Objective:To construct a refined management model based on the Interrupt Time Series(ITS)algorithm model for medical consumables,so as to provide a scientific basis for hospitals in implementing refined management for medical consumables.Methods:The supply,inventory and usage data of medical consumables were collected,and the ITS algorithm was used to analyze the prediction for the usage of medical consumables,and the influence of policy intervention.The outliers and key influencing factors of the full-cycle data of medical consumables were mined,and management countermeasures were formulated from three aspects:early warning for dynamic inventory,feedback of policy effect,and abnormal rectification handling.A total of 10,614 medical consumables that were routinely used at Taizhou People's Hospital from January 2023 to December 2024 were selected.The empirical management method was adopted to manage 4,924 medical consumables during the period from January to December 2023,and the refined management method was adopted to manage 5,690 medical consumables during the period from January to December 2024.The utilization rate of medical consumables,the quality of management decisions and the performance of clinical services of the two management methods were compared.Results:The average inventory turnover rate and usage rate of medical consumables of the refined management method were respectively(91.11±3.02)%and(91.19±2.75)%,which were higher than those of the empirical management method.The proportion of average inventory cost,and loss rate of overstock of the refined management method were respectively(2.53±0.57)%and(2.84±0.44)%,which were lower than those of the empirical management method,and the differences were all statistically significant(t=4.698,5.976,4.518,8.496,P<0.05).The average accuracy rate of prediction,detection rate of abnormity,and the mean of timely handling rate of the refined management method for medical consumables were all higher than those of the empirical management method,and the differences were statistically significant(t=6.423,8.587,6.102,P<0.05).The satisfaction of doctors,nurses,warehouse managers and purchasing managers who used and managed medical consumables for the clinical service performance of adopting the refined management method was higher than that of adopting the empirical management method,and the differences were statistically significant(x2=7.416,12.793,4.267,4.667,P<0.05).Conclusion:The application of the refined management model based on the ITS algorithm model for medical consumables can enhance the predictive ability for the demand of medical staffs for medical consumables,and reduce the pressure for inventory,and management costs of medical consumables,and accurately detect abnormal problems in the use of medical consumables,and improve the level of clinical services.

Objective:To construct a refined management model based on the Interrupt Time Series(ITS)algorithm model for medical consumables,so as to provide a scientific basis for hospitals in implementing refined management for medical consumables.Methods:The supply,inventory and usage data of medical consumables were collected,and the ITS algorithm was used to analyze the prediction for the usage of medical consumables,and the influence of policy intervention.The outliers and key influencing factors of the full-cycle data of medical consumables were mined,and management countermeasures were formulated from three aspects:early warning for dynamic inventory,feedback of policy effect,and abnormal rectification handling.A total of 10,614 medical consumables that were routinely used at Taizhou People's Hospital from January 2023 to December 2024 were selected.The empirical management method was adopted to manage 4,924 medical consumables during the period from January to December 2023,and the refined management method was adopted to manage 5,690 medical consumables during the period from January to December 2024.The utilization rate of medical consumables,the quality of management decisions and the performance of clinical services of the two management methods were compared.Results:The average inventory turnover rate and usage rate of medical consumables of the refined management method were respectively(91.11±3.02)%and(91.19±2.75)%,which were higher than those of the empirical management method.The proportion of average inventory cost,and loss rate of overstock of the refined management method were respectively(2.53±0.57)%and(2.84±0.44)%,which were lower than those of the empirical management method,and the differences were all statistically significant(t=4.698,5.976,4.518,8.496,P<0.05).The average accuracy rate of prediction,detection rate of abnormity,and the mean of timely handling rate of the refined management method for medical consumables were all higher than those of the empirical management method,and the differences were statistically significant(t=6.423,8.587,6.102,P<0.05).The satisfaction of doctors,nurses,warehouse managers and purchasing managers who used and managed medical consumables for the clinical service performance of adopting the refined management method was higher than that of adopting the empirical management method,and the differences were statistically significant(x2=7.416,12.793,4.267,4.667,P<0.05).Conclusion:The application of the refined management model based on the ITS algorithm model for medical consumables can enhance the predictive ability for the demand of medical staffs for medical consumables,and reduce the pressure for inventory,and management costs of medical consumables,and accurately detect abnormal problems in the use of medical consumables,and improve the level of clinical services.

