Objective:Exploring the association rules of factors influencing high hospitalization costs for cancer patients, providing references for hospitals to optimize medical cost management measures.Methods:In the inpatient case information system of a tertiary general hospital, the medical record homepages of inpatients in the DRG groups of the oncology department in 2022 were obtained. The upper four scores of hospitalization costs was used as the threshold for patient grouping. Patients with hospitalization costs≥this threshold were the high-cost group, while other patients were control group; 12 factors, including age, gender, and admission condition, etc, were considered as potential influencing factors of high hospitalization costs. FP-Growth and Apriori algorithms were used to excavate the potential association rules between the influencing factors of high hospitalization costs. Logistic regression was used to analyze the independent influencing factors of high hospitalization costs.Results:A total of 5 512 hospitalized patients were included, including 1 378 patients in the high-cost group. Thirteen validated strong association rules for factors influencing high hospitalization costs were obtained, of which the rule antecedents included age (≥70 years), number of days in hospital (≥7 days), other diagnoses (≥5), surgery, planned readmission, use of antibiotics, admission (general/critical), living admission score (61~99), level of care (level 1/level 2), non-day ward, criticality during hospitalisation. Logistic regression results showed that all nine influencing factors except gender, use of antibiotics, and readmission plans were independent influences on high hospitalization costs ( P<0.05). Conclusions:The joint application of FP-Growth and Apriori algorithm could effectively explore the association rules of high hospitalization costs for oncology patients. The early warning information mainly included the number of hospitalization days, the number of other diagnoses, surgeries, and so on. It was suggested that medical institutions can reasonably control the high hospitalization costs through clinical pathway management, diagnosis and treatment process reengineering, admission risk assessment, and multidisciplinary collaborative diagnosis and treatment strategies.

Objective:Exploring the association rules of factors influencing high hospitalization costs for cancer patients, providing references for hospitals to optimize medical cost management measures.Methods:In the inpatient case information system of a tertiary general hospital, the medical record homepages of inpatients in the DRG groups of the oncology department in 2022 were obtained. The upper four scores of hospitalization costs was used as the threshold for patient grouping. Patients with hospitalization costs≥this threshold were the high-cost group, while other patients were control group; 12 factors, including age, gender, and admission condition, etc, were considered as potential influencing factors of high hospitalization costs. FP-Growth and Apriori algorithms were used to excavate the potential association rules between the influencing factors of high hospitalization costs. Logistic regression was used to analyze the independent influencing factors of high hospitalization costs.Results:A total of 5 512 hospitalized patients were included, including 1 378 patients in the high-cost group. Thirteen validated strong association rules for factors influencing high hospitalization costs were obtained, of which the rule antecedents included age (≥70 years), number of days in hospital (≥7 days), other diagnoses (≥5), surgery, planned readmission, use of antibiotics, admission (general/critical), living admission score (61~99), level of care (level 1/level 2), non-day ward, criticality during hospitalisation. Logistic regression results showed that all nine influencing factors except gender, use of antibiotics, and readmission plans were independent influences on high hospitalization costs ( P<0.05). Conclusions:The joint application of FP-Growth and Apriori algorithm could effectively explore the association rules of high hospitalization costs for oncology patients. The early warning information mainly included the number of hospitalization days, the number of other diagnoses, surgeries, and so on. It was suggested that medical institutions can reasonably control the high hospitalization costs through clinical pathway management, diagnosis and treatment process reengineering, admission risk assessment, and multidisciplinary collaborative diagnosis and treatment strategies.

BACKGROUND:Reducing the rate of abnormal fertilization is an effective approach to improving the efficacy of in vitro fertilization and reducing patients'financial strain.However,the current research on abnormal fertilization has focused on exploring the types of prokaryotic nuclei and their generation mechanisms,as well as analyzing embryos formed by abnormal fertilization,chromosomal ploidy and utilization value.There is a lack of clinical prediction models for abnormal fertilization based on retrospective studies. OBJECTIVE:To construct a nomogram model to predict abnormal female factors in in vitro fertilization. METHODS:A total of 5 075 patients undergoing treatment for conventional in vitro fertilization at Nanxishan Hospital of Guangxi Zhuang Autonomous Region from March 2017 to March 2022 were retrospectively analyzed.The male confounders were calibrated on a 1:1 propensity score with a match tolerance of 0.02,and 1 672 cases were successfully matched.According to the Vienna Consensus,patients with≥60%normal fertilization capacity were included in the normal fertilization group(n=836)and those with<60%normal fertilization capacity were included in the abnormal fertilization group(n=836).The model and validation groups were obtained by random sampling at a ratio of 7:3.Factors related to the occurrence of abnormal fertilization following conventional in vitro fertilization in the model group were screened using univariate analysis and the best matching factors were selected using the Least Absolute Shrinkage and Selection Operator(LASSO)and included in a multifactorial forward stepwise Logistic regression to identify their independent influencing factors and plot a nomogram.Finally,the prediction model was validated for discrimination,accuracy and clinical application efficacy using receiver operating characteristic curves,calibration curves,clinical decision curves and clinical impact curves. RESULTS AND CONCLUSION:The univariate analysis indicated the factors influencing the occurrence of abnormal fertilization were age,controlled ovarian hyperstimulation protocol,number of assisted pregnancies,years of infertility,infertility factors,anti-mullerian hormone,sinus follicle count,basal luteinizing hormone,luteinizing hormone concentration on the human chorionic gonadotropin day,and estradiol level on human chorionic gonadotropin injection day(P<0.05).LASSO regression further identified the best matching factors,including age,microstimulation protocol,number of assisted pregnancies,years of infertility,anti-mullerian hormone,luteinizing hormone level on human chorionic gonadotropin injection day,and estradiol level on human chorionic gonadotropin injection day(P<0.05).Multifactorial forward stepwise Logistic regression results showed that age,microstimulation protocol,number of assisted conceptions,years of infertility,anti-mullerian hormone,and estradiol level on human chorionic gonadotropin injection day were independent influencing factors for the occurrence of abnormal fertilization following conventional in vitro fertilization.The receiver operating characteristic curves showed an area under the curve of 0.761(0.746,0.777)for the model group and 0.767(0.733,0.801)for the validation group,indicating that the model has good discrimination.The mean absolute error of the calibration curve was 0.044,and the Hosmer-Lemeshow test indicated that there was no significant difference between the predicted probability of abnormal fertilization and the actual probability of abnormal fertilization(P>0.05),indicating the prediction model has good consistency and accuracy.The clinical decision curves and clinical impact curves showed that the model and validation groups had the maximum net clinical benefit at valve probability values of 0.00-0.52 and 0.00-0.48,respectively,and there was a good clinical application efficacy in this valve probability range.To conclude,the nomogram model has good discrimination and accuracy as well as clinical application efficacy for predicting the occurrence of abnormal fertilization in women undergoing conventional in vitro fertilization based on age,microstimulation protocol,number of assisted conceptions,years of infertility,anti-mullerian hormone,and estradiol level on human chorionic gonadotropin injection day.

Objective To construct and identify an organoid model of human placental site trophoblastic tumor(PSTT).Methods The tumor cells were obtained by digesting and separating the PSTT tissues and then embedded in Matrigel.The organoids were cultured in the specific organoid medium.The histological morphology of the organoid model was observed by HE staining and the expression levels of the PSTT specific markers[human placental prolactin(HPL),human leukocyte antigen-G(HLA-G)and placental alkaline phosphatase(PLAP)]were detected by immunohistochemistry and immunofluorescence,so as to evaluate the consistency between the organoid model and the PSTT tissue.Meanwhile,the morphology and forming efficiency of the constructed model were observed under a microscope after primary culture,passage generation and cryopreservation to evaluate its potential application as an organoid model in basic and clinical translational research of PSTT.Results The constructed organoid model could proliferate stably,growing from small microspheres into compact solid spheres or spheres with follicle-like structures,and could passage after fully grown in 7-10 days.The cell state remained stable after passage,frozen storage and recovery.HE staining showed that the morphology of the cells in the organoids was similar to that of the primary PSTT tumor cells,and immunofluorescence staining showed that the organoids highly expressed HLA-G and lowly expressed β-HCG,indicating that the constructed organoid model mainly contained intermediate trophoblast.Conclusion The adult-derived PSTT organoid(ADPO)models were successfully established.

italic>Platycodon grandiflorum (Jacq.) A. DC is one of the most commonly used bulk medicinal herbs. It has important value in the fields of medicine, food and cosmetics, and its market demand is increasing year by year, and it has a good development prospect. In this study, based on 403 distribution records and 8 environmental variables, we used Maxent model to predict the potential distribution of P. grandiflorum under climate change. The results showed that the model simulation effect was the best when the Maxent parameter was FC (feature combination) = LQPH (L: linear features; Q: quadratic features; P: product features; H: hinge features) and RM (regularization multiplier) = 2.1, AUC (area under curve) = 0.901, and the model prediction showed that P. grandiflorum was widely distributed in 28 provinces of China under the current climate conditions, with a suitable area of 2 337 419.98 km². Under the influence of climate change in the future, the area of suitable habitat of P. grandiflorum showed a decreasing trend, and the distribution center as a whole showed a trend of northward and eastward shift. The distribution area of P. grandiflorum in the three main producing areas is affected by climate change, and the overall trend is that the future suitable distribution area of Chifeng in Inner Mongolia increases, while the future suitable distribution area of Zibo in Shandong and Taihe, Bozhou in Anhui decreases or even disappears under the SSP5-8.5 scenario (shared socioeconomic pathways). It is suggested to maintain and protect the ecological environment and germplasm resources of the reserved area suitable for the growth of P. grandiflorum, and increase the cultivation research in the expansion area of P. grandiflorum, so as to lay a foundation for the subsequent expansion of P. grandiflorum planting and to promote the protection and sustainable development of P. grandiflorum resources.

