G Protein-Coupled Receptor 56 Characterizes CTLs and Reflects the Progression of Lung Cancer Patients.

CTLs play important roles in host immune responses to tumors. CD4 CTLs are characterized by their ability to secrete cytotoxic effector molecules, such as granzyme B and perforin, and kill target cells in a MHC class II-restricted manner. However, the cell surface markers of CD4 CTLs remain unknown, which hinders their separation and research on their function. In this study, we performed a bioinformatics analysis and experimental validation that revealed that G protein-coupled receptor 56 (GPR56) is a cell surface marker that can be used to characterize CD4 CTLs. We found that GPR56 and granzyme B were coexpressed in extremely high levels in human peripheral blood T cells, and that anti-GPR56 stimulation significantly upregulated the expression of granzyme B in both CD4+GPR56+ and CD8+GPR56+ T cells. These findings suggest that GPR56 expression and the GPR56 signaling pathway could contribute directly to the toxic function of either CD4+ or CD8+ T cells. We also used GPR56 as a biomarker to investigate the clinical significance of CD4 CTLs. GPR56+ T cell levels were increased in patients with lung cancer, and GPR56 expression was significantly correlated with lung cancer progression. A further analysis revealed an increase in exhausted cell states in lung cancer patients because of upregulation of programmed cell death protein 1 expression in GPR56+ T cells. The findings of this study suggest that GPR56 characterizes the cytotoxic states of either CD4+ or CD8+ T cells.

Id1 expression in CD4 T cells promotes differentiation and function of follicular helper T cells and upregulation of related functional molecules.

Although the roles of E proteins and inhibitors of DNA-binding (Id) in T follicular helper (TFH) and T follicular regulatory (TFR) cells have been previously reported, direct models demonstrating the impact of multiple E protein members have been lacking. To suppress all E proteins including E2A, HEB and E2-2, we overexpressed Id1 in CD4 cells using a CD4-Id1 mouse model, to observe any changes in TFH and TFR cell differentiation. Our objective was to gain better understanding of the roles that E proteins and Id molecules play in the differentiation of TFH and TFR cells. The CD4-Id1 transgenic (TG) mice that we constructed overexpressed Id1 in CD4 cells, inhibiting E protein function. Our results showed an increase in the proportion and absolute numbers of Treg, TFH and TFR cells in the spleen of TG mice. Additionally, the expression of surface characterisation molecules PD-1 and ICOS was significantly upregulated in TFH and TFR cells. The study also revealed a downregulation of the marginal zone B cell precursor and an increase in the activation and secretion of IgG1 in spleen B cells. Furthermore, the peripheral TFH cells of TG mice enhanced the function of assisting B cells. RNA sequencing results indicated that a variety of TFH-related functional molecules were upregulated in TFH cells of Id1 TG mice. In conclusion, E proteins play a crucial role in regulating TFH/TFR cell differentiation and function and suppressing E protein activity promotes germinal centre humoral immunity, which has important implications for immune regulation and treating related diseases.

Hypervalent Iodine-Catalyzed Fluorination of Diene-Containing Compounds: A Computational Study.

Studies have shown that the incorporation of fluorine into materials can improve their properties, but C-F bonds are not readily formed in nature. Although some researchers have studied the reaction of fluorinating alkenes catalyzed by hypervalent iodine, far too little attention has been paid to its reaction mechanism. This study aimed to explore the mechanism of the hypervalent iodine-catalyzed 1,4-difluorination of dienes. We found that the catalyst is favorable for the activation of C1=C2 double bonds through halogen bonds, and then two HFs interact with one F atom in the catalyst via hydrogen bonds, resulting in the cleavage of I-F bonds and the formation of [F-H∙∙∙F]-. Subsequently, the catalyst interacts with C1, and the roaming [F-H···F]- attacks C4 from the opposite side of the catalyst. After the fluorination step is completed, the nucleophile F- substitutes the catalyst via the SN2 mechanism. Our calculations demonstrated that the interaction between HF and F- is favorable for the stabilization of the transition state within the fluorination process for which the presence of two HFs in the reaction is the best. We also observed that [F-H∙∙∙F]- attacking C4 from the opposite side of the catalyst is more advantageous than attacking from the same side. This study therefore offers a novel perspective on the mechanism of the hypervalent iodine-catalyzed fluoridation of dienes.

