Aim: To construct a prokaryotic expression vector carrying NuBCP-9-tumstatin(74-98) (abbreviated as NT) gene and to obtain the fusion peptide with antitumor activity. Methods: Nucleotide sequences of antitumor peptides, NuBCP-9 and Tumstatin( 74-98), were connected via a linker(G_4S)_3 based on biased codons of E. coli the fused NT gene was reconstructed using SOE PCR, and inserted into pET32a(+) vector, and transformed in E. coli BL21(DE3). After expression, the novel fusion peptide was purified through nickel-affinity chromatogra-phy, Factor Xa digestion and ultrafiltration. Biological activity of the fusion peptide on ECV304 and A549 cells was evaluated by MTT assay. Results: A prokaryotic expression system with NT gene was successfully constructed. The soluble fusion peptide was accounted for approximately 25% when induced by 0. 5 mmol/L IPTG at 30 ℃ for 4 h. The purified fusion peptide could inhibit cell growth of ECV304 and A549 with inhibition rates of 60. 8% and 65. 2% at 20 μmol/L, respectively. Conclusion: A novel fusion peptide with antitumor activity was cloned, expressed and purified.

Persistent fetal vasculature syndrome (PFVS) is a rare congenital vitreous dysplasia, which is classified as anterior, posterior and combined types according to the location of the vascular abnormalities. The clinical manifestations of PFVS are diverse, and early surgical intervention is very important. The main objective of surgical treatment is to remove the anterior and posterior traction between fibrovascular membranes and retina as well as lens, and to reconstruct clear visual axis. Surgical treatments include pupilloplasty, lensectomy with or without intraocular lens implantation and vitrectomy via limbal or scleral approach. For new technologies, the applications of ophthalmic viscosurgical device and femtosecond lasers have desirable results . In addition to focusing on improving the success rate of surgery, it is also necessary to systematically and comprehensively assess the overall preoperative condition and postoperative visual function of the patients. PFVS eyes have limited improvement in postoperative vision, which is related to the extent of lesion involvement and the occurrence of complications. Eyes with macular dysplasia and tractional retinal detachment, as well as elongated ciliary process, have a poor prognosis of vision after surgery. How to improve postoperative vision in the eye affecting the posterior segment of the eye with PFVS from the microscopic anatomical relationship between the fibrous vascular pedicle and the retina is worth further study. On the other hand, reducing surgical trauma and optimizing surgical procedures in order to improve postoperative visual acuity and reduce postoperative complications are also the key research directions of future PFVS treatments.

Objective:To explore the correlation of serum albumin level at admission with clinical prognoses in patients with acute traumatic brain injury (TBI).Methods:One hundred and fifty-four patients with acute moderate-extreme severe TBI (Glasgow Coma Scale [GCS] scores of 3-12 at admission) in Department of Neurosurgery, General Hospital of Western Theater Command from January 1, 2019 to December 31, 2020 were chosen. The comprehensive clinical data of these patients were collected, including age, gender, GCS scores, serum albumin level (hypoalbuminemia defined as<35 g/L), hemoglobin level, comorbidities, treatment measures, and prognoses 6 months after discharge (poor prognosis defined as Glasgow outcome Scale [GOS] scores of 1-2, and good prognosis defined as GOS scores of 3-5). Univariate and multivariate Logistic regressions were used to identify the independent factors for clinical prognoses of these patients, and differences in poor prognosis rate, length of ICU stay, and total hospital cost were compared between different groups.Results:Among the 154 patients, 43 had poor prognosis and 111 had good prognosis. Serum albumin level at admission ( OR=0.916, 95% CI: 0.843-0.996, P=0.001) and GCS scores at admission ( OR=0.701, 95% CI: 0.594-0.828, P<0.001) were independent factors for prognosis. Patients with hypoalbuminemia ( n=70) displayed significantly higher poor prognosis rate, longer ICU stays, and increased total hospitalization cost compared with those without hypoalbuminemia ( n=84, P<0.05); specifically, in patients with GCS scores of 9-12 at admission ( n=58), those with hypoalbuminemia ( n=27) exhibited significantly higher poor prognosis rate, longer ICU stays, and higher total hospitalization cost than their non-hypoalbuminemia counterparts ( n=31, P<0.05); similarly, in patients with GCS scores of 3-8 at admission ( n=96), those with hypoalbuminemia ( n=74) had significantly higher poor prognosis rate than their non-hypoalbuminemia counterparts ( n=22, P<0.05). In patients with good prognosis, those with hypoalbuminemia ( n=56) showed significantly longer total hospital stays, prolonged ICU stays, and increased total hospitalization cost compared with those without hypoalbuminemia ( n=55, P<0.05). Conclusion:Low serum albumin level at admission is likely to lead to poor prognosis, prolonged ICU stays and increased total hospitalization cost in patients with acute TBI.

