Search

With the rapid development of uncrewed aerial vehicle (UAV) technology, detecting aerial images has found significant applications across various domains. However, existing algorithms overlook the impact of illumination on target detection, resulting in less satisfactory detection performance under low-light conditions. We propose EfficientFuseDet, a visible and infrared image fusion detection network to overcome this issue. First, an effective multilevel cross-modal fusion network called EfficientFuse is presented to combine complementary information from both modalities better. EfficientFuse captures local dependencies and global contextual information in shallow and deep layers, seamlessly combining complimentary local and global features throughout the network. The generated fused images can exhibit clear target contours and abundant texture information. Second, we propose a detection network called AFI-YOLO, which employs an inverted residual vision transformer backbone (IRViT) to effectively address the challenges associated with background interference in fused images. We design an efficient feature pyramid network (EFPN) that efficiently integrates multiscale information, enhancing multiscale detection capability using aerial images. A reparameterization decoupling head (RepHead) is proposed to further improve target classification and localization precision. Finally, experiments on the DroneVehicle dataset indicate that the detection accuracy using fused images can reach 47.2 %, which is higher than that observed with visible light images of 45 %. Compared to state-of-the-art detection algorithms, EfficientFuseDet exhibits a slight decrease in speed. However, it demonstrates superior detection capabilities and effectively enhances the detection accuracy using aerial images under low-light conditions.

Hepatocellular carcinoma (HCC) stands as the sixth most prevalent cancer and the third leading cause of cancer mortality globally. Despite surgical resection being the preferred approach for early-stage HCC, most patients are diagnosed at intermediate to advanced stages, limiting treatment options to chemotherapy and immunotherapy, which often yield poor outcomes. Extracellular vesicles (EVs), minute lipid-bilayered particles released by diverse cells under various physiological and pathological conditions, are crucial for mediating communication between cells. Mounting evidence indicates that EVs sourced from different cells can profoundly influence the HCC tumor microenvironment (TME), thereby affecting the progression of HCC. Given their immunogenicity and liver-targeting properties, these EVs not only hold promise for HCC treatment but also provide avenues for advancing early diagnostic methods and assessing prognosis. This review not only describes the function of EVs within the HCC tumor microenvironment but also analyzes their therapeutic advantages and explores their significance in various therapeutic approaches for HCC, including chemotherapy, immunotherapy, combination therapy, and their role as innovative drug delivery carriers. Furthermore, it highlights the potential of EVs as biomarkers for the diagnosis and prognosis of HCC.

The exploration of efficient lead-free perovskite photoelectric active materials to develop high-performance photoelectrochemical (PEC) systems in aqueous solution is crucial to expand their PEC applications. Herein, we successfully constructed a high-performance PEC platform using ligand-free perovskite Cs2PdBr6 microcrystals (MCs) as the photoactive substance. The Cs2PdBr6 MCs showed narrow bandgap, wide absorption range, high electronic mobility and good stability in aqueous solutions. Particularly, the Cs2PdBr6 MCs exhibited an excellent photoresponse, the photocurrent density could reach as high as 98 μA/cm2 under 10.18 mW/cm2 light irradiation in the absence of other electron acceptors. In addition of the extremely wide range of response wavelength, wide pH range and accelerated interfacial carrier transfer, the Cs2PdBr6 MCs demonstrated the significant potential of photocathode active material for applications in PEC sensors and optoelectronic devices. Therefore, this work indicates that Cs2PdBr6 MCs design is a highly efficient way to solve the intrinsic issues of perovskite material, predicting a promising strategy for high performance PEC application in aqueous ambience.

Primary aldosteronism (PA), often due to aldosteronoma, commonly causes secondary hypertension and typically requires surgery. We present a case of an elderly man with longstanding hypertension, complicated by cerebral hemorrhage and myocardial infarction. Enhanced CT imaging identified a right-sided aldosteronoma and left adrenal gland fullness. Combined with upright supine aldosterone ratio, captopril challenge test, bilateral adrenal venous sampling, and CYP11B1/CYP11B2 fusion gene testing, the diagnosis of PA was confirmed. Despite the absence of surgical intervention in this patient, pharmacotherapy effectively managed hypertension and enhanced cardiac function, thereby underscoring the advantageous utilization of aldosterone antagonists in non-surgical candidates diagnosed with PA.

