECTIVE: To investigate the regulation trend of Jinxin Oral Liquid (JXOL) on the expression of negative regulatory factor of TLR3 signaling pathway SOCS1 in the lung tissue of RSV infected BALB/c mice at different time points. METHODS: Totally 75 BALB/c mice were randomly divided into 5 groups, i.e., the normal control group, the model group, the ribavirin group, the high dose JXOL group, and the equivalent dose JXOL group, 15 in each group. Each group had 3 intervention ways (I, II, and III) with 5 mice treated in each group. BALB/c mice were nasally infected with respiratory syncytial virus (RSV), and treated by different intervention ways. After intervention, mice were killed and their lung tissues were sampled, mRNA expression levels of RSV-M, SOCS1, and IFN-ß were detected by Real time PCR. The expression of SOCSl at the protein level was detected by Western blot. RESULTS: Compared with the normal control group, the mRNA expression level of SOCS1 and IFN-ß, and the protein expression level of SOCS1 increased significantly in the model group intervened by intervention I and II (all P < 0.01), but the mRNA expression level of IFN-ß decreased significantly in model group intervened by intervention III (P < 0.01). Compared with the model group, the mRNA expression level of RSV-M all significantly decreased in the high dose JXOL group and the equivalent dose JXOL group intervened by 3 intervention ways (all P < 0.01). The mRNA expression level of SOCS1 significantly decreased in the high dose JXOL group intervened by intervention I and III and the equivalent dose JXOL group intervened by 3 intervention ways (all P < 0.01). The mRNA expression level of IFN-ß significantly decreased in the high dose JXOL group intervened by intervention I and II and the equivalent dose JXOL group intervened by intervention I (all P < 0.01), while it significantly increased in the high dose JXOL group intervened by intervention III and the equivalent dose JXOL group intervened by intervention III (all P < 0.01). The protein expression level of SOCS1 significantly decreased in the high dose JXOL group intervened by intervention I and the equivalent dose JXOL group intervened by 3 intervention ways (all P < 0.01), while it significantly increased in the high dose JXOL group intervened by intervention III (all P < 0.01). Compared with the high dose JXOL group, the mRNA expression level of RSV-M decreased significantly in the equivalent dose JXOL group intervened by intervention I and II (P < 0.01). The mRNA expression level of SOCS1 and IFN-ß decreased significantly in the equivalent dose JXOL group intervened by intervention I (P < 0.01), but the mRNA expression level of IFN-ß increased significantly in the equivalent dose JXOL group intervened by intervention II and III (all P < 0.01). The protein expression level of SOCS1 decreased significantly in the equivalent dose JXOL group intervened by 3 intervention ways (all P < 0.01). CONCLUSIONS: JXOL could inhibit the expression of SOCS1 in the lung tissue of RSV infected BALB/c mice at different time points. Its regulatory effect might be associated with promoting the expression of interferon type I and further fighting against RSV.

Near-infrared spectral expansion method based on active semi-supervised regression.

BACKGROUND: The utilization of near-infrared (NIR) spectroscopy, in conjunction with chemometric techniques, has been widely used in a variety of sectors, including agricultural production and pharmaceutical production. Nevertheless, the laborious and arduous procedure of gathering samples and evaluating their physicochemical properties leads to relatively limited training set sizes for modeling. This problem severely limits the optimization and practical application of NIR spectrum analysis models. The Safer Active Semi-Supervised Sample Augmentation Learning Model (Safer-AS3A) proposed in this paper tries to address the problem by incorporating active learning (AL) and semi-supervised learning (SSL) techniques. RESULTS: Experiments were conducted on two sets of publicly available NIR spectral datasets, and the Safer-AS3A model was compared to other models with similar characteristics. The experimental results indicate that the Safer-AS3A model proposed in our study outperforms comparable models in terms of accuracy and robustness when dealing with scenarios having a limited number of labeled samples. Furthermore, after the training set was expanded with the Safer-AS3A model, the Partial least squares regression (PLSR), Bayesian ridge regression (BRR), and Support vector regression (SVR) models on the Diesel dataset improved their R2 on the test set by 5.923 %, 3.018 %, and 7.331 %, respectively, compared to the models using only the labeled sample set. On the other hand, the Ridge regression (RR), BRR, and SVR models on the test set on the Shoot dataset improved the R2 by 4.169 %, 4.449 %, and 11.597 %, respectively. Overall, the Safer-AS3A model can effectively expand the NIR spectral dataset and considerably improve the performance of the NIR spectral analysis model. SIGNIFICANCE AND NOVELTY: Using the AL method, the SSL method, and the co-training method together, a novel and effective method is presented for generating high-quality pseudo-labels. This method opens up new avenues for enhancing the efficiency and precision of NIR spectrum analysis. It also provides novel perspectives on sample diversification and prospective applications in other disciplines.

