Triple-negative breast cancer (TNBC) represents a distinctive subtype, characterized by the absence of estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2 (HER2). Due to its high inter-tumor and intra-tumor heterogeneity, TNBC poses significant chanllenges for personalized diagnosis and treatment. The advant of clustered regular interspaced short palindromic repeats (CRISPR) technology has profoundly enhanced our understanding of the structure and function of the TNBC genome, providing a powerful tool for investigating the occurrence and development of diseases. This review focuses on the application of CRISPR/Cas technology in the personalized diagnosis and treatment of TNBC. We begin by discussing the unique attributes of TNBC and the limitations of current diagnostic and treatment approaches: conventional diagnostic methods provide limited insights into TNBC, while traditional chemotherapy drugs are often associated with low efficacy and severe side effects. The CRISPR/Cas system, which activates Cas enzymes through complementary guide RNAs (gRNAs) to selectively degrade specific nucleic acids, has emerged as a robust tool for TNBC research. This technology enables precise gene editing, allowing for a deeper understanding of TNBC heterogeneity by marking and tracking diverse cell clones. Additionally, CRISPR facilitates high-throughput screening to promptly identify genes involved in TNBC growth, metastasis, and drug resistance, thus revealing new therapeutic targets and strategies. In TNBC diagnostics, CRISPR/Cas was applied to develop molecular diagnostic systems based on Cas9, Cas12, and Cas13, each employing distinct detection principles. These systems can sensitively and specifically detect a variety of TNBC biomarkers, including cell-specific DNA/RNA and circulating tumor DNA (ctDNA). In the realm of precision therapy, CRISPR/Cas has been utilized to identify key genes implicated in TNBC progression and treatment resistance. CRISPR-based screening has uncovered potential therapeutic targets, while its gene-editing capabilities have facilitated the development of combination therapies with traditional chemotherapy drugs, enhancing their efficacy. Despite its promise, the clinical translation of CRISPR/Cas technology remains in its early stages. Several clinical trials are underway to assess its safety and efficacy in the treatment of various genetic diseases and cancers. Challenges such as off-target effects, editing efficiency, and delivery methods remain to be addressed. The integration of CRISPR/Cas with other technologies, such as 3D cell culture systems, human induced pluripotent stem cells (hiPSCs), and artificial intelligence (AI), is expected to further advance precision medicine for TNBC. These technological convergences can offer deeper insights into disease mechanisms and facilitate the development of personalized treatment strategies. In conclusion, the CRISPR/Cas system holds immense potential in the precise diagnosis and treatment of TNBC. As the technology progresses and becomes more costs-effective, its clinical relevance will grow, and the translation of CRISPR/Cas system data into clinical applications will pave the way for optimal diagnosis and treatment strategies for TNBC patients. However, technical hurdles and ethical considerations require ongoing research and regulation to ensure safety and efficacy.

ObjectiveTo investigate the perceived experience and acceptance of intrapartum ultrasound （IPUS） as a novel method for labor progress assessment among pregnant women. MethodsFrom February 2023 to December 2024， a total of 180 pregnant women admitted to the Labor Ward of Lingnan Hospital， the Third Affiliated Hospital of Sun Yat-sen University， who were planned for vaginal trial of labor ， were accessed for labor progress using IPUS and vaginal examination （VE） after the onset of labor and prior to the initiation n of labor analgesia. A self-designed questionnaire was used to investigate the women's perceived experiences with both examination methods and their acceptance of IPUS. The pain intensity associated with the examinations was evaluated using the visual analogue pain scale （VAS）. Differences in the women's experiences and pain intensity between the two labor progress assessment methods were compared. ResultsThe acceptance rate of IPUS was 96.67% （174/180）， with the remaining 6 cases undecided. Over 60% of the pregnant women reported IPUS assessment as comfortable and none of them felt discomfort， whereas 32.8% felt uncomfortable with VE （χ²=196.02， P＜0.001）. Nearly two-thirds of the pregnant women believed that VE would cause psychological distress， while none reported such effect with IPUS （χ²=261.52， P＜0.001）. Approximately 77.78% （140/180） of the pregnant women believed that IPUS could reduce their fear of vaginal delivery and enhance their confidence if it replaced VE. The VAS score for IPUS ［0 （0， 2）］ was significantly lower than that for VE ［4 （4， 6）］ （Z=-14.62， P＜0.001）. Further stratified analysis showed that over 90% （164/180） of the pregnant women found IPUS painless， with no moderate or severe pain reported， compared to 43.33% （78/180） experienced moderate or severe pain with VE （P＜0.001）. ConclusionAs a novel approach for labor progress assessment， IPUS not only alleviates the pain and discomfort associated with traditional VE and reduces the fear of childbirth but also enhances women's confidence in delivery， thereby achieving a high level of acceptance among parturient women in China.

