Supported bilayer membranes for reducing cell adhesion in microfluidic devices [post-print]

The high surface area-to-volume ratio of microfluidic channels makes them susceptible to fouling and clogging when used for biological analyses, including cell-based assays. We evaluated the role of electrostatic and van der Waals interactions in cell adhesion in PDMS microchannels coated with supported lipid bilayers and identified conditions that resulted in minimal cell adhesion. For low ionic strength buffer, optimum results were obtained for a zwitterionic coating of pure egg phosphatidylcholine; for a rich growth medium, the best results were obtained for zwitterionic bilayers or those with slight negative or moderate positive charge from the incorporation of 5-10 mol% egg phosphatidylglycerol or 30 mol% ethylphosphocholine. In both solutions, the presence of 10 g L-1 glucose in the cell suspension reduced cell adhesion. Under optimum conditions, all cells were consistently removed from the channels, demonstrating the utility of these coatings for whole-cell microfluidic assays. These results provide practical information for immediate application and suggest future research areas on cell-lipid interactions

Centrality Graph Convolutional Networks for Skeleton-based Action Recognition

The topological structure of skeleton data plays a significant role in human action recognition. Combining the topological structure with graph convolutional networks has achieved remarkable performance. In existing methods, modeling the topological structure of skeleton data only considered the connections between the joints and bones, and directly use physical information. However, there exists an unknown problem to investigate the key joints, bones and body parts in every human action. In this paper, we propose the centrality graph convolutional networks to uncover the overlooked topological information, and best take advantage of the information to distinguish key joints, bones, and body parts. A novel centrality graph convolutional network firstly highlights the effects of the key joints and bones to bring a definite improvement. Besides, the topological information of the skeleton sequence is explored and combined to further enhance the performance in a four-channel framework. Moreover, the reconstructed graph is implemented by the adaptive methods on the training process, which further yields improvements. Our model is validated by two large-scale datasets, NTU-RGB+D and Kinetics, and outperforms the state-of-the-art methods

Cavity-enhanced and spatial-multimode spin-wave-photon quantum interface

Practical realizations of quantum repeaters require quantum memory simultaneously providing high retrieval efficiency, long lifetime and multimode storages. So far, the combination of high retrieval efficiency and spatially multiplexed storages into a single memory remains challenging. Here, we set up a ring cavity that supports an array including 6 TEM00 modes and then demonstrated cavity enhanced and spatially multiplexed spin wave photon quantum interface (QI). The cavity arrangement is according to Fermat' optical theorem, which enables the six modes to experience the same optical length per round trip. Each mode includesn horizontal and vertical polarizations. Via DLCZ process in a cold atomic ensemble, we create non classically correlated pairs of spin waves and Stokes photons in the 12 modes. The retrieved fields from the multiplexed SWs are enhanced by the cavity and the average intrinsic retrieval efficiency reaches 70% at zero delay. The storage time for the case that cross-correlation function of the multiplexed QI is beyond 2 reaches 0.6ms

Altered cerebral neurovascular coupling in medication-overuse headache: A study combining multi-modal resting-state fMRI with 3D PCASL

AimStructural and functional changes in the brain have been identified in individuals with medication-overuse headache (MOH) using MRI. However, it has not been clearly established whether neurovascular dysfunction occurs in MOH, which could be elucidated by examining neurovascular coupling (NVC) from the viewpoints of neuronal activity and cerebral blood flow. The aim of this study was to investigate potential alterations in NVC function of the brain in individuals with MOH using resting-state functional MRI (rs-fMRI) and 3D pseudo-continuous arterial spin labeling (3D PCASL) imaging techniques.MethodsA total of 40 patients with MOH and 32 normal controls (NCs) were recruited, and rs-fMRI and 3D PCASL data were obtained using a 3.0 T MR scanner. Standard preprocessing of the rs-fMRI data was performed to generate images representing regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuation (fALFF), and degree centrality (DC); cerebral blood flow (CBF) images were generated using 3D PCASL sequence data. These functional maps were all normalized into Montreal Neurological Institute (MNI) space, and NVC was subsequently determined on the basis of Pearson correlation coefficients between the rs-fMRI maps (ReHo, fALFF, and DC) and CBF maps. The statistical significance of differences between the MOH and NC groups in terms of NVC in different brain regions was established via Z-test. Further analysis was performed to examine correlations between NVC in the brain regions with NVC dysfunction and clinical variables among patients with MOH.ResultsNVC mainly presented a negative correlation in patients with MOH and NCs. No significant difference between the two groups was detected in terms of average NVC over the entire gray matter area. However, several brain regions with significantly decreased NVC in patients with MOH compared to NCs were identified: the left orbital region of the superior frontal gyrus, the bilateral gyrus rectus, and the olfactory cortex (P < 0.05). A correlation analysis revealed that the DC of the brain regions with NVC dysfunction was significantly positively correlated with disease duration (r = 0.323, P = 0.042), and DC–CBF connectivity was negatively correlated with VAS score (r = −0.424, P = 0.035).ConclusionThe current study demonstrated that cerebral NVC dysfunction occurs in patients with MOH, and the NVC technique could function as a new imaging biomarker in headache research

