Risk factors of non-arteritic anterior ischaemic optic neuropathy and central retinal artery occlusion

AIM:To investigate the difference in risk factors between non-arteritic anterior ischaemic optic neuropathy(NAION)and central retinal artery occlusion(CRAO)and develop a predictive diagnostic nomogram.METHODS:The study included 37 patients with monocular NAION,20 with monocular CRAO,and 24 with hypertension.Gender,age,and systemic diseases were recorded.Blood routine,lipids,hemorheology,carotid and brachial artery doppler ultrasound,and echocardiography were collected.The optic disc area,cup area,and cup-to-disc ratio(C/D)of the unaffected eye in the NAION and CRAO group and the right eye in the hypertension group were measured.RESULTS:The carotid artery intimal medial thickness(C-IMT)of the affected side of the CRAO group was thicker(P=0.039)and its flow-mediated dilation(FMD)was lower(P=0.049)than the NAION group.Compared with hypertension patients,NAION patients had higher whole blood reduced viscosity low-shear(WBRV-L)and erythrocyte aggregation index(EAI;P=0.045,0.037),and CRAO patients had higher index of rigidity of erythrocyte(IR)and erythrocyte deformation index(EDI;P=0.004,0.001).The optic cup and the C/D of the NAION group were smaller than the other two groups(P<0.0001).The diagnostic prediction model showed high diagnostic specificity(83.7%)and sensitivity(85.6%),which was highly related to hypertension,the C-IMT of the affected side,FMD,platelet(PLT),EAI,and C/D.CONCLUSION:CRAO patients show thicker C-IMT and worse endothelial function than NAION.NAION and CRAO may be related to abnormal hemorheology.A small cup and small C/D may be involved in NAION.The diagnostic nomogram can be used to preliminarily identify NAION and CRAO.

Establishment of a Predictive Diagnostic Model for Acute Mycoplasma Pneumoniae Infection in Elderly Patients with Community-acquired Pneumonia

We established a diagnostic model to predict acute Mycoplasma pneumoniae （M. pneumonia） infection in elderly Community-acquired pneumonia （CAP） patients. We divided 456 patients into acute and non-acute M. pneumoniae infection groups. Binary logistic regression and receiver operating characteristic （ROC） curves were used to establish a predictive model. The following independent factors were identified： age 〉 70 years; serum cTNT level 〉 0.0S ng/mL; lobar consolidation; mediastinal lymphadenopathy; and antibody titer in the acute phase 〉 1：40. The area under the ROC curve of the model was 0.923 and a score of 2 7 score predicted acute M. pneumoniae infection in elderly patients with CAP. The predictive model developed in this study has high diagnostic accuracy for the identification of elderly acute M. pneumoniae infection.

Modern Predictive Diagnostic Method of Induction Traction Motor Based on FEM, BEM

The paper presents a mathematical model of special construction induction traction motor. On the base of predictive filtering, analytical studies, fuzzy logic control, relying on the virtual data generated by FEM （Finite Element Method） and BEM （Boundary Element Method） is detected faults of induction motor. Digital predictive filter is used to separate a fundamental harmonic from spectrum current and voltage harmonics. Fuzzy logic control is used to identify a motor state. Magnetic fields distribution in the traction motor, of the wheel vehicle is presented in the paper. Modem diagnostics method has been used for faulty motor simulation and shows results of motor fault effects. Some computer programs were applied in calculation of magnetic fields distribution. On the base of magnetic field distributions were analyzed different failures situations. Some laboratory experiments realized for induction traction motor were verified by results of computer calculations.

DEVELOPMENT OF A MATERIALS RISK INFORMATION PLATFORM FOR A SAFE SOCIETY

A Materials Risk Information Platform is being developed under the cooperation of companies and academic societies with NIMS as project core. To combine safety and economy, which are sometimes contrary to each other, it is important to find an optimal solution using a new concept: risk based engineering. A 5-year project, the Materials Risk Information Platform, was started in 2001 in NIMS on the boilers of thermal power plants and the objectives of this project, research areas and its present state were outlined.

Epigenetic regulation of prostate cancer: the theories and the clinical implications

Epigenetics is the main mechanism that controls transcription of specific genes with no changes in the underlying DNA sequences. Epigenetic alterations lead to abnormal gene expression patterns that contribute to carcinogenesis and persist throughout disease progression. Because of the reversible nature, epigenetic modifications emerge as promising anticancer drug targets. Several compounds have been developed to reverse the aberrant activities of enzymes involved in epigenetic regulation, and some of them show encouraging results in both preclinical and clinical studies. In this article, we comprehensively review the up-to-date roles of epigenetics in the development and progression of prostate cancer. We especially focus on three epigenetic mechanisms: DNA methylation, histone modifications, and noncoding RNAs. We elaborate on current models/theories that explain the necessity of these epigenetic programs in driving the malignant phenotypes of prostate cancer cells. In particular, we elucidate how certain epigenetic regulators crosstalk with critical biological pathways, such as androgen receptor (AR) signaling, and how the cooperation dynamically controls cancer-oriented transcriptional profiles. Restoration of a “normal” epigenetic landscape holds promise as a cure for prostate cancer, so we concluded by highlighting particular epigenetic modifications as diagnostic and prognostic biomarkers or new therapeutic targets for treatment of the disease.

Recent Advances and Future Directions of Diagnostic and Prognostic Prediction Models in Ovarian Cancer

Ovarian cancer has one of the highest mortality rates among gynecological malignancies.This disease has a low early detection rate,a high postoperative recurrence rate,and a 5-year survival rate of only 40%.Hence,there is an urgent need to improve the early diagnosis and prognosis of ovarian cancer.Prediction models can effectively estimate the risk of disease occurrence,as well as its prognosis.Recently,many studies have established multiple ovarian cancer prediction models based on different regions and populations.These models can improve the detection rate and optimize the prognosis management to a certain extent.Herein,the construction principle of the ovarian cancer risk prediction model and its validation are summarized;furthermore,comprehensive reviews and comparisons of the different types of these models are made.Therefore,our review may be of great significance for the whole course of ovarian cancer management.