Objective To explore the characteristics,potential challenges,and optimization paths of high-quality development policies for public hospitals in China,and to provide reference for policy formulation and implementation.Methods The 31 policy documents on high-quality development of public hospitals issued at the central and provincial levels between 2021 and 2024 were coded using Nvivo 11 software,and a three-dimensional analytical framework was constructed on the basis of the policy tools,stakeholders,and system dimensions,combining both quantitative and qualitative methods for content analysis.Results It found that the distribution of policy content in the three dimensions was characterized by stage imbalance:policy tools were mainly environmental,followed by supply,and demand;stakeholder attention was focused on public hospitals and healthcare administrations,and patient attention was low;The system dimension focused mainly on the macro level,with less distribution at the meso and micro levels.Although the unbalanced distribution of policy instruments is relevant at certain stages,the long-term structural imbalance may lead to insufficient systemic policies and weakened stakeholder synergies,and there is an urgent need to optimize the structure of instruments.Conclusions It is recommended to increase the proportion of demand-based policy instruments in policy design and dynamically adjust the synergistic application of the three types of instruments;to strengthen the attention to vulnerable stakeholders,such as patients;and to optimize the design of policies at the meso-levels and micro-levels in order to enhance the systemicity and sustainability of policy implementation.

Objective To explore the characteristics,potential challenges,and optimization paths of high-quality development policies for public hospitals in China,and to provide reference for policy formulation and implementation.Methods The 31 policy documents on high-quality development of public hospitals issued at the central and provincial levels between 2021 and 2024 were coded using Nvivo 11 software,and a three-dimensional analytical framework was constructed on the basis of the policy tools,stakeholders,and system dimensions,combining both quantitative and qualitative methods for content analysis.Results It found that the distribution of policy content in the three dimensions was characterized by stage imbalance:policy tools were mainly environmental,followed by supply,and demand;stakeholder attention was focused on public hospitals and healthcare administrations,and patient attention was low;The system dimension focused mainly on the macro level,with less distribution at the meso and micro levels.Although the unbalanced distribution of policy instruments is relevant at certain stages,the long-term structural imbalance may lead to insufficient systemic policies and weakened stakeholder synergies,and there is an urgent need to optimize the structure of instruments.Conclusions It is recommended to increase the proportion of demand-based policy instruments in policy design and dynamically adjust the synergistic application of the three types of instruments;to strengthen the attention to vulnerable stakeholders,such as patients;and to optimize the design of policies at the meso-levels and micro-levels in order to enhance the systemicity and sustainability of policy implementation.

Humans ; Vitamins/therapeutic use* ; Iron, Dietary ; Diabetes Mellitus, Type 2 ; Nutrition Surveys ; Diet ; Vitamin A ; Vitamin K

Objective:To explore the risk factors of linezolid-induced thrombocytopenia (LIT) and evaluate their predictive value.Methods:Medical records of hospital acquired pneumonia (HAP) patients who admitted in Suzhou Municipal Hospital from July 2019 to October 2021 and received linezolid were collected and retrospectively analyzed. Clinical data including general information, comorbidities, linezolid application, laboratory test results, and trough concentration of linezolid was extracted. Patients were divided into LIT group and non-LIT group according to the occurrence of LIT. Clinical characteristics were compared between the 2 groups; risk factors of LIT in patients with HAP were analyzed using a binary logistic regression model, and the odds ratio ( OR) and its 95% confidence interval ( CI) were calculated; the predictive value of the risk factors for LIT were evaluated using receiver operating characteristic (ROC) curve. Results:A total of 74 patients were included in the study, including 55 males and 19 females, aged 82 (73, 88) years. LIT occurred in 25 patients (33.8%). Compared with the non-LIT group, the age and trough concentration of linezolid in patients in the LIT group were higher [88 (81, 92) years vs. 79(70, 86) years, P=0.001; (19.6±10.3) mg/L vs. (9.8±6.4) mg/L, P<0.001], and the baseline platelet count and baseline creatinine clearance rate were lower [181(162, 212) ×10 9/L vs. 229 (169, 289) ×10 9/L, P=0.025; 31(19, 44) ml/(min·1.73 m 2) vs. 46 (27, 65) ml/(min·1.73 m 2), P=0.018]. Binary logistic regression analysis showed that the lower baseline creatinine clearance rate ( OR=0.974, 95 %CI: 0.951-0.998, P=0.035) and higher trough concentration of linezolid ( OR=1.156, 95 %CI: 1.059-1.261, P=0.001) were independent risk factors for LIT in HAP patients. ROC curve analysis showed that the threshold of the age, trough concentration of linezolid, baseline platelet count, and baseline creatinine clearance rate were 87 years (sensitivity 56.0%, specificity 83.7%), 15.4 mg/L (sensitivity 64.0%, specificity 87.8%), 189×10 9/L (sensitivity 67.3%, specificity 68.0%), and 45 ml/(min·1.73 m 2) (sensitivity 53.1%, specificity 80.0%), respectively. Patients were respectively divided into 2 groups according to the thresholds and the incidences of LIT were compared. The results showed that the incidences of LIT in patients with age and trough concentration of linezolid exceeding the thresholds and in patients with baseline plate count and baseline creatinine clearance rate lower than or equal to the thresholds were significantly higher than those in the other patients (all P<0.01). Conclusions:Baseline creatinine clearance rate, trough concentration of linezolid, age, and plate count are risk factors for LIT in HAP patients and their thresholds are 45 ml/(min·1.73 m 2), 15.4 mg/L, 87 years, and 189×10 9/L, respectively. These risk factors have good predictive value for the occurrence of LIT.

