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Zhou G, Gao L, Fang BZ, Wang YS, Tao HB, Wen X, Wang Q, Huang XM, Shi QS, Li WJ, Xie XB. Fundicoccus culcitae sp. nov., a novel potential bacteriocin producing bacterium isolated from a spoiled eye mask. Antonie Van Leeuwenhoek 2023; 116:1185-1195. [PMID: 37704902 DOI: 10.1007/s10482-023-01866-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/05/2023] [Indexed: 09/15/2023]
Abstract
A Gram-positive, facultatively anaerobic, oval beaded-shape, oxidase-negative, and non-motile bacterium designated DM20194951T was isolated from a spoiled eye mask obtained from Guangdong, China. Based on the 16S rRNA gene sequence, phylogenetic analysis indicated that strain DM20194951T showed the highest sequence similarity (95.8%) to Fundicoccus ignavus WS4937T. Meanwhile, strain DM20194951T could be distinguished from the type strains in the genus Fundicoccus by distinct phenotypic and genotypic traits. Strain DM20194951T grew variably with 1-2% (w/v) NaCl and tolerated pH 6.0-10.0. Growth was observed from 28 to 37 °C. The diagnostic diamino acids in the cell-wall peptidoglycan consisted of aspartic and glutamic acids as well as alanine. The predominant fatty acids were C18:1 ω9c, C16:0, and C16:1 ω9c. In the polar lipid profile, two glycolipids, three phospholipids, one phosphatidylglycerol, and one diphosphatidylglycerol were found. No respiratory quinones were detected. The DM20194951T genome is 3.2 Mb in size and contains a G + C content of 38.1%. A gene cluster for lactococcin 972 family bacteriocin production was found in the DM20194951T genome. Based on morphological, genotypic, and phylogenetic data, strain DM20194951T should be considered to represent a novel species in the genus Fundicoccus, for which the name Fundicoccus culcitae sp. nov. is proposed with the type strain DM20194951T (= KCTC 43472T = GDMCC 1.3614T).
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Affiliation(s)
- Gang Zhou
- Key Laboratory of Agricultural Microbiomics and Precision Application (MARA), Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Key Laboratory of Agricultural Microbiome (MARA), State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, 510070, People's Republic of China
| | - Lei Gao
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, People's Republic of China
| | - Bao-Zhu Fang
- State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, People's Republic of China
| | - Ying-Si Wang
- Key Laboratory of Agricultural Microbiomics and Precision Application (MARA), Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Key Laboratory of Agricultural Microbiome (MARA), State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, 510070, People's Republic of China
| | - Hong-Bing Tao
- Guangdong Dimei Biotechnology Co, Ltd, Guangzhou, 510070, People's Republic of China
| | - Xia Wen
- Key Laboratory of Agricultural Microbiomics and Precision Application (MARA), Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Key Laboratory of Agricultural Microbiome (MARA), State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, 510070, People's Republic of China
| | - Qian Wang
- Key Laboratory of Agricultural Microbiomics and Precision Application (MARA), Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Key Laboratory of Agricultural Microbiome (MARA), State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, 510070, People's Republic of China
| | - Xiao-Mo Huang
- Guangdong Dimei Biotechnology Co, Ltd, Guangzhou, 510070, People's Republic of China
| | - Qing-Shan Shi
- Key Laboratory of Agricultural Microbiomics and Precision Application (MARA), Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Key Laboratory of Agricultural Microbiome (MARA), State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, 510070, People's Republic of China
| | - Wen-Jun Li
- State Key Laboratory of Biocontrol, Guangdong Provincial Key Laboratory of Plant Resources and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
| | - Xiao-Bao Xie
- Key Laboratory of Agricultural Microbiomics and Precision Application (MARA), Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Key Laboratory of Agricultural Microbiome (MARA), State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, 510070, People's Republic of China.
