The molecular mechanisms underlying the develop-ment and metastasis of prostate cancer remain elusive.This comprehensive review delves into the intricate role of cofilin,an actin-binding protein,in the pathogenesis and progression of prostate cancer.Cofilin is a significant protein in cytoskeletal dynamics,and any dysregulation may result in the morphological changes in normal cells and the invasion and metastasis of tumor cells.Research has revealed that the activity of cofilin is regula-ted by various mechanisms,including phosphorylation/dephos-phorylation and interactions with other molecules.Moreover,this review discusses promising therapeutic interventions,such as co-filin inhibitors and gene therapy,which have demonstrated effica-cy in preclinical models.The challenge of clinically preventing the transition to castration-resistant prostate cancer and tumor metastasis is widely recognized,necessitating the development of precise drug treatments and biomarker identification.As a key regulatory protein,cofilin provides a more comprehensive refer-ence for the prevention and treatment of prostate diseases.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

The molecular mechanisms underlying the develop-ment and metastasis of prostate cancer remain elusive.This comprehensive review delves into the intricate role of cofilin,an actin-binding protein,in the pathogenesis and progression of prostate cancer.Cofilin is a significant protein in cytoskeletal dynamics,and any dysregulation may result in the morphological changes in normal cells and the invasion and metastasis of tumor cells.Research has revealed that the activity of cofilin is regula-ted by various mechanisms,including phosphorylation/dephos-phorylation and interactions with other molecules.Moreover,this review discusses promising therapeutic interventions,such as co-filin inhibitors and gene therapy,which have demonstrated effica-cy in preclinical models.The challenge of clinically preventing the transition to castration-resistant prostate cancer and tumor metastasis is widely recognized,necessitating the development of precise drug treatments and biomarker identification.As a key regulatory protein,cofilin provides a more comprehensive refer-ence for the prevention and treatment of prostate diseases.

Non-syndromic oral cleft (NSOC), a common birth defect, remains to be a critical public health problem in China. In the context of adjustment of childbearing policy for two times in China and the increase of pregnancy at older childbearing age, NSOC risk prediction will provide evidence for high-risk population identification and prenatal counseling. Genome-wide association study and second generation sequencing have identified multiple loci associated with NSOC, facilitating the development of genetic risk prediction of NSOC. Despite the marked progress, risk prediction models of NSOC still faces multiple challenges. This paper summarizes the recent progress in research of NSOC risk prediction models based on the results of extensive literature retrieval to provide some insights for the model development regarding research design, variable selection, model-build strategy and evaluation methods.
Humans ; Cleft Palate/genetics* ; Cleft Lip/genetics* ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Risk Factors ; Polymorphism, Single Nucleotide

ObjectiveTo assess the prognostic value of ¹⁸F-FDG PET/CT parameters for predicting therapeutic response in diffuse large B-cell lymphoma （DLBCL）. MethodsWe retrospectively analyzed the clinical data and ¹⁸F-FDG PET/CT radiomics features of 81 DLBCL patients enrolled between June 2015 and October 2020. Multivariate logistic regression analysis was used to identify the predictive factors for therapeutic response of DLBCL， based on which a predictive model was developed accordingly. The performance of the model was evaluated by receiver operating characteristic （ROC） curves and calibration plots. ResultsDuring the two years after first chemotherapy， 23 patients （28.3%） developed relapse and 58 patients （71.7%） had progression-free survival （PFS）. The analysis for the predictive capability of the binary logistic regression model incorporating the PET/CT features revealed that the imaging features of ¹⁸F-FDG PET/CT after chemotherapy were independent prognostic factors for PFS. Among them， SUV_THR-mean2 was the most important factor for predicting therapeutic response in DLBCL patients after chemotherapy， with a cutoff value of 2.00 （AUC=0.81）. Conclusions¹⁸F-FDG PET/CT showed a valuable prognostic performance for PFS in DLBCL patients after chemotherapy， with the imaging feature after chemotherapy SUV_TLR-mean2 being the optimal independent predictor. Our predictive model of imaging features might have an important prognostic value in assessing the risk of disease progression， guiding the treatment and follow-up protocol， improving therapeutic efficiency and cutting down the medical cost.

OBJECTIVE: To explore the association between de novo mutations (DNM) and non-syndromic cleft lip with or without palate (NSCL/P) using case-parent trio design. METHODS: Whole-exome sequencing was conducted for twenty-two NSCL/P trios and Genome Analysis ToolKit (GATK) was used to identify DNM by comparing the alleles of the cases and their parents. Information of predictable functions was annotated to the locus with SnpEff. Enrichment analysis for DNM was conducted to test the difference between the actual number and the expected number of DNM, and to explore whether there were genes with more DNM than expected. NSCL/P-related genes indicated by previous studies with solid evidence were selected by literature reviewing. Protein-protein interactions analysis was conducted among the genes with protein-altering DNM and NSCL/P-related genes. R package "denovolyzeR" was used for the enrichment analysis (Bonferroni correction: P=0.05/n, n is the number of genes in the whole genome range). Protein-protein interactions among genes with DNM and genes with solid evidence on the risk factors of NSCL/P were predicted depending on the information provided by STRING database. RESULTS: A total of 339 908 SNPs were qualified for the subsequent analysis after quality control. The number of high confident DNM identified by GATK was 345. Among those DNM, forty-four DNM were missense mutations, one DNM was nonsense mutation, two DNM were splicing site mutations, twenty DNM were synonymous mutations and others were located in intron or intergenic regions. The results of enrichment analysis showed that the number of protein-altering DNM on the exome regions was larger than expected (P < 0.05), and five genes (KRTCAP2, HMCN2, ANKRD36C, ADGRL2 and DIPK2A) had more DNM than expected (P < 0.05/(2×19 618)). Protein-protein interaction analysis was conducted among forty-six genes with protein-altering DNM and thirteen genes associated with NSCL/P selected by literature reviewing. Six pairs of interactions occurred between the genes with DNM and known NSCL/P-related genes. The score measuring the confidence level of the predicted interaction between RGPD4 and SUMO1 was 0.868, which was higher than the scores for other pairs of genes. CONCLUSION Our study provided novel insights into the development of NSCL/P and demonstrated that functional analyses of genes carrying DNM were warranted to understand the genetic architecture of complex diseases.
Asians ; Case-Control Studies ; Cleft Lip/genetics* ; Cleft Palate/genetics* ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Genotype ; Humans ; Mutation ; Parents ; Polymorphism, Single Nucleotide ; Whole Exome Sequencing

Next-generation sequencing has revolutionized family-based association tests for rare variants. As the lower power of genome wide association study for detecting casual rare variants, methods aggregating effects of multiple variants have been proposed, such as burden tests and variance component tests. This paper summarizes the methods of rare variants association test that can be applied for family data, introduces their principles, characteristics and applicable conditions and discusses the shortcomings and the improvement of the present methods.
Computer Simulation ; Family Relations ; Genetic Association Studies ; Genetic Variation ; Genome-Wide Association Study/methods* ; Humans

Child ; Consensus ; Drugs, Chinese Herbal ; Humans ; Medicine, Chinese Traditional ; Respiratory System

Diagnosis, Differential ; Humans ; Sarcoma, Myeloid/diagnosis*