The triglyceride-glucose (TyG) index is a simple and inexpensive new marker of insulin resistance that is being increasingly used for the clinical prediction of metabolic syndrome (MetS). Nevertheless, there are only a few comparative studies on its predictive capacity for MetS versus those using the traditional homeostasis model assessment (HOMA). We conducted a cross-sectional study using a database from the National Health and Nutrition Examination Survey (1999 March to 2020 pre-pandemic period). Using statistical methods, we compared the predictive abilities of the TyG index and HOMA (including HOMA of insulin resistance [HOMA-IR] and HOMA of beta-cell function [HOMA-β]) for MetS. A total of 34,195 participants were enrolled and divided into the MetS group (23.1%) or no MetS group (76.9%) according to the International Diabetes Federation (IDF) diagnostic criteria. After applying weighted data, the baseline characteristics of the population were described. Following the exclusion of medication influences, the final count was 31,304 participants. Receiver operating characteristic curve analysis revealed that while distinguishing between MetS and no MetS, the TyG index had an area under the curve (AUC) of 0.827 (sensitivity = 71.9%, specificity = 80.5%), and the cutoff was 8.75, slightly outperforming HOMA-IR (AUC = 0.784) and HOMA-β (AUC = 0.614) with a significance of P < 0.01. The prevalence of MetS in the total population calculated using the TyG index cutoff value was 30.9%, which was higher than that reported in the IDF diagnostic criteria. Weighted data analysis using univariate and multivariate logistic regression displayed an independent association between elevated TyG and HOMA-IR with the risk of MetS. Subgroup analysis further revealed differences in the predictive ability of the TyG index among adult populations across various genders and ethnicities, whereas such differences were not observed for children and adolescents. The TyG index is slightly better than HOMA in predicting MetS and may identify more patients with MetS; thus, its applications in a clinical setting can be appropriately increased.

Glucose and lipid metabolism disorders caused by insulin resistance (IR) can lead to metabolic disorders such as diabetes, obesity, and the metabolic syndrome. Early and targeted intervention of IR is beneficial for the treatment of various metabolic disorders. Although significant progress has been made in the development of IR drug therapies, the state of the condition has not improved significantly. There is a critical need to identify novel therapeutic targets. Mitophagy is a type of selective autophagy quality control system that is activated to clear damaged and dysfunctional mitochondria. Mitophagy is highly regulated by various signaling pathways, such as the AMPK/mTOR pathway which is involved in the initiation of mitophagy, and the PINK1/Parkin, BNIP3/Nix, and FUNDC1 pathways, which are involved in mitophagosome formation. Mitophagy is involved in numerous human diseases such as neurological disorders, cardiovascular diseases, cancer, and aging. However, recently, there has been an increasing interest in the role of mitophagy in metabolic disorders. There is emerging evidence that normal mitophagy can improve IR. Unfortunately, few studies have investigated the relationship between mitophagy and IR. Therefore, we set out to review the role of mitophagy in IR and explore whether mitophagy may be a potential new target for IR therapy. We hope that this effort serves to stimulate further research in this area.

In this paper, the mobility of network nodes is explored as a new promising approach for jamming defense. To fulfill it, properly designed node motion that can intelligently adapt to the jammer's action is crucial. In our study, anti-jamming mobility control is investigated in the context of the single and multiple commodity flow problems, in the presence of intelligent mobile jammers which can respond to the evasion of legitimate nodes as well. Based on spectral graph theory, two new spectral quantities, single- and multi-weighted Cheeger constants and corresponding eigenvalue variants, are constructed to direct motions of the defender and the attacker in this dynamic adaptive competition. Both analytical and simulation results are presented to justify the effectiveness of the proposed approach. Furthermore, the proposed scheme can also be applied in cognitive radio networks to reconfigure the secondary users in the presence of mobile primary users.

Objectives: The choice of the debridement method is very important for the healing of diabetic foot ulcers (DFUs), but the relative effectiveness of different debridement methods in the healing of DFUs remains unclear. This study conducted a network meta-analysis of the relative healing effectiveness of different debridement methods in patients with DFUs.Methods: We performed a literature search in PubMed, Embase, and Cochrane Library from database inception up to 30 June 2023 for screening randomized controlled trials on the healing effectiveness of debridement in DFUs. Outcome measures included ulcer healing rate and ulcer area reduction rate. The Cochrane Risk Bias Tool, version 2.0, was used to assess the risk of bias in the included trials. R software was used for performing statistical analysis and GraphPad Prism was used for image plotting.Results: A total of 19 randomized controlled trials were included, and 900 patients with DFUs were assessed in this analysis. The proteolytic fraction from the latex of Vasconcellea cundinamarcensis (P1G10) in enzymatic debridement showed the best ulcer healing rate (SURCA = 0.919) when compared with the standard of care (SOC) group, with a mean difference (MD) and 95% confidence interval (CI) of 1.40 (0.57, 2.36). Kiwifruit extract demonstrated the best effect on the ulcer area reduction rate (SURCA = 0.931), when compared with that in the SOC group, with an MD and 95% CI of 0.47 (0.27, 0.66).Conclusion: Enzymatic debridement was superior to other debridement methods in terms of ulcer healing rate and ulcer area reduction rate in patients with DFUs. However, as the quality of the included trials is low, enzymatic debridement can be used as a candidate debridement method in addition to sharp-based debridement in clinical practice.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023441715.

Sensor networks are often subject to physical attacks. Once a node's cryptographic key is compromised, an attacker may completely impersonate it, and introduce arbitrary false information into the network. Basic cryptographic security mechanisms are often not effective in this situation. Most techniques to address this problem focus on detecting and tolerating false information introduced by compromised nodes. They cannot pinpoint exactly where the false information is introduced and who is responsible for it. We still lack effective techniques to accurately identify compromised nodes so that they can be excluded from a sensor network once and for all. In this paper, we propose an application-independent framework for identifying compromised sensor nodes. The framework provides an appropriate abstraction of application-specific detection mechanisms, and models the unique properties of sensor networks. Based on the framework, we develop alert reasoning algorithms to identify compromised nodes. The algorithm assumes that compromised nodes may collude at will. We show that our algorithm is optimal in the sense that it identifies the largest number of compromised nodes without introducing false positives. We evaluate the effectiveness of the designed algorithm through comprehensive experiments