Progression of Myopic Maculopathy Based on the ATN Classification System.

INTRODUCTION: Myopic maculopathy is a sight-threatening disease, which causes irreversible vision faults and central vision loss. The purpose of this study is evaluating the risk factors of the myopic maculopathy progression according to the ATN classification system. METHODS: Clinic data of 69 high myopia patients aged older than 40 years with a follow-up time of more than 2 years, who underwent fundus photography and OCT examination were retrospectively collected. Fundus changes were evaluated with ATN classification at the first and last follow-up times. The related factors affecting progress including axial length (AL), spherical equivalence (SE), subfoveal choroidal thickness (SFCT), disc-foveal distance (DFD), optic disc tilt, and parapapillary atrophy (PPA) were analyzed. RESULTS: This study included 69 high-myopia patients with mean age 54.29 ± 10.41 years. The progression rate of myopic maculopathy (MM) was approximately 25.56%. Elongated DFD (5.37 ± 0.11 mm vs. 4.86 ± 0.37 mm; p < 0.001) and thinner SFCT (138.52 ± 29.38 µm vs. 184.87 ± 48.72 µm; p = 0.008) at baseline were linked with MM progression. In multiple logistic regression analysis, DFD was a substantial hazard risk factor (adjusted OR = 1.672, 95% CI: 1.135-2.498, p < 0.05) after adjusting for age, AL and SFCT. Receiver operating characteristic curve showed that DFD might serve as a predictor to discriminate the MM progression with a cut-off value of 5.15 mm and a substantial receiver operating characteristic curve area (AUC: 0.794). Compared with the non-progression group, the progression group had older age (p < 0.001), longer AL (p = 0.001), higher optic disc tilt rate (p < 0.001), and higher proportion of pre-existing PPA (p = 0.038) at baseline, the differences were statistically significant. CONCLUSION: Based on the ATN classification system, we found that the progression of MM was related to older age, longer AL, high disc tilt, pre-existing PPA, thinner SFCT, and longer DFD. The parameter of DFD was an important factor affecting the progression of MM, which is considered to have a higher probability of progression when the length is beyond 5.15 mm.

The influence of intrathecal injection of methotrexate and dexamethasone on neuropsychiatric systemic lupus erythematosus (NPSLE): a retrospective cohort study of 386 patients with NPSLE.

BACKGROUND: Neuropsychiatric involvement is one of the major concerns in systemic lupus erythematosus (SLE). The therapeutic effect of intrathecal treatment of methotrexate and dexamethasone has been investigated in some exploratory studies, but its influence on the long-term prognosis of neuropsychiatric SLE (NPSLE) remains unknown. METHODS: This was a propensity score-matched retrospective study. Outcomes at discharge and time free from NPSLE relapse or death were evaluated by multivariate logistic regression, survival analysis, and Cox regression as appropriate. RESULTS: Among 386 hospitalized patients with NPSLE, the median [IQR] age was 30.0 [23.0-40.0] years, and 342 patients (88.4%) were female. Of those, 194 patients received intrathecal treatment. Patients in the intrathecal treatment group had higher Systemic Lupus Erythematosus Disease Activity Index 2000 scores (median 17 vs. 14 points, IQR 12-22 vs. 10-19 points, P <0 .001) and were more likely to receive methylprednisolone pulse therapy (71.6% vs. 49.5%, P < 0.001) than those who did not receive intrathecal therapy. Intrathecal treatment was associated with a higher probability of survival and being free from NPSLE relapse than control treatment among the 386 unmatched patients (P =0.042 by log-rank test) and within 147 propensity score-matched pairs (P =0.032 by log-rank test). In the subgroup of NPSLE patients with increased levels of protein in cerebrospinal fluid, intrathecal treatment had a positive influence on their prognosis (P < 0.001). CONCLUSIONS: Intrathecal treatment of methotrexate and dexamethasone was associated with a more favorable prognosis of NPSLE and may serve as a valuable additional therapy for NPSLE patients, especially for those with elevated levels of protein in cerebrospinal fluid.

SbMYB3 transcription factor promotes root-specific flavone biosynthesis in Scutellaria baicalensis.

