Objective To investigate the incidence and types of patient-ventilator asynchrony(PVA)in mechanically ventilated patients within the intensive care unit(ICU),and to identify associated high-risk factors,thereby providing a basis for reducing PVA,enhancing mechanical ventilation efficiency,and refining ventilation strategies.Methods A prospective observational study was conducted among patients admitted to the general ICU of the Affiliated Hospital of Guizhou Medical University from October to December 2024 who were receiving mechanical ventilation.Inclusion criteria were as follows:age ≥18 years and mechanical ventilation duration ≥12 hours.Exclusion criteria included complete controlled mechanical ventilation,palliative care or do-not-resuscitate status,and lack of informed consent.Senior respiratory therapists performed daily bedside observations of ventilator waveforms for 10～15 minutes between 08:00 and 12:00.PVA was diagnosed based on pressure-time and flow-time waveforms,with the types of PVA being recorded.Demographic and clinical data,including age,sex,body mass index(BMI),primary diagnosis,comorbidities,APACHEⅡ score at ICU admission,blood gas analysis,ventila-tion mode and parameters,analgesia and sedation status,duration of mechanical ventilation,and length of ICU stay,were collected.The incidence and types of PVA during the observation period were analyzed.Univariate and multivariate logistic regression analyses were performed to identify high-risk factors for PVA.Clinical outcomes were compared between patients with and without PVA.Results A total of 105 patients and 453 episodes of assisted mechanical ventilation waveforms were analyzed.Among these,60.95％(64/105)experienced at least one episode of PVA.Of the 453 ventilation waveforms assessed,35.76％(162/453)demonstrated PVA.The types of PVA,ranked by incidence,were as follows:cycling mismatch(12.58％,57/453),double triggering(11.92％,54/453),ineffective triggering(9.49％,43/453),flow starvation(5.30％,24/453),and exhalation flow limitation(1.77％,8/453).The incidence of PVA varied significantly across different ventilation modes:45.7％in volume-assist/control ventilation(V-A/C),38.1％in pressure-assist/control ventilation(P-A/C),42.9％in synchronized intermittent mandatory ventilation(SIMV),and 16.7％in pressure support ventilation(PSV)(P<0.001).Multi-variate logistic regression analysis revealed that the mechanical ventilation mode[reference:PSV;V-A/C:OR=4.687,95％CI:2.140～10.263,P<0.001;P-A/C:OR=2.922,95％CI:1.489～5.734,P=0.002;SIMV:OR=4.682,95％CI:1.758～12.466,P=0.002]and actual respiratory rate(OR=1.07,95％CI:1.016～1.127,P=0.011)were significant high-risk factors for PVA.Patients with PVA had a significantly longer duration of mechanical ventilation[8.21(5.35,13.91)days vs.3.00(1.96,5.71)days,P<0.001]compared to those without PVA.Conclusions PVA is commonly observed in ICU patients receiving assisted invasive mechanical ventilation,with cycling mismatch,double triggering,and ineffective triggering being the most prevalent types.The incidence of PVA tends to be lower when using the PSV mode.Clinically,real-time monitoring of patient-ventilator synchrony via ventilator waveforms,along with the optimization of ventilator modes and parameters,should be employed to minimize the occurrence of PVA and enhance the efficiency of mechanical ventilation.

