A multi-objective optimization method for identification of module biomarkers for disease diagnosis.

Biomarker identification aims at finding a set of biological indicators that best discriminate biological samples of different phenotypes. In this paper, we take the module containing the significant disease-related genes and their interactions from biological networks as a module biomarker, and propose an evolutionary multi-objective optimization method to identify module biomarkers for disease diagnosis. To be specific, we take the classification accuracy on control and disease samples, the association with disease and the intra-link density in the module as the optimization objectives. To achieve the best performance, a novel population initiation strategy is tailored to generate dense-connected initial solutions, and a specific population update strategy is employed to direct the evolution towards the global optimums with abundant diversity. Experimental results show that our method outperforms the previous state-of-the-art disease diagnosis methods. Meantime, the detected biomarker module can reflect the basic and significant biological functions and has a great correlation with a disease phenotype.

CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model.

In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance.

A Novel Smart Contract Vulnerability Detection Method Based on Information Graph and Ensemble Learning.

Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities.

SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection.

With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people's lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.

Huanglianjiedu Decoction as an effective treatment for oral squamous cell carcinoma based on network pharmacology and experimental validation.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is one of malignant tumors in oral and maxillofacial region with high fatality. Huanglianjiedu Decoction (HLJDD) is a well-known traditional Chinese medicinal prescription, which consists of Coptis chinensis Franch, Scutellaria baicalensis Georgi, Phellodendron amurense Rupr and Gardenia jasminoides J.Ellis. Some clinical studies showed HLJDD had good effectiveness on OSCC, but the mechanism is unclear. METHODS: In this study, potential components of HLJDD and putative targets were screened by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Combining with potential targets of OSCC searched from Therapeutic Target Database (TTD) and Online Mendelian Inheritance in Man (OMIM), we drew protein-protein interaction (PPI) network by Cytoscape v3.2.0 software. After topological analysis we got core targets and further did Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then we did the in vitro experiments to verify the major biological processes (cell cycle, apoptosis and proliferation) and signaling pathways (mitogen-activated protein kinase (MAPK), nuclear factor-kappa B (NF-κB), protein kinase B (AKT)) on OSCC cell lines, SCC-25 and CAL-27. RESULTS: The potential component targets number of Coptis chinensis Franch, Scutellaria baicalensis Georgi, Phellodendron amurense Rupr and Gardenia jasminoides J.Ellis were 39, 93, 81and 88, respectively. Then we got 52 core targets which enriched in cell cycle, apoptosis, proliferation, MAPK activation etc. and obtained TOP30 pathways. On SCC-25 and CAL-27, HLJDD suppressed cell proliferation, induced late apoptosis and inhibited cell invasion and migration which were consistent with the results from network pharmacology analysis. Additionally, in cell cycle, we confirmed HLJDD inhibited G1 phase and arrested in S phase to reduce cell proliferation on SCC-25. In signaling pathways, HLJDD inhibited the phosphorylation of extracellular regulatory protein kinase 1/2 (ERK1/2) and NF-κB p65 (S468) on SCC-25 and CAL-27. CONCLUSIONS: HLJDD played a potential therapeutic role on OSCC via inhibiting p-ERK1/2 and p-NF-κB p65 (S468).

Meta-analysis of association between TCF7L2 polymorphism rs7903146 and type 2 diabetes mellitus.

BACKGROUND: Large scale association studies have found a significant association between type 2 diabetes mellitus (T2DM) and transcription factor 7-like 2 (TCF7L2) polymorphism rs7903146. However, the quality of data varies greatly, as the studies report inconsistent results in different populations. Hence, we perform this meta-analysis to give a more convincing result. METHODS: The articles, published from January 1st, 2000 to April 1st, 2017, were identified by searching in PubMed and Google Scholar. A total of 56628 participants (34232 cases and 22396 controls) were included in the meta-analysis. A total of 28 studies were divided into 4 subgroups: Caucasian (10 studies), East Asian (5 studies), South Asian (5 studies) and Others (8 studies). All the data analyses were analyzed by the R package meta. RESULTS: The significant association was observed by using the dominant model (OR = 1.41, CI = 1.36 - 1.47, p < 0.0001), recessive model (OR = 1.58, CI = 1.48 - 1.69, p < 0.0001), additive model(CT vs CC) (OR = 1.34, CI = 1.28-1.39, p < 0.0001), additive model(TT vs CC) (OR = 1.81, CI = 1.69-1.94, p < 0.0001)and allele model (OR = 1.35, CI = 1.31-1.39, p < 0.0001). CONCLUSION: The meta-analysis suggested that rs7903146 was significantly associated with T2DM in Caucasian, East Asian, South Asian and other ethnicities.