OBJECTIVE: To explore the clinical characteristics and genetic etiology of three children with Menkes disease. METHODS: Three children who had presented at the Children's Medical Center, the Affiliated Hospital of Guangdong Medical University from January 2020 to July 2022 were selected as the study subjects. Clinical data of the children were reviewed. Genomic DNA was extracted from peripheral blood samples of the children, their parents and sister of child 1. Whole exome sequencing (WES) was carried out. Candidate variants were verified by Sanger sequencing, copy number variation sequencing (CNV-seq), and bioinformatic analysis. RESULTS: Child 1 was a 1-year-and-4-month male, and children 2 and 3 were monozygotic twin males aged 1-year-and-10-month. The clinical manifestations of the three children have included developmental delay and seizures. WES showed that child 1 has harbored a c.3294+1G>A variant of the ATP7A gene. Sanger sequencing confirmed that his parents and sister did not carry the same variant, suggesting that it was de novo. Children 2 and 3 had carried a c.77266650_77267178del copy number variation. CNV-seq results showed that their mother has carried the same variant. By searching the HGMD, OMIM and ClinVar databases, the c.3294+1G>A was known to be pathogenic. No carrier frequency has been recorded in the 1000 Genomes, ESP, ExAC and gnomAD databases. Based on the Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics (ACMG), the ATP7A gene c.3294+1G>A variant was predicted to be pathogenic. The c.77266650_77267178del variant has involved exons 8 to 9 of the ATP7A gene. ClinGen online system score for it was 1.8, which was also considered to be pathogenic. CONCLUSION The c.3294+1G>A and c.77266650_ 77267178del variants of the ATP7A gene probably underlay the Menkes disease in the three children. Above finding has enriched the mutational spectrum of Menkes disease and provided a basis for clinical diagnosis and genetic counseling.
Humans ; Male ; Computational Biology ; Copper-Transporting ATPases/genetics* ; DNA Copy Number Variations ; Exons ; Menkes Kinky Hair Syndrome/genetics* ; Mutation ; Peptide Fragments ; Seizures ; Infant

Objective:To establish a model for predicting the mental health status of nurses based on machine learning algorithm.Methods:In February 2020, the nurses from Shantou Central Hospital and Cancer Hospital of Shantou University Medical College were recruited by convenience sampling, investigated using the Self-reporting Inventory, Coping Style Questionnaire, Social Support Rating Scale and Work Attitude Scale. Mental health status was treated as a dichotomous variable, and candidate predictors were screened out by univariate and multivariate Logistic regression analysis. The subjects were randomly divided into a training set (80%) and a test set (20%). Then five prediction models of nursing staff mental health status were constructed using the five machine learning methods (Logistic Regression, Artificial Neural Network, C5.0 Decision Tree, Bayesian Network and Support Vector Machine), verified and compared to screen out the model with the highest predictive efficiency.Results:A total of 415 nurses were enrolled, and the positive detection rate of mental health symptoms was 20.48%. According to univariate and multiple Logistic regression analysis, candidate predictors were work attitude ( OR=1.098, 95% CI 1.028-1.174), self-accusation ( OR=7.703, 95% CI 2.014-29.468), problem-solving( OR=0.131, 95% CI 0.025-0.686), the number of night shifts per month ( OR=0.204, 95% CI 0.073-0.573)and support availability ( OR=0.830, 95% CI 0.701-0.984). The accuracy of prediction of Logistic Regression, Artificial Neural Network, C5.0 Decision Tree, Bayesian Network and Support Vector Machine were 84.21%, 85.53%, 82.89%, 78.95%, 84.21%. The area under the ROC curve was 0.801, 0.825, 0.777, 0.583, 0.774. Artificial Neural Network was significantly more effective than Logistic regression, C5.0 Decision Tree, Bayesian Network and Support Vector Machine (DeLong test, P<0.05). Conclusions:The machine learning based predictive models for nursing staff mental health status has higher predictive value, which can be applied into nursing staff mental health screening decisions to accurately grasp its dynamic changes, early identification of high-risk mental health abnormalities and early intervention. Work attitude, self-accusation, problem-solving, the number of night shifts per month and support availability was predictors to construct predictive models.

Objective:To understand the mental health of nurses, and to explore the influence of general information, coping styles, social support and work adaptation barriers on the mental health of nurses, and to initially construct a prediction model of clinical nurses' mental health.Methods:In February 2020, convenience sampling was used to select 374 clinical nurses from two hospitals in a city as the research objects. All nurses were investigated with the self-designed general information questionnaire, Symptom Checklist 90 (SCL-90) , Coping Style Questionnaire, Social Support Rating Scale (SSRS) and Work Adjustment Disorder Scale. The mental health was processed as binary data, and the multi-layer perceptron in the neural network was used to construct a prediction model of the clinical nurses' mental health, and the model was verified and analyzed.Results:Among 374 clinical nurses, the scores of SCL-90, Coping Style Questionnaire, SSRS and Work Adjustment Disorder Scale were (123.06±41.70) , (27.90±12.14) , (38.84±8.46) and (9.72±6.35) respectively. Correlation analysis results showed that working years, self-blame, fantasy, avoidance, rationalization and work adjustment disorder were positively correlated with mental health ( P<0.05) ; whether they were authorized strength, problem solving, asking for help, subjective support, support utilization were negatively correlated with mental health ( P<0.05) . The above variables related to mental health were incorporated into the prediction model, and 374 samples were divided into training samples (177) , test samples (81) and persistence samples (116) . The results showed that the modeling accuracy of training, testing and persistence samples were 79.7%, 86.4% and 81.0%, respectively, and the area under the receiver operating characteristic curve was 0.810. The variable which normalization importance was 100% in the prediction model was the work adjustment disorder. Conclusions:The prediction model of clinical nurses' mental health constructed in this study has good accuracy, and the application of neural network model is relatively simple, which can be used as an effective method to evaluate the mental health of clinical nurses.

Objective: To investigate the incidence and proportion of salivary gland tumors in order to provide new thinking for clinical diagnosis and treatment. Methods: Collected 3 724 cases salivary gland tumors diagnosed by Pathology Department of Hospital of Stomatology, Jilin University from January 1961 to December 2016. The pathological diagnosis referred to the fourth edition of head and neck-salivary gland tumor histopathological classification standard of WHO. The database was established with Microsoft Excel and analyzed with SPSS 18.0. Made a retrospective analysis and comparison on the numbers of all cases in terms of types, site, gender and age and estimate the trend with the time interval of 8 years, and then make a judgement of the trend of salivary tumors. Results: The benign tumors were more common than the malignant among all periods, the proportion of all tumors was about 2.92∶1; The top three benign tumors were polymorphous adenoma [73.78% (2 046/2 773)], Warthin tumor [15.80% (438/2 773)] and base cell adenoma [8.37% (232/2 773)]. Polymorphous adenoma took up 54.94% (2 046/3 724) of all tumors. The top three malignant tumors were mucous epidermoid carcinoma [31.44% (299/951)], adenoid cystic carcinoma [26.92% (256/951)] and adenocarcinoma [11.88% (113/951)]. As for sex, male female ratio was 0.83∶1. As for site, the pathogenic site of tumors was mainly in parotid gland [63.75% (2 374/3 724)], followed by palatal gland [16.50% (615/3 724)], then submandibular gland [12.67% (472/3 724)]; As for age, the common age was between 51 and 60 years old [23.74% (884/3 724)], followed by 41 to 50 years old [21.56%(803/3 724)]. Conclusions The incidence of benign and malignant salivary gland tumor increased in the 56 years. Females showed a higher incidence. The majority tumors occurred in parotid gland. The most common salivary gland tumor was pleomorphic adenoma and the most common malignant tumor was mucous epidermoid carcinoma. The most common age was in 51-60 years old period.

