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The ability to rapidly assay morphological and intracellular molecular variations within large heterogeneous populations of cells is essential for understanding and exploiting cellular heterogeneity. Optofluidic time-stretch microscopy is a powerful method for meeting this goal, as it enables high-throughput imaging flow cytometry for large-scale single-cell analysis of various cell types ranging from human blood to algae, enabling a unique class of biological, medical, pharmaceutical, and green energy applications. Here, we describe how to perform high-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Specifically, this protocol provides step-by-step instructions on how to build an optical time-stretch microscope and a cell-focusing microfluidic device for optofluidic time-stretch microscopy, use it for high-throughput single-cell image acquisition with sub-micrometer resolution at >10,000 cells per s, conduct image construction and enhancement, perform image analysis for large-scale single-cell analysis, and use computational tools such as compressive sensing and machine learning for handling the cellular ‘big data’. Assuming all components are readily available, a research team of three to four members with an intermediate level of experience with optics, electronics, microfluidics, digital signal processing, and sample preparation can complete this protocol in a time frame of 1 month.

This letter reports a proof‐of‐concept experiment conducted on a novel application of the multi‐core fibre (MCF), where image reconstruction is based on a single‐pixel imaging (SPI) technique and the diffraction pattern emitted from an MCF. The technique is intended to reduce the size of the SPI system, the applications of which can now be extended with the help of this study.

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software–hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.

Background Although fat accumulation in human organs is associated with a variety of diseases, there is little evidence about the effect of a fatty pancreas on the development of subclinical chronic pancreatitis over the clinical course. Methods We conducted a prospective cohort study from 2008 to 2014 of patients who underwent a medical checkup consultation for fat accumulated in the pancreas. Patients included in the analysis were divided into a non-fatty pancreas group ( n  = 9710) and fatty pancreas group ( n  = 223). The primary end point was the odds ratio (OR) for chronic pancreatitis associated with fatty pancreas, which was diagnosed using ultrasonography. We used a multiple logistic regression model to estimate the OR and the corresponding 95% confidence interval (CI). Results Ninety-two people were diagnosed with chronic pancreatitis, including both presumptive and definitive diagnoses. Twelve people were diagnosed with chronic pancreatitis by ultrasonography among the 223 patients with fatty pancreas, and 80 patients among 9710 were diagnosed with non-fatty pancreas. The crude OR was 6.85 (95% CI 3.68, 12.75), and the multiple adjusted OR was 3.96 (95% CI 2.04, 7.66). Conclusions Fat accumulation in the pancreas could be a risk factor for developing subclinical chronic pancreatitis.

In recent years, the interest in cell transplantation therapy using human dental pulp cells (DPCs) has been increasing. However, significant differences exist in the individual cellular characteristics of human DPC clones and in their therapeutic efficacy in rodent models of spinal cord injury (SCI); moreover, the cellular properties associated with their therapeutic efficacy for SCI remain unclear. Here, using DPC clones from seven different donors, we found that most of the clones were highly resistant to H2O2 cytotoxicity if, after transplantation, they significantly improved the locomotor function of rats with complete SCI. Therefore, we examined the effects of the basic fibroblast growth factor 2 (FGF2) and bardoxolone methyl (RTA402), which is a nuclear factor erythroid 2-related factor 2 (Nrf2) chemical activator, on the total antioxidant capacity (TAC) and the resistance to H2O2 cytotoxicity. FGF2 treatment enhanced the resistance of a subset of clones to H2O2 cytotoxicity. Regardless of FGF2 priming, RTA402 markedly enhanced the resistance of many DPC clones to H2O2 cytotoxicity, concomitant with the upregulation of heme oxygenase-1 (HO-1) and NAD(P)H-quinone dehydrogenase 1 (NQO1). With the exception of a subset of clones, the TAC was not increased by either FGF2 priming or RTA402 treatment alone, whereas it was significantly upregulated by both treatments in each clone, or among all seven DPC clones together. Thus, the TAC and resistance to H2O2 cytotoxicity were, to some extent, independently regulated and were strongly enhanced by both FGF2 priming and RTA402 treatment. Moreover, even a DPC clone that originally exhibited no therapeutic effect on SCI improved the locomotor function of mice with SCI after transplantation under both treatment regimens. Thus, combined with FGF2, RTA402 may increase the number of transplanted DPCs that migrate into and secrete neurotrophic factors at the lesion epicenter, where reactive oxygen species are produced at a high level.

Various types of fiber reinforced cementitious composites (FRCCs) were experimentally studied to evaluate their self-healing capabilities regarding their watertightness and mechanical properties. Cracks were induced in the FRCC specimens during a tensile loading test, and the specimens were then immersed in static water for self-healing. By water permeability and reloading tests, it was determined that the FRCCs containing synthetic fiber and cracks of width within a certain range (<0.1 mm) exhibited good self-healing capabilities regarding their watertightness. Particularly, the high polarity of the synthetic fiber (polyvinyl alcohol (PVA)) series and hybrid fiber reinforcing (polyethylene (PE) and steel code (SC)) series showed high recovery ratio. Moreover, these series also showed high potential of self-healing of mechanical properties. It was confirmed that recovery of mechanical property could be obtained only in case when crack width was sufficiently narrow, both the visible surface cracks and the very fine cracks around the bridging of the SC fibers. Recovery of the bond strength by filling of the very fine cracks around the bridging fibers enhanced the recovery of the mechanical property.