Development and validation of a nomogram for predicting depressive symptoms in dentistry patients: A cross-sectional study.

Depressive symptoms are frequently occur among dentistry patients, many of whom struggle with dental anxiety and poor oral conditions. Identifying the factors that influence these symptoms can enable dentists to recognize and address mental health concerns more effectively. This study aimed to investigate the factors associated with depressive symptoms in dentistry patients and develop a clinical tool, a nomogram, to assist dentists in predicting these symptoms. Methods: After exclusion of ineligible participants, a total of 1355 patients from the dentistry department were included. The patients were randomly assigned to training and validation sets at a 2:1 ratio. The LASSO regression method was initially employed to select highly influrtial features. This was followed by the application of a multi-factor logistic regression to determine independent factors and construct a nomogram. And it was evaluated by 4 methods and 2 indicators. The nomograms were formulated based on questionnaire data collected from dentistry patients. Nomogram2 incorporated factors such as medical burden, personality traits (extraversion, conscientiousness, and emotional stability), life purpose, and life satisfaction. In the training set, Nomogram2 exhibited a Concordance index (C-index) of 0.805 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.805 (95% CI: 0.775-0.835). In the validation set, Nomogram2 demonstrated an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.810 (0.768-0.851) and a Concordance index (C-index) of 0.810. Similarly, Nomogram1 achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.816 (0.788-0.845) and a Concordance index (C-index) of 0.816 in the training set, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.824 (95% CI: 0.784-0.864) and a Concordance index (C-index) of 0.824 in the validation set. Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) indicated that Nomogram1, which included oral-related factors (oral health and dental anxiety), outperformed Nomogram2. We developed a nomogram to predict depressive symptoms in dentistry patients. Importantly, this nomogram can serve as a valuable psychometric tool for dentists, facilitating the assessment of their patients' mental health and enabling more tailored treatment plans.

[Optimization of diagnosis indicator selection and inspection plan by 3.0T MRI in breast cancer].

OBJECTIVE: To optimize 3.0T MRI diagnosis indicator in breast cancer and to select the best MRI scan program. METHODS: Totally 45 patients with breast cancers were collected, and another 35 patients with benign breast tumor served as the control group. All patients underwent 3.0T MRI, including T1- weighted imaging (T1WI), fat suppression of the T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), 1H magnetic resonance spectroscopy (1H-MRS) and dynamic contrast enhanced (DCE) sequence. With operation pathology results as the gold standard in the diagnosis of breast diseases, the pathological results of benign and malignant served as dependent variables, and the diagnostic indicators of MRI were taken as independent variables. We put all the indicators of MRI examination under Logistic regression analysis, established the Logistic model, and optimized the diagnosis indicators of MRI examination to further improve MRI scan of breast cancer. RESULTS: By Logistic regression analysis, some indicators were selected in the equation, including the edge feature of the tumor, the time-signal intensity curve (TIC) type and the apparent diffusion coefficient (ADC) value when b=500 s/mm2. The regression equation was Logit (P)=-21.936+20.478X6+3.267X7+ 21.488X3. CONCLUSION: Valuable indicators in the diagnosis of breast cancer are the edge feature of the tumor, the TIC type and the ADC value when b=500 s/mm2. Combining conventional MRI scan, DWI and dynamic enhanced MRI is a better examination program, while MRS is the complementary program when diagnosis is difficult.

Corrigendum to "Influence of on emotions and behavior of adolescents with major depressive disorder" [Heliyon 9(5) (May 2023) e15890].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e15890.].

Protective effect of Huanglianjiedu Decoction on microcystin-LR induced nerve injury.