Differential expression of coagulation pathway-related proteins in diabetic urine exosomes.

BACKGROUND: Coagulation function monitoring is important for the occurrence and development of diabetes. A total of 16 related proteins are involved in coagulation, but how these proteins change in diabetic urine exosomes is unclear. To explore the expression changes of coagulation-related proteins in urine exosomes and their possible roles in the pathogenesis of diabetes, we performed proteomic analysis and finally applied them to the noninvasive monitoring of diabetes. METHODS: Subject urine samples were collected. LC-MS/MS was used to collect the information on coagulation-related proteins in urine exosomes. ELISA, mass spectrometry and western blotting were used to further verify the differential protein expression in urine exosomes. Correlations with clinical indicators were explored, and receiver operating characteristic (ROC) curves were drawn to evaluate the value of differential proteins in diabetes monitoring. RESULTS: Analyzing urine exosome proteomics data, eight coagulation-related proteins were found in this study. Among them, F2 was elevated in urine exosomes of diabetic patients compared with healthy controls. The results of ELISA, mass spectrometry and western blotting further verified the changes in F2. Correlation analysis showed that the expression of urine exosome F2 was correlated with clinical lipid metabolism indexes, and the concentration of F2 was strongly positively correlated with blood TG levels (P < 0.05). ROC curve analysis showed that F2 protein in urine exosomes had a good monitoring value for diabetes. CONCLUSION: Coagulation-related proteins were expressed in urine exosomes. Among them, F2 was increased in diabetic urine exosomes and may be a potential biomarker for monitoring diabetic changes.

Lymphocyte migration regulation related proteins in urine exosomes may serve as a potential biomarker for lung cancer diagnosis.

BACKGROUND: The migration of lymphocytes shares many similarities in mode and mechanism with the metastasis of lung cancer tumor cells. But changes in the expression of lymphocyte migration regulation related proteins in urine exosomes remain unclear. This study is to investigate the expression changes of lymphocyte migration regulation related proteins in urine exosomes of lung cancer patients, and further verify their correlation with the development and progression of lung cancer. METHODS: Urine exosomes were collected from lung cancer patients and healthy people aged 15-79 years. Mass spectrometry was used to screen and explore the expression changes of lymphocyte migration regulation related proteins in healthy people of different ages. Enzyme-linked immunosorbent assay and western blotting were used to detect the expression changes of lymphocyte migration regulation related proteins in lung cancer patients. RESULTS: Analyzing the data of urine exosome proteomics, a total of 12 lymphocyte related proteins were identified, 5 of which were lymphocyte migration regulation related proteins. Among these proteins, WASL and STK10 proteins showed a gradual decrease in expression with age, and WNK1 protein showed a gradual increase. Lung cancer patients had reduced expression of WASL and increased expression of STK10 and WNK1 proteins in urine exosomes compared to normal people. Urine exosome WASL, STK10, and WNK1 were diagnosed with lung cancer, with a combined AUC of 0.760. CONCLUSIONS: Lymphocyte migration regulation related proteins were differentially expressed in the urine exosome of lung cancer patients, and WASL, STK10 and WNK1 may serve as potential biomarkers for lung cancer diagnosis.

The altered HLA-DQ expression in peripheral blood T cells of chronic hepatitis B patients characterizes the function of T cells.