Objective: To investigate the incidence and constituent ratio of odontogenic tumors or odontogenic cysts in School and Hospital of Stomatology, Jilin University and to provide the reference for the clinical treatment. Methods: According to the WHO 2017 histological classification criteria, the pathological data of 4181 patients diagnosed as odontogenic tumors or odontogenic cysts in the Department of Pathology, Jilin University Stomatological Hospital from January 1961 to December 2017 were collected. Statistical analysis of the pathological types, gender, age and location of various tumors and cysts was conducted. Results: Of 4 181 cases, 1 055 were tumors and 3 126 were cysts. Among odontogenic tumors, benign tumors accounted for 96.11% (1 014/1 055), and malignant tumors accounted for 3.89% (41/1 055). The most common pathological type of odontogenic tumors was ameloblastoma [53.27% (562/1 055)], followed by cemento-ossifying fibroma [21.23% (224/1 055)] and odontoma [12.99% (137/1 055)]. The male-female ratio was 1∶1.04. The high-risk ages were 10-39. Maxilla-mandible ratio was 1∶2.85.As for cysts, radicular cysts [50.45% (1 577/3 126)] was the most common pathological type, followed by odontogenic keratocyst [25.59% (800/3 126)] and dentigerous cysts [21.56% (674/3 126)]. The male-female ratio was 1.37∶1. The high-risk ages were 20-49. Maxilla-mandible ratio was 1.37∶1. Conclusions There was no gender preference for odontogenic tumors in Jilin Province area in the 57 years. The majority tumors occurred in the radicular. The most common pathological type was ameloblastoma. As for odontogenic cysts, males showed a higher incidence and the majority cysts occurred in the maxilla. The most common pathological type was radicular cysts.

Objective To evaluate the application values of two kinds of single-platform flow cytometric methods,the Volumetric method based on flow sensor and the Trucount method based on Trucount beads,in the counts of T-lymphocyte subsets in peripheral blood of patients after transplantation.Methods The absolute number and percentage of CD4 +,CD8 +,and CD3 + T cells in peripheral blood samples from 107 patients after liver or renal transplantation were determined by the Trucount method and the Volumetric method,respectively,and their results were compared using paired t-test and linear regression analysis.Five samples with low CD3 + counts were selected and the precisions of the absolute number of CD4 +,CD8 + and CD3 + T ceils detected by the Volumetric method were evaluated.Results There was no significant difference in the levels of CD4+,CD4+/CD3+,CD8+,CD8+/CD3+,and CD4+/CD8 + in peripheral blood between the Trucount method and the Volumetric method (P ＞ 0.05),and the linear regression coefficients between them were from 0.9 to 1.1.When the concentration of CD3 + was equal or more than 40/μL,the coefficients of variation (CVs) were below 5.5％ for the Volumetric method.When the concentration of CD3 + was 20/μL,the CVs of CD3 +,CD4 +,and CD8 + were 5.19％,10.28％ and 6.48％,respectively.Conclusion The single-platform method based on flow sensor is accurate and reproducible for counting T-lymphocyte subsets in peripheral blood,which may be used to monitor the immune state of the patients after liver or renal transplantation.

