Chronic pancreatitis （CP） is a common disease in clinical practice characterized by progressive inflammatory fibrosis of the pancreas. Gut microbiota， known as the “second genome” of humans， bidirectionally modulates the progression of fibrosis in CP via the gut-pancreas axis. This article systematically elaborates on the characteristics of gut microbiota during the progression of CP and its molecular mechanism in mediating pancreatic fibrosis through bacterial translocation， metabolites， immune regulatory networks， and microbe-pancreatic stellate cell interactions， with a focus on the pivotal role of short-chain fatty acids and inflammatory cytokine networks in pancreatic stellate cell activation and extracellular matrix deposition. In addition， this article explores the potential value of gut microbiota-targeted interventions in the prevention and treatment of CP fibrosis， such as probiotics， prebiotics， and fecal microbiota transplantation， and discusses the translational potential of using multi-omics technologies to identify diagnostic biomarkers and novel therapeutic targets for CP， in order to provide new ideas for the precise diagnosis and treatment of CP.

Objective To observe the value of machine learning model based on MR T2WI and diffusion weighted imaging(DWI)radiomics for predicting perineural invasion(PNI)of rectal cancer.Methods Totally 343 patients with rectal cancer were retrospectively collected and divided into training set(n=275,92 PNI[+]and 183 PNI[-])and test set(n=68,23 PNI[+]and 45 PNI[-])at the ratio of 8∶2.Univariate and multivariate logistic regression(LR)were used to analyze clinical data and screen the independent predictors of PNI in rectal cancer,so as to construct a clinical model.The best radiomics features were extracted and screened based on preoperative T2WI and DWI.Then extremely randomized trees,multilayer perceptron,light gradient boosting machine,extreme gradient boosting,support vector machine(SVM),LR,K-nearest neighbor and random forest algorithms were used to construct ML models,respectively,and the optimal ML model was selected to establish a clinical-radiomics ML model combined with clinical relevant independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Patients' age was the independent predictor of PNI of rectal cancer(OR=0.988,P＜0.001),and the area under the curve(AUC)of the clinical model constructed based on it was 0.435 and 0.458 in training and test sets,respectively.SVM model was the best one among 8 ML models,with AUC in training and test set of 0.887 and 0.854,respectively.The AUC of clinical-radiomics ML model in training and test sets was 0.887 and 0.860,respectively,not different with AUC of SVM model(both P＞0.05).Decision curve analysis showed that when the threshold value was 0.20-0.45,clinical net benefit of SVM model was higher than that of other models.Conclusion SVM model based on T2WI and DWI radiomics could effectively predict PNI of rectal cancer.

G protein-coupled receptors (GPCRs) are significant drug targets, but their potential in cancer therapy remains underexplored. Conventional GPCR agonists or antagonists have shown limited effectiveness in cancer treatment, necessitating new GPCR-targeting strategies for more effective therapies. This study discovers that Yersinia pestis LcrV, a crucial linker protein for plague infection, acts as a biased agonist of a GPCR, the formyl peptide receptor 1 (FPR1). The LcrV protein induces unique conformational changes in FPR1, resulting in G proteins being activated in a distinctive state without subunit dissociation. This leads to a biased signaling profile characterized by cyclic adenosine monophosphate (cAMP) responses and β-arrestin2 recruitment, but not calcium mobilization. In FPR1-expressing triple-negative breast cancer (TNBC) cells, LcrV bi-directionally modulates intracellular signaling pathways, downregulating extracellular signal-regulated kinases (ERK1/2) and Akt pathways while upregulating Jun N-terminal kinase (JNK) and p38 pathways. This dual modulation results in cell cycle arrest and the inhibition of TNBC cell proliferation. In TNBC xenograft mouse models, long-term LcrV treatment inhibits tumor growth more effectively than a conventional FPR1 antagonist. Additionally, LcrV treatment reprograms tumor cells by reducing stemness-associated proteins OCT4 and c-MYC. Our findings highlight the potential of biased GPCR agonists as a novel GPCR-targeting strategy for cancer treatment.