Artemisia argyi leaf polysaccharide (AALPs) were prepared through ultrasound-assisted extraction (UAE), and their antifatigue activities were evaluated. Extraction was optimized using response surface methodology (RSM), which yielded the following optimal UAE conditions: ultrasonication power of 300 W, extraction temperature of 51 °C, liquid:solid ratio of 20 mL/g, and ultrasonication time of 47 mins. The above optimal conditions resulted in the maximum extraction rate of 10.49 %. Compared with hot water extraction (HWE), UAE supported higher yields and total sugar, uronic acid, and sulfate contents of AALPs. Meanwhile, AALP prepared through UAE (AALP-U) exhibited higher stability due to its smaller particle size and higher absolute value of zeta potential than AALP prepared through HWE (AALP-H). In addition, AALP-U demonstrated stronger antioxidant activity than AALP-H. In forced swimming tests on mice, AALP-U could significantly prolong swimming time with a dose-dependent effect, increase liver and muscle glycogen levels, and improve other biochemical indices, thus showing great potential for application in functional food.

Abstract Solar desalination is a promising strategy to utilize solar energy to purify saline water. However, the accumulation of salt on the solar evaporator surface severely reduces light absorption and evaporation performance. Herein, a simple and eco‐friendly method to fabricate a 3D gradient graphene spiral sponge (GGS sponge) is presented that enables high‐rate solar evaporation and zero liquid discharge (ZLD) desalination of high‐salinity brine. The spiral structure of the GGS sponge enhances energy recovery, while the gradient network structures facilitate radial brine transport and directional salt crystallization, which cooperate to endow the sponge with superior solar evaporation (6.5 kg m−2 h−1 for 20 wt.% brine), efficient salt collection (1.5 kg m−2 h−1 for 20 wt.% brine), ZLD desalination, and long‐term durability (continuous 144 h in 20 wt.% brine). Moreover, the GGS sponge shows an ultrahigh freshwater production rate of 3.1 kg m−2 h−1 during the outdoor desalination tests. A continuous desalination–irrigation system based on the GGS sponge for crop growth, which has the potential for self‐sustainable agriculture in remote areas is demonstrated. This work introduces a novel evaporator design and also provides insight into the structural principles for designing next‐generation solar desalination devices that are salt‐tolerant and highly efficient.

Glucose and lipid metabolism disorders are the core pathological mechanism of a variety of metabolic diseases, and the incidence of related diseases is increasing year by year, which seriously threatens human life and health. Traditional Chinese medicine with medicinal and edible properties refers to Chinese medicinal resources that have both medicinal and edible characteristics. Due to its safety and its health-promoting and medicinal functions, traditional Chinese medicine has received increasing attention in the development of functional health foods. Phenolic acids are important secondary metabolites that are ubiquitous in medicinal and edible homologous plants, and the regulation of glycolipid metabolism is an important activity and plays a key role in many diseases. In this paper, we focus on the alleviation of glycolipid disorders using MEHH phenolic acids, which regulate glucose metabolism and lipid metabolism, improve insulin resistance, inhibit inflammatory responses, alleviate oxidative stress, and regulate intestinal flora; additionally, we summarize the mechanism in order to provide a reference for MEHH phenolic acids in the treatment of glycolipid metabolism diseases.

