BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

ObjectiveTo explore the effect of respiratory training based on core stabilization training on lumbar disc herniation. MethodsFrom January, 2023 to October, 2024, 96 patients with lumbar disc herniation admitted to the First Affiliated Hospital of Bengbu Medical University were divided into control group (n = 32), core group (n = 32) and respiratory group (n = 32). All the groups underwent conventional rehabilitation therapy, with core stabilization training in the core group and respiratory training combined with core stabilization training in the respiratory group, additionally, for four weeks. Before and after training, the scores of Visual Analogue Scale, Japanese Orthopaedic Association (JOA) and Oswestry Dysfunction Index (ODI) were compared, the average electromyographic value (AEMG) and root mean square (RMS) value of the multifidus and transversus abdominis were detected by surface electromyography (sEMG); and the thickness of the multifidus and transversus abdominis were measured by musculoskeletal ultrasonography bilaterally. ResultsThe intra-group effect (F > 597.796, P < 0.001), inter-group effect (F > 16.535, P < 0.001) and interaction effect (F > 49.622, P < 0.001) were significant in the scores of VAS, JOA and ODI; which were better in the respiratory group than in the control group and the core group (P < 0.05), and were better in the core group than in the control group (P < 0.001). The intra-group effect (F > 7971.631, P < 0.001), inter-group effect (F > 177.760, P < 0.001) and interaction effect (F > 478.771, P < 0.001) were significant in the thickness of the transversus abdominis and multifidus; which were better in the respiratory group than in the control group and the core group (P < 0.001), and were better in the core group than in the control group (P < 0.001). The intra-group effect (F > 144303.007, P < 0.001), inter-group effect (F > 1495.458, P < 0.001) and interaction effect (F > 3121.361, P < 0.001) were significant in the RMS of the multifidus and transversus abdominis; which were better in the respiratory group than in the control group and the core group (P < 0.001), and were better in the core group than in the control group (P < 0.001). The intra-group effect (F > 1890.532, P < 0.001), inter-group effect (F > 607.132, P < 0.001) and interaction effect (F > 824.923, P < 0.001) were significant in the AEMG of the multifidus and transversus abdominis; which were better in the respiratory group than in the control group and core group (P < 0.001), and were better in the core group than in the control group (P < 0.001). ConclusionCore training combined with respiratory training can more effectively reduce pain and improve dysfunction by enhancing the strength and control of the core muscles， thus improving the quality of life of patients with lumbar disc herniation.

Objective To explore the predictive value of radiomics for postoperative pancreatic fistula, infection, and long-term survival in patients with pancreatic ductal adenocarcinoma (PDAC). Methods 206 patients who received radical resection of pancreatic cancer in Zhongshan Hospital, Fudan University from January 2014 to December 2020 and were pathologically confirmed as PDAC after surgery were retrospectively selected, all of whom had complete surgical data and long-term follow-up data. Pyradiomics was used to analyze the enhanced CT images of all patients and extract radiomics features. LASSO dimensionality reduction combined with logistic regression analysis was used to construct a predictive model for pancreatic fistula and abdominal infection after PDAC surgery, and evaluating the model’s effectiveness using ROC curves. A long-term survival prediction model for PDAC patients was constructed using LASSO dimensionality reduction combined with Cox regression analysis, and patient risk scores were calculated. The patients were divided into high-risk and low-risk groups based on the median, and the survival curves were compared to evaluate the effectiveness of the model. The imaging omics features with the highest weight were divided into high expression group and low expression group according to the median, and the prognostic differences and clinical features were compared. Radiomics and clinical features were combined to analyze the influencing factors of long-term prognosis and construct a clinical imaging comprehensive model. Results A total of 1 595 radiomics features were extracted. A predictive model for pancreatic fistula and infection after PDAC surgery was constructed, with AUC values of 0.81 and 0.79, respectively. The PDAC long-term survival prediction model was successfully constructed, and the prognosis of the high-risk group was worse than that of the low-risk group (P<0.001). The weight of the radiomics feature “log-sigma-5-mm-3D_glszm_ZonePercentage” was 59.557. The CA19-9 level in the high expression group is higher than that in the low expression group (P=0.017), and there is a statistically significant difference in survival curves between the two groups (P=0.021). The comprehensive clinical imaging model suggested that age, AJCC stage, lymph infiltration, CA19-9 level and imaging characteristics were risk factors for long-term prognosis of PDAC patients (HR=1.028, 4.084, 2.566, 1.232 and 2.536). Conclusions The predictive model based on radiomics has good predictive performance for pancreatic fistula, infection, and long-term prognosis after PDAC surgery. Patients with high expression of the radiomics feature “log-sigma-5-mm-3D_glszm_ZonePercentage” face poorer prognosis.