Wavelength selection method for near-infrared spectroscopy based on Max-Relevance Min-Redundancy.

Near-infrared spectroscopy (NIRS) is a rapid, nondestructive analytical technique utilized in various fields. However, the NIR data, which consists of hundreds of dimensions, may exhibit considerable duplication in the spectrum information. This redundancy might impair modeling effectiveness. As a result, feature selection on the spectral data becomes critical. The Max-Relevance Min-Redundancy (mRMR) method stands out among the different feature selection techniques for dimensional reduction. The approach depends on mutual information (MI) between random variables as the basis for feature selection and is unaffected by modeling methods. However, it is necessary to clarify the benefits of the maximum correlation minimal redundancy algorithm in the context of near-infrared spectral feature selection, as well as its adaptability to various modeling methods. This research focuses on the NIR spectral dataset of maize germination rate, and the mRMR method is utilized to select spectral features. Based on the preceding foundation, we create models for Support Vector Regression, Gaussian Process Regression, Random Forest, and Neural Networks. The experimental findings demonstrate that, among the feature selection methods employed in this paper, the Max-Relevance Min-Redundancy algorithm outperforms others regarding the corn germination rate dataset.

Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics.

As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.

Evaluation of the effect of kidney transplantation on left ventricular myocardial work by noninvasive pressure-strain loops.

Purpose: Kidney transplantation (KT) has the potential to reverse the cardiac changes caused by end-stage renal disease, and it may be inaccurate to analysis the left ventricular function by conventional echocardiography due to afterload. This study aimed to investigate the utility of pressure strain loops (PSLs) in evaluating left ventricular performance in patients underwent KT. Methods: We enrolled 60 patients with end-stage renal disease who underwent KT between January 2022 and July 2023, and 60 healthy controls with a similar distribution of gender and age to the patients. All participants underwent conventional echocardiography and three-dimensional speckle tracking echocardiography (3D-STE). Long axis, short axis, and four cavity images were collected and cardiac parameters were measured. The echocardiographic changes of cardiac structure and function of all patients before KT and about 12 months after KT were recorded. Left ventricular myocardial work parameters were acquired by PSLs, including the global work index (GWI), global constructive work (GCW), global wasted work (GWW), global work efficiency (GWE) and global longitudinal strain (GLS). In addition, the correlation between PSLs and clinical data were explored. Results: Compared with controls, the conventional echocardiographic parameters, myocardial function indicators GWI and GCW appeared no difference in post-KT group, while the GWE and GLS decreased (p < 0.05), and the GWW increased (p < 0.05). Compared with pre-KT, the GLS, GWI, GCW and GWE increased in post-KT group, while the GWW decreased (all p < 0.05). The above indicators were correlated with left ventricular GLS and left ventricular ejection fraction. Conclusion: PSLs were more sensitive than traditional echocardiographic indicators in detecting changes in myocardial work and predicting left ventricular myocardial damage. This indicator could quantitatively evaluate myocardial work and provide a new and reliable non-invasive reference for clinical diagnosis and treatment of patients underwent KT.