Objective To investigate the changes of serum IL-1β,IL-6 ,TNF-α,NGF and BDNF levels and their correlation with clinical symptoms of schizophrenia inpatiens and their value in the auxiliary diagnosis of schizophrenia .Methods The case-control study was used .The levels of serum IL-1β,IL-6 ,TNF-α,NGF and BDNF were measured by using the enzyme linked immunosorbent assay(ELISA) in 85 inpatients with schizophrenia and 85 healthy controls .Their changes in the case group were compared between before treatment and after 3-month treatment .The Pearson correlation analysis was used to analyze the correlation between the lev-els of IL-1β,IL-6 ,TNF-α,NGF and BDNF with the positive and negative syndrome scale (PANSS) score and the auxiliary diagnosis value of serum cytokine and neurotrophic factor levels were evaluated with the receiver operating characteristic (ROC) curve .Re-sults The levels of serum IL-1β(t=4 .560) ,IL-6(t= 4 .957) and TNF-α(t= 4 .799) before treatment in the schizophrenia case group were significantly higher than those in control group ,while the NGF(t= -4 .806) and BDNF(t= -4 .881) levels were sig-nificantly lower than those in the control group ,the differences were statistically significant (P<0 .01) .After 3-month treatment , the levels of serum IL-1β(t=4 .543) ,IL-6(t=4 .327) and TNF-α(t=4 .654) in the schizophrenia case group were significantly de-creased compared with before treatment ,while the NGF(t= -4 .641) and BDNF(t= -4 .876) levels were significantly increased , the differences were statistically significant (P< 0 .01) .IL-1β was positively correlated with the positive symptoms scores (r=0 .325 ,P<0 .01) ,IL-6 was positively correlated with the negative symptoms scores (r=0 .319 ,P<0 .01) ,TNF-α was positively correlated with the positive symptoms scores (r= 0 .281 ,P< 0 .01) ,NGF was negatively correlated with the positive symptoms scores(r= -0 .229 ,P<0 .05) ,BDNF was negatively correlated with the positive symptoms scores (r= -0 .272 ,P< 0 .05) .The cut-off values of serum IL-1β,IL-6 ,TNF-α,NGF and BDNF in the auxiliary diagnosis of schizophrenia were 40 .083 ,20 .037 ,17 .115 ,19 .998 ,584 .157pg/mL respectively ,the corresponding areas under the ROC were 0 .723 ,0 .772 ,0 .686 ,0 .712 and 0 .708 respectively ,the sensitivities were 0 .565 ,0 .871 ,0 .894 ,0 .859 and 0 .729 respectively ,and the specificities were 0 .871 ,0 .565 ,0 . 365 ,0 .494 and 0 .624 respectively .Conclusion The levels of serum IL-1 ,IL-6 ,TNF-α,NGF and BDNF have the correlation with the clinical symptoms of schizophrenic inpatients and have a certain value in the auxiliary diagnosis of schizophrenia .

Objective: To use autophagy inhibitors combined with radiation to treat the oral squamous cell carcinoma CAL-27 and KB cells,and to explore the influence of autophagy in the oral cancer radiation sensitivity and its mechanisms. Methods:The human oral squamous cell carcinoma CAL-27 and KB cells were divided into control group,CQ group,3-MA group,IR group,CQ+IR group,and 3-MA+IR group. The survival rate was detected by MTT method and the autophagy of CAL-27 cells was observed by immunofluorescence method and laser scanning confocal microscope.The expression levels of LC3 and beclin-1 were detected by Western blotting method. The apoptotic rate was determined by flow cytometry with Annexin Ⅴ/PI doulde staining. Results:Compared with IR group,the survival rates in 3-MA + IR and CQ+ IR groups were signifcantly decreased (P < 0.05 ).The autophagy levels of cells in IR group were significantly higher than those in control group, CQ group, 3-MA group,CQ+IR group,and 3-MA+IR group (P <0.05).The expression levels of LC3 and beclin-1 proteins in IR group were significantly higher than those in control group,CQ+ IR group,and 3-MA+ IR group (P < 0.05). The apoptotic rates in IR,3-MA+ IR,and CQ+ IR groups were markedly higher than those in control group. Compared with IR group,the apoptotic rates in CQ+IR and 3-MA+IR groups were significantly increased (P <0.05).Conclusion:Radiotherapy can induce the increase of autophagy level of oral squamous cell carcinoma cells. Inhibiors of autophagy can increase the radio-sensitivity of oral squamous cell carcinoma cells by inhibiting the proliferation and inducing the apoptosis.