ObjectiveTo investigate risk factors for postoperative sore throat in patients with double-lumen endotracheal intubation. MethodsThe data used in this post-hoc analysis were prospectively collected from a randomized， controlled trial. Age from 18 to 65 years old， ASAI-Ⅲ patients undergoing general anesthesia with a double-lumen endotracheal tube were enrolled. The perioperative data collected retrospectively were as follows： gender， age， smoking history， endotracheal tube diameter， duration of endotracheal tube， dose of Sufentanil， use of Flurbiprofen Axetil， cough after extubation， etc..Dynamometer was applied to assess extubation force. According to occurrence of postoperative sore throat， patients were divided into two groups： those who experienced sore throats and those who did not. Comparative analysis and multivariate logistic regression analysis were performed to screen the risk factors. ROC curve was used for predicting the predictive value of risk factors. ResultsAmong the 163 patients ， 74 （45.4%） had postoperative sore throat vs 89 （54.6%） not had. Multivariate logistic regression showed female ［OR95%CI=3.83（1.73， 8.50）， P=0.000 1］ and extubation force ［OR95%CI=1.78（1.45， 2.17）， P＜0.001］ were independent risk factors for postoperative sore throat. AUC value showed the extubation force was 0.773［95%CI（0.701， 0.846）， P＜0.001］. Youden index was 0.447， and the cut-off valve of extubation force was 13N. ConclusionFemale and extubation force were risk factors for sore throat in patients with double lumen endotracheal intubation.

Humans ; Consensus ; Liver Neoplasms/therapy* ; China

Objective:To investigate the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with maintenance hemodialysis (MHD) in Jiangsu province during SARS-CoV-2 pandemic in China from December 7, 2022 to January 27, 2023, and to analyze the influencing factors of all-cause death.Methods:It was a multi-center cross-sectional investigation. Structured questionnaire was used to collect patient information by medical staff of each hemodialysis center (room) as investigators. Part of the demography data and laboratory examination data came from the Jiangsu Province Hemodialysis Data Information System. MHD patients from hemodialysis centers (rooms) at all levels of medical institutions and independent hemodialysis institutions in Jiangsu province during the outbreak of SARS-CoV-2 infection were included, and the clinical characteristics and all-cause mortality of confirmed and suspected cases of SARS-CoV-2 infection were analyzed.Results:Questionnaire surveys and data analysis on 57 278 patients in 407 hemodialysis centers (rooms) were completed, accounting for 90.41% of the total number of MHD patients (63 357 cases) in Jiangsu province during the same period. There were 24 038 cases (41.97%) of SARS-CoV-2 infection and 14 805 cases (25.85%) of suspected infection, which were widely distributed in all dialysis centers in Jiangsu province. After clinical classification of 38 843 confirmed and suspected SARS-CoV-2 infection cases, 3 662 cases were severe and critical cases, accounting for 9.43% of the infected and suspected cases. Among the patients who had completed the questionnaires, there were 1 812 all-cause deaths, with an all-cause mortality rate of 3.16%. Multivariate logistic regression analysis showed that elderly (taking ≤50 years as a reference, 51-59 years: OR=1.583, 95% CI 1.279-1.933, P=0.001; 60-69 years: OR=3.972, 95% CI 3.271-4.858, P<0.001; 70-79 years: OR=7.236, 95% CI 5.917-8.698, P<0.001; ≥80 years: OR=11.738, 95% CI 9.459-14.663, P<0.001), male ( OR=1.371, 95% CI 1.229-1.529, P<0.001), and co-infection with hepatitis B virus (HBV) (positive serum HBV surface antigen, OR=0.629, 95% CI 0.484-0.817, P<0.001) were independent influencing factors for all cause mortality. Receiver-operating characteristic curve analysis showed that the area under the curve for male, age and current HBV infection prediction of all-cause death was 0.529 ( P<0.001), 0.724 ( P<0.001) and 0.514 ( P=0.042), respectively, and the cut-off value for age prediction of all-cause death was 65.5 years old. Compared with patients without HBV infection, MHD patients with HBV infection significantly reduced the proportion of severe and critically ill patients, all-cause hospitalizations and all cause deaths when infected with SARS-CoV-2 (4.99% vs. 6.41%, χ2=6.136, P=0.013; 8.90% vs. 11.44%, χ2=11.662, P<0.001; 2.01% vs. 3.37%, χ2=10.713, P=0.001, respectively). Conclusion:The MHD patients in Jiangsu province are susceptible to SARS-CoV-2. Elderly age and male gender are independent risk factors for death in MHD patients during the epidemic, while the HBV infection may be a protective factor for death of MHD patients infected with SARS-CoV-2.

