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In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

Vegetation, as a key component of land cover, plays a vital role in regulating energy exchange and water balance at different spatial and temporal scales. It is thus important to explore dynamic processes of changes in vegetation cover under changing environmental conditions in the context of global climate change. In this study, the Jialing River Basin (JRB) was selected as a case study, with the leaf area index (LAI) used as the primary indicator to represent JRB vegetation cover and growth status. The Biome-BGC model was employed to simulate the growth of various vegetation types within the basin. We calibrated the optimal range of multiple physiological and ecological parameters of vegetation and analyzed vegetation responses to climate change. The results showed that under four CMIP6 climate scenarios (SSP126, SSP245, SSP370, and SSP585), both temperature and precipitation in the basin are projected to increase. From 1976 to 2016, the vegetation coverage of the basin remained high, and on a monthly timescale, the grasslands are more responsive to climate-induced variability than woodlands. Under the influence of a warmer, more humid climate from 2023 to 2100, the LAI of vegetation in the basin is projected to show an increasing trend, and the vegetation coverage of woodland will still exceed that of grassland. These findings contribute to a more accurate simulation of vegetation dynamics under climate change and can inform the development of effective vegetation conservation and management strategies.

Carbon is one of the most crucial elements within plants, with its production and supply determining growth behaviors and physiological strategies. Nonstructural carbohydrates (NSC) serve as the “currency” of plant carbon flow, playing a key role in the balance between structural growth and carbon storage. However, the response patterns of NSC pools to varying concentrations and durations of nitrogen and phosphorus enrichment remain unclear. We conducted a meta-analysis compiling 1,313 independent data points from four plant organs-leaves, branches, stems, and roots-across global experiments to evaluate the impact of N and P enrichment on NSC pools in different organs. Our findings indicate that N limitation is widespread in ecosystems, whereas P limitation is not. Both the concentration and duration of N enrichment exhibit significant threshold effects on NSCs. Low to moderate levels of N enrichment led to varied increases in soluble sugar content (0.24% to 19.14%) and decreases in starch content (1.22% to 32.35%) in the leaves and branches of woody plants. However, this trend weakened or disappeared at high N concentrations. The NSC content in herbaceous plants was more sensitive to nutrient enrichment, with N enrichment significantly reducing their NSC reserves across all organs (by up to 90.72%). By integrating global data, this study not only addresses the gaps left by individual experiments in elucidating the spatio-temporal threshold responses of NSC to nutrient enrichment but also reveals the scarcity of studies on P addition and of long-term experiments in the existing literature. It reveals the growth-carbon storage strategies of plants under different nutrient conditions, contributing to biodiversity conservation and resource utilization in the context of future nitrogen deposition.

Aim To investigate the association between changes in estimated Glucose Disposal Rate (eGDR) and Composite Kidney Outcome (CKO) among middle-aged and older adults. Methods This study examined data from 4,007 individuals in the China Health and Retirement Longitudinal Study (CHARLS). The eGDR was estimated based on waist circumference, HbA1c, and hypertension status. Participants were stratified by cumulative eGDR quartiles and change patterns. The primary outcome was CKO, which included Chronic Kidney Disease (CKD)​ and rapid kidney function decline (RKFD). Logistic regression and restricted cubic spline (RCS) models were employed to assess the relationship between eGDR changes and outcomes, while receiver operating characteristic (ROC) curves evaluated the predictive performance of eGDR. Additionally, the Weighted Quantile Sum (WQS) regression model was used to quantify the contributions of eGDR components. Results Cumulative eGDR exhibited a linear and inverse relationship with the risk of CKO (OR = 0.96 (0.94 ~ 0.99), P for nonlinear = 0.214), and the greatest cumulative eGDR group (Q4) had a 53% lower risk of CKO than the reference group (OR = 0.47 (0.28 ~ 0.79), P  = 0.004). The predictive performance of cumulative eGDR surpassed both baseline eGDR and traditional insulin resistance indices. HbA1c and hypertension were the most influential components in the model. Conclusion Long-term declines in eGDR were significantly associated with an increased risk of CKO. Cumulative eGDR showed enhanced predictive value, underscoring its promise as a biomarker for early CKD risk assessment.

