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Objective To investigate the pathogenic role of neutrophil extracellular traps （NETs） in the pathogenesis and progression of severe Mycoplasma pneumoniae pneumonia （SMPP） in children and to assess their predictive value for SMPP. Methods Children with Mycoplasma pneumoniae pneumonia （MPP） admitted to the Second Hospital of Lanzhou University from October to December 2023 were selected as the study subjects. They were divided into the non-severe group （MPP group， n = 45） and severe group （SMPP group， n = 39）. The levels of NETs in the serum and bronchoalveolar lavage fluid （BALF） of all children were measured using ELISA. Additionally， BALF from both the affected and contralateral sides was collected from children in the SMPP group， and the content of NETs in BALF was determined using ELISA. Results A significant difference was observed in the serum concentration of NETs between MPP and SMPP patients （P < 0.01）. In the SMPP group， the level of NETs in the BALF of the affected lung was significantly higher than those of the contralateral side （P < 0.001）. Serum NETs levels were significantly higher than those in BALF in SMPP group （P < 0.001）. The area under the ROC curve （AUC） for predicting SMPP based on serum NETs levels was 0.892 （95%CI 0.810-0.963， P < 0.001）， with a sensitivity of 0.821 and specificity of 0.875， and a cut-off value of 17.24 ng/mL，respectively. The AUC of the combination of serum NETs with C-reactive protein （CRP） and lactate dehydrogenase （LDH） levels for predicting SMPP was 0.974 （95%CI 0.946-1.000， P < 0.001）， with a sensitivity of 0.923 and specificity of 0.950， respectively. Conclusions Excessive activation of NETs may contribute to the incidence of SMPP and localized lung injury in children with MPP. Monitoring the serum levels of NETs， CRP and LDH can effectively predict the incidence of SMPP. The combination of NETs， CRP， and LDH yields the best predictive performance.

Abstract Background To identify the differences of lumbar lordosis (LL) and sacral slope (SS) angles between two types of postoperative lumbar disc re-herniation, including the recurrence of same level and adjacent segment herniation (ASH). Methods We searched the medical records of lumbar disc herniation (LDH) patients with re-herniation with complete imaging data (n = 58) from January 1, 2013 to December 30, 2020 in our hospital. After matching for age and sex, 58 patients with LDH without re-herniation from the same period operated by the same treatment group in our hospital were served as a control group. Re-herniation patients were divided into two groups, same-level recurrent lumbar disc herniation group (rLDHG) and adjacent segment herniation group with or without recurrence (ASHG). The preoperative, postoperative and one month after operation LL and SS were measured on standing radiographs and compared with the control group by using t-test, ANOVA, and rank-sum test. Next, we calculated the odds ratios (ORs) by unconditional logistic regression, progressively adjusted for other confounding factors. Results Compared with the control group, the postoperative LL and SS were significantly lower in LDH patients with re-herniation. However, there were no differences in LL and SS between ASHG and rLDHG at any stage. After progressive adjustment for confounding factors, no matter what stage is, LL and SS remained unassociated with the two types of re-herniation. Conclusions Low postoperative LL and SS angles are associated with degeneration of the remaining disc. Low LL and SS may be independent risk factors for re-herniation but cannot determine type of recurrence (same or adjacent disc level).

ObjectiveNausea and vomiting are the distressing and debilitating side effects of chemotherapy. This study explores the relationship between the degree of nausea and vomiting and physical activity in patients with lung cancer during the first chemotherapy cycle.DesignA total of 107 patients with lung cancer who received platinum drugs during chemotherapy in a hospital in Shanghai, China, in 2023 were involved in this study. Data were collected with medical record system and self-reported questionnaires.Questionnaires included the International Physical Activity Questionnaire (IPAQ) and Index of Nausea, Vomiting, and Retching (INVR). IPAQ was used before chemotherapy; INVR was used on the second and sixth day of chemotherapy, followed by the analysis of the correlation between physical activity status and degree of nausea and vomiting during chemotherapy. The influencing factors of nausea and vomiting during chemotherapy in patients with lung cancer were analyzed using logistic regression analysis.ResultsMore than half of the patients experienced nausea, vomiting or retching related symptoms after chemotherapy, and the proportion of moderate to high physical activity was 50.5%. Univariate analysis showed that the degree of nausea and vomiting was influenced by factors such as age, gender, and history of drinking (P < 0.05). The degree of nausea and vomiting was negatively correlated with physical activity (P < 0.05). The multivariate linear analysis showed that gender, history of drinking, and moderate and high physical activity are contributing factors to nausea and vomiting during chemotherapy (P < 0.05).ConclusionModerate and high physical activity before chemotherapy is a protective factor for nausea and vomiting in patients with lung cancer. Physical activity can not only improve the degree of nausea and vomiting in patients with lung cancer but also reduce the incidence in these patients during the first chemotherapy cycle.