Scutellaria baicalensis Georgi produces abundant root-specific flavones (RSFs), which provide various benefits to human health. We have elucidated the complete biosynthetic pathways of baicalein and wogonin. However, the transcriptional regulation of flavone biosynthesis in S. baicalensis remains unclear. We show that the SbMYB3 transcription factor functions as a transcriptional activator involved in the biosynthesis of RSFs in S. baicalensis. Yeast one-hybrid and transcriptional activation assays showed that SbMYB3 binds to the promoter of flavone synthase II-2 (SbFNSII-2) and enhances its transcription. In S. baicalensis hairy roots, RNAi of SbMYB3 reduced the accumulation of baicalin and wogonoside, and SbMYB3 knockout decreased the biosynthesis of baicalein, baicalin, wogonin, and wogonoside, whereas SbMYB3 overexpression enhanced the contents of baicalein, baicalin, wogonin, and wogonoside. Transcript profiling by qRT-PCR demonstrated that SbMYB3 activates SbFNSII-2 expression directly, thus leading to more abundant accumulation of RSFs. This study provides a potential target for metabolic engineering of RSFs.

Expression of osteopontin and integrin αvß 3 receptor in retina of diabetic guinea pigs with high myopia.

INTRODUCTION: To investigate the effect of high myopia on the expression of retinal osteopontin (OPN) and integrin αVß 3 receptor in guinea pigs and determined the relationship between high myopia and diabetic retinopathy. METHODS: Ninety 3-week-old male guinea pigs were randomly divided into four groups that included normal control group (NOR，n=18), high myopia group (HM，n=24), diabetes group (DR，n=24), and diabetes with high myopia group (DR+HM，n=24). HM was induced by form deprivation (FDHM) in the right eye. The DR group was injected with 5% streptozotocin(STZ) 280 mg/kg intraperitoneally in the lower left abdomen of guinea pigs. The DRHM group was subjected to the same treatment as the HM and DR groups. Eighteen guinea pigs in each group were randomly selected to complete the experimental measurement. After enucleation of eyeballs, HE and immunohistochemical staining were performed to observe the retina morphology and count the positive rate of OPN and integrin αvß 3 receptor. RESULTS: Diabetic retinal changes were found in group DR and HM+DR. The degree of retinal change in group HM+DR was less than that in group DR. In the DR group, the morphology of retinal tissue was loose, the number of cells decreased, increased retinal microaneurysms, and a small amount of small artery embolism and venous thrombosis were observed. Although the retinal structure in the HM+DR group also became thinner, looser, and disordered, only a small number of microaneurysms were observed compared with the diabetic group. Immunohistochemical staining showed that the expression of OPN and integrin αvß 3 receptors in the diabetic groups (DR, HM+DR) was significantly higher than in the HM and NOR groups. The positive expression rates of OPN and integrin αvß 3 receptors in group HM+DR were significantly lower than those in group DR (P < 0.05). CONCLUSION: The expression of OPN and integrin αvß 3 receptor in the retina of diabetic guinea pigs with high myopia was lower than that of diabetic models, which may be due to the influence of high myopia on neovascularization in DR.

A deep neural network with subdomain adaptation for motor imagery brain-computer interface.

BACKGROUND: The nonstationarity problem of EEG is very serious, especially for spontaneous signals, which leads to the poor effect of machine learning related to spontaneous signals, especially in related tasks across time, which correspondingly limits the practical use of brain-computer interface (BCI). OBJECTIVE: In this paper, we proposed a new transfer learning algorithm, which can utilize the labeled motor imagery (MI) EEG data at the previous time to achieve better classification accuracies for a small number of labeled EEG signals at the current time. METHODS: We introduced an adaptive layer into the full connection layer of a deep convolution neural network. The objective function of the adaptive layer was designed to minimize the Local Maximum Mean Discrepancy (LMMD) and the prediction error while minimizing the distance within each class (DWC) and maximizing the distance between classes within each domain (DBCWD). We verified the effectiveness of the proposed algorithm on two public datasets. RESULTS: The classification accuracy of the proposed algorithm was higher than other comparison algorithms, and the paired t-test results also showed that the performance of the proposed algorithm was significantly different from that of other algorithms. The results of the confusion matrix and feature visualization showed the effectiveness of the proposed algorithm. CONCLUSION: Experimental results showed that the proposed algorithm can achieve higher classification accuracy than other algorithms when there was only a small amount of labeled MI EEG data at the current time. It can be promising to be applied to the field of BCI.

