Reporting and influencing factors of patient-reported outcomes in acupuncture randomised controlled trials: a cross-sectional study protocol.

INTRODUCTION: Patient-reported outcomes (PROs) are health reports that come directly from the patients themselves and represented the experience and insights of the patient's perspective on the impact of the intervention. PROs were increasingly emphasised in acupuncture randomised controlled trials (RCTs). However, the reporting quality of PROs in acupuncture RCTs has not been investigated to date. Therefore, we constructed this study to reveal the basic characteristics and reporting quality of PROs in acupuncture RCTs, and explore the relationship between concealment, blinding and RROs. We hope our findings can provide guidance for the reporting standards and future development of PROs in acupuncture RCTs in reverse. METHODS AND ANALYSIS: RCTs using acupuncture treatment as the intervention and PROs as primary outcomes or secondary outcomes will be systematically searched through seven databases MEDLINE, EMBASE, CENTRAL, CBM, CNKI, Wanfang and VIP between 1 January 2012 and 15 October 2022. The basic characteristics, concealment, blinding design and the characteristics of PROs in included RCTs will be summarised. The reporting quality of PROs will be assessed based on the CONSORT PRO extension. Logistic analysis will be performed to identify the association between concealment, blinding and RROs. ETHICS AND DISSEMINATION: Ethical approval is not required for this study. This protocol has been registered in Open Science Framework (OSF) Registries. The findings of this study will be submitted to a peer-reviewed academic journal.

Reporting and data-sharing level of acupuncture randomised controlled trials: a cross-sectional study protocol.

INTRODUCTION: Randomised controlled trials (RCTs) play an important role in evidence-based medicine. However, an article with low reporting quality may mislead both experts and the general public into an erroneous decision. Data sharing can contribute to the truthfulness and transparency of trials. Acupuncture RCTs have been increasing rapidly these years, but the reporting quality and data-sharing level of acupuncture RCTs are not clear. Thus, this study will provide the current status of the reporting quality and data-sharing level of acupuncture RCTs. METHODS AND ANALYSIS: A cross-sectional study will be conducted. The seven databases including MEDLINE, EMBASE, CENTRAL, CBM, CNKI, Wanfang Database and VIP will be searched between 1 January 2012 and 15 October 2022 to identify acupuncture RCTs. The basic characteristics of included trials will be summarised. The reporting quality for included RCTs will be assessed by the Consolidated Standards for Reporting Trials 2010 statement and the Standards for Reporting Interventions in Controlled Trials of Acupuncture. The data-sharing level will be assessed by open science practices. ETHICS AND DISSEMINATION: Ethical approval is not required for this study. This protocol has been registered in Open Science Framework Registries. The findings of this study will be submitted to a peer-reviewed academic journal.

In Situ Determination of Cannabidiol in Hemp Oil by Near-Infrared Spectroscopy.

Cannabidiol (CBD, 1) is an active component of hemp oil and many other products that offers diverse health benefits. Near-infrared spectroscopy (NIRS) coupled with chemometrics was utilized to quantify the CBD (1) concentration in the hemp oil through the containing glass vial. NIRS provided a fast and cost-effective tool to measure chemical profiles for the hemp oil samples with various concentrations of CBD (1) and its acid precursor, i.e., cannabidiolic acid (CBDA, 2). The measured NIR spectra were transformed by using a Savitzky-Golay first-derivative filter to remove baseline drift. Two self-optimizing chemometric methods, super partial least-squares regression (sPLSR) and self-optimizing support vector elastic net (SOSVEN), were applied to construct automatically multivariate models that predict the concentrations of CBD (1) and total CBD (sum of 1 and 2 concentrations) of the hemp oil samples. The SOSVEN had validation errors of 6.4 mg/mL for the prediction of CBD (1) concentration and 6.6 mg/mL for the prediction of total CBD concentration, which are significantly lower than the errors given by sPLSR. Other than the lower validation errors, SOSVEN has another advantage over sPLSR in that it builds a multivariate model while selecting spectral features at the same time. These results demonstrated that NIR spectroscopy combined with chemometrics can be used as a rapid and cost-effective approach to determine the CBD (1) and total CBD concentrations in hemp oil. Manufacturers would benefit from the fast and reliable approach in quality assurance.

Endothelium-Independent Vasodilatory Effects of Isodillapiolglycol Isolated from Ostericum citriodorum.