BackgroundThe prognosis of patients with linitis plastica (type 4) and large (≥ 8 cm) ulcero-invasive-type (type 3) gastric cancer is extremely poor, even after extended surgery and adjuvant chemotherapy. Given the promising results of our previous phase II study evaluating neoadjuvant chemotherapy (NAC) with S-1 plus cisplatin (JCOG0210), we performed a phase III study to confirm the efficacy of NAC in these patients, with the safety and surgical results are presented here.MethodsEligible patients were randomized to gastrectomy plus adjuvant chemotherapy with S-1 (Arm A) or NAC followed by gastrectomy + adjuvant chemotherapy (Arm B). The primary endpoint was the overall survival (OS). This trial is registered at the UMIN Clinical Trials Registry as C000000279.ResultsFrom February 2007 to July 2013, 300 patients were randomized (Arm A 149, Arm B 151). NAC was completed in 133 patients (88%). Major grade 3/4 adverse events during NAC were neutropenia (29.3%), nausea (5.4%), diarrhea (4.8%), and fatigue (2.7%). Gastrectomy was performed in 147 patients (99%) in Arm A and 139 patients (92%) in Arm B. The operation time was significantly shorter in Arm B than in Arm A (median 255 vs. 240 min, respectively; p = 0.024). There were no significant differences in Grade 2–4 morbidity and mortality (25.2% and 1.3% in Arm A and 15.8% and 0.7% in Arm B, respectively).ConclusionsNAC for type 4 and large type 3 gastric cancer followed by D2 gastrectomy can be safely performed without increasing the morbidity or mortality.

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Center for Epidemiologic Studies-Depression Scale (CES-D). The nested fivefold cross-validation was used for developing and evaluating the prediction models. The HSIC Lasso-based prediction model showed better predictive power than the other prediction models, including Lasso, support vector machine, partial least squares, random forest, and neural network. l -leucine, 3-hydroxyisobutyrate, and gamma-linolenyl carnitine frequently contributed to the prediction. We have demonstrated that the HSIC Lasso-based prediction model integrating nonlinear feature selection showed improved predictive power for depressive symptoms based on metabolome data as well as on risk metabolites based on nonlinear statistics in the Japanese population. Further studies should use HSIC Lasso-based prediction models with different ethnicities to investigate the generality of each risk metabolite for predicting depressive symptoms.

Purpose The influence of vertebral cement augmentation on spinal sagittal balance is unknown. The present study aimed to analyze the changes in total spinal alignment after Kyphoplasty in VCF patients. Methods The study involved 21 VCF patients who underwent Kyphoplasty. In all patients, lateral radiographs of the entire spine were taken preoperatively and 1 month after surgery, to measure the pelvic incidence (PI), sacral slope (SS), pelvic tilt (PT), lumbar lordosis (LL), sagittal vertical axis (SVA), and spinosacral angle (SSA). These parameters were compared between VCF patients and 30 healthy volunteers. In VCF patients, the parameters were compared before and after Kyphoplasty. Results In VCF patients, preoperative SVA was 7.00 ± 3.9 cm, showing a significant shift to anterior sagittal balance as compared to the healthy group (1.45 ± 2.7 cm) ( P  < 0.0001). Preoperative SS was smaller and PT was larger in VCF group than in the healthy group ( P  < 0.05). After Kyphoplasty, SVA decreased to 5.02 ± 2.91 ( P  = 0.0007) and LL and SSA increased (LL P  = 0.028; SSA P  = 0.0031). Postoperative decrease of SVA was correlated with the kyphotic change of treated vertebra ( r  = 0.792, P  < 0.01). VAS score decreased from 7.98 ± 1.8 before Kyphoplasty to 2.38 ± 2.3 postoperatively ( P  < 0.0001). Conclusions Total spinal alignment is shifted to anterior sagittal balance in VCF patients. Kyphoplasty plays a role not only in reducing pain associated with fractures but also in improving sagittal imbalance in the treatment of painful vertebral compression fracture.

In this study, we consider the problem of self-supervised learning (SSL) utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where TWD is defined as the L1 distance between two tree-embedded vectors. In SSL methods, the cosine similarity is often utilized as an objective function; however, it has not been well studied when utilizing the Wasserstein distance. Training the Wasserstein distance is numerically challenging. Thus, this study empirically investigates a strategy for optimizing the SSL with the Wasserstein distance and finds a stable training procedure. More specifically, we evaluate the combination of two types of TWD (total variation and ClusterTree) and several probability models, including the softmax function, the ArcFace probability model, and simplicial embedding. We propose a simple yet effective Jeffrey divergence-based regularization method to stabilize optimization. Through empirical experiments on STL10, CIFAR10, CIFAR100, and SVHN, we find that a simple combination of the softmax function and TWD can obtain significantly lower results than the standard SimCLR. Moreover, a simple combination of TWD and SimSiam fails to train the model. We find that the model performance depends on the combination of TWD and probability model, and that the Jeffrey divergence regularization helps in model training. Finally, we show that the appropriate combination of the TWD and probability model outperforms cosine similarity-based representation learning.