BackgroundDespite the rapid increase in the global prevalence of Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD), there are no approved therapeutic drugs for MAFLD yet. Nutrient supplementation might mitigate the risk of MAFLD. It is more typical for individuals to consume multiple nutrients simultaneously. However, the studies exploring the combined effects of multiple nutrients on MAFLD are limited. This study aimed to investigate the relationship between both individual nutrients and their combined influence on the risk of MAFLD.MethodsData were obtained from National Health and Nutrition Examination Survey (NHANES), and 18 types of nutrients were considered in this study. Logistic regression analysis was performed to evaluate the correlation between single nutrients and the risk of MAFLD. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to pinpoint the most relevant nutrient associated with the risk of MAFLD. Subsequently, both Weighted Quantile Sum (WQS) regression and Quantile g-computation (Qgcomp) were used to assess the combined effects of multiple nutrients on the risk of MAFLD.ResultsA total of 3,069 participants were included in this study. LASSO regression analysis showed that Se, α-tocopherol, and γ-tocopherol exhibited a positive association with the risk of MAFLD. In contrast, the serum levels of Co, P, α-cryptoxanthin, LZ, and trans-β-carotene were inversely associated with the prevalence of MAFLD. When Se and two types of vitamin E were excluded, the WQS index showed a significant inverse relationship between the remaining 15 nutrients and the risk of MAFLD; α-cryptoxanthin showed the most substantial contribution. Similarly, Qgcomp suggested that the combined effects of these 15 nutrients were associated with a lower risk of MAFLD, with α-cryptoxanthin possessing the most significant negative weights.ConclusionThis study suggested that the complex nutrients with either a low proportion of Se, α-tocopherol, and γ-tocopherol or without them should be recommended for patients with MAFLD to reduce its risk.

Aldo-Keto Reductase Family 1 Member C3 (AKR1C3), also known as type 5 17β-hydroxysteroid dehydrogenase (17β-HSD5) or prostaglandin F (PGF) synthase, functions as a pivotal enzyme in androgen biosynthesis. It catalyzes the conversion of weak androgens, estrone (a weak estrogen), and PGD2 into potent androgens (testosterone and 5α-dihydrotestosterone), 17β-estradiol (a potent estrogen), and 11β-PGF2α, respectively. Elevated levels of AKR1C3 activate androgen receptor (AR) signaling pathway, contributing to tumor recurrence and imparting resistance to cancer therapies. The overexpression of AKR1C3 serves as an oncogenic factor, promoting carcinoma cell proliferation, invasion, and metastasis, and is correlated with unfavorable prognosis and overall survival in carcinoma patients. Inhibiting AKR1C3 has demonstrated potent efficacy in suppressing tumor progression and overcoming treatment resistance. As a result, the development and design of AKR1C3 inhibitors have garnered increasing interest among researchers, with significant progress witnessed in recent years. Novel AKR1C3 inhibitors, including natural products and analogues of existing drugs designed based on their structures and frameworks, continue to be discovered and developed in laboratories worldwide. The AKR1C3 enzyme has emerged as a key player in carcinoma progression and therapeutic resistance, posing challenges in cancer treatment. This review aims to provide a comprehensive analysis of AKR1C3’s role in carcinoma development, its implications in therapeutic resistance, and recent advancements in the development of AKR1C3 inhibitors for tumor therapies.

Microscopic examination of visible components based on micrographs is the gold standard for testing in biomedical research and clinical diagnosis. The application of object detection technology in bioimages not only improves the efficiency of the analyst but also provides decision support to ensure the objectivity and consistency of diagnosis. However, the lack of large annotated datasets is a significant impediment in rapidly deploying object detection models for microscopic formed elements detection. Standard augmentation methods used in object detection are not appropriate because they are prone to destroy the original micro-morphological information to produce counterintuitive micrographs, which is not conducive to build the trust of analysts in the intelligent system. Here, we propose a feature activation map-guided boosting mechanism dedicated to microscopic object detection to improve data efficiency. Our results show that the boosting mechanism provides solid gains in the object detection model deployed for microscopic formed elements detection. After image augmentation, the mean Average Precision (mAP) of baseline and strong baseline of the Chinese herbal medicine micrograph dataset are increased by 16.3% and 5.8% respectively. Similarly, on the urine sediment dataset, the boosting mechanism resulted in an improvement of 8.0% and 2.6% in mAP of the baseline and strong baseline maps respectively. Moreover, the method shows strong generalizability and can be easily integrated into any main-stream object detection model. The performance enhancement is interpretable, making it more suitable for microscopic biomedical applications.

