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Background The current coronavirus disease 2019 (COVID-19) is a public health emergency. In this study, we aimed to evaluate the risk factors for mortality in severe and critical COVID-19 patients. Methods We performed a retrospective study of patients diagnosed with severe and critical COVID-19 from four hospitals in Wuhan, China, by evaluating the clinical characteristics and laboratory results, and using Cox proportional hazards model to assess the risk factors involved in disease progression. Results In total, 446 patients with COVID-19 were enrolled. The study indicated a high mortality rate (20.2%) in severe and critical COVID-19 patients. At the time of admission, all patients required oxygen therapy, and 52 (12%) required invasive mechanical ventilation, of which 50 (96%) died. The univariate Cox proportional hazards model showed a white blood cell count of more than 10 × 109/L (HR 3.993,95%CI 2.469 to 6.459) that correlated with an increased mortality rate. The multivariable Cox proportional hazards model demonstrated that older age (HR 1.066, 95% CI 1.043 to 1.089) and higher white blood cell count (HR 1.135, 95% CI 1.080 to 1.192) were independent risk factors for determining COVID-19 associated mortality. Conclusions COVID-19 is associated with a significant risk of morbidity and mortality in the population. Older age and higher white blood cell count were found to be independent risk factors for mortality.

Purpose Implant location is performed after placement to verify that the safety of neighboring anatomic structure and the realizability of prosthetic plan. Routine postoperative location is based on radiological scanning and raises the concerns on radiation exposure and inconveniency in practice. In the present study a location method based on surface scanning was introduced and the accuracy of this method was assessed in vitro. Material and methods A total of 40 implants were placed in 10 resin mandible models. The models were scanned with intraoral scanner (IS group) and extraoral scanner (ES group). The implant position was located with fusing the images of surface scanning and cone beam computerized tomography (CBCT) after implant placement. Deviations were measured between positions located by surface scanner and postoperative CBCT with the parameters: central deviation at apex (cda), central deviation at hex (cdh), horizontal deviation at apex (hda), horizontal deviation at hex (hdh), vertical deviation at apex (vda), vertical deviation at hex (vdh) and angular deviation (ad). Results In IS group, the mean value of cda, cdh, hda, hdh, vda, vdh and ad was 0.27 mm, 0.23 mm, 0.12 mm, 0.10 mm, 0.21 mm, 0.19 mm and 0.72°, respectively. In ES group, the mean value of cda, cdh, hda, hdh, vda, vdh and ad was 0.28 mm, 0.25 mm 0.14 mm, 0.11 mm, 0.22 mm, 0.20 mm and 0.68°, respectively. The implant deviations in IS and ES groups were of no significant difference for any of the measurements. Conclusions Dental implant can be located via surface scanner with acceptable accuracy for postoperative verification. Further clinical investigation is needed to assess the feasibility of the method.

Background Rapeseed (Brassica napus L.) and soybean (Glycine max L.) seeds are rich in both protein and oil, which are major sources of biofuels and nutrition. Although the difference in seed oil content between soybean (~ 20%) and rapeseed (~ 40%) exists, little is known about its underlying molecular mechanism. Results An integrated omics analysis was performed in soybean, rapeseed, Arabidopsis (Arabidopsis thaliana L. Heynh), and sesame (Sesamum indicum L.), based on Arabidopsis acyl-lipid metabolism- and carbon metabolism-related genes. As a result, candidate genes and their transcription factors and microRNAs, along with phylogenetic analysis and co-expression network analysis of the PEPC gene family, were found to be largely associated with the difference between the two species. First, three soybean genes (Glyma.13G148600, Glyma.13G207900 and Glyma.12G122900) co-expressed with GmPEPC1 are specifically enriched during seed storage protein accumulation stages, while the expression of BnPEPC1 is putatively inhibited by bna-miR169, and two genes BnSTKA and BnCKII are co-expressed with BnPEPC1 and are specifically associated with plant circadian rhythm, which are related to seed oil biosynthesis. Then, in de novo fatty acid synthesis there are rapeseed-specific genes encoding subunits β-CT (BnaC05g37990D) and BCCP1 (BnaA03g06000D) of heterogeneous ACCase, which could interfere with synthesis rate, and β-CT is positively regulated by four transcription factors (BnaA01g37250D, BnaA02g26190D, BnaC01g01040D and BnaC07g21470D). In triglyceride synthesis, GmLPAAT2 is putatively inhibited by three miRNAs (gma-miR171, gma-miR1516 and gma-miR5775). Finally, in rapeseed there was evidence for the expansion of gene families, CALO, OBO and STERO, related to lipid storage, and the contraction of gene families, LOX, LAH and HSI2, related to oil degradation. Conclusions The molecular mechanisms associated with differences in seed oil content provide the basis for future breeding efforts to improve seed oil content. Electronic supplementary material The online version of this article (10.1186/s12870-018-1542-8) contains supplementary material, which is available to authorized users.

Background 3-Hydroxypropionic acid (3-HP) is an important platform chemical, serving as a precursor for a wide range of industrial applications such as the production of acrylic acid and 1,3-propanediol. Although Escherichia coli or Saccharomyces cerevisiae are the primary industrial microbes for the production of 3-HP, alternative engineered hosts have the potential to generate 3-HP from other carbon feedstocks. Methylobacterium extorquens AM1, a facultative methylotrophic α-proteobacterium, is a model system for assessing the possibility of generating 3-HP from one-carbon feedstock methanol. Results Here we constructed a malonyl-CoA pathway by heterologously overexpressing the mcr gene to convert methanol into 3-HP in M. extorquens AM1. The engineered strains demonstrated 3-HP production with initial titer of 6.8 mg/l in shake flask cultivation, which was further improved to 69.8 mg/l by increasing the strength of promoter and mcr gene copy number. In vivo metabolic analysis showed a significant decrease of the acetyl-CoA pool size in the strain with the highest 3-HP titer, suggesting the supply of acetyl-CoA is a potential bottleneck for further improvement. Notably, 3-HP was rapidly degraded after the transition from exponential phase to stationary phase. Metabolomics analysis showed the accumulation of intracellular 3-hydroxypropionyl-CoA at stationary phase with the addition of 3-HP into the cultured medium, indicating 3-HP was first converted to its CoA derivatives. In vitro enzymatic assay and β-alanine pathway dependent 13C-labeling further demonstrated that a reductive route sequentially converted 3-HP-CoA to acrylyl-CoA and propionyl-CoA, with the latter being reassimilated into the ethylmalonyl-CoA pathway. The deletion of the gene META1_4251 encoding a putative acrylyl-CoA reductase led to reduced degradation rate of 3-HP in late stationary phase. Conclusions We demonstrated the feasibility of constructing the malonyl-CoA pathway in M. extorquens AM1 to generate 3-HP. Furthermore, we showed that a reductive route coupled with the ethylmalonyl-CoA pathway was the major channel responsible for degradation of the 3-HP during the growth transition. Engineered M. extorquens AM1 represents a good platform for 3-HP production from methanol. Electronic supplementary material The online version of this article (10.1186/s12934-017-0798-2) contains supplementary material, which is available to authorized users.

Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw .