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The gut microbiota is closely related to parenteral noncommunicable diseases through intestinal immunity and plays an important role in the occurrence of diabetes and diabetic nephropathy. The aim of the study was to understand the gut–kidney axis by an analysis of gut microbiota composition among patients with biopsy-proven diabetic nephropathy (DN), patients with type 2 diabetes for more than 10 years without kidney damage (DM), and healthy controls (NC). Thirty-five DN patients, 40 DM patients and 40 healthy subjects matched by age and sex were enrolled between January 2022 and December 2022. Baseline information and clinical parameters were collected. 16S rDNA sequencing was performed to characterize the gut microbiome and identify gut microbes that were differentially abundant between patients and healthy controls. The relationship between the relative abundance of specific bacterial taxa in the gut and clinical phenotype and pathological indicators was evaluated. Substantial differences were found in the richness of the gut microbiota and the variation in the bacterial population among DN patients, DM patients and healthy controls. DM patients could be accurately distinguished from age- and sex-matched healthy controls by variations in g_Clostridium-XVIII (AUC = 0.929), and DN patients could be accurately distinguished from age- and sex-matched healthy controls by variations in g_Gemmiger (AUC = 0.842). DN patients could be accurately distinguished from age- and sex-matched DM patients by variations in g_Flavonifractor or g_Eisenbergiella (AUC = 0.909 and 0.886, respectively). The gut microbiota was also closely related to clinical phenotypes and pathological indicators. The study of gut microbiota composition was explored to determine its relationship to the occurrence of DN and a long history of diabetes without kidney damage. The renal pathological progression of DN may be delayed by regulating changes in the gut microbiota.

Rhizosphere ecological factors that affect sugarcane ratoons are crucial components in the feedback mechanisms between the sugarcane plant and soil environment. However, systematic investigations on these dynamics are lacking. Therefore, this study investigated the relationship between sugarcane ratoon decline and rhizosphere ecological factors. In first-year sugarcane ratoons, ecological factors such as soil available potassium content, soil nitrogen fixation, and soil peroxidase activity were significantly positively correlated with sugarcane growth ( P  < 0.05) compared to that of third-year sugarcane ratoons. Significant intergroup disparities in the rhizosphere soil microbial community structure were observed based on different ratoon ages ( P  < 0.01), while highly significant intergroup differences in endophytic microbial community structure were observed based on a Jaccard distance analysis ( P  < 0.01). Generalised additive model analysis revealed a significant positive correlation ( P  < 0.05) between sugarcane growth properties and the alpha diversity of rhizosphere soil bacteria and endophytic bacteria but a predominantly negative correlation ( P  > 0.05) between the alpha diversity of endophytic fungi and key sugarcane growth indicators. The deterioration of mainly non-microbial ecological factors in rhizosphere soil ( P  < 0.05) with increasing ratoon age may represent a significant factor contributing to sugarcane ratoon decline. The fungal community significantly impacted soil enzyme activity, while the microbial community indirectly influenced sugarcane yield through its effect on soil enzyme activity. Therefore, endophytic fungi, particularly Ascomycota species, may play a crucial role in sugarcane diseases.

Background Integrin αvβ3 has been proposed as crucial determinant for tumor sustained progression and a molecular marker for the estimation of tumor angiogenesis. Our study suggested that integrin αvβ3 could efficiently promote lung cancer cell proliferation and stem-like phenotypes in a tribbles homolog 3 (TRIB3) dependent manner. Result Integrin αvβ3 could mediate the activation of FAK/AKT pro-survival signaling pathway. Meanwhile, activated TRIB3 interacted with AKT to upregulated FOXO1 and SOX2 expression, resulting in sustained tumor progression in lung cancer. Our further analysis revealed that TRIB3 was significantly upregulated in lung tumor tissues and correlated with the poor outcome in clinical patients, indicating the potential role of TRIB3 in diagnostic and prognostic estimation for patients with lung cancer. Conclusion Our study showed here for the first time that integrin αvβ3 promote lung cancer development by activating the FAK/AKT/SOX2 axis in a TRIB3 dependent signaling pathway, and interrupting TRIB3/AKT interaction significantly improved the outcome of chemotherapy in tumor-bearing mice, representing a promising therapeutic strategy in lung cancer.

