Search

Abstract Most classification efforts for primary subtypes of lung adenocarcinoma (LUAD) have not yet been integrated into clinical practice. This study explores the feasibility of combining deep learning and pathomics to identify tumor invasiveness in LUAD patients, highlighting its potential clinical value in assisting junior and intermediate pathologists. We retrospectively analyzed whole slide image (WSI) data from 289 patients with surgically resected ground-glass nodules (GGNs). First, three ResNet deep learning models were used to identify tumor regions. Second, features from the best-performing model were extracted to build pathomics using machine learning classifiers. Third, the accuracy of pathomics in predicting tumor invasiveness was compared with junior and intermediate pathologists’ diagnoses. Performance was evaluated using the area under receiver operator characteristic curve (AUC). On the test cohort, ResNet18 achieved the highest AUC (0.956) and sensitivity (0.832) in identifying tumor areas, with an accuracy of 0.904; Random Forest provided high accuracy and AUC values of 0.814 and 0.807 in assessing tumor invasiveness. Pathology assistance improved diagnostic accuracy for junior and intermediate pathologists, with AUC values increasing from 0.547 to 0.759 and 0.656 to 0.769. This study suggests that deep learning and pathomics can enhance diagnostic accuracy, offering valuable support to pathologists.

In the virtual geographic environment, conducting status analysis on urban structures and similar objects is crucial for enhancing their detailed management level. However, it is challenging to directly convert the same object across various software systems with different modalities (such as spatial analysis, BIM design, numerical simulation etc.). Therefore, the effective conversion of multi-modal models becomes pivotal. Due to the characteristics of inconsistent spatial description and complex association relationship among multimodal models, resulting in low knowledge reuse rate, poor accuracy of unit mapping, and low efficiency of state sharing in the process of model conversion. Aiming at these problems, this article delves into the knowledge-driven semantic conversion techniques for multi-modal models from a geospatial viewpoint. The mapping relationships between multimodal models in terms of spatial, geometric, and semantic information were first clarified. Subsequently, a structural matching template based on knowledge reuse was established, and a knowledge-guided algorithm for multimodal model transformation was designed. Finally, using a suspension bridge as a case study, a prototype system was developed and experimental analysis was conducted. The experimental results show that the method proposed in this article can accurately convert between BIM models, numerical analysis models, and GIS scene models, with spatial coordinate accuracy controlled within 1 mm and a conversion efficiency increase of more than 10 times. This can effectively enhance the integrated performance of models in applications such as digital geospatial twin scenarios.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy and a leading cause of cancer-related deaths globally. The asymptomatic progression of early-stage HCC often results in diagnosis at advanced stages, significantly limiting therapeutic options and worsening prognosis. Immunotherapy, with immune checkpoint inhibitors (ICIs) at the forefront, has revolutionized HCC treatment. Nevertheless, tumor heterogeneity, immune evasion, and the presence of immunosuppressive components within the tumor immune microenvironment (TIME) continue to compromise its efficacy. Furthermore, resistance or non-responsiveness to ICIs in some patients underscores the urgent need to unravel the complexities of the TIME and to design innovative strategies that enhance immunotherapeutic outcomes. Emerging evidence has revealed the pivotal role of N6-methyladenosine (m6A), a prominent RNA methylation modification, in shaping the TIME in HCC. By regulating RNA stability and translation, m6A influences immune-related factors, including cytokines and immune checkpoint molecules. This modification governs PD-L1 expression, facilitating immune escape and contributing to resistance against ICIs. Advances in this field have also identified m6A-related regulators as promising biomarkers for predicting immunotherapy response and as potential therapeutic targets for optimizing treatment efficacy. This review examines the regulatory mechanisms of m6A modification within the TIME of HCC, with a focus on its impact on immune cells and cytokine dynamics. It also explores the therapeutic potential of targeting m6A pathways to improve immunotherapy efficacy and outlines emerging directions for future research. These insights aim to provide a foundation for developing novel strategies to overcome immune resistance and advance HCC treatment.

BackgroundTo accurately identify spread through air spaces (STAS) in clinical stage IA lung adenocarcinoma, our study developed a non-invasive and interpretable biomarker combining clinical and radiomics features using preoperative CT.MethodsThe study included a cohort of 1,325 lung adenocarcinoma patients from three centers, which was divided into four groups: a training cohort (n = 930), a testing cohort (n = 238), an external validation 1 cohort (n = 93), and 2 cohort (n = 64). We collected clinical characteristics and semantic features, and extracted radiomics features. We utilized the LightGBM algorithm to construct prediction models using the selected features. Quantifying the contribution of radiomics features of CT to prediction model using Shapley additive explanations (SHAP) method. The models' performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), negative predictive value (NPV), positive predictive value (PPV), sensitivity, specificity, calibration curve, and decision curve analysis (DCA).ResultsIn the training cohort, the clinical model achieved an AUC value of 0.775, the radiomics model achieved an AUC value of 0.836, and the combined model achieved an AUC value of 0.837. In the testing cohort, the AUC values of the models were 0.743, 0.755, and 0.768. In the external validation 1 cohort, the AUC values of the models were 0.717, 0.758, and 0.765, while in the external validation 2 cohort, 0.725, 0.726 and 0.746. The DeLong test results indicated that the combined model outperformed the clinical model (p

Abstract Background The neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) has emerged as a promising biomarker for assessing inflammation and lipid dysregulation. Increasing evidence indicates that these metabolic disturbances play a crucial role in the development of metabolic dysfunction-associated steatotic liver disease(MASLD). This study aims to investigate the association between NHR, MASLD, and liver fibrosis. Methods This cross-sectional study analyzed data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES). Weighted multivariate logistic regression models were used to investigate the association between NHR and both MASLD and liver fibrosis. Smoothed curve fitting and threshold effect analysis were performed to detect potential nonlinear relationships. Subgroup analyses were conducted to assess the consistency of these associations across different groups. Results The study involved 4,761 participants. We observed a significant positive association between NHR and MASLD (OR = 1.20, 95% CI: 1.09–1.31). However, there was no significant association between NHR and liver fibrosis (OR = 1.01; 95% CI: 0.94–1.09). The analysis of smoothed curve fitting and threshold effect revealed an inverted U-shaped relationship between NHR and MASLD, with a turning point at 5.63. Conclusion Our findings indicate a positive correlation between elevated NHR levels and MASLD prevalence. However, we did not observe a significant association between NHR and liver fibrosis prevalence. Further prospective research is needed to validate these findings in a longitudinal setting.

Abstract Background Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) recommended for the patients with subsolid nodule in early lung cancer stage is not routinely. The clinical value and impact in patients with EGFR mutation on survival outcomes is further needed to be elucidated to decide whether the application of EGFR-TKIs was appropriate in early lung adenocarcinoma (LUAD) stage appearing as subsolid nodules. Materials and methods The inclusion of patients exhibiting clinical staging of IA-IIB subsolid nodules. Clinical information, computed tomography (CT) features before surgical resection and pathological characteristics including tertiary lymphoid structures of the tumors were recorded for further exploration of correlation with EGFR mutation and prognosis. Results Finally, 325 patients were enrolled into this study, with an average age of 56.8 ± 9.8 years. There are 173 patients (53.2%) harboring EGFR mutation. Logistic regression model analysis showed that female (OR = 1.944, p = 0.015), mix ground glass nodule (OR = 2.071, p = 0.003, bubble-like lucency (OR = 1.991, p = 0.003) were significant risk factors of EGFR mutations. Additionally, EGFR mutations were negatively correlated with TLS presence and density. Prognosis analysis showed that the presence of TLS was associated with better recurrence-free survival (RFS)(p = 0.03) while EGFR mutations were associated with worse RFS(p = 0.01). The RFS in patients with TLS was considerably excel those without TLS within EGFR wild type group(p = 0.018). Multivariate analyses confirmed that EGFR mutation was an independent prognostic predictor for RFS (HR = 3.205, p = 0.037). Conclusions In early-phase LUADs, subsolid nodules with EGFR mutation had specific clinical and radiological signatures. EGFR mutation was associated with worse survival outcomes and negatively correlated with TLS, which might weaken the positive impact of TLS on prognosis. Highly attention should be paid to the use of EGFR-TKI for further treatment as agents in early LUAD patients who carrying EGFR mutation.