OBJECTIVE: This study aimed to clarify the expression of HLA-DQ and granulysin in peripheral blood T-cell subsets in patients with chronic hepatitis B virus (CHB) and to evaluate their significance in assisting CHB diagnosis and immune status assessment. METHODS: Peripheral blood from 34 CHB patients, 36 inactive HBsAg carriers and 33 healthy controls were collected, and HLA-DQ and granulysin in a series of T-cell subsets were analysed by flow cytometry. The ability to secrete IL-10 and IFN-γ and the functional T-cell subsets were measured in Treg and CD4 cells expressing HLA-DQ or not. Correlation analyses were further conducted between HLA-DQ/granulysin-related subsets and clinical indicators of HBV infection, and ROC curves were built to evaluate diagnosis efficiency of HLA-DQ-related subsets. RESULTS: HLA-DQ+ percentages in circulating CD4 T cells were downregulated in CHB patients. The proportions of HLA-DQ + Tfh in CHB were upregulated while HLA-DQ+ percentages in Treg were decreased. In terms of function, the IFN-γ secretion ability of CD4 + T cells and IL-10 secretion in Tregs were stronger in HLA-DQ+ than HLA-DQ- subsets. HLA-DQ + CD4 + T cells and HLA-DQ + Treg were negatively correlated with HBV-DNA, while HLA-DQ + Tfh and Tfc cells were positively correlated with HBV-DNA and ALT. HLA-DQ + Treg/Tfh/Tfc could help to distinguish CHB from inactive HBsAg carriers. CONCLUSION: HLA-DQ on T cells can characterize the function of T-cell subsets and analysis of HLA-DQ can help to evaluate immune status and assist in diagnosis of CHB.

Changes in circulating TCF1- and GARP-associated regulatory T cell subsets reflect the clinical status of patients with chronic HBV infection.

This study aimed to clarify the expression changes and clinical significance of regulatory T (Treg) cells and follicular regulatory T (TFR) cell subsets divided by glycoprotein A repetitions predominant protein (GARP) and T cell factor 1(TCF1) in peripheral blood of patients with chronic HBV infection. The peripheral blood of 26 chronic hepatitis B (CHB) patients, 27 inactive HBsAg carriers and 32 healthy controls were collected and GARP + percentages in Treg and TFR cells were analyzed by flow cytometry. In addition, Treg and TFR cell subsets sorted by CD62L and TCF1 were analyzed and compared. Correlation analyses were performed between Treg and TFR cell subpopulations and clinical parameters as well as cytokine concentrations, including IL-21, IL-10 and TGF-ß1 in plasma. Circulating Treg and TFR levels were elevated in CHB patients. Moreover, GARP and TCF1 were up-regulated in circulating Treg and TFR cells of CHB patients. TCF1 + CD62L- Treg cells were increased while TCF1-CD62L + Treg cells were decreased in CHB patients. TCF1 + CD62L- and TCF1-CD62L- TFR cells were increased while TCF1 + CD62L + TFR cells were decreased in CHB patients. TCF1 + CD62L- Treg cells were positively correlated with HBV DNA, ALT and plasma IL-10, while TCF1 + CD62L + TFR cells were negatively correlated with HBV DNA, HBeAg, HBsAg, ALT, AST, T-BIL and positively correlated with plasma IL-21. Treg and TFR subsets sorted by TCF1, CD62L and GARP were changed in CHB patients. Changes in Treg and TFR functional subsets are associated with antiviral immunity in CHB patients.

Cytotoxic T Lymphocytes Expressing GPR56 are Up-regulated in the Peripheral Blood of Patients with Active Rheumatoid Arthritis and Reflect Disease Progression.

OBJECTIVE: This study aims to elucidate the changes in the percentage of GPR56 and/or granzyme B (GZMB) positive cells in rheumatoid arthritis (RA) CD4 and CD8 T lymphocytes, and to explore their clinical value in diagnosing and reflecting the progression of RA. METHODS: The percentages of GPR56 and/or GZMB positive cells were analyzed in peripheral blood (PB) and spleen T cells in a collagen-induced arthritis (CIA) model established in DBA/1 mice. The percentages of GPR56+ and/or GZMB+ cells were further analyzed in PBs from RA patients and healthy controls. Correlation analysis was performed between clinical indicators and GPR56+, GZMB+, and GPR56+ GZMB+ T cells. Receiver operating characteristic (ROC) curves were used to evaluate the value of GPR56 and GZMB in differentiating active and stable remitting RA. RESULTS: GPR56+ levels were increased in CD4 and CD8 T cells in the PB of CIA mice. The percentages of GPR56+ and GZMB+ cells were increased in both CD4 and CD8 T cell subsets in patients with active RA. GPR56+, GZMB+, and GPR56+ GZMB+ cells were positively correlated with rheumatoid factor and DAS28. ROC analysis revealed that AUCs for GPR56+, GZMB+, and GPR56+ GZMB+ cell percentages to distinguish active RA from stable remission RA were 0.7106, 0.6941, 0.7024, with cut-off values of 16.35, 16.40, 14.80 in CD4 + T cells, and 0.8031, 0.8086, 0.8196 with cut-off values 60.25, 62.15, 40.15 in CD8 + T cells, respectively. CONCLUSIONS: GPR56+ and/or GZMB+ T cells are up-regulated in patients with active RA and reflect their condition. The detection of GPR56 and GZMB is helpful for RA disease assessment.