OBJECTIVE: To detect variants of IVD gene among 4 neonates with suspected isovalerate acidemia in order to provide a guidance for clinical treatment. METHODS: 111 986 newborns and 7461 hospitalized children with suspected metabolic disorders were screened for acyl carnitine by tandem mass spectrometry. Those showing a significant increase in serum isovaleryl carnitine (C5) were analyzed for urinary organic acid and variants of the IVD gene. RESULTS: Four cases of isovalerate acidemia were detected, which included 2 asymptomatic newborns (0.018‰, 2/111 986) and 2 children suspected for metabolic genetic diseases (0.268‰, 2/7461). The formers had no obvious clinical symptoms. Analysis of acyl carnitine has suggested a significant increase in C5, and urinary organic acid analysis has shown an increase in isovaleryl glycine and 3-hydroxyisovalerate. Laboratory tests of the two hospitalized children revealed high blood ammonia, hyperglycemia, decreased red blood cells, white blood cells, platelets and metabolic acidosis. The main clinical manifestations have included sweaty foot-like odor, feeding difficulty, confusion, drowsiness, and coma. Eight variants (5 types) were detected, which included c.158G>A (p.Arg53His), c.214G>A (p.Asp72Asn), c.548C>T (p.Ala183Val), c.757A>G (p.Thr253Ala) and 1208A>G (p.Tyr403Cys). Among these, c.548C>T and c.757A>G were unreported previously. None of the variants was detected by next generation sequencing of 2095 healthy newborns, and all variants were predicted to be likely pathogenic based on the guidelines from the American College of Medical Genetics and Genomics. CONCLUSION The incidence of isovalerate acidemia in Liuzhou area is quite high. Screening of metabolic genetic diseases is therefore recommended for newborns with abnormal metabolism. The discovery of novel variants has enriched the mutational spectrum of the IVD gene.
Infant, Newborn ; Child ; Humans ; Acidosis ; Carnitine ; Erythrocytes ; High-Throughput Nucleotide Sequencing

Background/Aims: Colorectal cancer (CRC) patients often exhibit peritoneal metastasis, which negatively impacts their prognosis. CD31 and D2-40 have recently been suggested to be predictors of breast cancer prognosis, but their role in colorectal peritoneal metastasis (CRPM) remains unknown. Methods: The expression profiles of CD31 and D2-40 were analyzed in CRC patients with or without CRPM and in CRC cell lines with increasing metastatic potential. Overexpression and short hairpin RNA knockdown assays were performed in CRC cells, and the effects of these alterations on epithelial-mesenchymal transition (EMT) in vitro, growth of xenograft tumors in vivo, and peritoneal metastasis potential in a mouse model of CRPM were examined. Results: The expressions of CD31 and D2-40 were upregulated in CRC tumor tissues and was elevated further in tumor tissues from patients with CRPM. CD31 and D2-40 expression levels exhibited increasing trends parallel to the EMT potential of CRC cells. CD31 and D2-40 are essential for CRC cell EMT in vitro as well as for xenograft tumor growth and peritoneal metastasis in vivo. Conclusions CD31 and D2-40 contribute to CRPM by promoting EMT and may serve as prognostic markers and therapeutic targets for CRC, particularly in patients with peritoneal metastasis.

