Ecological cultivation is an important way for the sustainable production of traditional Chinese medicine in the context of the carbon peaking and carbon neutrality goals. Facility cultivation and simulative habitat cultivation modes have been developed and applied to develop the endangered Dendrobium huoshanense on the basis of protection. However, the differences in the greenhouse gas emissions and global warming potential of these cultivation modes remain unexplored, which limits the accurate assessment of carbon-friendly ecological cultivation modes of D. huoshanense. Greenhouse gas emission flux monitoring based on the static chamber method provides an effective way to solve this problem. Therefore, this study conducted a field experiment in the facility cultivation and simulative habitat cultivation modes at a D. huoshanense cultivation base in Dabie Mountains, Anhui Province. From April 2023 to March 2024, samples of greenhouse gases were collected every month, and the concentrations of CO_2, CH_4, and N_2O of the samples were then detected by gas chromatography. The greenhouse gas emission fluxes, cumulative emissions, and global warming potential were further calculated, and the following results were obtained.(1)The two cultivation modes of D. huoshanense showed significant differences in greenhouse gas emission fluxes, especially the CO_2 emission flux, with a pattern of facility cultivation>simulative habitat cultivation [(35.60±11.70)mg·m~(-2)·h~(-1) vs(2.10±4.59)mg·m~(-2)·h~(-1)].(2) The annual cumulative CO_2 emission flux in the case of facility cultivation was significantly higher than that of simulative habitat cultivation[(3 077.00±842.00)kg·hm~(-2) vs(221.00±332.00)kg·hm~(-2)], while no significant difference was found in annual cumulative CH_4 and N_2O emission fluxes.(3) The facility cultivation mode had a significantly higher global warming potential than the simulative habitat cultivation mode [(3 053.00±847.00)kg·hm~(-2) vs(196.00±362.00)kg·hm~(-2)]. Overall, the simulative habitat cultivation of D. huoshanense has obvious carbon-friendly characteristics compared with facility cultivation, which is in line with the concept of ecological cultivation of medicinal plants. This study is of great reference significance for the implementation and promotion of the ecological cultivation mode of D. huoshanense under carbon peaking and carbon neutrality goals.
Dendrobium/chemistry* ; Greenhouse Gases/metabolism* ; Carbon/analysis* ; Ecosystem ; Carbon Dioxide/metabolism* ; China ; Global Warming

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.

Gastric outlet obstruction (GOO) is a condition characterized by a mechanical obstruction of the stomach or duodenum, caused by either benign or malignant disease. Traditionally, surgical gastrojejunostomy (SGJ) has been the standard treatment for malignant GOO and endoscopic stenting (ES) offers a less invasive option, but it often requires repeat interventions. Recently, endoscopic ultrasound (EUS)-guided gastroenterostomy (EUS-GE), an innovative technique, has been applied as an alternative to SGJ and ES for GOO patients. Direct EUS-GE, device-associated EUS-GE, and EUS-guided double balloon-occluded gastrojejunostomy bypass are the most commonly used techniques with reported technical success rates ranging from 80% to 100%, and clinical success rates between 68% and 100%. Adverse event (AE) rates range from 0% to 28.2% and the stent misdeployment is the most common while other AEs include abdominal pain, bleeding, infection, peritonitis, bowel perforation, gastric leakage, and stent migration. It is clear that EUS-GE may achieve a similar clinical success to SGJ with fewer AEs and a shorter hospital stay. Compared to ES, EUS-GE showed higher clinical success, fewer stent obstructions, and lower reintervention rates.

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.