Objective:To explore the risk factors of linezolid-induced thrombocytopenia (LIT) and evaluate their predictive value.Methods:Medical records of hospital acquired pneumonia (HAP) patients who admitted in Suzhou Municipal Hospital from July 2019 to October 2021 and received linezolid were collected and retrospectively analyzed. Clinical data including general information, comorbidities, linezolid application, laboratory test results, and trough concentration of linezolid was extracted. Patients were divided into LIT group and non-LIT group according to the occurrence of LIT. Clinical characteristics were compared between the 2 groups; risk factors of LIT in patients with HAP were analyzed using a binary logistic regression model, and the odds ratio ( OR) and its 95% confidence interval ( CI) were calculated; the predictive value of the risk factors for LIT were evaluated using receiver operating characteristic (ROC) curve. Results:A total of 74 patients were included in the study, including 55 males and 19 females, aged 82 (73, 88) years. LIT occurred in 25 patients (33.8%). Compared with the non-LIT group, the age and trough concentration of linezolid in patients in the LIT group were higher [88 (81, 92) years vs. 79(70, 86) years, P=0.001; (19.6±10.3) mg/L vs. (9.8±6.4) mg/L, P<0.001], and the baseline platelet count and baseline creatinine clearance rate were lower [181(162, 212) ×10 9/L vs. 229 (169, 289) ×10 9/L, P=0.025; 31(19, 44) ml/(min·1.73 m 2) vs. 46 (27, 65) ml/(min·1.73 m 2), P=0.018]. Binary logistic regression analysis showed that the lower baseline creatinine clearance rate ( OR=0.974, 95 %CI: 0.951-0.998, P=0.035) and higher trough concentration of linezolid ( OR=1.156, 95 %CI: 1.059-1.261, P=0.001) were independent risk factors for LIT in HAP patients. ROC curve analysis showed that the threshold of the age, trough concentration of linezolid, baseline platelet count, and baseline creatinine clearance rate were 87 years (sensitivity 56.0%, specificity 83.7%), 15.4 mg/L (sensitivity 64.0%, specificity 87.8%), 189×10 9/L (sensitivity 67.3%, specificity 68.0%), and 45 ml/(min·1.73 m 2) (sensitivity 53.1%, specificity 80.0%), respectively. Patients were respectively divided into 2 groups according to the thresholds and the incidences of LIT were compared. The results showed that the incidences of LIT in patients with age and trough concentration of linezolid exceeding the thresholds and in patients with baseline plate count and baseline creatinine clearance rate lower than or equal to the thresholds were significantly higher than those in the other patients (all P<0.01). Conclusions:Baseline creatinine clearance rate, trough concentration of linezolid, age, and plate count are risk factors for LIT in HAP patients and their thresholds are 45 ml/(min·1.73 m 2), 15.4 mg/L, 87 years, and 189×10 9/L, respectively. These risk factors have good predictive value for the occurrence of LIT.

