Objective:To conduct a scoping review of risk prediction models for the development of intraoperative hypothermia in patients undergoing cancer surgery to inform clinical nursing practice and future research.Methods:Relevant literature on constructing or validating intraoperative hypothermia risk prediction models for cancer surgery patients in five foreign language databases (PubMed, Embase, Web of Science, Cochrane Library, CINAHL) and four Chinese language databases (China National Knowledge Infrastructure, Wanfang, VIP, Chinese Biomedical Database) were searched from the time of library construction to June 1, 2024, extracted information on the applicable target, incidence of intraoperative hypothermia, methodology of model construction, predictors and performance, etc. The Prediction model Risk Of Bias Assessment Tool was used to evaluate the risk of bias of the studies, and the included literature was analyzed and discussed.Results:A total of 15 pieces of literature involving 18 models were included, with the study population focussing on patients undergoing surgery for colorectal cancer. The rate of intraoperative hypothermia ranged from 15.14% to 61.5%. Model construction methods included 2 types of Logistic regression models and machine learning, and model presentation was based on column-line plots. There were 8 predictors that appeared with a frequency of ≥5, including age, body mass index, operation time, anaesthesia time, operating room temperature, intraoperative rehydration volume, intraoperative bleeding volume, and heat preservation method.Conclusions:The performance of the included model was good, but the risk of bias was high for the predictors and the analysis part, and nursing staff should pay close attention to the risk factors of intraoperative hypothermia in patients undergoing cancer surgery, construct a risk prediction model with low bias and high applicability, and validate and improve the existing risk prediction model.

Objective To optimize scanning parameters of SPECIAL 1H-MR spectroscopy(MRS),and to observe the feasibility of combining with self-made simulated metabolite spectral data set(B set)of LCModel software for quantitative analysis of pig liver glucose(Glc)in vitro.Methods Metabolite mixture of cod liver oil,Glc and choline with different concentrations of Glc(10,20,30,40,50 mmol/L)and the fixed concentration of cod liver oil(0.125 mg/ml)and choline(100 mmol/L)were prepared with saline to simulate liver metabolism phantoms.There were 5 tube models for each Glc concentration,with 25 tube models configured.SPECIAL sequence was used to scan tube models with different parameters,and 1H-MRS images were obtained.The acquired SPECIAL data of tube models were analyzed using LCModel software and built-in metabolite basic set(A set)and B set,respectively.SPECIAL 1H-MRS images were generated,and signal-to-noise ratio(SNR),standard deviation％(SD％)and Glc signal intensity were obtained.The quality of SPECIAL 1H-MRS images were evaluated according to SNR and SD％,and the optimal scanning parameters were selected.The correlation of Glc signal intensity of phantoms obtained by combining optimal parameters with B set and Glc concentration were analyzed.The optimal SPECIAL sequence was used to scan pig liver in vitro(n=5).Then updated fat suppression(FS)-SPECIAL sequence scanning were performed,the corresponding 1H-MRS images were obtained based on B set,and the quality was observed.Results The optimal scanning parameters of SPECIAL sequence included TR 3 500 ms,TE 4.42 ms,TM 20.00 ms,and the number of repetitions(averages)was 256.SNR of phantoms SPECIAL 1H-MRS acquired with the optimal scanning parameters and B set was 40.5±1.1 and SD％was(13.5±1.0)％,with clearer spectral lines,smoother baselines and higher Glc peak resolution.There was positive correlation between Glc signal intensity obtained with 1H-MRS and Glc concentration of phantoms(r=0.997,P＜0.001).SNR of SPECIAL 1H-MRS of pig liver in vitro was 24.0±2.7 and SD％was(13.5±1.1)％,while SNR of FS-SPECIAL 1H-MRS was 29.5±2.3 and SD％was(4.0±0.8)％,the methylene peak was suppressed and the resolution of Glc peak was higher.Conclusion SPECIAL 1H-MRS with optimized parameters combining with self-made simulation data set of LCModel software could be used for accurately quantitative analysis of pig liver Glc in vitro.