Multiple linear stapler firings is a risk factor for anastomotic leakage (AL) in laparoscopic low anterior resection (LAR) using double stapling technique (DST) anastomosis. In this study, our objective was to establish the risk factors for ≥ 3 linear stapler firings, and to create and validate a predictive model for ≥ 3 linear stapler firings in laparoscopic LAR using DST anastomosis. We retrospectively enrolled 328 mid–low rectal cancer patients undergoing laparoscopic LAR using DST anastomosis. With a split ratio of 4:1, patients were randomly divided into 2 sets: the training set (n = 260) and the testing set (n = 68). A clinical predictive model of ≥ 3 linear stapler firings was constructed by binary logistic regression. Based on three-dimensional convolutional networks, we built an image model using only magnetic resonance (MR) images segmented by Mask region-based convolutional neural network, and an integrated model based on both MR images and clinical variables. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and Youden index were calculated for each model. And the three models were validated by an independent cohort of 128 patients. There were 17.7% (58/328) patients received ≥ 3 linear stapler firings. Tumor size ≥ 5 cm (odds ratio (OR) = 2.54, 95% confidence interval (CI) = 1.15–5.60, p = 0.021) and preoperative carcinoma embryonic antigen (CEA) level > 5 ng/mL [OR = 2.20, 95% CI = 1.20–4.04, p = 0.011] were independent risk factors associated with ≥ 3 linear stapler firings. The integrated model (AUC = 0.88, accuracy = 94.1%) performed better on predicting ≥ 3 linear stapler firings than the clinical model (AUC = 0.72, accuracy = 86.7%) and the image model (AUC = 0.81, accuracy = 91.2%). Similarly, in the validation set, the integrated model (AUC = 0.84, accuracy = 93.8%) performed better than the clinical model (AUC = 0.65, accuracy = 65.6%) and the image model (AUC = 0.75, accuracy = 92.1%). Our deep-learning model based on pelvic MR can help predict the high-risk population with ≥ 3 linear stapler firings in laparoscopic LAR using DST anastomosis. This model might assist in determining preoperatively the anastomotic technique for mid–low rectal cancer patients.

Colorectal cancer (CRC) is one of most common tumors worldwide, causing a prominent global health burden. Cell senescence is a complex physiological state, characterized by proliferation arrest. Here, we investigated the role of cellular senescence in the heterogeneity of CRC. Based on senescence-associated genes, CRC samples were classified into different senescence patterns with different survival, cancer-related biological processes and immune cell infiltrations. A senescence-related model was then developed to calculate the senescence-related score to comprehensively explore the heterogeneity of each CRC sample such as stromal activities, immunoreactivities and drug sensitivity. Single-cell analysis revealed there were different immune cell infiltrations between low and high senescence-related model genes enrichment groups, which was confirmed by multiplex immunofluorescence staining. Pseudotime analysis indicated model genes play a pivotal role in the evolution of B cells. Besides, intercellular communication modeled by NicheNet showed tumor cells with higher enrichment of senescence-related model genes highly expressed CXCL2/3 and CCL3/4, which attracted immunosuppressive cell infiltration and promoted tumor metastasis. Finally, top 6 hub genes were identified from senescence-related model genes by PPI analysis. And RT-qPCR revealed the expression differences of hub genes between normal and CRC cell lines, indicating to some extent the clinical practicability of senescence-related model. To sum up, our study explores the impact of cellular senescence on the prognosis, TME and treatment of CRC based on senescence patterns. This provides a new perspective for CRC treatment.

Allelopathy is a process whereby a plant directly or indirectly promotes or inhibits growth of surrounding plants. Perennial sugarcane root extracts from various years significantly inhibited Bidens pilosa, Digitaria sanguinalis , sugarcane stem seedlings, and sugarcane tissue-cultured seedlings ( P  < 0.05), with maximum respective allelopathies of − 0.60, − 0.62, − 0.20, and − 0.29. Allelopathy increased with increasing concentrations for the same-year root extract, and inhibitory effects of the neutral, acidic, and alkaline components of perennial sugarcane root extract from different years were significantly stronger than those of the control for sugarcane stem seedlings ( P  < 0.05). The results suggest that allelopathic effects of perennial sugarcane root extract vary yearly, acids, esters and phenols could be a main reason for the allelopathic autotoxicity of sugarcane ratoons and depend on the type and content of allelochemicals present, and that allelopathy is influenced by other environmental factors within the rhizosphere such as the presence of old perennial sugarcane roots. This may be a crucial factor contributing to the decline of perennial sugarcane root health.