Diabetic cardiomyopathy (DCM) is a medical condition characterized by cardiac remodeling and dysfunction in individuals with diabetes mellitus. Sarcoplasmic reticulum (SR) and mitochondrial Ca²⁺ overload in cardiomyocytes have been recognized as biological hallmarks in DCM; however, the specific factors underlying these abnormalities remain largely unknown. In this study, we aimed to investigate the role of a cardiac-specific long noncoding RNA, D830005E20Rik (Trdn-as), in DCM. Our results revealed the remarkably upregulation of Trdn-as in the hearts of the DCM mice and cardiomyocytes treated with high glucose (HG). Knocking down Trdn-as in cardiac tissues significantly improved cardiac dysfunction and remodeling in the DCM mice. Conversely, Trdn-as overexpression resulted in cardiac damage resembling that observed in the DCM mice. At the cellular level, Trdn-as induced Ca²⁺ overload in the SR and mitochondria, leading to mitochondrial dysfunction. RNA-seq and bioinformatics analyses identified calsequestrin 2 (Casq2), a primary calcium-binding protein in the junctional SR, as a potential target of Trdn-as. Further investigations revealed that Trdn-as facilitated the recruitment of METTL14 to the Casq2 mRNA, thereby enhancing the m⁶A modification of Casq2. This modification increased the stability of Casq2 mRNA and subsequently led to increased protein expression. When Casq2 was knocked down, the promoting effects of Trdn-as on Ca²⁺ overload and mitochondrial damage were mitigated. These findings provide valuable insights into the pathogenesis of DCM and suggest Trdn-as as a potential therapeutic target for this condition.
Animals ; Diabetic Cardiomyopathies/pathology* ; RNA, Long Noncoding/genetics* ; Myocytes, Cardiac/metabolism* ; Mice ; Calsequestrin/genetics* ; Calcium/metabolism* ; Male ; Sarcoplasmic Reticulum/metabolism* ; Methyltransferases/metabolism* ; Mice, Inbred C57BL ; Mitochondria, Heart/metabolism* ; Disease Models, Animal ; Mitochondria/metabolism*

Inflammation plays a pivotal role in the etiology and progression of various diseases. In traditional Chinese medicine, the whole plants of Thesium chinense Turcz. and its preparations (e.g. Bairui Granules) have been employed to manage inflammatory conditions. While flavonoids were previously considered the primary anti-inflammatory components, other potentially active constituents have been largely overlooked and not thoroughly investigated. This study presents a novel finding that the total alkaloids of T. chinense (BC-Alk) are potent active substances underlying the traditional and clinical applications of T. chinense and Bairui Granules as anti-inflammatory agents. UPLC-MS/MS analysis identified the composition of BC-Alk as quinolizidine alkaloids. The anti-inflammatory efficacy of BC-Alk was evaluated using a lipopolysaccharide (LPS)-induced lung inflammation model in mice. Results demonstrated that BC-Alk significantly mitigated LPS-induced lung inflammation, attenuated the overproduction of IL-1β and the overproduction of inflammatory factors (TNF-α), and ameliorated lung tissue hyperplasia in mice in vivo. Mechanistic studies in vitro revealed that BC-Alk upregulated the expression of Nrf2 and its downstream proteins NQO1 and glutamate-cystine ligase and modifier subunit (GCLM), inhibited NF-κB phosphorylation, and suppressed NLRP3 activation. Collectively, these findings indicate that BC-Alk exerts potent inhibitory effects against lung inflammation by modulating Nrf2, NF-κB, and NLRP3 pathways. This study provides new insights into the anti-inflammatory constituents of T. chinense and Bairui Granules.
Animals ; Lipopolysaccharides/adverse effects* ; Alkaloids/pharmacology* ; NF-kappa B/metabolism* ; NF-E2-Related Factor 2/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Mice ; Signal Transduction/drug effects* ; Anti-Inflammatory Agents/pharmacology* ; Male ; Mice, Inbred C57BL ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Pneumonia/genetics*