Objective To optimize scanning parameters of SPECIAL 1H-MR spectroscopy(MRS),and to observe the feasibility of combining with self-made simulated metabolite spectral data set(B set)of LCModel software for quantitative analysis of pig liver glucose(Glc)in vitro.Methods Metabolite mixture of cod liver oil,Glc and choline with different concentrations of Glc(10,20,30,40,50 mmol/L)and the fixed concentration of cod liver oil(0.125 mg/ml)and choline(100 mmol/L)were prepared with saline to simulate liver metabolism phantoms.There were 5 tube models for each Glc concentration,with 25 tube models configured.SPECIAL sequence was used to scan tube models with different parameters,and 1H-MRS images were obtained.The acquired SPECIAL data of tube models were analyzed using LCModel software and built-in metabolite basic set(A set)and B set,respectively.SPECIAL 1H-MRS images were generated,and signal-to-noise ratio(SNR),standard deviation％(SD％)and Glc signal intensity were obtained.The quality of SPECIAL 1H-MRS images were evaluated according to SNR and SD％,and the optimal scanning parameters were selected.The correlation of Glc signal intensity of phantoms obtained by combining optimal parameters with B set and Glc concentration were analyzed.The optimal SPECIAL sequence was used to scan pig liver in vitro(n=5).Then updated fat suppression(FS)-SPECIAL sequence scanning were performed,the corresponding 1H-MRS images were obtained based on B set,and the quality was observed.Results The optimal scanning parameters of SPECIAL sequence included TR 3 500 ms,TE 4.42 ms,TM 20.00 ms,and the number of repetitions(averages)was 256.SNR of phantoms SPECIAL 1H-MRS acquired with the optimal scanning parameters and B set was 40.5±1.1 and SD％was(13.5±1.0)％,with clearer spectral lines,smoother baselines and higher Glc peak resolution.There was positive correlation between Glc signal intensity obtained with 1H-MRS and Glc concentration of phantoms(r=0.997,P＜0.001).SNR of SPECIAL 1H-MRS of pig liver in vitro was 24.0±2.7 and SD％was(13.5±1.1)％,while SNR of FS-SPECIAL 1H-MRS was 29.5±2.3 and SD％was(4.0±0.8)％,the methylene peak was suppressed and the resolution of Glc peak was higher.Conclusion SPECIAL 1H-MRS with optimized parameters combining with self-made simulation data set of LCModel software could be used for accurately quantitative analysis of pig liver Glc in vitro.

Objective:To conduct a scoping review of risk prediction models for the development of intraoperative hypothermia in patients undergoing cancer surgery to inform clinical nursing practice and future research.Methods:Relevant literature on constructing or validating intraoperative hypothermia risk prediction models for cancer surgery patients in five foreign language databases (PubMed, Embase, Web of Science, Cochrane Library, CINAHL) and four Chinese language databases (China National Knowledge Infrastructure, Wanfang, VIP, Chinese Biomedical Database) were searched from the time of library construction to June 1, 2024, extracted information on the applicable target, incidence of intraoperative hypothermia, methodology of model construction, predictors and performance, etc. The Prediction model Risk Of Bias Assessment Tool was used to evaluate the risk of bias of the studies, and the included literature was analyzed and discussed.Results:A total of 15 pieces of literature involving 18 models were included, with the study population focussing on patients undergoing surgery for colorectal cancer. The rate of intraoperative hypothermia ranged from 15.14% to 61.5%. Model construction methods included 2 types of Logistic regression models and machine learning, and model presentation was based on column-line plots. There were 8 predictors that appeared with a frequency of ≥5, including age, body mass index, operation time, anaesthesia time, operating room temperature, intraoperative rehydration volume, intraoperative bleeding volume, and heat preservation method.Conclusions:The performance of the included model was good, but the risk of bias was high for the predictors and the analysis part, and nursing staff should pay close attention to the risk factors of intraoperative hypothermia in patients undergoing cancer surgery, construct a risk prediction model with low bias and high applicability, and validate and improve the existing risk prediction model.

