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BackgroundAcute respiratory distress syndrome (ARDS) is a life-threatening condition that can develop in critically ill patients. Early identification of risk factors associated with ARDS development is essential for timely intervention and improved patient outcomes. This study aimed to investigate the potential predictors of ARDS in critically ill patients admitted to the intensive care unit (ICU).MethodsWe conducted a retrospective study involving 502 critically ill patients admitted to the ICUs of three hospitals. Demographic and clinical data, including laboratory test results, were collected during their ICU stay. Multivariable logistic regression analysis was performed to identify independent risk factors associated with the development of ARDS.ResultsAmong the 502 critically ill patients, 104 (20.7%) patients developed ARDS during their ICU stay, with a median time to development of 5.2 days. Multivariable logistic regression analysis revealed that age (odds ratio [OR], 1.07; 95% confidence interval [CI], 1.01–1.13; P = 0.002), C-reactive protein (CRP) levels (OR, 1.11; 95% CI, 1.05–1.17; P = 0.013), T lymphocyte count (OR, 0.82; 95% CI, 0.69–0.93; P = 0.011), and interleukin-6 (IL-6) levels (OR, 1.17; 95% CI, 1.08–1.23; P = 0.003) were independently associated with the development of ARDS in critically ill patients.ConclusionsOur study identified age, CRP, T lymphocyte count, and IL-6 as independent predictors of ARDS in critically ill patients admitted to the ICU. These findings highlight the importance of monitoring these parameters in critically ill patients to identify those at high risk of developing ARDS. Early recognition and intervention based on these risk factors may improve patient outcomes in the ICU setting. Further prospective studies are warranted to validate these results and develop a reliable predictive model for ARDS in critically ill patients.

Summary: Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition with a high incidence and mortality rate in intensive care unit (ICU) admissions. Early identification of patients at high risk for developing ARDS is crucial for timely intervention and improved clinical outcomes. However, the complex pathophysiology of ARDS makes early prediction challenging. This study aimed to develop an artificial intelligence (AI) model for automated lung lesion segmentation and early prediction of ARDS to facilitate timely intervention in the intensive care unit. Methods: A total of 928 ICU patients with chest computed tomography (CT) scans were included from November 2018 to November 2021 at three centers in China. Patients were divided into a retrospective cohort for model development and internal validation, and three independent cohorts for external validation. A deep learning-based framework using the UNet Transformer (UNETR) model was developed to perform the segmentation of lung lesions and early prediction of ARDS. We employed various data augmentation techniques using the Medical Open Network for AI (MONAI) framework, enhancing the training sample diversity and improving the model's generalization capabilities. The performance of the deep learning-based framework was compared with a Densenet-based image classification network and evaluated in external and prospective validation cohorts. The segmentation performance was assessed using the Dice coefficient (DC), and the prediction performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The contributions of different features to ARDS prediction were visualized using Shapley Explanation Plots. This study was registered with the China Clinical Trial Registration Centre (ChiCTR2200058700). Findings: The segmentation task using the deep learning framework achieved a DC of 0.734 ± 0.137 in the validation set. For the prediction task, the deep learning-based framework achieved AUCs of 0.916 [0.858–0.961], 0.865 [0.774–0.945], 0.901 [0.835–0.955], and 0.876 [0.804–0.936] in the internal validation cohort, external validation cohort I, external validation cohort II, and prospective validation cohort, respectively. It outperformed the Densenet-based image classification network in terms of prediction accuracy. Moreover, the ARDS prediction model identified lung lesion features and clinical parameters such as C-reactive protein, albumin, bilirubin, platelet count, and age as significant contributors to ARDS prediction. Interpretation: The deep learning-based framework using the UNETR model demonstrated high accuracy and robustness in lung lesion segmentation and early ARDS prediction, and had good generalization ability and clinical applicability. Funding: This study was supported by grants from the Shanghai Renji Hospital Clinical Research Innovation and Cultivation Fund (RJPY-DZX-008) and Shanghai Science and Technology Development Funds (22YF1423300).

