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Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. Methods Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k -nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model. Results A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. Conclusion The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Background Health-Related Quality of Life (HRQoL) of hypertensive patients is not only affected by the disease itself but also by some subjective factors. Low health literacy is prevalent among ethnic minorities . Considering the Kazakh-Chinese people have the highest prevalence of hypertension in Xinjiang, and the High Blood Pressure-Health Literacy (HBP-HL) has not been included in the study of HRQoL. The synergistic effects and the potential mechanism HBP-HL, self-management behavior, therapeutic adherence, self-efficacy, social support on HRQoL remain unclear. This study aimed to introduce the HBP-HL, and develop a structural equation model (SEM) to identify the factors influencing of the HRQoL among Kazakh hypertensive patients. Methods The data was obtained by questionnaire survey and physical examination in 2015. Patients with hypertension were recruited through random cluster sampling in Kazakh settlements in Xinjiang. Firstly, the blood pressure was measured. Then the one-for-one household interviews were conducted by Kazakh investigators. The questionnaires regarding HBP-HL, HRQoL, self-management behavior, therapeutic adherence, self-efficacy, and social support were used to collect data. Finally, SEM was constructed, and p  ≤ 0.05 was taken as significant. Results The data was analysed by SPSS18.0 and AMOS18.0 software. 516 Kazakh hypertension patients were recruited, and 94.4% of them had a relatively low HBP-HL score. The mean standardized scores of HRQoL, self-management, therapeutic adherence were poor; they were 63.5, 66.2, and 64.4, respectively. But 96.1% and 98.3% of the participants had high levels of self-efficacy and social support. The SEM of the HRQoL had a good overall fit (χ 2 /df = 2.078, AGFI = 0.944, GFI = 0.968, CFI = 0.947, IFI = 0.949, RMSEA = 0.046). The model indicated that the HBP-HL has the highest correlation with HRQoL, following with self-management behavior, social support, and self-efficacy. Conclusions Low HBP-HL is a major influenced factor of HRQoL among Kazakh hypertensive patients. Future programs should consider HBP-HL as the breakthrough point when designing targeting intervention strategies.

This study aimed to evaluate repression of Klotho on unilateral ureteral obstruction (UUO)-induced renal fibrosis and endothelial-to-mesenchymal transition (EndoMT) in mice. Renal fibrosis model was established by UUO in C57BL/6 male mice. Recombinant Klotho protein was administered to UUO mice as treatment group, and the mice in sham and UUO group were administered with an equal volume of vehicle. EndoMT biomarkers and TGF-β1/Smad2/Snail1 signaling were examined by immunofluorescence, immunohistochemistry, and western blotting assays. UUO deteriorated kidney function and resulted in increased expression of the mesenchymal marker α-smooth muscle actin and decreased expression of vascular endothelial cadherin, an endothelial marker. Moreover, UUO enhanced TGF-β1, phosphorylated Smad2 (p-Smad2), and Snail1 expression. Interestingly, Klotho treatment suppressed UUO-induced TGF-β1, p-Smad3, and Snail1 expression, which was accompanied by alleviation of the EndoMT process. Our findings demonstrated that Klotho significantly ameliorated EndoMT progression by targeting TGF-β1/Smad/Snail1 signaling in UUO mice, which provides the possibility for Klotho-based therapeutic protection against renal fibrosis.