Crowd evacuation simulation using virtual reality (VR) is significant for digital emergency response construction. However, existing evacuation simulation studies suffer from poor adaptation to complex environments, inefficient evacuations, and poor simulation effects and do not fully consider the impacts of specific disaster environments on crowd evacuation. To more realistically express the crowd evacuation results obtained under the influence of fire environments and the subjective consciousness of pedestrians in subway stations, we designed a dynamic pedestrian evacuation path planning method under multiple constraints, analysed the influences of an ‘environmental role’ and a ‘subjective initiative’ on crowd evacuation, established an improved social force model (ISFM)-based crowd evacuation simulation method in VR, developed a prototype system and conducted experimental analyses. The experimental results show that the crowd evacuation time of the ISFM is affected by the disaster severity. In simulation experiments without disaster scenarios, the improved model's crowd evacuation efficiency improved by averages of 12.53% and 15.37% over the commercial Pathfinder software and the original social force model, respectively. The method described herein can effectively support real-time VR crowd evacuation simulation under multiexit and multifloor conditions and can provide technical support for emergency evacuation learning and management decision analyses involving subway fires.

Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene. However, pre-rendering methods in the existing research work are usually used to implement holographic visualization, which is static display. The above-mentioned methods for static display have many shortcomings, such as poor adaptability, low rendering efficiency and lack of real-time. A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction. Firstly, a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes. Secondly, a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics. Finally, a prototype system was developed, and the corresponding experimental analysis was completed. The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction. The amount of scene data can be reduced to more than 30%, which significantly improves rendering efficiency and reduces glare.

Rationale and objectives: This study aimed to investigate the effect of intestinal dysbiosis on the hippocampal volume using proton magnetic resonance spectroscopy (1H-MRS) in a type 2 diabetes mellitus (T2DM) rat model. Materials and methods: We established a T2DM animal model with high-fat diet and streptozotocin (HFD/STZ) administration to Sprague-Dawley rats. Short-term ceftriaxone sodium administration was used to establish a T2DM intestinal dysbiosis (T2DM-ID) model. After establishing the model, fecal microbiota were detected using 16S rRNA sequencing. The models were then subjected to magnetic resonance imaging (MRI). Associations between MRI findings and fecal microbiota were evaluated. Results: Magnetic resonance imaging (MRI) showed that the bilateral hippocampal voxel value and N-acetylaspartate (NAA) level were lower in the experimental group than in the normal control (NC) group (p

Abstract. Background:. Management of gastric leak after sleeve gastrectomy (SG) is challenging due to its unpredictable outcomes. We aimed to summarize the characteristics of SG leaks and analyze interventions and corresponding outcomes in a real-world setting. Methods:. To retrospectively review of 15,721 SG procedures from 2010 to 2020 based on a national registry. A cumulative sum analysis was used to identify a fitting curve of gastric leak rate. The Kaplan-Meier method and log-rank tests were performed to calculate and compare the probabilities of relevant outcomes. The logistic regression analysis was conducted to determine the predictors of acute leaks. Results:. A total of 78 cases of SG leaks were collected with an incidence of 0.5% (78/15,721) from this registry (6 patients who had the primary SG in non-participating centers). After accumulating 260 cases in a bariatric surgery center, the leak rate decreased to a stably low value of under 1.17%. The significant differences presented in sex, waist circumference, and the proportion of hypoproteinemia and type 2 diabetes at baseline between patients with SG leak and the whole registry population (P = 0.005, = 0.026, 50%, whereas the cumulative healing probability in the patients who required surgical treatment was significantly lower than those requring non-surgical treatment (P = 0.013). Precise dissection in the His angle area was independently associated with a lower acute leak rate, whereas preservation ≥2 cm distance from the His angle area was an independent risk factor. Conclusions:. Male sex, elevated waist circumference, hypoproteinaemia, and type 2 diabetes are risk factors of gastric leaks after SG. Optimizing surgical techniques, including precise dissection of His angle area and preservation of smaller gastric fundus, should be suggested to prevent acute leaks.

Under the background of “dual carbon” development goals, the rapid expansion of internet data centers driven by advancements in 5G technology has led to increased energy consumption and elevated heat densities within the server rooms in these facilities. In this study, the modular data center is taken as the research object for the purpose of figuring out a way to improve the thermal environment of the computer room, reduce power consumption, and ensure the safe and stable running of servers. To this end, this study established an airflow organization model for the modular data center and verified this model through experimental methods. Computational Fluid Dynamics (CFD) simulations were employed to investigate the effects of raised floor height, floor opening rate, and cold/hot air channel closure on airflow organization. Furthermore, the efficiency of airflow organization was evaluated using entransy loss metrics. The results show that optimal airflow conditions are achieved when the height of the raised floor is 600–800 mm, the opening rate is 40%, and the combined opening is 40%. Additionally, the closure of either the cold channel or both the cold and hot channels significantly improves airflow performance. Specifically, cold channel closure is recommended for new data centers with underfloor air supply systems, while combined cold and hot channel closure is suitable for data centers with high power density and extended air supply distances.

ObjectivesThis study aimed to explore the value of an artificial intelligence (AI)-assisted diagnostic system in the prediction of pulmonary nodules.MethodsThe AI system was able to make predictions of benign or malignant nodules. 260 cases of solitary pulmonary nodules (SPNs) were divided into 173 malignant cases and 87 benign cases based on the surgical pathological diagnosis. A stratified data analysis was applied to compare the diagnostic effectiveness of the AI system to distinguish between the subgroups with different clinical characteristics.ResultsThe accuracy of AI system in judging benignity and malignancy of the nodules was 75.77% (p

As an important technology of digital construction, real 3D models can improve the immersion and realism of virtual reality (VR) scenes. The large amount of data for real 3D scenes requires more effective rendering methods, but the current rendering optimization methods have some defects and cannot render real 3D scenes in virtual reality. In this study, the location of the viewing frustum is predicted by a Kalman filter, and eye-tracking equipment is used to recognize the region of interest (ROI) in the scene. Finally, the real 3D model of interest in the predicted frustum is rendered first. The experimental results show that the method of this study can predict the frustrum location approximately 200 ms in advance, the prediction accuracy is approximately 87%, the scene rendering efficiency is improved by 8.3%, and the motion sickness is reduced by approximately 54.5%. These studies help promote the use of real 3D models in virtual reality and ROI recognition methods. In future work, we will further improve the prediction accuracy of viewing frustums in virtual reality and the application of eye tracking in virtual geographic scenes.

Objective: To discuss the accuracy of the automatic measurement of the volume of pure ground glass nodules (pGGN) by the artificial intelligence pulmonary nodule detection software and the influencing factors of the measurement error. Methods: 170 pGGNs from 90 patients who underwent routine chest CT scan from January 1 to 31, 2021 in our hospital were selected in this retrospective study. The original CT scan data (including 1 mm thin-slice images) was sent to the AI server of Inference Technology to automatically measure the volume of lung nodules and record the measurement data. Two senior chest imaging diagnosticians manually carried out pGGNs layer-by-layer measurement and added the volume value, then took the average of three measurements as the "gold standard" data to compare with the AI measurement results, and then analyzed the influence of pGGN location, size, proximity and other factors on the AI measurement error. SPSS 26.0 was used for statistical analysis. Results: Among the total 170 pGGNs in the 90 patients in this study, 49 (28.82%) were in the right upper lobe, 21 (12.35%) were in the right middle lobe, 27 (15.88%) were in the right lower lobe, and left upper lobe 49 patients (28.82%) were involved, and 24 patients were in the lower lobe of the left lung (14.12%). Among the adjacent relationships of pGGN, 82 (48.24%) were completely located in the lung parenchyma without adjacency, 29 (17.06%) were close to the blood vessel, and 59 (34.70%) were close to the pleura. (1) There was no statistically significant difference in the volume values of pGGNs between two observers and the same observer at different time points. (2) For the measurement of the same pGGN, there was no statistically significant difference between the results of automatic measurement by AI and manual measurement and the correlation between the two was quite high (r=0.981), and the agreement was also very high (ICC value is 0.987). (3) The size, location, and adjacent relationship of pGGN lesions were not statistically significant for the error of AI volume measurement. Conclusions: The InferRead lung nodule measurement software shows high accuracy in the measurement of lung pGGN three-dimensional volume, which can be applied in clinical lung nodule diagnosis and related research. (2) For the measurement of the same pGGN volume, there was no difference between the results of automatic measurement by AI and manual measurement, and the correlation between them was quite high (r = 0.981), and the consistency was high (ICC value is 0.987). (3) The volume size, occurrence position and adjacent relationship of pGGN have no statistical significance on the error of AI volume measurement. Conclusion: The InferRead lung nodule detection software shows high accuracy in measuring the three-dimensional volume of lung pGGN, and can be applied to clinical diagnosis and related research of lung nodules.

