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Hepatocellular carcinoma (HCC) ranks the most common primary liver malignancy and the third leading cause of tumor-related mortality worldwide. Unfortunately, despite advances in HCC treatment, less than 40% of HCC patients are eligible for potentially curative therapies. Recently, cancer immunotherapy has emerged as one of the most promising approaches for cancer treatment. It has been proven therapeutically effective in many types of solid tumors, such as non-small cell lung cancer and melanoma. As an inflammation-associated tumor, it’s well-evidenced that the immunosuppressive microenvironment of HCC can promote immune tolerance and evasion by various mechanisms. Triggering more vigorous HCC-specific immune response represents a novel strategy for its management. Pre-clinical and clinical investigations have revealed that various immunotherapies might extend current options for needed HCC treatment. In this review, we provide the recent progress on HCC immunology from both basic and clinical perspectives, and discuss potential advances and challenges of immunotherapy in HCC.

Accurate sand control design requires that the sand particles that break through the sand control facilities and enter the wellbore can be completely transported to the wellhead to prevent wellbore sand settling and blocking. At present, there are few studies on the migration and deposition of formation sand with a certain particle size distribution (PSD) after entering the wellbore with formation fluid. Most studies do not consider the sand PSD and treat sand particles as pseudo fluid. Therefore, this paper uses CFD-DEM (computational fluid dynamics-discrete element method) coupling method to conduct numerical simulation of sand migration in the wellbore, and obtains the influence rule of fluid velocity, viscosity, well deviation angle, formation sand production concentration, and PSD on cross-section sand concentration (sand carrying capacity of the fluid) and axial pressure drop in the wellbore section. Based on the simulation results and dimensional analysis, a prediction model for the volume concentration of sand particles in the wellbore was established, which is convenient for engineering applications to quickly determine the flow state of the wellbore. An engineering method for determining the critical non-deposition velocity of sand particles in wellbore was proposed, and the critical non-deposition velocity of sand particles under three effective particle sizes, three sand production concentrations, and three fluid viscosities was obtained. The research results can provide a theoretical basis for sand carrying and the production systems coordinated optimization design in weakly consolidated sandstone reservoirs.

Inflammatory bowel disease (IBD) is one relapsing and lifelong disease that affects millions of patients worldwide. Increasing evidence has recently highlighted immune-system dysfunction, especially toll-like receptors (TLRs)-mediated innate immune dysfunction, as central players in the pathogenesis of IBD. TLRs and TLR-activated signaling pathways are involved not only in the pathogenesis but also in the efficacy of treatment of IBD. By understanding these molecular mechanisms, we might develop a strategy for relieving the experience of long-lasting suffering of those patients and improving their quality of life. The purpose of this review article is to summarize the potential mechanisms of TLR signaling pathways in IBD and the novel potential therapeutic strategies against IBD.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignant diseases worldwide. It is refractory to conventional treatments, and consequently has a documented 5-year survival rate as low as 7%. Increasing evidence indicates that activated pancreatic stellate cells (PSCs), one of the stromal components in tumor microenvironment (TME), play a crucial part in the desmoplasia, carcinogenesis, aggressiveness, metastasis associated with PDAC. Despite the current understanding of PSCs as a “partner in crime” to PDAC, detailed regulatory roles of PSCs and related microenvironment remain obscure. In addition to multiple paracrine signaling pathways, recent research has confirmed that PSCs-mediated tumor microenvironment may influence behaviors of PDAC via diverse mechanisms, such as rewiring metabolic networks, suppressing immune responses. These new activities are closely linked with treatment and prognosis of PDAC. In this review, we discuss the recent advances regarding new functions of activated PSCs, including PSCs-cancer cells interaction, mechanisms involved in immunosuppressive regulation, and metabolic reprogramming. It’s clear that these updated experimental or clinical studies of PSCs may provide a promising approach for PDAC treatment in the near future.

We present the ggtreeExtra package for visualizing heterogeneous data with a phylogenetic tree in a circular or rectangular layout (https://www.bioconductor.org/packages/ggtreeExtra). The package supports more data types and visualization methods than other tools. It supports using the grammar of graphics syntax to present data on a tree with richly annotated layers and allows evolutionary statistics inferred by commonly used software to be integrated and visualized with external data. GgtreeExtra is a universal tool for tree data visualization. It extends the applications of the phylogenetic tree in different disciplines by making more domain-specific data to be available to visualize and interpret in the evolutionary context.

High cancer mortality is attributed to metastasis to a large extent. However, cancer metastasis remains devoid of dynamic monitoring and early prevention in terms of current advances in diagnostic means and therapeutic modalities. Meanwhile, studies have shown that reciprocal crosstalk among cells via exosomes plays a critical role in maintaining normal physiological state or triggering disease progression, including cancer metastasis. Therefore, in this review, we focus on the latest literature (primarily from 2018) to summarize action mechanisms and experimental studies of exosomes in cancer metastasis and put forward some problems as well as new outlooks of these studies.