Application of the combination method based on RF and LE in near infrared spectral modeling.

The dimensionality of near-infrared (NIR) spectral data is often extremely large. Dimensionality reduction of spectral data can effectively reduce the redundant information and correlation between spectral variables and simplify the model, which is crucial to increasing the model's performance. As a nonlinear feature extraction method, Laplacian Eigenmaps (LE) may preserve the local neighborhood information of the dataset, has high robustness, and is simple to compute. However, when the LE algorithm maps the data from high-dimensional space to low-dimensional space, it is often disturbed by irrelevant information and multicollinearity in the spectral data, which lowers the model's prediction performance. Random Frog (RF) algorithm can eliminate noise and collinearity in the spectrum. Therefore, before using the LE algorithm, we use the RF algorithm to eliminate irrelevant information in the spectrum and reduce the correlation between the spectra variables to increase the efficiency of the LE algorithm. We used the RF + LE algorithm to reduce the dimensionality of two public NIRS datasets (soil datasets and pharmaceutical tablets datasets) and compared it with RF and LE algorithms alone. We utilized Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) to establish regression models. The experimental findings demonstrate that compared with the RF algorithm and LE algorithm, the RF + LE combination method can reduce the dimension of spectral variables and model complexity, and improve regression models' prediction accuracy and stability. It is an effective dimensionality reduction method for the near-infrared spectrum.

Development of a multimodal kidney age prediction based on automatic segmentation CT image in patients with normal renal function.

Background: For decades, description of renal function has been of interest to clinicians and researchers. Serum creatinine (Scr) and estimated glomerular filtration rate (eGFR) are familiar but also limited in many circumstances. Meanwhile, the physiological volumes of the kidney cortex and medulla are presumed to change with age and have been proven to change with decreasing kidney function. Methods: We recruited 182 patients with normal Scr levels between October 2021 and February 2022 in Peking Union Medical College Hospital (PUMCH) with demographic and clinical data. A 3D U-Net architecture is used for both cortex and medullary separation, and volume calculation. In addition, we included patients with the same inclusion criteria but with diabetes (PUMCH-DM test set) and diabetic nephropathy (PUMCH-DN test set) for internal comparison to verify the possible clinical value of "kidney age" (K-AGE). Results: The PUMCH training set included 146 participants with a mean age of 47.5 ± 7.4 years and mean Scr 63.5 ± 12.3 µmol/L. The PUMCH test set included 36 participants with a mean age of 47.1 ± 7.9 years and mean Scr 66.9 ± 13.0 µmol/L. The multimodal method predicted K-AGE approximately close to the patient's actual physiological age, with 92% prediction within the 95% confidential interval. The mean absolute error increases with disease progression (PUMCH 5.00, PUMCH-DM 6.99, PUMCH-DN 9.32). Conclusion: We established a machine learning model for predicting the K-AGE, which offered the possibility of evaluating the whole kidney health in normal kidney aging and in disease conditions.

Analysis of Chinese Medical Syndrome Features of Ischemic Stroke Based on Similarity of Symptoms Subgroup.