Nucleic acid detection technique has good sensitivity and specificity and is widely used in in vitro diagnosis, animal and plant commodity quarantine, forensic identification, and other fields. However, it is susceptible to carryover contamination during the operation and leads to false-positive results, which seriously affects the detection accuracy. Therefore, finding an effective solution to prevent and eliminate nucleic acid carryover contamination has become particularly urgent. This study compared several different methods for removing nucleic acid contamination and confirmed that sodium hypochlorite solution and PCRguard reagent could effectively eliminate nucleic acid carryover in the liquid and on surfaces of different materials. Besides, the combination of sodium hypochlorite solution and PCRguard can solve the nucleic acid aerosol contamination. This study proposes solutions for the routine prevention of carryover contamination and removal of aerosol that has occurred in molecular diagnostic laboratories.
Laboratories ; Nucleic Acids ; Pathology, Molecular

Objective:To construct a prognosis associated micro RNA(miRNA) prediction model based on bioinformatics analysis and evaluate its application value in pancreatic cancer patients.Methods:The retrospective cohort study was conducted. The clinicopathological data of 171 pancreatic cancer patients from the Cancer Genome Atlas (TCGA) (https: //cancergenome.nih.gov/) between establishment of database and September 2017 were collected. There were 93 males and 78 females, aged from 35 to 88 years, with a median age of 65 years. Of the 171 patients, 64 had complete clinicopathological data. Patients were allocated into training dataset consisting of 123 patients and validation dataset consisting of 48 patients using the random sampling method, with a ratio of 7∶3. The training dataset was used to construct a prediction model, and the validation dataset was used to evaluate performance of the prediction model. Nine pairs of miRNA sequencing data (GSE41372) of pancreatic cancer and adjacent tissues were downloaded from Gene Expression Omnibus database. The candidate miRNAs were selected from differentially expressed miRNAs in pancreatic cancer and adjacent tissues for LASSO-COX regression analysis based on the patients of training dataset. A prognosis associated miRNA prediction model was constructed upon survival associated miRNAs which were selected from candidate differentially expressed miRNAs. The performance of prognosis associated miRNA prediction model was validated in training dataset and validation dataset, the accuracy of model was evaluated using the area under curve (AUC) of the receiver operating characteristic curves and the efficiency was evaluated using the consistency index (C-index). Observation indicarors: (1) survival of patients; (2) screening results of differentially expressed miRNAs; (3) construction of prognosis associated miRNA model; (4) validation of prognosis associated miRNA model; (5) comparison of clinicopathological factors in pancreatic cancer patients; (6) analysis of factors for prognosis of pancreatic cancer patients; (7) comparison of prediction performance between prognosis associated miRNA model and the eighth edition TNM staging. Measurement data with normal distribution were represented as Mean± SD, comparison between groups was analyzed by the student- t test, and comparison between multiple groups was analyzed by the AVONA. Measurement data with skewed data were represented as M (range), and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Ordinal data were analyzed using the rank sum test. Correlation analysis was conducted based on count data to mine the correlation between prognosis associated miRNA model and clinicopathological factors. COX univariate analysis and multivariate analysis were applied to evaluate correlation with the results described as hazard ratio ( HR) and 95% confidence interval ( CI). HR<1 indicated the factor as a protective factor, HR>1 indicated the factor as a risk factor, and HR equal to 1 indicated no influence on survival. The Kaplan-Meier method was used to draw survival curve and calculate survival rates, and the Log-rank test was used for survival analysis. Results:(1) Survival of patients: 123 patients in the training dataset were followed up for 31-2 141 days, with a median follow-up time of 449 days. The 3- and 5-year survival rates were 16.67% and 8.06%. Forty-eight patients in the validation dataset were followed up for 41-2 182 days, with a median follow-up time of 457 days. The 3- and 5-year survival rates were 15.63% and 9.68%. There was no significant difference in the 3- or 5-year survival rates between the two groups ( χ2=0.017, 0.068, P>0.05). (2) Screening results of differentially expressed miRNAs. Results of bioinformatics analysis showed that 102 candidate differentially expressed miRNAs were selected, of which 63 were up-regulated in tumor tissues while 39 were down-regulated. (3) Construction of prognosis associated miRNA model: of the 102 candidate differentially expressed miRNAs, 5 survival associated miRNAs were selected, including miR-21, miR-125a-5p, miR-744, miR-374b, miR-664. The differential expression patterns of pancreatic cancer to adjacent tissues were up-regulation, up-regulation, down-regulation, up-regulation, and down-regulation, respectively, with the fold change of 4.00, 3.43, 3.85, 2.62, and 2.35. A prognostic expression equation constructed based on 5 survival associated miRNAs = 0.454×miR-21 expression level-0.492×miR-125a-5p expression level-0.49×miR-744 expression level-0.419×miR-374b expression level-0.036×miR-664 expression level. (4) Validation of prognosis associated miRNA model: The C-index of prognosis associated miRNA model was 0.643 and 0.642 for the training dataset and validation dataset, respectively. (5) Comparison of clinicopathological factors in pancreatic cancer patients: results of COX analysis showed that the prognosis associated miRNA model was highly related with pathological T stage and location of pancreatic cancer ( Z=45.481, χ2=10.176, P<0.05). (6) Analysis of factors for prognosis of pancreatic cancer patients: results of univariate analysis showed that pathological N stage, radiotherapy, molecular targeted therapy, score of prognosis associated miRNA model were related factors for prognosis pf pancreatic cancer patients ( HR=2.471, 0.290, 0.172, 2.001, 95% CI: 1.012-6.032, 0.101-0.833, 0.082-0.364, 1.371-2.922, P<0.05). Results of multivariate analysis showed that molecular targeted therapy was an independent protective factor for prognosis of pancreatic cancer patients ( HR=0.261, 95% CI: 0.116-0.588, P<0.05) and score of prognosis associated miRNA model≥1.16 was an independent risk factor for prognosis of pancreatic cancer patients ( HR=1.608, 95% CI: 1.091-2.369, P<0.05). (7) Comparison of prediction performance between prognosis associated miRNA model and the eighth edition TNM staging: in the training dataset, there was a significant difference in the prediction probability for 3- and 5-year survival of pancreatic cancer patients between prognosis associated miRNA model and the eighth edition TNM staging ( Z=-1.671, -1.867, P<0.05). The AUC of the prognosis associated miRNA model and the eight edition TNM staging for 3- and 5-year survival prediction was 0.797, 0.935 and 0.737 , 0.703, with the 95% CI of 0.622-0.972, 0.828-1.042 and 0.571-0.904 , 0.456-0.951. The C-index was 0.643 and 0.534. In the validation dataset, there was a significant difference in the prediction probability for 3- and 5-year survival of pancreatic cancer patients between prognosis associated miRNA model and the eighth edition TNM staging ( Z=-1.729, -1.923, P<0.05). The AUC of the prognosis associated miRNA model and the eight edition TNM staging was 0.750, 0.873 and 0.721 , 0.703, with the 95% CI of 0.553-0.948, 0.720-1.025 and 0.553-0.889, 0.456-0.950, respectively. The C-index was 0.642 and 0.544. Conclusions:A prognosis associated miRNA prediction model can be constructed based on 5 survival associated miRNAs in pancreatic cancer patients, as a complementation to current TNM staging and other clinicopathological parameters, which provides individual and accurate prediction of survival for reference in the clinical treatment.