Objective:To evaluate the clinical efficacy and adverse events of 192Ir high-dose rate brachytherapy (HDR-BT) in the treatment of locally recurrent non-small cell lung cancer (NSCLC). Methods:Clinical data of 22 cases of recurrent NSCLC after radiotherapy admitted to our hospital from September 2013 to March 2018 were retrospectively analyzed. 192Ir HDR-BT was adopted for reradiotherapy. The prescription dose was 30Gy for 1 fraction. CT scan was reviewed every 1 month in the first 3 months after treatment and every 3 months after 3 months. Local control rate and adverse events were evaluated. The 1-and 2-year overall survival (OS) rates of re-treatment after relapse were calculated. Results:All the 22 patients completed the treatment successfully. The 1-, 3-and 6-month complete response (CR) rates were 9%, 14% and 14%, 82%, 82% and 82% for the partial response (PR) rates, 5%, 0% and 0% for the stable disease (SD) rates, 5%, 5% and 5% for the progressive disease (PD) rates, 91%, 96% and 96% for the objective response rates (ORR), respectively. The 1-and 2-year OS rates of re-treatment after relapse were 59% and 27%. Five patients (23%) experienced acute radiation-induced pneumonitis (3 cases of grade 1 and 2 cases of grade Ⅱ), 4 cases (18%) of radiation-induced bone marrow suppression (3 cases of grade I leukopenia and 1 case of grade I thrombocytopenia) and 1 case of postoperative pneumothorax. All these adverse events were mitigated after symptomatic treatment.Conclusion:192Ir HDR-BT is an efficacious and safe treatment of locally recurrent NSCLC.

Objective:To investigate the definition and influencing factors of early recurrence after resection for synchronous colorectal cancer liver metastases (sCRLM).Methods:Patients with sCRLM in Department of Hepatobiliary Surgery, Cancer Hospital, Chinese Academy of Medical Sciences from December 2008 to December 2016 were included. Restricted cubic spline was used to determine the correlations between the time of recurrence and the long-term prognosis. The univariable and multivariable Cox was performed to measure the feasibility of recurrence within 6 months as the early recurrence. Then apply logistic regression, support vector machine, decision tree, random forest, artificial neural network and XGBoost, these machine learning algorithm to comprehensively rank the importance of every clinicopathological variable to early recurrence, and according to the comprehensively ranks, we introduced variables into the multivariable logistic regression model and observed the receiver operating characteristic curve (ROC) of the logistic regression model, based on the ROC area under curve, Akaike information criterion, and Bayesian information criterion, we identified the best performed variable combination and introduced them into the multivariate logistic regression analysis to confirm the independent risk factors for early recurrence. Subsequently, inverse probability weighting (IPTW) was performed on the therapy-associated independent risk factor to evaluate and validate its influence on the early recurrence of sCRLM patients after reducing the standardized mean difference of all covariates.Results:A total of 228 sCRLM patients who received resection were enrolled and followed up from 2.10 to 108.57 months. There were 142 males and 86 females, aged (55.89±0.67) years old. In 170 (74.6%) patients with recurrence, restricted cube analysis determined that the hazard ratio (HR) of disease free survival (DFS) and overall survival (OS) satisfies a linear relationship ( P<0.05), and Cox analysis indicated that 6 months as the time cutoff for defining early recurrence was feasible ( HR=3.405, 95% CI: 2.098-5.526, P<0.05). Early recurrence was occurred in 93 (40.79%) patients. The survival rate of patients in early recurrence group was significantly lower than that in the late recurrence group ( HR=3.405, 95% CI: 2.098-5.526, P<0.05, and the 5-year survival rate was 14.0% vs 52.0%). Comprehensive analysis of 6 machine learning algorithms identified that the total number of lymph node dissection >22 ( OR=0.258, 95% CI: 0.132-0.506, P<0.05) is an independent protective factor for early recurrence, while the number of liver metastases>3 ( OR=4.715, 95% CI: 2.467-9.011, P<0.05) and postoperative complications ( OR=2.334, 95% CI: 1.269-4.291, P<0.05) are independent risk factors. Finally, the IPTW analysis fully reduced the influence of covariate confounding influence via causal inference to prove lymph node dissection associated with early recurrence (IPTW OR=0.29, P<0.05), benefiting the DFS (IPTW HR=0.4887, P<0.05), but without influence on OS (IPTW HR=0.6951, P>0.05). Conclusion:Six months after sCRLM as the definition of early recurrence, it has significant feasibility. The long-term survival of patients with early recurrence is poor. The independent influencing factors of early recurrence after sCRLM are the total number of lymph node dissection, the number of liver metastases and postoperative complications disease.