Carbon-neutral architectural design focuses on rationally utilizing the building’s surroundings to reduce its environmental impact. Resilient ventilation systems, developed according to the thermal comfort requirements of building energy-saving research, have few applications. We studied the Jin-an Shopping Mall in Harbin and established the middle point height (h), middle point horizontal location (d), roof angle (α), and exposure to floor ratio (k) as the morphological parameters of the atrium. Using computational fluid dynamics (CFD), the mean radiant temperature (MRT), and the universal thermal climate index calculations (UTCI), this program was set to switch off air conditioning when the resilient ventilation met the thermal comfort requirement to achieve energy savings. The energy-saving efficiency (U) was calculated based on the energy consumption of the original model, and U could reach 7.34–9.64% according to the simulation and prediction. This study provides methods and a theoretical basis for renovating other commercial complexes to improve comfort and control energy consumption.

Iris typhifolia Kitag is a perennial herbaceous species with high ornamental and applied value. Elucidating the mechanism of saline–alkali tolerance in Iris is crucial for their promotion in saline–alkali areas. Saline–alkali stress is one of the factors that affects plant growth, which has become a significant global issue. In this study, we measured the physiological and biochemical indexes of I. typhifolia, through germination and potting trials, to evaluate the resistance of I. typhifolia to different levels of artificial saline–alkali stress (0, 50, 100, 150, and 200 mmol·L−1). The results showed that artificial saline–alkali stress negatively impacted germination parameters, cell membrane integrity, and photosynthetic parameters. Different trends in osmoregulatory substances and endogenous hormones were observed. It was shown that I. typhifolia had a potential adaptability to the saline–alkali environment by enhancing its internal defense mechanism. Based on regression analyses, the germination threshold of I. typhifolia was calculated to be 87.15 mmol·L−1, which provided a theoretical basis for the application in soil saline–alkalization areas.

This article aims to precisely evaluate the catalytic impact of digital inclusive finance on economic growth, enhance the implementation of policies pertaining to digital inclusive finance, and foster high-quality economic development. Based on China's provincial panel data and the digital inclusive finance index from 2011 to 2021, this research investigates the influence of digital inclusive finance on high-quality economic development and the associated underlying mechanisms. The findings suggest that digital inclusive finance exerts a notable spatial impact on high-quality economic development. Moreover, there is heterogeneity in the spatial effects between different dimensions of digital inclusive finance and high-quality economic development. Through the threshold model and intermediary effect model, it is found that the Internet penetration rate has a dual-threshold effect on the impact of digital inclusive finance on promoting high-quality economic development. Specifically, digital inclusive finance contributes to elevating the level of high-quality economic development through its role in promoting the transformation of consumption structure. The findings of this study offer valuable insights for countries aiming to attain high-quality economic development through the enhancement of digital inclusive finance.

Abstract Objective: To assess the relationship between dietary intake of α-carotene, β-carotene, β-cryptoxanthin, lycopene and lutein+zeaxanthin (LZ) and occurrence of metabolic dysfunction-associated fatty liver disease (MAFLD). Design: Cross-sectional study design. The MAFLD diagnosis was based on hepatic steatosis and metabolic dysregulation. Carotenoid intake was adjusted for using an energy-adjusted model. Logistic regression and restricted cubic spline (RCS) analyses were used to assess the relationships, with sensitivity analysis to validate the findings. Weighted quantile sum regression (WQS) was used to explore the combined effect of these carotenoids on MAFLD. Subgroup analyses were conducted to identify population-specific associations. Setting: National Health and Nutrition Examination Survey (NHANES) 2017–March 2020. Participants: This study included 5098 individuals aged 18 years and older. Results: After adjusting for potential confounders, a weak inverse association was observed between α-carotene and β-carotene intakes and MAFLD occurrence (all P value

This paper deals with the semigroup generation of anti-triangular operator matrices M with unbounded entries in Hilbert space. Based on the space decomposition, some necessary and sufficient conditions are given for M to generate contraction semigroups. In addition, the anti-triangular differential system, converted from the damping wave equation, is used to explain our work, and it is proved that the corresponding anti-triangular operator matrix satisfies the conditions and generates a contraction semigroup.

