This study aimed to develop a risk prediction model for long-term atelectasis in children with pneumonia.
A retrospective study of 532 children with atelectasis was performed at the Children's ...Hospital of Chongqing Medical University from February 2017 to March 2020. The predictive variables were screened by LASSO regression analysis and the nomogram was drawn by R software. The area under the Receiver Operating Characteristic (ROC) curve, calibration chart and decision curve were used to evaluate the predictive accuracy and clinical utility. 1000 Bootstrap resampling was used for internal verification.
Multivariate logistic regression analysis showed that clinical course before bronchoscopy, length of stay, bronchial mucus plug formation, age were independent risk factors for long-term atelectasis in children. The area under the ROC curve of nomogram was 0.857(95% CI = 0.8136 ~ 0.9006) in training set and 0.849(95% CI = 0.7848-0.9132) in the testing set. The calibration curve demonstrated that the nomogram was well-fitted, and decision curve analysis (DCA) showed that the nomogram had good clinical utility.
The model based on the risk factors of long-term atelectasis in children with pneumonia has good predictive accuracy and consistency, which can provide a certain reference value for clinical prevention and treatment of long-term atelectasis in children.
In this case of splenic infarction we reported, D-dimer was also significantly elevated, suggesting that D-dimer may have a certain early warning effect on ST.Ethics approval and consent to ...participate This study was approved by the ethics committee of Ethics Review Committee of the Children's Hospital of KUNMING Medical University. ...all methods were carried out in accordance with relevant guidelines and regulations or Declaration of Helsinki.Funding No.Authors’ contributions YL and JH conceptualized the study, conducted literature review, and drafted and revised the manuscript for intellectual content. WD, HC, and YW did data collection work and conducted literature review.Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Massive Open Online Course (MOOC) has been criticized for low completion rates, and one of the major reasons is that it fails to offer personalized course recommendations for different users with ...different demands. To solve this problem, this paper proposes a personalized course recommendation model based on convolutional neural network combined with negative sequence pattern mining. The model first models the course-learning sequence as a negative sequence pattern according to the user’s course registration, degree of completion, and final grades, in which, the negative term means that students should not choose and misoperate the principle of courses. Then, it employs a convolutional neural network structure to extract the internal features of negative sequence patterns for representation learning. Finally, through the convolutional sequence-embedding model, each user is recommended with a course list that includes the user’s maximized needs in recent temporal terms and the courses that are easy to be misselected. Experiment results show that the recommended model achieves higher recommendation performance with lower course dropout rate compared to baselines, which provides a new insight for both online and offline course recommendation.
To analyze the predictive factors for necrotizing pneumonia (NP) in children with
pneumonia (MPP) and construct a prediction model.
The clinical data with MPP at the Children's Hospital of Kunming ...Medical University from January 2014 to November 2022 were retrospectively analyzed. Eighty-four children with MPP who developed NP were divided into the necrotizing group, and 168 children who did not develop NP were divided into the non-necrotizing group by propensity-score matching. LASSO regression was used to select the optimal factors, and multivariate logistic regression analysis was used to establish a clinical prediction model. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the nomogram. Clinical decision curve analysis was used to evaluate the clinical predictive value.
LASSO regression analysis showed that bacterial co-infection, chest pain, LDH, CRP, duration of fever, and D-dimer were the influencing factors for NP in children with MPP (P < 0.05). The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting necrotizing MPP was 0.870 (95% CI: 0.813-0.927, P < 0.001) in the training set and 0.843 (95% CI: 0.757-0.930, P < 0.001) in the validation set. The Bootstrap repeated sampling for 1000 times was used for internal validation, and the calibration curve showed that the model had good consistency. The Hosmer-Lemeshow test showed that the predicted probability of the model had a good fit with the actual probability in the training set and the validation set (P values of 0.366 and 0.667, respectively). The clinical decision curve showed that the model had good clinical application value.
The prediction model based on bacterial co-infection, chest pain, LDH, CRP, fever duration, and D-dimer has a good predictive value for necrotizing MPP.
This study aimed to develop a nomogram model for predicting massive necrotizing pneumonia (NP) in children.
A total of 282 children with NP admitted to Kunming Children's Hospital from January 2014 ...to November 2022 were enrolled. The children with NP were divided into massive necrotizing pneumonia (MNP) group and non-MNP group according to the severity of the lung necrosis. The clinical data of the children were collected, and least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression models were used to analyze the influencing factors of MNP. A nomogram model was constructed, and its predictive efficacy was evaluated.
The predictors selected by LASSO regression analysis were: haematogenous spread, white blood cell (WBC), hemoglobin (Hb), C-reactive protein (CRP), lactate dehydrogenase (LDH), and activated partial thromboplastin time (APTT) (P < 0.05). Based on the above independent influencing factors, a nomogram model for MNP was constructed. The bootstrap method was used to repeat sampling 1000 times. The results showed that the consistency index of the nomogram model in predicting MNP was 0.833 in the training set and 0.810 in the validation set. The results of ROC curve analysis showed that the area under the receiver-operating-characteristic curve (AUC) of the nomogram model for predicting MNP was 0.889 95% CI (0.818, 0.959) in the training set and 0.814 95% CI (0.754, 0.874) in the validation set. The calibration curve of the nomogram predicting MNP was basically close to the actual curve. The decision curve showed that the nomogram had good clinical utility.
We developed a nomogram for predicting MNP, which can help clinicians identify the severity of lung necrosis early.
To explore the clinical characteristics of necrotizing pneumonia (NP) caused by different pathogens.
