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Li L, Wan Q, Long Q, Nie T, Zhao S, Mao L, Cheng C, Zou C, Loomes K, Xu A, Lai L, Liu X, Duan Z, Hui X, Wu D. Comparative transcriptomic analysis of rabbit interscapular brown adipose tissue whitening under physiological conditions. Adipocyte 2022; 11:529-549. [PMID: 36000239 PMCID: PMC9427046 DOI: 10.1080/21623945.2022.2111053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 07/27/2022] [Accepted: 08/04/2022] [Indexed: 01/29/2023] Open
Abstract
Interscapular brown adipose tissue (iBAT) of both rabbits and humans exhibits a similar whitening phenomenon under physiological conditions. However, a detailed characterization of iBAT whitening in them is still lacking. Here, we chose rabbits as a model to gain a better understanding of the molecular signature changes during the whitening process of iBAT by transcriptomic analysis of rabbit iBAT at day 1, day 14, 1 month and 4 months after birth. We applied non-invasive MRI imaging to monitor the whitening process and correlated these changes with analysis of morphological, histological and molecular features. Principal component analysis (PCA) of differentially expressed genes delineated three major phases for the whitening process as Brown, Transition and Whitened BAT phases. RNA-sequencing data revealed that whitening of iBAT was an orchestrated process where multiple types of cells and tissues participated in a variety of physiological processes including neovascularization, formation of new nervous networks and immune regulation. Several key metabolic and signalling pathways contributed to whitening of iBAT, and immune cells and immune regulation appeared to play an overarching role.
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Affiliation(s)
- Lei Li
- Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qian Wan
- University of Chinese Academy of Sciences, Beijing, China
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qiaoyun Long
- School of Biomedical Sciences, the Chinese University of Hong Kong, Hong Kong SAR
| | - Tao Nie
- Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- China-New Zealand Joint Laboratory on Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Shiting Zhao
- Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Liufeng Mao
- Clinical Department of Guangdong Metabolic Disease Research Center of Integrated Chinese and Western Medicine, the First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Chuanli Cheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chao Zou
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Kerry Loomes
- School of Biological Sciences and Maurice Wilkins Centre, University of Auckland, New Zealand
| | - Aimin Xu
- Department of Medicine, University of Hong Kong, Hong Kong SAR
| | - Liangxue Lai
- Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- China-New Zealand Joint Laboratory on Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ziyuan Duan
- Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- China-New Zealand Joint Laboratory on Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Ziyuan Duan Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou510530, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiaoyan Hui
- School of Biomedical Sciences, the Chinese University of Hong Kong, Hong Kong SAR
- Xiaoyan Hui
| | - Donghai Wu
- Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- China-New Zealand Joint Laboratory on Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- CONTACT Donghai Wu
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Silva MAJG, Ferraz PFP, dos Santos LM, Ferraz GAES, Rossi G, Barbari M. Effect of the Spatial Distribution of the Temperature and Humidity Index in a New Zealand White Rabbit House on Respiratory Frequency and Ear Surface Temperature. Animals (Basel) 2021; 11:1657. [PMID: 34199567 PMCID: PMC8226593 DOI: 10.3390/ani11061657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/20/2021] [Accepted: 05/31/2021] [Indexed: 11/16/2022] Open
Abstract
The objective of this study was to characterize and evaluate the temperature and humidity index (THI) of New Zealand white (NZW) rabbits kept in a rabbit house using geostatistical techniques. Furthermore, we sought to evaluate its relationship with respiratory frequency (RF) and ear surface temperature (EST). The experiment was conducted at the Federal University of Lavras, Brazil. A total of 52 NZW rabbits were used. For the characterization of the thermal environment, the dry bulb temperature (tdb, °C), relative humidity (RH, %), and dew point temperature (tdp, °C) were collected at 48 points in the rabbit house at 6:00 a.m., 12:00 p.m., and 6:00 p.m. for seven days. The RF and EST of the animals was monitored. Subsequently, the THI was calculated and the data were analyzed using geostatistical tools and kriging interpolation. In addition, the RF and EST data were superimposed on the rabbit house's THI data maps. The magnitude of the variability and structure of the THI inside the rabbit house were characterized and the heterogeneity was visualized. Critical THI points inside the rabbit house and in locations where animals with high RF and ESTs were housed were identified, thus providing information about improving the production environment.
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Affiliation(s)
| | - Patrícia Ferreira Ponciano Ferraz
- Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Minas Gerais, Brazil; (P.F.P.F.); (L.M.d.S.); (G.A.eS.F.)
| | - Luana Mendes dos Santos
- Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Minas Gerais, Brazil; (P.F.P.F.); (L.M.d.S.); (G.A.eS.F.)
| | - Gabriel Araújo e Silva Ferraz
- Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Minas Gerais, Brazil; (P.F.P.F.); (L.M.d.S.); (G.A.eS.F.)
| | - Giuseppe Rossi
- Department of Agriculture, Food, Environment and Forestry, University of Firenze, 50145 Firenze, Italy; (G.R.); (M.B.)
| | - Matteo Barbari
- Department of Agriculture, Food, Environment and Forestry, University of Firenze, 50145 Firenze, Italy; (G.R.); (M.B.)
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A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers. ACTA ACUST UNITED AC 2021; 57:medicina57020099. [PMID: 33499377 PMCID: PMC7911834 DOI: 10.3390/medicina57020099] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/13/2021] [Accepted: 01/15/2021] [Indexed: 01/21/2023]
Abstract
Background and Objective: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths. Materials and Methods: Statistical methods such as Cox regression are usually used to detect the prognosis factors of a disease. This study investigated survival prediction using machine learning algorithms. The clinical data of 28,458 patients with primary lung cancers were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Results: This study indicated that the survival rate of women with primary lung cancer was often higher than that of men (p < 0.001). Seven popular machine learning algorithms were utilized to evaluate one-year, three-year, and five-year survival prediction The two classifiers extreme gradient boosting (XGB) and logistic regression (LR) achieved the best prediction accuracies. The importance variable of the trained XGB models suggested that surgical removal (feature “Surgery”) made the largest contribution to the one-year survival prediction models, while the metastatic status (feature “N” stage) of the regional lymph nodes was the most important contributor to three-year and five-year survival prediction. The female patients’ three-year prognosis model achieved a prediction accuracy of 0.8297 on the independent future samples, while the male model only achieved the accuracy 0.7329. Conclusions: This data suggested that male patients may have more complicated factors in lung cancer than females, and it is necessary to develop gender-specific diagnosis and prognosis models.
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