Objective To investigate the incidence and types of patient-ventilator asynchrony(PVA)in mechanically ventilated patients within the intensive care unit(ICU),and to identify associated high-risk factors,thereby providing a basis for reducing PVA,enhancing mechanical ventilation efficiency,and refining ventilation strategies.Methods A prospective observational study was conducted among patients admitted to the general ICU of the Affiliated Hospital of Guizhou Medical University from October to December 2024 who were receiving mechanical ventilation.Inclusion criteria were as follows:age ≥18 years and mechanical ventilation duration ≥12 hours.Exclusion criteria included complete controlled mechanical ventilation,palliative care or do-not-resuscitate status,and lack of informed consent.Senior respiratory therapists performed daily bedside observations of ventilator waveforms for 10～15 minutes between 08:00 and 12:00.PVA was diagnosed based on pressure-time and flow-time waveforms,with the types of PVA being recorded.Demographic and clinical data,including age,sex,body mass index(BMI),primary diagnosis,comorbidities,APACHEⅡ score at ICU admission,blood gas analysis,ventila-tion mode and parameters,analgesia and sedation status,duration of mechanical ventilation,and length of ICU stay,were collected.The incidence and types of PVA during the observation period were analyzed.Univariate and multivariate logistic regression analyses were performed to identify high-risk factors for PVA.Clinical outcomes were compared between patients with and without PVA.Results A total of 105 patients and 453 episodes of assisted mechanical ventilation waveforms were analyzed.Among these,60.95％(64/105)experienced at least one episode of PVA.Of the 453 ventilation waveforms assessed,35.76％(162/453)demonstrated PVA.The types of PVA,ranked by incidence,were as follows:cycling mismatch(12.58％,57/453),double triggering(11.92％,54/453),ineffective triggering(9.49％,43/453),flow starvation(5.30％,24/453),and exhalation flow limitation(1.77％,8/453).The incidence of PVA varied significantly across different ventilation modes:45.7％in volume-assist/control ventilation(V-A/C),38.1％in pressure-assist/control ventilation(P-A/C),42.9％in synchronized intermittent mandatory ventilation(SIMV),and 16.7％in pressure support ventilation(PSV)(P<0.001).Multi-variate logistic regression analysis revealed that the mechanical ventilation mode[reference:PSV;V-A/C:OR=4.687,95％CI:2.140～10.263,P<0.001;P-A/C:OR=2.922,95％CI:1.489～5.734,P=0.002;SIMV:OR=4.682,95％CI:1.758～12.466,P=0.002]and actual respiratory rate(OR=1.07,95％CI:1.016～1.127,P=0.011)were significant high-risk factors for PVA.Patients with PVA had a significantly longer duration of mechanical ventilation[8.21(5.35,13.91)days vs.3.00(1.96,5.71)days,P<0.001]compared to those without PVA.Conclusions PVA is commonly observed in ICU patients receiving assisted invasive mechanical ventilation,with cycling mismatch,double triggering,and ineffective triggering being the most prevalent types.The incidence of PVA tends to be lower when using the PSV mode.Clinically,real-time monitoring of patient-ventilator synchrony via ventilator waveforms,along with the optimization of ventilator modes and parameters,should be employed to minimize the occurrence of PVA and enhance the efficiency of mechanical ventilation.

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning. METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation. RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate. CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Bronchiectasis is a chronic, structural lung disease characterized by chronic cough, sputum production, and decreased exercise capacity. These symptoms worsen during exacerbations and impact negatively on health-related quality of life. Airway mucus clearance and pulmonary rehabilitation play an important role in the management of bronchiectasis, despite a relatively weak evidence base. Thus, the paper reviews the physiological rationale and clinical evidence of airway clearance techniques (ACTs), application and research progress of pulmonary rehabilitation in the management of bronchiectasis.

Objective To investigate the strengths and weaknesses of SPAIR and WE in iShim-IVIM of breast gland MRI scanning. Methods Twenty patients with breast tumor underwent iShim-IVIM and DCE-MRI scanning by SPAIR and WE technology. SNR ,CNR and ADC of the SPAIR-iShim and WE-iShim imaging were cal-culated and imaging quality were evaluated. Statistical analysis was done afterwards. Results The SNR and CNR of WE technology is higher than those of SPAIR statistically (P<0.05). There is no significant statistical differ-ence in score of the images between SPAIR and WE(P>0.05). The ADC of the center of the tumor has no signifi-cant statistical difference between SPAIR and WE(P>0.05). Conclusion The quality of the images using WE is significantly improved than SPAIR ,but both of them can meet the diagnostic requirements ,and the ADC has no statistical difference as well.