OBJECTIVE：To study the effects of augmented renal clearance （ARC）on blood trough concentration of patients receiving high-dose regimen of teicoplanin. METHODS ：Patients who received high-dose regimen of teicoplanin in the ICU were prospectively collected from the Affiliated Suzhou Hospital of Nanjing Medical University/Suzhou Municipal Hospital during Jul. 2018-Jun. 2020. They were divided into ARC group and normal renal function group according to corrected creatinine clearance. The dosage regimen of teicoplanin in the two groups were loading dose of 600 mg，q12 h×3 doses，maintenance dose of 6-10 mg/kg，qd，and the dosage was adjusted in combination with creatinine clearance rate and blood trough concentration. The trough concentration of blood samples which were collected 30 min before the 4th and 8th-10th dosage of teicoplanin were determined by HPLC. Trough concentration ，clinical efficacy ，Gram-positive bacterial clearance rate and the occurrence of ADR were compared between 2 groups. RESULTS ：A total of 56 patients were included and divided into ARC group （18 cases）and normal renal function group （38 cases）. ARC group had younger age （P＜0.001）and lower serum albumin level （P＝0.025）than normal renal function group. The trough concentrations before administration of the 4th and 8th-10th dosage in ARC group were lower than normal renal function group （P＝0.034；P＝0.035）. The trough concentrations in the ARC group and normal renal function group before 8th-10th dosage were all higher than 30 min before the 4th dosage （P＝0.003；P＜0.001）. The clinical efficacy rate and the clearance rate of Gram-positive bacteria in ARC group were 77.8％ and 76.2％，which were lower than those of the normal renal function group ，but there was no statistical difference （P＝0.195；P＝0.223）. There was no liver function damage ，hemocytopenia and allergic reaction in both groups ，but in the normal renal function group ，the causal relationship between acute renal damage and teicoplanin was assessed as “very likely ”in one patient. CONCLUSIONS ：ARC patients are younger ，most of them have hypoproteinemia，and the blood trough concentrations of teicoplanin in high-dose regimen are significantly lower than those of normal renal function patients. For critical ill ARC patients ，it is advisable to increase the loading dose of teicoplanin to make the trough concentration reach the target concentration range quickly.

