Objective The objective of this study was to analyze the longitudinal trend of multidimensional treatment quality of breast cancer based on the latent growth mixture model(LGMM),identify potential change patterns and influencing factors,and pro-vide scientific basis for improving treatment quality and patient prognosis.Methods The quality monitoring data of breast cancer from four consecutive years were obtained in the National"Quality Monitoring System for Specific(single)Disease";Based on the item response theory(IRT),the treatment quality of breast cancer in the three dimensions of preoperative examination,treatment,and out-come was calculated;LGMM was constructed to analyze the independent and joint change trend of breast cancer treatment quality in all dimensions,and the optimal model was determined based on practical significance and statistical indicators.Results In the inde-pendent trend analysis,2 potential categories were identified for preoperative examination,treatment,and outcome dimensions.Among them,9%showed a rapid upward trend in the preoperative examination dimension,and 91%showed a relatively stable trend;The sta-ble growth accounted for 23%and slow decline accounted for 77%in the treatment dimension;13%of the outcome dimensions showed an upward trend,while 87%showed a downward trend.In the joint trend analysis of changes,2 potential categories were identified:the first category accounted for about 8%,and the preoperative examination dimension of this category had a good treatment quality,with mean intercepts and slopes of 3.326 and 3.367,respectively.The treatment quality in the treatment and outcome dimensions had steadily improved;The second category accounted for about 92%,and the treatment quality in this dimension was relatively good.Its mean intercept and slope were 0.548 and 0.018,respectively.There is still room for improvement in the treatment quality of the pre-operative examination and outcome dimensions;BMI and M stage in patient characteristics are important influencing factors on the trend of combined changes in treatment quality.Conclusion The treatment quality of breast cancer during this study period has im-proved to varying degrees in all dimensions of preoperative examination,treatment and outcome;In the joint trend analysis of the three dimensions,the improvement of treatment quality in the preoperative examination dimension can provide feasible references for subse-quent treatment and achieve the goal of reducing complications.

Objective Based on polynomial logistic regression model,this study aimed to analyze the optimal length of hospi-tal stay for patients with non-small cell lung cancer(NSCLC)at different stages to achieve the best treatment quality,providing refer-ence for improving treatment quality and formulating relevant policies.Methods The data of NSCLC cases were collected and 16 di-agnosis and treatment process indicators were selected.Patients were stratified according to the stage of lung cancer.A polynomial lo-gistic regression model was constructed,including patient characteristics to analyze the impact of first hospitalization days on the quali-ty of comprehensive treatment.Results A total of 10,053 patients with NSCLC were collected in this study,with a median compre-hensive treatment quality score of 0.60.According to the staging of lung cancer,patients were divided into the early stage group(stageⅠ-Ⅱ),locally advanced stage group(stage Ⅲ),and advanced stage group(stage Ⅳ).The first hospitalization days and treatment quality of each subgroup showed a non-linear relationship.The polynomial model results showed that after adjusting the characteristics of patients,the length of hospitalization day and the quadratic term of hospital stay had a statistically significant impact on treatment quality in each subgroup:early patients had a first hospital stay of 18 days,and locally advanced and advanced patients had a first hos-pital stay of 22 days,with the highest probability of achieving high treatment quality.Conclusion Patients in different stages have va-rying degrees of illness and treatment plans,resulting in different first hospitalization days corresponding to the highest probability of obtaining high-quality treatment.Hospitals can improve the treatment quality and medical efficiency by implementing standardized di-agnosis and treatment guidelines,strengthening the management of the diagnosis and treatment process,and reasonably controlling the first hospitalization time of patients in different stages.

