Background: Previous genome-wide association study (GWAS) has revealed the association between MYP10 at 8p23 and MYP15 at 10q21.1 and high myopia (HM) in a French population. This study is managed to discover the connection between some single nucleotide polymorphism (located at MYP10 and MYP15) and Han Chinese HM.
Methods and Results: This case-control association study contained 1673 samples, including 869 ophthalmic patients and 804 controls. Twelve tag SNPs have been selected from the MYP10 and MYP15 loci and genotyped by SNaPshot method. Among 12 SNPs, rs4840437 and rs6989782 in TNKS gene were found significant association with HM. Carriers of rs4840437G allele and rs4840437GG genotype created a low risk of high myopia (P = .036, OR = 0.81, 95%CI = 0.71–0.93; P = .016, OR = 0.73, 95%CI = 0.56–0.96; respectively). Carriers of rs6989782T allele and rs6989782TT+CT genotype also had a decreased risk of high myopia (P = .048, OR = 0.82, 95%CI = 0.71–0.94; P = .006, OR = 0.74, 95%CI = 0.59–0.92; respectively). Other 10 SNPs displaced nonsignificant association with HM. Additionally, the risk haplotype AC and the protective haplotype GT, generated by two SNPs in TNKS, were considerably more likely to be association with HM (for AC, P = .002 and OR = 1.26; for GT, P = .027 and OR = 0.84).
Conclusions: Our results demonstrated that some heritable variants in the TNKS gene are associated with HM in the Han population. The possible functions of TNKS in the development and pathogenesis of hereditary high myopia still require further researches to identify. 相似文献
BACKGROUND: Researches on diabetic nervous system lesion are mainly focus on peripheral nerve and vegetative nerve, so there are few investigations on diabetic pseudotabes.
OBJECTIVE: To investigate the electrophysiological examinations on the diagnosis of diabetic pseudotabes.
DESIGN: Case study.
SETTING: Department of Electrophysiology and Department of Neurology, Zhongshan Hospital Affiliated to Xiamen University.
PARTICIPANTS: A total of 4 patients with type 2 diabetes mellitus, including 3 males and 1 female aged from 50 to 72 years, were selected from Department of Neurology, Zhongshan Hospital Affiliated to Xiamen University from March 2002 to February 2005. All accepted subjects met the modified diagnostic criteria of diabetes mellitus, which was set by American Diabetes Mellitus Association (ADA) in 1997. Otherwise, the subjects had typical symptoms and physical signs of spinal posterior funiculus damage. However, patients with spinal cord lesion which was caused by other factors were excluded. All accepted subjects provided the confirmed consent.
METHODS: Nicolet NT electromyography (EMG)/evoked potential meter (made in the USA) was used to detect spinal cord conduction velocity (SCCV), somatosensory evoked potential (SEP) of lower limbs, motor nerve conduction velocity (MNCV) and sensory nerve conduction velocity (SNCV) of extremities. Determining criteria: Measurements were performed based on the laboratory standards. SCCV, which was less than lower limit of normal value (T2–12: 40–55 m/s, T12–L4: 20–41 m/s, T2–L4: 36–45 m/s), was regarded as abnormal. SEP value of lower limbs: P40, P60 and PF, which were more than standard deviation of normal value (x(—)+2.5), were regarded as the abnormality. Normal value of P40, P60 and PF latencies (x(—)±s) in this study: P40, P60 and PF in males were (37.6±1.9) ms, (59.8±3.9) ms and (7.6±0.9) ms, respectively; meanwhile, those in females were (35.5±1.7) ms, (55.2±2.7) ms and (6.3±0.7) ms, respectively. MNCV and SNCV, which were less than 50 m/s in upper limbs and 40 m/s in lower limbs, were regarded as the abnormality.
MAIN OUTCOME MEASURES: Electrophysiological examinations.
