The clinical diagnosis and treatment of urinary tract infection has long faced the challenges of insufficient standardization of diagnosis and treatment pathways,irrational use of antimicrobial drugs and high recurrence rate.How to optimize the hierarchical diagnosis and treatment pathway of urinary tract infection,standardize the use of antimicrobial drugs,and reduce the recurrence rate have always been the focus of clinical attention.There is significant heterogeneity in the existing diagnostic criteria for urinary tract infection,which seriously affects the comparability and evidence integration of clinical and research studies.In order to solve the above problems,a consensus on global multidisciplinary diagnostic criteria for urinary tract infection has been formed by international multidisciplinary experts after three rounds of Delphi method.Breaking through the traditional classification framework,the consensus innovatively established a four-dimensional quantitative scoring system including local symptoms and signs,systemic inflammatory response,quantitative analysis of pyuria and urine culture results,and established a hierarchical standard for stepwise urinary tract diagnosis according to the scoring threshold.Based on the key citations related to the consensus,this paper interprets in detail the basis for the selection of core indicators and the establishment of thresholds for the diagnosis of urinary tract infection in the consensus,and focuses on the key issues and implementation paths of the consensus in localization practice.This consensus provides a unified standard for standardizing the clinical diagnosis and treatment of urinary tract infection,improving the homogeneity of clinical research through standardized diagnostic processes,and promoting the standardization of UTI drug research and development and the rational use of antibiotics and precision.

Objective To construct a prediction model for targeted biopsy(TB)of the prostate based on multiparameter magnetic resonance imaging(mp-MRI)and prostate-specific antigen density(PSAD)to predict the outcomes TB in patients with a score of 4-5 on the Prostate Imaging Reporting and Data System(PI-RADS).Methods Clinical data of 669 patients with PI-RADS 4-5 receiving transperineal TB in our hospital during Jan.2022 and Dec.2023 were retrospectively analyzed.The data were divided into the training set and validation set with a ratio of 2∶1.Independent predictors of TB results were identified with univariate and multivariate logistic regression to construct a formula for the prediction model.A prediction model was subsequently constructed and validated using the validation set to assess its efficacy and predictive performance with the area under the receiver operating characteristic curve(AUC).The relative importance of each independent predictor in the formula was analyzed.Results Univariate and multivariate logistic regression analyses showed that age,total number of lesions,histological location,PI-RADS score and PSAD were significantly associated with the TB outcomes(P＜0.05)and could be used as independent predictors,with PI-RADS score and PSAD making the highest contribution to outcome prediction,accounting for 27.59％and 37.58％,respectively.The training set had an AUC of 0.840(95％CI:0.800-0.881),which was more predictive than other single predictors,and the high-risk group based on the optimal threshold of 0.833 increased the positive biopsy rate from 79.3％to 94.4％.The validation set had an AUC of 0.865(95％CI:0.810-0.920),and the high-risk group based on the optimal threshold of 0.594 increased the positive biopsy rate from 80.0％to 96.2％.Conclusion The prediction model has good predictive ability for lesions with PI-RADS 4-5,which can significantly improve the positive detection rate and reduce a large number of unnecessary systematic puncture.

Objective To construct a prediction model for targeted biopsy(TB)of the prostate based on multiparameter magnetic resonance imaging(mp-MRI)and prostate-specific antigen density(PSAD)to predict the outcomes TB in patients with a score of 4-5 on the Prostate Imaging Reporting and Data System(PI-RADS).Methods Clinical data of 669 patients with PI-RADS 4-5 receiving transperineal TB in our hospital during Jan.2022 and Dec.2023 were retrospectively analyzed.The data were divided into the training set and validation set with a ratio of 2∶1.Independent predictors of TB results were identified with univariate and multivariate logistic regression to construct a formula for the prediction model.A prediction model was subsequently constructed and validated using the validation set to assess its efficacy and predictive performance with the area under the receiver operating characteristic curve(AUC).The relative importance of each independent predictor in the formula was analyzed.Results Univariate and multivariate logistic regression analyses showed that age,total number of lesions,histological location,PI-RADS score and PSAD were significantly associated with the TB outcomes(P＜0.05)and could be used as independent predictors,with PI-RADS score and PSAD making the highest contribution to outcome prediction,accounting for 27.59％and 37.58％,respectively.The training set had an AUC of 0.840(95％CI:0.800-0.881),which was more predictive than other single predictors,and the high-risk group based on the optimal threshold of 0.833 increased the positive biopsy rate from 79.3％to 94.4％.The validation set had an AUC of 0.865(95％CI:0.810-0.920),and the high-risk group based on the optimal threshold of 0.594 increased the positive biopsy rate from 80.0％to 96.2％.Conclusion The prediction model has good predictive ability for lesions with PI-RADS 4-5,which can significantly improve the positive detection rate and reduce a large number of unnecessary systematic puncture.

