Morphological and regional spontaneous functional aberrations in the brain associated with Crohn's disease: a systematic review and coordinate-based meta-analyses.

Crohn's disease is an acknowledged "brain-gut" disorder with unclear physiopathology. This study aims to identify potential neuroimaging biomarkers of Crohn's disease. Gray matter volume, cortical thickness, amplitude of low-frequency fluctuations, and regional homogeneity were selected as indices of interest and subjected to analyses using both activation likelihood estimation and seed-based d mapping with permutation of subject images. In comparison to healthy controls, Crohn's disease patients in remission exhibited decreased gray matter volume in the medial frontal gyrus and concurrently increased regional homogeneity. Furthermore, gray matter volume reduction in the medial superior frontal gyrus and anterior cingulate/paracingulate gyri, decreased regional homogeneity in the median cingulate/paracingulate gyri, superior frontal gyrus, paracentral lobule, and insula were observed. The gray matter changes of medial frontal gyrus were confirmed through both methods: decreased gray matter volume of medial frontal gyrus and medial superior frontal gyrus were identified by activation likelihood estimation and seed-based d mapping with permutation of subject images, respectively. The meta-regression analyses showed a positive correlation between regional homogeneity alterations and patient age in the supplementary motor area and a negative correlation between gray matter volume changes and patients' anxiety scores in the medial superior frontal gyrus. These anomalies may be associated with clinical manifestations including abdominal pain, psychiatric disorders, and possibly reflective of compensatory mechanisms.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. CLINICAL RELEVANCE STATEMENT: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. KEY POINTS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features. METHODS: A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features. RESULTS: A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models. CONCLUSION: Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION: CRD42022375712.

Effect of calcium sources on enzyme-induced carbonate precipitation to solidify desert aeolian sand.

Enzyme-induced carbonate precipitation (EICP) is a promising technique for soil reinforcement. To select a suitable calcium source and a suitable solution amount for aeolian sand stabilization using EICP, specimens treated with different solution amounts (1.5, 2, 2.5, 3, and 3.5 L/m2). Surface strength, crust thickness, calcium carbonate content (CCC) and water vapor adsorption tests were performed to evaluate the effect of two calcium sources (calcium acetate and calcium chloride) on aeolian sand solidification. The plant suitability of solidified sand was investigated by the sea buckthorn growth test. The suitable calcium source was then used for the laboratory wind tunnel test and the field test to examine the erosion resistance of solidified sand. The results demonstrated that Ca(CH3COO)2-treated specimens exhibited higher strength than CaCl2-treated specimens at the same EICP solution amount, and the water vapor equilibrium adsorption mass of Ca(CH3COO)2-treated specimens was less, indicating that Ca(CH3COO)2-solidified sand was more effective and had better long-term stability. In addition, plants grown in Ca(CH3COO)2-treated sand had greater seedling emergence percentage and higher average height, which indicated that calcium acetate is a more suitable calcium source for EICP treatment. Furthermore, the surface strength and crust thickness of solidified sand increased with increasing the solution amount. For sand treated with 3 L/m2 of solution, the excessive strength and thickness of the crust made plants growth difficult, and the performance of sand treated with more than 2 L/m2 of solution significantly improved. Thus, the solution amount of 2-3 L/m2 is suggested for engineering applications. The sand solidified using EICP in the field could effectively mitigate wind erosion and facilitate the growth of native plants. Therefore, EICP can be combined with vegetative method to achieve long-term wind erosion control in the future.

Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.

OBJECTIVES: To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies. METHODS: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4-19), while all of included studies were assessed as having a high risk of bias (ROB) overall. CONCLUSIONS: Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed. KEY POINTS: • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4-19), and all of studies were considered to have a high risk of bias overall.

The long-term course of subsolid nodules and predictors of interval growth on chest CT: a systematic review and meta-analysis.

OBJECTIVES: To calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT. METHODS: A search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle-Ottawa scale, and publication bias was assessed by Egger's test. RESULTS: Of the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15-29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12-39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3-7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth. CONCLUSIONS: The pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth. KEY POINTS: • Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules. • After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%. • Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred.

