OBJECTIVETo assess the influence of mimic cardiac rate on hydrodynamics of different mechanical prosthetic cardiac valves.

METHODSUS-made CarboMedics bileaflet valve, China-made Jiuling bileaflet valve and C-L tilting disc valve were tested via a pulsatile flow simulator in the aortic position. Testing conditions were set at mimic cardiac rates of 55 bpm, 75 bpm, 100 bpm with a constant mimic cardiac output of 4 L/min. The mean pressure differences (deltaP), leakage volumes (L(E)V) and closing volumes (C(L)V) across each valve, and effective orifice areas (EOA) were analyzed.

RESULTSWithin physiological range, deltaP, L(E)V, and C(L)V decreased as mimic cardiac rate increased, with a large extent of variance. EOA increased along with an increase in mimic cardiac rate. It was a different response in terms of cardiac rate alteration for different types of mechanical prosthetic cardiac valves.

CONCLUSIONMimic cardiac rate change affects hydrodynamics of mechanical prosthetic cardiac valves. Within physiological range, the hydrodynamic of prosthetic bileaflet valve is better than that of tilting disc valve.

Biomechanical Phenomena ; Cardiac Output ; Cardiac Volume ; Heart Rate ; Heart Valve Prosthesis ; Hemodynamics ; In Vitro Techniques ; Prosthesis Design ; Pulsatile Flow

OBJECTIVETo investigate the application of percutaneous endoscopic gastrostomy (PEG) to patients with severe craniocerebral injury for the purpose of nutritional support therapy and pulmonary infection prevention.

METHODSA total of 43 patients with severe craniocerebral injury admitted to our department from January 2008 to December 2012 received PEG followed by nutritional therapy. There were other 82 patients who were prescribed nasal-feeding nutrition. Nutrition status was evaluated by comparing serum albumin levels, and the incidence of pulmonary infection 1 week before and 2 weeks after operation was identified and compared.

RESULTSBoth PEG and nasal-feeding nutrition therapies have significantly elevated serum albumin levels (P<0.05). Serum albumin levels before and after nutritional therapies showed no significant difference between the two groups (P>0.05). The incidence of pulmonary infection in PEG group was significantly decreased compared with that in nasal-feeding nutrition group (P<0.05).

CONCLUSIONPEG is an effective method for severe craniocerebral injury patients. It can not only provide enteral nutrition but also prevent pulmonary infection induced by esophageal reflux.

Craniocerebral Trauma ; therapy ; Gastroesophageal Reflux ; prevention & control ; Gastroscopy ; Gastrostomy ; methods ; Humans ; Lung Diseases ; prevention & control ; Nutritional Support ; Serum Albumin ; analysis

Objective:To comprehensively evaluate the clinical value of Elecsys serum abnormal prothrombin (PIVKA-Ⅱ) test reagent for auxiliary diagnosis of hepatocellular carcinoma (HCC) in the Chinese population.Methods:A multicenter case-control design was used in the study. Samples from patients with first-time confirmed, diagnosed, and untreated HCC, benign liver disease and interfering controls were collected continuously. Elecsys PIVKA-II and alpha-fetoprotein (AFP) were tested for analysis. Various clinical details of the subjects were collected and analyzed. The efficacy of PIVKA-II (21.29 ng/ml) and AFP (400 ng/ml) for HCC diagnosis was calculated at specific positive cut-off values. Statistical analysis was performed using the Kruskal-Wallis test or receiver operating characteristic curve.Results:A total of 448 subjects were eventually enrolled from five centers, including 185 HCC cases. PIVKA-II had a higher diagnostic sensitivity and accuracy than AFP (84.86% vs. 30.81% and 89.01% vs. 63.66%) when the benign liver disease group was used as the control group, while the specificity was slightly lower. A sensitive analysis showed that PIVKA-II had a sensitivity of >80% at this specific positive cut-off value in the subgroup of AFP-negative subjects, patients with different etiologies, and HCC patients with multiple Barcelona Clinic liver cancer stages (including early-stage HCC). At the same time, the PIVKA-II subject had a slightly higher area under the receiver operating characteristic curve than the AFP (0.920 0 vs. 0.880 9).Conclusion:The clinical efficacy of Elecsys PIVKA-Ⅱ is good and stable in the Chinese population. Additionally, it has the clinical potential to improve the current missed diagnosis status of AFP-negative HCC and HCC monitoring at an early stage, as well as the effectiveness of accuracy promotion for HCC auxiliary diagnosis in China.

Objective To investigate the effect of varying ureteral wall thickness(UWT)at the site of ureteral stones on the clinical efficacy of ureteroscopic lithotripsy(URL).Methods The clinical data of 164 patients with ureteral stones in our hospital were retrospectively analyzed.According to different UWT,the patients were divided into the mild thickening group(84 cases,UWT<3.16 mm),the moderate thickening group(31 cases,UWT 3.16 to 3.49 mm),and the severe thickening group(49 cases,UWT>3.49 mm),and the differences of clinical related indicators among the three groups were compared.Results The incidence of postoperative renal colic and leukocyte disorder in the mild thickening group and the moderate thickening group were lower than those in the severe thickening group,and the differences were statistically significant(P<0.05).The postoperative catheterization time in the mild thickening group and the moderate thickening group were shorter than that in the severe thickening group,and the incidences of secondary lithotripsy,residual stones and stone return to kidney in the mild thickening group and the moderate thickening group were lower than those in the severe thickening group,with statistically significant differences(P<0.05).The length of hospital stay and hospitalization cost in the mild thickening group and the moderate thickening group were shorter/less than those in the severe thickening group,with statistically significant differences(P<0.05).Conclusion With the increase of UWT(especially when UWT>3.49 mm),the incidence of postoperative complications and hospitalization cost of URL increase to varying degrees,and the surgical efficacy decreases.In clinical work,UWT measurement holds potential value in predicting the surgical efficacy and complications of URL.

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: 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: 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: 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: 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.

OBJECTIVE: To explore the efficacy and action mechanism of penetrating moxibustion at governor vessel for persistent allergic rhinitis of deficiency-cold syndrome. METHODS: Ninety patients with persistent allergic rhinitis of deficiency-cold syndrome were randomly divided into an observation group ( RESULTS: Compared before treatment, the TCM symptom scores, VAS scores, RQLQ scores, serum levels of IgE and complete blood count of EOS in the two groups were all reduced after treatment ( CONCLUSION Based on the momethasone furoate nasal spray, the adjuvant treatment of penetrating moxibustion at governor vessel could significantly improve the clinical symptoms in patients with persistent allergic rhinitis of deficiency-cold syndrome, and its mechanism may be related to the regulation of immune disorder.
Acupuncture Points ; Humans ; Moxibustion ; Quality of Life ; Rhinitis, Allergic/drug therapy* ; Syndrome ; Treatment Outcome