Objective To investigate the association of HLA-A and -B alleles with syphilis in Shandong Han population. Methods The allele frequencies of HLA-A and -B were detected in 205 patients with syphilis and 5844 normal human controls by PCR-sequence specific oligonucleotide probe (PCR-SSOP)method. Results The patients with syphilis showed a higher frequency of HLA-A*02, B*15, B*40 alleles(all P＜0.01, Pc＜0.05) and a lower frequency of HLA-A*26 allele (P= 0.003, Pc = 0.039) than the normal human controls did. There was an increased frequency of HLA-B*15 and B*40 alleles in patients with symptomatic syphilis (both P＜0.01, Pc＜0.05), as well as an elevated frequency of HLA-A*02, 11, 29, B*15 and 40 alleles (all P＜0.01, Pc＜0.05) and a decreased frequency of HLA-A*30 and 33 in patients with asymptomatic syphilis(P=0.002, 0.026, Pc=0.001, 0.013 respectively), compared with the normal human controls. The frequency of HLA-A*30 allele was significantly higher in patients with symptomatic syphilis than in those with asymptomatic syphilis (P = 0.001, Pc = 0.013). Conclusions There seems to be an association between HLA-A*02, B* 15 and B*40 alleles and syphilis, between HLA-A*30 allele and symptomic syphilis, and between HLA-A*02, 11 and 29 alleles and asymptom1atic syphilis, in Shandong Han population.

Machine learning has developed rapidly in recent years.Using machine learning to predict the radiotherapy outcomes and complications can more accurately evaluate the patients' conditions and take appropriate treatment measures as soon as possible.The non-dose and dose related factors generated during radiotherapy are filtered and input into the algorithm model,then corresponding prediction result can be obtained.There are many algorithm models to predict survival rate,tumor control rate and radiotherapy complications,and the predicted result are more accurate now.However,the algorithm model also has various problems,and it needs constant exploration and improvement.

Objective To explore the relationship between meteorological conditions and emergency visiting of acute onset of renal colic caused by kidney stones. Methods Retrospective study design was applied to collect the emergency visiting data of acute renal colic attack in zhongnan hospital of Wuhan university from January 1, 2016 to December 31, 2017, as well as the average daily temperature and humidity in wuhan, hubei province during the same period. Pearson correlation analysis and linear regression analysis were used to study the relationship between meteorological conditions and emergency visiting of acute onset of renal colic caused by kidney stones. Results The results of multivariate linear regression analysis showed that the R 2 of daily visits of patients with renal colic and daily meteorological conditions was 0.309 (P < 0.05), and the R 2 of monthly visits of patients and monthly meteorological conditions was 0.642 (P<0.05). Conclusions Both temperature and humidity are correlated with the number of emergency visits of acute attack patients with renal calculi and colic.