Search

With the increasing energy consumption in buildings, the proportion of energy consumption in public buildings continues to grow. As an essential component of public buildings, sports buildings are receiving more attention regarding energy-saving technologies. This paper aims to study the passive energy-saving design methods of small-and medium-sized sports halls in hot summer and cold winter regions, exploring how to reduce building energy consumption by improving the spatial design and thermal performance of the enclosure structures of sports halls. Taking the Wuhu County Sports Center as an example, this study uses computer simulation software to analyze the building’s wind environment and the thermal performance of its external walls and roof. The results show that the large volume of the sports hall significantly impacts the distribution of wind speed and pressure around it, and this impact decreases with height. The thermal simulation of the enclosure structures demonstrates that adding insulation layers to the interior and exterior of the walls and roof of the sports hall is an effective way to reduce energy consumption in both winter and summer. Additionally, wind environment simulations of different roof shapes reveal that flat roofs have the most significant blocking effect on wind and are prone to inducing strong vortices on the leeward side; concave arch roofs have the least blocking effect on airflow, and arch and wave-shaped roofs maintain lower vortex intensity on the leeward side. Hopefully, this study can provide significant references for the energy-saving design of future small- and medium-sized sports buildings.

Abstract Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking β-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8 iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.

Abstract Earthworm cultivation can effectively promote the resource utilization of agricultural waste. The efficient utilization of agricultural waste by earthworms mainly depends on the microbial communities in the guts. This study used silkworm excrement and cow manure as substrates for earthworm cultivation and investigated the associated bacterial communities during earthworms’ growth. The survival rate of earthworms remained above 89% after 21 days of feeding with the two substrates. Proteobacteria, Actinobacteria, and Firmicutes constituted the predominant bacterial communities in earthworm growth, accounting for over 81% of the relative abundance in both guts and vermicompost. The bacteria richness and diversity in the foregut and midgut of earthworm were lower than those in the hindgut. The prediction function of intestinal bacterial communities of earthworms cultured with two substrates mainly involved biosynthesis, decomposition and energy production.

Abstract Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs.

Abstract X-ray diffraction (XRD) is used for characterizing the crystal structure of molecular sieves after synthetic experiments. However, for a high-throughput molecular sieve synthetic system, the huge amount of data derived from large throughput capacity makes it difficult to analyze timely. While the kernel step of XRD analysis is to search peaks, an automatic way for peak search is needed. Thus, we proposed a novel semantic mask-based two-step framework for peak search in XRD patterns: (1) mask generation, we proposed a multi-resolution net (MRN) to classify the data points of XRD patterns into binary masks (peak/background). (2) Peak search, based on the generated masks, the background points are used to fit an n-order polynomial background curve and estimate the random noises in XRD patterns. Then we proposed three rules named mask, shape, and intensity to screening peaks from initial peak candidates generated by maximum search. Besides, a voting strategy is proposed in peak screening to obtain a precise peak search result. Experiments show that the proposed MRN achieves the state-of-the-art performance compared with other semantic segmentation methods and the proposed peak search method performs better than Jade when using f1 score as the evaluation index.

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody–antigen interactions. This structural prediction tool can be used to optimize antibody–antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.

Direct aging of water-quenched metastable β-titanium alloys usually follows the sequence: rapid growth of ωiso and then slow transformation into α. To inhibit ωiso growth and promote α nucleation, 6% pre-deformation was introduced in Ti-10V-6Cu alloy before 500°C/60s aging. An ultrafast α-formation and a few α-free kink-bands are observed, indicating the alteration of the precipitation sequence. For α-nucleation without ωiso as the sites, it shows that O' nanodomains induced by stress accumulation around slip bands mediate the ultrafast nucleation of hcp-α structure, while dislocation pipe diffusion accelerates the necessary phase separation. This provides new insights into regulating precipitation kinetics of age-hardenable β-titanium alloys.

Abstract Asthma exacerbations caused by respiratory viral infections are a serious global health problem. Impaired antiviral immunity is thought to contribute to the pathogenesis, but the underlying mechanisms remain understudied. Here using mouse models we find that Cullin5 (CUL5), a key component of Cullin-RING E3 ubiquitin ligase 5, is upregulated and associated with increased neutrophil count and influenza-induced exacerbations of house dust mite-induced asthma. By contrast, CUL5 deficiency mitigates neutrophilic lung inflammation and asthma exacerbations by augmenting IFN-β production. Mechanistically, following thymic stromal lymphopoietin stimulation, CUL5 interacts with O-GlcNAc transferase (OGT) and induces Lys48-linked polyubiquitination of OGT, blocking the effect of OGT on mitochondrial antiviral-signaling protein O-GlcNAcylation and RIG-I signaling activation. Our results thus suggest that, in mouse models, pre-existing allergic injury induces CUL5 expression, impairing antiviral immunity and promoting neutrophilic inflammation for asthma exacerbations. Targeting of the CUL5/IFN-β signaling axis may thereby serve as a possible therapy for treating asthma exacerbations.

Inflammatory bowel disease (IBD) is a formidable disease due to its complex pathogenesis. Macrophages, as a major immune cell population in IBD, are crucial for gut homeostasis. However, it is still unveiled how macrophages modulate IBD. Here, we found that LIM domain only 7 (LMO7) was downregulated in pro-inflammatory macrophages, and that LMO7 directly degraded 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) through K48-mediated ubiquitination in macrophages. As an enzyme that regulates glycolysis, PFKFB3 degradation led to the glycolytic process inhibition in macrophages, which in turn inhibited macrophage activation and ultimately attenuated murine colitis. Moreover, we demonstrated that PFKFB3 was required for histone demethylase Jumonji domain-containing protein 3 (JMJD3) expression, thereby inhibiting the protein level of trimethylation of histone H3 on lysine 27 (H3K27me3). Overall, our results indicated the LMO7/PFKFB3/JMJD3 axis is essential for modulating macrophage function and IBD pathogenesis. Targeting LMO7 or macrophage metabolism could potentially be an effective strategy for treating inflammatory diseases.