Transcutaneous Electrical Nerve Stimulation (TENS) Alleviates Brain Ischemic Injury by Regulating Neuronal Oxidative Stress, Pyroptosis, and Mitophagy.

Background: As a noninvasive treatment, transcutaneous electrical nerve stimulation (TENS) has been utilized to treat various diseases in clinic. However, whether TENS can be an effective intervention in the acute stage of ischemic stroke still remains unclear. In the present study, we aimed to explore whether TENS could alleviate brain infarct volume, reduce oxidative stress and neuronal pyroptosis, and activate mitophagy following ischemic stroke. Methods: TENS was performed at 24 h after middle cerebral artery occlusion/reperfusion (MCAO/R) in rats for 3 consecutive days. Neurological scores, the volume of infarction, and the activity of SOD, MDA, GSH, and GSH-px were measured. Moreover, western blot was performed to detect the related protein expression, including Bcl-2, Bax, TXNIP, GSDMD, caspase-1, NLRP3, BRCC3, HIF-1α, BNIP3, LC3, and P62. Real-time PCR was performed to detect NLRP3 expression. Immunofluorescence was performed to detect the levels of LC3. Results: There was no significant difference of neurological deficit scores between the MCAO group and the TENS group at 2 h after MCAO/R operation (P > 0.05), while the neurological deficit scores of TENS group significantly decreased in comparison with MCAO group at 72 h following MACO/R injury (P < 0.05). Similarly, TENS treatment significantly reduced the brain infarct volume compared with the MCAO group (P < 0.05). Moreover, TENS decreased the expression of Bax, TXNIP, GSDMD, caspase-1, BRCC3, NLRP3, and P62 and the activity of MDA as well as increasing the level of Bcl-2, HIF-1α, BNIP3, and LC3 and the activity of SOD, GSH, and GSH-px (P < 0.05). Conclusions: In conclusion, our results indicated that TENS alleviated brain damage following ischemic stroke via inhibiting neuronal oxidative stress and pyroptosis and activating mitophagy, possibly via the regulation of TXNIP, BRCC3/NLRP3, and HIF-1α/BNIP3 pathways.

Development and validation of the potential biomarkers based on m6A-related lncRNAs for the predictions of overall survival in the lung adenocarcinoma and differential analysis with cuproptosis.

BACKGROUND: The treatment and prognosis of lung adenocarcinoma (LUAD) remains a challenge. The study aimed to conduct a systematic analysis of the predictive capacity of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in the prognosis of LUAD. METHODS: 594 samples were totally selected from a dataset from The Cancer Genome Atlas. The identification of prognostic m6A-related lncRNAs were performed by Pearson correlation analysis and Cox regression analysis. Systematic analyses, including cluster analysis, survival analysis, and immuno-correlated analysis, were conducted. A prognosis model was built from the optimized subset of m6A-related lncRNAs. The assessment of model was performed by survival analysis, and receiver operating characteristic (ROC) curve. Finally, the risk score of patients with LUAD calculated by the prognosis model was implemented by the analysis of Cox regression. Differential analysis was for further evaluation of the cuproptosis-related genes in two risk sets. RESULTS: These patients were grouped into two clusters according to the expression levels of 22 prognostic m6A-related lncRNAs. The patients with LUAD in cluster 2 was significantly worse in the overall survival (OS) (P = 0.006). Three scores calculated by the ESTIMATE methods in cluster 2 were significantly lower. After the least absolute shrinkage and selection operator algorithm, 10 prognostic m6A-related lncRNAs were totally selected to construct the final model to obtain the risk score. Then the area under the ROC curve of the prognosis model for 1, 3, and 5-year OS was 0.767, 0.709, and 0.736 in the training set, and 0.707, 0.691, and 0.675 in the test set. The OS of the low-risk cohort was significantly higher than that of the high-risk cohort in both the training set (P < 0.001) and test set (P < 0.001). After the analysis of Cox regression, the risk score [Hazard ratio (HR) = 5.792; P < 0.001] and stage (HR = 1.576; P < 0.001) were both considered as independent indicators of prognosis for LUAD. The expression levels of five cuproptosis-related genes were significantly different in two risk sets. CONCLUSIONS: The study constructed a predictive model for the OS of patients with LUAD and these OS-related m6A-lncRNAs might have potential roles in LUAD progression.