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Zhou G, Tao HB, Wen X, Wang YS, Peng H, Liu HZ, Yang XJ, Huang XM, Shi QS, Xie XB. Metagenomic analysis of microbial communities and antibiotic resistance genes in spoiled household chemicals. Chemosphere 2022; 291:132766. [PMID: 34740703 DOI: 10.1016/j.chemosphere.2021.132766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/26/2021] [Accepted: 10/31/2021] [Indexed: 06/13/2023]
Abstract
Numerous attempts have been utilized to unveil the occurrences of antibiotic resistance genes (ARGs) in human-associated and non-human-associated samples. However, spoiled household chemicals, which are usually neglected by the public, may be also a reservoir of ARGs because of the excessive and inappropriate uses of industrial drugs. Based upon the Comprehensive Antibiotic Research Database, a metagenomic sequencing method was utilized to detect and quantify Antibiotic Resistance Ontology (AROs) in six spoiled household chemicals, including hair conditioner, dishwashing detergent, bath shampoo, hand sanitizer, and laundry detergent. Proteobacteria was found to be the dominant phylum in all the samples. Functional annotation of the unigenes obtained against the KEGG pathway, eggNOG and CAZy databases demonstrated a diversity of their functions. Moreover, 186 types of AROs that were members of 72 drug classes were identified. Multidrug resistance genes were the most dominant types, and there were 17 AROs whose resistance mechanisms were categorized into the resistance-nodulation-cell division antibiotic efflux pump among the top 20 AROs. Moreover, Proteobacteria was the dominant carrier of AROs with the primary resistance mechanism of antibiotic efflux. The maximum temperature of the months of collection significantly affected the distributions of AROs. Additionally, the isolated individual bacterium from spoiled household chemicals and artificial mixed communities of isolated bacteria demonstrated diverse resistant abilities to different biocides. This study demonstrated that there are abundant microorganisms and a broad spectrum profile of AROs in spoiled household chemicals that might induce a severe threat to public healthy securities and merit particular attention.
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Affiliation(s)
- Gang Zhou
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Hong-Bing Tao
- Guangdong Dimei Biotechnology Co., Ltd, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Xia Wen
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Ying-Si Wang
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Hong Peng
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Hui-Zhong Liu
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Xiu-Jiang Yang
- Guangdong Dimei Biotechnology Co., Ltd, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Xiao-Mo Huang
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, 510070, People's Republic of China; Guangdong Dimei Biotechnology Co., Ltd, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Qing-Shan Shi
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, 510070, People's Republic of China.
| | - Xiao-Bao Xie
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, 510070, People's Republic of China.
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Wang ML, Fang HQ, Tao HB, Cheng ZH, Lin XJ, Cai M, Xu C, Jiang S. Bootstrapping data envelopment analysis of efficiency and productivity of county public hospitals in Eastern, Central, and Western China after the public hospital reform. Curr Med Sci 2017; 37:681-692. [PMID: 29058280 DOI: 10.1007/s11596-017-1789-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Revised: 05/12/2017] [Indexed: 10/18/2022]
Abstract
China implemented the public hospital reform in 2012. This study utilized bootstrapping data envelopment analysis (DEA) to evaluate the technical efficiency (TE) and productivity of county public hospitals in Eastern, Central, and Western China after the 2012 public hospital reform. Data from 127 county public hospitals (39, 45, and 43 in Eastern, Central, and Western China, respectively) were collected during 2012-2015. Changes of TE and productivity over time were estimated by bootstrapping DEA and bootstrapping Malmquist. The disparities in TE and productivity among public hospitals in the three regions of China were compared by Kruskal-Wallis H test and Mann-Whitney U test. The average bias-corrected TE values for the four-year period were 0.6442, 0.5785, 0.6099, and 0.6094 in Eastern, Central, and Western China, and the entire country respectively, with average non-technical efficiency, low pure technical efficiency (PTE), and high scale efficiency found. Productivity increased by 8.12%, 0.25%, 12.11%, and 11.58% in China and its three regions during 2012-2015, and such increase in productivity resulted from progressive technological changes by 16.42%, 6.32%, 21.08%, and 21.42%, respectively. The TE and PTE of the county hospitals significantly differed among the three regions of China. Eastern and Western China showed significantly higher TE and PTE than Central China. More than 60% of county public hospitals in China and its three areas operated at decreasing return scales. There was a considerable space for TE improvement in county hospitals in China and its three regions. During 2012-2015, the hospitals experienced progressive productivity; however, the PTE changed adversely. Moreover, Central China continuously achieved a significantly lower efficiency score than Eastern and Western China. Decision makers and administrators in China should identify the causes of the observed inefficiencies and take appropriate measures to increase the efficiency of county public hospitals in the three areas of China, especially in Central China.