The clinical diagnosis and treatment of urinary tract infection has long faced the challenges of insufficient standardization of diagnosis and treatment pathways,irrational use of antimicrobial drugs and high recurrence rate.How to optimize the hierarchical diagnosis and treatment pathway of urinary tract infection,standardize the use of antimicrobial drugs,and reduce the recurrence rate have always been the focus of clinical attention.There is significant heterogeneity in the existing diagnostic criteria for urinary tract infection,which seriously affects the comparability and evidence integration of clinical and research studies.In order to solve the above problems,a consensus on global multidisciplinary diagnostic criteria for urinary tract infection has been formed by international multidisciplinary experts after three rounds of Delphi method.Breaking through the traditional classification framework,the consensus innovatively established a four-dimensional quantitative scoring system including local symptoms and signs,systemic inflammatory response,quantitative analysis of pyuria and urine culture results,and established a hierarchical standard for stepwise urinary tract diagnosis according to the scoring threshold.Based on the key citations related to the consensus,this paper interprets in detail the basis for the selection of core indicators and the establishment of thresholds for the diagnosis of urinary tract infection in the consensus,and focuses on the key issues and implementation paths of the consensus in localization practice.This consensus provides a unified standard for standardizing the clinical diagnosis and treatment of urinary tract infection,improving the homogeneity of clinical research through standardized diagnostic processes,and promoting the standardization of UTI drug research and development and the rational use of antibiotics and precision.

The discussion on the connotation of children’s subjectivity is not only a response to the lack of children’s subjectivity at the current stage of health management, but also a reference for children’s medical science popularization. Based on the perspective of social critical theory, this study used empirical research methods to review the "Dream Medical College" project of Children’s Hospital of Fudan University. The current situation and influencing factors of health management experience of 1 520 children participating in the "Dream Medical College" project were analyzed. The study showed that 96.35% of 1 316 subjects had diagnosis and treatment experience in specialized hospitals, and the overall negative emotional performance was at a low level (0~12 points). There was significant correlation between diagnosis and treatment, invasive experience and children’s emotional performance (P<0.05). The study revealed that the diagnosis and treatment field is the main practice place of children’s health management, while the subjective of children with different diagnosis and experience perform significantly different. Children over 4 years old have better language anxiety than physical anxiety when receiving diagnosis and treatment. Although medical science popularization is an important practical form of children’s health management, it lacks the science popularization content of invasive diagnosis and treatment and emotional management, and creative popular science form is more suitable for children with long-term and frequent diagnosis and treatment experience.

Objective:To investigate the factors contributing to in-hospital mortality among elderly patients aged 80 and above with gastrointestinal bleeding(GIB).Additionally, it seeks to assess the predictive ability of the electronic frailty index(eFI)in determining the risk of in-hospital mortality in GIB patients.Methods:A retrospective analysis was performed among 624 patients aged 80 and above with GIB who were admitted to Beijing Hospital between July 2013 and September 2019.The patients were categorized into two groups based on their discharge outcomes: those who survived and those who did not.The eFI was developed using a cumulative deficit model utilizing data from the hospital's electronic medical records.The study examined the clinical features and risk factors associated with in-hospital mortality among these elderly patients.The effectiveness of eFI in predicting in-hospital mortality in elderly patients with gastrointestinal bleeding was evaluated by calculating the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.Results:Among a total of 624 patients aged between 80 and 102 years, the average age was(83.0±6.4)years, with 339 being male.A majority of the patients, 581 cases(93.1%), had an eFI ≥ 0.15.A comparison between the survival group(380 cases)and the death group(244 cases)revealed that the latter had higher eFI values(0.39±0.09 vs.0.29±0.11, t=-11.452, P<0.001), along with higher rates of heart failure, chronic kidney disease, and malignant tumors, as well as lower body mass index, hemoglobin, albumin, and total cholesterol levels, and higher alanine aminotransferase and D-dimer levels(all P<0.05).Logistic regression analysis indicated that eFI( OR=2.322, 95% CI: 1.840-2.929, P<0.001), malignant tumor( OR=1.833, 95% CI: 1.141-2.860, P<0.001), and albumin<35 g/L( OR=1.826, 95% CI: 1.200-2.777, P<0.001)were independent risk factors for in-hospital death in elderly patients aged 80 and over with gastrointestinal bleeding.With every 0.1 increase in eFI, the risk of in-hospital death rose by 1.322 times.The AUC of eFI for predicting in-hospital mortality was 0.751(95% CI: 0.713-0.789, P<0.001).An eFI of ≥0.33 demonstrated a sensitivity of 77.9% and a specificity of 60.3% in predicting in-hospital mortality in elderly patients aged 80 and over with GIB. Conclusions:The eFI serves as an important independent risk factor for in-hospital mortality among patients aged 80 and above who experience GIB.It can effectively assess the prognosis of elderly individuals facing GIB.

