OBJECTIVE To summarize the current situation of pharmaceutical clinic service in our hospital over the past five years， and explore sustainable development strategies for service models of pharmaceutical clinics. METHODS A retrospective analysis was conducted on the consultation records of patients who registered and established files at the pharmaceutical clinic in our hospital from January 2019 to December 2023. Statistical analysis was performed on patients’ general information， medication- related problems， and types of pharmaceutical services provided by pharmacists. RESULTS A total of 963 consultation records were included， among which females aged 20-39 years accounted for the highest proportion （66.04%）； obstetrics and gynecology- related consultations accounted for the largest number of cases. Additionally， 80 patients attended follow-up visits at our hospital’s pharmaceutical clinic. A total of 1 029 medication-related issues were resolved， including 538 cases of drug consultations （52.28%）， 453 medication recommendations （44.02%）， 22 medication restructuring（2.14%）， and 16 medication education （1.55%）； the most common types of medication-related problems identified were adverse drug events（70.07%）. CONCLUSIONS Although the pharmaceutical clinic has achieved recognition from clinicians and patients， challenges such as low awareness among healthcare providers and the public persist. Future efforts should focus on strengthening information technology construction， enhancing pharmacist training， and establishing various forms of outpatient pharmaceutical service models.

Objective: To analyze respiratory syncytial virus(RSV) outbreaks surveillance results and the epidemiological characteristics in kindergarten and school in Shenzhen during 2017-2023 , so as to provide a scientific reference for control and prevention of RSV. Methods: Epidemiological data and surveillance results of RSV outbreaks in kindergarten and school from 2017 to 2023 were collected for descriptive analyses. Results: A total of 31 RSV outbreaks were identified in kindergarten and school in 2017-2023 in Shenzhen, 346 cases were reported, the average incidence rate was 22.02%. The most annual RSV outbreaks were reported in 2020 with 14 outbreaks, followed by 8 outbreaks in 2023. A total of 64.52% of RSV outbreaks were identified in kindergarten with rest occurring in primary school or middle school. The greatest monthly count of outbreak was 18 (58.06%) in September, followed by 3 outbreaks (9.68%) in March and October. A total of 244 swab samples were collected, 169 samples were positive for respiratory viruses, the positive rate was 69.26%, 121 samples were positive for RSV,from 31 respiratory syncytical virus outbreaks 57 and samples were positive for other respiratory viruses(9 samples were positive for two respiratory viruses). A toral of 14(45.16%) outbreaks are caused by RSV alone, 17 outbreaks (54.84%) were caused by RSV and other respiratory viruses. Conclusions Most RSV outbreaks in kindergarten and school are reported after 2020 in Shenzhen, most RSV outbreaks occur in kindergarten, peak seasons of RSV outbreaks are autumn and spring.

Objective:To analyze the spatiotemporal correlation between the surveillance data of influenza in students reported by medical institutions and school absenteeism due to illness, and evaluate the application of Bayesian Structural Time Series model (BSTS) in the prediction of school influenza epidemic.Methods:A total of 13 schools in Dapeng new district of Shenzhen were selected. The incidence data of influenza in schools in Shenzhen from January 1, 2015 to December 31, 2019 were collected from China Disease Control and Prevention Information System and the illness related school absentence data during this period were collected from Shenzhen Student Health Surveillance System, and the spatiotemporal correlation between the data from two systems was analyzed and compared. BSTS was used to make long-term predictions of the monthly incidence of influenza in students in 2019 and short-term predictions of the weekly incidence of influenza in week 1-8 and week 45-52 of 2019 by using the data from two systems.Results:There was a temporal correlation between the data from China Disease Control and Prevention Information System and the data from Shenzhen Student Health Surveillance System ( r=0.93, P<0.001), and the lag of the former one was 1 day ( r=0.73, P<0.001). Influenza outbreaks were randomly distributed in different schools in Shenzhen, and there was no spatial correlation. The root mean square error ( RMSE) and mean absolute error ( MAE) were 0.35 and 0.28, respectively, in the long-term prediction, and the RMSE was 0.33 and 0.34, and the MAE was 0.26 and 0.28, respectively, in the short-term predictions of week 1-8 and week 45-52 of 2019, respectively, showing good prediction accuracy and fitting effect. Conclusion:By analyzing the data from China Disease Control and Prevention Information System and Shenzhen Student Health Surveillance System with BSTS, the dynamics of the school influenza epidemic can be accurately predicted, and effective technical support can be provided for the early warning and prevention and control of influenza epidemic.

