Clinical characteristics of lower extremity deep vein thrombosis in young vs. middle-aged adults: a single-center retrospective study

BackgroundThe incidence of deep vein thrombosis (DVT) in the lower extremities is increasing in the younger population. However, there are fewer reported comparisons in the literature for lower extremity DVT.MethodsPatients aged <40 years admitted with lower-extremity DVT between January 2018 and December 2023 were retrospectively analyzed and followed up for 1 year.ResultsA total of 61 patients were included in the study and divided into two groups: 33 patients over 30 years of age (middle-aged group) and 28 patients under 30 years of age (young group). A significant gender difference was observed, with a higher proportion of males in the young group compared to the middle-aged group (P < 0.001). Five patients in the young group were treated with anticoagulation alone, whereas all patients in the middle-aged group underwent endovascular therapy. A higher prevalence of inferior vena cava thrombosis in the young group compared to the middle-aged group (60.71% vs. 33.3%, P = 0.032). The proportion of iliac vein stenosis was significantly higher in the middle-aged groups than in the young group (P = 0.002). There was no statistically significant difference in venous function scores (Villalta and rVCSS) between the two groups during both the preoperative period and the postoperative follow-up (P > 0.05). The incidence of lower-extremity DVT post-thrombotic syndrome and thrombus recurrence was higher in the young group than in the middle-aged group at 1 year postoperatively (PTS: 78.57% vs. 33.3%, P < 0.001, and thrombus recurrence: 28.57% vs. 9.09%, P < 0.05). Univariate and multivariate analyses revealed that inferior vena cava thrombosis was an independent risk factor for severe DVT post-thrombotic syndrome and recurrent DVT (P < 0.05), whereas gender was an independent risk factor for recurrent DVT (P < 0.05).ConclusionsThis study suggests differences in the clinical characteristics and prognosis of lower-extremity DVT

BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy

Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME

Unveiling the spatial-temporal variation of urban land use efficiency of Yangtze River Economic Belt in China under carbon emission constraints

Under the constraint of carbon emission, measuring and analyzing the spatial-temporal evolution characteristics of urban land use efficiency in the Yangtze River Economic Belt is the inherent requirement of its ecological protection and sustainable development. In this paper, we calculated the urban land use efficiency of 107 cities in the Yangtze River Economic Belt from 2006 to 2020 by using the SBM-Undesirable model with unexpected output, and analyzed its temporal evolution trend and spatial correlation relationship by using kernel density and spatial autocorrelation method. The results showed that: except in 2020, the urban land use efficiency was generally low due to the COVID-19 epidemic, and the urban land use efficiency in other years was mostly concentrated in the middle levels, and showed a trend of slow fluctuation and rise year by year. The difference of urban land use efficiency level between regions increased, and the dispersion degree in upstream, midstream and downstream increased with each passing year. Urban land use efficiency spatial imbalance was significant, and the urban land use efficiency level of large and medium-sized cities was generally lower than that of cities with low economic development level. The spatial correlation was weak, and the global spatial autocorrelation was basically insignificant, while the local spatial agglomeration areas were mainly distributed in the upstream and downstream regions, with a small distribution range and weak spatial interaction. The distribution areas of the standard deviation ellipse were gradually flattened, and the center of gravity as a whole shift significantly to the southwest. The research results are helpful to understand the development history and future trend of urban land use efficiency in various regions, and propose that cities should consider the impact of public crisis events in advance, reasonably control the scale of land expansion, and lead coordinated development and other reasonable suggestions when formulating land use policies

Loss of PDZK1 expression activates PI3K/AKT signaling via PTEN phosphorylation in gastric cancer

Phosphorylation of PTEN plays an important role in carcinogenesis and progression of gastric cancer. However, the underlying mechanism of PTEN phosphorylation regulation remains largely elusive. In the present study, PDZK1 was identified as a novel binding protein of PTEN by association of PTEN through its carboxyl terminus and PDZ domains of PDZK1. By direct interaction with PTEN, PDZK1 inhibited the phosphorylation of PTEN at S380/T382/T383 cluster and further enhanced the capacity of PTEN to suppress PI3K/AKT activation. PDZK1 suppressed gastric cancer cell proliferation by diminishing PI3K/AKT activation via inhibition of PTEN phosphorylation in vitro and in vivo. The expression of PDZK1 was frequently downregulated in gastric cancer specimens and correlated with progression and poor prognosis of gastric cancer patients. Downregulation of PDZK1 was associated with PTEN inactivation, AKT signaling and cell proliferation activation in clinical specimens. Thus, low levels of PDZK1 in gastric cancer specimens lead to increase proliferation of gastric cancer cells via phosphorylation of PTEN at the S380/T382/T383 cluster and constitutively activation of PI3K/AKT signaling, which results in poor prognosis of gastric cancer patients

Autocatalytic reduction-assisted synthesis of segmented porous PtTe nanochains for enhancing methanol oxidation reaction

Morphology engineering has been developed as one of the most widely used strategies for improving the performance of electrocatalysts. However, the harsh reaction conditions and cumbersome reaction steps during the nanomaterials synthesis still limit their industrial applications. Herein, one-dimensional (1D) novel-segmented PtTe porous nanochains (PNCs) were successfully synthesized by the template methods assisted by Pt autocatalytic reduction. The PtTe PNCs consist of consecutive mesoporous architectures that provide a large electrochemical surface area (ECSA) and abundant active sites to enhance methanol oxidation reaction (MOR). Furthermore, 1D nanostructure as a robust sustaining frame can maintain a high mass/charge transfer rate in a long-term durability test. After 2,000 cyclic voltammetry (CV) cycles, the ECSA value of PtTe PNCs remained as high as 44.47 m2·gPt–1, which was much larger than that of commercial Pt/C (3.95 m2·gPt–1). The high catalytic activity and durability of PtTe PNCs are also supported by CO stripping test and density functional theory calculation. This autocatalytic reduction-assisted synthesis provides new insights for designing efficient low-dimensional nanocatalysts

A deep learning–based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols

Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different centers cause unavoidable data heterogeneity across centers, its impact on reliability in study outcomes has also drawn much attention recently. One fundamental issue arises in how to derive model parameters reliably from image data of varying quality. This issue is even more challenging for advanced diffusion methods such as diffusion kurtosis imaging (DKI). Recently, deep learning–based methods have been demonstrated with their potential for robust and efficient computation of diffusion-derived measures. Inspired by these approaches, the current study specifically designed a framework based on a three-dimensional hierarchical convolutional neural network, to jointly reconstruct and harmonize DKI measures from multicenter acquisition to reformulate these to a state-of-the-art hardware using data from traveling subjects. The results from the harmonized data acquired with different protocols show that: 1) the inter-scanner variation of DKI measures within white matter was reduced by 51.5% in mean kurtosis, 65.9% in axial kurtosis, 53.7% in radial kurtosis, and 61.5% in kurtosis fractional anisotropy, respectively; 2) data reliability of each single scanner was enhanced and brought to the level of the reference scanner; and 3) the harmonization network was able to reconstruct reliable DKI values from high data variability. Overall the results demonstrate the feasibility of the proposed deep learning–based method for DKI harmonization and help to simplify the protocol setup procedure for multicenter scanners with different hardware and software configurations