Objective·To determine hyaluronan(HA)expression in the endocrine-resistant microenvironment of estrogen receptor-positive(ER+)breast cancer and elucidate its impact on the acquired resistance.Methods·Chemiluminescent immunoassay was used to quantify HA levels in the culture supernatants of fulvestrant-resistant breast cancer cells.An immunofluorescence(IF)assay was performed to visualize the colocalization of CD44 and HA in MCF7/FulR cells.Using an established adaptive endocrine-resistant breast cancer mouse model,HA expression in resistant breast cancer tissues was assessed by immunohistochemistry(IHC)assay.Single-cell RNA sequencing(scRNA-seq)and RNA sequencing(RNA-seq)were conducted to examine transcriptomic profiles and alterations in HA-related genes in resistant breast cancer cells.Flow cytometry(FCM)was utilized to measure the proportion of CD44+CD24-cells in MCF7/FulR.The correlation between HA synthesis genes and cell stemness was investigated in clinical ER+breast cancers from GEO data sets.Hyaluronidase(HAase)treatment was applied to remove the"HA coat",and RT-qPCR and Western blotting analysis were carried out to monitor changes in stemness-related molecules.CCK-8 assays,flow cytometry(FCM),and Hoechst 33258 staining were performed to determine changes in apoptosis and fulvestrant efficiency after HAase treatment.Results·IF results revealed that compared with MCF7 cells,the"HA coat"on the surface of MCF7/FulR cells was significantly thickened.IHC demonstrated markedly increased HA retention in fulvestrant-resistant mouse breast cancer tissues.ScRNA-seq and RNA-seq analyses indicated elevated expression of stemness-related genes and HA synthesis-associated genes in fulvestrant-resistant breast cancer cells.Correlation analysis revealed a positive association between HA synthesis and cancer stemness in ER+breast cancer.IF and RT-qPCR results demonstrated that removing the HA coating from the surface of MCF7/FulR cells led to a significant reduction in the expression of stemness-related molecules;concurrently,CCK-8 assays,FCM analysis,and Hoechst 33258 staining revealed that"HA coat"clearance reduced MCF7/FulR'tolerance to fulvestrant and increased apoptosis.Conclusion·Endocrine-resistant breast cancer cells develop an enriched"HA coat",which promotes stemness in fulvestrant-resistant tumors.Disruption of this HA coat through HAase treatment effectively reduces cell stemness,induces apoptosis,and re-sensitizes breast cancer cells to fulvestrant.

Breast cancer is the leading cause of cancer-related mortality among women worldwide and has drawn extensive research attention.Owing to its molecular heterogeneity,drug resistance,and low therapeutic response,single-modality treatments often fail to achieve satisfactory efficacy or broad applicability.Combination therapy,designed based on the pathophysiological characteristics,related signaling pathways,and biomarkers of breast cancer,has emerged as a promising approach for improving therapeutic outcomes.With the advancement of research on combination strategies,the understanding of their molecular mechanisms—particularly key signaling pathways and biomarkers—has become increasingly important.However,comprehensive reviews addressing these molecular mechanisms and synergistic strategies remain scarce.This article summarizes recent advances in combination therapy for breast cancer,providing a comprehensive review of recent combination therapies for breast cancer and their underlying molecular mechanisms,and focusing on key signaling pathways involved in combination therapy and synergistic strategies,thereby providing theoretical insights and reference for researchers,graduate students,and clinicians engaged in the development of novel combination therapeutic strategies for breast cancer and related malignancies.

Objective·To determine hyaluronan(HA)expression in the endocrine-resistant microenvironment of estrogen receptor-positive(ER+)breast cancer and elucidate its impact on the acquired resistance.Methods·Chemiluminescent immunoassay was used to quantify HA levels in the culture supernatants of fulvestrant-resistant breast cancer cells.An immunofluorescence(IF)assay was performed to visualize the colocalization of CD44 and HA in MCF7/FulR cells.Using an established adaptive endocrine-resistant breast cancer mouse model,HA expression in resistant breast cancer tissues was assessed by immunohistochemistry(IHC)assay.Single-cell RNA sequencing(scRNA-seq)and RNA sequencing(RNA-seq)were conducted to examine transcriptomic profiles and alterations in HA-related genes in resistant breast cancer cells.Flow cytometry(FCM)was utilized to measure the proportion of CD44+CD24-cells in MCF7/FulR.The correlation between HA synthesis genes and cell stemness was investigated in clinical ER+breast cancers from GEO data sets.Hyaluronidase(HAase)treatment was applied to remove the"HA coat",and RT-qPCR and Western blotting analysis were carried out to monitor changes in stemness-related molecules.CCK-8 assays,flow cytometry(FCM),and Hoechst 33258 staining were performed to determine changes in apoptosis and fulvestrant efficiency after HAase treatment.Results·IF results revealed that compared with MCF7 cells,the"HA coat"on the surface of MCF7/FulR cells was significantly thickened.IHC demonstrated markedly increased HA retention in fulvestrant-resistant mouse breast cancer tissues.ScRNA-seq and RNA-seq analyses indicated elevated expression of stemness-related genes and HA synthesis-associated genes in fulvestrant-resistant breast cancer cells.Correlation analysis revealed a positive association between HA synthesis and cancer stemness in ER+breast cancer.IF and RT-qPCR results demonstrated that removing the HA coating from the surface of MCF7/FulR cells led to a significant reduction in the expression of stemness-related molecules;concurrently,CCK-8 assays,FCM analysis,and Hoechst 33258 staining revealed that"HA coat"clearance reduced MCF7/FulR'tolerance to fulvestrant and increased apoptosis.Conclusion·Endocrine-resistant breast cancer cells develop an enriched"HA coat",which promotes stemness in fulvestrant-resistant tumors.Disruption of this HA coat through HAase treatment effectively reduces cell stemness,induces apoptosis,and re-sensitizes breast cancer cells to fulvestrant.

