Ulcerative colitis （UC）， a chronic， non-specific inflammatory bowel disease with typical symptoms such as abdominal pain， diarrhea， and bloody stools， demonstrates a high relapse rate and difficulty in curing. Sishenwan， first recorded in Internal Medicine Abstract （Nei Ke Zhai Yao）， are a classic prescription for treating diarrhea caused by deficiency of the spleen and kidney Yang. The core therapeutic principle of Sishenwan is warming and tonifying the spleen and kidney， and astringing the intestine and stopping diarrhea. In recent years， Sishenwan have demonstrated distinct advantages in the clinical treatment of UC. The pathogenesis of UC involves multiple factors， including immune dysregulation and gut microbiota imbalance. Although Western medicine is effective in the short term， its side effects， high relapse rate， and resistance associated with long-term use pose substantial challenges. Sishenwan have shown excellent clinical outcomes in the treatment of UC due to deficiency of the spleen and kidney Yang. Modern clinical studies indicate that Sishenwan， used alone or in combination with Western medicine or other Chinese medicine compound prescriptions， significantly improve the clinical efficacy in treating UC due to deficiency of the spleen and kidney Yang. Sishenwan effectively alleviate core symptoms such as mucus， pus， and blood in stools， and persistent abdominal pain， reduce Mayo scores and the relapse rate， and improve patients' quality of life. Research on the material basis reveals that Sishenwan contain multiple active ingredients such as psoralen， isopsoralen， and evodiamine. Mechanism studies indicate that Sishenwan inhibit the inflammatory cascade reactions by regulating the signal network through multiple targets. Sishenwan regulate cellular immunity and restore intestinal immune homeostasis. At the microecological level， Sishenwan promote the intestinal barrier repair through the "microbiota-metabolism-immunity" axis. The current research still needs to be deepened in aspects such as the mining of specific biomarkers for syndromes and the exploration of the collaborative mechanism of traditional Chinese and Western medicine. In the future， a full-chain system covering syndrome differentiation， targeting， and monitoring needs to be constructed for promoting the paradigm transformation of Sishenwan into precision drugs. This review systematically explains the treatment mechanism of Sishenwan regarding the combination of disease and syndrome and its multi-target regulatory characteristics， providing a theoretical basis and transformation direction for the treatment of UC with integrated traditional Chinese and Western medicine.

Ulcerative colitis （UC）， a chronic， non-specific inflammatory bowel disease with typical symptoms such as abdominal pain， diarrhea， and bloody stools， demonstrates a high relapse rate and difficulty in curing. Sishenwan， first recorded in Internal Medicine Abstract （Nei Ke Zhai Yao）， are a classic prescription for treating diarrhea caused by deficiency of the spleen and kidney Yang. The core therapeutic principle of Sishenwan is warming and tonifying the spleen and kidney， and astringing the intestine and stopping diarrhea. In recent years， Sishenwan have demonstrated distinct advantages in the clinical treatment of UC. The pathogenesis of UC involves multiple factors， including immune dysregulation and gut microbiota imbalance. Although Western medicine is effective in the short term， its side effects， high relapse rate， and resistance associated with long-term use pose substantial challenges. Sishenwan have shown excellent clinical outcomes in the treatment of UC due to deficiency of the spleen and kidney Yang. Modern clinical studies indicate that Sishenwan， used alone or in combination with Western medicine or other Chinese medicine compound prescriptions， significantly improve the clinical efficacy in treating UC due to deficiency of the spleen and kidney Yang. Sishenwan effectively alleviate core symptoms such as mucus， pus， and blood in stools， and persistent abdominal pain， reduce Mayo scores and the relapse rate， and improve patients' quality of life. Research on the material basis reveals that Sishenwan contain multiple active ingredients such as psoralen， isopsoralen， and evodiamine. Mechanism studies indicate that Sishenwan inhibit the inflammatory cascade reactions by regulating the signal network through multiple targets. Sishenwan regulate cellular immunity and restore intestinal immune homeostasis. At the microecological level， Sishenwan promote the intestinal barrier repair through the "microbiota-metabolism-immunity" axis. The current research still needs to be deepened in aspects such as the mining of specific biomarkers for syndromes and the exploration of the collaborative mechanism of traditional Chinese and Western medicine. In the future， a full-chain system covering syndrome differentiation， targeting， and monitoring needs to be constructed for promoting the paradigm transformation of Sishenwan into precision drugs. This review systematically explains the treatment mechanism of Sishenwan regarding the combination of disease and syndrome and its multi-target regulatory characteristics， providing a theoretical basis and transformation direction for the treatment of UC with integrated traditional Chinese and Western medicine.

Breast cancer is one of the most common and fatal malignancies among women worldwide, and its treatment efficacy is often limited by drug resistance and the presence of undruggable targets. Traditional small-molecule drugs have difficulty effectively modulating certain critical targets such as transcription factors and non-coding RNAs, necessitating new therapeutic strategies. Proteolysis-targeting chimeras (PROTACs) function by recruiting pathogenic proteins to the cellular ubiquitin-proteasome system, thereby inducing their specific degradation. In contrast, ribonuclease-targeting chimeras (RIBOTACs) utilize small-molecule ligands but bind to RNA and direct endogenous RNases to selectively degrade pathogenic RNA molecules. By employing a "degradation rather than inhibition" mechanism, targeting chimera technology broadens the druggable landscape and offers a novel precision therapeutic strategy for breast cancer, particularly for refractory and drug-resistant cases. This approach not only overcomes the limitations of traditional drugs, such as the absence of suitable binding sites or poor selectivity, but also reduces required dosages and potential adverse effects. Recent studies have preliminarily demonstrated the therapeutic potential of PROTACs and RIBOTACs in breast cancer, encompassing target design, mechanistic investigation, and preclinical as well as early clinical applications. Research into these technologies reveals their ability to tackle previously undruggable targets, thereby providing theoretical support for the development of safer and more effective precision therapies for breast cancer. In the future, with advances in drug delivery systems and clinical trials, PROTACs and RIBOTACs are expected to be used synergistically with immunotherapy and chemotherapy, offering breast cancer patients more promising comprehensive treatment options and potentially driving oncology toward broader intervention of undruggable targets.
Humans ; Breast Neoplasms/drug therapy* ; Female ; Proteolysis ; Ribonucleases/metabolism* ; Molecular Targeted Therapy/methods* ; Antineoplastic Agents/therapeutic use*

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions,thereby offering optimized guidance for clinical diagnosis and treatment plan-ning,and ultimately enhancing medical efficiency and treatment outcomes. Methods First,TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people,from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ),and a dataset encompassing labelled constitutions was constructed.Second,heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition,a dual-branch deep network was employed to collect deep features from the full-body standing images.Last,the random forest(RF)algorithm was utilized to learn the extracted multifea-tures,which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy,precision,and F1 score were the three measures selected to assess the perfor-mance of the model. Results It was found that the accuracy,precision,and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842,0.868,and 0.790,respectively.In comparison with the identification models that encompass a single feature,either a single facial complexion feature,a body shape feature,or deep features,the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105,0.105,and 0.079,the precision increased by 0.164,0.164,and 0.211,and the F1 score rose by 0.071,0.071,and 0.084,respectively. Conclusion The research findings affirmed the viability of the proposed model,which incor-porated multifeatures,including the facial complexion feature,the body shape feature,and the deep feature.In addition,by employing the proposed model,the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.