Isolation and Identification of Alkali-Resistant 1,3-Propanediol Producing Strain

1,3-Propanediol is a promising renewable resource produced by microbial production. It is mainly used in many synthetic reactions, particularly applied to the polymer synthesis and cosmetics industry. We described here the isolation of strain ZH-1, which has the ability of high production with 1,3-propanediol, from Fenhe River in China. It was classified as a member of K. pneumoniae after the study of phenotypic, physio-logical, biochemical and phylogenetic (16S rDNA). The initial glycerol concentration, fermentation time and pH value of strain ZH-1 were determined to be 50 g·L-1, 36 h and 8.0. Under these conditions, the practical yield of 1,3-PD was 18.53 g·L-1 and a molar yield (mol1,3-PD molGlycerol-1 of 1,3-propanediol to glycerol of 0.497. In addition, we found that for the strain ZH-1, the optimum grown pH was 9.0, so we can deter-mine that it is a new member of alkali-resistant strains.

High loading carbon nanotubes deposited onto porous nickel yarns by solution imbibition as flexible wire-shaped supercapacitor electrodes

The deposition of active materials directly onto metal wires is a general strategy to prepare wire-shaped electrodes for flexible and wearable energy storage devices. However, it is still a critical challenge to coat active materials onto the aimed metal wires because of their smooth surface and small specific surface area. In this work, high porous nickel yarns（PNYs） was fabricated using commercial nylon yarns as templates through step-wise electroless plating, electroplating and calcination processes. The PNYs are composed of multiplied fibers with hollow tubular structure of 5–10 μm in diameter, allowing the imbibition of carbon nanotubes（CNTs） solution by a facile capillary action process. The prepared CNTs/PNY electrodes showed a typical electrochemical double layer capacitive performance and the constructed allsolid flexible wire-shaped symmetric supercapacitors provided a specific capacitance of 4.67 F/cm3 with good cycling stability at a current density of 0.6 A/cm3.

A computed tomography-based radiomic model for the prediction of strangulation risk in patients with acute intestinal obstruction

Background We created and validated a computed tomography(CT)-based radiomic model using both clinical factors and the radiomic signature for assessing the strangulation risk of acute intestinal obstruction.This would assist surgeons in accurately predicting intestinal ischemia and strangulation in patients with intestinal obstruction.Methods We recruited 289 patients with acute intestinal obstruction admitted in the Affiliated Hospital of Qingdao University from January 2019 to February 2022.The patients were allocated to a training(n=226)and validation cohort(n=63).Radiomic features were collected from CT images,and the radiomic signature was extracted and used to calculate a radiomic score(Rad-score).A nomogram was constructed using the clinical features and the Rad-score,and the performance of the clinical,radiomics,and nomogram models was assessed in the two cohorts.Results Six robust features were used to construct the radiomic signature.The nomogram incorporating hemoglobin levels,C-reactive protein levels,American Society of Anesthesiologists score,time of obstruction,CT image of mesenteric fluid(P<0.05),and the signature demonstrated good predictive ability for intestinal ischemia in patients with acute intestinal obstruction,with areas under the curve of 0.892(95%confidence interval,0.837–0.947)and 0.781(95%confidence interval,0.619–0.944)for the training and validation sets,respectively.The decision curve analysis showed that this model outperformed the clinical and radiomic signature models.Conclusion The radiomic nomogram may effectively predict intestinal ischemia in patients with acute intestinal disease and may assist clinical decision-making。

Melanin nanoparticles alleviate sepsis-induced myocardial injury by suppressing ferroptosis and inflammation

Myocardial injury as one of the severe complications leads to the increasing morbidity and mortality in patients with sepsis.Recent studies reported that reactive oxygen species(ROS)-mediated ferroptosis plays a critical role in the development of heart diseases.Therefore,we hypothesized that anti-ferroptosis agent might be a novel potential therapeutic strategy for sepsis-induced cardiac injury.Herein,we demonstrated that a small biocompatible and MRI-visible melanin nanoparticles(MMPP)improves myocardial function by inhibiting ROS-related ferroptosis signaling pathway.In LPS-induced murine sepsis model,after a single dose intravenously injection of MMPP treatment,MMPP markedly alleviated the myocardial injury including cardiac function and heart structure disorder through suppressing iron-accumulation induced ferroptosis.In vitro,MMPP inhibited cardiomyocyte death by attenuating oxidative stress,inflammation and maintaining mitochondrial homeostasis.Collectively,our findings demonstrated that MMPP protected heart against sepsis-induced myocardial injury via inhibiting ferroptosis and inflammation,which might be a novel therapeutic approach in future.

GSDMD介导的细胞焦亡在感染性疾病中的研究进展

细胞焦亡是细胞感染时由炎症小体介导,以裂解细胞为特点的程序性死亡形式。其激活途径分为依赖半胱氨酸蛋白酶-1或半胱氨酸蛋白酶-4/5/11活化的经典与非经典途径。目前的研究表明细胞焦亡过程中主要效应蛋白是具有膜成孔活性的gasdermin(也作GSDM)家族成员。因此,细胞焦亡也被称为gasdermin介导的程序性坏死。当宿主受到感染时,细胞焦亡与宿主自身其他免疫防御机制存在互相调节机制,保证宿主在清除感染的同时降低自身损伤程度。本文笔者将从研究最为广泛的GSDMD在细胞焦亡途径中的作用机制、细胞焦亡在感染性疾病中的研究进展以及细胞焦亡与其他程序性死亡在感染性疾病中的相互作用这三个方面作系统叙述,期望为今后研究如何通过细胞焦亡途径治疗感染性疾病提供理论基础。