Abstract We compare the Hilbert series approach with explicit constructions of higher-order Lagrangians for the O(N) nonlinear sigma model. We use the Hilbert series to find the number and type of operators up to mass dimension 16, for spacetime dimension D up to 12 and N up to 12, and further classify the operators into spacetime parity and parity of the internal symmetry group O(N). The explicit construction of operators is done up to mass dimension 12 for both parities even and dimension 10 for the other three cases. The results of the two methods are in full agreement. This provides evidence for the Hilbert series conjecture regarding co-closed but not co-exact k-forms, which takes into account the integration-by-parts relations.

In recent years, caffeic acid and its derivatives have received increasing attention due to their obvious physiological activities and wide distribution in nature. In this paper, to clarify the status of research on plant-derived caffeic acid and its derivatives, nuclear magnetic resonance spectroscopy data and possible biosynthetic pathways of these compounds were collected from scientific databases (SciFinder, PubMed and China Knowledge). According to different types of substituents, 17 caffeic acid and its derivatives can be divided into the following classes: caffeoyl ester derivatives, caffeyltartaric acid, caffeic acid amide derivatives, caffeoyl shikimic acid, caffeoyl quinic acid, caffeoyl danshens and caffeoyl glycoside. Generalization of their 13C-NMR and 1H-NMR data revealed that acylation with caffeic acid to form esters involves acylation shifts, which increase the chemical shift values of the corresponding carbons and decrease the chemical shift values of the corresponding carbons of caffeoyl. Once the hydroxyl group is ester, the hydrogen signal connected to the same carbon shifts to the low field (1.1~1.6). The biosynthetic pathways were summarized, and it was found that caffeic acid and its derivatives are first synthesized in plants through the shikimic acid pathway, in which phenylalanine is deaminated to cinnamic acid and then transformed into caffeic acid and its derivatives. The purpose of this review is to provide a reference for further research on the rapid structural identification and biofabrication of caffeic acid and its derivatives.

As the impact of climate change intensifies, meeting the energy demand of buildings in China’s cold regions is becoming increasingly challenging, particularly in terms of cooling energy consumption. The effectiveness of integrating phase change material (PCM) into building envelopes for energy saving in China’s cold regions is unclear. The aim of this study is to assess the effectiveness of PCM integration in building enclosures for energy efficiency in these regions. The research monitored and recorded indoor temperature data from typical residential cases from May to September. This measured data was then used to validate the accuracy of EnergyPlus22-1 software simulation models. Subsequently, the calibrated model was utilized to conduct a comparative analysis on the effects of PCM on indoor temperatures and cooling energy consumption across these regions. The results of these comparative analyses indicated that PCM can alleviate indoor overheating to varying degrees in severe cold regions of China. Focusing on north-facing bedrooms, applying PCMs reduced the duration of overheating in non-air-conditioned buildings in severe cold regions of China by 136 h (Yichun), 340 h (Harbin), 356 h (Shenyang), and 153 h (Dalian). In terms of cooling energy consumption, the energy saved by applying PCMs ranged from 1.48 to 13.83 kWh/m2. These results emphasize that the performance of PCM varies with climate change, with the most significant energy-saving effects observed in severe cold regions. In north-facing bedrooms in Harbin, the energy-saving rate was as high as 60.30%. Based on these results, the study offers guidance and recommendations for feasible passive energy-saving strategies for buildings in severe cold and cold regions of China in the face of climate change. Additionally, it provides practical guidance for applying PCMs in different climatic zones in China.

