BackgroundThe possible advantages of laparoscopic radical hysterectomy (LRH) versus open radical hysterectomy (RH) have not been well reviewed systematically. The aim of this study was to systematically review the comparative effectiveness between LRH and RH in the treatment of cervical cancer based on the evaluation of the Perioperative outcomes, oncological clearance, complications and long-term outcomes.MethodsThe systematic review was conducted by searching PubMed, MEDLINE, EMBASE, the Cochrane Library and BIOSIS databases. All original studies that compared LRH with RH were included for critical appraisal. Data were pooled and analyzed.ResultsA total of twelve original studies that compared LRH (n = 754) with RH (n = 785) in patients with cervical cancer fulfilled quality criteria were selected for review and meta-analysis. LRH compared with RH was associated with a significant reduction of intraoperative blood loss (weighted mean difference = −268.4 mL (95 % CI −361.6, −175.1; p < 0.01), a reduced risk of postoperative complications (OR = 0.46; 95 % CI 0.34–0.63) and shorter hospital stay (weighted mean difference = −3.22 days; 95 % CI–4.21, −2.23 days; p < 0.01). These benefits were at the cost of longer operative time (weighted mean difference = 26.9 min (95 % CI 8.08–45.82). The rate of intraoperative complications was similar in the two groups. Lymph nodes yield and positive resection margins were similar between the two groups. There were no significant differences in 5-year overall survival (HR 0.91, 95 % CI 0.48–1.71; p = 0.76) and 5-year disease-free survival (hazard ratio [HR] 0.97, 95 % CI 0.56–1.68; p = 0.91).ConclusionsLRH shows better short term outcomes compared with RH in patients with cervical cancer. The oncologic outcome and 5-year survival were similar between the two groups.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1818-4) contains supplementary material, which is available to authorized users.

Garlic, an economically important vegetable, spice, and medicinal crop, produces highly enlarged bulbs and unique organosulfur compounds. Here, we report a chromosome-level genome assembly for garlic, with a total size of approximately 16.24 Gb, as well as the annotation of 57 561 predicted protein-coding genes, making garlic the first Allium species with a sequenced genome. Analysis of this garlic genome assembly reveals a recent burst of transposable elements, explaining the substantial expansion of the garlic genome. We examined the evolution of certain genes associated with the biosynthesis of allicin and inulin neoseries-type fructans, and provided new insights into the biosynthesis of these two compounds. Furthermore, a large-scale transcriptome was produced to characterize the expression patterns of garlic genes in different tissues and at various growth stages of enlarged bulbs. The reference genome and large-scale transcriptome data generated in this study provide valuable new resources for research on garlic biology and breeding.

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package GPUMD.We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach.We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models, and we demonstrate their application in large-scale atomistic simulations.By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient.These results demonstrate that the GPUMD package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations.To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model.Finally, we introduce three separate Python packages, GPYUMD, CALORINE, and PYNEP, which enable the integration of GPUMD into Python workflows.