Objective The aim of this study was to use the fast causal inference(FCI)algorithm to construct a causal graph model,analyze the direct and indirect factors that affect the quality of treatment for non-small cell lung cancer(NSCLC),and provide a basis for improving the quality of patient treatment.Methods Case information of NSCLC patients from 10 tertiary hospitals was collected;the influencing factors were determined as the research variable,and the incidence of adverse events was the evaluation indi-cator of patient treatment quality,i.e.the outcome variable;the FCI algorithm to mine case data were used to construct a causal dia-gram model of research variables and outcomes,and analyze causal relationships between research variables and outcome variables,as well as between different research variables.Results A total of 2,846 patients with an average age of 56.00±7.70 years were includ-ed in this study,and the incidence of adverse events was 9.63%.The causal diagram model consisted 24 nodes and 71 edges,inclu-ding 54 directed edges and 7 bidirectional edges.The direct factors affecting the occurrence of adverse events included hospital type,histological grade,lymph node dissection,and length of hospitalization;indirect factors included occupation,medical insurance type,current medical history,pathological stage,comprehensive treatment,surgical nature,and type of lung resection;The analysis of the in-teraction between factors showed that the current medical history,histological classification,comprehensive treatment,surgical nature,and type of lung resection determined whether the patient received lymph node dissection;The nature of surgery,method of lung resec-tion,and comprehensive treatment affected the length of hospitalization;Medical history affected the histological classification of lung cancer;The type of occupation and medical insurance affected the type of hospital where patients sought medical treatment.Conclusion In the analysis of factors affecting the quality of NSCLC treatment,the causal diagram model can obtain direct and indi-rect factors that affect the occurrence of adverse events,identify target variables that can be intervened,and provide a basis for impro-ving the quality of NSCLC treatment;Hospitals can reduce the incidence of adverse events by increasing the acceptance rate of lymph node dissection and comprehensive treatment.

Objective The aim of this study was to estimate the causal effects of non-small cell lung cancer(NSCLC)treat-ment process on in-hospital mortality based on the double robust estimation(DR)method,and provide a reference basis for reducing in-hospital mortality of NSCLC.Methods According to the quality evaluation system of NSCLC treatment,the utilization rate of treatment process indicators was calculated,and patients were divided into the high-quality or low-quality groups based on the aver-age score of treatment process quality.In-hospital mortality was used as the outcome indicator,Kaplan-Meier method and Cox regres-sion adjusted for propensity score inverse probability of treatment weighting(IPTW)correction were used to analyze the impact of treat-ment process quality on in-hospital mortality in NSCLC.DR was combined to estimate the causal effects of the treatment process on in-hospital mortality.Results The median utilization rate of treatment process indicators was 66.88%,and the mean and standard de-viation of patients′ treatment process quality scores were 0.270±0.124,including 0.358±0.069 in the high-quality group,and 0.158±0.081 in the low-quality group.After the IPTW weighting,the standardized mean difference(SMD)of patients′baseline characteris-tics decreased;The difference in survival curves between the two groups of patients before and after ITPW was statistically significant(P<0.05),and the prognosis of patients in the high-quality group was better than that of patients in the low-quality group(pre-IPTW:HR=0.367,95%CI:0.275-0.491;post-IPTW:HR=0.228,95%CI:0.167-0.312).Compared with the low-quality group,the average causal effect of treatment process on in-hospital mortality was-0.026 in the high-quality group.Conclusion DR can compensate for the shortcomings of logistic or IPTW,avoid the risk of model error,and obtain for the causal effect of treatment process on in-hospital mortality.In medical practice,the utilization rate of treatment process indicators should be increased to improve patient prognosis;The study of causal effects suggests that besides the treatment process,other factors that affect in-hospital mortality cannot be ignored.

Glioma-associated microglial cells, a key component of the tumor microenvironment, play an important role in glioma progression. In this study, the mouse glioma cell line GL261 and the mouse microglia cell line BV2 were chosen. First, circadian gene expression in glioma cells co-cultured with either M1 or M2 microglia was assessed and the exosomes of M2-polarized and unpolarized BV-2 microglia were extracted. Subsequently, we labeled the exosomes with PKH67 and treated GL261 cells with them to investigate the exosome distribution. GL261 cell phenotypes and related protein expression were used to explore the role of M2 microglial exosomes in gliomas. Then a specific miR-7239-3p inhibitor was added to verify miR-7239-3p functions. Finally, the mouse subcutaneous tumorigenic model was used to verify the tumorigenic effect of M2 microglial exosomes in vivo. Our results showed that in gliomas co-cultured with M2 microglia, the expression of the BMAL1 protein was decreased (P < 0.01), while the expression of the CLOCK protein was increased (P < 0.05); opposite results were obtained in gliomas co-cultured with M1 microglia. After treatment with M2 microglial exosomes, the apoptosis of GL261 cells decreased (P < 0.001), while the viability, proliferation, and migration of GL261 cells increased. Increased expression of N-cadherin and Vimentin, and decreased E-cadherin expression occurred upon treatment with M2 microglial exosomes. Addition of an miR-7239-3p inhibitor to M2 microglial exosomes reversed these results. In summary, we found that miR-7239-3p in the glioma microenvironment is recruited to glioma cells by exosomes and inhibits Bmal1 expression. M2 microglial exosomes promote the proliferation and migration of gliomas by regulating tumor-related protein expression and reducing apoptosis.