N-myc downstream-regulated gene 1 (NDRG1) is a key regulator that interacts with many classic tumor signaling pathways, including some molecules downstream of the epidermal growth factor receptor (EGFR). However, whether NDRG1 is involved in the mechanism of resistance to cetuximab (CTX), the first monoclonal antibody targeting the EGFR has not been reported. Here, we found that NDRG1 enhanced the sensitivity of CTX in colorectal cancer (CRC) cell lines. Afterwards, we determined the underlying mechanism of this phenomenon. We demonstrated that NDRG1 inhibited the expression of EGFR; blocked EGFR phosphorylation and reduced the EGFR distribution in the cell membrane, cytoplasm and nucleus. And then, NDRG1 suppressed the EGFR downstream signaling: RAS/RAF/ERK and PI3k/AKT/mTOR pathways. Moreover, we discovered that NDRG1 attenuated the endocytosis and degradation of EGFR induced by caveolin-1 (Cav1). Additionally, our findings were further observed in an animal model and human tissues. Our results represent a potentially significant discovery that explains the mechanisms of NDRG1 in CTX resistance. NDRG1 could be a promising biomarker to predict optimum responses to CTX, and a key target to enhance CTX activity in the treatment of metastatic CRC (mCRC).

Subgroup analysis aims to identify subgroups (usually defined by baseline/demographic characteristics), who would (or not) benefit from an intervention under specific conditions. Often performed post hoc (not pre-specified in the protocol), subgroup analyses are prone to elevated type I error due to multiple testing, inadequate power, and inappropriate statistical interpretation. Aside from the well-known Bonferroni correction, subgroup treatment interaction tests can provide useful information to support the hypothesis. Using data from a previously published randomized trial where a p value of 0.015 was found for the comparison between standard and Hemopatch® groups in (the subgroup of) 135 patients who had hand-sewn pancreatic stump closure we first sought to determine whether there was interaction between the number and proportion of the dependent event of interest (POPF) among the subgroup population (patients with hand-sewn stump closure and use of Hemopatch®), Next, we calculated the relative excess risk due to interaction (RERI) and the “attributable proportion” (AP). The p value of the interaction was p  = 0.034, the RERI was − 0.77 ( p  = 0.0204) (the probability of POPF was 0.77 because of the interaction), the RERI was 13% (patients are 13% less likely to sustain POPF because of the interaction), and the AP was − 0.616 (61.6% of patients who did not develop POPF did so because of the interaction). Although no causality can be implied, Hemopatch® may potentially decrease the POPF after distal pancreatectomy when the stump is closed hand-sewn. The hypothesis generated by our subgroup analysis requires confirmation by a specific, randomized trial, including only patients undergoing hand-sewn closure of the pancreatic stump after distal pancreatectomy. Trial registration: INS-621000-0760.

This study investigates the integration of non-economic policies into the framework for assessing macroeconomic coherence as applied by the Chinese government, with a particular focus on green policies. We examine the impact of non-economic factors on social disagreement and investor disagreement (expectations), and how these influences interact with macroeconomic regulation, employing both empirical evidence and dynamic stochastic general equilibrium (DSGE) theoretical models. In the basic analysis section, we merge statistical data on social divergence with policy implementation, utilizing multiple regression and deep neural network models. Our findings provide direct evidence that non-economic policies significantly regulate social sentiment. Additionally, theoretical analyses using contagion models, grounded in real textual data on social and investor divergence, demonstrate that expectations of social sentiment can ultimately affect economic variables. In the extended analysis, we enhance the classic DSGE model to delineate the pathways through which non-economic policies impact the macroeconomy. Drawing from our analyses, we propose specific optimization measures for non-economic policies. The results indicate that targeted policy optimization can effectively manage social disagreement, thereby shaping expectations and harmonizing non-economic with economic policy initiatives. This alignment enhances the coherence of macroeconomic policy interventions. The innovative contribution of this study lies in its provision of both theoretical and empirical evidence supporting the formulation of non-economic policies for the first time, alongside specific recommendations for improving the consistency of macroeconomic policies.

We investigate the dynamic correlation between the Bitcoin price (BTC) and the U.S. economic policy uncertainty index (USEPU) from the perspective of multifractality. Utilizing the multifractal detrended cross-correlation analysis (MF-DCCA), we confirm a long-range cross-correlation between BTC and USEPU. Moreover, the empirical results of MF-DCCA show that the power-law properties and multifractal characteristics between BTC and USEPU are significant. We further examine the long-range dependency of cross-correlation between BTC and USEPU series via the Hurst exponent test and confirm the durable cross-correlation. Finally, we introduce another multifractal indicator and examine the extent of multifractality among time series. The empirical results indicate that the BTC series, USEPU series, and the cross-correlation of BTC-USEPU present apparent multifractality, where BTC shows the strongest degree of multifractality.