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Elucidating the mechanisms by which short chain fatty acids (SCFA) reduce body weight may assist in the development of an effective weight control strategy. Dietary supplementation of acetate, propionate, butyrate or their admixture was shown to significantly inhibit the body weight gain induced by high-fat diet feeding. Supplementation of SCFAs caused significant changes in the expressions of G-protein coupled receptor 43 (GPR43) and GPR41 characterized by increases in the adipose tissue and reductions in the colon. Additionally, they influenced the bacterial community structure in feces, with a reduction in the proportion of Firmicutes and an increase in the proportion of Bacteroidetes. The effects of dietary SCFAs on the GPR expression and gut microbiota composition may further result in body weight reduction by enhancing triglyceride hydrolysis and FFA oxidation in the adipose tissue, promoting beige adipogenesis and mitochondrial biogenesis, and inhibiting chronic inflammation.

Following dramatic success in many types of advanced solid tumors, interest in immunotherapy for the treatment of colorectal cancer (CRC) is increasingly growing. Given the compelling long-term durable remission, two programmed cell death 1 (PD-1)-blocking antibodies, pembrolizumab and nivolumab (with or without Ipilimumab), have been approved for the treatment of patients with metastatic colorectal cancer (mCRC) that is mismatch-repair-deficient and microsatellite instability-high (dMMR-MSI-H). Practice-changing results of several randomized controlled trials to move immunotherapy into the first-line treatment for MSI-H metastasis cancer and earlier stage were reported successively in the past 2 years. Besides, new intriguing advances to expand the efficacy of immunotherapy to mCRC that is mismatch-repair-proficient and low microsatellite instability (pMMR-MSI-L) demonstrated the potential benefits for the vast majority of mCRC cases. Great attention is also paid to the advances in cancer vaccines and adoptive cell therapy (ACT). In this review, we summarize the above progresses, and also highlight the current predictive biomarkers of responsiveness in immunotherapy with broad clinical utility.

Multimodal emotion recognition leverages multiple modalities to capture emotional cues more comprehensively, thereby improving the accuracy and robustness of emotion recognition. From the perspective of multimodal data and feature learning, reducing information redundancy in multimodal data and enhancing the discriminability of deep feature co-learning can effectively boost recognition performance. Based on this, this paper proposes a multimodal emotion recognition method based on an Adaptive High-order Transformer Network (AHOT). This method constructs Adaptive Selection Transformer block (AST) and Cross-modal Feature Fusion block (CMFF) for each modality branch, aiming to fully capture non-redundant feature representations from each modality and the interactions between modalities. In addition, a sparse high-order feature learning module is designed to enable the learning of highly discriminative high-order features across modalities. Experimental results on two multimodal emotion recognition datasets (IEMOCAP and CMU-MOSEI) demonstrate that, compared with several related methods, the proposed AHOT effectively improves emotion recognition accuracy. Moreover, ablation studies and parameter analyses further validate the effectiveness of AHOT.

Glyphosate, which has been widely reported to be a toxic pollutant, is often present at trace amounts in the environment. In this study, a novel copper-aluminum metal hydroxide doped graphene nanoprobe (labeled as CuAl–LDH/Gr NC) was first developed to construct a non-enzymatic electrochemical sensor for detection trace glyphosate. The characterization results showed that the synthesized CuAl–LDH had a high-crystallinity flowered structure, abundant metallic bands and an intercalated functional group. After mixed with Gr, the nanocomposites provided a larger surface area and better conductivity. The as-prepared CuAl–LDH/Gr NC dramatically improved the enrichment capability for glyphosate to realize the stripping voltammetry detection. The logarithmic linear detection range of the sensor was found to be 2.96 × 10−9–1.18 × 10−6 mol L−1 with the detection limit of 1 × 10−9 mol L−1 with excellent repeatability, good stability and anti-interference ability. Further, the sensor achieved satisfactory recovery rates in spiked surface water, ranging from 97.64% to 108.08%, demonstrating great accuracy and practicality.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, characterized by a highly immunosuppressive tumor microenvironment (TME) that facilitates immune evasion and tumor progression. Integrated analysis of TCGA and GEO datasets was performed to evaluate CALB2 expression and its prognostic significance in CRC. Functional experiments including proliferation, migration, colony formation, and apoptosis assays were conducted following CALB2 knockdown. Co-culture assays and multiplex immunohistochemistry (mIHC) were used to assess TME remodeling. Elevated CALB2 expression correlated significantly with poor prognosis in CRC patients. CALB2 knockdown suppressed CRC cell proliferation, migration, and colony formation, while promoting apoptosis. CALB2 silencing also reprogrammed TME by inducing macrophage M1 polarization, enhancing CD8 T cell cytotoxicity, and reducing immunosuppressive cell infiltration. Mechanistically, CALB2 activated the STAT3 signaling pathway to promote CCL5 secretion, facilitating M2 polarization of macrophages and activation of fibroblasts. Our findings identify CALB2 as a critical regulator of tumor progression and immune escape in CRC, acting through dual oncogenic and immunomodulatory mechanisms. CALB2 represents a promising therapeutic target for enhancing immunotherapy efficacy in CRC.

