OBJECTIVE To optimize the simmering technology of Rheum palmatum from Menghe Medical School and compare the difference of chemical components before and after processing. METHODS Using appearance score， the contents of gallic acid， 5-hydroxymethylfurfural （5-HMF）， sennoside A+sennoside B， combined anthraquinone and free anthraquinone as indexes， analytic hierarchy process （AHP）-entropy weight method was used to calculate the comprehensive score of evaluation indicators； the orthogonal experiment was designed to optimize the processing technology of simmering R. palmatum with fire temperature， simmering time， paper layer number and paper wrapping time as factors； validation test was conducted. The changes in the contents of five anthraquinones （aloe-emodin， rhein， emodin， chrysophanol， physcion）， five anthraquinone glycosides （barbaloin， rheinoside， rhubarb glycoside， emodin glycoside， and emodin methyl ether glycoside）， two sennosides （sennoside A， sennoside B）， gallic acid and 5-HMF were compared between simmered R. palmatum prepared by optimized technology and R. palmatum. RESULTS The optimal processing conditions of R. palmatum was as follows: each 80 g R. palmatum was wrapped with a layer of wet paper for 0.5 h， simmered on high heat for 20 min and then simmered at 140 ℃， the total simmering time was 2.5 h. The average comprehensive score of 3 validation tests was 94.10 （RSD＜1.0%）. After simmering， the contents of five anthraquinones and two sennosides were decreased significantly， while those of 5 free anthraquinones and gallic acid were increased to different extents； a new component 5-HMF was formed. CONCLUSIONS This study successfully optimizes the simmering technology of R. palmatum. There is a significant difference in the chemical components before and after processing， which can explain that simmering technology slows down the relase of R. palmatum and beneficiate it.

BACKGROUND:Developmental dysplasia of the hip often leads to limb deformities in children,and the research related to its diagnosis and treatment has been gradually clarified.Recently,the finite element method has been paid attention to by scholars in the research related to developmental dysplasia of the hip because of its advantages. OBJECTIVE:Through literature search and review of the relevant research progress of finite element method in children's developmental dysplasia of the hip and treatment,analyze and summarize its advantages and disadvantages,and explore the direction of further research in the future. METHODS:PubMed,SCI,CBM,and CNKI were searched for relevant articles published from January 2014 to November 2023 with the key words of"developmental dysplasia(dislocation)of the hip,dysplasia of the hip,finite element analysis(method),pavlik harness,fixation in herringbone position,biomechanics,pelvic osteotomies,pemberton,salter,dega,periacetabular osteotomy,children"in Chinese and English.A small number of long-term articles were included,and 62 articles were finally included for analysis through screening. RESULTS AND CONCLUSION:(1)The mechanical environment of hip joint in children with developmental dysplasia of the hip was abnormal.The pressure in acetabulum was uneven.The stress increased and concentrated;the joint contact area decreased,and the local stress concentrated in femoral neck.(2)In the Pavlik sling and herringbone fixation,the mechanical environment of the hip was improved;the concentrated high stress area disappeared and the joint contact area increased,but the excessive abduction angle led to the increase of stress in the acetabulum and the lateral femoral head.(3)After pelvic osteotomy,the stress environment of hip joint and sacroiliac joint was improved.There was no single hinge in the three kinds of osteotomy,and the stress load position was different according to the age of the children.(4)After peri-acetabular osteotomy,the joint contact pressure was close to normal,but it was difficult to recover in patients with non-spherical femoral head.(5)The postoperative X-ray film findings could not show that the joint contact mechanics was the best.(6)It is indicated that the information that cannot be measured in the body can be obtained by using the finite element method,which can be operated in a virtual environment without the limitation of time and ethics.It can directly see the stress change area of normal and developmental dysplasia of the hip,explain the effectiveness of treatment from the point of view of mechanics,establish a specific finite element model and tailor-made operation plan for patients who need osteotomy.There is no standard or unified standard for the finite element modeling of developmental dysplasia of the hip and the material characteristic parameters of children's hip joint.Due to the inherent limitations of finite element method,it is impossible to analyze the model that contains bone,cartilage,ligament,muscle and other elements at the same time.The operation of finite element analysis is difficult,although it has advantages,it is not universal,and the current research sample size is small,which needs to be further expanded and verified.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

AIM: To investigate the changes of retinal nerve fiber layer(RNFL)thickness, retinal thickness and blood flow density in different stages of diabetic retinopathy(DR)patients based on optical coherence tomography angiography(OCTA).METHODS: A retrospective analysis was conducted on 382 patients(382 eyes)diagnosed with DR in our hospital from February 2023 to February 2024. According to the staging criteria, the patients were divided into mild group(n=121), moderate group(n=133), severe group(n=72), and proliferative group(n=56). The general clinical data of the four groups of patients was compared; OCTA was used to scan and collect data from all patients, and the RNFL thickness, retinal thickness, and blood flow density were compared among the four groups of patients.RESULTS: There was no statistically significant difference in age, gender, hypertension, chronic kidney disease, and random blood glucose among patients in the mild, moderate, severe, and proliferative groups(all P>0.05). As the stage of DR worsened, the duration of the disease gradually prolonged(P<0.05). The thickness of the RNFL(superior, inferior, temporal, nasal, and average thickness)and retinal thickness significantly increased with the severity of DR(all P<0.001); however, there was no statistically significant difference in inferior RNFL thickness between the moderate and mild groups(P>0.05). The blood flow density in the superficial and deep retinal layers, as well as in the choroidal capillary layer, significantly decreased with the progression of DR(all P<0.05). Nevertheless, there was no statistically significant difference in superficial retinal blood flow density between the moderate and severe groups(P>0.05).CONCLUSION: OCTA can accurately observe the changes in RNFL thickness, retinal thickness, and blood flow density in patients with DR at different stages, which can serve as sensitive indicators for monitoring DR progression.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Background: In acute and chronic renal inflammatory diseases, the activation of inflammatory cells is involved in the defect of erythropoietin (EPO) production. Ras guanine nucleotide-releasing protein-4 (RasGRP4) promotes renal inflammatory injury in type 2 diabetes mellitus (T2DM). Our study aimed to investigate the role and mechanism of RasGRP4 in the production of renal EPO in diabetes. Methods: The degree of tissue injury was observed by pathological staining. Inflammatory cell infiltration was analyzed by immunohistochemical staining. Serum EPO levels were detected by enzyme-linked immunosorbent assay, and EPO production and renal interstitial fibrosis were analyzed by immunofluorescence. Quantitative real-time polymerase chain reaction and Western blotting were used to detect the expression of key inflammatory factors and the activation of signaling pathways. In vitro, the interaction between peripheral blood mononuclear cells (PBMCs) and C3H10T1/2 cells was investigated via cell coculture experiments. Results: RasGRP4 decreased the expression of hypoxia-inducible factor 2-alpha (HIF2A) via the ubiquitination–proteasome degradation pathway and promoted myofibroblastic transformation by activating critical inflammatory pathways, consequently reducing the production of EPO in T2DM mice. Conclusion RasGRP4 participates in the production of renal EPO in diabetic mice by affecting the secretion of proinflammatory cytokines in PBMCs, degrading HIF2A, and promoting the myofibroblastic transformation of C3H10T1/2 cells.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.