Geriatric nutritional risk index is correlated with islet function but not insulin resistance in elderly patients with type 2 diabetes: A retrospective study.

The geriatric nutritional risk index (GNRI) is a simple nutritional assessment tool that can predict poor prognosis in elderly subjects. The aim of this study was to evaluate the association between GNRI and both islet function and insulin sensitivity in patients with type 2 diabetes mellitus. This research carries significant implications for the integrated treatment and nutritional management of this patient population. A total of 173 patients with type 2 diabetes mellitus, aged 60 years or older, who were hospitalized in the Endocrinology Department at Hebei General Hospital from February 2018 to June 2021, were selected as the research subjects. These subjects were divided into 4 groups according to the quartile of their GNRI values: T1 (GNRI < 99.4, n = 43), T2 (99.4 ≤ GNRI < 103, n = 43), T3 (103 ≤ GNRI < 106.3, n = 43), and T4 (GNRI ≥ 106.3, n = 44). Glucose, insulin, and C-peptide concentrations were tested at 0, 30, 60, 120, and 180 minutes during a 75 g oral glucose tolerance test. The homeostasis model assessment for insulin resistance and the homeostasis model assessment for ß cell function index were calculated. As the GNRI value increased, the levels of total protein, albumin, hemoglobin, alanine transaminase, aspartate aminotransferase, and 25-hydroxyvitamin D increased significantly. The area under the curve for blood glucose decreased significantly across the 4 groups, while the AUCs for insulin and C-peptide showed an overall increasing trend. ß Cell function index increased significantly with the increase of GNRI; meanwhile, both the early-phase insulin secretion index and the late-phase insulin secretion index increased significantly. Although there was an increasing trend, homeostasis model assessment for insulin resistance did not change significantly among the 4 groups. This study indicates that elderly type 2 diabetes patients with higher nutritional risk have worse islet function, while insulin sensitivity is not associated with nutritional risk.

Machine learning-enabled performance prediction and optimization for iron-chromium redox flow batteries.

Iron-chromium flow batteries (ICRFBs) are regarded as one of the most promising large-scale energy storage devices with broad application prospects in recent years. However, transitioning from laboratory-scale development to industrial-scale deployment can be a time-consuming process due to the multitude of complex factors that impact ICRFB stack performance. Herein, a data-driven optimization methodology applying active learning, informed by an extensive survey of the literature encompassing diverse experimental conditions, is proposed to enable exceptional precision in predicting ICRFB system performance considering both operation conditions and key materials selection. Specifically, multitask ML models are trained on experimental data with a high prediction accuracy (R2 > 0.92) to link ICRFB properties to energy efficiency, coulombic efficiency, and capacity. We also interpret the ML models based on Shapley additive explanations and extract valuable insights into the importance of descriptors. It is noted that the operation conditions (current density and cycle number) and the electrode type are the most critical descriptors affecting the voltage efficiency and coulombic efficiency while the electrode size strongly affects the capacity. Moreover, active learning is used to explore the most optimized cases considering the highest energy efficiency and capacity. The versatility and robustness of the approach are demonstrated by the successful validation between ML prediction and our experiments of energy efficiency (±0.15%) and capacity (±0.8%). This work not only affords fruitful data-driven insight into the property-performance relationship, but also unveils the explainability of critical properties on the performance of ICRFBs, which accelerates the rational design of next-generation ICRFBs.

Relationship between geriatric nutritional risk index and osteoporosis in type 2 diabetes in Northern China.

BACKGROUND: Osteoporosis is a very common bone disease in the elderly population and can lead to fractures and disability. Malnutrition can lead to osteoporosis. The geriatric nutritional risk index (GNRI) is a tool used to assess the risk of malnutrition and complications associated with nutritional status in older patients and is a crucial predictor of many diseases. Hence, this study investigated the association between the GNRI and the presence of osteoporosis and assessed the value of this index for predicting osteoporosis in patients with type 2 diabetes mellitus (T2DM). METHODS: This cross-sectional study enrolled 610 elderly patients with T2DM. General and laboratory data of the patients were collected, along with their measurements of bone mineral density (BMD). The GNRI was calculated based on ideal body weight and serum albumin (ABL) levels. Correlation analysis was performed to determine the relationship between the GNRI and BMD and bone metabolism indices. The GNRI predictive value for osteoporosis development was analyzed through logistic regression analysis and by creating a receiver operating characteristic curve (ROC), calculating the area under the curve (AUC). RESULTS: All patients were divided into the no-nutritional risk and nutritional risk groups. Compared with the no-nutritional risk group, the nutritional risk group had a longer diabetes course, older age, higher HbA1c levels, higher prevalence of osteoporosis; lower BMI, ABL,triglyceride (TG),Calcium (Ca),25-hydroxy-vitamin-D(25(OH)D),and parathyroid hormone(PTH) and lower femoral neck BMD,total hip BMD (P < 0.05). All patients were also assigned to the non-osteoporosis and osteoporosis groups. The non-osteoporosis group had higher GNRI values than the osteoporosis group (P < 0.05). Correlation analysis revealed a positive correlation between the GNRI and lumbar BMD, femoral neck BMD, and total hip BMD (P < 0.05). After the adjustment for confounding factors, Spearman's correlation analysis revealed that the GNRI was positively correlated with Ca, 25(OH)D, and PTH and negatively correlated with alkaline phosphatase (ALP) and procollagen of type-1 N-propeptide (P1NP). Regression analysis exhibited that the GNRI was significantly associated with osteoporosis. The ROC curve analysis was performed using the GNRI as the test variable and the presence of osteoporosis as the status variable. This analysis yielded an AUC for the GNRI of 0.695 and was statistically significant (P < 0.05). CONCLUSIONS: A lower GNRI among T2DM patients in northern China is associated with a higher prevalence of osteoporosis.

