Combination Therapy With Fingolimod and Neural Stem Cells Promotes Functional Myelination

Myelination, which occurs predominantly postnatally and continues throughout life, is important for proper neurologic function of the mammalian central nervous system (CNS). We have previously demonstrated that the combination therapy of fingolimod (FTY720) and transplanted neural stem cells (NSCs) had a significantly enhanced therapeutic effect on the chronic stage of experimental autoimmune encephalomyelitis, an animal model of CNS autoimmunity, compared to using either one of them alone. However, reduced disease severity may be secondary to the immunomodulatory effects of FTY720 and NSCs, while whether this therapy directly affects myelinogenesis remains unknown. To investigate this important question, we used three myelination models under minimal or non-inflammatory microenvironments. Our results showed that FTY720 drives NSCs to differentiate into oligodendrocytes and promotes myelination in an ex vivo brain slice culture model, and in the developing CNS of healthy postnatal mice in vivo. Elevated levels of neurotrophic factors, e.g., brain-derived neurotrophic factor and glial cell line-derived neurotrophic factor, were observed in the CNS of the treated infant mice. Further, FTY720 and NSCs efficiently prolonged the survival and improved sensorimotor function of shiverer mice. Together, these data demonstrate a direct effect of FTY720, beyond its known immunomodulatory capacity, in NSC differentiation and myelin development as a novel mechanism underlying its therapeutic effect in demyelinating diseases

Cotton Fusarium wilt diagnosis based on generative adversarial networks in small samples

This study aimed to explore the feasibility of applying Generative Adversarial Networks (GANs) for the diagnosis of Verticillium wilt disease in cotton and compared it with traditional data augmentation methods and transfer learning. By designing a model based on small-sample learning, we proposed an innovative cotton Verticillium wilt disease diagnosis system. The system uses Convolutional Neural Networks (CNNs) as feature extractors and applies trained GAN models for sample augmentation to improve classification accuracy. This study collected and processed a dataset of cotton Verticillium wilt disease images, including samples from normal and infected plants. Data augmentation techniques were used to expand the dataset and train the CNNs. Transfer learning using InceptionV3 was applied to train the CNNs on the dataset. The dataset was augmented using GAN algorithms and used to train CNNs. The performances of the data augmentation, transfer learning, and GANs were compared and analyzed. The results have demonstrated that augmenting the cotton Verticillium wilt disease image dataset using GAN algorithms enhanced the diagnostic accuracy and recall rate of the CNNs. Compared to traditional data augmentation methods, GANs exhibit better performance and generated more representative and diverse samples. Unlike transfer learning, GANs ensured an adequate sample size. By visualizing the images generated, GANs were found to generate realistic cotton images of Verticillium wilt disease, highlighting their potential applications in agricultural disease diagnosis. This study has demonstrated the potential of GANs in the diagnosis of cotton Verticillium wilt disease diagnosis, offering an effective approach for agricultural disease detection and providing insights into disease detection in other crops

Risk factors, clinical features, and outcomes of patients with hypertrophic cardiomyopathy complicated by ischemic stroke: A single-center retrospective study

ObjectiveThis study aimed to explore risk factors, clinical features, and prognosis of patients with hypertrophic cardiomyopathy (HCM) complicated by ischemic stroke (IS).MethodsWe conducted a retrospective analysis of all HCM patient data and a 1-year follow-up study.ResultsTotally, 506 patients with HCM, including 71 with IS, were enrolled. Older age (≥63 years) was associated with an increased risk of IS in HCM patients (OR = 1.045, 95% CI: 1.018–1.072, p = 0.001). Among 37 patients complicated by IS, 22 (59.5%, 22/37) manifested as cardioembolism (CE) subtype, and 13 (35.1%, 3/37) small artery occlusion (SAO) subtype, according to TOAST classification. In the acute phase, the IS patients presented with NIHSS 4 (interquartile range: 1, 10). Multi-infarction was more common than single infarction (72.7 vs. 27.3%), while cortical + subcortical infarction (CE group: 50%) or subcortical infarction (SAO group: 53.8%) constituted most IS cases. Additionally, the blood supply areas of anterior circulation (CE group: 45.5%; SAO group: 92.3%) or anterior + posterior circulation (CE group: 50%) were mainly involved. The 1-year survival rate of HCM patients with concomitant IS was 81.8%, and IS was associated with 1-year all-cause death in HCM patients (HR = 5.689, 95% CI: 1.784–18.144, p = 0.003).ConclusionOlder age is a risk factor for IS occurrence in HCM patients. Patients with HCM complicated by IS had mild or moderate neurologic deficits at disease onset. CE and SAO subtypes predominate in patients with concomitant IS, especially the former. Multiple cortical and subcortical infarctions are their neuroimaging characteristics, mainly involving the anterior circulation or anterior + posterior circulation. Is is a risk factor for all-cause death in HCM patients within 1 year

