Objective The purpose of writing the"Expert consensus on the prevention and control of intracranial hypertension in adult critical illness"(here in after referred to as the"Consensus")aimed to standardize the nursing work related to the prevention and control of elevated intracranial pressure in adult critical illness,and prevent the occurrence of complications such as cerebral herniation.Methods Guided by evidence-based practice,domestic and foreign databases were searched for guidelines,expert consensuses,systematic evaluation,evidence summaries,and original research related to increased intracranial pressure.The search period is from database establishment to March 2024.The high-quality evidence and suggestions in the field was evaluated,extracted,and summarized to form a preliminary consensus.27 experts were invited to conduct 2 rounds of expert inquiry and 8 experts were invited to conduct 2 expert discussion meetings,to revise and improve the content of the initial draft,and to ultimately form a final consensus.Results The effective response rates for both rounds of inquiry questionnaires were 100％,with expert authority coefficients of 0.884,judgment coefficients of 0.964,and familiarity levels of 0.804.The Kendall harmony coefficients for 2 rounds of inquiry were 0.107 and 0.083(P＜0.01),respectively.The consensus includes 4 aspects,including identification,monitoring,prevention and control strategies,emergency treatment and care for increased intracranial pressure.Conclusion This"Consensus"has strong scientific validity and can provide reference basis for nurses to carry out prevention and control of intracranial pressure increase.

Objective:To evaluate the relationship between prognostic nutritional index (PNI) and myocardial injury after non-cardiac surgery (MINS).Methods:This was a retrospective cohort study. The clinical data of adult patients ( n=2 203) who underwent liver resection surgery with general anesthesia at our center from January 2016 to August 2019 were retrospectively collected. The predictive value of preoperative PNI for MINS and the optimal cut-off value of PNI were evaluated and determined according to the receiver operating characteristic curve, and the patients were divided into 2 groups based on the cut-off value: high PNI group and low PNI group. Logistic regression analyses were applied to investigate the relationship between preoperative PNI and MINS. According to the same inclusion and exclusion criteria, the clinical data of patients at our center from January 2022 to December 2023 were collected as the validation set ( n=2 525), and they were grouped using the same PNI cutoff value. Logistic regression analyses were used to verify the relationship between PNI and MINS. Results:The receiver operating characteristic curve analysis showed that the area under the curve of preoperative PNI for predicting MINS was 0.651 (95% confidence interval [ CI] 0.602-0.699), with an optimal cut-off value of 46.193, and the specificity and sensitivity were 0.729 and 0.519 respectively. The integer 46 was considered as the optimal cutoff value for PNI, and the patients were divided into low PNI group (PNI<46, n=606) and high PNI group (PNI≥46, n=1 597). Both univariate and multivariate logistic regression analyses showed that preoperative low PNI was an independent risk factor for the occurrence of MINS (univariate: OR=2.873, 95% CI 2.063-4.003, P<0.001; multivariate: OR=1.844, 95% CI 1.241-2.600, P=0.003). The results in the validation set were still robust (univariate: OR=2.694, 95% CI 1.890-3.833, P<0.001; multivariate: OR=1.602, 95% CI 1.071-2.385, P=0.021). Conclusions:Preoperative low-level PNI is an independent risk factor for MINS, with a certain predictive value.

The surface plasmon resonance(SPR)biosensor technology is a novel optical analysis method for studying intermolecular interactions.Owing to in-depth research on traditional Chinese medicine(TCM)in recent years,comprehensive and specific identification of components and target interactions has become key yet difficult tasks.SPR has gradually been used to analyze the active components of TCM owing to its high sensitivity,strong exclusivity,large flux,and real-time monitoring capabilities.This review sought to briefly introduce the active components of TCM and the principle of SPR,and provide historical and new insights into the application of SPR in the analysis of the active components of TCM.

Gray matter heterotopia is a neurodevelopmental disorder characterized by abnormal distribution of gray matter in the brain cortex,and epilepsy is the most common clinical symptom of this disease.This article summarizes the etiology,pathogenesis,clinical manifestations,examination,and treatment of gray matter heterotopia and epilepsy,so as to improve the understanding of the disease.