Objective:To establish a set of artificial intelligence (AI) verification rules for blood routine analysis.Methods:Blood routine analysis data of 18 474 hospitalized patients from the First Hospital of Jilin University during August 1st to 31st, 2019, were collected as training group for establishment of the AI verification rules,and the corresponding patient age, microscopic examination results, and clinical diagnosis information were collected. 92 laboratory parameters, including blood analysis report parameters, research parameters and alarm information, were used as candidate conditions for AI audit rules; manual verification combining microscopy was considered as standard, marked whether it was passed or blocked. Using decision tree algorithm, AI audit rules are initially established through high-intensity, multi-round and five-fold cross-validation and AI verification rules were optimized by setting important mandatory cases. The performance of AI verification rules was evaluated by comparing the false negative rate, precision rate, recall rate, F1 score, and pass rate with that of the current autoverification rules using Chi-square test. Another cohort of blood routine analysis data of 12 475 hospitalized patients in the First Hospital of Jilin University during November 1sr to 31st, 2023, were collected as validation group for validation of AI verification rules, which underwent simulated verification via the preliminary AI rules, thus performance of AI rules were analyzed by the above indicators. Results:AI verification rules consist of 15 rules and 17 parameters and do distinguish numeric and morphological abnormalities. Compared with auto-verification rules, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score of AI rules in training group were 22.7%, 1.6%, 74.5%, 1.3%, 75.7%, 97.2%, 93.5%, 94.7%, 94.1, respectively.All of them were better than auto-verification rules, and the difference was statistically significant ( P<0.001), and with no important case missed. In validation group, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score were 19.2%, 8.2%, 70.1%, 2.5%, 72.6%, 89.2%, 70.0%, 88.3%, 78.1, respectively, Compared with the auto-verification rules, The false negative rate was lower, the false positive rate and the recall rate were slightly higher, and the difference was statistically significant ( P<0.001). Conclusion:A set of the AI verification rules are established and verified by using decision tree algorithm of machine learning, which can identify, intercept and prompt abnormal results stably, and is moresimple, highly efficient and more accurate in the report of blood analysis test results compared with auto-vefication.

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, which seriously endangers the health of mothers and infants. Its incidence is gradually increasing worldwide. Research has found that, in addition to insulin resistance and pancreatic β-cell dysfunction, immune disorders play an important role in the pathogenesis of GDM. This study reviews the recent research on the involvement of common immune cells in the pathophysiological process of GDM to explore the functional changes of immune cells related to the occurrence and development of GDM and provides a reference for the prevention and treatment of GDM.

Objective:By conducting a retrospective analysis of the clinical data of 14 patients diagnosed with invasive fungal rhinosinusitis (IFRS) confirmed by metagenomics next generation sequencing (mNGS) technology, we aim to explore the rapid diagnosis value of mNGS in IFRS.Methods:The clinical data of 14 IFRS patients admitted to TianJin First Central Hospital were retrospectively analyzed from February 2021 to October 2023. The study cohort comprised 8 males and 6 females, with ages ranging from 14 to 77 years. All patients were diagnosed as IFRS by performing mNGS sequencing technology of nasal sinus lesion biopsy specimens. Clinical data such as laboratory examination, imaging examination, histopathological examination results, treatment plan and prognosis were summarized and analyzed.Results:All 14 patients were diagnosed as IFRS, with mNGS detecting pathogens such as Rhizopus (7 cases), Aspergillus (5 cases), Trichoderma (1 case), and Scedosporium apiospermum (1 case). Follow-up evaluations were conducted for a period ranging from 2 months to 2 years post-treatment. At the end of follow-up, 11 out of 14 IFRS patients achieved a complete cure with no signs of recurrence, while the symptoms of the remaining 3 patients significantly improved with comprehensive treatment. Conclusion:mNGS emerges as a highly effective diagnostic tool for IFRS, providing valuable microbiological evidence for clinical diagnosis and demonstrating promising clinical utility.

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, which seriously endangers the health of mothers and infants. Its incidence is gradually increasing worldwide. Research has found that, in addition to insulin resistance and pancreatic β-cell dysfunction, immune disorders play an important role in the pathogenesis of GDM. This study reviews the recent research on the involvement of common immune cells in the pathophysiological process of GDM to explore the functional changes of immune cells related to the occurrence and development of GDM and provides a reference for the prevention and treatment of GDM.