Objective:To investigate the application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma (HCC) after resection.Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 375 patients with HCC who underwent radical resection in the Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College from January 2012 to January 2017 were collected. There were 304 males and 71 females, aged from 21 to 79 years, with a median age of 57 years. According to the random numbers showed in the computer, 375 patients were divided into training dataset consisting of 300 patients and validation dataset consisting of 75 patients, with a ratio of 8∶2. Machine learning algorithms including logistic regression (LR), supporting vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used to construct survival prediction models for HCC after resection, so as to identify the optimal machine learning algorithm prediction model. A COX nomogram prediction model for predicting postoperative survival in patients with HCC was also constructed. Comparison of performance for predicting postoperative survival of HCC patients was conducted between the optimal machine learning algorithm prediction model and the COX nomogram prediction model. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) follow-up and survival of patients in the training dataset and validation dataset; (3) construction and evaluation of machine learning algorithm prediction models; (4) construction and evaluation of COX nomogram prediction model; (5) evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model. Follow-up was performed using outpatient examination or telephone interview to detect survival of patients up to December 2019 or death. Measurement data with normal distribution were expressed as Mean± SD, and comparison between groups was analyzed by the paired t test. Measurement data with skewed distribution were expressed as M ( P25, P75) or M (range), and comparison between groups was analyzed by the Mann-Whitney U test. Count data were represented as absolute numbers. Comparison between groups was performed using the chi-square test when Tmin ≥5 and N ≥40, using the calibration chi-square test when 1≤ Tmin ≤5 and N ≥40, and using Fisher exact probability when Tmin <1 or N <40. The Kaplan-Meier method was used to calculate survival rate and draw survival curve. The COX proportional hazard model was used for univariate analysis, and variables with P<0.2 were included for the Lasso regression analysis. According to the lambda value, variables affecting prognosis were screened for COX proportional hazard model to perform multivariate analysis. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: cases without microvascular invasion or with microvascular invasion, cases without liver cirrhosis or with liver cirrhosis of the training dataset were 292, 8, 105, 195, respectively, versus 69, 6, 37, 38 of the validation dataset, showing significant differences between the two groups ( χ2=4.749, 5.239, P<0.05). (2) Follow-up and survival of patients in the training dataset and validation dataset: all the 375 patients received follow-up. The 300 patients in the training dataset were followed up for 1.1-85.5 months, with a median follow-up time of 50.3 months. Seventy-five patients in the validation dataset were followed up for 1.0-85.7 months, with a median follow-up time of 46.7 months. The postoperative 1-, 3-year overall survival rates of the 375 patients were 91.7%, 79.5%. The postoperative 1-, 3-year overall survival rates of the training dataset were 92.0%, 79.7%, versus 90.7%, 81.9% of the validation dataset, showing no significant difference in postoperative survival between the two groups ( χ2=0.113, P>0.05). (3) Construction and evaluation of machine learning algorithm prediction models. ① Selection of the optimal machine learning algorithm prediction model: according to information divergence of variables for prediction of 3 years postoperative survival of HCC, five machine learning algorithms were used to comprehensively rank the variables of clinicopathological factors of HCC, including LR, SVM, DT, RF, and ANN. The main predictive factors were screened out, as hepatitis B e antigen (HBeAg), surgical procedure, maximum tumor diameter, perioperative blood transfusion, liver capsule invasion, and liver segment Ⅳ invasion. The rank sequence 3, 6, 9, 12, 15, 18, 21, 24, 27, 29 variables of predictive factors were introduced into 5 machine learning algorithms in turn. The results showed that the area under curve (AUC) of the receiver operating charateristic curve of LR, SVM, DT, and RF machine learning algorithm prediction models tended to be stable when 9 variables are introduced. When more than 12 variables were introduced, the AUC of ANN machine learning algorithm prediction model fluctuated significantly, the stability of AUC of LR and SVM machine learning algorithm prediction models continued to improve, and the AUC of RF machine learning algorithm prediction model was nearly 0.990, suggesting RF machine learning algorithm prediction model as the optimal machine learning algorithm prediction model. ② Optimization and evaluation of RF machine learning algorithm prediction model: 29 variables of predictive factors were sequentially introduced into the RF machine learning algorithm to construct the optimal RF machine learning algorithm prediction model in the training dataset. The results showed that when 10 variables were introduced, results of grid search method showed 4 as the optimal number of nodes in DT, and 1 000 as the optimal number of DT. When