Mn3+/Mn4+ ion-doped carbon dots as fenton-like catalysts for fluorescence dual-signal detection of dopamine.

Carbon dots (CDs), a new zero-dimensional material, have ignited a revolution in the fields of sensing, bioimaging, and biomedicine. However, the difficulty of preparing CDs with Fenton-like catalytic properties has seriously hindered their application in the diagnosis of oxidation/reduction biomolecules or metal ions. Here, an innovative method was successfully established to synthesize Mn3+/Mn4+ ion-doped blue-green fluorescent CDs with Fenton-like catalytic properties using manganese acetate as the manganese source. Specifically, the CDs prepared here were equipped with functional groups of -COOH, NH2, C=O, and Mn-O, offering the possibility to function as a fluorescence sensor. More importantly, the introduction of manganese acetate resulted in the preparation of CDs with Fenton-like catalytic properties, and the dual-signal fluorescence detection of dopamine (DA) was realized with linear ranges of 100-275 nM and 325-525 nM, and the detection limits were 3 and 12 nM, respectively. In addition, due to the Fenton-like catalytic activity of Mn3+/Mn4+ ion-doped CDs, the material has broad application prospects in the detection of oxidation/reduction biomolecules or metal ions related to disease diagnosis and prevention.

Highly Fluorescent Collagen-Based Quantum Dots as an Efficient Interlinkage in the 2D Perovskite Bulk for Improved Solar Cells.

A design-inexpensive, effective, and easy-to-prepare additive in the large-scale preparation of perovskite solar cells (PSCs) is urgently desired to alleviate the future energy crisis. Carbon-based quantum dots have demonstrated novel nanomaterials with excellent chemical stability and high electrical conductivity, which exhibit great potential as additives for perovskite optoelectronics. Herein, we designed novel highly fluorescent collagen-based quantum dots (Col-QDs) and thoroughly studied the micromorphological characteristics, photoluminescence properties, and the states of surface-functionalized groups on the Col-QDs. It is found that the introduction of Col-QDs in the two-dimensional (2D) perovskite precursor can be further confirmed as an efficient interlinkage via Col-Pb bands in the pure 2D perovskite heterojunction, which significantly improves the crystallinity, orientation, and interlayer coupling of perovskite crystal plates, as observed by grazing incidence X-ray diffraction (GIWAXS) and X-ray photoelectron spectroscopy (XPS). Finally, the champion Col-QD additive can efficiently modulate the photovoltaic performance of pure 2D PSCs with a significant increase of photoelectric conversion efficiency (PCE) from 8.18% up to 10.45%, which ranks among the best efficiencies of highly pure 2D PSCs. These results provide a facile and feasible approach to modulate the interlayer interaction of pure 2D perovskites and further improve their output of PSCs, which would further facilitate the burgeoning applications of the Col-QDs in various perovskite-based optical-related fields.

Energy Saving and Energy Generation Smart Window with Active Control and Antifreezing Functions.

Windows are the least energy efficient part of the buildings, as building accounts for 40% of global energy consumption. Traditional smart windows can only regulate solar transmission, while all the solar energy on the window is wasted. Here, for the first time, the authors demonstrate an energy saving and energy generation integrated smart window (ESEG smart window) in a simple way by combining louver structure solar cell, thermotropic hydrogel, and indium tin oxides (ITO) glass. The ESEG smart window can achieve excellent optical properties with ≈90% luminous transmission and ≈54% solar modulation, which endows excellent energy saving performance. The outstanding photoelectric conversion efficiency (18.24%) of silicon solar cells with louver structure gives the smart window excellent energy generation ability, which is more than 100% higher than previously reported energy generation smart window. In addition, the solar cell can provide electricity to for ITO glass to turn the transmittance of hydrogel actively, as well as the effect of antifreezing. This work offers an insight into the design and preparation together with a disruptive strategy of easy fabrication, good uniformity, and scalability, which opens a new avenue to realize energy storage, energy saving, active control, and antifreezing integration in one device.

