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Effects and Mechanisms of Probiotics, Prebiotics, Synbiotics, and Postbiotics on Metabolic Diseases Targeting Gut Microbiota: A Narrative Review
Metabolic diseases are serious threats to public health and related to gut microbiota. Probiotics, prebiotics, synbiotics, and postbiotics (PPSP) are powerful regulators of gut microbiota, thus possessing prospects for preventing metabolic diseases. Therefore, the effects and mechanisms of PPSP on metabolic diseases targeting gut microbiota are worth discussing and clarifying. Generally, PPSP benefit metabolic diseases management, especially obesity and type 2 diabetes mellitus. The underlying gut microbial-related mechanisms are mainly the modulation of gut microbiota composition, regulation of gut microbial metabolites, and improvement of intestinal barrier function. Moreover, clinical trials showed the benefits of PPSP on patients with metabolic diseases, while the clinical strategies for gestational diabetes mellitus, optimal formula of synbiotics and health benefits of postbiotics need further study. This review fully summarizes the relationship between probiotics, prebiotics, synbiotics, postbiotics, and metabolic diseases, presents promising results and the one in dispute, and especially attention is paid to illustrates potential mechanisms and clinical effects, which could contribute to the next research and development of PPSP.
Journal Article
Pathogenesis of sarcopenia and the relationship with fat mass: descriptive review
Age‐associated obesity and muscle atrophy (sarcopenia) are intimately connected and are reciprocally regulated by adipose tissue and skeletal muscle dysfunction. During ageing, adipose inflammation leads to the redistribution of fat to the intra‐abdominal area (visceral fat) and fatty infiltrations in skeletal muscles, resulting in decreased overall strength and functionality. Lipids and their derivatives accumulate both within and between muscle cells, inducing mitochondrial dysfunction, disturbing β‐oxidation of fatty acids, and enhancing reactive oxygen species (ROS) production, leading to lipotoxicity and insulin resistance, as well as enhanced secretion of some pro‐inflammatory cytokines. In turn, these muscle‐secreted cytokines may exacerbate adipose tissue atrophy, support chronic low‐grade inflammation, and establish a vicious cycle of local hyperlipidaemia, insulin resistance, and inflammation that spreads systemically, thus promoting the development of sarcopenic obesity (SO). We call this the metabaging cycle. Patients with SO show an increased risk of systemic insulin resistance, systemic inflammation, associated chronic diseases, and the subsequent progression to full‐blown sarcopenia and even cachexia. Meanwhile in many cardiometabolic diseases, the ostensibly protective effect of obesity in extremely elderly subjects, also known as the ‘obesity paradox’, could possibly be explained by our theory that many elderly subjects with normal body mass index might actually harbour SO to various degrees, before it progresses to full‐blown severe sarcopenia. Our review outlines current knowledge concerning the possible chain of causation between sarcopenia and obesity, proposes a solution to the obesity paradox, and the role of fat mass in ageing.
Journal Article
Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions
This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. It is then driven by bias corrected general circulation model (GCM) predictions to generate soil moistures for the forecasting months. Finally, model‐simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future monthly streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN‐GRU) as the DL model, is applied to predict 1‐, 3‐, and 6‐month ahead reservoir inflows for the Danjiangkou Reservoir in China. The results show that the hybrid model consistently performs better than VIC and CNN‐GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. Additional tests indicate that hybrid models based on CNN‐GRU outperform those based on LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model greatly reduces the computation burden of rolling prediction. It also saves decision‐makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. Overall, the new hybrid model demonstrates great potential for monthly streamflow prediction where training data are limited.
Key Points
Deep learning and physically based distributed hydrological models are coupled for monthly streamflow predictions
A simplified hydrological model is developed on the basis of the distributed model to reduce computational cost in streamflow predictions
The hybrid model is an efficient and accurate surrogate tool for real‐time monthly streamflow predictions
Journal Article
Toward Monitoring Short-Term Droughts Using a Novel Daily Scale, Standardized Antecedent Precipitation Evapotranspiration Index
Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices [Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI)] and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings.
Journal Article
لقاء في القرية العالمية = An encounter in the global village : قصص مختارة من المؤتمر الدولي الرابع عشر للقصة القصيرة
هذا الكتاب يحتوي على قصص مختارة من المؤتمر الدولي الرابع عشر للقصة القصيرة وهذا اللقاء الذي نظم من قبل جمعية دراسة القصص القصيرة الإنجليزية (أس أس أس أس إي) وهي جمعية عالمية أنشئت في الولايات المتحدة عام 1992 وينعقد كل عامين ويعتبر اللقاء العالمي الوحيد الذي يركز بشكل خاص على دراسات القصة القصيرة أما القصص المشاركة في اللقاء فهي مكتوبة من قبل 29 كاتبا ينتمون إلى عشرة دول هي الصين وتايوان والهند والولايات المتحدة وكندا ونيوزلندا وفرنسا وإيرلندا والنمسا وسنغافورا وجامايكا.
BOOK
The combination of procalcitonin and C-reactive protein or presepsin alone improves the accuracy of diagnosis of neonatal sepsis: a meta-analysis and systematic review
Background
Sepsis is an important cause of neonatal morbidity and mortality; therefore, the early diagnosis of neonatal sepsis is essential.
Method
Our aim was to compare the diagnostic accuracy of procalcitonin (PCT), C-reactive protein (CRP), procalcitonin combined with C-reactive protein (PCT + CRP) and presepsin in the diagnosis of neonatal sepsis. We searched seven databases to identify studies that met the inclusion criteria. Two independent reviewers performed data extraction. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under curve (AUC), and corresponding 95% credible interval (95% CI) were calculated by true positive (TP), false positive (FP), false negative (FN), and true negative (TN) classification using a bivariate regression model in STATA 14.0 software. The pooled sensitivity, specificity, PLR, NLR, DOR, AUC, and corresponding 95% CI were the primary outcomes. Secondary outcomes included the sensitivity and specificity in multiple subgroup analyses.
Results
A total of 28 studies enrolling 2661 patients were included in our meta-analysis. The pooled sensitivity of CRP (0.71 (0.63, 0.78)) was weaker than that of PCT (0.85 (0.79, 0.89)), PCT + CRP (0.91 (0.84, 0.95)) and presepsin (0.94 (0.80, 0.99)) and the pooled NLR of presepsin (0.06 (0.02, 0.23)) and PCT + CRP (0.10 (0.05, 0.19)) were less than CRP (0.33 (0.26, 0.42)), and the AUC for presepsin (0.99 (0.98, 1.00)) was greater than PCT + CRP (0.96 (0.93, 0.97)), CRP (0.85 (0.82, 0.88)) and PCT (0.91 (0.89, 0.94)). The results of the subgroup analysis showed that 0.5–2 ng/mL may be the appropriate cutoff interval for PCT. A cut-off value > 10 mg/L for CRP had high sensitivity and specificity.
Conclusions
The combination of PCT and CRP or presepsin alone improves the accuracy of diagnosis of neonatal sepsis. However, further studies are required to confirm these findings.
Journal Article