Triclosan (TCS) and triclocarban (TCC) are widely used synthetic broad-spectrum antibacterial agents that can enter the human body through the skin, gastrointestinal tract, and other pathways. More and more studies have found that exposure to TCS and TCC can affect human health, but currently, review reports on the health effects of human exposure to TCS and TCC are limited. Therefore, this study reviewed population studies on the relationship between TCS and TCC exposure and health effects by searching the PubMed database, summarized the associated health outcomes, and elucidated the biological mechanisms. A total of 56 studies were retrieved, among which cross-sectional studies (25 studies, 44.64%) and cohort studies (25 studies, 44.64%) accounted for a relatively large proportion, while case-control studies (6 studies, 10.72%) were relatively few. Studies on TCS exposure (48 studies, 85.71%) were far more prevalent than those on TCC exposure (2 studies, 3.57%). The remaining 6 studies involved both TCS and TCC exposure. The research results revealed that TCS exposure was associated with male and female abnormal reproductive functions, fetal growth restriction, abnormal behavior development in children, obesity, gestational diabetes mellitus (GDM), and immune-related diseases. Although the results of different studies show significant differences, they have indicated that exposure to TCS is a potential risk factor for these health problems. Due to the limited number of studies, the evidence for the relationship between TCC exposure and most of the aforementioned health effects is insufficient. Population studies and in vitro and in vivo studies have shown that exposure to TCS and TCC can interfere with the microbial homeostasis, the endocrine system, oxidative stress and immune function of the body, which are potential mechanisms causing adverse health effects. In the future, large-scale prospective cohort studies, as well as in vivo and in vitro studies, are still needed to further clarify the associations between TCS and TCC exposure and health effects, and to deeply explore its mechanism of action. These efforts will provide references for clarifying the human health hazards of TCS and TCC exposure and formulating targeted prevention and control strategies.

Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. Methods Patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. Results A total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.

Objective Currently, there is no commercially available quantitative detection kit for the main Felis domestic allergen (Fel d 1) in China. To establish a rapid detection method for Fel d 1, this study aims to prepare monoclonal antibodies against Fel d 1 protein. Methods The codon preference of Escherichia coli was utilized to optimize and synthesize the Fel d 1 gene. The prokaryotic expression plasmid pET-28a-Fel d 1 was constructed and used to express and purify the recombinant Fel d 1 protein. Subsequently, the recombinant protein was immunized into BALB/c mice and monoclonal antibodies (mAbs) were prepared by the hybridoma technique. An indirect ELISA was established using the recombinant Fel d 1 as the coating antigen, and hybridoma cell lines were screened for positive clones. The specificity and antigenic epitopes of the mAbs were confirmed by Western blot analysis. Finally, the selected hybridoma cells were injected into the peritoneal cavities of BALB/c mice for large-scale monoclonal antibody production. Results The recombinant plasmid pET-28a-Fel d 1 was successfully constructed, and soluble Fel d 1 protein was obtained after optimizing the expression conditions. Western blot and antibody titer assays confirmed the successful isolation of two hybridoma cell lines, 7D11 and 5H4, which stably secreted mAbs specific to Fel d 1. Antibody characterization revealed that the 5H4 mAb was of the IgG2a subtype and could recognize the amino acid region 105-163 of Fel d 1, while the 7D11 mAb was the IgG1 subtype and could recognize the amino acid region 1-59. Conclusion The high-purity recombinant Fel d 1 protein produced in this study provides a promising alternative for clinical immunotherapy of cat allergies. Furthermore, the monoclonal antibody prepared in this experiment lays a material foundation for the in-depth study of the biological function of Fel d 1 and the development of ELISA detection.
Animals ; Antibodies, Monoclonal/biosynthesis* ; Mice, Inbred BALB C ; Cats ; Mice ; Allergens/genetics* ; Glycoproteins/genetics* ; Enzyme-Linked Immunosorbent Assay ; Hybridomas/immunology* ; Recombinant Proteins/genetics* ; Female ; Antibody Specificity

Objective:To systematically synthesize the experiences and needs of thyroid cancer patients and their stakeholders in participating in treatment decision-making, so as to provide evidence-based theoretical support for the development of localized decision aids and the implementation of shared decision-making in China.Methods:A systematic search was conducted in PubMed, Web of Science, Cochrane Library, Embase, CINAHL, China National Knowledge Infrastructure, VIP, Wanfang Data, and China Biomedical Literature Database for qualitative studies on the experiences and needs of thyroid cancer patients and their stakeholders in treatment decision-making. The search covered the period from the establishment of the databases to May 20, 2025. The 2020 version of the Joanna Briggs Institute (JBI) critical appraisal tool for qualitative research was used to assess study quality. Aggregative synthesis was applied to integrate the findings.Results:A total of 19 studies were included, from which 94 individual findings were extracted. These findings were categorized into 15 new categories and further synthesized into four overarching themes: role dynamics in treatment decision-making; challenging trade-offs between risks and benefits of treatment choices; multifactorial influences on treatment decisions; and multidimensional support needs.Conclusions:Treatment decision-making among thyroid cancer patients involves complex role identification, conflicting emotional experiences, and risk-benefit deliberations influenced by multiple factors. It is essential to build a "clinician-family-society" decision-support ecosystem encompassing informational, psychological, and social support. Future efforts should focus on developing culturally appropriate decision aids that integrate emotional support and innovative technologies to promote shared decision-making, enhance decision quality, and improve patient satisfaction.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.

Epigenetic modifications of RNA play a crucial role in the initiation and progression of tumors.Among these modifications,N6-methyladenosine(m6A)modification catalyzed by the METTL3 methyltransferase is one of the most common RNA modifications.It is essential for regulating RNA transcription,stability,and translation.At the same time,long non-coding RNAs(lncRNAs)also serve as important regulatory molecules and play a significant role in the development and progression of tumors.However,the reciprocal interaction between lncRNAs and METTL3,as well as their functional mechanisms within tumors,are not fully understood.This paper aims to provide a comprehensive over-view of the mutual regulation between lncRNAs and METTL3 in tumors.The goal is to offer new insights and theoretical foundations for a better understanding of tumor pathogenesis.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.