Soil organic matter (SOM) is a key index of soil fertility. Calculating spectral index and screening characteristic band reduce redundancy information of hyperspectral data, and improve the accuracy of SOM prediction. This study aimed to compare the improvement of model accuracy by spectral index and characteristic band. This study collected 178 samples of topsoil (0–20 cm) in the central plain of Jiangsu, East China. Firstly, visible and near-infrared (VNIR, 350–2500 nm) reflectance spectra were measured using ASD FieldSpec 4 Std-Res spectral radiometer in the laboratory, and inverse-log reflectance (LR), continuum removal (CR), first-order derivative reflectance (FDR) were applied to transform the original reflectance (R). Secondly, optimal spectral indexes (including deviation of arch, difference index, ratio index, and normalized difference index) were calculated from each type of VNIR spectra. Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. Meanwhile, SOM prediction models were established using characteristic wavelengths, denoted here as CARS-based models. Finally, this research compared and assessed accuracy of SI-based models and CARS-based models, and selected optimal model. Results showed: (1) The correlation between optimal spectral indexes and SOM was enhanced, with absolute value of correlation coefficient between 0.66 and 0.83. The SI-based models predicted SOM content accurately, with the coefficient of determination ( R 2 ) and root mean square error ( RMSE ) values ranging from 0.80 to 0.87, 2.40 g/kg to 2.88 g/kg in validation sets, and relative percent deviation ( RPD ) value between 2.14 and 2.52. (2) The accuracy of CARS-based models differed with models and spectral transformations. For all spectral transformations, PLSR and SVR combined with CARS displayed the best prediction ( R 2 and RMSE values ranged from 0.87 to 0.92, 1.91 g/kg to 2.56 g/kg in validation sets, and RPD value ranged from 2.41 to 3.23). For FDR and CR spectra, DNN and RF models achieved more accuracy ( R 2 and RMSE values ranged from 0.69 to 0.91, 1.90 g/kg to 3.57 g/kg in validation sets, and RPD value ranged from 1.73 to 3.25) than LR and R spectra ( R 2 and RMSE values from 0.20 to 0.35, 5.08 g/kg to 6.44 g/kg in validation sets, and RPD value ranged from 0.96 to 1.21). (3) Overall, the accuracy of SI-based models was slightly lower than that of CARS-based models. But spectral index had a good adaptability to the models, and each SI-based model displayed the similar accuracy. For different spectra, the accuracy of CARS-based model differed from modeling methods. (4) The optimal CARS-based model was model CARS-CR-SVR ( R 2 and RMSE : 0.92 and 1.91 g/kg in validation set, RPD : 3.23). The optimal SI-based model was model SI3-SVR ( R 2 and RMSE : 0.87 and 2.40 g/kg in validation set, RPD : 2.57) and model SI-SVR ( R 2 and RMSE : 0.84 and 2.63 g/kg in validation set, RPD : 2.35).

Background: Machine learning (ML) is transforming dentistry by setting new standards for precision and efficiency in clinical practice, while driving improvements in care delivery and quality. Objectives: This review: (1) states the necessity to develop ML in dentistry for the purpose of breaking the limitations of traditional dental technologies; (2) discusses the principles of ML-based models utilised in dental clinical practice and care; (3) outlines the application respects of ML in dentistry; and (4) highlights the prospects and challenges to be addressed. Data and sources: In this narrative review, a comprehensive search was conducted in PubMed/MEDLINE, Web of Science, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases. Conclusions: Machine Learning has demonstrated significant potential in dentistry with its intelligently assistive function, promoting diagnostic efficiency, personalised treatment plans and related streamline workflows. However, challenges related to data privacy, security, interpretability, and ethical considerations were highly urgent to be addressed in the next review, with the objective of creating a backdrop for future research in this rapidly expanding arena. Clinical significance: Development of ML brought transformative impact in the fields of dentistry, from diagnostic, personalised treatment plan to dental care workflows. Particularly, integrating ML-based models with diagnostic tools will significantly enhance the diagnostic efficiency and precision in dental surgeries and treatments.