Breast cancer is the leading cause of cancer-related mortality among women worldwide and has drawn extensive research attention.Owing to its molecular heterogeneity,drug resistance,and low therapeutic response,single-modality treatments often fail to achieve satisfactory efficacy or broad applicability.Combination therapy,designed based on the pathophysiological characteristics,related signaling pathways,and biomarkers of breast cancer,has emerged as a promising approach for improving therapeutic outcomes.With the advancement of research on combination strategies,the understanding of their molecular mechanisms—particularly key signaling pathways and biomarkers—has become increasingly important.However,comprehensive reviews addressing these molecular mechanisms and synergistic strategies remain scarce.This article summarizes recent advances in combination therapy for breast cancer,providing a comprehensive review of recent combination therapies for breast cancer and their underlying molecular mechanisms,and focusing on key signaling pathways involved in combination therapy and synergistic strategies,thereby providing theoretical insights and reference for researchers,graduate students,and clinicians engaged in the development of novel combination therapeutic strategies for breast cancer and related malignancies.

Objective:To observe the efficacy of plasma beam combined with intense pulsed light on facial burn scars and its impact on scar score and pain level.Method:This study was a prospective study,and a random number table method was used to divided 92 patients with facial burn scars who were admitted to Maoming People's Hospital from March 2024 to August 2024 into control group(received intense pulsed light treatment,46 cases)and study group(received plasma beam treatment in addition to the control group,46 cases).The efficacy,related scale scores,scar recovery and incidence of adverse reaction rates between two groups were compaerd.Result:Compared with control group after treatment,the clinical total effective rate and the chinese version of the simplified burn health scale(BSHS-B)score of the study group were higher,while vancouver scar scale(VSS),visual analogue scale(VAS)scores,scar thickness,and scar blood flow perfusion were lower(P＜0.05).There was no significant difference in the incidence of adverse reaction rates between the two groups(P＞0.05).Conclusion:Plasma beam combined with intense pulsed light on facial burn scars,can improve clinical efficacy,improve scar thickness and blood flow perfusion,alleviate scar itching and pain,and has good safety in treating facial burn scar patients.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Objective:To observe the efficacy of plasma beam combined with intense pulsed light on facial burn scars and its impact on scar score and pain level.Method:This study was a prospective study,and a random number table method was used to divided 92 patients with facial burn scars who were admitted to Maoming People's Hospital from March 2024 to August 2024 into control group(received intense pulsed light treatment,46 cases)and study group(received plasma beam treatment in addition to the control group,46 cases).The efficacy,related scale scores,scar recovery and incidence of adverse reaction rates between two groups were compaerd.Result:Compared with control group after treatment,the clinical total effective rate and the chinese version of the simplified burn health scale(BSHS-B)score of the study group were higher,while vancouver scar scale(VSS),visual analogue scale(VAS)scores,scar thickness,and scar blood flow perfusion were lower(P＜0.05).There was no significant difference in the incidence of adverse reaction rates between the two groups(P＞0.05).Conclusion:Plasma beam combined with intense pulsed light on facial burn scars,can improve clinical efficacy,improve scar thickness and blood flow perfusion,alleviate scar itching and pain,and has good safety in treating facial burn scar patients.

The genome tagging project (GTP) plays a pivotal role in addressing a critical gap in the understanding of protein functions. Within this framework, we successfully generated a human influenza hemagglutinin-tagged sperm-specific protein 411 (HA-tagged Ssp411) mouse model. This model is instrumental in probing the expression and function of Ssp411. Our research revealed that Ssp411 is expressed in the round spermatids, elongating spermatids, elongated spermatids, and epididymal spermatozoa. The comprehensive examination of the distribution of Ssp411 in these germ cells offers new perspectives on its involvement in spermiogenesis. Nevertheless, rigorous further inquiry is imperative to elucidate the precise mechanistic underpinnings of these functions. Ssp411 is not detectable in metaphase II (MII) oocytes, zygotes, or 2-cell stage embryos, highlighting its intricate role in early embryonic development. These findings not only advance our understanding of the role of Ssp411 in reproductive physiology but also significantly contribute to the overarching goals of the GTP, fostering groundbreaking advancements in the fields of spermiogenesis and reproductive biology.
Animals ; Female ; Humans ; Male ; Mice ; Spermatids/metabolism* ; Spermatogenesis/physiology* ; Spermatozoa/metabolism* ; Thioredoxins/genetics*