RESULTS: All 4 patients with type 2 diabetes mellitus were involved in the final analysis. ① SCCV: Among 4 patients, SCCV of three patients was decreased in T2–12, T12–L4 and T2–L4, and that of the other one was decreased in T2–12 and T2–L4; however, SCCV in T12–L4 was normal. There was significant difference as compared with normal value (P < 0.01). ② SEP of lower limbs: SEP values of lower limbs were abnormal in all 4 patients. Among them, P40, P60 and PF latencies of two patients were delayed; P40 of one patient was delayed and PF was not drained out; P40 and P60 of the last one were delayed and PF was normal. ③ MNCV and SNCV: The MNCV and SNCV were normal in one patient and abnormal in three patients. The results demonstrated that MNCV and SNCV of extremities decreased; especially, sensory nerve action potential (SNAP) of both lower extremities of one patient were not drained out.
CONCLUSION: Detections of SCCV, SEP of lower limbs, MNCV and SNCV of extremities are helpful to investigate whether peripheral nerve and deep sensory passage are damaged or not and determine whether deep sensory damage is caused by peripheral nerve and spinal posterior funiculus. 相似文献
BACKGROUND: Cognitive impairment after stroke associates with various factors, such as age, educational years, etc. Besides concerning about the recovery of limb function after stroke, we should also focus on the rehabilitation of cognition. Moreover, we'd better pay attention to the control of all the risk factors of stroke, and improve the quality of life in stroke patients.
OBJECTIVE: To analyze the factors that affect cognitive impairment after stroke.
DESIGN: A related factors analysis.
SETTINGS: Department of Neurology of Dalian Port Hospital and Dalian Second People's Hospital.
PARTICIPANTS: Totally 148 stroke inpatients were selected from Department of Neurology, Dalian Port Hospital and Dalian Second People's Hospital from April 2004 to December 2005, including 100 males and 48 females, aging 45-75 years with an average age of (67±8) years; Their educational years ranged 2-10 years with an average of (6.1±3.7) years; The disease course ranged 15-30 days; All were right-handed. Inclusive criteria: All were accorded with the diagnostic standard set by the Fourth National Academic Meeting for Cerebrovascular Disease; Confirmed by CT or MRI; Informed consents were obtained from all the subjects.
METHODS: After the disease conditions were stable, the patients were assessed with Wechsler memory scale (WMS) and Wisconsin card sorting test (WCST). WMS included forward and backward recitation of numbers and short-term memory (verbal memory, visual recognition). The number of times for correct and wrong classifications in WCST and the time to complete the trail making tests A and B were recorded. The focal volume, area and layer were recorded at 24 hours after admission. The general data of the patients were recorded, including name, sex, age, educational years, history of hypertension, history of diabetes mellitus. Electroencepalograph (EEG) was examined to record the wave shape, blood lipids were detected, and the cognition related indexes were analyzed with the Pearson correlation analysis. The correlation between cognitive indexes after stroke and the influencing factors were analyzed with multiple linear regression analysis.
MAIN OUTCOME MEASURES: ① Correlation between cognitive indexes and imaging indexes; ② Influencing factors for cognitive indexes after stroke.
RESULTS: All the 148 stroke patients were involved in the analysis of results. ① Correlation between cognitive indexes and CT indexes: There were obvious negative correlations between numerical symbol and focal layer (r =-0.234, P < 0.05), as well as between verbal memory and the focal area and volume (r =-0.363, -0.279, P < 0.05); Trail making test A had obvious positive correlation with focal area and volume (r =0.256, 0.256, P < 0.05). Results of multiple linear regression analysis: Correct classification was correlated with triglyceride (partial wave and θ wave (partial regression coefficient=0.231-0.908, P < 0.05); Verbal memory was correlated with EEG α wave, focal volume, sex, educational years and diabetes mellitus (partial regression coefficient=0.219-1.017, P < 0.05-0.01). Visual recognition had correlation with educational years and hypertension (partial regression coefficient=0.326, -1.163, P < 0.01). Trail making tests A and B were correlated with focal volume (partial regression coefficient=4.680, -18.561, P < 0.05).
CONCLUSION: The factors that affect the cognitive function after stroke include sex, age, educational years, hypertension, diabetes mellitus, triglyceride, EEG wave shape, and the focal area, volume and layer 相似文献