Specific adipokines, such as adiponectin and resistin, are secreted from adipose tissue and are associated with the development of obesity. Supplementation of dietary SCFA can prevent and reverse high-fat-diet (HFD)-induced obesity. However, it is not clear whether SCFA ameliorate abnormal expression of adiponectin and resistin in the obese state. The aim of this study was to investigate the effects of SCFA on adiponectin and resistin’s expressions in diet-induced obese mice, as well as the potential mechanisms associated with DNA methylation. C57BL/6J male mice were fed for 16 weeks with five types of HFD (34·9 % fat by wt., 60 % kJ) – a control HFD and four HFD with acetate (HFD-A), propionate (HFD-P), butyrate (HFD-B) and their admixture (HFD-SCFA). Meanwhile, a low-fat diet (4·3 % fat by wt., 10 % kJ) was used as the control group. The reduced mRNA levels of adiponectin and resistin in the adipose tissue of the HFD-fed mice were significantly reversed by dietary supplementation of acetate, propionate, butyrate or their admixture to the HFD. Moreover, the expressional changes of adiponectin and resistin induced by SCFA were associated with alterations in DNA methylation at their promoters, which was mediated by reducing the expressions of enzyme-catalysed DNA methyltransferase ( DNMT1 , 3a , 3b ) and the methyl-CpG-binding domain protein 2 ( MBD2 ) and suppressing the binding of these enzymes to the promoters of adiponectin and resistin. Our results indicate that SCFA may correct aberrant expressions of adiponectin and resistin in obesity by epigenetic regulation.

Background Epithelial-to-mesenchymal transition (EMT) is a process linked to metastasis and drug resistance with non-coding RNAs (ncRNAs) playing pivotal roles. We previously showed that miR-100 and miR-125b, embedded within the third intron of the ncRNA host gene MIR100HG , confer resistance to cetuximab, an anti-epidermal growth factor receptor (EGFR) monoclonal antibody, in colorectal cancer (CRC). However, whether the MIR100HG transcript itself has a role in cetuximab resistance or EMT is unknown. Methods The correlation between MIR100HG and EMT was analyzed by curating public CRC data repositories. The biological roles of MIR100HG in EMT, metastasis and cetuximab resistance in CRC were determined both in vitro and in vivo. The expression patterns of MIR100HG, hnRNPA2B1 and TCF7L2 in CRC specimens from patients who progressed on cetuximab and patients with metastatic disease were analyzed by RNAscope and immunohistochemical staining. Results The expression of MIR100HG was strongly correlated with EMT markers and acted as a positive regulator of EMT. MIR100HG sustained cetuximab resistance and facilitated invasion and metastasis in CRC cells both in vitro and in vivo. hnRNPA2B1 was identified as a binding partner of MIR100HG. Mechanistically, MIR100HG maintained mRNA stability of TCF7L2, a major transcriptional coactivator of the Wnt/β-catenin signaling, by interacting with hnRNPA2B1. hnRNPA2B1 recognized the N6-methyladenosine (m 6 A) site of TCF7L2 mRNA in the presence of MIR100HG. TCF7L2, in turn, activated MIR100HG transcription, forming a feed forward regulatory loop. The MIR100HG/hnRNPA2B1/TCF7L2 axis was augmented in specimens from CRC patients who either developed local or distant metastasis or had disease progression that was associated with cetuximab resistance. Conclusions MIR100HG and hnRNPA2B1 interact to control the transcriptional activity of Wnt signaling in CRC via regulation of TCF7L2 mRNA stability. Our findings identified MIR100HG as a potent EMT inducer in CRC that may contribute to cetuximab resistance and metastasis by activation of a MIR100HG/hnRNPA2B1/TCF7L2 feedback loop.

Ferroptosis, a type of cell death triggered by excessive accumulation of iron-dependent lipid peroxidation, possesses an excellent potential in cancer treatment. However, many colorectal cancer (CRC) cell lines are resistant to ferroptosis induced by erastin and RSL3, the classical ferroptotic inducers. Moreover, the underlying mechanism of resistance remains poorly elucidated. This study sought to discover the major factor contributing to ferroptosis resistance in CRC. The study findings will help design strategies for triggering ferroptosis for application in individualized tumor therapy. Here, we show that tetrahydrobiopterin (BH4) determines the sensitivity of CRC cells to ferroptosis induced by erastin. GTP cyclohydrolase-1 (GCH1) is the first rate-limiting enzyme of BH4. Genetic or pharmacological inhibition of GCH1 decreased BH4 and assisted erastin in cell death induction, lipid peroxidation enhancement, and ferrous iron accumulation. BH4 supplementation completely inhibited ferroptotic features resulting from GCH1 knockdown. Unexpectedly, GCH1 knockdown failed to enhance RSL3-induced cell death in CRC. Mechanistically, GCH1 knockdown drastically activated ferritinophagy during erastin treatment rather than RSL3 treatment. Administration of an autophagy inhibitor reversed erastin resistance in GCH1-knockdown cells. GCH1 inhibitor and erastin co-treatment in vivo synergistically inhibited tumor growth in CRC. Overall, our results identified GCH1/BH4 metabolism as a burgeoning ferroptosis defense mechanism in CRC. Inhibiting GCH1/BH4 metabolism promoted erastin-induced ferroptosis by activating ferritinophagy, suggesting that combining GCH1 inhibitors with erastin in the treatment of CRC is a novel therapeutic strategy.