Objective To explore the impact of sluggish cognitive tempo(SCT)on the efficacy of atomoxetine in the treatment of attention-deficit/hyperactivity disorder(ADHD).Methods A prospective study was conducted.Pediatric patients aged 6-12 years with a diagnosis of ADHD were selected from the Department of Psychiatry,Xijing Hospital,Air Force Military Medical University from May 2020 to May 2023.All the patients were treated with atomoxetine for 8 weeks.ADHD symptoms and SCT symptoms were assessed by Swanson,Nolan,and Pelham-Ⅳrating scale(SNAP-Ⅳ)and SCT rating scale at baseline(before treatment),4 weeks and 8 weeks after treatment.Pearson correlation analysis was used to analyze the correlation between SCT and SNAP-Ⅳscores.Results A total of 61 pediatric patients were enrolled,including 10 cases of inattentive type and 51 cases of mixed type.Baseline SCT score was positively correlated with baseline SNAP-Ⅳscore(r=0.490,P＜0.001)and reduction rate of SNAP-Ⅳscore at week 8(r=0.670,P＜0.001).The total SCT score at week 8 was positively correlated with total SNAP-Ⅳscore at baseline(r=0.320,P=0.012).The baseline SCT score of patients with mixed type was positively correlated with reduction rate of SNAP-Ⅳscore at week 8(r=0.578,P＜0.001).Conclusions The severity of SCT symptoms is positively correlated with the SNAP-Ⅳreduction rate 8 weeks after atomoxetine treatment,which could be a predictor of the efficacy of atomoxetine.

Artemisia argyi is a perennial herbaceous herb of the Artemisia family, with leaves for medical use. However, the germplasm of A. argyi is seriously unclear and mixed during production, and it is urgent to protect new varieties of A. argyi. The distinctness, uniformity, and stability(DUS) testing of the new varieties of plants is the basis for the protection of new varieties of plants, and the development of the DUS testing guidelines is the technical basis for DUS testing. To develop the DUS testing guidelines of A. argyi, A. argyi of 100 germplasm was used as the research objects, and their agronomic and medicinal quality characters were observed and measured during six growth stages, and each character was graded and described. A total of 53 test characters were determined, including 19 characters that must be tested; there were four plant characters, two rhizome characters, five stem characters, three branching characters, 29 leaf characters, three floral characters, five medicinal quality characters, and two other characters. It also involved 16 quality characters, 22 quantitative characters, and 15 pseudo-quantitative characters. Seven grouping characters were determined from 53 characters, including "emergence period" "plant-plant type" "branching-primary branching site" "stem-color" "middle leaf-number of leaf splits" "budding period", and "plant-height". By searching for standard characters, 16 standard varieties were ultimately determined. The preparation of this guideline was of great significance for the review and protection of new A. argyi varieties, the protection of breeders' rights, and the promotion of the development of A. argyi industry.
Artemisia/chemistry* ; Plant Leaves/chemistry* ; Quality Control ; Plants, Medicinal/classification* ; Guidelines as Topic

To clarify the content characteristics of mineral elements in different Artemisia argyi germplasm resources and their relationship with the quality properties of Artemisiae Argyi Folium, this study measured the content of 10 mineral elements including nitrogen(N), phosphorus(P), potassium(K), calcium(Ca), magnesium(Mg), aluminum(Al), manganese(Mn), iron(Fe), copper(Cu), and zinc(Zn) in 100 Artemisia argyi germplasm samples. Besides, their relationship with the quality properties of Artemisiae Argyi Folium was explored by correlation analysis, path analysis, and cluster analysis. The results demonstrated that the variation coefficient of the 10 mineral elements in Artemisiae Argyi Folium ranged from 12.23% to 64.38%, and the genetic diversity index from 0.97 to 3.09. The genetic diversities of N, P, and Zn were obvious. As revealed by the correlation analysis, N, P, and K showed strong positive correlations with each other. Except that Mg and Al were negatively correlated, Ca, Mg, Al, Mn, Fe, Cu, and Zn were positively correlated. The correlation analysis of mineral elements with the quality properties of Artemisiae Argyi Folium proved the significant correlations of 17 pairs of characters. According to the path analysis, P, K, Ca, and Mn greatly affected the yield of Artemisiae Argyi Folium, P, K, and Mg the output rate of moxa, N, P, and K the content of total volatile oil, P and K the content of eucalyptol, and P, K, and Ca the content of eupatilin. The 100 germplasm samples were clustered into three groups. Specifically, in cluster Ⅰ, the enrichment capacity of P, K, and Mg elements was strong, and the comprehensive properties of mineral elements were better, implying good development potential. Ca, Mn, Fe, and Zn elements in cluster Ⅱ and N and Al in cluster Ⅲ displayed strong enrichment capacities. This study has provided new ideas for resource evaluation and variety breeding of A. argyi and also reference for fertilizer application.
Artemisia/genetics* ; Iron ; Minerals/analysis* ; Plant Breeding ; Plant Leaves/chemistry*