AbstractTemozolomide (TMZ) is a conventional chemotherapeutic drug for glioma, however, its clinical application and efficacy is severely restricted by its drug resistance properties. O6-methylguanine-DNA methyltransferase (MGMT) is a DNA repair enzyme, which can repair the DNA damage caused by TMZ. A large number of clinical data show that reducing the expression of MGMT can enhance the chemotherapeutic efficacy of TMZ. Therefore, in order to improve the resistance of glioma to TMZ, an angiopep-2 (A2) modified nanoprodrug of polytemozolomide (P(TMZ)n) that combines with MGMT siRNA (siMGMT) targeting MGMT was developed (A2/T/D/siMGMT). It not only increased the amount of TMZ within tumor lesion site, but also reduced MGMT expression in glioma. The in vitro experiments indicated that the A2/T/D/siMGMT effectively enhanced the cellular uptake of TMZ and siMGMT, and resulted in a significant cell apoptosis and cytotoxicity in the glioma cells. The in vivo experiments showed that glioma growth was inhibited and the survival time of animals were prolonged remarkably after A2/T/D/siMGMT was injected via tail vein. The results showed that the therapeutic effect of A2/T/D/siMGMT in the treatment of glioma was significantly improved.

NiOx, prepared via the sputtering method, exhibits low conductivity and energy level mismatch with the perovskite layer, thereby limiting further enhancements in the performance of perovskite solar modules (PSMs). Unlike traditional methods that enhance the performance of NiOx through reactive sputtering or directly doping NiOx targets with metal ions, both of which incur high costs and low efficiency, we employ an evaporation method using LiF to achieve efficient and low-cost doping of NiOx. Compared to the pristine NiOx, the incorporation of LiF significantly increases the conductivity of NiOx. Additionally, the incorporation of LiF enhances the quality of the deposited perovskite films, as well as the energy level alignment and symmetry between NiOx and the perovskite, effectively improving the hole extraction and transport capabilities between NiOx and the perovskite. As a result, the PSM (active area of 57.30 cm²) fabricated in air achieves an impressive efficiency of 19.54%. Furthermore, the unencapsulated PSM retains 80% of its initial efficiency after 700 h of continuous illumination, whereas the NiOx-based PSM drops to 80% after only 150 h. This study provides a simple and low-cost method for doping NiOx, which is of great significance for the further industrialization of PSMs.

Common complications following laparoscopic appendectomy include wound infection, bleeding, intra-abdominal abscess, small bowel obstruction, stump leakage, and stump appendicitis. Here, we presented a case reporting detailing a rare complication following laparoscopic appendectomy: the development of a metastatic neck abscess induced by Klebsiella pneumoniae(K. pneumoniae). A 49-year-old male underwent emergency laparoscopic surgery with prophylactic antibiotic administration for acute appendicitis. Subsequently, he experienced persistent neck pain and fever postoperatively, prompting further investigation. Pus and blood cultures revealed K. pneumoniae, with magnetic resonance imaging confirming the presence of a neck abscess. Antibiotic therapy was adjusted, and surgical drainage of the abscess was performed after multidisciplinary consultation. The patient was discharged without complications. While rare, metastatic abscesses following appendectomy warrant consideration, particularly in K. pneumoniae infections. Comprehensive clinical assessment, imaging, and laboratory evaluation are crucial for timely diagnosis and management of such complications.

The MTCH2 protein is located on the mitochondrial outer membrane and regulates mitochondria-related cell death. This study set out to investigate the role of MTCH2 in the underlying pathophysiological mechanisms of breast cancer (BC). MTCH2 expression levels in BC were analyzed using bioinformatics prior to verification by cell lines in vitro. Experiments of over-expression and siRNA-mediated knockdown of MTCH2 were conducted to assess its biological functions, including its effects on cellular proliferation and cycle progression. Xenografts were utilised for in vivo study and signaling pathway alterations were examined to identify the mechanisms driven by MTCH2 in BC proliferation and cell-cycle regulation. MTCH2 was up-regulated in BC and correlated with patients’ overall survival. Over-expression of MTCH2 promoted cellular proliferation and cycle progression, while silencing MTCH2 had the opposite effect. Xenograft experiments were utilised to confirm the in vitro cellular findings and it was identified that the PI3K/Akt signaling pathway was activated by MTCH2 over-expression and suppressed by its silencing. Moreover, the activation of IGF-1R rescued cellular growth and cycle arrest induced by MTCH2-silencing. Overall, this study reveals that expression of MTCH2 in BC is upregulated and potentiates cellular proliferation and cycle progression via the PI3K/Akt pathway.

Abstract Intravitreal (IVT) injection of anti‐vascular endothelial growth factor (anti‐VEGF) has greatly improved the treatment of many retinal disorders, including wet age‐related macular degeneration (wAMD), which is the third leading cause of blindness. However, frequent injections can be difficult for patients and may lead to various risks such as elevated intraocular pressure, infection, and retinal detachment. To address this issue, researchers have found that IVT injection of anti‐VEGF proteins at their maximally viable concentration and dose can be an effective strategy. However, the intrinsic protein structure can limit the maximum concentration due to stability and solution viscosity. To overcome this challenge, we developed a novel anti‐VEGF protein called nanoFc by fusing anti‐VEGF nanobodies with a crystallizable fragment (Fc). NanoFc has demonstrated high binding affinity to VEGF165 through multivalency and potent bioactivity in various bioassays. Furthermore, nanoFc maintains satisfactory chemical and physical stability at 4°C over 1 month and is easily injectable at concentrations up to 200 mg/mL due to its unique architecture that yields a smaller shape factor. The design of nanoFc offers a bioengineering strategy to ensure both strong anti‐VEGF binding affinity and high protein concentration, with the goal of reducing the frequency of IV injections.

Abstract Fibrocystin/Polyductin (FPC), encoded by PKHD1, is associated with autosomal recessive polycystic kidney disease (ARPKD), yet its precise role in cystogenesis remains unclear. Here we show that FPC undergoes complex proteolytic processing in developing kidneys, generating three soluble C-terminal fragments (ICDs). Notably, ICD15, contains a novel mitochondrial targeting sequence at its N-terminus, facilitating its translocation into mitochondria. This enhances mitochondrial respiration in renal epithelial cells, partially restoring impaired mitochondrial function caused by FPC loss. FPC inactivation leads to abnormal ultrastructural morphology of mitochondria in kidney tubules without cyst formation. Moreover, FPC inactivation significantly exacerbates renal cystogenesis and triggers severe pancreatic cystogenesis in a Pkd1 mouse mutant Pkd1 V/V in which cleavage of Pkd1-encoded Polycystin-1 at the GPCR Proteolysis Site is blocked. Deleting ICD15 enhances renal cystogenesis without inducing pancreatic cysts in Pkd1 V/V mice. These findings reveal a direct link between FPC and a mitochondrial pathway through ICD15 cleavage, crucial for cystogenesis mechanisms.

