OBJECTIVE To investigate the ameliorative effect and potential mechanism of imperatorin （IMP） on doxorubicin （DOX） resistance in breast cancer. METHODS The effects of maximum non-toxic concentration （100 μg/mL） of IMP combined with different concentrations of DOX （12.5， 25， 50， 75， 100 μg/mL） on the proliferation of MCF-7/DOX cells were determined by MTT method. MCF-7/DOX cells were divided into blank control group （1‰ dimethyl sulfoxide）， DOX group （50 μg/mL）， IMP+DOX group （100 μg/mL IMP+50 μg/mL DOX） and IMP group （100 μg/mL）. mRNA and protein expressions of multidrug resistance protein 1 （MDR1） and multidrug resistance-associated protein 1 in each group were measured. The relevant pathways and targets involved in the improvement of DOX resistance in breast cancer cells by IMP were screened and validated by using transcriptome sequencing technology， along with gene ontology （GO） enrichment analyses and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. RESULTS Compared with DOX alone， the combination of IMP and DOX reduced the half inhibitory concentration of DOX on MCF-7/DOX cells from 81.965 μg/mL to 43.170 μg/mL， the reverse fold was 1.90， and the mRNA expression of MDR1 was significantly down-regulated （P＜0.05）. The results of GO enrichment analyses and KEGG pathway enrichment analyses indicated that the reversal of DOX resistance in breast cancer by IMP was mainly associated with the regulation of biological processes such as detoxification， multiple biological processes， and cell killing. The main pathway involved was the p53 signaling pathway， and the key targets mainly included constitutively photomorphogenic protein 1 （COP1）， cyclin E1 （CCNE1）， growth arrest and DNA damage-inducible protein 45A E-mail：tangxilan1983@163.com （GADD45A） and GADD45B. The results of the verification experiments showed that compared with DOX group， there was a trend of up-regulation of COP1 mRNA， and significant down- regulation of CCNE1， GADD45A， and GADD45B mRNA expression in IMP+DOX group （P＜0.05）. CONCLUSIONS The effect of IMP in ameliorating DOX resistance in breast cancer is related to its regulation of COP1， CCNE1， GADD45A and GADD45B targets in the p53 signaling pathway.

Immunotherapy has become a pivotal modality in clinical cancer treatment. However, its effectiveness is limited to a small subset of patients due to the low antigenicity, impaired innate response, and various adaptive immune resistance mechanisms of the tumor microenvironment (TME). Accumulating evidence reveals the critical roles of metal elements in shaping immunity against tumor progression and metastasis. The marriage of metalloimmunotherapy and nanotechnology further presents new opportunities to optimize the physicochemical and pharmacokinetic properties of metal ions in a precise spatiotemporal control manner. Several metallodrugs have demonstrated encouraging immunotherapeutic potential in preliminary studies and are currently undergoing clinical trials at different stages, yet challenges persist in scaling up production and addressing long-term biosafety concerns. This review delineates how metal materials modulate biological activities across diverse cell types to orchestrate antitumor immunity. Moreover, it summarizes recent progress in smart drug delivery-release systems integrating metal elements, either as cargo or vehicles, to enhance antitumor immune responses. Finally, the review introduces current clinical applications of nanomedicines in metalloimmunotherapy and discusses potential challenges that impede its widespread translation into clinical practice.

Paradoxical psoriasis is a special adverse drug reaction characterized by the new onset, exacerbation, or phenotypic change of psoriatic lesions during treatment with biological agents. In recent years, with the increasing use of biologics, this condition has garnered growing attention from clinicians. The pathogenesis of paradoxical psoriasis is complex and its clinical manifestations are highly heterogeneous. Diagnosis currently relies primarily on clinical features and medication history due to the lack of unified diagnostic criteria. Furthermore, treatment strategies—such as whether to discontinue the original biologic agent or switch therapies—remain controversial, posing significant challenges in clinical management.This article presents a case of paradoxical psoriasis occurring in a patient with ankylosing spondylitis following treatment with the tumor necrosis factor-α inhibitor (TNFi) infliximab. By discussing the clinical characteristics of this case, we aim to enhance clinicians' understanding of this condition, reduce misdiagnosis and underdiagnosis, and provide valuable insights for its diagnosis and treatment.

