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The use of proton pump inhibitors (PPIs) may potentially predispose to the development of small intestinal bacterial overgrowth (SIBO), but this association is controversial due to conflicting results from studies conducted to date. The aim of this meta-analysis was to evaluate the association between the use of PPIs and the risk of SIBO. We systematically searched the online PubMed, Embase, and Cochrane Library databases and Web of Science for relevant articles published up to November 2016. Two researchers identified and extracted data independent of each other. The pooled analysis was performed using the generic inverse-variance random-effects model. Subgroup and sensitivity analysis were conducted to assess the stability and heterogeneity of the pooled results. The risk of publication bias was evaluated by assessing for funnel plot asymmetry and by Egger’s test and Begg’s test. A total of 19 articles met the eligibility criteria for the meta-analysis, reporting on 7055 subjects. The pooled odds ratio (OR) showed a statistically significant association between increased risk of SIBO and PPI use (OR 1.71, 95% confidence interval 1.20–2.43). Subgroup analyses demonstrated an association between SIBO and PPI use in studies that employed small bowel aspirates culture and glucose hydrogen breath tests (GHBT) as diagnostic tests for SIBO. Our meta-analysis suggests that the use of PPI moderately increases the risk of SIBO, thereby highlighting the need for appropriate prescribing of PPIs.

The gut microbiome plays a pivotal role in the development and progression of liver disease. Liver sinusoidal endothelial cells (LSECs), as the first hepatic barrier exposed to blood from the portal circulation, may be influenced by gut-derived microbiota and their byproducts. This study aimed to investigate the interaction between gut microbiota and LSECs and to clarify how this interaction impacts the progression of liver cirrhosis. Liver cirrhosis was induced by carbon tetrachloride (CCl4) injection and bile duct ligation (BDL). CCl4 and BDL mice were administered rifaximin. The primary LSECs were isolated from mice and treated with LPS. 16S rRNA sequencing was conducted to examine changes in the gut microbiota of cirrhotic mice following rifaximin treatment. Rifaximin attenuated liver fibrosis and LSEC dysfunction in CCl4 and BDL mice. Liver cirrhosis induced remarkable changes in the gut microbiome while rifaximin treatment could partially reverse these alterations. Serum lipopolysaccharides (LPS) level was elevated in cirrhotic mice, while reduced following rifaximin treatment. Furthermore, LPS treatment could induce LSEC dysfunction by inhibiting eNOS mRNA expression, which was attenuated by TLR4 inhibitor, indicating that TLR4 signaling was involved in LPS-induced LSEC dysfunction. Intestinal microbiota dysbiosis allows more LPS to enter the portal circulation, which may in turn exacerbate LSEC dysfunction and liver fibrosis. Intestinal decontamination with rifaximin improves LSEC function and alleviates liver fibrosis, a process linked to the reconstruction of the gut microbiome and a reduction in gut-derived LPS.

Biodegradation characteristics of polycaprolactone (PCL) films with three different molecular weights after being degraded by Candida antarctica lipase were investigated. The changes of the PCL films before and after degradation were characterized. The results showed that the degradation process of PCL films could be roughly divided into two phrases: a rapid degradation phase and a slow degradation phase. Molecular weights of PCL films had no influence on the performance of lipase; however, increasing molecular weight caused the numbers of pores on the degraded PCL films to increase, and the pores became deeper as the degradation time increased. The results also showed that the crystallinity of the films decreased with increasing degradation time, and both the crystalline and the amorphous regions of the films were simultaneously degraded. The thermal stability of the films also gradually decreased with the increase of degradation time. Despite the above changes, the degradation did not cause the chemical composition and structure of PCL to change, and PCL remained the main component.