Microcystin-LR (MC-LR) presented in eutrophic water has been identified as having the capacity to induce damage to the mammalian nervous system by crossing the blood-brain barrier through organic anion transporting polypeptides. However, the lack of effective preventive and protective strategies remains a concern. Huanglianjiedu Decoction (HLJD), a classical Chinese traditional formula originating from the Tang Dynasty and comprising Rhizoma Coptidis, Radix Scutellariae, Cortex Phellodendri, and Fructus Gardeniae, has exhibited neuroprotective effects attributed to its antioxidant properties. In this study, we investigated the potential of HLJD in counteracting the neurotoxic effects induced by MC-LR. Our findings revealed that MC-LR dose-dependently inhibited the activity of the PP2A enzyme in PC 12 cells and significantly elevated the phosphorylation levels of JNK, ERK1/2, and p38. Moreover, MC-LR administration resulted in synaptic damage in mouse neurons, hyperphosphorylation of the microtubule-related protein Tau, cognitive impairment, and deficits in learning and memory in C57BL/6J mice. Notably, HLJD effectively reversed the cytotoxicity caused by MC-LR in PC 12 cells, and attenuated MC-LR-induced neuronal damage while improving learning ability in mice. These results highlight the potential of HLJD as a promising protective strategy against MC-LR-induced neurological injury.

Study on the Deterioration Mechanism of Pb on TiO2 Oxygen Sensor.

Previous studies have shown that the pollutants in exhaust gas can cause performance deterioration in air-fuel oxygen sensors. Although the content of Pb in fuel oil is as low as 5 mg/L, the effect of long-term Pb accumulation on TiO2 oxygen sensors is still unclear. In this paper, the influence mechanism of Pb-containing additives in automobile exhaust gas on the response characteristics of TiO2 oxygen sensors was simulated and studied by depositing Pb-containing pollutants on the surface of a TiO2 sensitive film. It was found that the accumulation of Pb changed the surface gas adsorption state and reduced the activation energy of TiO2, thus affecting the steady-state response voltage and response speed of the TiO2-based oxygen sensor.

Hierarchical Harris hawks optimizer for feature selection.

INTRODUCTION: The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection. OBJECTIVES: To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features. METHODS: We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets. RESULTS: Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms. CONCLUSION: Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.

Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising.

Removing the noise in low-dose CT (LDCT) is crucial to improving the diagnostic quality. Previously, many supervised or unsupervised deep learning-based LDCT denoising algorithms have been proposed. Unsupervised LDCT denoising algorithms are more practical than supervised ones since they do not need paired samples. However, unsupervised LDCT denoising algorithms are rarely used clinically due to their unsatisfactory denoising ability. In unsupervised LDCT denoising, the lack of paired samples makes the direction of gradient descent full of uncertainty. On the contrary, paired samples used in supervised denoising allow the parameters of networks to have a clear direction of gradient descent. To bridge the gap in performance between unsupervised and supervised LDCT denoising, we propose dual-scale similarity-guided cycle generative adversarial network (DSC-GAN). DSC-GAN uses similarity-based pseudo-pairing to better accomplish unsupervised LDCT denoising. We design a Vision Transformer-based global similarity descriptor and a residual neural network-based local similarity descriptor for DSC-GAN to effectively describe the similarity between two samples. During training, pseudo-pairs, i.e., similar LDCT samples and normal-dose CT (NDCT) samples, dominate parameter updates. Thus, the training can achieve equivalent effect as training with paired samples. Experiments on two datasets demonstrate that DSC-GAN beats the state-of-the-art unsupervised algorithms and reaches a level close to supervised LDCT denoising algorithms.

Influence of on emotions and behavior of adolescents with major depressive disorder.

Background: Major depressive disorder in adolescents is characterized by high prevalence rate, high recurrence rate, high suicide rate and high disability rate. However, the recognition rate and cure rate are low, and the disease has a very bad influence on the family and society. The lack of psychiatrists and psychotherapists in villages and small towns makes it difficult to get timely and professional intervention and treatment for adolescent with major depressive disorder. Methods: A total of 84 adolescents with major depressive disorder who received treatment in the department of psychosomatic medicine of the Second Affiliated Hospital of Nanchang University participated in this survey, and they were divided into the control group and the intervention group by random number table. Adolescent Non-suicidal Self-injury Assessment Questionnaire (ANSSIAQ), Self-rating Questionnaire for Adolescent Problematic Mobile Phone Use (SQAPMPU), Screen for Child Anxiety Related Emotional Disorders (SCARED) and Depression Self-Rating Scale for Childhood (DSRS) were used to investigate the negative emotions and behavior of adolescents with major depressive disorder at baseline and intervention for 12 weeks. Results: There were no significant differences in the baseline information of adolescents (sex ratio, age, education level), including the total score of SCARED, DSRS and SQAPMPU, the total mean score of ANSSIAQ between the two groups (P > 0.05). After 12-week intervention, the score of SCARED, DSRS and SQAPMPU, the total mean score of ANSSIAQ in both groups were lower than that of the baseline, and the score of the intervention group showed a more obvious downward trend (P < 0.05). Conclusions: In-person and remote Satir family therapy not only effectively reduced the anxiety and depression level among participants, but also validly reduced their non-suicidal self-injury behavior and mobile phone use behavior. The results verified that the model we adopted can be well applied for the out-patient management of adolescents with major depressive disorder, especially in villages and small towns.