2D PtS nanorectangles/g-C3N4 nanosheets with a metal sulfide-support interaction effect for high-efficiency photocatalytic H2 evolution.

Cocatalyst design is a key approach to acquire high solar-energy conversion efficiency for photocatalytic hydrogen evolution. Here a new in situ vapor-phase (ISVP) growth method is developed to construct the cocatalyst of 2D PtS nanorectangles (a length of ∼7 nm, a width of ∼5 nm) on the surface of g-C3N4 nanosheets. The 2D PtS nanorectangles/g-C3N4 nanosheets (PtS/CN) show an unusual metal sulfide-support interaction (MSSI), which is evidenced by atomic resolution HAADF-STEM, synchrotron-based GIXRD, XPS and DFT calculations. The effect of MSSI contributes to the optimization of geometrical structure and energy-band structure, acceleration of charge transfer, and reduction of hydrogen adsorption free energy of PtS/CN, thus yielding excellent stability and an ultrahigh photocatalytic H2 evolution rate of 1072.6 µmol h-1 (an apparent quantum efficiency of 45.7% at 420 nm), up to 13.3 and 1532.3 times by contrast with that of Pt nanoparticles/g-C3N4 nanosheets and g-C3N4 nanosheets, respectively. This work will provide a new platform for designing high-efficiency photocatalysts for sunlight-driven hydrogen generation.

Comparison of toxicity of Ti3 C2 and Nb2 C Mxene quantum dots (QDs) to human umbilical vein endothelial cells.

Recently, we developed highly fluorescent Ti3 C2 and Nb2 C Mxene quantum dots (QDs) for labeling of in vitro models. However, the mechanism of the toxicity of the prepared QDs was not explored before. In this study, we addressed the possible mechanism associated with cytotoxicity of the QDs to human umbilical vein endothelial cells (HUVECs). Exposure to up to 100 µg/ml Ti3 C2 but not Nb2 C QDs for 24 h significantly induced cytotoxicity. The exposure also increased intracellular Ti and Nb elements, indicating the internalization of both types of QDs. None of the QDs promoted interleukin 6 (IL-6) and IL-8 releases. Rather, Ti3 C2 QDs significantly reduced IL-6 and IL-8 release, indicating that the toxicity of Ti3 C2 QDs was not due to elevated inflammatory responses. Exposure to Ti3 C2 but not Nb2 C QDs resulted in increased LC3B-II/I ratio and beclin-1 proteins, biomarkers of autophagy, as well as the accumulation of autophagic substance p62. Ti3 C2 QDs also more effectively promoted pro-caspase 3 but not pro-caspase 8 compared with Nb2 C QDs. Furthermore, pre-treatment with autophagic modulators altered the cytotoxicity of Ti3 C2 QDs, which further confirmed the role of autophagic dysfunction in Ti3 C2 QD-induced toxicity to HUVECs. In conclusion, the results from this study suggested that high levels of Ti3 C2 QDs could induce cytotoxicity to HUVECs by inducing the dysfunction of autophagy. Nb2 C QDs appeared to be more biocompatible to HUVECs compared with Ti3 C2 QDs at the same mass concentrations, which suggested a role of composition of Mxene QDs to determine their toxicity to human endothelial cells.

Biomimicry Surface-Coated Proppant with Self-Suspending and Targeted Adsorption Ability.

Proppant is a key material, which can increase the production of unconventional petroleum and gas. Excellent proppants with a long migration distance are required in the fracture network. Resin-coated proppants have been confirmed as a good choice because of the long migration and the self-suspending ability in fracturing fluids. However, the distribution of the resin-coated proppants in fracture networks is random. The design of proppants with targeted adsorption is urgently needed. In this study, a novel proppant coated with a phenolic resin shell doped with Fe3O4 nanoparticles on ceramic (coated proppant) was designed and investigated. Based on the results, the coated proppant was adsorbed on the magnetic component's parts of the fracture network surface, which helps in enhancing the uniform distribution of the proppant in the fracture rock cracks. Meanwhile, the self-suspending ability of the coated proppant is five times higher than that of the uncoated proppant and can migrate a longer distance in the fracture network. Moreover, the liquid conductivity of the coated proppant is 30% higher than that of the uncoated ones at a closure pressure of 6.9 MPa. In summary, new insights into the design of functional proppants and further guidelines on the production of unconventional petroleum and gas have been provided in this study.