Combination Therapy With Fingolimod and Neural Stem Cells Promotes Functional Myelination in vivo Through a Non-immunomodulatory Mechanism

Myelination, which occurs predominantly postnatally and continues throughout life, is important for proper neurologic function of the mammalian central nervous system (CNS). We have previously demonstrated that the combination therapy of fingolimod (FTY720) and transplanted neural stem cells (NSCs) had a significantly enhanced therapeutic effect on the chronic stage of experimental autoimmune encephalomyelitis, an animal model of CNS autoimmunity, compared to using either one of them alone. However, reduced disease severity may be secondary to the immunomodulatory effects of FTY720 and NSCs, while whether this therapy directly affects myelinogenesis remains unknown. To investigate this important question, we used three myelination models under minimal or non-inflammatory microenvironments. Our results showed that FTY720 drives NSCs to differentiate into oligodendrocytes and promotes myelination in an ex vivo brain slice culture model, and in the developing CNS of healthy postnatal mice in vivo. Elevated levels of neurotrophic factors, e.g., brain-derived neurotrophic factor and glial cell line-derived neurotrophic factor, were observed in the CNS of the treated infant mice. Further, FTY720 and NSCs efficiently prolonged the survival and improved sensorimotor function of shiverer mice. Together, these data demonstrate a direct effect of FTY720, beyond its known immunomodulatory capacity, in NSC differentiation and myelin development as a novel mechanism underlying its therapeutic effect in demyelinating diseases

The effect of Shengmai injection in patients with coronary heart disease in real world and its personalized medicine research using machine learning techniques

Objective: Shengmai injection is a common treatment for coronary heart disease. The accurate dose regimen is important to maximize effectiveness and minimize adverse reactions. We aim to explore the effect of Shengmai injection in patients with coronary heart disease based on real-world data and establish a personalized medicine model using machine learning and deep learning techniques.Methods: 211 patients were enrolled. The length of hospital stay was used to explore the effect of Shengmai injection in a case-control study. We applied propensity score matching to reduce bias and Wilcoxon rank sum test to compare results between the experimental group and the control group. Important variables influencing the dose regimen of Shengmai injection were screened by XGBoost. A personalized medicine model of Shengmai injection was established by XGBoost selected from nine algorithm models. SHapley Additive exPlanations and confusion matrix were used to interpret the results clinically.Results: Patients using Shengmai injection had shorter length of hospital stay than those not using Shengmai injection (median 10.00 days vs. 11.00 days, p = 0.006). The personalized medicine model established via XGBoost shows accuracy = 0.81 and AUC = 0.87 in test cohort and accuracy = 0.84 and AUC = 0.84 in external verification. The important variables influencing the dose regimen of Shengmai injection include lipid-lowering drugs, platelet-lowering drugs, levels of GGT, hemoglobin, prealbumin, and cholesterol at admission. Finally, the personalized model shows precision = 75%, recall rate = 83% and F1-score = 79% for predicting 40 mg of Shengmai injection; and precision = 86%, recall rate = 79% and F1-score = 83% for predicting 60 mg of Shengmai injection.Conclusion: This study provides evidence supporting the clinical effectiveness of Shengmai injection, and established its personalized medicine model, which may help clinicians make better decisions

Clonal Spread of Escherichia coli ST93 Carrying mcr-1-Harboring IncN1-IncHI2/ST3 Plasmid Among Companion Animals, China