Abstract Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

Abstract Breast cancer patients often have a poor prognosis largely due to lack of effective targeted therapy. It is now well established that monosaccharide enhances growth retardation and chemotherapy sensitivity in tumor cells. We investigated whether d-arabinose has capability to restrict the proliferation of tumor cells and its mechanism. Here, we report that d-arabinose induced cytotoxicity is modulated by autophagy and p38 MAPK signaling pathway in breast cancer cell lines. The proliferation of cells was evaluated by CCK-8 and Colony formation assay. The distribution of cells in cell cycle phases was analyzed by flow cytometry. Cell cycle, autophagy and MAPK signaling related proteins were detected by western blotting. Mouse xenograft model was used to evaluate the efficacy of d-arabinose in vivo. The proliferation of cells was dramatically inhibited by d-arabinose exposure in a dose-dependent manner, which was relevant to cell cycle arrest, as demonstrated by G2/M cell cycle restriction and ectopic expression of cell cycle related proteins. Mechanistically, we further identified that d-arabinose is positively associated with autophagy and the activation of the p38 MAPK signaling in breast cancer. In contrast, 3-Ma or SB203580, the inhibitor of autophagy or p38 MAPK, reversed the efficacy of d-arabinose. Additionally, d-arabinose in vivo treatment could significantly inhibit xenograft growth of breast cancer cells. Our findings were the first to reveal that d-arabinose triggered cell cycle arrest by inducing autophagy through the activation of p38 MAPK signaling pathway in breast cancer cells.

[Objective] A large number of tunnel diseases usually occur during the operation of urban rail transit, and the main cause of the diseases is the wheel-rail force. Therefore, it is necessary to study the method of measuring the wheel-rail vertical force without affecting the normal train operation. [Method] With the descending section between Chadianzi Station and Yipintianxia Station of Chengdu Metro Line 7 as the research object and based on the moment difference theory, a finite element model of steel rail under static load and dynamic load is established by using ANSYS software, and a numerical simulation analysis is conducted on it. The calibration test on a 2-meter-long, 3-span rail is carried out in the laboratory. [Result & Conclusion] The numerical simulation analysis of the static load shows that the rail measuring points can be chosen on both sides of the upper surface edge of the rail bottom in the middle of the rail span. When the vertical load is applied to the middle of the span, the strain at the measuring points along the rail direction is the largest, and the strain increases linearly with the vertical load. The calibration coefficient measured by calibration test is 0.626 0×106 kN, while that obtained by numerical simulation is 0.644 1×106 kN. The deviation between the two is only 2.8%. Therefore, the calibration coefficient can be determined by static load calibration test. The measurement method of the wheel-rail vertical force can better evaluate the safety of track structure and train operation. The hidden hazards can be timely detected and corresponding measures be taken for repair and improvement.

AbstractRecent research has revealed that mechanical ventilation (MV) could initiate ventilator-induced lung injury along with the initiation of the process of pulmonary fibrosis (PF), leading to MV-induced PF (MVPF). However, the underlying mechanism remains unclear. This study aimed to explore the role of MV-induced extracellular vesicles (MV-EVs) and the c-Jun N-terminal kinase (JNK) signalling pathway in the pathogenesis of MVPF in vivo and in vitro. The process of MV is accompanied by the secretion of MV-EVs, which could induce lung fibroblast activation. Furthermore, single-cell RNA-sequencing analysis revealed that the JNK pathway in lung fibroblasts was activated after MV initiation. Inhibiting the JNK pathway could both restrain MV-EV-induced lung fibroblast activation in vitro or reduce the severity of MVPF in vivo. In conclusion, this study demonstrated that MV-EVs contribute to MVPF progression by activating lung fibroblasts via the JNK signalling pathway and that inhibiting the secretion of EV and the activation of the JNK signalling pathway is a promising strategy for treating MVPF.