Ostericum citriodorum is a plant with a native range in China used in herbal medicine for treating angina pectoris. In this study, we investigated the vasodilatory effects of isodillapiolglycol (IDG), which is one of the main ingredients isolated from O. citriodorum ethyl acetate extract, in Sprague-Dawley rat aortic rings, and measured intracellular Ca2+ ([Ca2+]in) using a molecular fluo-3/AM probe. The results show that IDG dose-dependently relaxed endothelium-intact or -denuded aortic rings pre-contracted with noradrenaline (NE) or potassium chloride (KCl), and inhibited CaCl2-induced contraction in high K+ depolarized aortic rings. Tetraethyl ammonium chloride (a Ca2+-activated K+ channel blocker) or verapamil (an L-type Ca2+ channel blocker) significantly reduced the relaxation of IDG in aortic rings pre-contracted with NE. In vascular smooth muscle cells, IDG inhibited the increase in [Ca2+]in stimulated by KCl in Krebs solution; likewise, IDG also attenuated the increase in [Ca2+]in induced by NE or subsequent supplementation of CaCl2. These findings demonstrate that IDG relaxes aortic rings in an endothelium-independent manner by reducing [Ca2+]in, likely through inhibition of the receptor-gated Ca2+ channel and the voltage-dependent Ca2+ channel, and through opening of the Ca2+-activated K+ channel.

Pipeline for High-Throughput Modeling of Marijuana and Hemp Extracts.

Authentication of Cannabis products is important for assuring the quality of manufacturing, with the increasing consumption and regulation. In this report, a two-stage pipeline was developed for high-throughput screening and chemotyping the spectra from two sets of botanical extracts from the Cannabis genus. The first set contains different marijuana samples with higher concentrations of tetrahydrocannabinol (THC). The other set includes samples from hemp, a variety of Cannabis sativa with the THC concentration below 0.3%. The first stage applies the technique of class modeling to determine whether spectra belong to marijuana or hemp and reject novel spectra that may be neither marijuana nor hemp. An automatic soft independent modeling of class analogy (aSIMCA) that self-optimizes the number of principal components and the decision threshold is utilized in the first pipeline process to achieve excellent efficiency and efficacy. Once these spectra are recognized by aSIMCA as marijuana or hemp, they are then routed to the appropriate classifiers in the second stage for chemotyping the spectra, i.e., identifying these spectra into different chemotypes so that the pharmacological properties and cultivars of the spectra can be recognized. Three multivariate classifiers, a fuzzy rule building expert system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and support vector machine tree type entropy (SVMtreeH), are employed for chemotyping. The discriminant ability of the pipeline was evaluated with different spectral data sets of these two groups of botanical samples, including proton nuclear magnetic resonance, mass, and ultraviolet spectra. All evaluations gave good results with accuracies greater than 95%, which demonstrated promising application of the pipeline for automated high-throughput screening and chemotyping marijuana and hemp, as well as other botanical products.

Automatic soft independent modeling for class analogies.

Soft independent modeling of class analogy (SIMCA) is an important method for authentication. The key parameters for SIMCA, the number of principal components and the decision threshold, determine the model's performance. In this report, a self-optimizing SIMCA that automatically determines these two parameters is devised and referred to as automatic SIMCA (aSIMCA). An efficient optimization is obtained by incorporating response surface modeling (RSM) and bootstrapped Latin partitions with the model-building dataset. A set of design points over the ranges of the two parameters are evaluated with respect to sensitivity and specificity by using the model-building data from target and non-target classes. Averages of the sensitivity and specificity are used as responses for the design points. A 2-dimensional interpolation and a bivariate cubic polynomial were used to model the response surface. As a control method, a grid search that evaluates all combinations of the two parameters over the same ranges was performed in parallel to determine the best conditions for SIMCA and the modeling performance was compared to aSIMCA with RSM. The developed aSIMCA methods were evaluated by authenticating two botanical extracts sets, i.e., marijuana and hemp, with spectral datasets collected from various spectroscopic techniques, including nuclear magnetic resonance, high-resolution mass, and ultraviolet spectrometry. Results of a paired t-test indicated that the aSIMCA with the RSM had similar performance with the one optimized by the grid search for modeling marijuana and hemp, while the RSM was more computationally efficient. The 2-dimensional interpolation is preferred because the better efficiency and the fit to the response surface is more precise.