Early start denver model(ESDM)is a comprehensive early intervention approach for the children with autism spectrum disorder(ASD)between 12-month-old-36-month-old.The model is built upon the theoretical foundations of applied behavior analysis,denver model(DM),and pivotal response treatment,and it is one of the naturalistic developmental behavioral interventions.Compared with the other early intervention methods,ESDM is not limited by the environment of intervention;it encompasses all the areas of development during teaching practice and has been widely adopted for the early intervention of the children with ASD,and achieves the satisfactory therapeutic effect.The ESDM typically uses an intensive one-on-one intervention approach,but variabilities have emerged in its practical application,such as group ESDM(G-ESDM),parent-implemented ESDM(P-ESDM),and peer-mediated ESDM.In particular,G-ESDM and P-ESDM have provided the learning opportunities for more families,showing a broad application prospect.This study reviews the theoretical foundations,teaching models,and the effects of various intervention modalities of the ESDM in the treatment of ASD;combined with the domestic and international research findings,this study offers a reference for further studies on the mechanism of ESDM intervention for ASD.

Objective To comprehensively evaluate the current situation of traditional Chinese medicine resource allocation in grassroots community health service centers in Guangzhou in 2022.Methods Based on the index system of traditional Chinese medicine resource allocation(community health service center)in Guangzhou,MATLAB R2021a and SPSS 27 software were used to comprehensively evaluate the current situation of traditional Chinese medicine resource allocation in 116 community health service centers in Guangzhou by TOPSIS method and RSR method.Results The allocation of TCM resources in 5 communities,including Xiaoguwei Street Community Health Service Center in Panyu District,Guangzhou City,Dadong Street Community Health Service Center in Yuexiu District,Guangzhou City,Fengyuan Street Community Health Service Center in Leiwan District,Guangzhou City,was evaluated as"excellent",and the allocation of TCM resources in 4 communities was rated as"poor".In addition,27,53 and 27 community health service centers were rated as"upper middle","medium"and"lower middle"respectively.Analysis of variance showed that the difference was statistically significant(F = 231.268,P<0.001).Conclusion TOPSIS method combined with RSR method can better evaluate the allocation of TCM resources in grass-roots communities:The allocation of TCM resources in grass-roots communities in Guangzhou is generally good,but there are still significant differences among different communities.In the future,health administrative departments at all levels in Guangzhou can rationally allocate resources according to the differences of different com-munities and better improve the capacity building of traditional Chinese medicine service in grassroots communities.

Background and purpose:Breast cancer is a major global public health problem.Bone is the most common site of distant metastasis of breast cancer,accounting for about 70%of all metastatic cases.Bone metastasis of breast cancer can cause a series of complications,including severe pain,pathological fracture,hypercalcemia,spinal cord compression,etc.,which bring great inconvenience to patients'physical activities and affect their quality of life.Metastatic recurrence is the leading cause of death in breast cancer patients.Therefore,there is an urgent need to build a diagnostic model of bone metastasis in breast cancer to identify patients with a high risk of bone metastasis.The aim of this study was to develop a predictive model based on machine learning to predict the probability of breast cancer developing bone metastasis.Methods:Data of breast cancer patients diagnosed between 2010 and 2015 were extracted from The Surveillance,Epidemiology,and End Results(SEER)database.The variables were screened by least absolute shrinkage and selection operator(LASSO)regression,univariate and multivariate logistic regression analysis,and statistically significant risk factors were included to build a prediction model.In this study,nine machine learning algorithms,including decision tree,elastic network,K-nearest neighbor,lightweight gradient elevator,logistic regression,neural network,random forest,support vector machine and limit gradient lifting,were used to adjust the model hyperparameters through random search and 5x cross-validation to build a breast cancer bone metastasis prediction model.The area under the receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the model,the optimal model was obtained,and the importance of variables was analyzed based on the optimal model.Finally,a network calculator for predicting the risk of bone metastasis of breast cancer was established using the optimal model.Results:The study included 10 106 patients with breast cancer,7 073 patients in the training set,and 3 033 patients in the validation set.We found that 4 494(63.5%)patients in the training set and 1 927(63.5%)patients in the validation set developed bone metastases,respectively.Race,pathologic grade,estrogen receptor(ER)status,progesterone receptor(PR)status,human epidermal growth factor receptor 2(HER2)status,N stage,lung metastasis,radiotherapy,chemotherapy and surgery were independent predictors of bone metastasis.The training set and verification set were used to verify the model,and the limit gradient lifting algorithm was superior to other machine learning algorithms by integrating the evaluation indexes such as the area under the ROC curve,calibration curve and decision curve.Finally,we used limit gradient algorithm to build network calculator for prediction of breast cancer bone metastases(https://bcbm.shinyapps.io/DynNomapp/).Conclusion:This study developed a predictive model based on machine learning to predict the probability of bone metastases in breast cancer patients,hoping to help clinicians make more rational treatment decisions.