To understand the survival stress of community-dwelling people with mental disorder. Fifteen cases were selected by purposive sampling and received semi-structured individualized interviews.The data were analyzed by Colaizzi framework and themes were extracted. Four themes were extracted:physiological stress due to psychiatric symptoms and side effects of drugs;psychological stress due to the outcome of mental illness and to conflict of roles in daily life;social and environmental stress such as social discrimination,lack of job opportunities,and difficulty in obtaining social welfare resources;and interpersonal stress caused by discrimination and deteriorating family relations. Community-dwelling people with mental illness have a higher level survival stress after returning to their families and society,with the stressors including symptoms of illness,social discrimination,and interpersonal relationship.Eliminating self-discrimination of the patients,improving social support and social welfare system,and increasing individualized community mental rehabilitation activities may reduce the survival stress of these patients and promote their rehabilitation.
Humans ; Independent Living ; Mental Disorders ; Qualitative Research ; Social Support

Objective To explore the value of machine learning(ML)models based on non-contrast CT(NCCT)radiomics features for predicting the severity of acute phase traumatic brain injury(TBI).Methods Totally 600 TBI patients were retrospectively collected as observation group,other 65 TBI patients were taken as external validation set,while 50 TBI patients were prospectively enrolled as prospective validation set.Patients in observation group were divided into high-risk subgroup(n=240)and low-risk subgroup(n=360)according to Glasgow outcome scale(GOS)at discharge.The severity of acute phase TBI in observation group was assessed by doctor A and B with the same criteria,then an artificial model was established based on clinical and NCCT data at the time of first diagnosis using logistic regression(LR)method for predicting the severity of acute phase TBI.Patients in observation group were divided into training set(n=420,including 168 in high-risk subgroup and 252 in low-risk subgroup)and test set(n=180,including 72 in high-risk subgroup and 108 in low-risk subgroup)at the ratio of 7∶3.Based on NCCT of training set,radiomics features were extracted and selected,and LR,support vector machine(SVM),random forest(RF)and K-nearest neighbor(KNN)were used to establish 4 ML models.The efficacies of the above models were validated in test set,external validation set(including 34 cases of high-risk and 31 cases of low-risk TBI)and prospective validation set(including 21 cases of high-risk and 29 cases of low-risk TBI),respectively.Results The area under the curve(AUC)of doctor A and B for evaluating the severity of acute phase TBI in observation group was 0.606 and 0.771,respectively,of artificial model was 0.824.Based on NCCT in training set,6 optimal radiomics features were selected to construct LR,SVM,RF and KNN ML models,with AUC of 0.983,0.971,0.970 and 0.984 in test set,respectively,while the AUC of artificial model was 0.708.The AUC of LR,SVM,RF,KNN ML models and artificial model in external validation set was 0.879,0.881,0.984,0.863 and 0.733,while in prospective validation set was 0.984,0.873,0.982,0.897 and 0.704,respectively.Conclusion ML models based on CT radiomics could effectively predict the severity of acute phase TBI.