Due to global warming, the overheating risk of residential buildings (especially with no air conditioning) in severe cold and cold regions of China is attracting increasing attention. This phenomenon will seriously affect people’s comfort and even health. Fewer studies and Chinese local building standards, however, are related to overheating in such region, which were not considered overheating in the past. It is not clear whether integrating phase change materials (PCM), which has been considered as a useful method to improve thermal comfort, into building envelopes can effectively alleviate the overheating phenomenon in the severe cold and cold regions. This paper aims to reveal the overheating risk by monitoring four residential buildings, and to verify the effectiveness of using PCM to alleviate the overheating in these regions through simulation. The results indicate a significant overheating risk. During the summertime (May to September), overheating hours were recorded for 395, 618, 857 and 713 h out of total 3672 h (accounted for 25.82%, 40.39%, 56.01% and 46.60%) in the south-facing bedrooms of Yichun, Harbin, Shenyang and Dalian respectively. The study finds that PCM reduces overheating hours in south-facing bedrooms in Yichun, Harbin, Shenyang and Dalian by 19.66%, 17.95%, 15.88% and 10.87%, and saves cooling energy by 25.67%, 15.36%, 14.61%, 15.34% respectively. This paper proposes a standard for evaluation and practical guidance for policy makers on reducing indoor overheating in severe cold and cold regions of China.

Introduction: Microglia and macrophages participate in hematoma clearance after intracerebral hemorrhage (ICH), thereby facilitating tissue restoration and neurological recovery. Triggering receptor expressed on myeloid cells 2 (Trem2) has been indicated as a major pathology-induced immune signaling hub on the microglial/macrophage surface. Soluble Trem2 (sTrem2), the proteolytic form of Trem2, is abundant in the body fluid and is positively correlated with the pathological process. Objectives: In the present study, we aimed to investigate the potential role of sTrem2 in hematoma resolution after ICH and to elucidate its underlying mechanisms. Methods: We explored the biological functions of sTrem2 in the murine ICH brain by stereotaxic injection of recombinant sTrem2 protein or by adeno-associated virus-mediated expression. Erythrocyte phagocytosis was assessed using flow cytometry and immunofluorescence. Western blotting was performed to evaluate protein expression. Changes in behavior, sTrem2-induced down-stream pathway, and microglia were examined. Results: sTrem2 impedes hematoma resolution and impairs functional motor and sensory recovery. Interestingly, sTrem2 bypasses full-length Trem2, negatively regulating microglial/macrophage erythrophagocytosis, and promotes an inflammatory phenotype, which is associated with reduced retromer levels and impaired recycling of the pro-erythrophagocytic receptor CD36. Rescue of retromer Vps35 abolishes the phagocytosis-inhibiting effects and lysosome-dependent CD36 degradation caused by sTrem2. Conclusion: These findings indicate sTrem2 as a negative factor against microglia/macrophage-mediated hematoma and related neuronal damage clearance, provide insight into the mechanisms by which erythrophagocytosis is regulated and how it may be impaired after ICH, and suggest that the anti-proteolytic activity of Trem2 can be explored for ICH therapy.

Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy. The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks (GNN) models on the internal test set. Furthermore, it also exhibited much better performance on two external test sets, suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity. Then, a naïve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms, and the consistency of the interpretability results for 5 typical epidermal growth factor receptor (EGFR) inhibitors with their structure‒activity relationships could be observed. Finally, a free online web server called KIP was developed to predict the kinome-wide polypharmacology effects of small molecules (http://cadd.zju.edu.cn/kip).