Association between dietary supplement use and mortality in cancer survivors with different body mass index and frailty status: a cohort study

BackgroundThe association between Body Mass Index (BMI), frailty index (FI), and dietary supplement in cancer survivors has been a subject of growing interest. This study investigates the relationship of BMI and FI with mortality in American cancer survivors and explores the impact of dietary supplement usage on different BMI and FI groups.MethodsThree thousand nine hundred and thirty-two cancer patients from the National Health and Nutrition Examination Survey (NHANES) database were included in the analyses. BMI, FI, and supplement usage were obtained through the NHANES structured survey and the 49-item FI tool. Weighted logistic and Cox proportional hazards models, Kaplan–Meier survival analyses, and propensity score matching (PSM) were used to elucidate the relationships between BMI, FI, dietary supplement, and mortality outcomes.ResultsThe study found significant associations between higher BMI and increased frailty (Odds ratio [OR] = 1.04, 95% confidence interval [95% CI], 1.02–1.06). BMI < 25 kg/m2 and FI > 0.2 are associated with an increased mortality rate. Dietary supplement use can reduce all-cause and cancer mortality in cancer patients with BMI < 25 kg/m2 (Hazard ratio [HR] = 0.63, 95% CI, 0.47–0.84; HR = 0.48, 95% CI, 0.29–0.80) or FI ≤ 0.2 (HR = 0.77, 95% CI, 0.60–0.99; HR = 0.59, 95% CI, 0.39–0.89). In cancer patients with BMI < 25 kg/m2 and FI ≤ 0.2, dietary supplement users had lower all-cause and cancer mortality (HR = 0.49, 95% CI, 0.30–0.79; HR = 0.25, 95% CI, 0.10–0.60).ConclusionThe study revealed a negative correlation between BMI and the FI among the cancer patient cohort as well as their complex impact on mortality and highlighted the role of dietary supplement in cancer prognosis, indicating benefits for non-frail patients with BMI < 25 kg/m2

Unraveling the microbial puzzle: exploring the intricate role of gut microbiota in endometriosis pathogenesis

Endometriosis (EMs) is a prevalent gynecological disorder characterized by the growth of uterine tissue outside the uterine cavity, causing debilitating symptoms and infertility. Despite its prevalence, the exact mechanisms behind EMs development remain incompletely understood. This article presents a comprehensive overview of the relationship between gut microbiota imbalance and EMs pathogenesis. Recent research indicates that gut microbiota plays a pivotal role in various aspects of EMs, including immune regulation, generation of inflammatory factors, angiopoietin release, hormonal regulation, and endotoxin production. Dysbiosis of gut microbiota can disrupt immune responses, leading to inflammation and impaired immune clearance of endometrial fragments, resulting in the development of endometriotic lesions. The dysregulated microbiota can contribute to the release of lipopolysaccharide (LPS), triggering chronic inflammation and promoting ectopic endometrial adhesion, invasion, and angiogenesis. Furthermore, gut microbiota involvement in estrogen metabolism affects estrogen levels, which are directly related to EMs development. The review also highlights the potential of gut microbiota as a diagnostic tool and therapeutic target for EMs. Interventions such as fecal microbiota transplantation (FMT) and the use of gut microbiota preparations have demonstrated promising effects in reducing EMs symptoms. Despite the progress made, further research is needed to unravel the intricate interactions between gut microbiota and EMs, paving the way for more effective prevention and treatment strategies for this challenging condition

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model