Evaluation of iron transport from ferrous glycinate liposomes using Caco-2 cell model

Background: Iron fortification of foods is currently a strategy employed to fight iron deficiency in countries. Liposomes were assumed to be a potential carrier of iron supplements.Objective: The objective of this study was to investigate the iron transport from ferrous glycinate liposomes, and to estimate the effects of liposomal carriers, phytic acid, zinc and particle size on iron transport using Caco-2 cell models.Methods: Caco-2 cells were cultured and seeded in DMEM medium. Minimum essential medium was added to the basolateral side. Iron liposome suspensions were added to the apical side of the transwell.Results: The iron transport from ferrous glycinate liposomes was significantly higher than that from ferrous glycinate. In the presence of phytic acid or zinc ion, iron transport from ferrous glycinate liposomes and ferrous glycinate was evidently inhibited, and iron transport decreased with increasing phytic acid concentration. Iron transport was decreased with increase of particle size increasing of ferrous glycinate liposome.Conclusion: Liposomes could behave as more than a simple carrier, and iron transport from liposomes could be implemented via a mechanism different from the regulated non-heme iron pathway.Keywords: Ferrous glycinate liposomes, iron transport, phytic acid, particle siz

Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors and Machine Learning Algorithms

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe strike) utilizing wearable body sensors. Thirty-six children (Age 9.4±2.8 years) diagnosed with ITW participated in this study. Six ML algorithms, consisting of Support Vector Machines (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), Multi-layer Perceptron (MLP), and Gaussian process (GP), could successfully classify initial contact walking patterns among ITW. We found that a simple KNN algorithm resulted in the highest accuracy of 92.92% and an F1-score of 93.20% to differentiate toe walking gait versus best heel strike when using all four body sensors. We also found that toe walking resulted in higher variability in the sacral vertical accelerations among children diagnosed with ITW. Accurate quantification of toe walking steps in clinical applications is critical for assessing rehabilitation progress and designing new interventions for children diagnosed with ITW

PointVotes: A Deep Learing Point Cloud Model for Tire Bubble Defect Detection

In order to eliminate the hidden dangers caused by tire bubble defects, considering that the two-dimensional technology is sensitive to light, the 3D point cloud technology is used to obtain the tire surface morphology. This paper proposes a 3D point cloud network model named PointVotes, a point based target detection method. The designed structural framework includes: the fusion sampling layer, the voting layer and the proposal refinement layer. By observing the spatial characteristics of the detected target, a new point sampling method named C-farthest point sampling (C-FPS) is proposed. Combining with the fusion sampling strategy, the FPS and the C-FPS are sampled in a certain proportion. It solves the problem that the proposal box cannot be generated due to less available prospect information when generating suggestions for small targets. The network model uses Set Abstraction layers in multiple PointNet++ to extract features, arranges and combines features of different scales, forms high-dimensional features of points and votes, judges whether there are bubble defects through classification, and then generates proposals and regression to the prediction frame. Experiment results show that the mean average precision of the model can reach 82.8 % with a detection time of 0.12 s

Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients

BackgroundImmune cells are crucial components of the tumor microenvironment (TME) and regulate cancer cell development. Nevertheless, the clinical implications of immune cell infiltration-related mRNAs for bladder cancer (BCa) are still unclear.MethodsA 10-fold cross-validation framework with 101 combinations of 10 machine-learning algorithms was employed to develop a consensus immune cell infiltration-related signature (IRS). The predictive performance of IRS in terms of prognosis and immunotherapy was comprehensively evaluated.ResultsThe IRS demonstrated high accuracy and stable performance in prognosis prediction across multiple datasets including TCGA-BLCA, eight independent GEO datasets, our in-house cohort (PUMCH_Uro), and thirteen immune checkpoint inhibitors (ICIs) cohorts. Additionally, IRS was superior to traditional clinicopathological features (e.g., stage and grade) and 94 published signatures. Furthermore, IRS was an independent risk factor for overall survival in TCGA-BLCA and several GEO datasets, and for recurrence-free survival in PUMCH_Uro. In the PUMCH_Uro cohort, patients in the high-IRS group were characterized by upregulated CD8A and PD-L1 and TME of inflamed and immunosuppressive phenotypes. As predicted, these patients should benefit from ICI therapy and chemotherapy. Furthermore, in the ICI cohorts, the high-IRS group was related to a favorable prognosis and responders have dramatically higher IRS compared to non-responders.ConclusionsGenerally, these indicators suggested the promising application of IRS in urological practices for the early identification of high-risk patients and potential candidates for ICI application to prolong the survival of individual BCa patients

Evaluation of iron transport from ferrous glycinate liposomes using Caco-2 cell model.

Background: Iron fortification of foods is currently a strategy employed to fight iron deficiency in countries. Liposomes were assumed to be a potential carrier of iron supplements. Objective: The objective of this study was to investigate the iron transport from ferrous glycinate liposomes, and to estimate the effects of liposomal carriers, phytic acid, zinc and particle size on iron transport using Caco-2 cell models. Methods: Caco-2 cells were cultured and seeded in DMEM medium. Minimum essential medium was added to the basolateral side. Iron liposome suspensions were added to the apical side of the transwell. Results: The iron transport from ferrous glycinate liposomes was significantly higher than that from ferrous glycinate. In the presence of phytic acid or zinc ion, iron transport from ferrous glycinate liposomes and ferrous glycinate was evidently inhibited, and iron transport decreased with increasing phytic acid concentration. Iron transport was decreased with increase of particle size increasing of ferrous glycinate liposome. Conclusion: Liposomes could behave as more than a simple carrier, and iron transport from liposomes could be implemented via a mechanism different from the regulated non-heme iron pathway

Posture-specific breathing detection

Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity

Leveraging a disulfidptosis-based signature to improve the survival and drug sensitivity of bladder cancer patients

BackgroundDisulfidptosis is a recently discovered form of cell death. However, its biological mechanisms in bladder cancer (BCa) are yet to be understood.MethodsDisulfidptosis-related clusters were identified by consensus clustering. A disulfidptosis-related gene (DRG) prognostic model was established and verified in various datasets. A series of experiments including qRT-PCR, immunoblotting, IHC, CCK-8, EdU, wound-healing, transwell, dual-luciferase reporter, and ChIP assays were used to study the biological functions.ResultsWe identified two DRG clusters, which exhibited distinct clinicopathological features, prognosis, and tumor immune microenvironment (TIME) landscapes. A DRG prognostic model with ten features (DCBLD2, JAM3, CSPG4, SCEL, GOLGA8A, CNTN1, APLP1, PTPRR, POU5F1, CTSE) was established and verified in several external datasets in terms of prognosis and immunotherapy response prediction. BCa patients with high DRG scores may be characterized by declined survival, inflamed TIME, and elevated tumor mutation burden. Besides, the correlation between DRG score and immune checkpoint genes and chemoradiotherapy-related genes indicated the implication of the model in personalized therapy. Furthermore, random survival forest analysis was performed to select the top important features within the model: POU5F1 and CTSE. qRT-PCR, immunoblotting, and immunohistochemistry assays showed the enhanced expression of CTSE in BCa tumor tissues. A series of phenotypic assays revealed the oncogenetic roles of CTSE in BCa cells. Mechanically, POU5F1 can transactivate CTSE, promoting BCa cell proliferation and metastasis.ConclusionsOur study highlighted the disulfidptosis in the regulation of tumor progression, sensitivity to therapy, and survival of BCa patients. POU5F1 and CTSE may be potential therapeutic targets for the clinical treatment of BCa