Begonia shenzhenensis, a new species of Begoniaceae from Guangdong, China.

Begonia shenzhenensis D.K.Tian & X.Yun Wang, sp. nov., a new species in Begonia sect. Platycentrum of Begoniaceae from Shenzhen of Guangdong province, China, is described and illustrated. Morphologically, it is primarily similar to B. coelocentroides in the same section but differs by its denser hairs on leaf, petiole, and pedicel, abtuse anther apex, hairy ovary, and narrower adaxial fruit wing. Based on only one small population found to date, its conservation status is assigned to Critical Endangered according to the IUCN Red List Categories and Criteria.

Using human umbilical cord mesenchymal stem cells combined with allogenic platelet-rich fibrin membrane for the treatment of dual limb ischemia in an elderly patient: A case report.

RATIONALE: To describe the clinical effects of human umbilical cord mesenchymal stem cells (UCMSCs) combined with allogenic platelet-rich fibrin (PRF) for the treatment of lower limb ischemia in an elderly patient. PATIENT CONCERNS: The patient was a 93-year-old Chinese woman with bilateral foot gangrene and ulcers lasting for 6 months. She had a prior history of Behcet's disease. DIAGNOSES: The admitting diagnosis for this episode was atherosclerosis bilateral limb ischemia. INTERVENTIONS: First, treatment consisting of immunosuppressants, anticoagulation, antiplatelets, and anti-microbials were instituted. A UCMSC suspension was administered intravenously and injected into the lower limbs twice. An allogenic PRF membrane was externally applied 15 times over the lower limbs. OUTCOMES: The patient's pain improved and the 6 ulcers healed. LESSONS: The combination of UCMSCs with a PRF membrane for the treatment of lower limb ischemia in an elderly patient is effective and safe. More and larger trials are needed before incorporating this therapy into mainstream treatment.

A new species (Begoniagiganticaulis) of Begoniaceae from southern Xizang (Tibet) of China.

Begoniagiganticaulis, a huge new species in Begoniasect.Platycentrum of Begoniaceae from southern Xizang (Tibet) of China, is described. Morphologically, it is mostly similar to B.longifolia and B.acetosella, but clearly differs from the former mainly by its dioecious and taller plants, sparse hairs on abaxial veins, longer inflorescence, unique shape of fruits, and differs from the latter mainly by its late and longer flowering time, 6-tepals of female flower and 3-loculed ovary. The phylogenetic analyses also support the separation of the new species from other taxa. Based on the current data, its conservation status is assigned to Endangered (B2a) according to the IUCN Red List Categories and Criteria.

Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks.

At present, only professional doctors can use the professional scales to diagnose depression and anxiety in clinical practice. In recent years, the problems of detecting the presence of anxiety or depression using Electroencephalography (EEG) has received attention as a way to implement assistant diagnosis, and some researchers explored that there are differences in the degree of prefrontal lateralization and functional connectivity of brain networks between patients with anxiety and depression and normal people. In this paper, we proposed a new approach that combines functional connectivity of brain networks and convolutional neural networks (CNN) for EEG-based anxiety and depression recognition. EEG data are collected from subjects consisting ten healthy controls and ten patients with anxiety or depression. In this way, we achieved 67.67% classification accuracy. It points out the way to further explore the application of functional connectivity of brain networks and deep learning technology in EEG about patients with anxiety and depression.

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectrum of EEG data related to motor imagery, the shared features across sessions or across subjects, namely, the mean and variance of model parameters, are extracted. Then, select the data sets that were most relevant to the new data set according to Euclidean distance to update the shared features. Finally, utilize the shared features and subject/session-specific features jointly to generate a new model. We evaluated our algorithm by analyzing the motor imagery EEG data from 10 healthy participants and a public data set from BCI competition IV. The classification accuracy of the proposed transfer learning is higher than that of traditional machine learning algorithms. The results of the paired t test showed that the classification results of PSD and the transfer learning algorithm were significantly different (p = 2.0946e-9), and the classification results of CSP and the transfer learning algorithm were significantly different (p = 1.9122e-6). The test accuracy of data set 2a of BCI competition IV was 85.7% ± 5.4%, which was higher than that of related traditional machine learning algorithms. Preliminary results suggested that the proposed algorithm can be effectively applied to the classification of motor imagery EEG signals across sessions and across subjects and the performance is better than that of the traditional machine learning algorithms. It can be promising to be applied to the field of brain-computer interface (BCI). Graphical abstract.

