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.

Short-chain fatty acids （SCFA） are the main metabolic products generated by the fermentation of dietary fiber by gut microbiota. Studies have shown that SCFA not only play a role in energy metabolism， but also act as important signaling molecules， exhibiting a significant potential in alleviating liver and pancreatic fibrosis. The core mechanism of SCFA mainly involves the regulation of various key signaling pathways by activating G protein-coupled receptors and inhibiting the activity of histone deacetylase， thereby suppressing the activation and proliferation of hepatic stellate cell （HSC） and pancreatic stellate cell （PSC）， which is a key link in fibrosis formation. In addition， SCFA can effectively alleviate tissue inflammation response， improve intestinal barrier function， and regulate gut microbiota balance， thus indirectly preventing the process of fibrosis mediated by the “gut-liver/pancreas axis”. Compared with the research on SCFA in liver fibrosis， studies on their role in pancreatic fibrosis are limited. Given that HSC and PSC are highly homologous， the transcription factors and proteins that have been confirmed in liver fibrosis-related studies are also similarly expressed in PSC， suggesting that they may also influence the activation of PSC. This article systematically summarizes the recent advances in the research on SCFA in alleviating liver and pancreatic fibrosis， in order to provide new perspectives for exploring the mechanism of pancreatic fibrosis and developing related interventional strategies.

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.

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*