Effectiveness and Safety of a New Nutrient Fixed Combination Containing Pollen Extract Plus Teupolioside, in the Management of LUTS in Patients with Benign Prostatic Hypertrophy: A Pilot Study

Lucia Muraca,Antonio Scuteri，Giovambattista De Sarro,Luca Gallelli

Life (Chicago, Ill. : 1978)（2022）SCI 3区

Cited 4|Views25

Abstract

Benign prostatic hyperplasia (BPH) is a common cause of male lower urinary tract symptoms (LUTS) that can reduce quality of life. Even if several drugs can be used in its treatment, the development of adverse drug reactions (ADRs) represents the most common cause of low adherence. In the present study, we evaluate both the efficacy and the safety of a new nutrient fixed combination of Pollen Extract plus Teupolioside, named Xipag®, in patients with LUTS. We conduct a pilot single center open label clinical study between 1 March 2020 and 30 June 2020 in patients with BPH referred to general practitioner’s ambulatories. Male patients > 45 years, sexually active, with clinical symptoms of LUTS and with a diagnosis of HPB were enrolled and received one tablet/day of Xipag® (T0), for three months (T1: end of treatment). The IPSS and IIEF-5 questionnaires were carried out at T0 and T1 and represent the first end point, whereas the primary safety end point was considered the absence of ADR or of drug–drug interactions related to Xipag® administration. During the study period, 25 subjects aged 43 to 76 years (mean 62.7 ± 9) were enrolled and completed the study. The clinical evaluation in T1 documented that Xipag® induced a statistically significant improvement (p < 0.01) in symptoms, as documented by the IPSS questionnaire (range 22.7–88.9; mean 55.2 ± 23.6), without the development of ADRs. In conclusion, this is the first real-world study that showed the efficacy and the safety of Xipag® in the BPH patients with LUTS.

Translated text

Key words

benign prostatic hyperplasia,LUTS,nutrients,pollen extract,teupolioside,clinical care,drug safety

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper