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Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC. Gastric cancer detection by endoscopy is intrusive and time-consuming, and early detection is key to improving survival. Here, the authors propose a metabolite-based model to enable early detection. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the predictive factors of therapeutic efficacy for chronic subdural hematoma (CSDH) patients receiving atorvastatin combined with dexamethasone therapy by using clinical imaging characteristics in conjunction with computed tomography (CT) texture analysis (CTTA). Clinical imaging characteristics and CT texture parameters at admission were retrospectively investigated in 141 CSDH patients who received atorvastatin combined with dexamethasone therapy from June 2019 to December 2022. The patients were divided into a training set (n = 81) and a validation set (n = 60). Patients in the training data were divided into two groups based on the effectiveness of the treatment. Univariate and multivariate analyses were performed to assess the potential factors that could indicate the prognosis of CSDH patients in the training set. The receiver operating characteristic (ROC) curve was used to analyze the predictive efficacy of the significant factors in predicting the prognosis of CSDH patients and was validated using a validation set. The multivariate analysis showed that the hematoma density to brain parenchyma density ratio, singal min (minimum) and singal standard deviation of the pixel distribution histogram, and inhomogeneity were independent predictors for the prognosis of CSDH patients based on atorvastatin and dexamethasone therapy. The area under the ROC curve between the two groups was between 0.716 and 0.806. As determined by significant factors, the validation's accuracy range was 0.816 to 0.952. Clinical imaging characteristics in conjunction with CTTA could aid in distinguishing patients with CSDH who responded well to atorvastatin combined with dexamethasone. [ABSTRACT FROM AUTHOR]

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Accurate motion feature extraction and recognition provide critical information for many scientific problems. Herein, a new paradigm for a wearable seamless multimode sensor with the ability to decouple pressure and strain stimuli and recognize the different joint motion states is reported. This wearable sensor is integrated into a unique seamless structure consisting of two main parts (a resistive component and a capacitive component) to decouple the different stimuli by an independent resistance-capacitance sensing mechanism. The sensor exhibits both high strain sensitivity (GF = 7.62, 0–140% strain) under the resistance mechanism and high linear pressure sensitivity (S = 3.4 kPa−1, 0–14 kPa) under the capacitive mechanism. The sensor can differentiate the motion characteristics of the positions and states of different joints with precise recognition (97.13%) with the assistance of machine learning algorithms. The unique integrated seamless structure is achieved by developing a layer-by-layer casting process that is suitable for large-scale manufacturing. The proposed wearable seamless multimode sensor and the convenient process are expected to contribute significantly to developing essential components in various emerging research fields, including soft robotics, electronic skin, health care, and innovative sports systems applications. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming evolves during cancer initiation and progression. However, thorough understanding of metabolic evolution from preneoplasia to lung adenocarcinoma (LUAD) is still limited. Here, we perform large-scale targeted metabolomics on resected lesions and plasma obtained from invasive LUAD and its precursors, and decipher the metabolic trajectories from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC), revealing that perturbed metabolic pathways emerge early in premalignant lesions. Furthermore, three panels of plasma metabolites are identified as non-invasive predictive biomarkers to distinguish IAC and its precursors with benign diseases. Strikingly, metabolomics clustering defines three metabolic subtypes of IAC patients with distinct clinical characteristics. We identify correlation between aberrant bile acid metabolism in subtype III with poor clinical features and demonstrate dysregulated bile acid metabolism promotes migration of LUAD, which could be exploited as potential targetable vulnerability and for stratifying patients. Collectively, the comprehensive landscape of the metabolic evolution along the development of LUAD will improve early detection and provide impactful therapeutic strategies. Metabolic reprogramming occurs during tumor progression. Here the authors decipher metabolic trajectories from preneoplasia to lung adenocarcinoma in tumor samples and identify plasma metabolites as potential predictive biomarkers for early detection. [ABSTRACT FROM AUTHOR]