the number of introduced variables were not less than 10, the AUC of RF machine learning algorithm prediction model was about 0.990. When 10 variables were introduced, the RF machine learning algorithm prediction model had an AUC of 0.992 for postoperative overall survival of 3 years, a sensitivity of 0.629, a specificity of 0.996 in the training dataset, an AUC of 0.723 for postoperative overall survival of 3 years, a sensitivity of 0.177, a specificity of 0.948 in the validation dataset. (4) Construction and evaluation of COX nomogram prediction model. ① Analysis of postoperative survival factors of HCC patients in the training dataset. Results of univariate analysis showed that HBeAg, alpha fetoprotein (AFP), preoperative blood transfusion, maximum tumor diameter, liver capsule invasion, and degree of tumor differentiation were related factors for postoperative survival of HCC patients [ hazard ratio ( HR)=1.958, 1.878, 2.170, 1.188, 2.052, 0.222, 95% confidence interval ( CI): 1.185-3.235, 1.147-3.076, 1.389-3.393, 1.092-1.291, 1.240-3.395, 0.070-0.703, P<0.05]. Clinico-pathological data with P<0.2 were included for Lasso regression analysis, and the results showed that age, HBeAg, AFP, surgical procedure, perioperative blood transfusion, maximum tumor diameter, tumor located at liver segment Ⅴ or Ⅷ, liver capsule invasion, and degree of tumor differentiation as high differentiation, moderate-high differentiation, moderate differentiation, moderate-low differentiation were related factors for postoperative survival of HCC patients. The above factors were included for further multivariate COX analysis, and the results showed that HBeAg, surgical procedure, maximum tumor diameter were independent factors affecting postoperative survival of HCC patients ( HR=1.770, 8.799, 1.142, 95% CI: 1.049- 2.987, 1.203-64.342, 1.051-1.242, P<0.05). ② Construction and evaluation of COX nomogram prediction model: the clinicopathological factors of P≤0.1 in the COX multivariate analysis were induced to Rstudio software and rms software package to construct COX nomogram prediction model in the training dataset. The COX nomogram prediction model for predicting postoperative overall survival had an consistency index of 0.723 (se=0.028), an AUC of 0.760 for postoperative overall survival of 3 years in the training dataset, an AUC of 0.795 for postoperative overall survival of 3 years in the validation dataset. The verification of the calibration plot in the training dataset showed that the COX nomogram prediction model had a good prediction performance for postoperative survival. COX nomogram score=0.627 06×HBeAg (normal=0, abnormal=1)+ 0.134 34×maximum tumor diameter (cm)+ 2.107 58×surgical procedure (laparoscopy=0, laparotomy=1)+ 0.545 58×perioperative blood transfusion (without blood transfusion=0, with blood transfusion=1)-1.421 33×high differentiation (non-high differentiation=0, high differentiation=1). The COX nomogram risk scores of all patients were calculated. Xtile software was used to find the optimal threshold of COX nomogram risk scores. Patients with risk scores ≥2.9 were assigned into high risk group, and patients with risk scores <2.9 were assigned into low risk group. Results of Kaplan-Meier overall survival curve showed a significant difference in the postoperative overall survival between low risk group and high risk group of the training dataset ( χ2=33.065, P<0.05). There was a significant difference in the postoperative overall survival between low risk group and high risk group of the validation dataset ( χ2=6.585, P<0.05). Results of further analysis by the decision-making curve showed that COX nomogram prediction model based on the combination of HBeAg, surgical procedure, perioperative blood transfusion, maximum tumor diameter, and degree of tumor differentiation was superior to any of the above individual factors in prediction performance. (5) Evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model: prediction difference between two models was investigated by analyzing maximun tumor diameter (the important variable shared in both models), and by comparing the predictive error curve of both models. The results showed that the postoperative 3-year survival rates predicted by RF machine learning algorithm prediction model and COX nomogram prediction model were 77.17% and 74.77% respectively for tumor with maximum diameter of 2.2 cm ( χ2=0.182, P>0.05), 57.51% and 61.65% for tumor with maximum diameter of 6.3 cm ( χ2=0.394, P>0.05), 51.03% and 27.52% for tumor with maximum diameter of 14.2 cm ( χ2=12.762, P<0.05). With the increase of the maximum tumor diameter, the difference in survival rates predicted between the two models turned larger. In the validation dataset, the AUC for postoperative overall survival of 3 years of RF machine learning algorithm prediction model and COX nomogram prediction model was 0.723 and 0.795, showing a significant difference between the two models ( t=3.353, P<0.05). Resluts of Bootstrap cross-validation for prediction error showed that the integrated Brier scores of RF machine learning algorithm prediction model and COX nomogram prediction model for predicting 3-year survival were 0.139 and 0.134, respectively. The prediction error of COX nomogram prediction model was lower than that of RF machine learning algorithm prediction model. Conclusion:Compared with machine learning algorithm prediction models, the COX nomogram prediction model performs better in predicting 3 years postoperative survival of HCC, with fewer variables, which is easy for clinical use.