BackgroundThe coronavirus disease 2019 (COVID-19) is an acute infectious pneumonia caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection previously unknown to humans. However, predictive studies of acute respiratory distress syndrome (ARDS) in patients with COVID-19 are limited. In this study, we attempted to establish predictive models to predict ARDS caused by COVID-19 via a thorough analysis of patients' clinical data and CT images.MethodThe data of included patients were retrospectively collected from the intensive care unit in our hospital from April 2022 to June 2022. The primary outcome was the development of ARDS after ICU admission. We first established two individual predictive models based on extreme gradient boosting (XGBoost) and convolutional neural network (CNN), respectively; then, an integrated model was developed by combining the two individual models. The performance of all the predictive models was evaluated using the area under receiver operating characteristic curve (AUC), confusion matrix, and calibration plot.ResultsA total of 103 critically ill COVID-19 patients were included in this research, of which 23 patients (22.3%) developed ARDS after admission; five predictive variables were selected and further used to establish the machine learning models, and the XGBoost model yielded the most accurate predictions with the highest AUC (0.94, 95% CI: 0.91–0.96). The AUC of the CT-based convolutional neural network predictive model and the integrated model was 0.96 (95% CI: 0.93-0.98) and 0.97 (95% CI: 0.95–0.99), respectively.ConclusionAn integrated deep learning model could be used to predict COVID-19 ARDS in critically ill patients.

Purpose: This study aims to investigate the therapeutic effects of wolfberry (Lycium barbarum L.) against breast cancer and the potential mechanisms using network pharmacology. Methods: The active ingredients and corresponding targets of wolfberry were screened by TCMSP database, and the potential targets of Wolfberry against breast cancer were obtained by intersecting them with the disease targets obtained from GeneCards database and OMIM database；The visualized network of drug-active component-disease-related target was constructed；Protein-protein interaction (PPI) relationships were demonstrated for the wolfberry-breast cancer intersection targets using the String database, and the core target is obtained by analyzing it. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on crossover targets via the DAVID online platform and the Bioinformatics online platform. Results: In this study, we screened the main active ingredients of Wolfberry against breast cancer as quercetin, β-sitosterol, Stigmasterol by network pharmacological methods. The PPI network showed that the core targets are ESR1, MYC, HIF1A, EGFR, VEGFA, CCND1etc. GO enrichment analysis showed that the main biological processes of wolfberry against breast cancer include response to steroid hormones, response to ketone, cellular response to chemical stress, epithelial cell proliferation. The results of KEGG enrichment analysis showed that the anti-breast cancer targets of wolfberry were mainly enriched in cancer pathways, estrogen signaling pathway, AGE-RAGE signaling pathway, P53 signaling pathway, HIF-1 signaling pathway. Conclusion: Through a network pharmacology approach, we predicted wolfberry can treat breast cancer mainly by acting on ESR1, MYC, HIF1A and other key targets, regulating cancer pathway, estrogen signaling pathway and so on.

Objective: Electrical impedance tomography (EIT) develops rapidly in technology and applications. Nowadays EIT is used in multiple clinical and experimental scenarios including pulmonary, brain, and tissue monitoring, etc. The present study explores the research trends and hotspots on EIT extrapulmonary application research by bibliometrics analysis. Approach: Publications on EIT extrapulmonary applications between 1987 and 2021 were retrieved from the Web of Science Core Collection database. For precise screening, search strategy “electrical impedance tomography” plus “hemodynamic” or “brain” or “nerve” or “cancer” or “venous” or “vessel” or “tumor” or “veterinary” or “tissue” or “cell” or “wearable” or “application” and excluding “lung”, “ventilation” “respiratory”, “pulmonary”, “algorithm”, “current”, “voltage” or “electrode” were used. CiteSpace and VOSviewer were used to analyze the publication features, collaboration, keywords co-occurrence, and co-cited reference. Main results: A total of 506 articles were finally identified. The global publication numbers on extrapulmonary applications gradually increased yearly in the past 30 years. The US, UK, and China contributed most three publications concerning EIT extrapulmonary applications. “tissues”, “conductivity”, “model” were research hotspots, and “cutaneous melanoma”, “microstructure”, “diagnosis” were recent topics (Portions of this research have previously been presented in poster form). Significance: Overall, EIT extrapulmonary applications bibliometrics analysis provides a unique insight into research focus, current trends, and future directions.