Supervised Dimensionality Reduction on Grassmannian for Image Set Recognition.

Modeling videos and image sets by linear subspaces has achieved great success in various visual recognition tasks. However, subspaces constructed from visual data are always notoriously embedded in a high-dimensional ambient space, which limits the applicability of existing techniques. This letter explores the possibility of proposing a geometry-aware framework for constructing lower-dimensional subspaces with maximum discriminative power from high-dimensional subspaces in the supervised scenario. In particular, we make use of Riemannian geometry and optimization techniques on matrix manifolds to learn an orthogonal projection, which shows that the learning process can be formulated as an unconstrained optimization problem on a Grassmann manifold. With this natural geometry, any metric on the Grassmann manifold can theoretically be used in our model. Experimental evaluations on several data sets show that our approach results in significantly higher accuracy than other state-of-the-art algorithms.

Unveiling the potential of SLURP1 protein as a biomarker for prostate cancer screening.

Background: Prostate cancer (PCa) develops slowly and lacks obvious symptoms in the early stage, which makes early screening and diagnosis difficult. Urine collection is simple and is an ideal source of biomarkers. In this study, we performed urinary proteomic studies in PCa patients to screen proteins and apply them to the non-invasive early diagnosis of PCa. Method: Urine samples from PCa patients, benign prostatic hyperplasia (BPH) patients and normal control group were collected. Mass spectrometry was used for proteomic analysis and screening target proteins. Western blot and enzyme-linked immunosorbent assay (ELISA) were used to verify the results. Correlations with clinical indicators were explored, and receiver operating characteristic (ROC) curves were drawn to evaluate the value of target proteins in PCa. Result: A total of 1065 proteins were identified. Urinary SLURP1 protein was significantly elevated in patients with PCa compared with normal controls and patients with BPH patients. Western blot and ELISA further verified the expression changes of SLURP1. The immunohistochemical staining results revealed a substantial increase in positive SLURP1 expression within PCa tumor tissue. Correlation analysis showed a positive correlation between the expression level of urine SLURP1 protein and serum PSA. ROC curve analysis of the SLURP1 protein in the urine of both normal individuals and PCa patients is determined to be 0.853 (95% CI=0.754 to 0.954). Conclusion: The concentration of SLURP1 protein in urine of PCa patients is increased, which can serve as a biomarker for screening PCa.

Helios characterized circulating follicular helper T cells with enhanced functional phenotypes and was increased in patients with systemic lupus erythematosus.

Helios was related to the immunosuppressive capacity and stability of regulatory T cells. However, the significance of Helios in follicular help T (TFH) and follicular regulatory T (TFR) cells is unclear. This research aimed to clarify the significance of Helios (IKZF2) in TFH and TFR cells and its clinical value in systemic lupus erythematosus (SLE). IKZF2 mRNA in different cell subsets was analyzed. Helios+ percentages in TFH and TFR cells were identified in the peripheral blood of 75 SLE patients and 62 HCs (healthy controls). PD-1 and ICOS expression were compared between Helios+ and Helios- cells. The capacity of TFH cells to secrete IL-21 and TFR cells to secrete IL-10 was measured. Correlation analysis and receiver operating characteristic (ROC) curve analysis were conducted to assess the clinical significance of Helios-related TFH and TFR cell subsets in SLE. There was Helios expression in TFH and TFR cells. PD-1 and ICOS were lower in Helios+ TFR than in Helios- TFR. ICOS was increased in Helios+ TFH cells compared with Helios- TFH cells, and ICOS in Helios+ TFH cells was downregulated in SLE. Helios+ TFH cells secreted more IL-21 than Helios- TFH cells, and Helios+ TFH cells from SLE patients had a stronger IL-21 secretion than HCs. Helios+ TFH percentages were negatively correlated with C3 and C4 and positively related to CRP and SLEDAI, and the AUC of Helios+ TFH to distinguish SLE from HC was 0.7959. Helios characterizes circulating TFH cells with enhanced function. Increased Helios+ TFH cells could reflect the autoimmune status of SLE.