OBJECTIVE: To derive the Chinese medicine (CM) syndrome classification and subgroup syndrome characteristics of ischemic stroke patients. METHODS: By extracting the CM clinical electronic medical records (EMRs) of 7,170 hospitalized patients with ischemic stroke from 2016 to 2018 at Weifang Hospital of Traditional Chinese Medicine, Shandong Province, China, a patient similarity network (PSN) was constructed based on the symptomatic phenotype of the patients. Thereafter the efficient community detection method BGLL was used to identify subgroups of patients. Finally, subgroups with a large number of cases were selected to analyze the specific manifestations of clinical symptoms and CM syndromes in each subgroup. RESULTS: Seven main subgroups of patients with specific symptom characteristics were identified, including M3, M2, M1, M5, M0, M29 and M4. M3 and M0 subgroups had prominent posterior circulatory symptoms, while M3 was associated with autonomic disorders, and M4 manifested as anxiety; M2 and M4 had motor and motor coordination disorders; M1 had sensory disorders; M5 had more obvious lung infections; M29 had a disorder of consciousness. The specificity of CM syndromes of each subgroup was as follows. M3, M2, M1, M0, M29 and M4 all had the same syndrome as wind phlegm pattern; M3 and M0 both showed hyperactivity of Gan (Liver) yang pattern; M2 and M29 had similar syndromes, which corresponded to intertwined phlegm and blood stasis pattern and phlegm-stasis obstructing meridians pattern, respectively. The manifestations of CM syndromes often appeared in a combination of 2 or more syndrome elements. The most common combination of these 7 subgroups was wind-phlegm. The 7 subgroups of CM syndrome elements were specifically manifested as pathogenic wind, pathogenic phlegm, and deficiency pathogens. CONCLUSIONS: There were 7 main symptom similarity-based subgroups in ischemic stroke patients, and their specific characteristics were obvious. The main syndromes were wind phlegm pattern and hyperactivity of Gan yang pattern.

Application of adaptive Laplacian Eigenmaps in near infrared spectral modeling.

Laplacian Eigenmaps is a nonlinear dimensionality reduction algorithm based on graph theory. The algorithm adopted the Gaussian function to measure the affinity between a pair of points in the adjacency graph. However, the scaling parameter σ in the Gaussian function is a hyper-parameter tuned empirically. Once the value of σ is determined and fixed, the weight between two points depends wholly on the Euclidian distance between them, which is not suitable for multi-scale sample sets. To optimize the weight between two points in the adjacency graph and make the weight reflect the scale information of different sample sets, an adaptive LE improved algorithm is used in this paper. Considering the influence of adjacent sample points and multi-scale data, the Euclidean distance between the k-th nearest sample point to sample point xi is used as the local scaling parameter σi of xi, instead of using a single scaling parameter σ. The efficiency of the algorithm is testified by applying on two public near-infrared data sets. LE-SVR and ALE-SVR models are established after LE and ALE dimension reduction of SNV preprocessed data sets. Compared with the LE-SVR model, the R2 and RPD of the ALE-SVR model established on the two data sets are improved, while RMSE is decreased, indicating that the prediction effect and stability of the regression model are established by the ALE algorithm are better than that of the traditional LE algorithm. Experiments show that the ALE algorithm can achieve a better dimensionality reduction effect than the LE algorithm.

Classification of Rice Varieties Using SIMCA Applied to NIR Spectroscopic Data.

The selection of suitable rice varieties is the key to achieve high and stable yields, and the correct identification of rice varieties is the prerequisite for seed selection. In this paper, with Kenjing No.5, No.6, and No.9 as the subjects, the effectiveness of near-infrared spectroscopy (NIRS) combined with soft independent modeling of class analogy (SIMCA) in the rapid identification of rice varieties was explored. The modeling sets of Kenjing No.5, No.6, and No.9 samples were respectively used to establish a SIMCA classification model based on principal component analysis (PCA). The accuracies of the model in classifying the rice samples in the modeling set were 100, 100, and 97.5%, respectively. Then, the established SIMCA model was used to identify the rice samples in the test set. According to the experimental findings, the SIMCA analytical method achieved 100% prediction accuracy for the Kenjing No.5, Kenjing No.6, and Hongyu 001-1 samples. For the Kenjing No.9 sample, the accuracy rate was 90% with a 10% sample of Kenjing No.9 misidentified as Kenjing No.6. Therefore, the analytical method of NIRS combined with SIMCA could effectively identify the rice varieties, providing a new approach for the correct selection of planting varieties.

Microstructural changes of the vestibulocochlear nerve in patients with Ménière's disease using diffusion tensor imaging.