Objective: Electrical impedance tomography (EIT) is a technique to measure electrical properties of tissue. With the progress of modern integrated circuits and microchips, EIT instrumentation becomes an active research area to improve all aspects of device performance. Plenty of studies on EIT hardware have been presented in prestigious journals. This study explores publications on EIT hardware to identify the developing hotspots and trends.Method: Publications covering EIT hardware on the Web of Science Core Collection (WoSCC) database from 1989 to 2021 were collected for bibliometric analysis. CiteSpace and VOS viewer were used to study the characteristics of the publications.Main results: A total of 592 publications were analyzed, showing that the number of annual publications steadily increased. China, England, and South Korea were the most prolific countries on EIT hardware publications with productive native institutions and authors. Research topics spread out in “bio-electrical impedance imaging”, “hardware optimization”, “algorithms” and “clinical applications” (e.g., tissue, lung, brain, and oncology). Hardware research in “pulmonary” and “hemodynamic” applications focused on monitoring and were represented by silhouette recognition and dynamic imaging while research in “tumor and tissue” and “brain” applications focused on diagnosis and were represented by optimization of precision. Electrode development was a research focus through the years. Imaging precision and bioavailability of hardware optimization may be the future trend.Conclusion: Overall, system performance, particularly in the areas of system bandwidth and precision in applications may be the future directions of hardware research.

Stem cell therapy is a promising treatment for knee osteoarthritis, but few bibliometric studies have been performed on the subject. Bibliometric analysis is helpful for identifying the most influential studies in a specific field and can evaluate the global research trends in stem cell therapy for knee osteoarthritis. The Web of Science Core Collection was searched for publications from 2001 to 2021. Publication performance was analyzed using several bibliometric parameters, including VOSviewer, to identify the research landscape of trends in topics, and CiteSpace was investigated to identify the keywords that have the strongest citation bursts. From 2001 to 2021, in total, 1,345 publications explored the research on stem cells in knee osteoarthritis. The United States contributed the largest number of publications and at the top list of international collaborations. Tokyo Medical and Dental University ranked first among institutions in the overall number of articles and citations. The journal of Osteoarthritis and Cartilage had the largest number of publications. Sekiya Ichiro was the most cited author, with 32 articles. The keywords with the most frequent occurrence were “osteoarthritis,” “mesenchymal stem cells,” and “cartilage,” in descending order of frequency. “fibroblast growth factor” and “extracellular vesicle” were the first and last searched theme terms, respectively. The number of publications on stem cells for knee osteoarthritis stays growing. Cartilage repair and paracrine function are current research hotspots for the stem cell therapy mechanism. Stem cell therapy has gradually advanced from basic research to the clinical application stage.

The striking dominance of groundwater-related defects in the operational high-speed railway tunnels in China calls for swift and accurate detection and identification. Thus, it is a new attempt to detect the water-bearing defects at 5 to 10 meters via train-borne transient electromagnetic method in operating tunnels. Due to the short detection distance, the interaction between transmitting and receiving coils is more important than those normally used coils. Thus, numerical and experimental methods are combined to investigate the mutual induction. The influence of turns, current and coil size on the mutual induction and the impact of damping coefficient on the receiving system are manifested. To further verify these findings, full-scale model experiments are conducted. During these physical experiments, the detection results of different coil parameters including coil size, number of turns, and emission current are compared and analyzed. Then, a special effort to minimize the induction between transmitting and receiving coils is expended to acquire the suitable coils for close range detection in the tunnel context. Finally, in order to verify the availability of the detection system, different detection distances are conducted. It turns out that different detection distances have slight difference at the detection results, but they are still within the measuring range of the detection instrument. Obviously, these findings can provide theoretical support for the detection of water-bearing anomalies in operating tunnels and it also has reference significance for the detection of anomalies at close distance.