Abstract Background Loss of G2-specific E3-like (G2E3) protein sensitizes tumor cells to chemotherapy. However, the role of G2E3 in breast cancer development and patient’s prognosis is unclear. Here, we explored the expression, prognostic significance, and regulatory pathway of G2E3 in breast cancer. Methods TCGA and UALCAN database were utilized to explore G2E3 expression in breast cancer and normal tissues and its expression in breast cancer based on clinicopathological characteristics, respectively. The Kaplan–Meier plotter database was utilized to determine the effect of G2E3 on the prognosis of breast cancer patients. RT-PCR was utilized to validate the G2E3 expression in cancerous and normal breast tissues. Immunohistochemistry analysis was utilized to validate the prognostic effect of G2E3 expression in breast cancer patients and the relationship between G2E3 expression and lymphocyte infiltration levels. Receiver operating characteristic (ROC) curves were also generated to validate the diagnostic value of G2E3 expression in recurrence/distant organ metastasis and death. The STRING database, DAVID database, and Sanger-box tools were utilized to perform GO functional, KEGG pathway enrichment, and GSEA analysis. The TISIDB database was utilized to determine the relationship between G2E3 expression and tumor immunity. Finally, CTD database was utilized to screen for potential therapeutic compounds that could reduce the G2E3 mRNA expression. Results TCGA data presented that G2E3 expression was higher in breast cancer tissues than in normal breast tissues. This result was further validated by RT-PCR (P = 0.003). The Kaplan–Meier plotter database suggested that patients with high G2E3 mRNA expression had significantly shorter RFS and OS than patients with low G2E3 mRNA expression. Immunohistochemistry analysis of 156 breast cancer clinical specimens also validated patients with G2E3-positive expression had a significantly shorter DFS and OS than patients with G2E3-negative expression. Thus, G2E3 expression was an independent prognostic predictor of DFS and OS. The G2E3-positive expression also has a high diagnostic value for recurrence/distant organ metastasis and death. GSEA analysis revealed that G2E3 might be enriched in the E2F, PI3K/AKT/mTOR signaling, DNA repair pathways, and other cancer-related signaling pathways. The TISIDB database showed that G2E3 expression was significantly negatively associated with lymphocyte infiltration. This result was further validated in clinical breast cancer samples (P = 0.048; R = −0.158). Using the CTD database, we found that (+)-JQ1 compound, 1,2-dimethylhydrazine, and other compounds may decrease the G2E3 mRNA expression. These compounds could serve as potential therapeutic compounds for the clinical treatment of breast cancer. Conclusions G2E3 expression was higher in breast cancer tissues than in normal tissues. G2E3-positive expression was related to a worse survival outcome in patients with breast cancer. Genes co-expressed with G2E3 may be enriched in the breast cancer-related signaling pathways. The G2E3 expression was significantly negatively associated with lymphocyte infiltration. G2E3 may serve as a novel prognostic biomarker and therapeutic target for breast cancer.

Abstract As an important member of ion channels family, the voltage-gated sodium channel (VGSC/Nav) is associated with a variety of diseases, including epilepsy, migraine, ataxia, etc., and has always been a hot target for drug design and discovery. Many subtype-selective modulators targeting VGSCs have been reported, and some of them have been approved for clinical applications. However, the drug design resources related to VGSCs are insufficient, especially the lack of accurate and extensive compound data toward VGSCs. To fulfill this demand, we develop the Voltage-gated Sodium Channels Database (VGSC-DB). VGSC-DB is the first open-source database for VGSCs, which provides open access to 6055 data records, including 3396 compounds from 173 references toward nine subtypes of Navs (Nav1.1 ~ Nav1.9). A total of 28 items of information is included in each data record, including the chemical structure, biological activity (IC50/EC50), target, binding site, organism, chemical and physical properties, etc. VGSC-DB collects the data from small-molecule compounds, toxins and various derivatives. Users can search the information of compounds by text or structure, and the advanced search function is also supported to realize batch query. VGSC-DB is freely accessible at http://cadd.zju.edu.cn/vgsc/ , and all the data can be downloaded in XLSX/SDF file formats. Graphical Abstract

Abstract To improve the performance of data-driven reaction prediction models, we propose an intelligent strategy for predicting reaction products using available data and increasing the sample size using fake data augmentation. In this research, fake data sets were created and augmented with raw data for constructing virtual training models. Fake reaction datasets were created by replacing some functional groups, i.e., in the data analysis strategy, the fake data as compounds with modified functional groups to increase the amount of data for reaction prediction. This approach was tested on five different reactions, and the results show improvements over other relevant techniques with increased model predictivity. Furthermore, we evaluated this method in different models, confirming the generality of virtual data augmentation. In summary, virtual data augmentation can be used as an effective measure to solve the problem of insufficient data and significantly improve the performance of reaction prediction.