Objective To observe the value of machine learning model based on MR T2WI and diffusion weighted imaging(DWI)radiomics for predicting perineural invasion(PNI)of rectal cancer.Methods Totally 343 patients with rectal cancer were retrospectively collected and divided into training set(n=275,92 PNI[+]and 183 PNI[-])and test set(n=68,23 PNI[+]and 45 PNI[-])at the ratio of 8∶2.Univariate and multivariate logistic regression(LR)were used to analyze clinical data and screen the independent predictors of PNI in rectal cancer,so as to construct a clinical model.The best radiomics features were extracted and screened based on preoperative T2WI and DWI.Then extremely randomized trees,multilayer perceptron,light gradient boosting machine,extreme gradient boosting,support vector machine(SVM),LR,K-nearest neighbor and random forest algorithms were used to construct ML models,respectively,and the optimal ML model was selected to establish a clinical-radiomics ML model combined with clinical relevant independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Patients' age was the independent predictor of PNI of rectal cancer(OR=0.988,P＜0.001),and the area under the curve(AUC)of the clinical model constructed based on it was 0.435 and 0.458 in training and test sets,respectively.SVM model was the best one among 8 ML models,with AUC in training and test set of 0.887 and 0.854,respectively.The AUC of clinical-radiomics ML model in training and test sets was 0.887 and 0.860,respectively,not different with AUC of SVM model(both P＞0.05).Decision curve analysis showed that when the threshold value was 0.20-0.45,clinical net benefit of SVM model was higher than that of other models.Conclusion SVM model based on T2WI and DWI radiomics could effectively predict PNI of rectal cancer.

Complete androgen insensitivity syndrome(CAIS) is characterized by lack of androgen response in target organs due to androgen receptor dysfunction, resulting in feminized external genitalia. Individuals with CAIS are typically advised to live as females. This article reports a patient diagnosed with CAIS and gender dysphoria in adulthood. Following the removal of a left pelvic mass, pathology indicated cryptorchidism with a concurrent Leydig cell tumor. Genetic testing revealed a deletion mutation in exon 3 of androgen receptor gene. During follow-up, the patient underwent gender reassignment, transitioning socially from female to male. This case provides new insights into gender allocation for CAIS patients.

Objective To study microbial characteristics of pulmonary in children with community-acquired pneumonia(CAP)of different severity,in order to provide a basis for accurate diagnosis and antibiotic treatment of pneumonia children,and provide new strategies and perspectives for the diagnosis and treatment of pulmonary microbiota in pneumonia children.Methods Bronchoalveolar lavage fluid(BALF)from 64 children with CAP of different severity hospitalized in Department of Pediatrics,Jingmen People's Hospital were collected from January to December 2023,the children were divided into severe pneumonia group(n=34)and common pneumonia group(n=30).Microbiome information of the lungs of children with CAP of different severity were obtained through metagenomic sequencing of BALF,microbial structure diversity analysis,species classification analysis,and differential analysis on the microbial bioinformatics data of two groups of samples obtained were performed.Results Alpha diversity analysis showed that there were statistically significant differences(P<0.05)in the Chao1 index,ACE index,Shannon index,and Simpson index between two groups.The principal coordinate analysis(PCoA)of Beta diversity showed a statistically significant difference in the composition of microbial communities between two groups(F=4.221,P=0.005).Through species classification analysis,it was found that at the genus level,mycoplasma was the main genus in the BALF samples of severe pneumonia group,followed by Streptococcus and Haemophilus,Streptococcus was the main genus in the BALF samples of common pneumonia group,followed by Mycoplasma and Haemophilus.Children of two groups showed statistically significant differences in microbial abundance among the top 20 species at the genus level(P<0.05),including Mycoplasma,Streptococcus,Rhodococcus,Neisseria,Prevotella,Corynebacterium,and Pseudomonas.Species diversity analysis showed that at the genus level,there were 47 species with differences(P<0.05).Conclusion There are differences in the abundance,diversity,structure,and composition of pulmonary microbiota in children with CAP of different severity.The dominant microbiota varies among children with CAP of different severity.This study enriches the pulmonary microbiome data of children with CAP.

Objective To study microbial characteristics of pulmonary in children with community-acquired pneumonia(CAP)of different severity,in order to provide a basis for accurate diagnosis and antibiotic treatment of pneumonia children,and provide new strategies and perspectives for the diagnosis and treatment of pulmonary microbiota in pneumonia children.Methods Bronchoalveolar lavage fluid(BALF)from 64 children with CAP of different severity hospitalized in Department of Pediatrics,Jingmen People's Hospital were collected from January to December 2023,the children were divided into severe pneumonia group(n=34)and common pneumonia group(n=30).Microbiome information of the lungs of children with CAP of different severity were obtained through metagenomic sequencing of BALF,microbial structure diversity analysis,species classification analysis,and differential analysis on the microbial bioinformatics data of two groups of samples obtained were performed.Results Alpha diversity analysis showed that there were statistically significant differences(P<0.05)in the Chao1 index,ACE index,Shannon index,and Simpson index between two groups.The principal coordinate analysis(PCoA)of Beta diversity showed a statistically significant difference in the composition of microbial communities between two groups(F=4.221,P=0.005).Through species classification analysis,it was found that at the genus level,mycoplasma was the main genus in the BALF samples of severe pneumonia group,followed by Streptococcus and Haemophilus,Streptococcus was the main genus in the BALF samples of common pneumonia group,followed by Mycoplasma and Haemophilus.Children of two groups showed statistically significant differences in microbial abundance among the top 20 species at the genus level(P<0.05),including Mycoplasma,Streptococcus,Rhodococcus,Neisseria,Prevotella,Corynebacterium,and Pseudomonas.Species diversity analysis showed that at the genus level,there were 47 species with differences(P<0.05).Conclusion There are differences in the abundance,diversity,structure,and composition of pulmonary microbiota in children with CAP of different severity.The dominant microbiota varies among children with CAP of different severity.This study enriches the pulmonary microbiome data of children with CAP.