Imbalance of Circulating Follicular Regulatory and Follicular Helper T Cell Subpopulations Is Associated with Disease Progression and Serum CYFRA 21-1 Levels in Patients with Non-small Cell Lung Cancer.

OBJECTIVE: This study aimed to investigate the changes of follicular helper T (TFH) and follicular regulatory T (TFR) cell subpopulations in patients with non-small cell lung cancer (NSCLC) and their significance. METHODS: Peripheral blood was collected from 58 NSCLC patients at different stages and 38 healthy controls. Flow cytometry was used to detect TFH cell subpopulation based on programmed death 1 (PD-1) and inducible co-stimulator (ICOS), and TFR cell subpopulation based on cluster determinant 45RA (CD45RA) and forkhead box protein P3 (FoxP3). The levels of interleukin-10 (IL-10), interleukin-17a (IL-17a), interleukin-21 (IL-21), and transforming growth factor-ß (TGF-ß) in the plasma were measured, and changes in circulating B cell subsets and plasma IgG levels were also analyzed. The correlation between serum cytokeratin fragment antigen 21-1 (CYFRA 21-1) levels and TFH, TFR, or B cell subpopulations was further explored. RESULTS: The TFR/TFH ratio increased significantly in NSCLC patients. The CD45RA+FoxP3int TFR subsets were increased, with their proportions increasing in stages II to III and decreasing in stage IV. PD-1+ICOS+TFH cells showed a downward trend with increasing stages. Plasma IL-21 and TGF-ß concentrations were increased in NSCLC patients compared with healthy controls. Plasmablasts, plasma IgG levels, and CD45RA+FoxP3int TFR cells showed similar trends. TFH numbers and plasmablasts were positively correlated with CYFRA 21-1 in stages I-III and negatively correlated with CYFRA 21-1 in stage IV. CONCLUSION: Circulating TFH and TFR cell subpopulations and plasmablasts dynamically change in different stages of NSCLC, which is associated with serum CYFRA 21-1 levels and reflects disease progression.

Morphological changes in the cerebellum during aging: evidence from convolutional neural networks and shape analysis.

The morphology and function of the cerebellum are associated with various developmental disorders and healthy aging. Changes in cerebellar morphology during the aging process have been extensively investigated, with most studies focusing on changes in cerebellar regional volume. The volumetric method has been used to quantitatively demonstrate the decrease in the cerebellar volume with age, but it has certain limitations in visually presenting the morphological changes of cerebellar atrophy from a three-dimensional perspective. Thus, we comprehensively described cerebellar morphological changes during aging through volume measurements of subregions and shape analysis. This study included 553 healthy participants aged 20-80 years. A novel cerebellar localized segmentation algorithm based on convolutional neural networks was utilized to analyze the volume of subregions, followed by shape analysis for localized atrophy assessment based on the cerebellar thickness. The results indicated that out of the 28 subregions in the absolute volume of the cerebellum, 15 exhibited significant aging trends, and 16 exhibited significant sex differences. Regarding the analysis of relative volume, only 11 out of the 28 subregions of the cerebellum exhibited significant aging trends, and 4 exhibited significant sex differences. The results of the shape analysis revealed region-specific atrophy of the cerebellum with increasing age. Regions displaying more significant atrophy were predominantly located in the vermis, the lateral portions of bilateral cerebellar hemispheres, lobules I-III, and the medial portions of the posterior lobe. This atrophy differed between sexes. Men exhibited slightly more severe atrophy than women in most of the cerebellar regions. Our study provides a comprehensive perspective for observing cerebellar atrophy during the aging process.

Vanin-2 is expressed in peripheral blood T cells and upregulated in SLE patients.