A Framework on Optimization Strategy for EEG Motor Imagery Recognition.

At present, in the process of encephalogram motor imagery decoding, facing the background of big data analysis, it has the necessity to design an effective system which is subject-independent. Pre-training is common to carry out before each experiment, which affects the practicability of the EEG system. In order to solve this problem, the most feasible method is to design a unified framework for deep learning optimization, which could capture the spatial and spectral dependence of original motor imagery EEG signals according to the features extracted by CNN and the temporal dependence extracted by RNN-LSTM. The framework is superimposed from both end-to-end and time-frequency domains so as to retain and learn interpretable motor imagery features. In addition, artificial EEG signals can be automatically generated by training the generated adversarial network, which can generate the feature distribution similar to the original EEG signals, increase the capacity of EEG samples, and ultimately improve the classification performance and robustness of EEG motor imagery recognition. This deep learning framework can improve the classification accuracy of motor imagery for different subjects. In addition, the network can learn from the original data with the least amount of preprocessing, thus eliminating the time-consuming data preparation process.

[Detection of glucocorticoids by pressurized capillary electrochromatography with ultra-violet detection and its application in hair analysis].

An effective and convenient method for glucocorticoid analysis in hair by reversed-phase pressurized capillary electrochromatography (pCEC) with ultra-violet (UV) detection was developed. Eight glucocorticoids (betamethasone, dexamethasone, prednisone, prednisolone, prednisolone acetate, hydrocortisone acetate, cortisone acetate and corticosterone) were separated within 20 min in an isocratic mode pCEC. The separations were performed on a reversed-phase C18 column, and with 245 nm as the UV detection wavelength. The mobile phase was composed of 1.5 mmol/L Tris buffer (pH 8.0) and acetonitrile (65: 35, v/v), at a pump flow rate of 0.105 mL/min. All of the compounds showed good linearity in the range of 0.036 - 4.0 mmol/L. The limits of detection (LODs) for all glucocorticoids were of microg/g levels. The proposed method was applied to the analysis of hair samples. The interference of hair matrices was effectively eliminated by protein enzymatic digestion, followed by a methanol extraction and a solid phase extraction (SPE) clean up step. The average recoveries of 71% - 85% at different fortified levels of glucocorticoids were achieved. This non-invasive method is useful for rapidly estimating the level of drug exposure in drug chronic abuse and monitoring the compliance of therapeutic drugs.

Simultaneous analysis of acetaminophen, p-aminophenol and aspirin metabolites by hydrophilic interaction and strong anion exchange capillary liquid chromatography coupled to amperometric detection.

A simple and sensitive method has been developed for the simultaneous determination of polar nonsteroidal pharmaceuticals and metabolites, including acetaminophen, p-aminophenol and several aspirin metabolites (salicylic acid, gentisic acid, salicyluric acid and 2,3-dihydroxybenzoic acid), by capillary liquid chromatography with amperometric detection. Using a capillary monolithic column with mixed mode stationary phases and a mobile phase composed of acetonitrile and Tris buffer, rapid separation of six polar analytes was achieved within 8 min, and a hydrophilic interaction and strong anion exchange separation mechanism were exhibited. Method detection limits of six analytes ranged from 10 to 50 ng/mL. In terms of precision, the intra- and interday relative standard deviation values in all analytes never exceeded 3.1% for migration time and 8.9% for peak areas, respectively. This method provided a simple, rapid and cost-effective approach for the analysis of polar pharmaceuticals. The applicability of the method in pharmacokinetics was verified by spiking human serum samples with the compounds and analyzing the recoveries.

Pressurized capillary electrochromatography with indirect amperometric detection for analysis of organophosphorus pesticide residues.