Abstract Despite the progressive decline in the virulence of the novel coronavirus, there has been no corresponding reduction in its associated hospital mortality. Our aim was to redefine an accurate predictor of mortality risk in COVID-19 patients, enabling effective management and resource allocation. We conducted a retrospective analysis of 2917 adult Chinese patients diagnosed with COVID-19 who were admitted to our hospital during two waves of epidemics, involving the Beta and Omicron variants. Upon admission, NT-proBNP levels were measured, and we collected demographic, clinical, and laboratory data. We introduced a new concept called the NT-proBNP ratio, which measures the NT-proBNP level relative to age-specific maximum normal values. The primary outcome was all-cause in-hospital mortality. Our analysis revealed a higher in-hospital mortality rate in 2022, as shown by the Kaplan–Meier Survival Curve. To assess the predictive value of the NT-proBNP ratio, we employed the time-dependent receiver operating characteristic (ROC) curve. Notably, the NT-proBNP ratio emerged as the strongest predictor of mortality in adult Chinese hospitalized COVID-19 patients (area under the curve, AUC = 0.826; adjusted hazard ratio [HR], 3.959; 95% confidence interval [CI] 3.001–5.221; P

Effective forecasting of carbon prices helps investors to judge carbon market conditions and promotes the environment and economic sustainability. The contribution of this paper is constructing a novel secondary decomposition hybrid carbon price forecasting model, namely CEEMD-SE-VMD-LSTM. It is noteworthy that the sample entropy is introduced to identify the highly complex signals rather than empirically determined in previous studies. Specifically, the complementary ensemble empirical mode decomposition (CEEMD) model is used to decompose the original price signals. The sample entropy (SE) and variational mode decomposition (VMD) are conducted to recognize and secondary decompose the highly complex components, while the long short-term memory (LSTM) model is employed to forecast the carbon price by summing up the predicted intrinsic mode function (IMF) components. The conclusion shows the proposed model has the smallest forecasting errors with the values of RMSE, MAE and MAPE are 0.2640, 0.1984 and 0.0044, respectively, the secondary decomposition models are better than other primary decomposition models and the forecasting performances of LSTM-type models are better than those of other GRU-type models. Further evidence convinces us that short-term forecasting accuracy is superior to long-term forecasting. Those conclusions and model innovation can provide a valuable reference for investors to make trading decisions.

Understanding how individuals collaborate with others is a complex undertaking, because collaborative problem-solving (CPS) is an interactive and dynamic process. We attempt to identify distinct collaborative problem-solver profiles of Chinese 15-year-old students on a computer-based CPS task using process data from the 2015 Program for International Student Assessment (PISA, N = 1,677), and further to examine how these profiles may relate to student demographics (i.e., gender, socioeconomic status) and motivational characteristics (i.e., achieving motivation, attitudes toward collaboration), as well as CPS performance. The process indicators we used include time-on-task, actions-on-task, and three specific CPS process skills (i.e., establish and maintain shared understanding, take appropriate action to solve the problem, establish and maintain team organization). The results of latent profile analysis indicate four collaborative problem-solver profiles: "Disengaged," "Struggling," "Adaptive," and "Excellent." Gender, socioeconomic status, attitudes toward collaboration and CPS performance are shown to be significantly associated with profile membership, yet achieving motivation was not a significant predictor. These findings may contribute to better understanding of the way students interact with computer-based CPS tasks and inform educators of individualized and adaptive instructions to support student collaborative problem-solving.