In this study, poly(lactic acid) (PLA)/starch blends were prepared through reactive melt blending by using PLA and starch as raw materials and vegetable oil polyols, polyethylene glycol (PEG), and citric acid (CA) as additives. The effects of CA and PEG on the toughness of PLA/starch blends were analyzed using a mechanical performance test, scanning electron microscope analysis, differential scanning calorimetry, Fourier-transform infrared spectroscopy, X-ray diffraction, rheological analysis, and hydrophilicity test. Results showed that the elongation at break and impact strength of the PLA/premixed starch (PSt)/PEG/CA blend were 140.51% and 3.56 kJ·m−2, which were 13.4 and 1.8 times higher than those of pure PLA, respectively. The essence of the improvement in the toughness of the PLA/PSt/PEG/CA blend was the esterification reaction among CA, PEG, and starch. During the melt-blending process, the CA with abundant carboxyl groups reacted in the amorphous region of the starch. The shape and crystal form of the starch did not change, but the surface activity of the starch improved and consequently increased the adhesion between starch and PLA. As a plasticizer for PLA and starch, PEG effectively enhanced the mobility of the molecular chains. After PEG was dispersed, it participated in the esterification reaction of CA and starch at the interface and formed a branched/crosslinked copolymer that was embedded in the interface of PLA and starch. This copolymer further improved the compatibility of the PLA/starch blends. PEGs with small molecules and CA were used as compatibilizers to reduce the effect on PLA biodegradability. The esterification reaction on the starch surface improved the compatibilization and toughness of the PLA/starch blend materials and broadens their application prospects in the fields of medicine and high-fill packaging.

The Hemoglobin Glycation Index (HGI) quantifies the difference between observed and predicted glycated hemoglobin (HbA1c) values, and has connections to multiple adverse outcomes. However, the relationship between HGI and the risk of diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM) remains underexplored. The objective of this study was to examine the relationship between baseline HGI and the risk of DN development among patients with T2DM through a retrospective cohort study. A single-center retrospective study was conducted on 1050 newly diagnosed T2DM patients with normal renal function at baseline. Participants were categorized into quartiles based on HGI values. The primary outcome was DN development, defined as persistent proteinuria or reduced estimated glomerular filtration rate (eGFR). Multivariable logistic regression, restricted cubic spline (RCS) analysis, and threshold effect models were employed to assess the association between HGI and DN risk. Subgroup and sensitivity analyses were conducted to validate the robustness of our findings, while mediation analysis was employed to explore potential underlying mechanisms. The study revealed a U-shaped relationship between HGI and DN risk. Both excessively low and high HGI levels were associated with an increased risk of DN, with the lowest risk observed at an HGI threshold of -0.648. In fully adjusted models, the highest HGI quartile (Q4) demonstrated a significantly increased risk of DN (OR = 1.54, 95% CI: 1.03-2.30, = 0.036), while the lowest HGI quartile (Q1) also showed a trend toward higher risk (OR = 1.40, 95% CI: 0.92-2.14, = 0.115). However, fasting plasma glucose (FPG) ( for overall = 0.217) and glycated hemoglobin (HbA1c) ( for overall = 0.529) did not show an association with the risk of DN. Subgroup and sensitive analyses confirmed the consistency of this U-shaped association across different patient demographics. Mediation analysis indicated that C-reactive protein (CRP) mediated 11.1% of the effect of |HGI| on DN. In T2DM patients, baseline HGI exhibits a U-shaped association with DN risk, serving as a potential indicator for assessing DN risk.

Tongqiang Liu, Department of Nephrology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, 68 Middle Gehu Road, Changzhou, Jiangsu, 213000, People's Republic of China, Email liuyf1106@126.com

Thyroid-associated ophthalmopathy (TAO) is a vision-threatening autoimmune disease that involves the extraocular muscles (EOMs) and periorbital fat. Typical signs of TAO include eyelid recession, proptosis, diplopia, and decreased visual acuity. As a self-limited disease, there is major bipolarity in clinical outcomes in TAO population. The early diagnosis and prediction of these refractory and relapsed patients is essential. Unfortunately, commonly used tools such as CAS/NOSPECTS, are based on clinical symptoms and signs alone, have significant limitations. Some imaging techniques or examinations, such as magnetic resonance imaging (MRI), can be very effective in assisting TAO assessment, from exhaustive whiteboard notes to optimized patient outcomes. Being one of the most commonly used and accurate objective examinations for TAO assessment, MRI boosts no ionizing radiation, high soft tissue contrast, better reflection of tissue water content, and the ability to quantify multiple parameters. In addition, novel MR sequences are becoming increasingly more familiar in TAO and other areas of clinical and scientific research. Moreover, radiomics, a method involving the extraction of a large number of features from medical images through algorithms, is a more recent approach used in the analysis and characterization of TAO data. Thus, this review aims to summarize and compare the value of routine and novel functional MRI sequences and radiomics prediction models in the diagnosis and evaluation of TAO.

