Objective:To discuss the composition and connotation of practical training objectives for nurses specialized in intravenous therapy, and provide guidance and reference for standardizing the practical training of nurses specialized in intravenous therapy.Methods:In this phenomenological analysis in qualitative research, 13 intravenous treatment and nursing experts from Shandong, Zhejiang, Sichuan, and Shaanxi provinces were selected from May to July 2021 for semi-structured interviews. Colaizzi's 7-step method and Nvivo 12.0 were used to organize data and analyze and refine themes.Results:Three themes and 12 subthemes were extracted for the practical training of intravenous therapy nurses, including knowledge objectives, ability objectives, and well-rounded objectives.Conclusions:Attention should be paid to the setting of clinical professional knowledge, skills and comprehensive quality goals for nurses specialized in intravenous therapy, so as to improve the pertinence and timeliness of training, promote the quality of training and the professional development of specialized training for intravenous therapy.

Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF–target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of chromatin immunoprecip-itation sequencing (ChIP-seq) provide great opportunities to discover the TF–target regulations across different conditions. In this study, we constructed a database named hTFtarget, which inte-grated huge human TF target resources (7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs) and epigenetic modification information to predict accurate TF–target regulations. hTFtarget offers the following functions for users to explore TF–target regulations:(1) browse or search general targets of a query TF across datasets;(2) browse TF–target regulations for a query TF in a specific dataset or tissue;(3) search potential TFs for a given target gene or non-coding RNA; (4) investigate co-association between TFs in cell lines; (5) explore potential co-regulations for given target genes or TFs; (6) predict candidate TF binding sites on given DNA sequences; (7) visualize ChIP-seq peaks for different TFs and conditions in a genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploringhuman TF–target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtar-get.