Objective To investigate the dosimetric difference between manual and inverse optimization in 3-dimensional (3D) brachytherapy for gynecologic tumors. Methods This retrospective study was conducted among a total of 110 patients with gynecologic tumors undergoing intracavitary combined with interstitial brachytherapy or interstitial brachytherapy. Based on the original images, the brachytherapy plans were optimized for each patient using Gro, IPSA1, IPSA2 (with increased volumetric dose limits on the basis of IPSA1) and HIPO algorithms. The dose-volume histogram (DVH) parameters of the clinical target volume (CTV) including V200, V150, V100, D90, D98 and CI, and the dosimetric parameters D2cc, D1cc, and D0.1cc for the bladder, rectum, and sigmoid colon were compared among the 4 plans. Results Among the 4 plans, Gro optimization took the longest time, followed by HIPO, IPSA2 and IPSA1 optimization. The mean D90, D98, and V100 of HIPO plans were significantly higher than those of Gro and IPSA plans, and D90 and V100 of IPSA1, IPSA2 and HIPO plans were higher than those of Gro plans (P<0.05), but the CI of the 4 plans were similar (P>0.05). For the organs at risk (OARs), the HIPO plan had the lowest D2cc of the bladder and rectum;the bladder absorbed dose of Gro plans were significantly greater than those of IPSA1 and HIPO (P<0.05). The D2cc and D1cc of the rectum in IPSA1, IPSA2 and HIPO plans were better than Gro (P<0.05). The D2cc and D1cc of the sigmoid colon did not differ significantly among the 4 plans. Conclusion Among the 4 algorithms, the HIPO algorithm can better improve dose coverage of the target and lower the radiation dose of the OARs, and is thus recommended for the initial plan optimization. Clinically, the combination of manual optimization can achieve more individualized dose distribution of the plan.

Endo-periodontal lesions(EPLs)involve both the periodontium and pulp tissue and have complicated etiologies and pathogenic mechanisms,including unique anatomical and microbiological characteristics and multiple contributing factors.This etiological complexity leads to difficulties in determining patient prognosis,posing great challenges in clinical practice.Furthermore,EPL-affected teeth require multidisciplinary therapy,including periodontal therapy,endodontic therapy and others,but there is still much debate about the appropriate timing of periodontal therapy and root canal therapy.By compiling the most recent findings on the etiology,pathogenesis,clinical characteristics,diagnosis,therapy,and prognosis of EPL-affected teeth,this consensus sought to support clinicians in making the best possible treatment decisions based on both biological and clinical evidence.

Perimenopause is a vulnerable stage for emotional disorders such as anxiety and depression,which is the result of a combination of bio-psycho-social factors,and it seriously affect the quality of life of perimenopausal women.Therefore,finding safe and effective treatments is one of the urgent problems in modern medicine.This paper summarises the etiology and treatment of emotional disorder in perimenopause in Chinese and western medicine,and on this basis,this paper discusses the clinical diagnostic and treatment strategies and research ideas of acupuncture in treating emotional disorder in perimenopause,thus providing a new idea for the prevention and treatment of emotional disorder in perimenopause.

Objective To investigate the dosimetric difference between manual and inverse optimization in 3-dimensional (3D) brachytherapy for gynecologic tumors. Methods This retrospective study was conducted among a total of 110 patients with gynecologic tumors undergoing intracavitary combined with interstitial brachytherapy or interstitial brachytherapy. Based on the original images, the brachytherapy plans were optimized for each patient using Gro, IPSA1, IPSA2 (with increased volumetric dose limits on the basis of IPSA1) and HIPO algorithms. The dose-volume histogram (DVH) parameters of the clinical target volume (CTV) including V200, V150, V100, D90, D98 and CI, and the dosimetric parameters D2cc, D1cc, and D0.1cc for the bladder, rectum, and sigmoid colon were compared among the 4 plans. Results Among the 4 plans, Gro optimization took the longest time, followed by HIPO, IPSA2 and IPSA1 optimization. The mean D90, D98, and V100 of HIPO plans were significantly higher than those of Gro and IPSA plans, and D90 and V100 of IPSA1, IPSA2 and HIPO plans were higher than those of Gro plans (P<0.05), but the CI of the 4 plans were similar (P>0.05). For the organs at risk (OARs), the HIPO plan had the lowest D2cc of the bladder and rectum;the bladder absorbed dose of Gro plans were significantly greater than those of IPSA1 and HIPO (P<0.05). The D2cc and D1cc of the rectum in IPSA1, IPSA2 and HIPO plans were better than Gro (P<0.05). The D2cc and D1cc of the sigmoid colon did not differ significantly among the 4 plans. Conclusion Among the 4 algorithms, the HIPO algorithm can better improve dose coverage of the target and lower the radiation dose of the OARs, and is thus recommended for the initial plan optimization. Clinically, the combination of manual optimization can achieve more individualized dose distribution of the plan.

The current situation of deep learning applied to single-and multi-lead ECG detection of arrhythmia was reviewed.The problems of deep learning during the application in generalization,interpretability and time complexity were analyzed,and the countermeasures were put forward accordingly.It's pointed out deep learning would be applied widely in arrhythmia ECG detection with the development of the algorithm,dataset and hardware.[Chinese Medical Equipment Journal,2024,45(2):105-112]