Objective: To evaluate the microstructural changes of the vestibulocochlear nerve in patients with Ménière's disease. Methods: A total of 26 subjects, 13 patients with MD and 13 healthy controls, underwent diffusion tensor imaging (DTI) on a 3T scanner. The independent sample t-test was used to compare the differences in fractional anisotropy (FA) and apparent diffusion coefficient (ADC) between the two groups. A Pearson correlation was used between DTI and the dizziness handicap inventory (DHI) scores. Results: There was a significant decrease in FA and an increase in ADC of the vestibulocochlear nerve in MD patients compared with healthy controls (P = 0.04, P = 0.001). FA had negative correlations with the DHI score (r = -0.62, P = 0.02) and DHI-functional score (r = -0.64, P = 0.02). Conclusion: These results are the first evidence of possible changes in the microstructure of the vestibulocochlear nerves in patients with MD. DTI is a potential technique for evaluating the vestibulocochlear nerve in patients with MD.

Uncoupled neuro-osseous growth in adolescent idiopathic scoliosis? A preliminary study of 90 adolescents with whole-spine three-dimensional magnetic resonance imaging.

Previous literatures revealed abnormal cross-sectional morphology of spinal cord in AIS, suggesting the presence of disproportional growth between the neural and skeletal system. No accurate measurement of whole spine by MRI multiplanar reconstruction and their correlation with Cobb angle were studied. In this study, MRI three-dimensional reconstruction of the whole spine was performed in 90 adolescents (49 AIS with thoracic/thoracolumbar curve, and 41 age-matched healthy controls). Measurements of the ratio of anteroposterior (AP) and transverse (TS) diameter of the cord, the concave and convex lateral cord space (LCS) were obtained at the apical level in AIS patients. Cerebellar tonsillar level related to the basion-opsithion line, location of conus medullaris, cord length, vertebral column length, cord/vertebral column length ratio were obtained. All of the same parameters were also measured in healthy controls at matched vertebral levels and their correlations with Cobb angle were made. We notice that AP, TS, AP/TS and LCS ratio were increased in AIS subjects with low-lying position of cerebellar tonsillar level and elevating position of conus medullary when compared with healthy controls (P < 0.01). AP, AP/TS and LCS ratio were correlated significantly with Cobb angle (P < 0.05). Cord length and vertebral column length were not significantly different between AIS and control group. However, cord/vertebral column length ratio was significantly smaller in AIS group (P < 0.01). Cord length, vertebral column length and cord/vertebral column length ratio were not related with age or Cobb angle (P > 0.05). These data suggest the presence of uncoupled neuro-osseous growth along the longitudinal axis of spinal cord with associated morphologic changes of cross-sectional configuration and relative position of the cord. Some changes are significantly relevant with Cobb angle, which may indicate pathogenesis of AIS.

A Novel Bionic Catalyst-Mediated Drug Delivery System for Enhanced Sonodynamic Therapy.

Ultrasound (US)-triggered sonodynamic therapy (SDT) proves itself to be a formidable tool in the fight against cancer, due to its large spectrum of uses as a non-invasive therapeutic measure, while also demonstrating itself to be a certain improvement upon traditional SDT therapeutics. However, tumor hypoxia remains to be a major challenge for oxygen-dependent SDT. This study describes the development of an innovative, multi-use, catalyst-based and improved SDT targeting cancer, through the employment of a sonosensitizing curcumin (Cur) load embedded within a MnO2 core, together with an extraneous tumor cell membrane component. The latter allows for efficient tumor recognition properties. Hollowed-out MnO2 allows for efficient drug delivery, together with catalyzing oxygen generation from hydrogen peroxide present in tumor tissue, leading to enhanced SDT efficacy through the induction of a reduced hypoxic state within the tumor. In addition, Cur acts as a cytotoxic agent in its own right. The results deriving from in vivo studies revealed that such a biomimetic approach for drug-delivery actually led to a reduced hypoxic state within tumor tissue and a raised tumor-inhibitory effect within mouse models. Such a therapeutic measure attained a synergic SDT-based tumor sensitization treatment option, together with the potential use of such catalysis-based therapeutic formulations in other medical conditions having hypoxic states.