Major histocompatibility complex-I (MHC-I) presents tumour antigens to CD8 + T cells and triggers anti-tumour immunity. Humans may have 30,000–60,000 long noncoding RNAs (lncRNAs). However, it remains poorly understood whether lncRNAs affect tumour immunity. Here, we identify a lncRNA, lncRNA inducing MHC-I and immunogenicity of tumour (LIMIT), in humans and mice. We found that IFNγ stimulated LIMIT, LIMIT cis -activated the guanylate-binding protein (GBP) gene cluster and GBPs disrupted the association between HSP90 and heat shock factor-1 (HSF1), thereby resulting in HSF1 activation and transcription of MHC-I machinery, but not PD-L1. RNA-guided CRISPR activation of LIMIT boosted GBPs and MHC-I, and potentiated tumour immunogenicity and checkpoint therapy. Silencing LIMIT , GBPs and/or HSF1 diminished MHC-I, impaired antitumour immunity and blunted immunotherapy efficacy. Clinically, LIMIT, GBP- and HSF1-signalling transcripts and proteins correlated with MHC-I, tumour-infiltrating T cells and checkpoint blockade response in patients with cancer. Together, we demonstrate that LIMIT is a cancer immunogenic lncRNA and the LIMIT–GBP–HSF1 axis may be targetable for cancer immunotherapy. Li et al. identify LIMIT as a lncRNA that modulates MHC-I expression through HSP90 and HSF1, thereby regulating antitumour immune response and the efficacy of immunotherapy.

Visual adaptive devices have potential to simplify circuits and algorithms in machine vision systems to adapt and perceive images with varying brightness levels, which is however limited by sluggish adaptation process. Here, the avalanche tuning as feedforward inhibition in bionic two-dimensional (2D) transistor is proposed for fast and high-frequency visual adaptation behavior with microsecond-level accurate perception, the adaptation speed is over 10 4 times faster than that of human retina and reported bionic sensors. As light intensity changes, the bionic transistor spontaneously switches between avalanche and photoconductive effect, varying responsivity in both magnitude and sign (from 7.6 × 10 4 to −1 × 10 3  A/W), thereby achieving ultra-fast scotopic and photopic adaptation process of 108 and 268 μs, respectively. By further combining convolutional neural networks with avalanche-tuned bionic transistor, an adaptative machine vision is achieved with remarkable microsecond-level rapid adaptation capabilities and robust image recognition with over 98% precision in both dim and bright conditions. Visual adaptive devices show promise for simplifying circuits and algorithms in machine vision systems. Here, the authors report a visual adaptive transistor with tunable avalanche effects and microsecond-level bionic vision capabilities, recognizing images in dim and bright conditions with over 98% accuracy.

Urbanization has profoundly reshaped the patterns and forms of modern urban landscapes. Understanding how urban transportation and mobility are affected by spatial planning is vital. Urban vibrancy, as a crucial metric for monitoring urban development, contributes to data-driven planning and sustainable growth. However, empirical studies on the relationship between urban vibrancy and the built environment in European cities remain limited, lacking consensus on the contribution of the built environment. This study employs Munich as a case study, utilizing night-time light, housing prices, social media, points of interest (POIs), and NDVI data to measure various aspects of urban vibrancy while constructing a comprehensive assessment framework. Firstly, the spatial distribution patterns and spatial correlation of various types of urban vibrancy are revealed. Concurrently, based on the 5Ds built environment indicator system, the multi-dimensional influence on urban vibrancy is investigated. Subsequently, the Geodetector model explores the heterogeneity between built environment indicators and comprehensive vibrancy along with its economic, social, cultural, and environmental dimensions, elucidating their influence mechanism. The results show the following: (1) The comprehensive vibrancy in Munich exhibits a pronounced uneven distribution, with a higher vibrancy in central and western areas and lower vibrancy in northern and western areas. High-vibrancy areas are concentrated along major roads and metro lines located in commercial and educational centers. (2) Among multiple models, the geographically weighted regression (GWR) model demonstrates the highest explanatory efficacy on the relationship between the built environment and vibrancy. (3) Economic, social, and comprehensive vibrancy are significantly influenced by the built environment, with substantial positive effects from the POI density, building density, and road intersection density, while mixed land use shows little impact. (4) Interactions among built environment factors significantly impact comprehensive vibrancy, with synergistic interactions among the population density, building density, and POI density generating positive effects. These findings provide valuable insights for optimizing the resource allocation and functional layout in Munich, emphasizing the complex spatiotemporal relationship between the built environment and urban vibrancy while offering crucial guidance for planning.

