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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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The objective of this study is to explore whether craniocervical manual lymphatic drainage (cMLD) can promote hematoma absorption and increase the efficiency of atorvastatin-based conservative treatment in chronic subdural hematoma (CSDH) patients. All CSDH patients treated with atorvastatin-based therapy between October 2020 and February 2022 in our department were retrospectively screened for enrollment. The patients were divided into the control and cMLD groups according to whether cMLD was performed. Head CT or MR images in both groups were obtained before the treatment and 2 weeks and 4 weeks after the treatment. MR images of the deep cervical lymphatic nodes (dCLNs) in 23 patients were obtained in the cMLD group before and approximately 2 weeks after treatment. The volumes of the dCLNs and hematoma were calculated. The primary outcomes are the differences in hematoma volume reduction after 4 weeks of treatment. The secondary outcomes were (1) the differences in hematoma volume reduction between the patients in these two groups in the 2nd week, (2) the dCLN volume change in the cMLD group before and after 2 weeks of treatment, and (3) the percentage of patients who transitioned to surgery because of failure to the conservative treatment. A total of 106 consecutive patients were enrolled in this study for analysis; 54 patients received atorvastatin-based treatment (control group), and 52 were treated with both atorvastatin-based treatment and cMLD (cMLD group). At baseline, the mean hematoma volume was 76.53 ± 42.97 ml in the control group and 88.57 ± 49.01 ml in the cMLD group (p = 0.181). In the 4th week, the absolute number of hematoma reductions (20.79 ± 34.73 ml vs. 37.28 ± 28.24 ml, p = 0.009) and percentage of hematoma reductions (22.58% ± 60.01% vs. 46.43% ± 30.12%, p = 0.012) in the cMLD group were greater than those in the control group. After 2 weeks of treatment, the absolute number of hematoma reductions showed no difference in the two groups, while the percentage of hematoma reduction was higher in the cMLD group (18.18% ± 24.61% vs. 2.08% ± 25.72%, p = 0.001). One patient in cMLD and 8 patients in the control group were transitioned to receive surgical treatment. The dCLN volumes in 23 experimental patients increased significantly after 2 weeks of treatment in the cMLD group (p = 0.032). There were no severe side effects that needed to be reported. Combined with atorvastatin-based therapy, cMLD can promote hematoma absorption and decrease the surgery rate, which provides a new therapeutic strategy for CSDH. [ABSTRACT FROM AUTHOR]

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]

Zika virus (ZIKV) infection during pregnancy can cause microcephaly in newborns, yet the underlying mechanisms remain largely unexplored. Here, we reveal extensive and large-scale metabolic reprogramming events in ZIKV-infected mouse brains by performing a multi-omics study comprising transcriptomics, proteomics, phosphoproteomics and metabolomics approaches. Our proteomics and metabolomics analyses uncover dramatic alteration of nicotinamide adenine dinucleotide (NAD + )-related metabolic pathways, including oxidative phosphorylation, TCA cycle and tryptophan metabolism. Phosphoproteomics analysis indicates that MAPK and cyclic GMP-protein kinase G signaling may be associated with ZIKV-induced microcephaly. Notably, we demonstrate the utility of our rich multi-omics datasets with follow-up in vivo experiments, which confirm that boosting NAD + by NAD + or nicotinamide riboside supplementation alleviates cell death and increases cortex thickness in ZIKV-infected mouse brains. Nicotinamide riboside supplementation increases the brain and body weight as well as improves the survival in ZIKV-infected mice. Our study provides a comprehensive resource of biological data to support future investigations of ZIKV-induced microcephaly and demonstrates that metabolic alterations can be potentially exploited for developing therapeutic strategies., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)