A novel physiological feature selection method for emotional stress assessment based on emotional state transition.

The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.

Dispersed differential hunger games search for high dimensional gene data feature selection.

The realms of modern medicine and biology have provided substantial data sets of genetic roots that exhibit a high dimensionality. Clinical practice and associated processes are primarily dependent on data-driven decision-making. However, the high dimensionality of the data in these domains increases the complexity and size of processing. It can be challenging to determine representative genes while reducing the data's dimensionality. A successful gene selection will serve to mitigate the computing costs and refine the accuracy of the classification by eliminating superfluous or duplicative features. To address this concern, this research suggests a wrapper gene selection approach based on the HGS, combined with a dispersed foraging strategy and a differential evolution strategy, to form a new algorithm named DDHGS. Introducing the DDHGS algorithm to the global optimization field and its binary derivative bDDHGS to the feature selection problem is anticipated to refine the existing search balance between explorative and exploitative cores. We assess and confirm the efficacy of our proposed method, DDHGS, by comparing it with DE and HGS combined with a single strategy, seven classic algorithms, and ten advanced algorithms on the IEEE CEC 2017 test suite. Furthermore, to further evaluate DDHGS' performance, we compare it with several CEC winners and DE-based techniques of great efficiency on 23 popular optimization functions and the IEEE CEC 2014 benchmark test suite. The experimentation asserted that the bDDHGS approach was able to surpass bHGS and a variety of existing methods when applied to fourteen feature selection datasets from the UCI repository. The metrics measured--classification accuracy, the number of selected features, fitness scores, and execution time--all showed marked improvements with the use of bDDHGS. Considering all results, it can be concluded that bDDHGS is an optimal optimizer and an effective feature selection tool in the wrapper mode.

Sparse-to-dense coarse-to-fine depth estimation for colonoscopy.

Colonoscopy, as the golden standard for screening colon cancer and diseases, offers considerable benefits to patients. However, it also imposes challenges on diagnosis and potential surgery due to the narrow observation perspective and limited perception dimension. Dense depth estimation can overcome the above limitations and offer doctors straightforward 3D visual feedback. To this end, we propose a novel sparse-to-dense coarse-to-fine depth estimation solution for colonoscopic scenes based on the direct SLAM algorithm. The highlight of our solution is that we utilize the scattered 3D points obtained from SLAM to generate accurate and dense depth in full resolution. This is done by a deep learning (DL)-based depth completion network and a reconstruction system. The depth completion network effectively extracts texture, geometry, and structure features from sparse depth along with RGB data to recover the dense depth map. The reconstruction system further updates the dense depth map using a photometric error-based optimization and a mesh modeling approach to reconstruct a more accurate 3D model of colons with detailed surface texture. We show the effectiveness and accuracy of our depth estimation method on near photo-realistic challenging colon datasets. Experiments demonstrate that the strategy of sparse-to-dense coarse-to-fine can significantly improve the performance of depth estimation and smoothly fuse direct SLAM and DL-based depth estimation into a complete dense reconstruction system.

Nomogram reliability for predicting potential risk in postgraduate medical students with anxiety symptoms.

Purpose: This research aims to develop a Nomogram for exact anxiety symptoms prediction in postgraduate medical students so that they may be identified as high-risk individuals early and get focused care. Methods: Using a convenient sampling method, for case-control matching, 126 participants with anxiety symptoms and 774 participants of the same age and gender but without anxiety symptoms were designated as the case group and control group, respectively. Multivariable logistic regression analysis was utilized to identify influencing factors for anxiety symptoms, then used to design and verify a Nomogram of anxiety symptoms. Results: Multivariate logistic regression analysis showed that lack of social support (OR = 0.95, 95%CI: 0.91-0.99), low life satisfaction (OR = 0.91, 95%CI: 0.86-0.95), low subjective well-being (OR = 0.58, 95%CI: 0.41-0.83) and frequent tobacco and alcohol use (OR = 1.75, 95%CI: 1.10-2.80) were independent predictors of anxiety symptoms in postgraduate medical students (P < 0.05). The Nomogram risk prediction model based on the above four independent prediction factors was established, and the verified C-index (Concordance index) is 0.787 (95%CI: 0.744-0.803, P < 0.001). Conclusions: Anxiety symptoms in postgraduate medical students are influenced by various variables. The Nomogram prediction model has high accuracy, validity, and reliability, which can provide reference for predicting anxiety symptoms in postgraduate medical students.