Cytokine release syndrome (CRS) is a major cause of the multi-organ injury and fatal outcome induced by SARS-CoV-2 infection in severe COVID-19 patients. Metabolism can modulate the immune responses against infectious diseases, yet our understanding remains limited on how host metabolism correlates with inflammatory responses and affects cytokine release in COVID-19 patients. Here we perform both metabolomics and cytokine/chemokine profiling on serum samples from healthy controls, mild and severe COVID-19 patients, and delineate their global metabolic and immune response landscape. Correlation analyses show tight associations between metabolites and proinflammatory cytokines/chemokines, such as IL-6, M-CSF, IL-1α, IL-1β, and imply a potential regulatory crosstalk between arginine, tryptophan, purine metabolism and hyperinflammation. Importantly, we also demonstrate that targeting metabolism markedly modulates the proinflammatory cytokines release by peripheral blood mononuclear cells isolated from SARS-CoV-2-infected rhesus macaques ex vivo, hinting that exploiting metabolic alterations may be a potential strategy for treating fatal CRS in COVID-19. Metabolism changes can modulate immune responses in many contexts, and vice versa. Here the authors associate metabolomic, as well as cytokine and chemokine, data from stratified COVID-19 patients to find that arginine, tryptophan and purine metabolic pathways correlate with hyperproliferation, thus hinting at potential therapeutic targets for severe COVID-19 patients. [ABSTRACT FROM AUTHOR]

Multi-pole arrangements in magnetorheological plane finishing technology have been investigated in this study. A method of combining the material removal mechanism of micro-points using the empirical Preston equation is proposed to establish a prediction model for surface flatness, and the new Semiconductor Equipment and Materials International (SEMI) standard has been used to evaluate workpiece surface flatness. Based on the model, the effects of process parameters (polishing time, speed ratio, translational amplitude, polishing gap, etc.) on the flatness of workpieces with different shapes are predicted through simulation, and the effects of multi-pole arrangements are explored. The results of the analysis indicate that with changes in process parameters, the extent of change in surface flatness differs based on the shape of the workpiece. After polishing, concave workpieces show the highest levels of surface flatness. From simulations of magnetic pole arrangements, it is also found that magnetic field generators with different magnetic pole arrangements can be used for workpieces with different shapes to improve their surface flatness. Experiments with a workpiece with its shape measured using a white light interferometer showed that the surface flatness improved from being 33.561 μm initially to 21.822 μm after polishing, thereby demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Magnetorheological finishing, considered as a promising polishing technique that can finish a nano-level smooth surface and free of surface or subsurface damage, has been widely utilized in ultra-smooth polishing of hard-to-machine materials. However, there are still several practical weaknesses in magnetorheological finishing (MRF) applied for wafer surface polishing. In this study, a new method using MRF effect was proposed for wafer polishing. Concretely, porous foam was used as a storage carrier of MR slurry, and thus, MR slurry can be bounded in porous foam in the finishing process (cluster MR-porous foam polishing, CMRPP). In this paper, an experimental rig was constructed to achieve CMRPP mechanism, the processing principle and material removal process in CMRPP were analyzed, and CMRPP experiments were conducted to investigate the effects of porous foam on surface polishing. The results indicate the following: (1) The polishing forces in CMRPP were increased compared to that of a cluster MR polishing without porous foam (CMRP), and CMRPP can get a greater shear force than of CMRP at a same normal force; (2) CMRPP can well hinder the solid-liquid separation and sustain the stability of the slurry performance; (3) CMRPP achieved a higher volumetric removal rate than that of CMRP and improving of material removal efficiency; (4) CMRPP can effectively achieve a nano-level smooth surface compared to that of CMRP. [ABSTRACT FROM AUTHOR]

Silicon carbide (SiC) ceramics play a key role in various engineering applications due to their desirable properties, however, they are typical hard-brittle materials and famous for their poor machinability. Ultrasonic-assisted grinding, a processing method hybridizing the conventional grinding and ultrasonic vibration (UV) machining, is employed as the one applicable machining method for hard-brittle materials. This study focuses on internal grinding of SiC ceramics with the assistance of the UV, and its ductile-brittle transition behavior in the grinding process were investigated experimentally. Following the processing principal of internal grinding and UV machining (i.e., UV-assisted internal grinding, UVAIG), the UVAIG experiment rig were constructed. Conventional internal grinding (CIG) tests, i.e., internal grinding without UV, were also performed on the constructed rig for comparison. In addition, the grinding force and the grinding chips variation behavior with the UV amplitude were also investigated to explore the ductile-brittle transition mechanism in UVAIG. The experiment results evidence that (1) ductile mode grinding is easily achieved in UVAIG, and the critical depth of the cut is deeper in UVAIG than that in CIG, i.e., 0.072 μm in CIG and 0.093 μm in UVAIG; (2) increasing of the critical depth of cut in UVAIG is likely owing to the lower grinding force in UVAIG than that in CIG; and (3) the ultrasonic vibration acted on the axis of the grinding wheel helps in the removal of material on the work-surface, but decreases the grinding energy in the ductile-brittle transition. [ABSTRACT FROM AUTHOR]