Industrialization and human activities have caused significant environmental challenges, with water pollution posing severe risks to human health. This underscores the urgent need for effective water treatment solutions. Zeolites, known for their high specific surface area and stability, have gained increasing attention as adsorbents for water treatment. Among zeolites, LTA varieties stand out due to their low Si/Al ratio, which enhances ion-exchange capacity, and their cost-effectiveness. This review focuses on the synthesis of low-silica LTA zeolites, particularly zeolite A, using natural materials and solid wastes without relying on organic-structure-directing agents (OSDAs). Common pretreatment processes for such synthesis are also highlighted. The review further explores the applications of LTA zeolites in water treatment, emphasizing their exceptional performance in adsorbing inorganic and organic pollutants. In particular, LTA zeolites are highly effective at removing inorganic cation pollutants through ion exchange. An updated ion-exchange selectivity order, based on previous studies, is provided to support these findings. Overall, this review aims to guide future research and development in water treatment technologies.

Plant health is strongly impacted by beneficial and pathogenic plant microbes, which are themselves structured by resource inputs. Organic fertilizer inputs may thus offer a means of steering soil-borne microbes, thereby affecting plant health. Concurrently, soil microbes are subject to top-down control by predators, particularly protists. However, little is known regarding the impact of microbiome predators on plant health-influencing microbes and the interactive links to plant health. Here, we aimed to decipher the importance of predator-prey interactions in influencing plant health. To achieve this goal, we investigated soil and root-associated microbiomes (bacteria, fungi and protists) over nine years of banana planting under conventional and organic fertilization regimes differing in Fusarium wilt disease incidence. We found that the reduced disease incidence and improved yield associated with organic fertilization could be best explained by higher abundances of protists and pathogen-suppressive bacteria (e.g. Bacillus spp.). The pathogen-suppressive actions of predatory protists and Bacillus spp. were mainly determined by their interactions that increased the relative abundance of secondary metabolite Q genes (e.g. nonribosomal peptide synthetase gene) within the microbiome. In a subsequent microcosm assay, we tested the interactions between predatory protists and pathogen-suppressive Bacillus spp. that showed strong improvements in plant defense. Our study shows how protistan predators stimulate disease-suppressive bacteria in the plant microbiome, ultimately enhancing plant health and yield. Thus, we suggest a new biological model useful for improving sustainable agricultural practices that is based on complex interactions between different domains of life.

Background The association between obesity and respiratory diseases has been confirmed. However, few studies have reported the relationship between obesity and the risk and mortality of chronic inflammatory airway disease (CIAD). The aim of this study was to reveal the association between obesity and the risk of CIAD, and mortality in patients with CIAD. Methods The study was conducted using data from the National Health and Nutrition Examination Survey (NHANES) 2013 to 2018 among adults aged 20 years and above. All participants were grouped according to body mass index (BMI) and waist circumference (WC) levels to study the relationship between obesity and CIAD. Multivariate logistic regression analysis was utilized to examine the connection between CIAD and obesity in a cross-sectional study. The association between obesity and all-cause mortality in individuals with CIAD was examined using multiple cox regression models and smooth curve fitting in a prospective cohort study. Results When stratified based on BMI in comparison to the normal weight group, the ORs with 95%CIs of CIAD for underweight and obesity were 1.39 (1.01–1.93) and 1.42 (1.27–1.58), respectively. The OR with 95%CI of CIAD for obesity was 1.20 (1.09–1.31) when stratified according to WC. Additionally, underweight was associated with a higher mortality (HR = 2.44, 95% CI = 1.31–4.55), whereas overweight (HR = 0.58,95% CI = 0.39–0.87) and obesity (HR = 0.59,95% CI = 0.4–0.87) were associated with a lower mortality ( P for trend < 0.05). There was a non-linear association between BMI and all-cause mortality (P for non-linear = 0.001). An analysis of a segmentation regression model between BMI and all-cause mortality revealed a BMI turning point value of 32.4 kg/m 2 . The mortality of CIAD patients was lowest when BMI was 32.4 kg/m 2 . When BMI ≤ 32.4 kg/m 2 , BMI was inversely associated with all-cause mortality in patients with CIAD (HR: 0.92, 95%CI:0.88–0.97). However, when BMI > 32.4 kg/m 2 , there was no association between BMI and all-cause mortality (HR:1.02, 95%CI:0.97–1.06). Conclusion Compared to normal weight, underweight and obesity were associated with the increased risk of CIAD. Underweight was associated with increased all-cause mortality, while overweight was associated with reduced all-cause mortality. There was a non-linear association between BMI and all-cause mortality in patients with CIAD. The all-cause mortality was lowest when BMI was 32.4 kg/m 2 .

Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years. Although research on MRC based on deep learning is flourishing, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. Specifically, we give a thorough review of this research field, covering different aspects including (1) typical MRC tasks: their definitions, differences, and representative datasets; (2) the general architecture of neural MRC: the main modules and prevalent approaches to each; and (3) new trends: some emerging areas in neural MRC as well as the corresponding challenges. Finally, considering what has been achieved so far, the survey also envisages what the future may hold by discussing the open issues left to be addressed.