ObjectiveThis study explores the emerging trends and hot topics concerning applications on electrical impedance tomography (EIT) in clinical lung monitoring.MethodsPublications on EIT applications in clinical lung monitoring in 2001–2021 were extracted from the Web of Science Core Collection (WoSCC). The search strategy was “electrical impedance tomography” and “lung.” CiteSpace, a VOS viewer was used to study the citation characteristics, cooperation, and keyword co-occurrence. Moreover, co-cited reference clustering, structural variation analysis (SVA), and future research trends were presented.ResultsSix hundred and thirty-six publications were included for the final analysis. The global annual publications on clinical lung monitoring gradually increased in the last two decades. Germany contributes 32.2% of total global publications. University Medical Center Schleswig-Holstein (84 publications, cited frequency 2,205), Physiological Measurement (105 publications, cited frequency 2,056), and Inéz Frerichs (116 articles, cited frequency 3,609) were the institution, journal, and author with the largest number of article citations in the research field. “Electrical impedance tomography” (occurrences, 304), “mechanical ventilation” (occurrences, 99), and “acute respiratory distress syndrome” (occurrences, 67) were the top most three frequent keywords, “noninvasive monitoring” (Avg, pub, year: 2008.17), and “extracorporeal membrane oxygenation” (Avg, pub, year: 2019.60) were the earliest and latest keywords. The keywords “electrical impedance tomography” (strength 7.88) and co-cited reference “Frerichs I, 2017, THORAX” (strength 47.45) had the highest burst value. “Driving pressure,” “respiratory failure,” and “titration” are the three keywords still maintaining a high brush value until now. The largest and smallest cluster of the co-cited references are “obstructive lung diseases” (#0, size: 97) and “lung perfusion” (#20, size: 5). Co-cited reference “Frerichs I, 2017, THORAX” (modularity change rate: 98.49) has the highest structural variability. Categories with most and least interdisciplinary crossing are “ENGINEERING” and “CRITICAL CARE MEDICINE.”ConclusionsEIT is a valuable technology for clinical lung monitoring, gradually converting from imaging techniques to the clinic. Research hot spots may continue monitoring techniques, the ventilation distribution of acute respiratory distress syndrome (ARDS), and respiratory therapy strategies. More diversified lung function monitoring studies, such as lung perfusion and interdisciplinary crossing, are potentially emerging research trends.

Objective: Spatial and temporal ventilation distributions in patients with acute respiratory failure during high flow nasal cannula (HFNC) therapy were previously studied with electrical impedance tomography (EIT). The aim of the study was to explore the possibility of predicting HFNC failure based on various EIT-derived parameters.Methods: High flow nasal cannula failure was defined reintubation within 48 h after HFNC. EIT was performed with the patients spontaneously breathing in the supine position at the start of HFNC. EIT-based indices (comprising the global inhomogeneity index, center of ventilation, ventilation delay, rapid shallow breathing index, minute volume, and inspiration to expiration time) were explored and evaluated at three time points (prior to HFNC, T1; 30 min after HFNC started, T2; and 1 h after, T3).Results: A total of 46 subjects were included in the final analysis. Eleven subjects had failed HFNC. The time to failure was 27.8 ± 12.4 h. The ROX index (defined as SpO2/FiO2/respiratory rate) for HFNC success patients was 8.3 ± 2.7 and for HFNC failure patients, 6.2 ± 1.8 (p = 0.23). None of the investigated EIT-based parameters showed significant differences between subjects with HFNC failure and success. Further subgroup analysis indicated that a significant difference in ventilation inhomogeneity was found between ARDS and non-ARDS [0.54 (0.37) vs. 0.46 (0.28) as evaluated with GI, p < 0.01]. Ventilation homogeneity significantly improved in ARDS after 60-min HFNC treatment [0.59 (0.20) vs 0.57 (0.19), T1 vs. T3, p < 0.05].Conclusion: Spatial and temporal ventilation distributions were slightly but insignificantly different between the HFNC success and failure groups. HFNC failure could not be predicted by changes in EIT temporal and spatial indexes of ventilation distribution within the first hour. Further studies are required to predict the outcomes of HFNC.

