Interaction of general obesity and abdominal obesity with frailty in patients with chronic kidney disease: a nationally representative analysis.

Background: General and abdominal obesity are prevalent, with established associations to frailty in the elderly. However, few studies have investigated these associations in patients with chronic kidney disease (CKD), yielding inconsistent results. Methods: This cross-sectional study analysed data from the National Health and Nutrition Examination Survey (NHANES 2003-2018). Frailty was evaluated by the 36-item frailty index. General obesity was defined as a body mass index (BMI) >30 kg/m2; abdominal obesity was identified if waist circumference (WC) reached 102 cm in men and 88 cm in women. The associations of general and abdominal obesity with frailty were analysed using weighted multivariate logistic regression and restricted cubic splines. The interaction of general and abdominal obesity with frailty was examined. Results: A total of 5604 adult patients (median age 71 years, 42% men) with CKD were included in this analysis, with a median estimated glomerular filtration rate of 57.3 ml/min/1.73 m2. A total of 21% were frail with general obesity and 32% were frail with abdominal obesity. Neither general nor abdominal obesity alone was associated with frailty. There was an interaction between general and abdominal obesity with frailty. Compared with individuals with normal BMI and WC, those with both general and abdominal obesity, rather than either alone, exhibited significantly increased odds of frailty {odds ratio [OR] 1.53 [95% confidence interval (CI) 1.20-1.95]}. General obesity was associated with being frail only when CKD patients had abdominal obesity [OR 1.59 (95% CI 1.08-2.36)]. Conclusions: There may be an interaction between general and abdominal obesity with frailty in patients with CKD. Interventions aimed at preventing frailty should consider both aspects.

Diminished levels of insulin-like growth factor-1 may be a risk factor for peripheral neuropathy in type 2 diabetes patients.

AIMS/INTRODUCTION: To investigate risk factors for diabetic peripheral neuropathy (DPN) and to explore the connection between insulin-like growth factor-1 (IGF-1) and DPN in individuals with type 2 diabetes. MATERIALS AND METHODS: A total of 790 patients with type 2 diabetes participated in a cross-sectional study, divided into two groups: those with DPN (DPN) and those without DPN (non-DPN). Blood samples were taken to measure IGF-1 levels and other biochemical markers. Participants underwent nerve conduction studies and quantitative sensory testing. RESULTS: Patients with DPN exhibited significantly lower levels of IGF-1 compared with non-DPN patients (P < 0.001). IGF-1 was positively correlated with the average amplitude of both motor (P < 0.05) and sensory nerves (P < 0.05), but negatively correlated with the vibration perception threshold (P < 0.05). No significant difference was observed between IGF-1 and nerve conduction velocity (P > 0.05), or the temperature detection threshold (P > 0.05). Multivariate regression analysis identified diabetes duration, HbA1c, and the low levels of IGF-1 as independent risk factors (P < 0.001). Receiver operating characteristic analysis determined that at 8 years duration of diabetes, 8.5% (69.4 mmol/mol) HbA1c and 120 ng/mL IGF-1, the optimal cut-off points, indicated DPN (P < 0.001). CONCLUSIONS: A reduction of IGF-1 in patients with DPN suggests a potential protective role against axon injury in large fiber nerves of type 2 diabetes patients.

E-M349E and NS2A/2B-P1(T) are compensatory mutations of rDTMUV-NS2AB-P1P1'(AA), which regain virus proliferation by enhancing the virus package and restoring NS2A/2B cleavage.

Duck Tembusu virus (DTMUV) belongs to the Flaviviridae family and mainly infects ducks. The genome of DTMUV is translated into a polyprotein, which is further cleaved into several protein by viral NS2B3 protease and host proteases. Crucially, the cleavage of the NS2A/2B precursor during this process is essential for the formation of replication complexes and viral packaging. Previous research has demonstrated that alanine mutations in NS2A/2B (P1P1' (AA)) result in an attenuated strain (rDTMUV-NS2A/2B-P1P1' (AA)) by disrupting NS2A/2B cleavage. In this study, we investigate the effects of the P1P1' (AA) mutation on the viral life cycle and explore compensatory mutations in rDTMUV-NS2A/2B-P1P1' (AA). Infected ducklings exhibit similar body weight gain and viral tissue loads to DTMUV-WT. Compensatory mutations E-M349E and P1(T) emerge, restoring proliferation levels to those of rDTMUV-WT. Specifically, E-M349E enhances viral packaging, while P1(T) reinstates NS2A/2B proteolysis in vitro. Thus, our findings reveal novel compensatory sites capable of restoring the attenuated DTMUV during polyprotein cleavage and packaging.

Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae.

Carbapenem-resistant Enterobacteriaceae (CRE) is a major pathogen that poses a serious threat to human health. Unfortunately, currently, there are no effective measures to curb its rapid development. To address this, an in-depth study on the surface-enhanced Raman spectroscopy (SERS) of 22 strains of 7 categories of CRE using a gold silver composite SERS substrate was conducted. The residual networks with an attention mechanism to classify the SERS spectrum from three perspectives (pathogenic bacteria type, enzyme-producing subtype, and sensitive antibiotic type) were performed. The results show that the SERS spectrum measured by the composite SERS substrate was repeatable and consistent. The SERS spectrum of CRE showed varying degrees of species differences, and the strain difference in the SERS spectrum of CRE was closely related to the type of enzyme-producing subtype. The introduced attention mechanism improved the classification accuracy of the residual network (ResNet) model. The accuracy of CRE classification for different strains and enzyme-producing subtypes reached 94.0% and 96.13%, respectively. The accuracy of CRE classification by pathogen sensitive antibiotic combination reached 93.9%. This study is significant for guiding antibiotic use in CRE infection, as the sensitive antibiotic used in treatment can be predicted directly by measuring CRE spectra. Our study demonstrates the potential of combining SERS with deep learning algorithms to identify CRE without culture labels and classify its sensitive antibiotics. This approach provides a new idea for rapid and accurate clinical detection of CRE and has important significance for alleviating the rapid development of resistance to CRE.