Members of the vanin gene family include VNN1, VNN2 and VNN3 in humans. Although the functions of vanins have been widely examined in myeloid cells, their expression and functions have not been clarified in T lymphocytes. This study aimed to elucidate the significance of Vanin-2 (VNN2) on human peripheral blood T lymphocytes and study its expression in systemic lupus erythematosus (SLE). The differential expression of Vanins was analysed by bioinformatics. VNN2 expressions in peripheral blood T cell subsets were analysed by single-cell RNA sequencing data and flow cytometry. Changes of VNN2 expression before and after T cell activation were further clarified by western blot. The function of VNN2+ cells was studied by granzyme B and perforin detection. Changes in VNN2+ proportions in T cell subsets of SLE patients were further analysed. In the present study, only VNN2 among vanins showed distinguishable expression in T cells. VNN2+ percentages were higher in CD8+ T cells than in CD4+ T cells. VNN2+ T cells were with a higher memory T cell composition. VNN2 expression was significantly increased after T cell stimulation. VNN2+ T cells had higher levels of granzyme B and perforin secretion than VNN2- T cells. Clinically, VNN2+ percentages in T cells of SLE patients were upregulated. Together, these data suggested that VNN2 is expressed in peripheral blood T cells characterized more granzyme B and perforin secretion, and increased VNN2+ T cells in SLE patients could reflect altered T cell functions in vivo.

Density regression and uncertainty quantification with Bayesian deep noise neural networks.

Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of applications. However, it remains a challenging task to accurately quantify the uncertainty in DNN predictions, especially those of continuous outcomes. To this end, we propose the Bayesian deep noise neural network (B-DeepNoise), which generalizes standard Bayesian DNNs by extending the random noise variable from the output layer to all hidden layers. Our model is capable of approximating highly complex predictive density functions and fully learn the possible random variation in the outcome variables. For posterior computation, we provide a closed-form Gibbs sampling algorithm that circumvents tuning-intensive Metropolis-Hastings methods. We establish a recursive representation of the predictive density and perform theoretical analysis on the predictive variance. Through extensive experiments, we demonstrate the superiority of B-DeepNoise over existing methods in terms of density estimation and uncertainty quantification accuracy. A neuroimaging application is included to show our model's usefulness in scientific studies.

Physically informed machine-learning algorithms for the identification of two-dimensional atomic crystals.

After graphene was first exfoliated in 2004, research worldwide has focused on discovering and exploiting its distinctive electronic, mechanical, and structural properties. Application of the efficacious methodology used to fabricate graphene, mechanical exfoliation followed by optical microscopy inspection, to other analogous bulk materials has resulted in many more two-dimensional (2D) atomic crystals. Despite their fascinating physical properties, manual identification of 2D atomic crystals has the clear drawback of low-throughput and hence is impractical for any scale-up applications of 2D samples. To combat this, recent integration of high-performance machine-learning techniques, usually deep learning algorithms because of their impressive object recognition abilities, with optical microscopy have been used to accelerate and automate this traditional flake identification process. However, deep learning methods require immense datasets and rely on uninterpretable and complicated algorithms for predictions. Conversely, tree-based machine-learning algorithms represent highly transparent and accessible models. We investigate these tree-based algorithms, with features that mimic color contrast, for automating the manual inspection process of exfoliated 2D materials (e.g., MoSe2). We examine their performance in comparison to ResNet, a famous Convolutional Neural Network (CNN), in terms of accuracy and the physical nature of their decision-making process. We find that the decision trees, gradient boosted decision trees, and random forests utilize physical aspects of the images to successfully identify 2D atomic crystals without suffering from extreme overfitting and high training dataset demands. We also employ a post-hoc study that identifies the sub-regions CNNs rely on for classification and find that they regularly utilize physically insignificant image attributes when correctly identifying thin materials.

Colorimetric Aptasensor for the Visual and Microplate Determination of Clusterin in Human Urine Based on Aggregation Characteristics of Gold Nanoparticles.

Clusterin has the potential to become the biomarker of multiple diseases, but its clinical quantitative detection methods are limited, which restricts its research progress as a biomarker. A rapid and visible colorimetric sensor for clusterin detection based on sodium chloride-induced aggregation characteristic of gold nanoparticles (AuNPs) was successfully constructed. Unlike the existing methods based on antigen-antibody recognition reactions, the aptamer of clusterin was used as the sensing recognition element. The aptamer could protect AuNPs from aggregation caused by sodium chloride, but clusterin bound with aptamer detached it from AuNPs, thereby inducing aggregation again. Simultaneously, the color change from red in the dispersed state to purple gray in the aggregated state made it possible to preliminarily judge the concentration of clusterin by observation. This biosensor showed a linear range of 0.02-2 ng/mL and good sensitivity with a detection limit of 5.37 pg/mL. The test results of clusterin in spiked human urine confirmed that the recovery rate was satisfactory. The proposed strategy is helpful for the development of label-free point-of-care testing equipment for clinical testing of clusterin, which is cost-effective and feasible.