This study is to establish a simple and practical co-culture method of cortical neurons and astrocytes of rats. The cortex of the new-born SD rats was digested by 0.125% pancreatic enzyme, and the differential adherence was applied to obtain the mixed cell suspension of neurons and astrocytes. A low concentration of cytarabine was used to inhibit the astrocytes in a moderate way to get neuronal and astrocyte co-culture. The morphological characteristics of the cells in different times were observed under the inverted microscope. The cells began to adhere the wall 2 h after the inoculation. Neurons and astrocytes grew in a good condition under the inverted microscope 9 days after the inoculation. The results of the immunofluorescence staining and Rosenfeld's staining indicated that the co-culture of neurons and astrocytes was successful and the ratio of neurons and astrocytes was close to 1:1. A new neurons and astrocytes co-culture method, which is simple and convenient, was successfully established. It will be an efficient method for the related researches about neuronal and astrocyte co-culture in vitro.

Objective To investigate the risk factors for leukoaraiosis (LA). Methods The clinical and imaging data in patients with stroke were collected retrospectively. LA was divided into periventricular LA and subcortical LA according to the findings of MRI, and they were scored and classified. Results A total of 113 patients with stroke were included. There were 39 women and 74 men (mean age 61.33 ± 1.32 years). The age (65.52 ± 12. 56 vs.47. 96 ±9. 23 years, t =5. 634, P =0. 000), hypertension (68. 60% vs. 29. 63% ,x2 = 12. 932,P =0. 000), diabetes (30. 23% vs. 3.70%, x2 = 7. 953, P = 0. 005), systolic blood pressure (SBP) (147. 42 ± 2. 78 mm Hg vs. 134. 00 ± 22. 45 mm Hg,t = 2. 862, P = 0. 004), glucose (6. 54 ± 3. 48 mmol/L vs. 5. 35 ± 1.37 mmol/L, t = 2. 808, P = 0. 005), and total cholesterol (TC) level (5. 17±0.89 mmol/L vs. 4.59±0.61 mmol/L, t=3. 152, P=0. 002) in patients with periventricular LA (n = 86) were significantly higher than those without periventricular LA (n =27). The age (66. 44 ± 11.33 vs. 47. 96 ±9. 23 years, t =4. 768, P =0. 000), hypertension (74. 29% vs. 34. 88%, x2 = 17. 134, P = 0. 000), SBP (85.46 ± 9. 80 mm Hg vs. 69. 81 ±8. 74 mm Hg, t =2. 999, P=0. 003), diastolic blood pressure (DBP) (85.46 ±9. 80 mm Hg vs.69. 81 ±8.74 mm Hg, t =2. 999, P =0. 003), and TC level (5.22±0.99 mmol/L vs. 4.91 ±0. 75 mmol/L, t =3. 330, P =0. 001) in patients with subcortical LA (n =70) were significantly higher than those without subcortical LA (n =43). Spearman correlation analysis showed that the periventricular LA classification was significantly correlated with the age (rs = 0. 606, P =0. 000), drinking (rs = -0. 257, P = 0. 006), hypertension (rs = 0. 428, P = 0. 000), diabetes (rs =0. 236, P =0. 012), SBP (rs =0. 382, P =0. 000), and DBP (rs =0. 258, P =0. 006). The subcortical LA classification was significantly correlated with the age (rs = 0.488, P = 0. 000),hypertension (rs = 0. 416, P = 0. 000), SBP (rs = 0. 386, P = 0. 000), DBP (rs = 0. 326, P =0. 006), and TC level (rs =0. 231, P =0. 014). Multivariate logistic regression analysis showed that the age (odds ratio[OR] = 1.071, 95% confidence interval [CI] 1.009-1. 137; P=O. 024), hypertension (OR =4. 106, 95% CI 1. 657-10. 174; P =0. 002), and SBP (OR =1. 049,95% CI 1. 162-7. 013; P = 0. 022) were independently correlated with LA. Conclusions The age, hypertension, and SBP are the independent risk factors for LA, in which the age is an uncontrollable factor, and the aggressive prevention and treatment of hypertension may reduce the occurrence of LA.