Infectious diseases continue to pose a threat to global public health. Successive global shocks caused by emerging and re-emerging pathogens have continuously challenged existing surveillance systems, highlighting the urgent need to build efficient and precise pathogen surveillance networks. Pathogen genomic databases have been developed rapidly in recent two decades, significantly improving the molecular identification, evolutionary analysis, and transmission tracking of pathogens, and changing disease surveillance strategies and patterns. This paper summarizes the developmental history and current state of pathogen genomic databases, and discusses their applications in public health, including pathogen variation surveillance, emerging or suspected pathogen identification, and epidemiological tracing. Furthermore, this paper systematically analyzes the limitations and key challenges faced by current global health prevention and control system, and suggests the focus of the development of online pathogen databases to address existing shortcomings, ultimately improve global infectious disease surveillance and early warning

Objective:To screen bile acid-related characteristic genes in IgA nephropathy (IgAN) based on the feature gene selection algorithm in the machine learning method, aiming to exploring the molecular biological mechanisms and biomarkers of IgAN.Methods:The gene expression data and sample grouping information of GSE93798, GSE116626 and GSE35487 were downloaded from the Gene Expression Omnibus (GEO). Bile acid-related gene sequences were obtained from the Molecular Signatures Database (MSigDB). R language was used to identify differentially expressed genes between IgAN samples and healthy control samples. Candidate genes were obtained by intersecting differentially expressed genes and bile acid-related genes. The least absolute shrinkage and selection operator (LASSO) algorithm in machine learning was used to screen the feature genes in the candidate genes as biomarkers, and the feature genes in the training set and validation set were analyzed by the rate of change index. Receiver operating characteristic curve (ROC) method was used to evaluate the diagnostic value of identified bile acid related characteristic genes for IgAN. Gene set enrichment analysis (GSEA) was used to analyze the Spearman correlation between the characteristic genes and all other genes and their related metabolic pathways. The expression of disease-characteristic genes in the kidney tissues of IgAN rats was validated by real-time PCR.Results:Gene expression information from kidney tissue samples of 20 IgAN cases and 22 healthy controls were obtained from GEO database. A total of 204 bile acid-related genes including 24 pathways were obtained from MSigDB. The results of gene differential expression analysis showed that 333 genes in the kidney tissues of IgAN patients were differentially expressed compared with those of healthy controls, including 102 up-regulated genes and 231 down-regulated genes, among which 12 differentially expressed genes were related to bile acid genes, as follows: NR1H4,SLC23A1, ALDH8A1, FABP1, ALB, SLC27A2, DIO1, CYP8B1, BBOX1, PIPOX, AKR1C1 and SLC10A2. Five characteristic genes ( NR1H4, SLC23A1, FABP1, ALB and AKR1C1) were screened by LASSO regression algorithm.ROC analysis results showed that in GSE93798 cohort genes, the AUC of NR1H4, SLC23A1, FABP1 and ALB genes with differential expression was >0.95 respectively in diagnosing IgAN, and that of AKR1C1 genes with differential expression was >0.85 in diagnosing IgAN. The gene expression data of SLC23A1 in GSE35487 cohort was missing. ROC analysis results of other four genes showed that the AUC of differential expression of ALB gene for IgAN was >0.95 respectively, that of NR1H4 gene was >0.70, and that of both FABP1 and AKR1C1 gene was >0.60. In the GSE116626 cohort genes, the AUC of five disease characteristic genes ( NR1H4, SLC23A1, FABP1, ALB, AKR1C1) for diagnosing IgAN was >0.60, respectively. These results suggested that 5 characteristic genes have certain distinguishing ability between IgAN group and control group. GSEA results were displayed that the characteristic genes were related to butyric acid metabolism, propionic acid metabolism, arginine and proline metabolism, valine leucine and isoleucine degradation, fatty acid metabolism, etc. These results suggested that five characteristic genes might be related to IgAN through the above metabolic mechanisms. The verification results of five bile acid characteristic genes in the rat model of IgAN in the kidney tissue showed that the expressions of four genes, NR1H4, SLC23A1, FABP1 and ALB, were higher than those of the control group, and there was no statistical significance in the expression of AKR1C1 gene between the two groups. Conclusions:The expression of bile acid-related characteristic genes is abnormal in the kidney tissue of IgAN patients. Four bile acid-related differentially expressed genes, NR1H4, SLC23A1, FABP1 and ALB, are expected to be biomarkers for non-invasive diagnosis and therapeutic targets .

Objective:To investigate the radioactivity levels and variation trends of 90Sr in drinking water and food around the Qinshan Nuclear Power Plant in operation. Methods:From 2012 to 2022, the source, factory, and tap water was collected within 30 km around the Qinshan Nuclear Power Plant during the wet and dry seasons (i.e., May and October, respectively) each year to determine the 90Sr concentration in water. According to the dietary habits of local residents, four kinds of food, including rice, cabbage, mullet, and crucian carp, were collected to determine and analyze the 90Sr radioactivity concentration in food using the bis-(2-ethylhexyl) phosphoric acid extraction chromatographic method. Results:From 2012 to 2022, the 90Sr radioactivity concentrations in the source, factory, and tap water around the Qinshan Nuclear Power Plant ranged from 3.73 to 11.89 mBq/L, 2.95 to 9.83 mBq/L, and 3.12 to 8.70 mBq/L, respectively, showing nonsignificant fluctuations over the years. The 90Sr radioactivity concentrations in rice, cabbage, mullet, and crucian carp ranged from 0.02 to 0.46 Bq/kg (dry weight), 0.26 to 1.07 Bq/kg (fresh weight), 0.38 to 1.05 Bq/kg (fresh weight), and 0.08 to 1.32 Bq/kg (fresh weight), respectively, all below the national standard limits. Conclusions:From 2012 to 2022, the 90Sr radioactivity levels in drinking water and food around the Qinshan Nuclear Power Plant were at the background level and remained stable.