Does the Electrocardiogram Machine Interpretation Affect the Ability to Accurately Diagnose ST-Elevation Myocardial Infarction by Emergency Physicians?

INTRODUCTION: An ST-elevation myocardial infarction (STEMI) can portend significant morbidity and mortality to the patient and therefore must be rapidly diagnosed by an emergency medicine (EM) physician. The primary aim of this study is to determine whether EM physicians are more or less likely to accurately diagnose STEMI on an electrocardiogram (ECG) if they are blinded to the ECG machine interpretation as opposed to if they are provided the ECG machine interpretation. METHODS: We performed a retrospective chart review of adult patients over 18 years of age admitted to our large, urban tertiary care center with a diagnosis of STEMI from January 1, 2016, to December 31, 2017. From these patients' charts, we selected 31 ECGs to create a quiz that was presented twice to a group of emergency physicians. The first quiz contained the 31 ECGs without the computer interpretations revealed. The second quiz, presented to the same physicians 2 weeks later, contained the same set of ECGs with the computer interpretations revealed. Physicians were asked "Based on the ECG above, is there a blocked coronary artery present causing a STEMI?" RESULTS: Twenty-five EM physicians completed two 31-question ECG quizzes for a total of 1550 ECG interpretations. On the first quiz with computer interpretations blinded, the overall sensitivity in identifying a "true STEMI" was 67.2% with an overall accuracy of 65.6%. On the second quiz in which the ECG machine interpretation was revealed, the overall sensitivity was 66.4% with an accuracy of 65.8 % in correctly identifying a STEMI. The differences in sensitivity and accuracy were not statistically significant. CONCLUSION: This study demonstrated no significant difference in physicians blinded versus those unblinded to computer interpretations of possible STEMI.

Chameleon-inspired active tunable structural color based on smart skin with multi-functions of structural color, sensing and actuation.

Tunable structural color has many potential applications in artificial camouflage, mechanical sensors, etc. Despite the extensive efforts to develop efficient tunable structural color, there is still a wide gap between the existing "passive" tuning methods and the "active" strategy found on organisms such as chameleons that can change color according to the environment. Inspired by the active tunable color system of chameleons, we propose a smart skin comprising a nanoscale hole array of photonic crystals, carbon nanotube coatings, and liquid crystal elastomers, to integrate multiple functions, i.e., structural color tunability, sensing, and actuation, in one structure. The smart skin was further coupled with an image acquisition unit (which mimics eyes to obtain colors from the environment) and a controller (which mimics the brain to process the signals transmitted from the image acquisition unit to the smart skin), to construct an active tunable structural color system. The proposed system autonomously modulates the color according to the environmental color. To validate the color tuning, color scanning from red to green to blue or vice versa is demonstrated in this work, which could certainly open up new paths to create active tunable structural color systems, and thus, push the development of structural color-based devices and systems.

Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder.

Multidimensional Item Response Theory (MIRT) is widely used in educational and psychological assessment and evaluation. With the increasing size of modern assessment data, many existing estimation methods become computationally demanding and hence they are not scalable to big data, especially for the multidimensional three-parameter and four-parameter logistic models (i.e., M3PL and M4PL). To address this issue, we propose an importance-weighted sampling enhanced Variational Autoencoder (VAE) approach for the estimation of M3PL and M4PL. The key idea is to adopt a variational inference procedure in machine learning literature to approximate the intractable marginal likelihood, and further use importance-weighted samples to boost the trained VAE with a better log-likelihood approximation. Simulation studies are conducted to demonstrate the computational efficiency and scalability of the new algorithm in comparison to the popular alternative algorithms, i.e., Monte Carlo EM and Metropolis-Hastings Robbins-Monro methods. The good performance of the proposed method is also illustrated by a NAEP multistage testing data set.