Evaluation of the efficacy and safety of pegloticase for the treatment of chronic refractory gout through meta-analysis.

Gout is the most common arthritis that affects more than 2% of adults in developed countries. 3% to 4% of gout is chronic refractory gout. Conventional treatments are considered invalid. A new drug, pegloticase is used to treat chronic refractory gout, and there are still many questions about efficacy and safety. We searched PubMed, web of science, and the Cochrane Library. Preprints and references of related literature were also considered. Related efficacy and safety indicators were statistically analyzed by Review Manager 5.4 to conduct meta-analysis. A total of one article and one clinical trial were included. Pegloticase is able to reduce serum uric acid and reduce tender joints, thereby improving joint function. But pegloticase has more adverse events. Pegloticase can be used to treat chronic refractory gout. However, Pegloticase has a higher risk of adverse events. Considering the efficacy and safety, the scope of clinical applications of pegloticase can be further widened in patients in good medical condition.

Development and validation of a risk prediction model for the recurrence of foot ulcer in type 2 diabetes in China: A longitudinal cohort study based on a systematic review and meta-analysis.

AIMS: To develop and validate a risk prediction model for Chinese patients with type 2 diabetes with the recurrence of diabetic foot ulcers (DFUs) based on a systematic review and meta-analysis. METHODS: A prospective analysis was performed with 1333 participants and followed up for 60 months. Three models were analysed using a derived cohort. The risk factors were screened using meta-analysis and logistic regression, and the missing variables were interpolated by multiple imputation. The internal validation was performed using the bootstrap procedure, and the validation cohort was applied to the external validation. The performance of the model was evaluated in the area under the discrimination Receiver Operating Characteristic Curve (ROC). Calibration and discrimination methods were used for the validation cohort. The variables were selected according to their clinical and statistical importance to construct the nomograms. RESULTS: Three models were developed and validated. Model 1 included seven social and clinical indicators like sex, diabetes mellitus duration, previous DFU, location of ulcer, smoking, history of amputation, and foot deformity. Model 2 included four more indicators besides those in Model 1, which were statin agents used, antiplatelet agents used, systolic blood pressure, and body mass index. Model 3 added further laboratory indicators to Model 2, such as LDL-C, HbA1C, fibrinogen, and blood urea nitrogen. In the derivation cohort, 20.1% (206/1027) participants with DFU recurred as compared to the validation cohort, which was 38.2% (117/306). The areas under the curve in the derivation cohort for Models 1-3 were 0.781 (0.744-0.817), 0.843 (0.813-0.873), and 0.899 (0.876-0.922), respectively. The Youden indexes for Models 1-3 were 0.430, 0.559, and 0.653, respectively. Model 3 showed the highest sensitivity and specificity. All models performed well for both discrimination and calibration. CONCLUSIONS: Models 1-2 were non-invasive, which indicate their role in general screening for patients at a high risk of recurrence of DFU. However, Model 3 offers a more specific screening due to its best performance in predicting the risk of DFU recurrence amongst the three models.

Characterization of volatile organic compounds in Polygonum multiflorum and two of its processed products based on multivariate statistical analysis for processing technology monitoring.

Herein we describe a comprehensive analysis of the volatile organic compounds (VOCs) of raw Polygonum multiflorum Thunb. (PM) and two of its processed products, as well as an effective and simple method based on volatile markers to determine to which extent the PM had been processed. Sixty-five VOCs were identified by headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), along with headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). Principal component analysis (PCA) of the HS-SPME-GC-MS spectra and fingerprint analysis of the HS-GC-IMS spectra allowed the identification of raw PM from its processed products based the VOCs identified. Furthermore, the content and distribution of VOCs in the samples were easily analyzed visually based on clustering-kernel density estimation (Cluster-KDE). Finally, exploratory factor analysis (EFA) allowed the screening of significant markers to identify the processing method and consequently distinguish the three studied groups of PM.