BackgroundCalcium and magnesium are essential minerals that have significant roles in nerve function and regulation. There may be a correlation between dietary calcium and magnesium intake and peripheral neuropathy. However, this relationship remains unclear and requires further study.MethodsData from 7,726 participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2004 were analyzed in this study. The relationship between total dietary calcium and magnesium intake, as well as each quantile, and peripheral neuropathy was analyzed using a multifactor logistic regression model. To illustrate the dose–response relationship between calcium and magnesium intake and peripheral neuropathy, we utilized a restricted cubic spline (RCS) plot.ResultsOur analysis found a positive correlation between dietary intake of calcium and magnesium and peripheral neuropathy (calcium: OR 1.000, 95% CI 1.000–1.000; magnesium: OR 1.001, 95% CI 1.00–1.002). Participants in the first and third quantiles of dietary calcium intake had a significantly higher incidence of peripheral neuropathy than those in the second quantile (OR 1.333, 95% CI 1.034–1.719, OR 1.497, 95% CI 1.155–1.941). Those in the first and third quantiles of dietary magnesium intake also had a significantly higher incidence of peripheral neuropathy than those in the second quantile (OR 1.275, 95% CI 1.064–1.528, OR 1.525, 95% CI 1.231–1.890). The restricted cubic spline analysis revealed a U-shaped nonlinear relationship between dietary intake of calcium and magnesium and peripheral neuropathy.ConclusionThe study found a U-shaped non-linear relationship between dietary calcium and magnesium intake levels and peripheral neuropathy, indicating that both excessive and insufficient intake of calcium and magnesium can increase the incidence of peripheral neuropathy.

Abstract Background Part-solid nodules (PSNs) have gradually shifted to defining special clinical subtypes. Commonly, the solid portions of PSNs show various radiological morphologies, of which the corresponding pathological basis and prognosis are unclear. We conducted a radiological–pathological evaluation to determine the histopathologic basis of different consolidation radiographic morphologies related to prognosis. Materials and methods A cohort of 275 patients with a surgical pathological diagnosis of lung adenocarcinoma were enrolled. Preoperative computed tomography (CT) images of the PSNs were recorded and assessed. A panel of 103 patients with complete pathological specimens was selected to examine the radiological–pathological associations, and follow-up was performed to identify the prognosis. Results Of the 275 patients, punctate consolidation was observed radiologically in 43/275 (15.7%), stripe consolidation in 68/275 (24.7%), and irregular consolidation in 164/275 (59.6%) patients. The radiological morphology of the solid components was significantly associated with the histopathological subtypes (P

Abstract Background Genotype status of glioma have important significance to clinical treatment and prognosis. At present, there are few studies on the prediction of multiple genotype status in glioma by method of multi-sequence radiomics. The purpose of the study is to compare the performance of clinical features (age, sex, WHO grade, MRI morphological features etc.), radiomics features from multi MR sequence (T2WI, T1WI, DWI, ADC, CE-MRI (contrast enhancement)), and a combined multiple features model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q of glioma. Methods In this retrospective analysis, 81 glioma patients confirmed by histology were enrolled in this study. Five MRI sequences were used for radiomic feature extraction. Finally, 107 features were extracted from each sequence on Pyradiomics software, separately. These included 18 first-order metrics, such as the mean, standard deviation, skewness, and kurtosis etc., 14 shape features and second-order metrics including 24 grey level run length matrix (GLCM), 16 grey level run length matrix (GLRLM), 16 grey level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix (NGTDM), and 14 grey level dependence matrix (GLDM). Then, Univariate analysis and LASSO (Least absolute shrinkage and selection operator regression model were used to data dimension reduction, feature selection, and radiomics signature building. Significant features (p

Abstract Apoptosis is a critical event in the pathogenesis of lung ischemia/reperfusion (I/R) injury. Sirtuin 3 (SIRT3), an important deacetylase predominantly localized in mitochondria, regulates diverse physiological processes, including apoptosis. However, the detailed mechanisms by which SIRT3 regulates lung I/R injury remain unclear. Many polyphenols strongly regulate the sirtuin family. In this study, we found that a polyphenol compound, procyanidin B2 (PCB2), activated SIRT3 in mouse lungs. Due to this effect, PCB2 administration attenuated histological lesions, relieved pulmonary dysfunction, and improved the survival rate of the murine model of lung I/R injury. Additionally, this treatment inhibited hypoxia/reoxygenation (H/R)-induced A549 cell apoptosis and rescued Bcl-2 expression. Using Sirt3-knockout mice and specific SIRT3 knockdown in vitro, we further found that SIRT3 strongly protects against lung I/R injury. Sirt3 deficiency or enzymatic inactivation substantially aggravated lung I/R-induced pulmonary lesions, promoted apoptosis, and abolished PCB2-mediated protection. Mitochondrial pyruvate kinase M2 (PKM2) inhibits apoptosis by stabilizing Bcl-2. Here, we found that PKM2 accumulates and is hyperacetylated in mitochondria upon lung I/R injury. By screening the potential sites of PKM2 acetylation, we found that SIRT3 deacetylates the K433 residue of PKM2 in A549 cells. Transfection with a deacetylated mimic plasmid of PKM2 noticeably reduced apoptosis, while acetylated mimic transfection abolished the protective effect of PKM2. Furthermore, PKM2 knockdown or inhibition in vivo significantly abrogated the antiapoptotic effects of SIRT3 upregulation. Collectively, this study provides the first evidence that the SIRT3/PKM2 pathway is a protective target for the suppression of apoptosis in lung I/R injury. Moreover, this study identifies K433 deacetylation of PKM2 as a novel modification that regulates its anti-apoptotic activity. In addition, PCB2-mediated modulation of the SIRT3/PKM2 pathway may significantly protect against lung I/R injury, suggesting a novel prophylactic strategy for lung I/R injury.

It is essential to establish a digital twin scene, which helps to depict the dynamically changing geographical environment accurately. Digital twins could improve the refined management level of intelligent tunnel construction; however, research on geographical twin models primarily focuses on modeling and visual description, which has low analysis efficiency. This paper proposes a knowledge-guided intelligent analysis method for the geometric deformation of tunnel excavation profile twins. Firstly, a dynamic data-driven knowledge graph of tunnel excavation twin scenes was constructed to describe tunnel excavation profile twin scenes accurately. Secondly, an intelligent diagnosis algorithm for geometric deformation of tunnel excavation contour twins was designed by knowledge guidance. Thirdly, multiple visual variables were jointly used to support scene fusion visualization of tunnel excavation profile twin scenes. Finally, a case was selected to implement the experimental analysis. The experimental results demonstrate that the method in this article can achieve an accurate description of objects and their relationships in tunnel excavation twin scenes, which supports rapid geometric deformation analysis of the tunnel excavation profile twin. The speed of geometric deformation diagnosis is increased by more than 90% and the cognitive efficiency is improved by 70%. The complexity and difficulty of the deformation analysis operation are reduced, and the diagnostic analysis ability and standardization of the geographic digital twin model are effectively improved.

Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

Abstract Objectives To retrospectively analyse the potential influencing factors of CT-guided hook wire localization failure prior to thoracoscopic resection surgery of ground glass nodules (GGNs), and determine the main risk elements for localization failure. Methods In all, 372 patients were included in this study, with 21 patients showing localization failure. The related parameters of patients, GGNs, and localization were analysed through univariate and multiple logistic regression analysis to determine the risk factors of localization failure. Results Univariate logistic regression analysis indicated that trans-fissure (odds ratio [OR] 4.896, 95% confidence interval [CI] 1.489–13.939); trans-emphysema (OR 3.538, 95% CI 1.343–8.827); localization time (OR 0.956, 95% CI 0.898–1.019); multi-nodule localization (OR 2.597, 95% CI 1.050–6.361); and pneumothorax (OR 10.326, 95% CI 3.414–44.684) were risk factors for localization failure, and the p-values of these factors were

Adoptive cell therapy (ACT) using chimeric antigen receptor (CAR)-modified T cells has revolutionized the field of immune-oncology, showing remarkable efficacy against hematological malignancies. However, its success in solid tumors is limited by factors such as easy recurrence and poor efficacy. The effector function and persistence of CAR-T cells are critical to the success of therapy and are modulated by metabolic and nutrient-sensing mechanisms. Moreover, the immunosuppressive tumor microenvironment (TME), characterized by acidity, hypoxia, nutrient depletion, and metabolite accumulation caused by the high metabolic demands of tumor cells, can lead to T cell “exhaustion” and compromise the efficacy of CAR-T cells. In this review, we outline the metabolic characteristics of T cells at different stages of differentiation and summarize how these metabolic programs may be disrupted in the TME. We also discuss potential metabolic approaches to improve the efficacy and persistence of CAR-T cells, providing a new strategy for the clinical application of CAR-T cell therapy.

Inhibitor of nuclear factor kappa-B kinase subunit beta (IKKβ) is one of important kinases in inflammation to phosphorylate inhibitor of nuclear factor kappa-B (IκBα) and then activate nuclear factor kappa-B (NF-κB). Inhibition of IKKβ has been a therapeutic strategy for inflammatory and autoimmune diseases. Here we report that IKKβ is constitutively activated in healthy donors and healthy IkkβC46A (cysteine 46 mutated to alanine) knock-in mice although they possess intensive IKKβ–IκBα–NF-κB signaling activation. These indicate that IKKβ activation probably plays homeostatic role instead of causing inflammation. Compared to IkkβWT littermates, lipopolysaccharides (LPS) could induce high mortality rate in IkkβC46A mice which is correlated to breaking the homeostasis by intensively activating p-IκBα–NF-κB signaling and inhibiting phosphorylation of 5ʹ adenosine monophosphate-activated protein kinase (p-AMPK) expression. We then demonstrated that IKKβ kinase domain (KD) phosphorylates AMPKα1 via interacting with residues Thr183, Ser184, and Thr388, while IKKβ helix–loop–helix motifs is essential to phosphorylate IκBα according to the previous reports. Kinase assay further demonstrated that IKKβ simultaneously catalyzes phosphorylation of AMPK and IκBα to mediate homeostasis. Accordingly, activation of AMPK rather than inhibition of IKKβ could substantially rescue LPS-induced mortality in IkkβC46A mice by rebuilding the homeostasis. We conclude that IKKβ activates AMPK to restrict inflammation and IKKβ mediates homeostatic function in inflammation via competitively phosphorylating AMPK and IκBα.

Natural plants are rich sources of various bioactive compounds. Consequently, the efficiently isolation of these bioactive components has always attracted considerable attention. Our work aims to demonstrate a framework for bioactivity guided isolation of potential effective compounds from the complex food materials. We demonstrated its application for isolation of phenolic compounds with anti-proliferative activity against colorectal cancer cells (CRCs) from Citrus aurantium L. Firstly, phenolic rich fraction was successfully identified as the main effective components that could simultaneously suppress the growth of CRCs and inhibit Wnt signaling. In order to obtain the bioactive phenolic constituents, a detailed study was performed by optimizing the purification conditions. Two phenolic rich fractions (40% and 60% ethanol elution fractions) were then obtained by AB-8 macroporous resins under optimized condition. Finally, the main components (65 compounds) were tentatively identified from the 40% ethanol eluant by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) analysis. Notably, there were five of the phytochemicals (Feruloylagmatine, Haploside C, Sagittatin A, Linderagalactone C and Koparin-2′-methyl ether) which were hitherto unidentified in Citrus aurantium L. fruit. In conclusion, this study showed that under the principle of bioactivity guided strategy, phenolic constituents with potential anti-CRCs activity were isolated from Citrus aurantium L.

BackgroundWhile the End Tuberculosis (TB) Strategy has been implemented worldwide, the cause of the TB epidemic is multifactorial and not fully understood. ObjectiveThis study aims to investigate the risk factors of TB and incorporate these factors to forecast the incidence of TB infection across different age groups in Sichuan, China. MethodsCorrelation and linear regression analyses were conducted to assess the relationships between TB cases and ecological factors, including environmental, economic, and social factors, in Sichuan Province from 2006 to 2017. The transfer function-noise model was used to forecast trends, considering both time and multifactor effects. ResultsFrom 2006 to 2017, Sichuan Province had a reported cumulative incidence rate of 1321.08 cases per 100,000 individuals in male patients and 583.04 cases per 100,000 individuals in female patients. There were significant sex differences in the distribution of cases among age groups (trend χ225=12,544.4; P

Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high complexity of mountainous road scene modeling and low visualisation efficiency. It is challenging to construct the digital twin scenarios efficiently for mountain highways. To solve this problem, this article proposes a knowledge-guided fusion expression method for digital twin scenes of mountain highways. First, we explore the expression features and interrelationships of mountain highway scenes to establish the knowledge graph of mountain highway scenes. Second, by utilizing scene knowledge to construct spatial semantic constraint rules, we achieve efficient fusion modeling of basic geographic scenes and dynamic and static ancillary facilities, thereby reducing the complexity of scene modeling. Finally, a multi-level visualisation publishing scheme is established to improve the efficiency of scene visualisation. On this basis, a prototype system is developed, and case experimental analysis is conducted to validate the research. The results of the experiment indicate that the suggested method can accomplish the fusion modelling of mountain highway scenes through knowledge guidance and semantic constraints. Moreover, the construction time for the model fusion is less than 5.7 ms; meanwhile, the dynamic drawing efficiency of the scene is maintained above 60 FPS. Thus, the construction of twinned scenes can be achieved quickly and efficiently, the effect of replicating reality with virtuality is accomplished, and the informatisation management capacity of mountain highways is enhanced.

ObjectiveTo investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index.MethodsA total of 107 pulmonary nodules were prospectively recruited and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical feature model alone to test its diagnostic validity, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic validity.ResultsThe nomogram was established using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under the ROC and calibration curve, which showed the best performance, area under the curve (AUC) = 0.982, 95% CI = 0.940–1.000, compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using the decision curve analysis (DCA) and using the nomogram for benign and malignant pulmonary nodules, and preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit.ConclusionThis study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than the radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant pulmonary nodules.

Due to the characteristics of aggressiveness and high risk of postoperative recurrence, non-small cell lung cancer (NSCLC) is a serious hazard to human health, accounting for 85% of all lung cancer cases. Drug therapies, including chemotherapy, targeted therapy and immunotherapy, are effective treatments for NSCLC in clinics. However, most patients ultimately develop drug resistance, which is also the leading cause of treatment failure in cancer. To date, the mechanisms of drug resistance have yet to be fully elucidated, thus original strategies are developed to overcome this issue. Emerging studies have illustrated that circular RNAs (circRNAs) participate in the generation of therapeutic resistance in NSCLC. CircRNAs mediate the modulations of immune cells, cytokines, autophagy, ferroptosis and metabolism in the tumor microenvironment (TME), which play essential roles in the generation of drug resistance of NSCLC. More importantly, circRNAs function as miRNAs sponges to affect specific signaling pathways, directly leading to the generation of drug resistance. Consequently, this review highlights the mechanisms underlying the relationship between circRNAs and drug resistance in NSCLC. Additionally, several therapeutic drugs associated with circRNAs are summarized, aiming to provide references for circRNAs serving as potential therapeutic targets in overcoming drug resistance in NSCLC.

ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC.ResultsIn patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model.ConclusionFunctional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM.

Abstract Background Part of tuberculosis (TB) patients were missed if symptomatic screening was based on the main TB likely symptoms. This study conducted to compare the yield and relative costs of different TB screening algorithms in active case-finding in the whole population in China. Methods The study population was screened based on the TB likely symptoms through a face-to-face interview in selected 27 communities from 10 counties of 10 provinces in China. If the individuals had any of the enhanced TB likely symptoms, both chest X-ray and sputum tests were carried out for them furtherly. We used the McNemar test to analyze the difference in TB detection among four algorithms in active case-finding. Of four algorithms, two were from WHO recommendations including 1a/1c, one from China National Tuberculosis Program, and one from this study with the enhanced TB likely symptoms. Furthermore, a two-way ANOVA analysis was performed to analyze the cost difference in the performance of active case-finding adjusted by different demographic and health characteristics among different algorithms. Results Algorithm with the enhanced TB likely symptoms defined in this study could increase the yield of TB detection in active case-finding, compared with algorithms recommended by WHO (p

ObjectiveTumor spread through air spaces (STAS) has recently been reported as a novel invasive pattern in lung adenocarcinoma. Thus, this study aimed to investigate the clinicopathological and radiological features in invasive lung adenocarcinoma with tumor STAS.MethodsData of 503 invasive lung adenocarcinoma patients who underwent surgery between 1 January 2015 and 31 December 2021 were collected. The correlations between STAS presence and clinicopathological and radiological characteristics were analyzed. Statistical analysis was performed using SPSS 22.0.ResultsAmong the 503 patients with invasive adenocarcinoma, 247 (47.9%) and 262 (52.1%) patients were positive and negative for STAS, respectively. Compared to STAS-negative adenocarcinoma, STAS was more common in papillary, micropapillary, and solid tumors (p < 0.01); STAS was associated with advanced pT (p = 0.024), pN (p < 0.001), and pTNM (p < 0.001) stage, more lymph node metastases (p < 0.01), more pleural invasion (p < 0.01), and more neurovascular invasion (p = 0.025). The maximum diameter (p < 0.01), the maximum diameters of the solid component (p < 0.01), and the consolidation/tumor ratio (CTR, p < 0.01) were significantly larger in STAS-positive than in STAS-negative adenocarcinoma. Other common computed tomography (CT) features of adenocarcinomas, i.e., lobulation (p < 0.01), spiculation (p < 0.01), vacuole (p < 0.01), air bronchogram (p = 0.020), vascular convergence (p < 0.01), and pleural indentation (p < 0.01) were significantly associated with STAS. In a multivariable analysis, the maximal diameter of the solid component (odds ratio [OR], 2.505; 95% confidence interval [CI], 1.886–3.329), vacuole (OR, 3.301; 95% CI, 1.822–5.980), and spiculation (OR, 2.162; 95% CI, 1.221–3.829) were independent predictors of STAS. The area under the curve (AUC) of the maximal diameter of the solid component was 0.757 (95% CI, 0.714–0.799; p < 0.001), the sensitivity was 73.9%, and the specificity was 69.1% at a cutoff value of 1.18 cm.ConclusionSTAS was significantly correlated with several invasive clinicopathological and radiological characteristics, and the maximal diameter was an independent predictor of STAS. These results will prove helpful in identifying STAS-positive adenocarcinoma by CT before surgical resection.

ObjectivePoor prognosis and limited treatments of liver metastases from non–small‐cell lung cancer (NSCLC) after radical surgery are critical issues. The current study aimed to evaluate the efficacy and safety of CalliSpheres® microsphere transarterial chemoembolization (CSM-TACE) plus 125I brachytherapy in these patients.MethodsA total of 23 patients with liver metastases from NSCLC after radical surgery were included. All patients received CSM-TACE 1–3 times, then 125I brachytherapy was carried out following the last CSM-TACE. Complete response (CR), objective response rate (ORR), disease control rate (DCR), survival, and adverse events were evaluated.ResultsCR, ORR and DCR were 43.5%, 87.0%, and 100%, respectively, at three months; furthermore, they were 78.3%, 100%, and 100% accordingly at six months. Moreover, most European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (QLQ-C30) subscales of functions (including physical and emotional function) and symptoms (including pain, nausea, and vomiting) were generally improved at three months (all P < 0.05). Furthermore, median progression-free survival (PFS) was 14.0 [95% confidence interval (CI): 10.4–17.6] months, with a 1-year PFS rate of 62.9%, but the 2-year PFS rate was not reached. Moreover, the median overall survival (OS) was 22.0 (95% CI: 16.8–27.2) months, with a 1-year OS rate of 91.3% and a 2-year OS rate of 43.5%. Additionally, the main adverse events included fever (100%), pain (65.2%), liver function impairment (65.2%), fatigue (56.5%), and nausea and vomiting (52.2%), which were all categorized as grade 1–2.ConclusionCSM-TACE plus 125I brachytherapy is effective and safe in patients with liver metastases from NSCLC after radical surgery, providing a potentially optimal option in these patients.

This study aimed to compare efficacy and safety of HepaSpheres and CalliSpheres in unresectable large hepatocellular carcinoma (HCC) patients. One hundred and twenty-seven unresectable large HCC patients receiving drug-eluting bead transarterial chemoembolization (DEB-TACE) treatment with CalliSpheres or HepaSpheres microspheres were analyzed. Treatment response, Karnofsky performance status (KPS) score, adverse events, main liver function indexes, time to progression (TTP), and overall survival (OS) were analyzed. Objective response rate (82.7% vs. 63.8%, p=.030) and disease control rate (100.0% vs. 91.5%, p=.030) were increased in CalliSpheres group compared to HepaSpheres group at 1 month after treatment, while no difference was found between the two groups regarding treatment response at 3 or 6 months post treatment (all p>.05). The KPS score at 1, 3, and 6 months was similar between the two groups (all p>.05). As for the liver function, the ALT, AST, ALB, and TBIL levels at 7 and 30 days were of no difference between the two groups (all p>.05). In addition, the adverse events including nausea/vomiting, pain, fever, myelosuppression, biloma, and abscess were of no difference between the two groups, either (all p>.05). In terms of survival profile, there was no difference regarding TTP (6.3 months (95%CI: 5.9–6.6 months) vs. 6.0 months (95%CI: 5.6–6.4 months), p=.082) or OS (23.0 months (95%CI: 20.1–25.9 months) vs. 22.0 months (95%CI: 20.2–23.8 months), p=.571) between the two groups. In conclusion, CalliSpheres seems to be superior in short-term efficacy and equal in long-term efficacy as well as safety compared to HepaSpheres for DEB-TACE treatment in unresectable large HCC patients.

Abstract Background The importance of lipid mediators in allergic diseases has been long recognized, whereas little is known about their role in allergic bronchopulmonary aspergillosis (ABPA). We investigated whether lipid mediators are associated with ABPA. Methods We recruited 12 ABPA patients, 23 asthma patients and 12 healthy control in our study. Serum of 11 ABPA patients were collected before and following treatment. 36 polyunsaturated fatty acid metabolites were measured in serum samples by using liquid chromatography–mass spectrometry. This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University, with ethics number GYFYY-2016-73. Results Levels of arachidonic acid (AA), 15(S)-hydroxyeicosatetraenoic acid (HETE), 12(S)-HETE, 8(S)-HETE, 5(S)-HETE, LTB4, PGB2, 12(S)-hydroxyeicosapentaenoic acid (HEPE), 12-hydro-xyheptadecatrienoic acid (HHTrE) were significantly higher in ABPA patients than that in HC groups. Compared with asthma group, ABPA group expressed lower levels of 15(S)-hy-droperoxyeicosatetraenoic acid (HPETE), 5(S)-HPETE, 13(S)-hydroperoxyoctadecadienoic acid (HPODE) and 9(S)-HPODE. In APBA patients, AA level was positively correlated with serumtotal IgE (tIgE). The levels of 12(S)-HPETE, 15(S)-HEPE and 12(S)-HEPE correlated with Asp-ergillus fumigatus specific IgE(A. fumigatus sIgE) positively. Peripheral blood eosinophilia correlated with high levels of 12(S)-HETE and 15(S)-HETE. In addition, the serum levels of15(S)-HETE and 12(S)-HETE in ABPA subjects both declined with the decrease of tIgE, A. fumigatus sIgE and sIgG concentrations after treatment. Conclusions We present data regarding the role of polyunsaturated fatty acid metabolites in APBA for the first time. Most of the tested metabolites increased in ABPA when co-mpared with healthy controls and 15(S)-HETE and 12(S)-HETE may play a role in the pat-hogenesis of ABPA. These findings can provide new ideas for diagnosis, therapy and mon-itor of ABPA.