Toll-Like Receptor 4: A Promising Therapeutic Target for Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that primarily manifests as memory deficits and cognitive impairment and has created health challenges for patients and society. In AD, amyloid ß-protein (Aß) induces Toll-like receptor 4 (TLR4) activation in microglia. Activation of TLR4 induces downstream signaling pathways and promotes the generation of proinflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-1ß (IL-1ß), which also trigger the activation of astrocytes and influence amyloid-dependent neuronal death. Therefore, TLR4 may be an important molecular target for treating AD by regulating neuroinflammation. Moreover, TLR4 regulates apoptosis, autophagy, and gut microbiota and is closely related to AD. This article reviews the role of TLR4 in the pathogenesis of AD and a range of potential therapies targeting TLR4 for AD. Elucidating the regulatory mechanism of TLR4 in AD may provide valuable clues for developing new therapeutic strategies for AD.

High salinity slowed organic acid production from acidogenic fermentation of kitchen wastewater by shaping functional bacterial community.

The high salinity of kitchen wastewater might have adverse effects on the production of short-chain fatty acids (SCFAs) in anaerobic fermentation. The effects and mechanisms of salinity on SCFA production in the anaerobic fermentation of kitchen wastewater were studied by varying the salt concentration, as follows: 0 g/L (S0), 2 g/L (S2), 6 g/L (S6), 10 g/L (S10), 15 g/L (S15), and 20 g/L (S20). Experimental results showed that hypersaline conditions (>10 g NaCl/L) accelerated the release of soluble proteins at the initial stage of anaerobic fermentation. They also significantly prohibited the hydrolysis and degradation of soluble proteins and carbohydrates. Compared with low salinity tests, the SCFA concentrations under hypersaline conditions (>10 g NaCl/L) only reached approximately 43% of the highest concentration on day 10, although the SCFA concentrations in all tests were very close on day 10 (14 g COD/L). High salinity delayed the production of n-butyric acid but did not change the composition of the total SCFAs. High salinity enriched Enterococcus and Bifidobacterium, the relative abundance levels of which reached 27.57% and 49.71%, respectively, before the depletion of substrate. High salinity showed a negative correlation with the relative abundance of the genera Clostridium_sensu_stricto_1, Prevotella and unclassified_f_Oscillospiraceae which are responsible for SCFA production. This study provided a theoretical basis for the fficient utilization of kitchen wastewater.

The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules.

OBJECTIVES: To evaluate the ability of CT radiomic features extracted from peritumoral parenchyma of 2 mm and 5 mm distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). METHODS: For this retrospective study, 121 lung adenocarcinomas appearing as ground-glass nodules on thin-section CT were evaluated. Quantitative radiomic features were extracted from the peritumoral parenchymal region of 2 mm and 5 mm on CT imaging, and the radiomic models of External2 and External5 were constructed. The ROC curves were used to evaluate the performance of different models. Differences between the AUCs were evaluated using DeLong's method. RESULTS: The radiomic scores of IAC were statistically higher than those of MIA/AIS in both the External2 and External5 models. The AUCs of the External2 and External5 models were 0.882, 0.778 in the training cohort and 0.888, 0.804 in the validation cohort, respectively. The AUC of the External2 model was not statistically different from the External5 model both in the training cohort (p = 0.116) and validation cohort (p = 0.423). CONCLUSIONS: The radiomic features extracted from the peritumoral region of 2 mm and 5 mm at thin-section CT showed good predictive values to differentiate the IAC from AIS/MIA. The radiomic features from the peritumoral region of 5 mm provide no additional benefit in distinguishing IAC from MIA/AIS than that of the 2 mm region. KEY POINTS: • The radiomic models from various peritumoral lung parenchyma were developed and validated to predict invasiveness of adenocarcinoma. • The peritumoral parenchyma of lung adenocarcinoma may contain useful information. • Radiomics from peritumoral lung parenchyma of 5 mm provides no added efficiency of the prediction for invasiveness of lung adenocarcinoma.