The demand for low-carbon and energy-efficient building designs is urgent, especially considering that building energy consumption constitutes a significant part of global energy usage. Unlike small to medium-sized buildings such as residential and office spaces, large public buildings, like sports facilities, have unique usage patterns and architectural forms, offering more significant potential for energy-saving strategies. This review focuses on sports buildings, selecting 62 high-quality papers published in building science over the past 30 years that investigate low-carbon and energy-efficient research. Summarizing and synthesizing these papers reveals that current studies predominantly concentrate on four main areas: indoor air quality, ventilation, thermal environment, and energy consumption. Notably, many studies emphasize improving indoor thermal comfort and reducing energy consumption in sports buildings through measurements and evaluations of indoor thermal environments, temperature distributions, heat transfer phenomena, and energy consumption analyses. Key outcomes indicate that green technology innovations, such as energy substitution technologies, significantly enhance energy efficiency and reduce CO2 emissions. However, present research emphasizes singular energy-saving approaches, suggesting future directions could integrate comprehensive green technologies, life-cycle assessments, and applications of intelligent technologies and the Internet of Things (IoT). These enhancements aim to provide more effective and sustainable solutions for implementing green, low-carbon energy practices in sports buildings. The review emphasizes that in order to accomplish sustainable urban growth and achieve global carbon neutrality targets, a comprehensive approach involving technical innovation, legislative assistance, and extensive preparation is crucial.

Weakly supervised semantic segmentation (WSSS) aims to segment objects without a heavy burden of dense annotations. Pseudo-masks serve as supervisory information for training segmentation models, which is crucial to the performance of segmentation models. However, the generated pseudo-masks contain significant noisy labels, which leads to poor performance of the segmentation models trained on these pseudo-masks. Few studies address this issue, as these noisy labels remain inevitable even after the pseudo-masks are improved. In this paper, we propose an uncertainty-weight transform module to mitigate the impact of noisy labels on model performance. It is noteworthy that our approach is not aimed at eliminating noisy labels but rather enhancing the robustness of the model to noisy labels. The proposed method adopts a frequency-based approach to estimate pixel uncertainty. Moreover, the uncertainty of pixels is transformed into loss weights through a set of well-designed functions. After dynamically assigning weights, the model allocates attention to each pixel in a significantly differentiated manner. Meanwhile, the impact of noisy labels on model performance is weakened. Experiments validate the effectiveness of the proposed method, achieving state-of-the-art results of 69.3% on PASCAL VOC 2012 and 39.3% on MS COCO 2014, respectively.

With the widespread application of lithium-ion batteries (LIBs) energy storage stations in high-altitude areas, the impact of ambient pressure on battery thermal runaway (TR) behavior and venting flow characteristics have aroused wide research attention. This paper conducts a lateral heating experiment on 280 Ah lithium iron phosphate batteries (LFPs) and proposes a method for testing battery internal pressure using an embedded pressure sensor. This paper analyzes the battery characteristic temperature, internal pressure, chamber pressure, and gas components under different chamber pressures. The experiment is carried out in a N2 atmosphere using a 1000 L insulated chamber. At 40 kPa, the battery experiences two instances of venting, with a corresponding peak in temperature on the battery’s side of 136.3 °C and 302.8 °C, and gas generation rates of 0.14 mol/s and 0.09 mol/s, respectively. The research results indicate that changes in chamber pressure significantly affect the center temperature of the battery side (Ts), the center temperature of the chamber (Tc), the opening time of the safety valve (topen), the triggering time of TR (tTR), the time difference (Δt), venting velocity, gas composition, and flammable limits. However, the internal pressure and gas content of the battery are apparently unaffected. Considering the TR characteristics mentioned above, a safety assessment method is proposed to evaluate the TR behavior and gas hazard of the battery. The results indicate that the risk at 40 kPa is much higher than the other three chamber pressures. This study provides theoretical references for the safe use and early warning of energy storage LIBs in high-altitude areas.

In order to better understand the role of coral reefs around an isolated island in mitigating tsunami hazards, this study performed a horizontally two-dimensional (2DH) numerical study of tsunami-like solitary wave propagation and run-up around an idealized reef-fringed island. The shock-capturing Boussinesq wave model, the FUNWAVE-TVD is used in the present study and well-validated with existing experimental data for its robustness in predicting 2DH solitary wave processes around an island. Based on the validated model, the typical solitary propagation process around the reef-fringed island and the effects of morphological and hydrodynamic parameters on the maximum run-up heights were systematically investigated. It is found that coral reefs can effectively reduce maximum run-up heights around an isolated island. The reef flat’s water depth, reef flat width, and reef surface roughness are the main factors affecting maximum run-up heights around an island, while the fore-reef slope has little impact. For the idealized reef-fringed island in this study, sea-level rise will cause coral reefs to lose their protective capability on the lee side, and the presence of coral reefs may even enhance tsunami hazards around an island when the reef flat width is very narrow or coral bleaching happens.

Growing environmental concerns have prompted the shipping industry to adopt stringent measures to address greenhouse gas emissions, with fuel-powered ships being the primary source of such emissions. Additionally, alternative forms of ship propulsion, such as internal combustion engine hybridization, low-carbon fuels, and zero-carbon fuels, face significant challenges either in terms of cost or emission-reduction capability at present. In order to decarbonize navigation, countries are focusing the maritime industry’s transition towards low-carbon alternatives on transforming energy consumption, with widespread attention on the electrification of ships. Therefore, this paper provides a comprehensive review of the feasibility of fully electrifying ships, covering aspects such as technological prospects, economic viability, and emission-reduction capabilities. Firstly, the current state of research on ship electrification technology is summarized; the applicability of different battery types to electric ship technology is compared. Subsequently, the economic viability and emission-reduction capabilities of five different electric ship lifecycles are discussed separately. The results indicate that ship electrification is a key pathway to achieving zero-emission shipping, with lithium-ion batteries being the most suitable battery technology for maritime use currently. Short-to-medium-range electric ship types have demonstrated economic advantages over traditional diesel ships. As battery costs continue to decline and energy density keeps improving, the economic feasibility of ship electrification is expected to expand.