OBJECTIVE To study the effects of transferrin-targeting peptide T7 （7pep） on intracellular transportation of polyethylene glycol-polycaprolactone （PEG-PCL） micelles in human cervical cancer HeLa cells. METHODS Using coumarin-6 （C6） as fluorescent indicator probe， both coumarin-6 （C6）-loaded PEG-PCL （PEG-PCL-C6） micelles and 7pep-modified PEG- PCL （7pep-PEG-PCL-C6） micelles were prepared by film-dispersion method. The particle size， polydispersity index and appearance morphology were compared between two types of micelles； the real-time uptake of two types of micelles by HeLa cells was compared， and the colocalization of two types of micelles with early endosomes （EE）， endocytic recycling compartments （ERC） and late endosomes （LE） after entry into the cells was observed. RESULTS The particle sizes of PEG-PCL-C6 and 7pep-PEG-PCL- C6 micelles were（75.0±2.3）and（82.0±1.5）nm； the polymer dispersity indexes were 0.17±0.20 and 0.17±0.32， respectively， with a regular spherical appearance. The colocalization results showed that entry speed and amount of 7pep-PEG-PCL-C6 micelles were significantly faster/more than those of PEG-PCL-C6 micelles. 7pep-PEG-PCL-C6 micelles entered EE faster than PEG-PCL-C6 micelles， while PEG-PCL-C6 micelles entered ERC at a faster rate than 7pep-PEG-PCL-C6 micelles， and both PEG-PCL-C6 micelles and 7pep-PEG-PCL-C6 micelles tended to accumulate gradually in LE； Pearson coefficient， signal overlap ratio， and colocalization ratio of 7pep-PEG-PCL-C6 micelles with LE were significantly lower 60 minutes after entering the cell than those 30 minutes after entering the cell （P＜0.05 or P＜0.01）. CONCLUSIONS Targeting 7pep modification can increase the entry speed and amount of PEG-PCL-C6 micelles， and also alter their intracellular transportation behavior.

Objective:Analyze the operation mode of the valuation investment of scientific and technological achievements in hospitals, and provide a certain reference for other hospitals to carry out valuation investment in scientific and technological achievements.Methods:This paper analyzed the origin and current situation of the valuation investment of scientific and technological achievements. Taking Peking University Cancer Hospital as an example, it made an in-depth analysis of the valuation investment model of scientific and technological achievements.Results:The study found that the valuation investment of scientific and technological achievements in hospitals includes several key links such as signing the Valuation Investment Agreement, signing the Shareholder Agreement and the Articles of Association, the registration and identification of technical contracts, the issuance of invoices, and deferred taxation. And several suggestions on how to apply this model for the transformation of scientific and technological achievements were put forward.Conclusions:Although the implementation process of scientific and technological achievements is cumbersome, the operation mode is diverse, and the ownership of equity is difficult to distinguish, the advantages are extremely obvious. It can closely combine technological capital and industrial capital, form a strong and effective technological alliance and a community of multiple interests, better use the market-oriented motivation of scientific and technological innovation, and carry out cutting-edge research in line with market prospects.

【Objective】 To investigate the expression of Toll-like receptor 9 (TLR9) in B cells in the peripheral blood of patients with allergic rhinitis (AR), allergic asthma (AA), AR combined with AA (ARA) and the blood or lung tissue of sensitized mice, as well as the effect of allergens on its expression. 【Methods】 A total of 100 volunteers from The First Affiliated Hospital of Jinzhou Medical University were recruited for outpatient and acute inpatient attacks, consisting of 19 healthy people (HC) with negative prick test result, 40 AR patients, 26 AA patients, and 15 ARA patients with positive prick test result. The expression of TLR9 in the peripheral blood B cells of the patients before and after stimulation by house dust mite allergen extract (HDME), Artemisia sieversiana wild allergen extract (ASWE), and Platanus pollen allergen extract (PPE) was detected by flow cytometry. AR and AA sensitization models were established in WT mice and FcεRI-KO mice to detect the effects of allergens and FcεRI on the expression of TLR9 in B cells. 【Results】 The expression and mean fluorescence intensity (MFI) of TLR9 in peripheral blood B cells of unstimulated AR, AA and ARA patients were higher than those of HC. After allergen stimulation, the expression of TLR9 and its MFI in blood B cells of AR and AA patients increased (P<0.05). In WT mice and FcεRI-KO mice, compared with NS control mice, MFI was increased in almost each group. Compared with the NS control group, there was no significant difference in the expression of TLR9⁺ in B cells in the lung tissues of AA mice with FcεRI-KO after allergen challenge, but their MFI increased. FcεRI-KO mice had lower TLR9⁺ MFI in B cells after allergen challenge compared with WT mice. 【Conclusion】 TLR9 in B cells may be involved in the occurrence of AR and AA, and detecting the expression of TLR9 in B cells may be a new direction for the diagnosis of AR and AA.

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.

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.