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Affiliation(s)
- Man-Li Wang
- Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Hai-Qing Fang
- Administration Office, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen, 518020, China
| | - Hong-Bing Tao
- Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Zhao-Hui Cheng
- Department of statistics and development research, Chongqing Health Information Center, Chongqing, 401120, China
| | - Xiao-Jun Lin
- Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Miao Cai
- Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, Saint Louis, Missouri, 63103, USA
| | - Chang Xu
- Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shuai Jiang
- Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
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Shu Q, Cai M, Tao HB, Cheng ZH, Chen J, Hu YH, Li G. What Does a Hospital Survey on Patient Safety Reveal About Patient Safety Culture of Surgical Units Compared With That of Other Units? Medicine (Baltimore) 2015; 94:e1074. [PMID: 26166083 PMCID: PMC4504589 DOI: 10.1097/md.0000000000001074] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The objective of this study was to examine the strengths and weaknesses of surgical units as compared with other units, and to provide an opportunity to improve patient safety culture in surgical settings by suggesting targeted actions using Hospital Survey on Patient Safety Culture (HSOPSC) investigation.A Hospital Survey on Patient Safety questionnaire was conducted to physicians and nurses in a tertiary hospital in Shandong China. 12 patient safety culture dimensions and 2 outcome variables were measured.A total of 23.5% of respondents came from surgical units, and 76.5% worked in other units. The "overall perceptions of safety" (48.1% vs 40.4%, P < 0.001) and "frequency of events reported" (63.7% vs 60.7%, P = 0.001) of surgical units were higher than those of other units. However, the communication openness (38.7% vs 42.5%, P < 0.001) of surgical units was lower than in other units. Medical workers in surgical units reported more events than those in other units, and more respondents in the surgical units assess "patient safety grade" to be good/excellent. Three dimensions were considered as strengths, whereas 5 other dimensions were considered to be weaknesses in surgical units. Six dimensions have potential to aid in improving events reporting and patient safety grade. Appropriate working times will also contribute to ensuring patient safety. Medical staff with longer years of experience reported more events.Surgical units outperform the nonsurgical ones in overall perception of safety and the number of events reported but underperform in the openness of communication. Four strategies, namely deepening the understanding about patient safety of supervisors, narrowing the communication gap within and across clinical units, recruiting more workers, and employing the event reporting system and building a nonpunitive culture, are recommended to improve patient safety in surgical units in the context of 1 hospital.
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Affiliation(s)
- Qin Shu
- From the Department of Health Administration (QS, MC, HT, ZC, JC, YH), School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology; and Tongji Hospital (GL), Tongji Medical college, Huazhong University of Science and Technology, Wuhan, Hubei Province, P.R. China
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Tao HB, Ye JJ, Miao WJ, Hou SY, Xiong GL, Yu Y, Guo SL, Chen P. [The incidence discriminant model for close contacts of active tuberculosis patients]. Zhonghua Liu Xing Bing Xue Za Zhi 2009; 30:676-678. [PMID: 19957588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
OBJECTIVE To establish a discriminant model and to provide a relatively accurate scientific basis for the early diagnosis of tuberculosis (TB) and detection of the close contacts. METHODS Through logistic regression analysis, key factors were selected according to Bayes theory and key factors of TB incidence of the close contacts were screened as well as a discriminant model was established. RESULTS The non-TB incidence discriminant function of the close contacts was described as: Y1= -39.831 (constant) + 1.927 X1 (sputum-frequency) + 3.528 X2 (education) + 0.309 X3 (contact time) + 5.893 X4 (evade) +2.140 X5 (ventilation) + 8.706 X6 (cough) + 30.970 X7 (fever). The discriminant function of non-TB incidence of the close contacts was as: Y2 =-57.875 (constant) + 2.343 X1 (sputum-frequency) + 3.965 X2 (education) + 0.361 X3 (contact time) + 6.296 X4 (evade) + 1.348 X5 (ventilation) + 12.984 X6 (cough) + 36.555 X7 (fever). CONCLUSION The discriminant model night be used to contribute to the early diagnosis, early intervention and timely treatment on those close contacts of tuberculosis cases.
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Affiliation(s)
- Hong-Bing Tao
- School of Medical and Health Management, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
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