The development of an efficient catalyst especially with a high productivity for decarboxylation of L-lysine to cadaverine, is of both industrial and economic significance. Here, we reported the synthesis of RuO2 well-confined in the supercage of FAU zeolite (RuO2@FAU) through in situ hydrothermal strategies. A set of characterizations, such as XRD, Raman, TEM, XPS, NH3-TPD and N2 physical adsorption, confirmed the successful encapsulation of RuO2 clusters (~1.5 nm) inside the FAU zeolite. RuO2@FAU had the higher cadaverine productivity of 120.9 g/L/h/mmol cat., which was almost six times that of traditionally supported ruthenium oxide catalysts (21.2 g/L/h/mmol cat.). RuO2@FAU catalysts with different ammonia exchange degrees, as well as different Si/Al ratios were further evaluated. After optimization, the highest cadaverine productivity of 480.3 g/L/h/mmol cat. was obtained. Deep analysis of the electronic properties of RuO2@FAU indicated that the surface defect structures, such as oxygen vacancies, played a vital role in the adsorption or activation of L-lysine which finally led to a boosted performance. Furthermore, the mechanism of decarboxylation of L-lysine to cadaverine was proposed.

Constructing a shield tunnel that crosses under a river poses considerable safety risks, and risk assessment is essential for guaranteeing the safety of tunnel construction. This paper studies a risk assessment system for a shield tunnel crossing under a river. Risk identification is performed for the shield tunnel, and the risk factors and indicators are determined. The relationship between the two is determined preliminarily by numerical simulation, the numerical simulation results are verified by field measurements, and a sample set is established based on the numerical simulation results. Fuzzy comprehensive evaluation and a backpropagation neural network are then used to evaluate and analyze the risk level. Finally, the risk assessment system is used to evaluate the risk for Line 5 of the Hangzhou Metro in China. Based on the evaluation results, adjustments to the slurry strength, grouting pressure, and soil chamber pressure are proposed, and the risk is mitigated effectively.

Medical electrical impedance tomography (EIT) reconstruction algorithms are approximately forty years old and are now being used to treat patients. At the same time, many advanced concepts are still active research areas, and literature regarding EIT algorithms has been presented in different professional journals and scientific conferences. Bibliometrics analysis method is used to assess the 30 years of scientific output and activity regarding EIT algorithm research from a global perspective, covering global publications, cooperation between countries, and clustering of keywords. 2467 research articles, reviews, letters, and proceeding papers published from 1987 to 2021 in English on EIT reconstruction algorithm were analyzed. Literature statistics show that the worldwide growth trend is rising, and global geographic distribution of publications in this field indicates that USA, South Korea, and China ranked as the top three prolific contributors in terms of EIT algorithm. "inverse problem" "regularization""electrode models" were research focus. Overall, EIT algorithms bibliometric research provides the current hotspots and future directions of algorithm research. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is a technique to image the electrical properties of tissue based on measuring electrical impedance by body-surface electrodes. With the progress of clinical experience, the major components of EIT instruments becomes an active area of research to improve all aspects of performance, and huge among of studies regard to EIT hardware have been presented in prestigious journals. Therefore, in this study publications on EIT hardware from 1987 to 2021 were extracted from the Web of Science Core Collection (WoSCC) database for bibliometric analysis to trace the EIT developing trends. A total of 2698 publications were analyzed, showing that the annual publications have steadily increased, and the USA was the first prolific contributor in the field of EIT hardware. Among institutions, Kyung Hee University (publications: 164; citing frequency: 3,563) contributed the most publications. "Electrical impedance tomography", "reconstruction", and "conductivity" were the top three frequent keywords. [ABSTRACT FROM AUTHOR]