The precise calculation and effective control of the horizontal displacement of deep foundation pit retaining structures are critical for foundation pit support design and construction. Based on stress–strain linear elastomer theory and considering the deformation coordination between an enclosure wall and its internal support member, a formula for the redundant restraint force acting on the retaining wall was derived through the unit load method and the principle of elastic superposition. Moreover, a method for calculating the horizontal displacement of the retaining structure of a deep foundation pit was formulated, which is convenient for engineering applications. The method can also be used to calculate the horizontal displacement of cantilevered and anchored retaining structures when the loading conditions of the deep foundation pit and the relevant parameters of the enclosure structure are known. A case study was conducted on a standard section with an excavation width H of 19.3 m and an excavation depth h of 17.8 m. The structural parameters of the enclosure wall, along with the elastic support stiffness coefficient and soil layer parameters of the pit, were inputted into a MATLAB calculation code. Then, four internal support constraint forces Fi and the calculated values for the horizontal displacement of the enclosure wall were obtained after running the code. The calculated curve closely matched the curve of values measured in the field. The horizontal displacements of the top of the wall of several cement–soil gravity enclosure structures mentioned in the literature were also calculated. The results of these calculations were then compared with the measured data and corresponding data from the literature. The examples provided clear evidence demonstrating that the proposed method is highly reliable for calculating the horizontal displacement of deep foundation pit enclosure structures.

[Objective] The effects of climate change on the stability of organic carbon were revealed from the perspective of the molecular structure of organic carbon in order to provide a theoretical basis for clarifying the impact of future climate change on organic carbon stability and soil fertility in mollisols. [Methods] A long-term field experiment dealing with the simulation of climate change through the use of open-top chambers (OTCs) at the Hailun Agroecological Experiment Station of the Chinese Academy of Sciences provided data for this study. We analyzed the organic carbon content and infrared spectral characteristics of various aggregates and density fractions in the 0—20 cm topsoil of black soil under three treatments: ① ambient temperature and CO2 concentration (aTaCO2); ② temperature elevated by 2 ℃ and ambient CO2 concentration (eTaCO2); ③ temperature elevated by 2 ℃ and CO2 enrichment to (700±25) μmol/mol (eTeCO2). [Results] Neither eTaCO2 nor eTeCO2 significantly affected organic carbon content of the bulk soil compared with aTaCO2 (p>0.05). However, eTaCO2 increased soil organic carbon (SOC) contents by 13.45% and 52.89% in the 0.25 mm size aggregate, but increased the -CH/C=C ratio of organic carbon in the 0.25—0.053 mm size aggregates (p

Converting a camera’s RAW image to an RGB format for human perception involves utilizing an imaging pipeline, and a series of processing modules. Existing modules often result in varying degrees of original information loss, which can render the reverse imaging pipeline unable to recover the original RAW image information. To this end, this paper proposes a new, almost reversible image imaging pipeline. Thus, RGB images and RAW images can be effectively converted between each other. Considering the impact of original information loss, this paper introduces a two-stage tone mapping operation (TMO). In the first stage, the RAW image with a linear response is transformed into an RGB color image. In the second stage, color scale mapping corrects the dynamic range of the image suitable for human perception through linear stretching, and reduces the loss of sensitive information to the human eye during the integer process. effectively preserving the original image’s dynamic information. The DCRAW imaging pipeline addresses the problem of high light overflow by directly highlighting cuts. The proposed imaging pipeline constructs an independent highlight processing module, and preserves the highlighted information of the image. The experimental results demonstrate that the two-stage tone mapping operation embedded in the imaging processing pipeline provided in this article ensures that the image output is suitable for human visual system (HVS) perception and retains more original image information.

Network alignment is an important technique with applications in diverse domains, such as social network analysis, bioinformatics, and knowledge graph construction. Many of the alignment methods rely on predefined anchor nodes, which are often unavailable in real-world scenarios. To overcome this limitation, we propose MANNA (MAtched Neighbor consistency for Network Alignment), an unsupervised approach to network alignment that exploits the concept of Matched Neighborhood Consistency (MNC). The hypothesis of MANNA is that nodes with higher similarity within their local neighborhood structures are more likely to be aligned across different networks. To learn the structural and attribute features of networks, MANNA uses a Graph Neural Network (GNN). It extracts multi-order node embeddings to capture multi-scale neighborhood features, which are then used to construct similarity matrices for the alignment process. MANNA introduces a key innovation by using pseudo-anchor nodes identified by the MNC strategy to provide self-supervised learning signals in the absence of real anchor nodes. This approach enhances the model’s ability to learn accurate network representations and improve alignment accuracy. Alignment results are iteratively refined by applying the MNC strategy, which strengthens the consistency of neighborhood structures between matched nodes. Extensive experiments on three public datasets show that MANNA outperforms existing network alignment methods.

PurposeElevated concentrations of thyroglobulin eluent is a risk factor for lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid cancer (PTC). We aimed to develop a practical nomogram based on the distribution of thyroid nodules and the presence of suspicious lateral cervical lymph nodes in fine-needle aspiration biopsies (LN-FNABs), including the cytopathology and the suspicious lateral cervical lymph node (LLN) thyroglobulin eluent (Tg), to predict the possibility of LLNM preoperatively in patients with PTC.MethodsThe clinical data of PTC patients who were admitted to the Third Affiliated Hospital of Soochow University from January 2022 to May 2023 to undergo fine-needle aspiration biopsy (FNAB) were included in this study. A total of 208 patients in 2022 served as the training set (70%), and 89 patients in 2023 served as the validation set (30%). The clinical characteristics and LN-FNAB results were collected to determine the risk factors of LLNM. A preoperative nomogram was developed for predicting LLNM based on the results of the univariate and multivariate analyses. Internal calibration, external calibration, and decision curve analysis (DCA) were performed for these models.ResultsThe multivariate logistic regression analysis showed that the maximum thyroid nodule diameter (Odds Ratio (OR) 2.323, 95% CI 1.383 to 3.904; p = 0.001), Tg level (OR 1.007, 95% CI 1.005 to 1.009; p = 0.000), Tg divided by serum thyroglobulin, (Tg/sTg) [odds ratio (OR) 1.005, 95% CI 1.001 to 1.008; p = 0.009], and cytopathology (OR 9.738, 95% CI 3.678 to 25.783; p = 0.000) (all p < 0.05) had a significant impact on the LLNM of patients with suspicious LLNs. The nomogram showed a better predictive value in both the training cohort [area under the curve, (AUC) 0.937, 95% CI 0.895 to 0.966] and the validation cohort (AUC 0.957, 95% CI 0.892 to 0.989). The nomogram also showed excellent internal and external calibration in predicting LLNM. According to the DCA, the diagnostic performance of this model was dependent on the following variables: maximum thyroid nodule diameter, Tg level, Tg/sTg, and cytopathology.ConclusionBased on the aforementioned risk factors, we believe that it is necessary to establish a personalized LLNM model for patients with PTC. Using this practical nomogram, which combines clinical and Tg risk factors, surgeons could accurately predict the possibility of LLNM preoperatively. The nomogram will also help surgeons to establish personalized treatment plans before surgery.