The adhesive protein secreted by marine sessile animals can resist the resistance of water and exert stickiness under the humid environment. It has become a candidate for the development of high-performance materials in the field of biomedicine and bionics. Barnacles are as one of the marine macrofoulers that can be firmly attached to the underwater substrate materials with different surface characteristics through its cement proteins. To date, the adhesion process of barnacle has been understood in-depth, but the specific underwater adhesion mechanism has not been elucidated and needs further exploration. This review first presented an overview of barnacle and its adhesion process, followed by summarizing the advances of barnacle adhesive protein, its production methods, and applications. Moreover, challenges and future perspectives were prospected.
Animals ; Thoracica/metabolism* ; Proteins/metabolism* ; Adhesives/metabolism*

Microbial induced calcium carbonate precipitation (MICP) refers to the natural biological process of calcium carbonate precipitation induced by microbial metabolism in its surrounding environment. Based on the principles of MICP, microbial cement has been developed and has received widespread attention in the field of biology, civil engineering, and environment owing to the merits of environmental friendliness and economic competence. Urease and carbonic anhydrase are the key enzymes closely related to microbial cement. This review summarizes the genes, protein structures, regulatory mechanisms, engineering strains and mutual synergistic relationship of these two enzymes. The application of bioinformatics and synthetic biology is expected to develop biocement with a wide range of environmental adaptability and high performance, and will bring the MICP research to a new height.
Calcium Carbonate/metabolism* ; Chemical Precipitation ; Urease/metabolism*

Objective:To investigate the diagnostic performance of MRI Liver Imaging Reporting and Data System version 2018 in high-risk hepatocellular carcinoma (HCC) patients with intrahepatic parenchymal substantial lesions ≤3.0 cm.Methods:A retrospective analysis was conducted in hospitals between September 2014 to April 2020. 131 pathologically confirmed non-HCC cases with lesions ≤3.0 cm in diameter were randomly matched with 131 cases with lesions ≤3.0 cm in diameter and divided into benign (56 cases), other hepatic malignant tumor (OM, 75 cases), and HCC group (131 cases) in a 1:1 ratio. MRI features of the lesions were analyzed and classified according to LI-RADS v2018 criteria (tie-break rule was applied to lesions with both HCC and LR-M features). Taking the pathological results as the gold standard, the sensitivity and specificity of the LI-RADS v2018 classification criteria and the more stringent LR-5 criteria (with three main signs of HCC at the same time) were calculated for HCC, OM or benign lesions diagnosis. Mann -Whitney U test was used to compare the classification results. Results:The number of cases classified as LR-M, LR-1, LR-2, LR-3, LR-4, and LR-5 in HCC group after applying the tie-break rule were 14, 0, 0, 12, 28, and 77, respectively. There were 40, 0, 0, 4, 17, 14 and 8, 5, 1, 26, 13, 3 cases in benign and OM group, respectively. There were 41 (41/77), 4 (4/14) and 1 (1/3) lesion case in the HCC, OM and benign group, respectively, that met the more stringent LR-5 criteria. The sensitivity of LR-4 combined with LR-5 (LR-4/5) criteria, LR-5 criteria and more stringent LR-5 criteria for HCC diagnosis were 80.2% (105/131), 58.8% (77/131) and 31.3% (41/131), respectively, and the specificity were 64.1% (84/131), 87.0% (114/131) and 96.2% (126/131), respectively. The sensitivity and specificity of LR-M were 53.3% (40/75) and 88.2% (165/187), respectively. The sensitivity and specificity using LR-1 combined with LR-2 (LR-1/2) criteria for the diagnosis of benign liver lesions were 10.7% (6/56) and 100% (206/206), respectively.Conclusions:LR-1/2, LR-5, and LR-M criteria have high diagnostic specificity for intrahepatic lesions with a diameter of ≤3.0 cm. Lesions classified as LR-3 are more likely to be benign. The specificity of LR-4/5 criteria is low, while the more stringent LR-5 criteria has a high specificity for HCC diagnosis.