Cancer-related sarcopenia is mainly characterized by protein degradation and muscle depletion caused by catabolism, leading to a decrease in the quality, strength, and function of skeletal muscles. Pancreatic cancer has the highest incidence of cancer-associated sarcopenia. More than 80% of pancreatic cancer patients have the Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations, which promote the progress of sarcopenia. The authors systematically elaborate the interaction mechanism between KRAS-mutant pancreatic cancer and sarcopenia, mainly in four aspects: (1) KRAS driven metabolic reprogramming leads to depletion of muscle energy reserves, affecting the redistribution of muscle fiber energy and resulting in unidirectional energy flow for cancer cell proliferation. (2) By altering the cytokine profile, activating the ubiquitin protease system and the autophagy lysosome pathway, the cancer cells promote myotube degradation, inhibit muscle regeneration, disrupt muscle homeostasis, and lead to unidirectional flow of nutrients to support tumor growth. (3) Oxidative stress caused by cancer cells damages skeletal muscles. (4) Cancer cells induce immune cell remodeling in skeletal muscle. Meanwhile, the authors review the treatment progress of KRAS-mutant pancreatic cancer-related sarcopenia. With the deepening research of KRAS-mutant pancreatic cancer-related sarcopenia, targeted therapy and personalized intervention are expected to become important means to improve the prognosis and quality of life of patients.

Objective To explore the clinical efficacy of transcranial combined peripheral magnetic stimu-lation on bladder and voiding ability in patients with NB after spinal cord injury.Methods From September 2023 to October 2024,60 patients with NB after spinal cord injury were chosen from the Department of Rehabilitation Medicine of the First Affiliated Hospital of Bengbu Medical University,and were separated into the regular group(n=15),M1 area stimulation group(n=15),sacral nerve root stimulation group(n=15),and combined stimulation group(n=15).The control group underwent conventional rehabilitation treatment,the M1 area stimu-lation group added repetitive transcranial magnetic stimulation(rTMS)of the M1 area of the motor cortex,the sacral nerve root stimulation group added repetitive peripheral magnetic stimulation(rPMS)of the sacral 3 nerve roots,and the co-stimulation group added rTMS of the M1 area and rPMS of the sacral 3 nerve roots,and the treat-ment was carried out for 4 weeks in total.Urodynamic indices,voiding diaries,and neurogenic bladder symptom scores(NBSS)were assessed before and after therapy in the four groups.Results Before therapy,the differences in maximum bladder capacity,bladder capacity at the time of the first urge to urinate,maximum urine flow rate,residual urine volume,average number of urination per day,average daily urinary output,and NBSS scores of the four groups were not statistically significant when compared with those of the pre-treatment group(P<0.05);after treatment,the maximum bladder capacity,bladder capacity at the time of the first urge to urinate,maximum urine flow rate,residual urine volume,average number of urination per day,average daily urinary output,and NBSS scores were all improved compared with those before treatment(P<0.05);in comparison between the groups,after treatment,the Urodynamic indices,voiding diaries,and NBSS scores of the combined group were better than those of the other three groups(P<0.05).Conclusion Transcranial combined peripheral bidirectional feedback magnetic stimulation therapy can improve urodynamic and voiding symptoms and promote the recovery of bladder and voiding function in patients with NB after spinal cord injury

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.