Objective: To develop a brief version of Quality of Life of College Students Questionnaire(QOLCS-51) to measure the quality of college students conveniently in time. Methods: Qualitative research and two investigations were applied to shorten and verify the brief questionnaire, and SPSS 22.0 and Lisrel 9.20 were used to analyze the reliability and validity of 2 questionnaires. Results: Quality of Life of College Students Questionnaire-bref was developed by deleting 22 items through qualitative research and 6 items by the first investigation. 952 college students from Jiangsu, Anhui and Shanxi Province were selected to participate in the second investigation, which consisted of five domains and 23 items (QOLCS-23). All 23 items were accepted by analysis of difficulty(0.44-0.68), and all have passed the test of critical ratio(P<0.01), the general related index was 0.33-0.60(P<0.01). 78.3% items distinguished the students with/without dyssomnia. Reliability was tested by test-retest reliability coefficient(0.71-0.86), homogeneity reliability coefficient (Cronbach α=0.845) and exploratory factor analysis (6 factors, i.e. physics domain, three subdomains of psychology domain, behavior domain, social domain and environment domain). Validity was tested by correlations of five domains between QOLCS-51 and QOLCS23(greater than 0.8) and confirmatory factor analysis(χ2/df=12.17, RMSEA=0.05, SRMR=0.07, GFI=0.84, AGFI=0.83, CFI=0.92, IFI=0.92, NFI=0.85, NNFI=0.91). Conclusion QOLCS51 consists of 23 items of QOLCS23, after deleting 28 items, to assess 5 dimensions of physiology, psychology, behavior, environment and social support with good construction validity and criterion-related validity, and good homogeneity reliability and re-test reliability.

Objective: To analyze variations of TYR and P genes among 14 patients with clinically diagnosed oculocutaneous albinism. Methods: Potential variations of the TYR and P genes were detected by Sanger sequencing. Novel variations were predicted with bioinformatics software including SIFT and PolyPhen-2. Results: No variation was found in the TYR gene, while 9 types of variations were found in the P gene among the 14 patients, which included c. 803-3C>G (7/26), c. 1327G>A (p.Val443Ile) (5/26), c. 632C>T (p.Pro211Leu) (4/26), c. 1832T>C (p.Leu611Pro) (3/26), c. 1349C>A (p.Thr450Lys) (2/26), c. 2363C>T (p.Ser788Leu) (2/26), c. 2228C>T (p.Pro743Leu) (1/26), c. 1525A>G(p.Thr509Ala) (1/26), and c. 1349C>T(p.Thr450Met) (1/26). Only 1 heterozygous variation was detected in 2 families. c. 2363C>T (p.Ser788Leu), c. 1832T>C (p.Leu611Pro) and c. 1525A>G (p.Thr509Ala) were not reported previously and predicted as "harmful" to the protein function. Conclusion The main type of ocular albinism is oculocutaneous albinism type Ⅱ in Liuzhou region, where the most common variations of the P gene were c. 803-3C>G and c. 1327G>A (p.Val443Ile). Above finding has enriched the variation spectrum of the P gene.