We developed large-scale preparation of phycoerythrin from Porphyra haitanensis, a main economic red algae in China. Firstly, P. haitanensis thallus was broken by using "swelling and smash" method. Then times of grads ammonium sulfate precipitation applied to the crude extraction were compared. Desalted solution was further purified with one-step chromatography using hydroxyapatite and properties on spectrum and molecular weight were identified finally. The results indicated that after four times of ammonium sulfate precipitation (15%, 50%, 10% and 40%), the absorption spectrum purity of P. haitanensis achieved 0.9 (A564/A280), and 507.82 mg phycoerythrin (A564/A280 > 3.2) was obtained from 7 kg fresh algae after further hydroxyapatite chromatography. This research provides a potential way for preparation of phycoerythrin in large sclae.
Ammonium Sulfate ; chemistry ; Chromatography ; methods ; Phycoerythrin ; isolation & purification ; Porphyra ; chemistry

Objective:To investigate the clinical application of multimodal low-opioid combined with saphenous nerve block analgesia in elderly patients undergoing knee arthroplasty.Methods:This study is a randomized controlled study.A total of 60 elderly patients who underwent elective knee arthroplasty in the Department of Orthopedics, the First Hospital of Shanxi Medical University from January 2021 to December 2021 were selected and divided into 2 groups by numerical randomization: mode low opioid analgesia regimen group(observation group)and traditional analgesia regimen group(control group), 30 cases in each group.Observation group: (1)Preemptive analgesia: Oral celecoxib 200 mg, qd.from 3 days before surgery, the mini-mental state examination(MMSE)score was used to evaluate the cognitive function; (2)Intraoperative analgesia: After the prosthesis was installed, choose to inject analgesics around the knee joint(ropivacaine 200 mg, morphine 5 mg, epinephrine 0.25 mg, dexamethasone 5 mg/100 ml normal saline, also known as "cocktail" solution); (3)Postoperative analgesia: After the operation, continuous saphenous nerve block(0.2% ropivacaine, 2 ml/h)was performed under ultrasound guidance, and the dose of nerve block was adjusted according to the degree of rehabilitation training.Control group: no special treatment before and during the operation, traditional postoperative patient-controlled intravenous analgesia(PCIA)was used after the operation, the formula WAs as follows: sufentanil(2 μg/kg)+ flurbiprofen axetil(200-300 mg)+ Tropisetron(5-10 mg). The numerical rating scale(NRS)scores were observed and recorded 1 day before surgery, 6, 12, 24, and 48 hours after surgery; the time of the first active straight leg raising after surgery, the time of landing; the range of motion(ROM)of the knee joint 3, 7, and 14 days after surgery; the American Hospital for Special Surgery Knee Score(HSS)at 14 days, 1, 3, and 6 months after surgery; serum substance P(SP)and interleukin-6(IL-6)concentrations 1 day before surgery and 48 hours after surgery, and adverse events were recorded.Results:Compared with the control group, the patients in the observation group had lower NRS scores at 6, 12, 24, and 48 hours after the operation( t=27.705, 27.532, 21.739, 25.780, all P<0.05); the first active straight leg raising time and the time of landing earlier after the operation, and the knee joint at 3, 7, and 14 days after the operation, the range of motion(ROM)was better( t=35.496, 43.716, 3.766, 5.216, 6.009, all P<0.05). And the American hospital for special surgery knee score(HSS)was higher at 14 days, 1 month, and 3 months after surgery( t=19.247, 32.337, 22.651, all P<0.05), but there was no significant difference at 6 months after surgery.Simultaneously, the serum SP and IL-6 concentrations in the observation group 48 h after the operation were (431.0±11.3)ng/L and(11.9±2.7)ng/L, respectively.Compared with the control group(442.5±15.6)ng/L, (14.4±2.9)ng/L( t=5.362, 4.144, both P<0.05). Compared with the control group, which were lower than those in the incidence of postoperative nausea and vomiting was lower, the length of hospital stay was shorter in the observation group( χ2=4.630, t=3.311, P=0.031, 0.002), and the other indicators had no statistical differences(all P>0.05). Conclusions:Multimodal low-opioid combined with saphenous nerve block analgesia can significantly reduce perioperative pain in elderly patients undergoing knee arthroplasty, improve early postoperative mobility, and speed up postoperative functional recovery.

OBJECTIVE: To determine the prevalence and influencing factors of work-related musculoskeletal disorders(WMSDs) among workers in a shipyard. METHODS: A total of 496 workers in a large shipyard in Guangdong Province were selected as research subjects using the convenient sampling method. The Questionnaire of Musculoskeletal Disorders was used to investigate the prevalence of WMSDs in various regions of the body in the past year.Multivariate logistic regression was used to analyze the potential influencing factors of WMSDs in the frequently affected body parts. RESULTS: The prevalence of WMSDs was 70.2%(348/496). The prevalence rates of WMSDs in different body regions were: lower back/waist(43.1%), neck(29.4%), shoulder(29.0%), hand/wrist(25.4%), knee(22.4%), hip/leg(14.3%), ankle/foot(12.1%), upper back(11.3%) and elbow(9.3%). Multivariate logistic regression analysis results showed that increased risk of lower back/waist WMSDs occurred in workers who carry heavy objects>5.0 kg, who work in uncomfortable positions, who repeatedly perform the same work every day, and who repeatedly perform the same action using the lower limbs and ankles(P<0.05). Employees working ≤8 hours or more than 8 hours per day had higher risk of developing lower back/waist WMSDs compared with workers working 8-10 hours per day(P<0.05). CONCLUSION: The prevalence of WMSDs among workers in the shipyard is high.The lower back/waist WMSD is the most common one. The influencing factors include work organization and adverse ergonomic factors.

Objective:Glioblastoma(GBM)and brain metastases(BMs)are the two most common malignant brain tumors in adults.Magnetic resonance imaging(MRI)is a commonly used method for screening and evaluating the prognosis of brain tumors,but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited.In recent years,deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system.This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases(SBMs),and to further explore the impact of multimodality data fusion on classification tasks. Methods:Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed.First,structural T1-weight,T1C-weight,and T2-weight were selected as 3 inputs to the entire model,regions of interest(ROIs)were manually delineated on the registered three modal MR images,and multimodality radiomics features were obtained,dimensions were reduced using a random forest(RF)-based feature selection method,and the importance of each feature was further analyzed.Secondly,we used the method of contrast disentangled to find the shared features and complementary features between different modal features.Finally,the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. Results:The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs.Furthermore,compared with single-modal data,the multimodal fusion models using machine learning algorithms such as support vector machine(SVM),Logistic regression,RF,adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT)achieved significant improvements,with area under the curve(AUC)values of 0.974,0.978,0.943,0.938,and 0.947,respectively;our comparative disentangled multi-modal MR fusion method performs well,and the results of AUC,accuracy(ACC),sensitivity(SEN)and specificity(SPE)in the test set were 0.985,0.984,0.900,and 0.990,respectively.Compared with other multi-modal fusion methods,AUC,ACC,and SEN in this study all achieved the best performance.In the ablation experiment to verify the effects of each module component in this study,AUC,ACC,and SEN increased by 1.6%,10.9%and 15.0%,respectively after 3 loss functions were used simultaneously. Conclusion:A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.