A new analytical method, pressurized capillary electrochromatography with indirect amperometric detection, has been developed for the determination of some non-electroactive organophosphorus pesticides (OPPs). When 0.1 mmol L(-1) of 3,4-dihydroxybenzylamine (DHBA) was added to the mobile phase containing 50% v/v of ACN and 50% v/v of MES buffer (10 mmol L(-1), pH 5.5), and +0.9 V (vs. Ag/AgCl) of working potential were used, maximal signal levels of analytes could be achieved. A separation voltage of +10 kV, a column pressure of 7.0 MPa and a pump flow rate of 0.05 mL min(-1) were selected as the other optimal conditions for separation of six OPPs, namely, dimethoate, methyl parathion, ethyl parathion, chlorpyrifos, chlorpyrifos-methyl, trichlorfon. The OPPs could be separated within 15 min and determined with the detection limits ranging from 0.008 to 0.2 mg/kg. Combining with a solid phase extraction procedure, mean recoveries between 78.9 and 87.2% for vegetable samples and from 81.4 to 98.6% for fruit samples were obtained.

Determination of trace mercury by solid substrate-room temperature phosphorimetry quenching method based on catalytic effect of Hg2+ on formation of the ion association complex [Sn(XO)6]4+.[(Fin)4].

A new method for the determination of trace mercury by solid substrate-room temperature phosphorimetry (SS-RTP) quenching method has been established. In glycine-HCl buffer solution, xylenol orange (XO) can react with Sn4+ to form the complex [Sn(XO)6]4+. [Sn(XO)6]4+ can interact with Fin- (fluorescein anion) to form the ion associate [Sn(XO)6]4+.[(Fin)4]-, which can emit strong and stable room temperature phosphorescence (RTP) on polyamide membrane (PAM). Hg2+ can catalyze H2O2 oxidizing the ion association complex [Sn(XO)6]4+.[(Fin)4]-, which causes the RTP to quench. The DeltaIp value is directly proportional to the concentration of Hg2+ in the range of 0.016-1.6 fg spot(-1) (corresponding concentration: 0.040-4.0 pg ml(-1), 0.40 microl spot(-1)), and the regression equation of working cure is DeltaIp=10.03+83.15 m Hg2+ (fg spot(-1)), (r=0.9987, n=6) and the detection limit (LD) is 3.6 ag spot(-1)(corresponding concentration: 9.0 x 10(-15) g ml(-1), the sample volume: 0.4 microl). This simple, rapid, accurate method is of high selectivity and good repeatability, and it has been successfully applied to the determination of trace mercury in real samples. The reaction mechanism for catalyzing H2O2 oxidizing the ion association complex ([Sn(XO)6]4+.[(Fin)4]-) SS-RTP quenching method to determine trace mercury is also discussed.

Determination of alkaline phosphatase based on affinity adsorption solid-substrate room temperature phosphorimetry using rhodamine 6G-dibromoluciferin luminescent nanoparticle to label lectin and prediction of diseases.

In the presence of heavy atom perturber LiAc, the silicon dioxide nanoparticle containing rhodamine 6G (R) and dibromoluciferin (D) (R-D-SiO(2)) can emit strong and stable solid-substrate room temperature phosphorescence signal of R (lambda(ex)/lambda(em)=481/648 nm) and D (lambda(ex)/lambda(em)=457/622 nm) on the surface of acetyl cellulose membrane (ACM). R-D-SiO(2) is used to label triticum vulgare lectin (WGA). Then two types of affinity adsorption reactions, R-D-SiO(2)-WGA- alkaline phosphatase (ALP) (direct method) and WGA-ALP-WGA-R-D-SiO(2) (sandwich method), are carried out on ACM. The conditions and the analytical characteristics for the determination of ALP using affinity adsorption solid-substrate room temperature phosphorimetry (AA-SS-RTP) were studied. For a 0.40-microl drop of sample, results show that the detection limits of the sandwich method are 0.16 ag spot(-1)(457/622 nm) and 0.17 ag spot(-1)(481/648 nm), and the detection limits of the direct method are 0.41 ag spot(-1) (457/622 nm) and 0.44 ag spot(-1) (481/648 nm). The contents of ALP in human serum correlated well with those obtained by enzyme-linked immunoassay. This study shows that AA-SS-RTP whether by the sandwich method or the direct method, can combine very well the characteristics of both high sensitivity of SS-RTP and specificity of the immunoreaction. Simultaneously, whether the phosphorescence excitation/emission wavelength of either R or D in R-D-SiO(2) is chosen to determine ALP, this can promote the agility and widen the adaptability of AA-SS-RTP.