Abstract Wet age‐related macular degeneration (wet AMD) is the most common cause of blindness, and chronic intravitreal injection of anti‐vascular endothelial growth factor (VEGF) proteins has been the dominant therapeutic approach. Less intravitreal injection and a prolonged inter‐injection interval are the main drivers behind new wet AMD drug innovations. By rationally engineering the surface residues of a model anti‐VEGF nanobody, we obtained a series of anti‐VEGF nanobodies with identical protein structures and VEGF binding affinities, while drastically different crystallization propensities and crystal lattice structures. Among these nanobody crystals, the P212121 lattice appeared to be denser and released protein slower than the P1 lattice, while nanobody crystals embedding zinc coordination further slowed the protein release rate. The polymorphic protein crystals could be a potentially breakthrough strategy for chronic intravitreal administration of anti‐VEGF proteins.

Summary: Background: Heterologous vaccine schedules have been recommended to provide superior immunity and protection against emergent SARS-CoV-2 variants of concern. We aimed to evaluate the safety, immunogenicity, and efficacy of an mRNA COVID-19 vaccine RQ3013 compared with adenoviral vectored vaccine Ad5-nCoV and protein subunit vaccine ZF2001 as the fourth dose in adults primed with three doses of inactivated vaccines in China. Methods: We conducted a double-blinded, randomised, controlled, phase 3b trial among healthy Chinese adults at Lancang County, Yunnan, China. Adults who had received three doses of inactivated COVID-19 vaccines at least 6 months prior were randomly allocated (3:1:1) to receive heterologous boosters with RQ3013, Ad5-nCoV, or ZF2001. We assessed safety within 28 days post boost and the serum geometric mean titres (GMTs) of neutralising antibodies (NAbs) against the live SARS-CoV-2 omicron variant BA.5 on day 14 post-boost. We used Poisson regression to assess the vaccine efficacy against the first episode of virologically confirmed symptomatic COVID-19 occurring at least 7 days post boost. Subgroup analyses categorized by age and sex were also performed for safety and immunogenicity outcomes. This trial has been registered with the Chinese Clinical Trial Registry (ChiCTR2200065281) and is now complete. Findings: Between December 12 and December 18, 2022, a total of 1382 adults were screened, and 1250 were enrolled and randomly assigned to receive one dose of RQ3013 (n = 750), Ad5-nCoV (n = 250), or ZF2001 (n = 250). Although solicited adverse reactions within 28 days post boost were more frequent in the RQ3013 group (175 [23.3%]) compared to the control groups (24 [9.6%] in both the Ad5-nCOV and ZF2001 groups, P 0.05). On day 14 post-boost, RQ3013 (GMT 69.14, 95% CI 47.90–99.81) elicited 4.8-fold and 5.6-fold higher concentrations of NAbs against BA.5 than did Ad5-nCoV (14.37, 7.78–26.56) and ZF2001 (12.21, 5.13–29.06), respectively. On day 28 post-boost, RQ3013 demonstrated a relative efficacy of 62.2% (95% CI 13.7–83.1, P = 0.02) compared to Ad5-nCoV, and of 69.0% (33.5–85.7, P = 0.002) compared to ZF2001. Interpretation: The administrations of all the three heterologous boosters were well tolerated. The heterologous prime-boost regimen with RQ3013 elicited superior immune responses and demonstrated better protection against symptomatic SARS-CoV-2 infections compared with Ad5-nCoV or ZF2001, supporting the use of RQ3013 as a booster vaccination in adults. Funding: Yunnan Province Science and Technology Department (grant no.202302AA310047).

The integrated CO2 capture and conversion (iCCC) technology has been booming for carbon neutrality. Here the authors optimized the Ni–CaO composite catalyst to promote iCCC involving consecutive high-temperature Calcium-looping and dry reforming of methane and illustrated their synergistic promotions at the suitable catalyst interface.

Seismic facies analysis (SFA) is a crucial step in the interpretation of subsurface structures, with the core challenge being the development of automatic approaches for the analysis of 4D prestack seismic data. The dominant isolated learning-based SFA schemes have gained considerable attention and primarily focus on learning the best representation of prestack data and generating facies maps by clustering the extracted features. However, in isolated learning, the independent nature of feature extraction and clustering leads to the ineffectiveness of clustering loss guidance on feature extraction, thereby resulting in derived features that unnecessarily facilitate the clustering task. As an alternative, we proposed a new unsupervised, end-to-end learning-based SFA method, which is referred to as the lognormal mixture-based variational autoencoder (LMVAE) and enhanced the existing Gaussian mixture variational autoencoder-based deep clustering framework (GMVAE framework). In this approach, both the extraction and clustering of seismic features are simultaneously performed by determining from which mode of the latent mixture distribution the seismic data were generated. Furthermore, the LMVAE extends the Gaussian mixture modeling of seismic features in the GMVAE framework to lognormal mixture modeling, improving the adaptability of SFA to field data. The effective performance of the LMVAE is demonstrated in synthetic and field prestack seismic data.

Axial field flux-switching magnetic gear composite motor (AFFSMGCM) combines the advantages of axial field flux-switching permanent magnet machine (AFFSPMM) and magnetic gear (MG). In order to reduce the time and cost of design and optimization due to the complicated structure of AFFSMGCM, the nonlinear varying-network magnetic circuit (VNMC) is researched and developed for the machine. First, the topology of AFFSMGCM is introduced in details. Then, the accurate VNMC model is established with consideration of magnetic saturation and leakage flux. Finally, multi-objective optimization of AFFSMGCM based on VNMC is carried out to obtain large torque and low torque ripple. Meanwhile, the finite element analysis (FEA) method is used to validate the VNMC model. It is shown that the calculation results based on VNMC model are similar to those of FEA. The proposed VNMC improves the efficiency and accuracy of the design and optimization of the machine.