Mineralization crust field experiment for desert sand solidification based on enzymatic calcification.

Sandstorms have been recognized as severe natural disasters worldwide and it is of great significance to propose an effective and environmentally friendly method to combat sandstorm. In this study, the enzymatic calcification (EC) treatment technology was used for mineralization crust and desert sand solidification. Both laboratory experiments and field site tests were conducted to demonstrate the feasibility of EC treatment to improve wind-erosion resistance and rainfall-erosion resistance. Results showed that with the concentration of reactants higher than 0.25 M or the ratio of urease solution to the cementation solution above 0.8, the improvement effects of wind-erosion resistance and rainfall-erosion resistance decreased. Therefore, the 0.25 M of reagent concentration and 0.8 of ratio of urease solution to the cementation solution were chosen for subsequent field site test. The two test sites had similar CaCO3 contents, thus obtaining a similar increasing range of surface strength. However, the test site one had larger surface strengths due to thicker cemented crust layers. Both the two test sites had sufficient wind-erosion resistance because of crust layer. Moreover, rainfalls decreased surface strength; the surface strength recovered to a high level after water evaporation. In addition, the effect of rainfall on thickness of crust layer and CaCO3 was small. The EC treatment had good ecological compatibility, and the combined EC and grass seed treatment was effective for mitigation of desertification. The results demonstrated that EC treatment significantly improved both wind-erosion and rainfall-erosion resistance, which presents promising potential for anti-desertification.

Exploring the structure, bonding and stability of noble gas compounds promoted by superhalogens. A case study on HNgMX3 (Ng = Ar-Rn, M = Be-Ca, X = F-Br) via combined high-level ab initio and DFT calculations.

A series of complexes (HNgMX3), formed from superhalogen MX3 (M = Be-Ca, X = F-Br) noble gas (Ar-Rn) and the hydrogen atom, were investigated via combined high-level ab initio and DFT calculations. The high vertical electron detachment energy (VDE) of the superhalogen part will lead to charge transfer from the noble gas hydride to it. This charge transfer gives rise to attractive ionic interaction between the two components and to the existence of these complexes as local minima on the potential energy surface eventually. However, the VDE value of the superhalogen part is not always monotonically correlated with the thermodynamic/kinetic stability of the whole complexes. Therefore the superhalogen itself might not be enough to provide information for the correct prediction of the properties of the whole composites. Although there are exothermic channels of dissociation, the existence of energy barrier might ensure the existence of these Ng hydrides under certain conditions. Our analysis indicates the existence of two important factors, functioning in opposite directions, for the energy barriers along the exothermic channel. To achieve a high energy barrier, the attractive interaction between superhalogen and the H atom in the TS, which lowers the barrier, needs to be suppressed effectively. An understanding of the superhalogen-based composites will provide valuable information on the functional properties and potential application of superhalogens. The details of the interaction between different parts of these composites should be one of the areas of focus in these studies.

Improvement of bio-cementation at low temperature based on Bacillus megaterium.

A low production rate for calcium carbonate with microbial solidification technology at low temperatures often restricts its application. For this reason, adding urea to the medium and the domestication of Bacillus megaterium at low temperature were proposed to produce more calcium carbonate based on an analysis of growth characteristics, urease activity, and the production rates for calcium carbonate under different conditions. Sand solidification tests were conducted to demonstrate improvements caused by the methods. The results showed that the higher the temperature, the faster the growth of Bacillus megaterium and the stronger the urease activity. Growth was fastest and urease activity strongest at a pH of 8. Adding urea to the medium and the domestication of B. megaterium at low temperature can both improve the production rate, effectively increasing calcium carbonate precipitation at low temperature. Combining the two methods resulted in greater improvement of the production rate for calcium carbonate. The two methods were also found to improve the effect of sand solidification. Therefore, our study provides a solid foundation for the actual engineering application of bio-cementation technology at low temperature.

Biomineralization for Reducing and Controlling Sand-Dust Storms.