Abstract Background: Diabetic foot is a complex condition with high incidence, recurrence, mortality, and disability rates. Current treatments for diabetic foot ulcers are often insufficient. This study was conducted to identify potential therapeutic targets for diabetic foot. Methods: Datasets related to diabetic foot and diabetic skin were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using R software. Enrichment analysis was conducted to screen for critical gene functions and pathways. A protein interaction network was constructed to identify node genes corresponding to key proteins. The DEGs and node genes were overlapped to pinpoint target genes. Plasma and chronic ulcer samples from diabetic and non-diabetic individuals were collected. Western blotting, immunohistochemistry, and enzyme-linked immunosorbent assays were performed to verify the S100 calcium binding protein A9 (S100A9), inflammatory cytokine, and related pathway protein levels. Hematoxylin and eosin staining was used to measure epidermal layer thickness. Results: In total, 283 common DEGs and 42 node genes in diabetic foot ulcers were identified. Forty-three genes were differentially expressed in the skin of diabetic and non-diabetic individuals. The overlapping of the most significant DEGs and node genes led to the identification of S100A9 as a target gene. The S100A9 level was significantly higher in diabetic than in non-diabetic plasma (178.40 ± 44.65 ng/mL vs. 40.84 ± 18.86 ng/mL) and in chronic ulcers, and the wound healing time correlated positively with the plasma S100A9 level. The levels of inflammatory cytokines (tumor necrosis factor-α, interleukin [IL]-1, and IL-6) and related pathway proteins (phospho-extracellular signal regulated kinase [ERK], phospho-p38, phospho-p65, and p-protein kinase B [Akt]) were also elevated. The epidermal layer was notably thinner in chronic diabetic ulcers than in non-diabetic skin (24.17 ± 25.60 μm vs. 412.00 ± 181.60 μm). Conclusions: S100A9 was significantly upregulated in diabetic foot and was associated with prolonged wound healing. S100A9 may impair diabetic wound healing by disrupting local inflammatory responses and skin re-epithelialization.

This prospective study evaluated the feasibility of chemical exchange saturation transfer (CEST) MRI for assessing disease activity in thyroid-associated ophthalmopathy (TAO). A total of 88 patients with active TAO, 76 with inactive TAO, and 30 healthy controls were enrolled. CEST MRI-derived magnetization transfer ratio (MTR) and MTR asymmetry (MTR ) at 1 ppm, 2 ppm, and 3.5 ppm were calculated. Clinical data, MTR, and MTR values for the extraocular muscles (one representative muscle per eye, yielding two measurements per participant) were compared among the groups. Spearman's correlation was used to examine associations between imaging parameters and the clinical activity score (CAS) in patients with TAO. Logistic regression analysis was used to identify independent associations between imaging parameters and disease activity in patients with TAO (active vs. inactive). Receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic performance for discriminating active from inactive TAO. Patients with active TAO showed lower MTR values ( < 0.001) and higher MTR , MTR , and MTR (all < 0.001) compared with those with inactive TAO. MTR was negatively correlated with CAS ( = -0.402; < 0.001), while MTR , MTR , and MTR were positively correlated with CAS ( = 0.369, 0.350, and 0.349, respectively; all < 0.001). MTR and MTR were independently associated with TAO activity. The areas under the ROC curve (AUCs) for MTR and MTR in discriminating active from inactive TAO were 0.772 and 0.730, respectively. Combining MTR with MTR significantly improved diagnostic performance compared with either parameter alone, achieving an AUC of 0.805 ( = 0.029 and 0.001). MTR and MTR were independently associated with TAO activity. Their combination further enhanced diagnostic performance in distinguishing active from inactive TAO, suggesting their potential as quantitative imaging biomarkers to guide treatment in patients with TAO.