Inspired by the tremendous achievements of meta-learning in various fields, this paper proposes the local quadratic embedding learning (LQEL) algorithm for regression problems based on metric learning and neural networks (NNs). First, Mahalanobis metric learning is improved by optimizing the global consistency of the metrics between instances in the input and output space. Then, we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the constraints. Based on the hypothesis of local quadratic interpolation, the algorithm introduces two lightweight NNs; one is used to learn the coefficient matrix in the local quadratic model, and the other is implemented for weight assignment for the prediction results obtained from different local neighbors. Finally, the two sub-models are embedded in a unified regression framework, and the parameters are learned by means of a stochastic gradient descent (SGD) algorithm. The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances. Moreover, it prevents the model degradation caused by sensor drift and unmeasurable variables by modeling variable differences with the LQEL algorithm. Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.

The marginal carbon emission factor can more accurately reflect the real-time carbon emission of the system. Based on the hourly output, load and external power data of Guangdong Province from 2017 to 2020, this paper uses the seemingly unrelated regressions (SUR) model to calculate the marginal carbon emission factor and carbon displacement coefficient of Guangdong’s power industry and compares them with the average carbon emission intensity, which can provide support for the formulation of emission reduction policies in the province. The study shows that the average carbon emission factor underestimates the actual carbon emission of the power system, and the marginal carbon emission factor is a more appropriate indicator to reflect the carbon emission of the system in policy regulation. In addition, this paper also analyzes the annual, quarterly and peak–valley heterogeneity of marginal carbon emissions. The results show that the marginal carbon emission factor at peak demand times increases significantly, featuring more gas turbines participating in power generation.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are life-threatening lung diseases with high mortality rates, predominantly attributable to acute and severe pulmonary inflammation. Lomerizine (LMZ) is a calcium channel blocker previously used in preventing and treating migraine. Here, we found that LMZ inhibited inflammatory responses and lung pathological injury by reducing pulmonary edema, neutrophil infiltration and pro-inflammatory cytokine production in lipopolysaccharide (LPS)-induced ALI mice. In vitro experiments, upon treating with LMZ, the expression of interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α was attenuated in macrophages. The phosphorylation of p38 MAPK, ERK1/2, JNK, and NF-κB p65 was inhibited after LMZ treatment. Furthermore, LPS-induced Ca2+ influx was reduced by treating with LMZ, which correlated with inhibition of pro-inflammatory cytokine production. And L-type Ca2+ channel agonist Bay K8644 (BK) could restore cytokine generation. In conclusion, our study demonstrated that LMZ alleviates LPS-induced ALI and is a potential agent for treating ALI/ARDS.

An important challenge in industrial laser ablation is laser-induced damage. In this study, reduced damage was achieved through the transition of the laser distribution from a Gaussian beam to a top-hat beam using diffractive optical elements (DOE), which overcome inhomogeneous irradiation. The higher peak fluence of a Gaussian beam far exceeded the ablation threshold and led to severely melted silicon at a higher depth covering the polished texture. The top-hat beam, with uniform irradiation, had a superior ablation characteristic and created a uniform square opening with the shallow melted silicon in the lift-off process. Thus, its effective minor carrier lifetime was 15.35% less at an ablated area fraction of 2% after re-passivation because of the decreased damage. After optimizing the ablation pattern with a top-hat beam, the local contacts improved the average open-circuit voltage (Voc) and short-circuit current (Isc) values of the cells due to the decreased damage and the uniform openings, but the damage induced by a Gaussian beam was too deep and can be partly restored under back surface field (BSF) formation. The overall increment in Isc and Voc enhanced the average efficiency by 0.05% of the absolute value for the PERC cells and 0.03% of the absolute value for bi-facial PERC cells.

Abstract Hazy image obtained in the low‐light environment has the characteristics of low contrast, non‐uniform illumination, color cast and much noise. In this paper, a method is put forward which can be properly applied to recover low‐light hazy images. The original image is first decomposed into glow layer and haze layer with a modified color channel transformation for glow artifacts and color balanced. A new light segmentation function is proposed next by using gamma correction of channel difference and setting threshold levels to determine if the pixel belongs to light source regions. Then the ambient illuminance map is estimated using maximum reflectance prior to computing the atmosphere light in the light and non‐light regions. Finally, a novel nonlinear image depth estimation model is established to build the relationship between the image depth map and three image features including luminance, saturation and gradient map for the light areas. The experimental results prove that the dehazing algorithm is reliable for removing haze and glow artifacts of active light sources, reducing much noise and improving the visibility.

Glucocorticoid‐induced TNF receptor‐related (GITR) can act as a co‐stimulatory receptor, representing a potential target for safely enhancing immunotherapy efficacy. GITR is triggered by a GITR ligand or an agonist antibody and activates CD8+ and CD4+ effector T cells, reducing tumor‐infiltrating Treg numbers and resulting in activation of immune responses and tumor cell destruction by effector T cells. GITR is an attractive target for immunotherapy, especially in combination therapy with immune checkpoint inhibitors, as is being explored in clinical trials. Using H2L2 transgenic mice encoding the human immunoglobulin variable region and hybridoma technology, we generated a panel of fully human antibodies that showed excellent specific affinity and strong activation of human T cells. After conversion to fully human antibodies and engineering modification, we obtained an anti‐GITR antibody hab019e2 with enhanced antitumor activity in a B‐hGITR MC38 mouse model compared to Tab9H6V3, an anti‐GITR antibody that activates T cells and inhibits Treg suppression from XenoMouse. As a fully human antibody with its posttranslational modification hot spot removed, the hab019e2 antibody exerted more potent therapeutic effects, and may have potential as a novel and developable antibody targeting GITR for follow‐up drug studies.

In computer vision, the detection and tracking of moving objects has become a hot topic today. The target tracking technology in this paper refers to the visual tracking of the ground moving target by the aircraft during the flight. Since both the aircraft and the target are moving, there are background and two motion vectors composed of the target and the background in the acquired image. Therefore, this paper proposed a research on the location and tracking of environmental pollution sources under multi-UAV vision based on the target motion model. This paper first introduced the UAV target tracking technology, and analyzed the development history and types of UAVs in detail. Then, based on the increasingly serious environmental pollution, this paper proposed to use UAV sensing technology to locate the pollution source. Finally, in the experimental part, this paper tested the UAV flight platform, and carried out the actual operation and positioning of the pollution source. The final experimental results showed that within 0 ~ 360 s, the attitude angle obtained by the gradient descent method had no divergence phenomenon, which could effectively reduce the error caused by the integration; the inclination angle deviation of the two groups of experimental equipment was within ±2.5°, the roll angle deviation was within ±3°, and the deviation angle was relatively large at some moments, but the average deviation was only 0.8°.