Peripheral nerve injury is one of the more common forms of peripheral nerve disorders, and the most severe type of peripheral nerve injury is a defect with a gap. Biosynthetic cellulose membrane (BCM) is a commonly used material for repair and ligation of nerve defects with gaps. Meanwhile, exosomes from mesenchymal stem cells can promote cell growth and proliferation. We envision combining exosomes with BCMs to leverage the advantages of both to promote repair of peripheral nerve injury. Prepared exosomes were added to BCMs to form exosome-loaded BCMs (EXO-BCM) that were used for nerve repair in a rat model of sciatic nerve defects with gaps. We evaluated the repair activity using a pawprint experiment, measurement and statistical analyses of sciatica function index and thermal latency of paw withdrawal, and quantitation of the number and diameter of regenerated nerve fibers. Results indicated that EXO-BCM produced comprehensive and durable repair of peripheral nerve defects that were similar to those for autologous nerve transplantation, the gold standard for nerve defect repair. EXO-BCM is not predicted to cause donor site morbidity to the patient, in contrast to autologous nerve transplantation. Together these results indicate that an approach using EXO-BCM represents a promising alternative to autologous nerve transplantation, and could have broad applications for repair of nerve defects.

Abstract Recently, deep learning has achieved considerable results in the hyperspectral image (HSI) classification. However, most available deep networks require ample and authentic samples to better train the models, which is expensive and inefficient in practical tasks. Existing few‐shot learning (FSL) methods generally ignore the potential relationships between non‐local spatial samples that would better represent the underlying features of HSI. To solve the above issues, a novel deep transformer and few‐shot learning (DT‐FSL) classification framework is proposed, attempting to realize fine‐grained classification of HSI with only a few‐shot instances. Specifically, the spatial attention and spectral query modules are introduced to overcome the constraint of the convolution kernel and consider the information between long‐distance location (non‐local) samples to reduce the uncertainty of classes. Next, the network is trained with episodes and task‐based learning strategies to learn a metric space, which can continuously enhance its modelling capability. Furthermore, the developed approach combines the advantages of domain adaptation to reduce the variation in inter‐domain distribution and realize distribution alignment. On three publicly available HSI data, extensive experiments have indicated that the proposed DT‐FSL yields better results concerning state‐of‐the‐art algorithms.

Ganoderma lucidum, used in East Asia for its health benefits, contains ganoderic acids (GA) which have various pharmacological activities but are limited by poor water solubility and low oral bioaccessibility. This study synthesized and characterized ganoderic acids loaded zein-chitosan nanoparticles (GA-NPs), and investigated its advantages in alleviating alcoholic liver injury (ALI) in mice model. The GA-NPs demonstrated high encapsulation efficiency (92.68%), small particle size (177.20 nm), and a +29.53 mV zeta potential. The experimental results of alcohol-induced liver injury mouse model showed that GA-NPs significantly improved liver metabolic function, reduced alcohol-induced liver oxidative stress in liver by decreasing lactate dehydrogenase activity and malondialdehyde level, while increasing the activities of liver antioxidant enzymes and alcohol dehydrogenase. Moreover, GA-NPs were favorable to ameliorate intestinal microbiota dysbiosis in mice exposed to alcohol by increasing the proportion of probiotics such as Romboutsia, Faecalibaculum, Bifidobacterium and Turicibacter, etc., which were highly correlated with the improvement of liver function. Furthermore, GA-NPs modulated the mRNA expression related to ethanol metabolism, oxidative stress and lipid metabolism. Conclusively, this study revealed that GA-NPs have stronger hepatoprotective effects than non-encapsulated ganoderic acids on alleviating ALI by regulating intestinal microbiota and liver metabolism.

To address the problem of ignoring unpaved roads when planning off-road emergency rescue paths, an improved A* algorithm that incorporates road factors is developed to create an off-road emergency rescue path planning model in this study. To reduce the number of search nodes and improve the efficiency of path searches, the current node is classified according to the angle between the line connecting the node and the target point and the due east direction. Additionally, the search direction is determined in real time through an optimization method to improve the path search efficiency. To identify the path with the shortest travel time suitable for emergency rescue in wilderness scenarios, a heuristic function based on the fusion of road factors and a path planning model for off-road emergency rescue is developed, and the characteristics of existing roads are weighted in the process of path searching to bias the selection process toward unpaved roads with high accessibility. The experiments show that the improved A* algorithm significantly reduces the travel time of off-road vehicles and that path selection is enhanced compared to that with the traditional A* algorithm; moreover, the improved A* algorithm reduces the number of nodes by 16.784% and improves the search efficiency by 27.18% compared with the traditional 16-direction search method. The simulation results indicate that the improved algorithm reduces the travel time of off-road vehicles by 21.298% and improves the search efficiency by 93.901% compared to the traditional A* algorithm, thus greatly enhancing off-road path planning.