The patient,a male newborn,was admitted to the hospital 2 hours after birth due to prematurity(gestational age 27+5 weeks)and respiratory distress occurring 2 hours postnatally.After admission,the infant developed fever and elevated C-reactive protein levels.On the fourth day after birth,metagenomic next-generation sequencing of cerebrospinal fluid indicated a positive result for Mycoplasma hominis(9 898 reads).On the eighth day,a retest of cerebrospinal fluid metagenomics confirmed Mycoplasma hominis(56 806 reads).The diagnosis of purulent meningitis caused by Mycoplasma hominis was established,and the antibiotic treatment was switched to moxifloxacin[5 mg/(kg·day)]administered intravenously for a total of 4 weeks.After treatment,the patient's cerebrospinal fluid tests returned to normal,and he was discharged as cured on the 76th day after birth.This article focuses on the diagnosis and treatment of neonatal Mycoplasma hominis purulent meningitis,introducing the multidisciplinary diagnosis and treatment of the condition in extremely preterm infants.[Chinese Journal of Contemporary Pediatrics,2024,26(4):432-436]

Objective:To investigate the predictive value of the quantitative parameters of dual-layer detector spectral CT in arterial and venous phases for the differentiation degree and the E-cadherin protein expression of gastric cancer.Methods:This was a cross-sectional study. The preoperative data from the dual-layer detector spectral CT images among 183 patients with gastric adenocarcinoma confirmed by operation and pathology was retrospectively analyzed from October 2021 to October 2022 in the First Affiliated Hospital of Fujian Medical University. According to the differentiation degree and E-cadherin protein expression of gastric cancer, all patients were divided into the moderately well differentiated group ( n=82) and the poorly differentiated group ( n=101), as well as the E-cadherin-negative group ( n=80) and the E-cadherin-positive group ( n=103). The CT images in arterial and venous phases were used to reconstruct the virtual monoenergetic images (VMI) at 40, 50, 60, 70, 80, 90, and 100 keV, effective atomic number (Z eff) images and iodine concentration (IC) images. The CT values (CT keV) from VMI, Z eff and IC were measured, and the normalized Z eff (NZ eff) and the normalized IC (NIC) were calculated. Independent-sample t test or Mann-Whitney U test were used to compare the differences in quantitative parameters between groups. The logistic regression analysis was used to screen for the independent predictors, after which a combined prediction model was constructed. The receiver operating characteristic curves were used to evaluate the predictive efficiency of the parameters for the differentiation degree and the E-cadherin protein expression of gastric cancer. Results:There were statistically significant differences in CT 40 keV to CT 70 keV, NZ eff and NIC in dual-phase, as well as Z eff and IC in the venous phase between the moderately well differentiated group and the poorly differentiated group ( P<0.05). The combined prediction model was constructed by CT 40 keV ( OR=1.03, 95% CI 1.02-1.05, P<0.001) in arterial phase and CT 40 keV ( OR=1.05, 95% CI 1.03-1.07, P<0.001) and Z eff ( OR=1.32, 95% CI 1.06-1.65, P=0.015) in venous phase, of which the area under the curve (AUC) for the prediction of the moderately-well group and the poor group was 0.932 (95% CI 0.897-0.967), with a sensitivity of 90.1% and a specificity of 85.4%. Between the E-cadherin-negative group and the E-cadherin-positive group, CT 40 keV and NZ eff in arterial phase, as well as CT 40 keV to CT 70 keV, Z eff, NZ eff, IC and NIC in venous phase, had statistically significant differences ( P<0.05). The AUC for the combined prediction model established by CT 40 keV ( OR=1.02, 95% CI 1.01-1.04, P<0.001) and Z eff ( OR=1.33, 95% CI 1.09-1.63, P=0.006) in venous phase was 0.800 (95% CI 0.736-0.864), with a sensitivity of 95.0% and a specificity of 60.2%. Conclusion:The combined prediction model from the quantitative parameters of dual-layer spectral detector CT can be used to predict the differentiation degree and the E-cadherin protein expression of gastric cancer preoperatively.