Background Existing studies on cardiovascular diseases (CVDs) often focus on individual‐level behavioral risk factors, but research examining social determinants is limited. This study applies a novel machine learning approach to identify the key predictors of county‐level care costs and prevalence of CVDs (including atrial fibrillation, acute myocardial infarction, congestive heart failure, and ischemic heart disease). Methods and Results We applied the extreme gradient boosting machine learning approach to a total of 3137 counties. Data are from the Interactive Atlas of Heart Disease and Stroke and a variety of national data sets. We found that although demographic composition (eg, percentages of Black people and older adults) and risk factors (eg, smoking and physical inactivity) are among the most important predictors for inpatient care costs and CVD prevalence, contextual factors such as social vulnerability and racial and ethnic segregation are particularly important for the total and outpatient care costs. Poverty and income inequality are the major contributors to the total care costs for counties that are in nonmetro areas or have high segregation or social vulnerability levels. Racial and ethnic segregation is particularly important in shaping the total care costs for counties with low poverty rates or social vulnerability level. Demographic composition, education, and social vulnerability are consistently important across different scenarios. Conclusions The findings highlight the differences in predictors for different types of CVD cost outcomes and the importance of social determinants. Interventions directed toward areas that have been economically and socially marginalized may aid in reducing the impact of CVDs.

Summary: Background: Affordability to novel anticancer drugs has become a major health issue in China. It is encouraging to note that China initiated its drug regulatory reform and national price negotiation policies since 2015. As a growing number of domestic within-class targeted anticancer drugs are approved in China, it is expected that this may reduce the price of novel anticancer drugs and improve the affordability of anticancer drugs. This study aimed to evaluate the price, efficacy, and safety of the within-class anticancer drugs between domestic and imported drugs approved in China from 2010 to 2022. Methods: The domestic and imported within-class targeted drugs for solid cancers approved in China between 2010 and 2022 were extracted. We classified it as a class of anticancer drugs based on the same indication and similar biological mechanism. The published literature derived from pivotal clinical trials of these domestic and imported drugs was identified based on the review report and the latest labels issued by the China National Medical Products Administration. We evaluated the monthly treatment price at launch and the latest (2022), primary efficacy endpoint and safety between domestic and imported anticancer drugs. Meta-analyses were further employed to evaluate the efficacy and safety of the domestic and imported anticancer drugs, including pooled hazard ratios (HR) for progression-free survival (PFS), overall survival (OS), objective response rates (ORR) for solid cancers, and relative risk for serious adverse events (SAE) and Grade ≥3 adverse events (AEs). Findings: In our cohort study, 12 within-class anticancer drugs with 7 cancer diseases were analyzed, including 18 domestic (21 indications; 21 pivotal trials) and 18 imported (21 indications; 27 pivotal trials) novel anticancer drugs, respectively. The median monthly treatment price of domestic and imported drugs from the years of launch to 2022 had significantly decreased by 71% and 62%, respectively. Moreover, the median monthly treatment price of domestic targeted anticancer drugs on the market at launch ($3786 vs. $5393, P = 0.007) and the latest ($1222 vs. $2077, P = 0.011) was significantly lower than that of imported drugs. No significant differences in median PFS gains (9.0 vs. 11.0 months; P = 0.24), OS gains (9.3 vs 10.6 months; P = 0.66), and ORR (57% vs 62%, P = 0.77) of targeted anticancer drugs in their pivotal trials were observed between the domestic and imported drugs. Additionally, there was no significant difference between domestic and imported drugs in the incidence of SAE (23% vs. 24%; P = 0.41) and Grade ≥3 AEs (59% vs. 57%; P = 0.45). These findings were also further confirmed in the meta-analyses for primary efficacy endpoints and safety outcomes. Interpretation: The prices of both domestic and imported anticancer drugs significantly decreased after market entry mainly due to the role of national price negotiations. The median monthly treatment price of domestic within-class targeted anticancer drugs was significantly lower than that of imported drugs. Furthermore, the efficacy and safety of domestic anticancer drugs were comparable to that of imported drugs. This evidence implicated that the development of within-class anticancer drugs with national price negotiations in China significantly improved the affordability for patients. Funding: This study was supported by postdoctoral fellowship from Tsinghua-Peking Joint Centers for Life Sciences (CLS).

Anaerobic activated sludge plays a key role in the anaerobic digestion (AD) treatment of wastewater. The ability of anaerobic activated sludge to endure stress shock determines the performance of AD. In this study, the resistance of anaerobic activated sludge acclimated by three feeding patterns (continuous, semi-continuous, and pulse) to four stress shocks, including low pH influent, high OLR (organic loading rate), high ammonium and high sulfate, was investigated respectively. The results showed that the anaerobic activated sludge acclimated by semi-continuous feeding had the best resistance to high OLR shock, followed by pulse feeding, and then continuous feeding. There was no significant difference in the resistance of the three activated sludge to the other stress shocks. Under stress shock, the microbial community structure and abundance of specific functional microorganisms in the activated sludge acclimated by different feeding patterns varied, while the relative abundance of Methanosarcinaceae in the anaerobic activated sludge increased. The variation in the relative abundance of specific functional microorganisms was in charge of the differences in the resistance of anaerobic activated sludge. Overall, the results presented herein provide reference for improving the stability and effectiveness of activated sludge under adverse conditions. HIGHLIGHTS Resistance of activated sludge acclimated by three feeding patterns was studied.; Acclimated activated sludge was well resistant to low pH influent and high sulfate.; Sludge acclimated by semi-continuous feeding can best resist to high OLR shock.; The relative abundance of Methanosarcinaceae increased under stress shock.; Differences in functional microbial abundance led to different resistance of sludge.;

When present in the cytosol, a variety of peptides encoded by the distal carboxyl-termini of the L-type CaV1 calcium channels downregulate channel activity and neurite outgrowth.

How mechanical forces drive fluid transport in the kidney remains unclear. Here, the authors use a microfluidic platform to show that kidney epithelial cells generate hydraulic pressure gradients across the epithelium, and that the fluid flux is from apical to basal for normal cells, and inverted in autosomal dominant polycystic kidney disease cells.