The sand-dust weather and sand-dust storms have become a serious environmental disaster worldwide. It is an important challenge to develop technologies for desert sand solidification in order to prevent and control sand-dust weather. The biomineralization technology for solidifying desert sands has been a novel method for reinforced soils in recent years. The biomineralization solidification sand field tests are completed at the Wuma Highway solidification section in the Tengger Desert. The superiority of the biomineralization for solidifying sands is verified by measuring the water storage capacity of different reinforcement zones including bare sand zone, plant zone, biomineralization solidifying sand zone, and biomineralization combined plant solidifying sand zone. Simultaneously, the molecular dynamics calculation analysis is used to verify the role of biomineralization solidifying sands in preventing sand-dust storms. All results demonstrate that the biomineralization solidification sand method is effective for controlling and preventing sandstorm disasters.

Trends and hotspots of energy-based imaging in thoracic disease: a bibliometric analysis.

OBJECTIVE: To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention. METHODS: The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source's production over time, corresponding author's countries, and a treemap of authors' keywords. RESULTS: A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the "European Radiology" (181 articles, h-index = 46), followed by the "European Journal of Radiology" (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest. CONCLUSION: Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future. CRITICAL RELEVANCE STATEMENT: This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice. KEY POINTS: Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications.

Transcutaneous electrical acupoint stimulation attenuated neuroinflammation and oxidative stress by activating SIRT1-induced signaling pathway in MCAO/R rat models.

BACKGROUND: Silent information regulator 1 (SIRT1) plays a beneficial role in cerebral ischemic injury. Previous reports have demonstrated that transcutaneous electrical acupoint stimulation (TEAS) exerts a beneficial effect on ischemic stroke; however, whether SIRT1 participates in the underlying mechanism for the neuroprotective effects of TEAS against ischemic brain damage has not been confirmed. METHODS: The rat models of middle cerebral artery occlusion/reperfusion (MCAO/R) were utilized in the current experiment. After MCAO/R surgery, rats in TEAS, EC and EX group received TEAS intervention with or without the injection of EX527, the SIRT1 inhibitor. Neurological deficit scores, infarct volume, hematoxylin eosin (HE) staining and apoptotic cell number were measured. The results of RNA sequencing were analyzed to determine the differential expression changes of genes among sham, MCAO and TEAS groups, in order to investigate the possible pathological processes involved in cerebral ischemia and explore the protective mechanisms of TEAS. Moreover, oxidative stress markers including MDA, SOD, GSH and GSH-Px were measured with assay kits. The levels of the proinflammatory cytokines, such as IL-6, IL-1ß and TNF-α, were detected by ELISA assay, and Iba-1 (the microglia marker protein) positive cells was measured by immunofluorescence (IF). Western blot and IF were utilized to examine the levels of key molecules in SIRT1/FOXO3a and SIRT1/BRCC3/NLRP3 signaling pathways. RESULTS: TEAS significantly decreased brain infarcted size and apoptotic neuronal number, and alleviated neurological deficit scores and morphological injury by activating SIRT1. The results of RNA-seq and bioinformatic analysis revealed that oxidative stress and inflammation were the key pathological mechanisms, and TEAS alleviated oxidative injury and inflammatory reactions following ischemic stroke. Then, further investigation indicated that TEAS notably attenuated neuronal apoptosis, neuroinflammation and oxidative stress damage in the hippocampus of rats with MCAO/R surgery. Moreover, TEAS intervention in the MCAO/R model significantly elevated the expressions of SIRT1, FOXO3a, CAT, BRCC3, NLRP3 in the hippocampus. Furthermore, EX527, as the inhibitor of SIRT1, obviously abolished the anti-oxidative stress and anti-neuroinflammatory roles of TEAS, as well as reversed the TEAS-mediated elevation of SIRT1, FOXO3a, CAT and reduction of BRCC3 and NLRP3 mediated by following MCAO/R surgery. CONCLUSIONS: In summary, these findings clearly suggested that TEAS attenuated brain damage by suppressing apoptosis, oxidative stress and neuroinflammation through modulating SIRT1/FOXO3a and SIRT1/BRCC3/NLRP3 signaling pathways following ischemic stroke, which can be a promising treatment for stroke patients.