Objective: To evaluate the safety and efficacy of surgical treatment after neoadjuvant chemotherapy (NCT) for patients with cT4N+ colon cancer, and to explore whether the indication of NCT for colon cancer can be extended from cT4b to cT4N+ . Methods: The clinical data of 40 patients with cT4N+ colon cancer who underwent neoadjuvant chemotherapy followed by surgical treatment was retrospectively analyzed. The safety of neoadjuvant chemotherapy, surgical complications, R0 resection rate, tumor regression grade and prognosis were evaluated. Results: Of the 40 patients, 23 were male and 17 were female; the median age was 57 years old. All patients were well tolerated with chemotherapy, and only one case (1/40, 2.5%) had grade 3 chemotherapy-related adverse event. They all underwent surgery after chemotherapy, and 95.0% (38/40) achieved microscopically clear resection (R0). Of the 11 patients with cT4b, 54.5% (6/11) had undergone multivisceral resection (MVR). Postoperative pathological results showed that 12 patients had moderate to severe tumor regression, including one(1/40, 2.5%) achieved pathologic complete response (pCR). 29(72.5%) and 22 (55.0%) patients achieved down-staging of tumor T stage and N stage, respectively. The occurrence of surgical complications was 22.5% (9/40), including one case of anastomotic leakage (1/40, 2.5%). The 3-year disease-free survival and overall survival of the whole group were 75.0% and 80.0%, respectively. Conclusion Surgery after neoadjuvant chemotherapy is safe and effective for patients with cT4N+ colon cancer, therefore indications for neoadjuvant chemotherapy for advanced colon cancer can be extended to cT4N+ stage.

Objective: To investigate the curative effect and prognostic factors of radical radiotherapy for cervical cancer. Methods: A total of 211 patients with stage Ⅰ_A-Ⅲ_B cervical cancer who underwent therapy in department of radiotherapy, Tumor Hospital of Jilin province between June 2014 and February 2017, were analyzed retrospectively. All patients received radical radiotherapy with or without concurrent chemotherapy. Short-term and long-term efficacy and related prognostic factors were observed. Kaplan-Meier method was used for survival analysis, Log-rank test was used for univariate analysis, and Cox proportional hazards regression model was used for multivariate analysis. Results: The 2-year overall survival (OS) and disease free survival (DFS) were 83.4% and 72.5%, respectively. During the follow-up periods, 46 patients (21.8%) died, including two from non-tumor-related diseases, and one from second primary colon cancer. Totally 57 patients (27%) had recurrence and metastasis, including 16 (28.1%) with local recurrence, 27 (47.4%) with distant metastasis, and 14 with local recurrence and distant metastasis(24.6%). Univariate analysis showed that 2-year OS and DFS were significantly correlated with pathological type, pre-treatment squamous cell carcinoma antigen (SCC) value and FIGO stage (OS: χ²=7.123, 6.014, 8.398, P<0.05; DFS: χ²=11.832, 8.003, 7.731, P<0.05). In addition to the above factors, 2-year DFS was also associated with pelvic lymph node metastasis (χ²=9.286, P<0.05). Multivariate analysis showed that pathological type, pre-treatment SCC value and FIGO stage were independent prognostic factors of OS(HR=2.963, 2.473, 2.574, P<0.05). The independent prognosis factors affecting DFS included pathological type, pre-treatment SCC value and pelvic lymph node metastasis (HR =3.014, 1.988, 1.914, P<0.05). Conclusions By means of radical radiotherapy, cervical cancer patients with adenocarcinoma, pre-treatment SCC levels ≥30 ng/ml and advanced stage have poor prognosis, so more active treatment strategy should be adopted.