In this paper, we propose a distributed multi-agent reinforcement learning (MARL) method to learn cooperative searching and tracking policies for multiple unmanned aerial vehicles (UAVs) with limited sensing range and communication ability. Firstly, we describe the system model for multi-UAV cooperative searching and tracking for moving targets and consider average observation rate and average exploration rate as the metrics. Moreover, we propose the information update and fusion mechanisms to enhance environment perception ability of the multi-UAV system. Then, the details of our method are demonstrated, including observation and action space representation, reward function design and training framework based on multi-agent proximal policy optimization (MAPPO). The simulation results have shown that our method has well convergence performance and outperforms other baseline algorithms in terms of average observation rate and average exploration rate.

Bloodstream infection is a major health problem worldwide, with extremely high mortality. Detecting infection in the early stage is challenging due to the extremely low concentration of bacteria in the blood. Digital PCR provides unparalleled sensitivity and can achieve absolute quantification, but it is time-consuming. Moreover, the presence of unavoidable background signals in negative controls poses a significant challenge for single-molecule detection. Here, we propose a novel strategy called “Ultrafast flexible thin tube-based droplet digital PCR (utPCR)” that can shorten the digital PCR process from 2 h to only 5 min, with primer annealing/extension time reduced from minutes to only 5 s. Importantly, the ultrafast PCR eliminates nonspecific amplification and thus enables single-molecule detection. The utPCR enabled the sensitive detection and digital quantification of E. coli O157 in the high background of a 106-fold excess of E. coli K12 cells. Moreover, this method also displayed the potential to detect rare pathogens in blood samples, and the limit of detection (LOD) could be as low as 10 CFU per mL of blood without false positive results. Considered ultrafast (

Abstract Background COVID-19 is an infectious disease characterized by multiple respiratory and extrapulmonary manifestations, including gastrointestinal symptoms. Although recent studies have linked gut microbiota to infectious diseases such as influenza, little is known about the role of the gut microbiota in COVID-19 pathophysiology. Methods To better understand the host-gut microbiota interactions in COVID-19, we characterized the gut microbial community and gut barrier function using metagenomic and metaproteomic approaches in 63 COVID-19 patients and 8 non-infected controls. Both immunohematological parameters and transcriptional profiles were measured to reflect the immune response in COVID-19 patients. Results Altered gut microbial composition was observed in COVID-19 patients, which was characterized by decreased commensal species and increased opportunistic pathogenic species. Severe illness was associated with higher abundance of four microbial species (i.e., Burkholderia contaminans, Bacteroides nordii, Bifidobacterium longum, and Blautia sp. CAG 257), six microbial pathways (e.g., glycolysis and fermentation), and 10 virulence genes. These severity-related microbial features were further associated with host immune response. For example, the abundance of Bu. contaminans was associated with higher levels of inflammation biomarkers and lower levels of immune cells. Furthermore, human-origin proteins identified from both blood and fecal samples suggested gut barrier dysfunction in COVID-19 patients. The circulating levels of lipopolysaccharide-binding protein increased in patients with severe illness and were associated with circulating inflammation biomarkers and immune cells. Besides, proteins of disease-related bacteria (e.g., B. longum) were detectable in blood samples from patients. Conclusions Our results suggest that the dysbiosis of the gut microbiome and the dysfunction of the gut barrier might play a role in the pathophysiology of COVID-19 by affecting host immune homeostasis.

Abstract Background Diosmetin is a bioflavonoid compound naturally abundant in citrus fruits. It is found to perform a variety of activities, while its antitumor property in osteosarcoma, a malignant tumor with unmet clinical treatment, remained unknown. Methods Colony formation assay, cell cycle analysis and apoptosis analysis were conducted respectively to observe the effect of diosmetin on cell proliferation and apoptosis in human osteosarcoma cells. Western blot and immunoprecipitation were used to detect the expression of apoptotic molecules and activation of STAT3/c-Myc pathway in Saos-2 and U2SO cells. Results Diosmetin significantly inhibited cell proliferation, induced cell cycle arrest at G2/M phase and promoted cell apoptosis in both Saos-2 and U2SO cells. Moreover, Diosmetin downregulated the expression of anti-apoptotic protein Bcl-xL while upregulated the levels of pro-apoptotic proteins including cleaved Caspase-3, cleaved-PARP and Bax. Furthermore, diosmetin dose-dependently inhibited STAT3 phosphorylation, reduced the expression of its downstream protein c-Myc and impeded the interaction between STAT3 molecules. Conclusions These results suggest that diosmetin exerts anti-osteosarcoma effects by suppressing cell proliferation and inducing apoptosis via inhibiting the activation of STAT3/c-Myc signaling pathway, which provide the possibility for diosmetin to be a chemotherapeutic candidate for osteosarcoma.

Abstract Background GPRIN1 may be a novel tumor regulator, but its role and mechanism in tumors are still unclear. Methods First, a pan‐cancer correlation analysis was conducted on the expression and prognosis of GPRIN1 based on the data downloaded from The Cancer Genome Atlas (TCGA) database. Second, the Starbase database was used to predict the upstream miRNAs and lncRNAs of GPRIN1, and the expression analysis, survival analysis, and correlation analysis were performed to screen the microRNA (miRNAs)/long non‐coding RNAs (lncRNAs) that had a correlation with kidney renal papillary cell carcinoma (KIRP) or lung adenocarcinoma (LUAD). Third, the CIBERSORT algorithm was employed to calculate the proportion of various types of immune cells, and then the R packages were used for evaluating the relation between GPRIN1 expression and tumor immune cell infiltration as well as between GPRIN1 and the immune cell biomarker. Finally, the correlation analysis was made on GPRIN1 and immune checkpoints (CD274, CTLA4, and PDCD1). Results The pan‐cancer analysis suggested that GPRIN1 was up‐expressed in KIRP and LUAD, and it correlated with poor prognosis. LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG‐miR‐140‐3p was likely to be the most promising upstream regulation pathway of GPRIN1. Upexpression of LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG and downexpression of miR‐140‐3p were found relevant with poor outcomes of KIRP and LUAD. GPRIN1 expression was significantly correlated with tumor immune cell infiltration, immune cell biomarkers, and immune checkpoints. Conclusions The competitive endogenous (ceRNA) of miR‐140‐3p‐GPRIN1 axis and its upstream lncRNAs are closely related to KIRP and LUAD, and might affect the prognosis and therapeutic effect of KIRP and LUAD.