Gastric outlet obstruction (GOO) is a condition characterized by a mechanical obstruction of the stomach or duodenum, caused by either benign or malignant disease. Traditionally, surgical gastrojejunostomy (SGJ) has been the standard treatment for malignant GOO and endoscopic stenting (ES) offers a less invasive option, but it often requires repeat interventions. Recently, endoscopic ultrasound (EUS)-guided gastroenterostomy (EUS-GE), an innovative technique, has been applied as an alternative to SGJ and ES for GOO patients. Direct EUS-GE, device-associated EUS-GE, and EUS-guided double balloon-occluded gastrojejunostomy bypass are the most commonly used techniques with reported technical success rates ranging from 80% to 100%, and clinical success rates between 68% and 100%. Adverse event (AE) rates range from 0% to 28.2% and the stent misdeployment is the most common while other AEs include abdominal pain, bleeding, infection, peritonitis, bowel perforation, gastric leakage, and stent migration. It is clear that EUS-GE may achieve a similar clinical success to SGJ with fewer AEs and a shorter hospital stay. Compared to ES, EUS-GE showed higher clinical success, fewer stent obstructions, and lower reintervention rates.

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.

Gastric outlet obstruction (GOO) is a condition characterized by a mechanical obstruction of the stomach or duodenum, caused by either benign or malignant disease. Traditionally, surgical gastrojejunostomy (SGJ) has been the standard treatment for malignant GOO and endoscopic stenting (ES) offers a less invasive option, but it often requires repeat interventions. Recently, endoscopic ultrasound (EUS)-guided gastroenterostomy (EUS-GE), an innovative technique, has been applied as an alternative to SGJ and ES for GOO patients. Direct EUS-GE, device-associated EUS-GE, and EUS-guided double balloon-occluded gastrojejunostomy bypass are the most commonly used techniques with reported technical success rates ranging from 80% to 100%, and clinical success rates between 68% and 100%. Adverse event (AE) rates range from 0% to 28.2% and the stent misdeployment is the most common while other AEs include abdominal pain, bleeding, infection, peritonitis, bowel perforation, gastric leakage, and stent migration. It is clear that EUS-GE may achieve a similar clinical success to SGJ with fewer AEs and a shorter hospital stay. Compared to ES, EUS-GE showed higher clinical success, fewer stent obstructions, and lower reintervention rates.

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.

INTRODUCTION: Management of aortic stenosis (AS) in patients with chronic kidney disease (CKD) may often be overlooked, and this could confer poorer outcomes. METHODS: Consecutive patients ( n = 727) with index echocardiographic diagnosis of moderate to severe AS (aortic valve area <1.5 cm 2 ) were examined. They were divided into those with CKD (estimated glomerular filtration rate < 60 mL/min) and those without. Baseline clinical and echocardiographic parameters were compared, and a multivariate Cox regression model was constructed. Clinical outcomes were compared using Kaplan-Meier curves. RESULTS: There were 270 (37.1%) patients with concomitant CKD. The CKD group was older (78.0 ± 10.3 vs. 72.1 ± 12.9 years, P < 0.001), with a higher prevalence of hypertension, diabetes mellitus, hyperlipidaemia and ischaemic heart disease. AS severity did not differ significantly, but left ventricular (LV) mass index (119.4 ± 43.7 vs. 112.3 ± 40.6 g/m 2 , P = 0.027) and Doppler mitral inflow E to annular tissue Doppler e' ratio (E: e' 21.5 ± 14.6 vs. 17.8 ± 12.2, P = 0.001) were higher in the CKD group. There was higher mortality (log-rank 51.5, P < 0.001) and more frequent admissions for cardiac failure (log-rank 25.9, P < 0.001) in the CKD group, with a lower incidence of aortic valve replacement (log-rank 7.12, P = 0.008). On multivariate analyses, after adjusting for aortic valve area, age, left ventricular ejection fraction and clinical comorbidities, CKD remained independently associated with mortality (hazard ratio 1.96, 95% confidence interval 1.50-2.57, P < 0.001). CONCLUSION Concomitant CKD in patients with moderate to severe AS was associated with increased mortality, more frequent admissions for cardiac failure and a lower incidence of aortic valve replacement.
Humans ; Aortic Valve Stenosis/surgery* ; Male ; Female ; Renal Insufficiency, Chronic/complications* ; Aged ; Aged, 80 and over ; Middle Aged ; Severity of Illness Index ; Glomerular Filtration Rate ; Proportional Hazards Models ; Echocardiography ; Kaplan-Meier Estimate ; Retrospective Studies ; Aortic Valve/surgery* ; Echocardiography, Doppler