ObjectiveTo explore the value of a predictive model combining the multiparametric magnetic resonance imaging (mpMRI) radiomics score (RAD-score), clinicopathologic features, and morphologic features for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in invasive breast carcinoma of no specific type (IBC-NST).MethodsWe enrolled, retrospectively and consecutively, 206 women with IBC-NST who underwent surgery after NAC and obtained pathological results from August 2018 to October 2021. Four RAD-scores were constructed for predicting the pCR based on fat-suppression T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI+C) and their combination, which was called mpMRI. The best RAD-score was combined with clinicopathologic and morphologic features to establish a nomogram model through binary logistic regression. The predictive performance of the nomogram was evaluated using the area under receiver operator characteristic (ROC) curve (AUC) and calibration curve. The clinical net benefit of the model was evaluated using decision curve analysis (DCA).ResultsThe mpMRI RAD-score had the highest diagnostic performance, with AUC of 0.848 among the four RAD-scores. T stage, human epidermal growth factor receptor-2 (HER2) status, RAD-score, and roundness were independent factors for predicting the pCR (P < 0.05 for all). The combined nomogram model based on these factors achieved AUCs of 0.930 and 0.895 in the training cohort and validation cohort, respectively, higher than other models (P < 0.05 for all). The calibration curve showed that the predicted probabilities of the nomogram were in good agreement with the actual probabilities, and DCA indicated that it provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and validation datasets.ConclusionThe combined nomogram model based on the mpMRI RAD-score combined with clinicopathologic and morphologic features may improve the predictive performance for the pCR of NAC in patients with IBC-NST.

Objective: Cognitive impairment (CI) is a common neurological complication in patients with end-stage renal disease undergoing maintenance hemodialysis (MHD). Brain network analysis based on graph theory is a promising tool for studying CI. Therefore, the purpose of this study was to analyze the changes of functional brain networks in patients on MHD with and without CI by using graph theory and further explore the underlying neuropathological mechanism of CI in these patients.Methods: A total of 39 patients on MHD (19 cases with CI and 20 without) and 25 healthy controls (HCs) matched for age, sex, and years of education were enrolled in the study. Resting-state functional magnetic resonance imaging (rs-fMRI) and T1-weighted high-resolution anatomical data were obtained, and functional brain networks for each subject were constructed. The brain network parameters at the global and regional levels were calculated, and a one-way analysis of covariance was used to compare the differences across the three groups. The associations between the changed graph-theory parameters and cognitive function scores in patients on MHD were evaluated using Spearman correlation analysis.Results: Compared with HCs, the global parameters [sigma, gamma, and local efficiency (Eloc)] in both patient groups decreased significantly (p < 0.05, Bonferroni corrected). The clustering coefficient (Cp) in patients with CI was significantly lower than that in the other two groups (p < 0.05, Bonferroni corrected). The regional parameters were significantly lower in the right superior frontal gyrus, dorsolateral (SFGdor) and gyrus rectus (REC) of patients with CI than those of patients without CI; however the nodal local efficiency in the left amygdala was significantly increased (all p < 0.05, Bonferroni corrected). The global Cp and regional parameters in the three brain regions (right SFGdor, REC, and left amygdala) were significantly correlated with the cognitive function scores (all FDR q < 0.05).Conclusion: This study confirmed that the topology of the functional brain network was disrupted in patients on MHD with and without CI and the disruption of brain network was more severe in patients with CI. The abnormal brain network parameters are closely related to cognitive function in patients on MHD.

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.

Abstract Background Pulmonary lobectomy has been a well-established curative treatment method for localized lung cancer. After left upper pulmonary lobectomy, the upward displacement of remaining lower lobe causes the distortion or kink of bronchus, which is associated with intractable cough and breathless. However, the quantitative study on structural and functional alterations of the tracheobronchial tree after lobectomy has not been reported. We sought to investigate these alterations using CT imaging analysis and computational fluid dynamics (CFD) method. Methods Both preoperative and postoperative CT images of 18 patients who underwent left upper pulmonary lobectomy are collected. After the tracheobronchial tree models are extracted, the angles between trachea and bronchi, the surface area and volume of the tree, and the cross-sectional area of left lower lobar bronchus are investigated. CFD method is further used to describe the airflow characteristics by the wall pressure, airflow velocity, lobar flow rate, etc. Results It is found that the angle between the trachea and the right main bronchus increases after operation, but the angle with the left main bronchus decreases. No significant alteration is observed for the surface area or volume of the tree between pre-operation and post-operation. After left upper pulmonary lobectomy, the cross-sectional area of left lower lobar bronchus is reduced for most of the patients (15/18) by 15–75%, especially for 4 patients by more than 50%. The wall pressure, airflow velocity and pressure drop significantly increase after the operation. The flow rate to the right lung increases significantly by 2–30% (but there is no significant difference between each lobe), and the flow rate to the left lung drops accordingly. Many vortices are found in various places with severe distortions. Conclusions The favorable and unfavorable adaptive alterations of tracheobronchial tree will occur after left upper pulmonary lobectomy, and these alterations can be clarified through CT imaging and CFD analysis. The severe distortions at left lower lobar bronchus might exacerbate postoperative shortness of breath.

Cholangiocarcinoma (CCA) is a malignant cancer with an unknown etiology and an unfavorable prognosis. Most patients are diagnosed at an advanced stage, thus making it essential to find novel curative targets for CCA. Metabolic reprogramming of the tumor cells includes metabolic abnormalities in glucose (known as the Warburg effect) and other substances such as amino acids and fats. Metabolic reprogramming produces anti-oxidant substances, reduces tumor oxidative stress, and finally promotes the proliferation of tumors. There is increasing evidence to imply that SIRT2, a histone deacetylase, and its downstream target cMYC, play metabolic regulatory roles in tumor cells. However, the role of the SIRT2/cMYC pathway in CCA is unclear. To assess the metabolic reprogramming function of the SIRT2/cMYC pathway in CCA and to determine the downstream targets as well as evaluate the therapeutic effect, the CCA RNA-Seq data were downloaded from the TCGA database. Differentially expressed genes were confirmed and KEGG pathway enrichment analysis was performed. Overall, 48 paired CCA samples were collected and subjected to immunohistochemical detection, and the clinical characteristics of participants were summarized. The CCA cells were suppressed or overexpressed with different downstream targets of SIRT2 and then subjected to apoptosis, immunoblotting, seahorse, and metabolites tracing analysis. In vivo experiments were also performed. We found that the SIRT2/cMYC pathway contributed to the proliferation of CCA cells and confirmed that the downstream target is PHDA1 and the serine synthesis pathway. The up-regulated SIRT2 and cMYC levels resulted in low levels of mitochondrial oxidative phosphorylation and increased conversion of glucose to serine and led to poor patient survival. The highly active SIRT2/cMYC pathway up-regulated the serine synthesis pathway pyruvate and increased antioxidant production, thus consequently protecting the CCA cells from oxidative stress-induced apoptosis. Our data revealed that the SIRT2/cMYC pathway plays a critical role in transforming glucose oxidative metabolism to serine anabolic metabolism, thus providing antioxidants for stress resistance. SIRT2/cMYC-induced metabolic reprogramming may represent a new therapeutic target for treating CCA.