In Hezhang county, Guizhou province, black spot tends to occur to Aconitum carmichaelii in the hot rainy summer, with the incidence up to 50%-70%, seriously impacting the yield and quality of the medicinal material. Thus, this study aims to clarify the pathogen and the occurrence characteristics. To be specific, the pathogen was isolated and identified according to Koch's postulates and the pathogenicity and biological characteristics were determined. In addition, the sensitivity of the pathogen to four microbial fungicides, four botanical fungicides, and five chemical fungicides was determined with the mycelium growth rate method for the purpose of screening out optimal fungicides. The pathogen was identified as Alternaria alternate, as evidenced by the similar colony morphology and microscopic characteristics and 99.55%-100% similarity in sequences of rDNA-ITS, LSU, 18S, and TEF of the two. The optimum growth conditions for A. alternata were 28 ℃, pH 8, and continuous darkness. Bacillus subtilis had strong inhibitory effect on the pathogen, and the inhibition rate was more than 90% when the concentration was 1 mg·L~(-1). In addition, difenoconazole and quinoline copper can also control the pathogen, with median effective concentration(EC_(50)) of 2.92 and 9.02 mg·L~(-1), respectively. This study lays a theoretical basis for the field control of black spot in A. carmichaelii.
Aconitum ; Alternaria ; Fungicides, Industrial/pharmacology* ; Mycelium

Objective: To explore the efficacy and safety of real-world eribulin in the treatment of metastatic breast cancer. Methods: From December 2019 to December 2020, patients with advanced breast cancer were selected from Beijing Chaoyang District Sanhuan Cancer Hospital, Shandong Cancer Hospital, Peking University Cancer Hospital, Baotou Cancer Hospital, Shengjing Hospital Affiliated to China Medical University, and Cancer Hospital of Chinese Academy of Medical Sciences. Kaplan-Meier method and Log rank test were used for survival analysis, and Cox regression model was used for multivariate analysis. Results: The median progression-free survival (PFS) of 77 patients was 5 months, the objective response rate (ORR) was 33.8%, and the disease control rate (DCR) was 71.4%. The ORR of patients with triple-negative breast cancer was 23.1%, and the DCR was 57.7%; the ORR of patients with Luminal breast cancer was 40.0%, and the DCR was 77.8%; the ORR of patients with HER-2 overexpression breast cancer was 33.3%, and the DCR was 83.3%. ORR of 50.0% and DCR of 66.7% for patients treated with eribulin as first to second line treatment, ORR of 29.4% and DCR of 76.5% for patients treated with third to fourth line and ORR of 28.6% and DCR of 71.4% for patients treated with five to eleven line. The ORR of patients in the eribulin monotherapy group was 40.0% and the DCR was 66.0%; the ORR of patients in the combination chemotherapy or targeted therapy group was 22.2% and the DCR was 81.5%. Patients with a history of treatment with paclitaxel, docetaxel, or albumin paclitaxel during the adjuvant phase or after recurrent metastasis had an ORR of 32.9% and a DCR of 69.9% when treated with eribulin. The treatment efficacy is an independent prognostic factor affecting patient survival (P<0.001). The main adverse reactions in the whole group of patients were Grade Ⅲ-Ⅳ neutrophil decline [29.9% (23/77)], and other adverse reactions were Grade Ⅲ-Ⅳ fatigue [5.2% (4/77)], Grade Ⅲ-Ⅳ peripheral nerve abnormality [2.6% (2/77)] and Grade Ⅲ-Ⅳ alopecia [2.6% (2/77)]. Conclusions: Eribulin still has good antitumor activity against various molecular subtypes of breast cancer and advanced breast cancer that has failed multiple lines of chemotherapy, and the adverse effects can be controlled, so it has a good clinical application value.
Breast Neoplasms/pathology* ; Female ; Furans/adverse effects* ; Humans ; Ketones/adverse effects* ; Paclitaxel/adverse effects* ; Treatment Outcome ; Triple Negative Breast Neoplasms/drug therapy*