Cutting-edge humanoid machine vision merely mimics human systems and lacks polarimetric functionalities that convey the information of navigation and authentic images. Interspecies-chimera vision reserving multiple hosts’ capacities will lead to advanced machine vision. However, implementing the visual functions of multiple species (human and non-human) in one optoelectronic device is still elusive. Here, we develop an optically-controlled polarimetry memtransistor based on a van der Waals heterostructure (ReS 2 /GeSe 2 ). The device provides polarization sensitivity, nonvolatility, and positive/negative photoconductance simultaneously. The polarimetric measurement can identify celestial polarizations for real-time navigation like a honeybee. Meanwhile, cognitive tasks can be completed like a human by sensing, memory, and synaptic functions. Particularly, the anti-glare recognition with polarimetry saves an order of magnitude energy compared to the traditional humanoid counterpart. This technique promotes the concept of interspecies-chimera visual systems that will leverage the advances of autonomous vehicles, medical diagnoses, intelligent robotics, etc. The implementation of polarimetric functionalities in machine vision is beneficial for real-time navigation. Here, the authors report an optically-controlled polarimetry memtransistor with polarization sensitivity and synaptic functions.

This paper studies whether the investment intensity of R&D expenses of listed companies can improve the profitability of enterprises. The data indicators of listed companies from 2015 to 2017 are obtained by using CSMAR database. R&D investment is divided into two indicators: relative number index and absolute number index, and regression analysis is carried out by establishing the econometric analysis model of profitability and absolute number index and relative number index of R&D investment. It is found that the intensity of R&D investment has a positive effect on the profitability of enterprises. Therefore, enterprises can improve their profitability through technological innovation and increasing R&D investment.

Thermal transport and phonon-phonon coupling in monolayer hexagonal boron nitride (h-BN) under equibiaxial strains are investigated from first principles. Phonon spectra at elevated temperatures have been calculated from perturbation theory using the third-order anharmonic force constants. The stiffening of the out-of-plane transverse acoustic mode (ZA) near the Brillouin zone center and the increase of acoustic phonon lifetimes are found to contribute to the dramatic increase of thermal transport in strained h-BN. The transverse optical mode (TO) at the K point, which was predicted to lead to mechanical failure of h-BN, is found to shift to lower frequencies at elevated temperatures under equibiaxial strains. The longitudinal and transverse acoustic modes exhibit broad phonon spectra under large strains in sharp contrast to the ZA mode, indicating strong in-plane phonon-phonon coupling.

Porcine deltacoronavirus (PDCoV) is a newly detected porcine coronavirus causing serious vomiting and diarrhea in piglets, especially newborn piglets. There has been an outbreak of PDCoV in worldwide since 2014, causing significant economic losses in the pig industry. The interferon (IFN)-mediated antiviral response is an important component of virus-host interactions and plays an essential role in inhibiting virus infection. However, the mechanism of PDCoV escaping the porcine immune surveillance is unclear. In the present study, we demonstrated that the PDCoV nucleocapsid (N) protein antagonizes porcine IFN-β production after vesicular stomatitis virus (VSV) infection or poly(I:C) stimulation. PDCoV N protein also suppressed the activation of porcine IFN-β promoter when it was stimulated by porcine RLR signaling molecules. PDCoV N protein targeted porcine retinoic acid-inducible gene I (pRIG-I) and porcine TNF receptor associated factor 3 (pTRAF3) by directly interacting with them. The N-terminal region (1-246 aa) of PDCoV N protein was important for interacting with pRIG-I and interfere its function. We confirmed that PDCoV N antagonizes IFN-β production by associating with pRIG-I to impede it from binding double-stranded RNA. Furthermore, porcine Riplet (pRiplet) was an important activator for pRIG-I by mediating the K63-linked polyubiquitination. However, PDCoV N protein restrained the pRiplet binding pRIG-I to inhibit pRIG-I K63-linked polyubiquitination. Taken together, our results revealed a novel mechanism by which PDCoV N protein interferes with the early activation of pRIG-I in the host antiviral response. The novel findings provide a new insight into PDCoV on evading the host innate immune response and may provide new therapeutic targets and more efficacious vaccines strategies for PDCoV infections.