Time­dependent changes in NLRP3 and Nrf2 levels in lipopolysaccharide­induced acute lung injury.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are severe clinical conditions with a high mortality rate. Nucleotide­binding oligomerization domain (NOD)­like receptor containing pyrin domain 3 (NLRP3) and nuclear factor E2­related factor 2 (Nrf2) have been reported to be associated with ALI. However, the dynamic changes in the levels of these factors in lipopolysaccharide (LPS)­induced lung injury remain unclear. Thus, the present study aimed to determine the LPS­induced activation of immunological cascades, as well as the NLRP3/Nrf2 signaling pathway at different stages of lung injury. For this purpose, mice were divided into six groups as follows: The control, LPS­4 h, LPS­24 h, LPS­48 h, LPS­96 h and LPS­144 h groups. LPS (4 mg/kg) was administered intratracheally to induce lung injury. Flow cytometry was used to determine the changes in macrophages, neutrophils and T­cell subsets in lung tissue, hematoxylin and eosin staining were used to measure the histopathological changes in lung tissues, ELISA was performed to evaluate the levels of cytokines, western blot analysis was used to measure the levels of inflammatory proteins, and reverse transcription­quantitative PCR used to determine the mRNA level of a target gene. Following LPS administration, evident histopathological damage with neutrophil infiltration was observed which peaked at 48 h. The levels of interleukin­1ß, keratinocyte­derived chemokine, macrophage inflammatory protein 2 and tumor necrosis factor a were markedly increased in bronchoalveolar lavage fluid and serum from the mice, and these levels peaked at 4 h. Moreover, LPS promoted Toll like receptor­4 expression and reactive oxygen species production, thus activating NLRP3/Nrf2 signaling and pyroptosis. Collectively, the present study demonstrates that LPS triggers multiple inflammatory molecules and immune cells during ALI, which may be closely involved in the irregular redox status, NLRP3/Nrf2 pathway and pyroptosis.

Family function and life satisfaction of postgraduate medical students during the COVID-19 pandemic: the mediating role of meaning in life and depression.

Recent studies have documented life satisfaction of people have changed during the COVID-19 pandemic. However, it is unknown about the influential factors and mechanisms of life satisfaction of postgraduate medical students during the COVID-19 pandemic. There is a strong link among life satisfaction and individual quality of life and achievement, so it is important to explore the influence mechanism of life satisfaction of postgraduate medical students and explore ways to improve life satisfaction for the development of postgraduate medical students. The current study was based on the Circumplex Model of Marital and Family System, The Theory of Family Functioning, The Meaning Maintenance Model, The Theory of Personal Meaning and Existential Theory to construct theoretical framework and examine whether meaning in life and depression would mediate the link between family function and postgraduate medical students' life satisfaction. By convenient sampling method, a total of 900 postgraduate medical students (Mage = 27.01 years, SD = 3.33) completed questionnaires including Family APGAR Scale, Chinese Version of Meaning In Life Questionnaire, Patient Health Questionnaire, and Satisfaction With Life Scale. In this study, SPSS 25.0 was used for correlation analysis, regression analysis and common method bias test, and AMOS 23.0 was used for structural equation modeling analysis. The results showed that (a) family function could predict life satisfaction of postgraduate medical students significantly; (b) both meaning in life and depression mediated the association between family function and life satisfaction in a parallel manner; (c) meaning in life and depression sequentially mediated the link between family function and life satisfaction of postgraduate medical students. The study illuminates the role of meaning in life and depression in improving life satisfaction and implies that it is necessary to focus on the changes of life satisfaction of postgraduate medical students during the COVID-19 pandemic, and medical educator can improve the sense of meaning in life of postgraduate medical students through improving their family function, further decreasing the risk of depression, finally improving their life satisfaction.