ObjectiveTo evaluate the diagnostic value of multi-slice CT (MSCT) and multi-dimensional reconstructions for congenital pulmonary stenosis (PS) in children.MethodsThe enhanced thin CT images of 33 patients with PS were retrospectively analyzed,the data was transmitted to the workstation for multi-planar reformation ( MPR),volume rendering technique (VRT) and maximum intensity projection (MIP).The CT imaging features of PS were analyzed combining with operation resul ts and Ultrasonic Cardiogram (UCG).ResultsIn 33 cases of PS,there were 17 cases with pulmonary valve stenosis (PVS),10 cases with right ventricular infundibulum stenosis (RVIS),and 6 cases with pulmonary trunk stenosis (PTS).The first two were correctly diagnosed by UCG,5 cases of PVS and Icases of RVIS were correctly diagnosed by MSCTA,the later was correctly diagnosed by UCG and MSCTA.In 14 cases with collateral circulations between aorta and pulmonary artery ( APC ),all were correctly diagnosed by MSCTA,only 3 case was diagnosed by UCG,1 case was misdiagnosed as PDA.UCG can clearly demonstrate the others intra-cardiac deformities,such as ASD,VSD,RVH,PFO,SV,DORV,TECD and TBD,the accuracy rate of MSCTA was 39.4％,and MSCTA can clearly demonstrate the origin,course and diameter of extracardiac vascular abnormalities,such as PDA,RAA,TGA,TAPVD,CoA,PLSV and VLSA,the accuracy rate of UCG was 697％.ConclusionsMSCT and multi-dimensional reconstruction were a kind of non-invasive method,it was a good approach for extra-cardiac vascular malformations and APC in PS.Combining with UCG,it can further be used to improve the diagnostic accuracy of intra-cardiac malformation and supply diagnostic evidence for clinical treatment.

OBJECTIVETo study whether the abilities of hepatitis C virus (HCV) E2 gene immunization to induce humoral and cellular immune responses to E2 protein were affected by hepatitis B virus (HBV) preS gene when they fused in DNA-immunized mice.

METHODSMice were immunized with E2, preS-E2 (preS gene was upstream of E2 gene), and E2-preS (preS gene was downstream of E2 gene) gene by their eukaryotic expression vectors, respectively. The anti-E2 or anti-preS antibodies were detected using the E2 and preS antigens. The cellular immune response to E2 protein in immunized mice was presented by its survival time after injecting SP2/O myeloma cells expressing HCV E2 protein into the abdominal cavity.

RESULTSChimeric E2 and preS gene immunization can induce mice to develop anti-preS and anti-E2 antibodies. The number of the mice developing anti-E2 antibody and the antibody titers in preS-E2 gene-injected group were higher than those in E2-preS gene-immunized group. However, the mice injected with E2 gene did not develop the detectable anti-E2 antibodies until 12 weeks after DNA immunization. After the mice was injected with target cells, the average survival time of the mice in the group immunized with E2 gene alone was longer than that of the group injected with E2 gene fused with HBV preS and was significantly longer than that of the control (P < 0.05).

CONCLUSIONHBV preS might be a humoral enhancer that can affect the abilities of HCV E2 protein to induce immune responses in DNA-immunized mice.

Animals ; Antibody Formation ; drug effects ; Hepacivirus ; chemistry ; Hepatitis B Surface Antigens ; genetics ; pharmacology ; Hepatitis B virus ; chemistry ; genetics ; Immunity, Cellular ; drug effects ; Male ; Mice ; Mice, Inbred BALB C ; Protein Precursors ; genetics ; pharmacology ; Recombinant Fusion Proteins ; immunology ; Viral Envelope Proteins ; genetics ; pharmacology