BackgroundRheumatoid arthritis (RA) is a long-term autoimmune disorder characterized by chronic inflammation that results in swollen and painful joints and even cartilage and bone damage. The gut microbiota, a novel anti-inflammatory target, is considered an important environmental factor in the development of RA. S-propargyl-cysteine (SPRC), an amino acid analogue, exerts anti-inflammatory, cardioprotective effects, and neuroprotective effects on various diseases. In recent studies, an SPRC treatment exerted anti-inflammatory effects on RA. Meanwhile, gut microbiome dysbiosis in individuals with RA has also been reported by many researchers. However, the relationship between SPRC and gut microbiota in individuals with RA remains unclear.MethodsThirty male Sprague-Dawley (SD) rats were randomly divided into three groups of 10 each, including the Control, Model, and SPRC groups. Adjuvant-induced arthritis (AIA) rats in SPRC group were treated with SPRC. Measurement of paw volume and serum tumor necrosis factor-α (TNF-α) and interleukin 6 (IL-6) levels were applied to evaluate the inflammatory status. Fecal samples were collected on the 14th day and 28th day. Gut microbiota were analyzed using 16S ribosomal RNA (rRNA) gene amplicon sequencing. Untargeted metabolomics on plasma samples was applied to investigate the metabolic changes induced by the altered gut microbiota by using derivatization-UHPLC-Q-TOF/MS.FindingsUsing 16S rRNA amplicon sequencing, we found that SPRC significantly altered the gut microbiota structure in AIA rats. In particular, Bifidobacterium, a genus of BSH (Bile Salt Hydrolase)-producing microbes, was overrepresented in SPRC-treated AIA rats. Additionally, a subsequent metabolomics analysis indicated that bile acid metabolism was also altered by SPRC treatment. Interestingly, glycochenodeoxycholic acid (GCDCA) and glycocholic acid (GCA), which are formed with the participation of BSH-producing microbes in the intestine, were identified as crucial biomarkers responding to SPRC treatment with significantly lowered levels.InterpretationA mechanistic link between the gut microbiota and plasma metabolites was revealed in this study, which provides insights into the mechanism of SPRC treatment for RA from the perspective of the gut microbiota.

To achieve zero-energy building target, both China and the US have planned pathway by formulating guidelines and standards. China has published National China technical standard for nearly zero energy building (CTS-NZEB), which defines detailed principles of zero-energy building. The US LEED zero energy evaluation system has been released based on LEED rating system. Defined target and scope have been introduced in this evaluation system; however, there are no technical and detailed approaches in the rating system. To support future projects achieving the LEED Zero Energy certification in China, accessible applications have been analyzed in perspectives of technology and economy in this article. The comparison and analysis results show that the strategies and technical index of CTS-NZEB can be adopted comprehensively in LEED Zero Energy rating system for Chinese buildings except for renewable energy resources because air source or ground source heat pump system, which are extensively used in China but not acceptable in LEED Zero Energy rating system. High-performance strategies, including low U-value envelope, exterior shading devices, high air tightness envelope, high-efficiency heating, ventilation, and air-conditioning system, and low thermal bridge impact, can be applied to pursue LEED Zero Energy rating system. The incremental costs of NZEBs in China are also supported by government through market and financial policies. This would make significant impacts on Chinese buildings to achieve the LEED Zero Energy target.

Type 2 diabetes mellitus (T2DM) leads to a higher risk of brain damage and adversely affects cognition. The underlying neural mechanism of T2DM-induced cognitive impairment (T2DM-CI) remains unclear. This study proposes to identify a small number of dysfunctional brain connections as imaging biomarkers, distinguishing between T2DM-CI, T2DM with normal cognition (T2DM-NC), and healthy controls (HC). We have recruited 22 T2DM-CI patients, 31 T2DM-NC patients, and 39 HCs. The structural Magnetic Resonance Imaging (MRI) and resting state fMRI images are acquired, and neuropsychological tests are carried out. Amplitude of low frequency fluctuations (ALFF) is analyzed to identify impaired brain regions implicated with T2DM and T2DM-CI. The functional network is built and all connections connected to impaired brain regions are selected. Subsequently, L1-norm regularized sparse canonical correlation analysis and sparse logistic regression are used to identify discriminative connections and Support Vector Machine is trained to realize three two-category classifications. It is found that single-digit dysfunctional connections predict T2DM and T2DM-CI. For T2DM-CI versus HC, T2DM-NC versus HC, and T2DM-CI versus T2DM-NC, the number of connections is 6, 7, and 5 and the area under curve (AUC) can reach 0.912, 0.901, and 0.861, respectively. The dysfunctional connection is mainly related to Default Model Network (DMN) and long-distance links. The strength of identified connections is significantly different among groups and correlated with cognitive assessment score (p < 0.05). Via ALFF analysis and further feature selection algorithms, a small number of dysfunctional brain connections can be identified to predict T2DM and T2DM-CI. These connections might be the imaging biomarkers of T2DM-CI and targets of intervention.

Colorectal cancer (CRC) ranks the third most common cancer type in both men and women. Besides the known genetic and epigenetic changes in the gut epithelial cells, we now know that disturbed gut microbes could also contribute to the onset and progression of CRC. Hence, keeping a balanced gut microbiota (GM) has become a novel pursue in the medical field, particularly in the area of gastrointestinal disorders. Gynostemma pentaphyllum (Gp) is a dietary herbal medicine. In our previous study, Gp saponins (GpS) displayed prebiotic and cancer-preventive properties through the modulation of GM in ApcMin/+ mice. However, the specific group(s) of GM links to the health effects of GpS remains unknown. To track down the missing link, we first investigated and found that inoculation with fecal materials from GpS-treated ApcMin/+ mice effectively reduces polyps in ApcMin/+ mice. From the same source of the fecal sample, we successfully isolated 16 bacterial species. Out of the 16 bacteria, Bifidobacterium animalis stands out as the responder to the GpS-growth stimulus. Biochemical and RNAseq analysis demonstrated that GpS enhanced expressions of a wide range of genes encoding biogenesis and metabolic pathways in B. animalis culture. Moreover, we found that colonization of B. animalis markedly reduces the polyp burden in ApcMin/+ mice. These findings reveal a mutualistic interaction between the prebiotic and a probiotic to achieve anticancer and cancer-preventive activities. Our result, for the first time, unveils the anticancer function of B. animalis and extend the probiotic horizon of B. animalis.

Probiotic-based therapies have been shown to be beneficial for chemotherapy-induced mucositis. Previous research has demonstrated that a probiotic mixture (Bifidobacterium brevis, Lactobacillus acidophilus, Lactobacillus casei, and Streptococcus thermophilus) can ameliorate chemotherapy-induced mucositis and dysbiosis in rats, but the underlying mechanism has not been completely elucidated. We aimed to determine the inhibitory effects of the probiotic mixture on cisplatin-induced mucositis and pica and the underlying mechanism, focusing on the levels of 5-hydroxytryptamine (5-HT, serotonin) regulated by the gut microbiota. A rat model of mucositis and pica was established by daily intraperitoneal injection of cisplatin (6 mg/kg) for 3 days. In the probiotic+cisplatin group, predaily intragastric injection of the probiotic mixture (1×109 CFU/kg BW) was administrated for 1 week before cisplatin injection. This was then followed by further daily probiotic injections for 6 days. Histopathology, pro-/anti-inflammatory cytokines, oxidative status, and 5-HT levels were assessed on days 3 and 6. The structure of the gut microbiota was analyzed by 16S rRNA gene sequencing and quantitative PCR. Additionally, 5-HT levels in enterochromaffin (EC) cells (RIN-14B cell line) treated with cisplatin and/or various probiotic bacteria were also determined. The probiotic mixture significantly attenuated kaolin consumption, inflammation, oxidative stress, and the increase in 5-HT concentrations in rats with cisplatin-induced intestinal mucositis and pica. Cisplatin markedly increased the relative abundances of Enterobacteriaceae_other, Blautia, Clostridiaceae_other, and members of Clostridium clusters IV and XIVa. These levels were significantly restored by the probiotic mixture. Importantly, most of the genera increased by cisplatin were significantly positively correlated with colonic 5-HT. Furthermore, in vitro, the probiotic mixture had direct inhibitory effects on the 5-HT secretion by EC cells. The probiotic mixture protects against cisplatin-induced intestine injury, exhibiting both anti-inflammatory and antiemetic properties. These results were closely related to the reestablishment of intestinal microbiota ecology and normalization of the dysbiosis-driven 5-HT overproduction.