Objective To describe and analyze the current status,changing trend and influencing factors of the disease burden of cervical,endometrial and ovarian cancer in China from 1990 to 2021.Methods Data on incidence,mortality,disability-adjusted life year(DALY),and other indicators for cervical,endometrial and ovarian cancer were collected from the 2021 Global Burden of Disease database.Joinpoint regression models were used to analyze time trends,and age-period-cohort(APC)models assessed their impact on incidence and mortality.Spearman correlation analysis was performed to evaluate the relationship between the sociodemographic index(SDI)and the cancer indicators.Finally,the attributable risk factors for the disease burden were analyzed.Results From 1990 to 2021,age-standardized incidence rates of cervical and endometrial cancers in China significantly increased,while ovarian cancer showed no significant change.Age-standardized mortality,DALY,and years of life lost due to premature death(YLL)decreased significantly.The disease burden was heavier in middle-aged and older groups.APC model indicated an increase in cervical cancer incidence and a decrease in mortality over time.Furthermore,the incidence risks of cervical and endometrial cancers were elevated in successive birth cohorts,whereas a lower risk was observed for ovarian cancer.Correlation analysis showed significant associations between cancer incidence and mortality with SDI.Obesity has significantly contributed to the disease burden of common gynecologic cancers in China.Conclusion Mortality rates of cervical,endometrial and ovarian cancer have declined,while the incidence of cervical and endometrial cancers has significantly increased.The trends in incidence and mortality are influenced by age,period and cohort effects.Future efforts should focus on controlling risk factors like obesity to reduce the disease burden.

Patients with myeloproliferative neoplasms(MPNs)are at risk of developing solid tumors.Although the connection between MPN and solid tumors has been widely discussed,the cause remains to be explored.Some studies show that the MPN is considered to trigger a second cancer,but others suggest both diseases seem to share the same origin.In recent years,more and more studies have found that genetic susceptibility,drugs,chronic inflammation,immune function abnormalities and clonal hematopoiesis of indeterminate potential are related to the development of second cancers.In this review,we try to summarize the new advances of biological characteristics and pathogenesis of second cancers in MPNs.

Dental implantology has developed rapidly for over half a century,since pure titanium(99.7％)dental cylindrical threaded implants were exploited and osseointegration was introduced in 1960s by Prof.Br?nemark.The long term retention rates of 10 years or more are over 95％.However,the biological complications jeopardize the long term effects of dental implant treatment seriously.The prevalence of dental implant biological complications varies greatly among different reports resulting from the disparities on the defini-tions of dental implant biological complications.After analyzing and summarizing the major opinions proposed internationally in recent years,the consensus for the definition of dental implant biological complications has been reached.Generally the dental implant biologi-cal implications can be classified into early stage(before restoration)biological complications and late stage(after restoration)biological complications.The early stage biological complications include acute and chronic infections,pain,soft tissue deficiency,and osseointegration failure,etc.The late stage complications include peri-implant diseases(peri-implant mucositis and peri-implantitis),soft tissue deficiency around implant,implant loosening and dropping off,etc.The various risk factors related to different dental implant biological complications,the strategies of the prevention and treatment for the dental implant biological complications have been discussed comprehensively,and the consensus has been reached.It is aimed to advocate the dentist to pay more attention to the early prevention of the biological implant complications,to promote more researches on the implant biological complications,and to help elevate the level of dental implantology in our country.

Objective To investigate the structural changes and influencing factors of the average inpatient cost per admis-sion in public hospitals in Guangdong Province.Methods Grey relational analysis and structural variation degree analysis were used to analyze the correlation and changes between the average inpatient cost per admission and various cost components in public hospitals of Guangdong Province from 2017 to 2023.Results The average inpatient cost per admission in public hospitals of Guangdong Province showed an overall upward trend from 2017 to 2023,with an average annual growth rate of 3.84％.Among the components,laboratory test fees and examination fees grew at average annual rates of 6.17％and 6.68％,respectively.The top four cost components with the highest grey relational degree with the average inpatient cost were laboratory test fees(0.867),exam-ination fees(0.835),nursing fees(0.784),and treatment fees(0.728).The top four components with the largest structural vari-ation values were surgery fees(2.57％),medical material fees(1.77％),laboratory test fees(1.56％),and examination fees(1.45％).Conclusion The growth of the average inpatient cost per admission has slowed,and the cost structure has been opti-mized to some extent.However,the relatively rapid increase in laboratory test and examination fees has a significant impact on the cost structure.It is necessary to deepen the coordinated governance of healthcare,medical insurance,and medicine,strengthen the leveraging role of medical insurance payment,improve the external governance system and scientific compensation mechanism,and combine these with refined hospital management to promote reasonable cost control and high-quality development in public hospitals.