Predicting the prognosis in patients with sepsis by an endoplasmic reticulum stress gene signature.

BACKGROUND: Prognostic stratification of patients with sepsis is important for the development of individualized treatment strategies. Endoplasmic reticulum stress (ERS) plays a key role in sepsis. This study aimed to identify a set of genes related to ER stress to construct a predictive model for the prognosis of sepsis. METHODS: The transcriptomic and clinical data of 479 sepsis patients were obtained from GSE65682 and divided into a training set (n=288) and a validation set (n=191) at a ratio of 3:2. The external test set was GSE95233 (n=51). LASSO and Cox regression analyses were performed to establish a signature to predict the prognosis of patients with sepsis. Moreover, we developed a nomogram that included the risk signature and clinical features to predict survival probability. RESULTS: A prognostic signature was constructed with ten endoplasmic reticulum related genes (ADRB2, DHCR7, GABARAPL2, MAOA, MPO, PDZD8, QDPR, SCAP, TFRC, and TLR4) in the training set, which significantly divided patients with sepsis into high- and low-risk groups in terms of survival. This signature was validated using validation and external test sets. A nomogram based on the risk signature was constructed to quantitatively predict the prognosis of patients with sepsis. CONCLUSIONS: We constructed an ERS signature as a novel prognostic marker for predicting survival in sepsis patients, which could be used to develop novel biomarkers for the diagnosis, treatment, and prognosis of sepsis and to provide new ideas and prospects for future clinical research.

A database of seed plants on taxonomy, geography and ecology in the Qinling-Daba Mountains and adjacent areas.

The Qinling-Daba Mountains span subtropical and warm temperate zones and are one of the most remarkable biodiversity hotspots in China. Establishing a complete checklist of seed plants organized by nature reserves in the Qinling-Daba Mountains and adjacent areas is an important basis for managing and utilizing plant resources. First, we collected seed plant species data from published checklists representing 58 nature reserves in the Qinling-Daba Mountains and adjacent areas; second, we comprehensively and systematically sorted and integrated these data; third, we proofread and revised the data with the help of the R language and Flora of China dataset; and finally, we set up a seed plant database containing 96148 records, including the name, order, family, genus, life form, and endemism of each species for the entirety of the Qinling-Daba Mountains. The database contains 9491 species of seed plants belonging to 1729 genera, 211 families, and 59 orders, accounting for 39% of China's seed plants.

Correction: Exploring on the climate regionalization of Qinling-Daba mountains based on Geodetector-SVM model.

[This corrects the article DOI: 10.1371/journal.pone.0241047.].

Exploring on the climate regionalization of Qinling-Daba mountains based on Geodetector-SVM model.

Qinling-Daba Mountains (QDM), which are located in central China, are considered as a significant climatic boundary delimiting north and south. However, the influence of complex topographic and climatic features makes it challenging to identify the exact location of the boundary, and different scholars delimit the boundary with significant differences. In addition, there is a gradual transition between climate zones, and no real dividing line exists. To explore the climate regionalization of the QDM, we focused on the identification of the transition zone rather than the exact location of the boundary between subtropical and temperate zones. Thus, we proposed a new workflow for climate regionalization based on the Geodetector-SVM model (a combination of Geodetector and support vector machines). First, we selected the spatial distribution data of six vegetation types (including typical subtropical and temperate vegetation) to represent the spatial distribution of climatic zones. Environmental factors (such as topography, temperature, precipitation, and soil) were used as explanatory variables for the spatial distribution of vegetation. Second, using the Geodetector-SVM model, the distribution characteristics and suitable environment of typical vegetation in different climatic zones are comprehensively explored. By analyzing the multiple boundaries between subtropical and temperate vegetation, the location of the transition zone of the QDM was identified. The results revealed the following: (1) The new workflow for climate regionalization based on the Geodetector-SVM model is powerful for the identification of the transition zone. The q-statistics are generally greater than 0.35, indicating that the transition zone between subtropical and temperate zones can highly reflect the character of the QDM; (2) From west to east, the transition zone mainly passes through the cities of Heishui County, Kang County, Liuba County, and Yichuan County and is approximately 30 km wide.

Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt.

Vegetation mapping is of considerable significance to both geoscience and mountain ecology, and the improved resolution of remote sensing images makes it possible to map vegetation at a finer scale. While the automatic classification of vegetation has gradually become a research hotspot, real-time and rapid collection of samples has become a bottleneck. How to achieve fine-scale classification and automatic sample selection at the same time needs further study. Stratified sampling based on appropriate prior knowledge is an effective sampling method for geospatial objects. Therefore, based on the idea of stratified sampling, this paper used the following three steps to realize the automatic selection of representative samples and classification of fine-scale mountain vegetation: 1) using Mountain Altitudinal Belt (MAB) distribution information to stratify the study area into multiple vegetation belts; 2) selecting and correcting samples through iterative clustering at each belt automatically; 3) using RF (Random Forest) classifier with strong robustness to achieve automatic classification. The average sample accuracy of nine vegetation formations was 0.933, and the total accuracy of the classification result was 92.2%, with the kappa coefficient of 0.910. The results showed that this method could automatically select high-quality samples and obtain a high-accuracy vegetation map. Compared with the traditional vegetation mapping method, this method greatly improved the efficiency, which is of great significance for the fine-scale mountain vegetation mapping in large-scale areas.

Clinical efficacy of laser combined with menstrual regulation in the treatment of female melasma: a retrospective study.

The aim of this study was to evaluate the clinical efficacy of using a Medlite C6 Q-switch Nd:YAG laser combined with menstrual regulation-based traditional Chinese medicine (TCM) in the treatment of female melasma. Forty cases of female patients with melasma, who were treated between December 2013 and December 2015 at the Jiangsu Provincial Hospital of Traditional Chinese Medicine, were reviewed retrospectively. Twenty patients received Q-switch Nd:YAG 1064 nm laser treatments combined with menstrual regulation treatments (experimental group), and 20 patients were treated only with the laser (control group). All treatments lasted for 6 months. The patients' faces were photographed before, immediately, and 6 months after treatment. The therapeutic efficacy was assessed by the reduction in the Melasma Area and Severity Index (MASI) score and the total skin damage score, and this was then compared between the two groups. Immediately and 6 months after the treatment, both the MASI and total skin damage scores in the experimental group were significantly lower compared to those in the control group (P < 0.001). The experimental group had significantly higher basic recovery and effectiveness rates than the control group (P = 0.020 and P = 0.008, respectively) and had a significantly lower invalidity rate (P < 0.001). Results from Medlite C6 Q-switch Nd:YAG laser treatment combined with menstrual regulation are superior than those obtained using only a laser for the treatment of female melasma.

A preliminary study of the heating effect of the Tibetan Plateau.

The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect of the plateau and its implications. This paper firstly collects climate data (2001-2007) from 109 observation stations and MODIS-based estimated monthly mean temperature data in the plateau and the neighboring Sichuan Basin, and conducts correlation and simple linear regression to reveal the altitudinal pattern of temperature. Then, according to the linear relationships of temperature and altitude for each month, it compares air temperature differences on the same elevation between the main plateau and surrounding mountains and the Sichuan Basin so as to quantify the heating effect and discuss its implication on timberline of the plateau. The results show that: 1) the heating effect of the plateau is significant. The temperature of the main plateau area was higher than that of free air on the same elevation above the neighboring areas; on the elevation of 4500 m (the main plateau), temperature is 1-6°C higher in the main Plateau than over the Sichuan Basin for different months and 5.9-10.7°C higher than in the Qilian Mountains in the northeastern corner of the plateau. 2) Even at altitudes of 5000-6000 m in the main Plateau, there are 4 months with a mean temperature above 0°C. The mean temperature of the warmest month (July) can reach 10°C at about 4600-4700 m. This may help explain why the highest timberline in the northern hemisphere is on the southeastern Tibetan Plateau.