A total of 282 children with NP admitted to Kunming Children's Hospital from January 2014 to ...November 2022 were enrolled. The clinical data of all children was collected. According to the different pathogens causing NP, the children were divided into three groups: the M
necrotizing pneumonia (MPNP) group, the bacterial necrotizing pneumonia (BNP) group, and necrotizing pneumonia with no pathogen detected (NNP) group. The basic information, symptoms, signs, laboratory tests, radiological features, treatment, and prognosis of the three groups were compared.
Among the 282 cases of NP, there were 62 (22.0%) cases of MPNP, 98 (34.75%) cases of BNP, and 142 (50.35%) cases of NNP. The most common bacteria causing NP were
, and
, respectively. Most clinical features of the three groups were statistically significant. The area under the concentration curve of white blood cell, C-reactive protein, albumin, neutrophil percentage, and fibrinogen in differentiating MPNP from BNP were 0.743 (0.638-0.849), 0.797 (0.711-0.883), 0.766 (0.671-0.861), 0.616 (0.509-0.724), and 0.634 (0.523-0.744), respectively. The decision curve showed that white blood cells, albumin, and C-reactive protein had good clinical application in differentiating MPNP from BNP. All patients were improved and discharged without death.
Bacteria are the most common cause of NP, and the most common bacteria are
. NP with no pathogen detected accounted for a large proportion. white blood, C-reactive protein, and albumin can identify the pathogens of NP. Patients with BNP were more severe, had a longer hospital stay, and were more likely to undergo closed drainage and surgery.
IntroductionWith the growing emphasis on swift recovery, minimally invasive thoracic surgery has advanced significantly. Video-assisted thoracoscopic surgery (VATS) has seen rapid development, and ...the double-lumen tube (DLT) remains the most dependable method for tracheal intubation in VATS. However, hypoxaemia during DLT intubation poses a threat to the perioperative safety of thoracic surgery patients. Recently, transnasal high-flow nasal oxygen (HFNO) has shown promise in anaesthesia, particularly in handling short-duration hypoxic airway emergencies. Yet, its application in the perioperative period for patients undergoing pulmonary surgery with compromised cardiopulmonary function lacks evidence, and there are limited reliable clinical data.Methods and analysisA prospective, randomised, controlled, single-blind design will be employed in this study. 112 patients aged 18–60 years undergoing elective VATS-assisted pulmonary surgery will be enrolled and randomly divided into two groups: the nasal high-flow oxygen group (H group) and the traditional mask transnasal oxygen group (M group) in a 1:1 ratio. HFNO will be used during DLT intubation for the prevention of asphyxia in group H, while conventional intubation procedures will be followed by group M. Comparison will be made between the two groups in terms of minimum oxygen saturation during intubation, hypoxaemia incidence during intubation, perioperative complications and postoperative hospital days.Ethics and disseminationApproval for this study has been granted by the local ethics committee at Shenzhen Second People’s Hospital. The trial results will be disseminated through peer-reviewed journals and scientific conferences.Trial registration numberNCT05666908.
The development of multianalyte immunoassays with an emphasis on food safety has attracted increasing interest, due to its high target throughput, short detection time, reduced sample consumption, ...and low overall cost. In this study, a superior polyclonal antibody (pAb) against sulfonamides (SAs) was raised by using a bioconjugate of bovine serum albumin with a rationally designed hapten 4-(4-aminophenyl) sulfonyl-amino-2-methoxybenzoic acid (SA10-X). The results showed that the pAb could recognize 19 SAs with 50% inhibition (IC
) below 100 µg L
and a recognition profile for SAs containing, either a five-atom ring or a six-atom ring, with highly uniform affinity. A three-dimensional quantitative structure-activity relationship analysis indicated that the electrostatic features of SAs play a considerably important role, during recognition with pAb than stereochemical effects. Skimmed milk samples were directly diluted five times before analysis. After optimization, the limit of detection for sulfamonomethoxine, sulfamethoxazole, sulfaquinoxaline, sulfadimethoxine, and sulfamethazine were 1.00, 1.25, 2.95, 3.35, and 6.10 µg L
, respectively. The average recoveries for these 5 SAs were 72.0⁻107.5% with coefficients of variation less than 14.1%. The established method, based on pAb, with broad specificity and uniform affinity, offered a simple, sensitive, and high-throughput screening tool for the detection of multi-SAs in milk samples.
Objective
Previous studies have found that sorafenib could induced mitochondrial autophagy in diverse malignant cells, however the detail mechanism and related relevance was unknown. This study was ...aimed to investigate whether and how mitochondrial protein Nix could play a role in the outcome of hepatoma cell treated with sorafenib.
Methods
By lentivirus, transfected HepG2 cell lines with stable overexpression of Nix were constructed and subcultured. Western blot and RT‐PCR was respectively performed to detect the Nix protein and mRNA production in cells in the present of sorafenib with different doses (0 to 20μM) and different time (0 to 48h). Trypan blue assay was then used to observe the sorafenib‐induced cell death in HepG2 cells, while flow cytometry was used to detect the apoptotic rate and mitochondrial membrane potential level in these cells.
Results
Basically, sorafenib was found to dose‐ and time‐dependently decreased the Nix translation in HepG2 cells (P < 0.05). Overexpression of Nix significantly attenuated the killing effect of sorafenib on HepG2 cells with 60% off versus control. In parallel, the overexpression of Nix was dramatically found to block sorafenib induced‐decrement in the mitochondrial membrane potential level in HepG2 cells. Moreover, western blotting results showed that the overexpression of Nix promoted the increment of LC3 as well as decrement of p62 in these cells, and substantially impaired the sorafenib induced‐ cleavage of caspase‐3.
Conclusion
Nix is an important protein that mediates mitochondrial autophagy and may have specific effect of “protecting” hepatoma cells, which may indicate a critical drug resistance mechanism of sorafenib in hepatoma.
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