Polystyrene (PS) is a widely used petroleum-based plastic, that pollutes the environment because it is difficult to degrade. In this study, a PS degrading bacterium identified as Massilia sp. FS1903 was successfully isolated from the gut of Galleria mellonella (Lepidoptera: Pyralidae) larvae that were fed with PS foam. Scanning electron microscopy and X-ray energy dispersive spectrometry showed that the structure and morphology of the PS film was destroyed by FS 1903, and that more oxygen appeared on the degraded PS film. A water contact angle assay verified the chemical change of the PS film from initially hydrophobic to hydrophilic after degradation. X-ray photoelectron spectroscopy further demonstrated that more oxygen-containing functional groups were generated during PS degradation. After 30 days of bacterial stain incubation with 0.15 g PS, 80 ml MSM, 30°C and PS of Mn 64400 and Mw 144400 Da, the weight of the PS film significantly decreased, with 12.97 ± 1.05% weight loss. This amount of degradation exceeds or is comparable to that previously reported for other species of bacteria reported to degrade PS. These results show that Massilia sp. FS1903 can potentially be used to degrade PS waste.

Klinotaxis is a strategy of chemotaxis behavior in Caenorhabditis elegans ( C. elegans ), and random walking is evident during its locomotion. As yet, the understanding of the neural mechanisms underlying these behaviors has remained limited. In this study, we present a connectome-based simulation model of C. elegans to concurrently realize realistic klinotaxis and random walk behaviors and explore their neural mechanisms. First, input to the model is derived from an ASE sensory neuron model in which the all-or-none depolarization characteristic of ASEL neuron is incorporated for the first time. Then, the neural network is evolved by an evolutionary algorithm; klinotaxis emerged spontaneously. We identify a plausible mechanism of klinotaxis in this model. Next, we propose the liquid synapse according to the stochastic nature of biological synapses and introduce it into the model. Adopting this, the random walk is generated autonomously by the neural network, providing a new hypothesis as to the neural mechanism underlying the random walk. Finally, simulated ablation results are fairly consistent with the biological conclusion, suggesting the similarity between our model and the biological network. Our study is a useful step forward in behavioral simulation and understanding the neural mechanisms of behaviors in C. elegans .

Forest ecosystems are crucial to the survival and development of human societies. Urbanization is expected to impact forest landscape patterns and consequently the supply of forest ecosystem services. However, the specific ways by which such impacts manifest are unclear. Therefore, to discuss the relationship between them is of great significance for realizing regional sustainable development. Here, we quantitatively assess the intensity of forest ecosystem service functions and forest landscape patterns in Renqiu City of China’s Hebei Province in 2019 using ArcGIS and FRAGSTATS. We characterize the relationships between forest ecosystem service capacity and landscape patterns, and identify strategies for the spatial optimization of forests. We find that the ecosystem service intensity of forests are significantly correlated with their spatial distribution, forest area ratio, and landscape patterns. Specifically, the percentage of landscape (PLAND) index, landscape shape index (LSI), and contagion (CONTAG) index indices display second-order polynomial relationships with various forest ecosystem service functions, with critical values of 80, 5, and 70, respectively. We propose that forest ecosystem functions can be optimized by optimizing forest landscape patterns. Specifically, to maximize the function of forest ecosystem services, managers should consider the integrity of forest ecosystems, optimize their ability to self-succession, repair service functions of key nodes within forests, enhance forests’ structural stability, optimize forest quality and community structure, and strengthen the efficiency of functional transformation per unit area. Finally, we propose a strategy for the spatial optimization of forests in Renqiu to optimize their associated ecosystem services. This involves protecting important areas for forest ecosystems, rationally organizing different ecological patches such as forests and water bodies to maximize their functions, strengthening the connectivity of scattered forests, and supplementing woodland areas.

Poly(butylene succinate) (PBS) and poly(lactic acid) (PLA) were melt-blended and formed into a film by hot press forming. The film was selectively degraded by cutinase and proteinase K to form a porous material. The porous materials were characterized with respect to their pore morphology, pore size, porosity and hydrophilicity. The porous materials were investigated in vitro degradation and in vivo compatibility. The results show that the pore size of the prepared porous materials could be controlled by the proportion of PBS and the degradation time. When the PBS composition of PBS/PLA blends was changed from 40 wt% to 50 wt%, the mean pore diameter of the porous materials significantly increased from 6.91 µm to 120 µm, the porosity improved from 81.52% to 96.90%, and the contact angle decreased from 81.08° to 46.56°. In vitro degradation suggests that the PBS-based porous materials have a good corrosion resistance but the PLA-based porous materials have degradability in simulated body fluid. Subcutaneous implantation of the porous materials did not cause intense inflammatory response, which revealed good compatibility. The results of hematoxylin and eosin and Masson's trichrome staining assays demonstrated that the porous materials promote chondrocyte production. Porous materials have great potential in preparing implants for tissue engineering applications.

