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The authors performed a systematic review and meta-analysis of all published randomized controlled trials with the aim to determine and quantify the anti-hyperglycemic effects of glutamine (Gln) in acute and chronic clinical settings.

The authors conducted a comprehensive search of all randomized clinical trials performed up to December 2018, to identify those investigating the impact of Gln supplementation on fasting blood sugar (FBS), insulin levels and homeostatic model assessment-insulin resistance (HOMA-IR) via ISI Web of Science, Cochrane library PubMed and SCOPUS databases. A meta-analysis of eligible studies was conducted using random effects model to estimate the pooled effect size. Fractional polynomial modeling was used to explore the dose–response relationships between Gln supplementation and diabetic indices.

The results of the present meta-analysis suggest that of Gln supplementation had a significant effect on FBS (weighted mean difference (WMD): –2.868 mg/dl, 95 per cent CI: –5.467, –0.269, p = 0.031). However, the authors failed to observe that Gln supplementation affected insulin levels (WMD: 1.06 units, 95 per cent CI: –1.13, 3.26, p = 0.34) and HOMA-IR (WMD: 0.001 units, 95 per cent CI: –2.031, 2.029, p = 0.999). Subgroup analyses showed that the highest decrease in FBS levels was observed when the duration of intervention was less than two weeks (WMD: –4.064 mg/dl, 95 per cent CI: –7.428, –0.700, p = 0.01) and when Gln was applied via infusion (WMD: –5.334 mg/dl, 95 per cent CI: –10.48, 0.17, p = 0.04).

The results from this meta-analysis show that Gln supplementation did not have a significant effect on insulin levels and HOMA-IR. However, it did significantly reduce the levels of FBS, obtaining a higher effect when the duration of the intervention period was less than two weeks.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.