Abstract Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to judge the generated reactions. More importantly, further organic synthesis experiments were performed to verify the accuracy and feasibility of representative reactions. The total process, from Heck reaction generation to experimental verification, required only 15 days, demonstrating that our model has well-learned reaction rules in-depth and can contribute to novel reaction discovery and chemical space exploration.

Host defense against helminth infection is mediated by mucosal type 2 immunity. Using gain- and loss-of-function mouse models, and mouse intestinal organoids, Xiong et al. show that SIRT6 modulates tuft and goblet cell expansion in intestinal epithelium by activating STAT6 to maintain type 2 mucosal immunity in response to helminth infection.

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer, representing approximately 85% of cases. The diagnosis is often made in the middle and late stages, necessitating systemic treatment as the primary therapeutic option. Despite sorafenib being the established standard of care for advanced HCC in the past decade, the efficacy of systemic therapy remains unsatisfactory, highlighting the need for novel treatment modalities. Recent breakthroughs in immunotherapy have shown promise in HCC treatment, particularly with immune checkpoint inhibitors (ICIs). However, the response rate to ICIs is currently limited to approximately 15%–20% of HCC patients. Recently, ICIs demonstrated greater efficacy in “hot" tumors, highlighting the urgency to devise more effective approaches to transform “cold" tumors into “hot" tumors, thereby enhancing the therapeutic potential of ICIs. This review presented an updated summary of the factors influencing the effectiveness of immunotherapy in HCC treatment, identified potential combination therapies that may improve patient response rates to ICIs, and offered an overview of ongoing clinical trials focusing on ICI-based combination therapy.

Breast cancer is one of the leading causes of cancer-related death in women. Currently, the treatment of breast cancer is limited by the lack of effectively targeted therapy and patients often suffer from higher severity, metastasis, and resistance. Extracellular vesicles (EVs) consist of lipid bilayers that encapsulate a complex cargo, including proteins, nucleic acids, and metabolites. These bioactive cargoes have been found to play crucial roles in breast cancer initiation and progression. Moreover, EV cargoes play pivotal roles in converting mammary cells to carcinogenic cells and metastatic foci by extensively inducing proliferation, angiogenesis, pre-metastatic niche formation, migration, and chemoresistance. The present update review mainly discusses EVs cargoes released from breast cancer cells and tumor-derived EVs in the breast cancer microenvironment, focusing on proliferation, metastasis, chemoresistance, and their clinical potential as effective biomarkers.

Birds-Eye-View (BEV) maps provide an accurate representation of sensory cues present in the surroundings, including dynamic and static elements. Generating a semantic representation of BEV maps can be a challenging task since it relies on object detection and image segmentation. Recent studies have developed Convolutional Neural networks (CNNs) to tackle the underlying challenge. However, current CNN-based models encounter a bottleneck in perceiving subtle nuances of information due to their limited capacity, which constrains the efficiency and accuracy of representation prediction, especially for multi-scale and multi-class elements. To address this issue, we propose novel neural networks for BEV semantic representation prediction that are built upon Transformers without convolution layers in a significantly different way from existing pure CNNs and hybrid architectures that merge CNNs and Transformers. Given a sequence of image frames as input, the proposed neural networks can directly output the BEV maps with per-class probabilities in end-to-end forecasting. The core innovations of the current study contain (1) a new pixel generation method powered by Transformers, (2) a novel algorithm for image-to-BEV transformation, and (3) a novel network for image feature extraction using attention mechanisms. We evaluate the proposed Models performance on two challenging benchmarks, the NuScenes dataset and the Argoverse 3D dataset, and compare it with state-of-the-art methods. Results show that the proposed model outperforms CNNs, achieving a relative improvement of 2.4 and 5.2% on the NuScenes and Argoverse 3D datasets, respectively.