Polybutylene succinate, polybutylene adipate, polybutylene suberate and their copolyesters were synthesized. The physical properties and biodegradability of these polyesters were controlled by adjusting the composition of carboxyl monomers. Compared with the homopolyesters, the addition of comonomer during polymerization resulted in the formation of copolyesters with lower melting temperatures and crystallinity. Among them, poly(butylene succinate-co-adipate) (PBSA) had the lowest crystallinity, and poly(butylene adipate-co-suberate) (PBASub) had the lowest melting point. The elongation at break and tensile strength of PBSA was 766.2% and 21.5 MPa, respectively. Enzymatic degradation by Fusarium solani cutinase (FsC) showed that both the crystalline and amorphous regions of the polyester were simultaneously degraded by FsC, and the crystal structure of the polyester was not disrupted. FsC preferentially got attached to the surface of polyesters, subsequently attacked the center of the films, and the water penetrated the amorphous region, leading to enhanced enzymatic hydrolysis. The biodegradability of copolyester was higher than that of homopolyesters. PBSA and poly(butylene succinate-co-suberate) (PBSSub) were completely degraded in about 10 h, and can be used in agricultural, automotive, electronics, biomedical materials, packaging, etc.

Objective Cirrhosis with acute kidney injury is associated with a high mortality rate, particularly in patients receiving continuous renal replacement therapy. This study aimed to develop a nomogram to predict in-hospital mortality in patients with cirrhosis and acute kidney injury receiving continuous renal replacement therapy. Methods A retrospective study was conducted using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients with cirrhosis with acute kidney injury who underwent continuous renal replacement therapy were identified. Predictors were selected using least absolute shrinkage and selection operator regression, and a multivariable logistic regression model was developed. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration analysis, and decision curve analysis. Internal validation was performed via bootstrapping. Results The following three independent predictors of in-hospital mortality were identified in the 452 eligible patients: international normalized ratio at continuous renal replacement therapy initiation, presence of sepsis, and vasoactive drug use. The nomogram showed strong discrimination (AUC = 0.828, 95% confidence interval: 0.790–0.866) and consistent internal validation (AUC = 0.825, 95% confidence interval: 0.781–0.870). Calibration and decision curve analyses indicated good agreement and clinical usefulness. Conclusions A nomogram incorporating international normalized ratio, sepsis, and vasoactive drug use may help estimate short-term mortality risk in patients with cirrhosis and acute kidney injury receiving continuous renal replacement therapy. As the analysis was based on single-center data, and no external validation was performed, the findings should be interpreted cautiously and verified in future studies.

Convolutional neural networks (CNNs) have significantly promoted the development of speaker verification (SV) systems because of their powerful deep feature learning capability. In CNN-based SV systems, utterance-level aggregation is an important component, and it compresses the frame-level features generated by the CNN frontend into an utterance-level representation. However, most of the existing aggregation methods aggregate the extracted features across time and cannot capture the speaker-dependent information contained in the frequency domain. To handle this problem, this paper proposes a novel attention-based frequency aggregation method, which focuses on the key frequency bands that provide more information for utterance-level representation. Meanwhile, two more effective temporal-frequency aggregation methods are proposed in combination with the existing temporal aggregation methods. The two proposed methods can capture the speaker-dependent information contained in both the time domain and frequency domain of frame-level features, thus improving the discriminability of speaker embedding. Besides, a powerful CNN-based SV system is developed and evaluated on the TIMIT and Voxceleb datasets. The experimental results indicate that the CNN-based SV system using the temporal-frequency aggregation method achieves a superior equal error rate of 5.96% on Voxceleb compared with the state-of-the-art baseline models.