Objectives Alzheimer’s disease (AD), impaired fasting glucose (IFG), and Type 2 diabetes mellitus (T2DM) were reported associated with smaller brain volumes. Nevertheless, the association of hyperglycemia with brain volume changes in AD patients remains unclear. To investigate this issue, structural magnetic resonance imaging was used to compare brain volumes among AD patients with different fasting glucose levels. Methods Eighty-five AD patients were divided into three groups based on their fasting glucose level as suggested by the American Diabetes Association: normal fasting glucose group (AD_NFG, n = 45), AD_IFG group (n = 15), and AD_T2DM group (n = 25). Sagittal 3D T1-weighted images were obtained to calculate the brain volume. Brain parenchyma and 33 brain structures were automatically segmented. Each regional volume was analyzed among groups. For regions with statistical significance, partial correlation analysis was used to evaluate their relationships with fasting glucose level, corrected for Mini-Mental State Examination score, age, education level, cholesterol, triglyceride, and blood pressure. Results Compared with the AD_IFG and AD_NFG groups, the volume of pons in AD_T2DM group was significantly smaller. Fasting glucose was negatively correlated with pontine volume. Conclusions T2DM may exacerbate pontine atrophy in AD patients, and fasting glucose level is associated with pontine volume.

Abstract Background Accurate and early prognosis of disease is essential to clinical decision making, particularly in diseases, such as HCC, that are typically diagnosed at a late stage in the course of disease and therefore carry a poor prognosis. CDCA5 is a cell cycle regulatory protein that has shown prognostic value in several cancers. Methods We retrospectively evaluated 178 patients with HCC treated with curative liver resection between September 2009 and September 2012 at Nanchong Central Hospital in Nanchong, Sichuan Province, China. Patients were screened for their CDCA5 expression levels and assigned to either the high or low expression group. Patient demographics, comorbidities, clinicopathologic data, such as tumor microvascular invasion status and size, and long-term outcomes were compared between the two groups. The effect of CDCA5 on the proliferation of liver cancer cells was analyzed using in vitro and in vivo assays. Results The present study found that increased CDCA5 expression was associated with increased tumor diameter and microvascular invasion in HCC. It was also found that CDCA5 overexpression may be associated with liver cancer cells. Additionally, this study confirmed that CDCA5 expression was increased in HCC tissue versus normal liver tissue, that CDCA5 expression was associated with decreased survival and that CDCA5 knockdown using shRNA led to cell cycle arrest in the G2/M phase. Conclusions These findings suggest that CDCA5 expression is associated with poor prognosis in patients with hepatocellular carcinoma.

Background and Objectives. This study investigated the effect of preoperative nutrition on the recovery and prognosis of colorectal cancer patients after laparoscopic radical resection. Methods and Study Design. A retrospective analysis was conducted on 120 colorectal cancer patients between January 2015 and August 2017. Patients were divided into two groups: those without nutritional risk (NRS 20020.05). However, patients with nutritional risk had higher postoperative blood transfusion volumes, albumin infusions, weight difference before and after surgery, and postoperative hospital stays than the nonnutritional risk group (all P

The non-covalent interactions between small drug molecules and disease-related proteins (ligand-target interactions) mediate various pharmacological processes in the treatment of different diseases. The development of the analytical methods to assess those interactions, including binding sites, binding energies, stoichiometry and association-dissociation constants, could assist in clarifying the mechanisms of action, precise treatment of targeted diseases as well as the targeted drug discovery. For the last decades, mass spectrometry (MS) has been recognized as a powerful tool to study the non-covalent interactions of the ligand-target complexes with the characteristics of high sensitivity, high-resolution, and high-throughput. Soft ionization mass spectrometry, especially the electrospray mass spectrometry (ESI-MS) and matrix assisted laser desorption ionization mass spectrometry (MALDI-MS), could achieve the complete transformation of the target analytes into the gas phase, and subsequent detection of the small drug molecules and disease-related protein complexes, and has exerted great advantages for studying the drug ligands-protein targets interactions, even in case of identifying active components as drug ligands from crude extracts of medicinal plants. Despite of other analytical techniques for this purpose, such as the NMR and X-ray crystallography, this review highlights the principles, research hotspots and recent applications of the soft ionization mass spectrometry and its hyphenated techniques, including hydrogen-deuterium exchange mass spectrometry (HDX-MS), chemical cross-linking mass spectrometry (CX-MS), and ion mobility spectrometry mass spectrometry (IMS-MS), in the study of the non-covalent interactions between small drug molecules and disease-related proteins.

BACKGROUND:China has a substantial tuberculosis (TB) disease burden and an aging population. Seniors have a higher risk of developing TB disease compared to younger age groups. Active case finding (ACF) could help identify seniors with TB disease. METHODS:From March to June 2017, we included ACF during annual physical check-ups for persons aged ≥ 65 years in Bayi, Sichuan Province. Seniors with clinical TB symptoms (i.e., cough lasting ≥ 2 weeks and/or hemoptysis) or one or more risk factors (e.g., previous TB disease, diabetes, and heavy alcohol consumption) were offered chest x-rays. We used acid-Fast Bacilli smear and solid culture laboratory testing for TB confirmation. We calculated the yield (i.e., cases identified among seniors screened) and cost per new each TB case detected. Focus group-interviews were conducted with health care workers and seniors to evaluate project acceptability. Participation rates and acceptability were used to assess feasibility. RESULTS:Of the 2,393 seniors residing in Bayi, 2,049 (85.6%) were enrolled in the pilot project. Of these seniors, 794 (38.7%) presented with at least one TB risk factor and 74 (3.6%) had symptoms consistent with active TB disease. Three seniors (0.2%)-each presenting with at least one risk factor-were diagnosed with active TB. The project yielded 146 TB cases per 100,000 seniors screened; the cost per case detected was $4,897. Most workers supported ACF if additional resources and staff could be provided. Seniors appreciated the convenience of this integrated health service approach. CONCLUSIONS:Although the yield was lower than expected, ACF appeared feasible in Bayi. Targeting seniors with at least one known TB risk factor could help detect previously unidentified TB cases. However, similar projects in communities with a higher TB prevalence are needed to further evaluate the yield and required resources prior to implementation on a larger scale. Findings from our pilot project should be combined with data from these future ACF projects to improve TB screening criteria.

Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a significant challenge during the analysis of breast MR images. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI) due to similar intensity levels and the close connection to BROI. This study proposes an innovative, fully automatic and fast segmentation approach to identify and remove landmarks such as the heart and pectoral muscles. The BROI segmentation is carried out with a framework consisting of three major steps. Firstly, we use adaptive wiener filtering and k-means clustering to minimize the influence of noises, preserve edges and remove unwanted artefacts. The second step systematically excludes the heart area by utilizing active contour based level sets where initial contour points are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed by using morphological operations and local adaptive thresholding on MR images. Prior to the elimination of the pectoral muscle, the MR image is sub divided into three sections: left, right, and central based on the geometrical information. Subsequently, a BD segmentation is achieved with 4 level fuzzy c-means (FCM) thresholding on the denoised BROI segmentation. The proposed method is validated using the 1350 breast images from 15 female subjects. The pixel-based quantitative analysis showed excellent segmentation results when compared with manually drawn BROI and BD. Furthermore, the presented results in terms of evaluation matrices: Acc, Sp, AUC, MR, P, Se and DSC demonstrate the high quality of segmentations using the proposed method. The average computational time for the segmentation of BROI and BD is 1 minute and 50 seconds.