The purpose of this study was to investigate the occurrence of plasmid-mediated colistin resistance gene mcr-1 in Enterobacteriaceae isolates from companion animals in Guangzhou, China. Enterobacteriaceae isolated from 180 samples collected from cats and dogs were screened for mcr-1 by PCR and sequencing. MCR-1-producing isolates were further characterized by multilocus sequence typing and pulsed-field gel electrophoresis (PFGE). Plasmid characterization was performed by conjugation, replicon typing, S1-PFGE, and Southern blot hybridization. Plasmid pHN6DS2 as a representative IncN1-IncHI2/ST3 plasmid from ST93 E. coli was fully sequenced. pHN6DS2-like plasmids were screened by PCR-mapping and sequencing. The mcr-1 gene was detected in 6.25% (8/128) Escherichia coli isolates, of which, five belonged to E. coli ST93 and had identical PFGE patterns, resistance profiles and resistance genes. mcr-1 genes were located on ∼244.4 kb plasmids (n = 6), ∼70 kb plasmids, and ∼60 kb plasmids, respectively. Among them, five mcr-1-carrying plasmids were successfully transferred to recipient by conjugation experiments, and were classified as IncN1-IncHI2/ST3 (∼244.4 kb, n = 4, all obtained from E. coli ST93), and IncI2 (∼70 kb, n = 1), respectively. Plasmid pHN6DS2 contained a typical IncHI2-type backbone, with IncN1 segment (ΔrepA-Iterons I-gshB-ΔIS1294) inserted into the multiresistance region, and was similar to other mcr-1-carrying IncHI2/ST3 plasmids from Enterobacteriaceae isolates of various origins in China. The remaining five mcr-1-bearing plasmids with sizes of ∼244.4 kb were identified to be pHN6DS2-like plasmids. In conclusion, clonal spread of ST93 E. coli isolates was occurred in companion animals in Guangzhou, China

MODEL OF CROATIAN SEA PASSENGER PORTS MANAGEMENT RATIONALIZATION

Predmet istraživanja u ovome znanstvenom radu je razvitak pomorskoputničkih luka u Republici Hrvatskoj do 2012. godine. Za definiranje svojstava i determinanti pomorskoputničkih luka koristilo se modelom na bazi matrice rasta. Analiza i vrednovanje pojedinih elemenata modela i dobivene izravne stope rasta imale su za cilj znanstveno formulirati rezultate istraživanja, prema najvažnijim teorijskim zakonitostima razvitka pomorskoputničkih luka u Republici Hrvatskoj. Autori su se u znanstvenom istraživanju i prezentiranju rezultata istraživanja ovog rada služili kombinaciju znanstvenih metoda kao što su: metoda analize i sinteze, metoda konkretizacije, komparativna metoda i metoda modeliranja (matrica rasta). Glavna znanstvena hipoteza dokazana je izravnim stopama rasta odabranih elemenata modela a ona glasi: Znanstveno utemeljenim spoznajama o funkcioniranju i poslovanju sustava pomorskoputničkih luka moguće je predložiti model, mjere i aktivnosti za racionalno upravljanje tim lukama kako bi se osigurao rast i razvoj sustava pomorskoputničkih luka.This paper analyses the sustainable development of sea passenger ports in the Republic of Croatia until 2012. A model of growth was used in order to define the main characteristics and determinants of sea passenger ports. The purpose of the paper was to present a scientifically-based formulation of sustainable development analysis of sea passenger ports in Croatia, based on the evaluation and analysis of relevant elements and resulting direct rates. The authors in their scientific research and presentation used a various combination of scientific methods like: analysis and syntheses method, concretization method, comparative method and modeling method (growth matrix). The main scientific hypothesis is: By using scientifically based acknowledgments about functioning and management of sea passenger port system it is possible to suggest a model, measurements and activities for the rational management of sea passenger ports in Croatia in order to secure their growth and development. This scientific hypothesis was confirmed by the direct rates of growth of the model elements

Systems Biology Modeling Reveals a Possible Mechanism of the Tumor Cell Death upon Oncogene Inactivation in EGFR Addicted Cancers

Despite many evidences supporting the concept of “oncogene addiction” and many hypotheses rationalizing it, there is still a lack of detailed understanding to the precise molecular mechanism underlying oncogene addiction. In this account, we developed a mathematic model of epidermal growth factor receptor (EGFR) associated signaling network, which involves EGFR-driving proliferation/pro-survival signaling pathways Ras/extracellular-signal-regulated kinase (ERK) and phosphoinositol-3 kinase (PI3K)/AKT, and pro-apoptotic signaling pathway apoptosis signal-regulating kinase 1 (ASK1)/p38. In the setting of sustained EGFR activation, the simulation results show a persistent high level of proliferation/pro-survival effectors phospho-ERK and phospho-AKT, and a basal level of pro-apoptotic effector phospho-p38. The potential of p38 activation (apoptotic potential) due to the elevated level of reactive oxygen species (ROS) is largely suppressed by the negative crosstalk between PI3K/AKT and ASK1/p38 pathways. Upon acute EGFR inactivation, the survival signals decay rapidly, followed by a fast increase of the apoptotic signal due to the release of apoptotic potential. Overall, our systems biology modeling together with experimental validations reveals that inhibition of survival signals and concomitant release of apoptotic potential jointly contribute to the tumor cell death following the inhibition of addicted oncogene in EGFR addicted cancers

Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data

© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5