ObjectiveTo describe the epidemic characteristics of COVID-19 after policy adjustment from “Category B notifiable disease with category A management” to “Category B notifiable disease with category B management”， and to explore the protective effect of previous infection with SARS-CoV-2 on common symptoms of reinfection. MethodsHealthcare workers infected with SARS-CoV-2 in a grade A tertiary hospital in Shanghai were included in the study from December 4， 2022 to January 11， 2023. Data on demographic characteristics， clinical symptoms， medical history， and COVID-19 vaccination history were collected. We determined the epidemiological curve and characteristics， and then compared the difference in the severity of clinical symptoms between primary and reinfection subjects. ResultsA total of 2 704 cases were included in the study， of which 45 had reinfection， 605 （22.4%）were males， 608 （22.5%）were doctors， 1 275 （47.2%） were nurses， and 2 351 （86.9%） received ≥3 doses of COVID-19 vaccination. The average age of these healthcare workers was （34.9±9.1） years old. The number of cases with mild/moderate illness， asymptomatic infection， fever， headache， dry cough， expectoration， and chest tightness were 2 704 （100.0%）， 92 （3.4%）， 2 385 （88.2%）， 2 066 （76.4%）， 1 642 （60.7%）， 1 807 （66.8%）， and 439 （16.2%）， respectively. Reinfection was a protective factor for fever （OR=0.161， P<0.001）， headache （OR=0.320， P<0.001）， and peak body temperature （β=-0.446， P<0.001）. ConclusionFollowing the COVID-19 policy adjustment as a category B notifiable disease， healthcare workers at a grade A tertiary hospital in Shanghai predominantly experiences mild to moderate COVID-19 symptoms. Reinfection results in milder clinical manifestations， with a lower proportion of being asymptomatic.

Objective:To analyze the electrocardiographic characteristics of patients with chronic pulmonary artery stenosis (PAS), and to explore their relationship with disease severity indicators.Methods:The study was a retrospective case-series analysis. Patients with chronic PAS admitted to Gansu Provincial Hospital from January 2018 to July 2021 were enrolled. The clinical data and the results of electrocardiography, transthoracic echocardiography, right cardiac catheterization, N-terminal B-type natriuretic peptide (NT-proBNP) measurement and 6-min walking distance test of patients were analyzed. The linear regression model or logistic regression model was used to analyze the relationship between electrocardiographic characteristics and the disease severity in patients with chronic PAS.Results:Sixty-three patients aged (62.1±9.7) years including 43 females (68.3%) were enrolled in the study. Among them, 62 patients (98.4%) had (R 1+S Ⅲ)-(S Ⅰ+R Ⅲ)<1.5 mV, and no patients had V 5lead R: S ratio to V 1 lead R: S<0.04 and V 6 lead R: S ratio<0.4. There were 55 patients (87.3%), with flat or inverted T-waves in V 1, and 10 patients (15.9%) with flat or inverted T-waves in all precordial leads (V 1-V 6). There were 18 patients (28.6%) with flat or inverted T-waves in inferior leads (Ⅱ, Ⅲ, aVF). Multiple liner regression analysis showed that Max R V1, 2+Max S I, aVL-S V1 combined with the number of flat or inverted T-waves in limb leads was independently correlated with atrial area ( R2=0.290, P=0.002); R V1+S V5 was independently correlated with right ventricular area ( R2=0.257, P=0.001); R peak V 1 combined with the number of flat or inverted T waves in precordial leads was independently correlated with tricuspid annular plane systolic excursion ( R2=0.407, P<0.001); (R 1+S Ⅲ)-(S Ⅰ+R Ⅲ) combined with the number of flat or inverted T waves in precordial leads was independently correlated with NT-proBNP ( R2=0.504, P<0.001); Max R V1, 2+Max S I, aVL-S V1 were independently correlated with right atrial pressure ( R2=0.803, P=0.036); (R 1+S Ⅲ)-(S Ⅰ+R Ⅲ) were independently correlated with mean pulmonary artery pressure ( R2=0.302, P<0.001); R aVRcombined with the number of flat or inverted T-waves in precordial leads was independently correlated with cardiac index ( R2=0.173, P=0.003); (R 1+S Ⅲ)-(S Ⅰ+R Ⅲ) was independently correlated with pulmonary vascular resistance ( R2=0.173, P=0.002); R peak V 1 combined with the number of flat or inverted T-waves in precordial leads was independently correlated with mixed vein oxygen saturation ( R2=0.302, P<0.001). Conclusion:The vast majority of patients with chronic PAS have (R 1+S Ⅲ)-(S Ⅰ+R Ⅲ)<1.5 mV and flat or inverted T-wave in V 1 lead, and some characteristic electrocardiographic manifestations are correlated with indicators of disease severity.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.