氢键控制断裂韧性的应力分散超灵敏应变传感器用于稳定的生物力监测

近年来,柔性应变传感器的迅速发展,拓宽了其在各种机械生理信号监测中的应用范围.在各种应变传感器中,基于裂纹的应变传感器由于其高灵敏度,在监测微小的机械变形方面越来越受到重视.然而,由于导电传感层中裂纹的早期产生和快速扩展导致裂纹传感器工作范围狭窄,限制了其在监测较大生物力学变形中的应用.本研究通过引入缺口不敏感的弹性基底和可调微裂纹的导电层,开发了具有超高灵敏度(应变系数高达2.3×10^(6))和宽工作范围(0%-50%)的应力分散超灵敏应变(SDUS)传感器.此外,高弹性胺基聚合物修饰的聚二甲基硅氧烷衬底没有明显的迟滞,使SDUS传感器对外界刺激能够快速响应(响应时间2.33 ms).通过对桡动脉脉搏、关节运动和声带振动的精确检测,SDUS传感器在医疗健康监测和人机通信方面的能力得到了验证.

基于纳米离子储层的双网络胃穿孔封堵水凝胶

近年来,对于可注射水凝胶用于内部组织损伤修复的研究取得了一定进展,但将此类水凝胶用于胃穿孔治疗时,依然面临强酸性胃液及胃壁的剧烈形变带来的严峻问题.鉴于此,我们提出了一种基于纳米离子储库(INR)的双网络水凝胶策略,该水凝胶具有优异的黏附和机械性能,可以简便地涂覆于穿孔部位,在封堵穿孔的同时促进组织修复.结果显示,在蓝光条件下,聚丙烯酰胺(PAM)可以于5 s内在胃组织上交联形成第一重网络,并通过机械互锁增强黏附性能;此外,纳米羟基磷灰石作为纳米离子储库可在酸性环境下释放Ca^(2+),与海藻酸钠(SA)形成第二重网络,抑制水凝胶在胃液中的溶胀.同时,通过在体系中引入碳二亚胺/N-羟基琥珀酰亚胺(EDC/NHS),可以促进水凝胶-组织界面上酯酰胺共价键的形成,从而进一步增强黏附力.因此我们认为,这种通过纳米离子储库策略获得的双网络水凝胶有望用于解决胃穿孔乃至其他类似的生物医学问题.

Efficient Hermite Spectral-Galerkin Methods for Nonlocal Diffusion Equations in Unbounded Domains

In this paper,we develop an efficient Hermite spectral-Galerkin method for nonlocal diffusion equations in unbounded domains.We show that the use of the Hermite basis can de-convolute the troublesome convolutional operations involved in the nonlocal Laplacian.As a result,the“stiffness”matrix can be fast computed and assembled via the four-point stable recursive algorithm with O(N^(2))arithmetic operations.Moreover,the singular factor in a typical kernel function can be fully absorbed by the basis.With the aid of Fourier analysis,we can prove the convergence of the scheme.We demonstrate that the recursive computation of the entries of the stiffness matrix can be extended to the two-dimensional nonlocal Laplacian using the isotropic Hermite functions as basis functions.We provide ample numerical results to illustrate the accuracy and efficiency of the proposed algorithms.

Development and application of a detection platform for colorectal cancer tumor sprouting pathological characteristics based on artificial intelligence

Objective Tumor sprouting can reflect independent risk factors for tumor malignancy and a poor clinical progno-sis.However,there are significant differences and difficulties associated with manually identifying tumor sprout-ing.This study used the Faster region convolutional neural network(RCNN)model to build a colorectal cancer tumor sprouting artificial intelligence recognition framework based on pathological sections to automatically identify the budding area to assist in the clinical diagnosis and treatment of colorectal cancer.Methods We retrospectively collected 100 surgical pathological sections of colorectal cancer from January 2019 to October 2019.The pathologists used LabelImg software to identify tumor buds and to count their numbers.Finally,1,000 images were screened,and the total number of tumor buds was approximately 3,226;the images were randomly divided into a training set and a test set at a ratio of 6:4.After the images in the training set were manually identified,the identified buds in the 600 images were used to train the Faster RCNN identification model.After the establishment of the artificial intelligence identification detection platform,400 images in the test set were used to test the identification detection system to identify and predict the area and number of tumor buds.Finally,by comparing the results of the Faster RCNN system and the identification information of pathologists,the performance of the artificial intelligence automatic detection platform was evaluated to determine the area and number of tumor sprouting in the pathological sections of the colorectal cancers to achieve an auxiliary diagnosis and to suggest appropriate treatment.The selected performance indicators include accuracy,precision,specificity,etc.ROC(receiver operator characteristic)and AUC(area under the curve)were used to quantify the performance of the system to automatically identify tumor budding areas and numbers.Results The AUC of the receiver operating characteristic curve of the artificial intelligence detection and identi-fication system was 0.96,the image diagnosis accuracy rate was 0.89,the precision was 0.855,the sensitivity was 0.94,the specificity was 0.83,and the negative predictive value was 0.933.After 400 test sets,pathological image verification showed that there were 356 images with the same positive budding area count,and the difference between the positive area count and the manual detection count in the remaining images was less than 3.The detection system based on tumor budding recognition in pathological sections is comparable to that of patholo-gists’accuracy;however,it took significantly less time(0.03±0.01)s for the pathologist(13±5)s to diagnose the sections with the assistance of the AI model.Conclusion This system can accurately and quickly identify the tumor sprouting area in the pathological sections of colorectal cancer and count their numbers,which greatly improves the diagnostic efficacy,and effectively avoids the need for confirmation by different pathologists.The use of the AI reduces the burden of pathologists in reading sections and it has a certain clinical diagnosis and treatment value.