Single-pixel Fresnel incoherent correlation holography compressed imaging using a Trumpet network.

Fresnel incoherent correlation holography (FINCH) can achieve high-precision and non-scanning 3D imaging. However, as a holographic imaging technology, the huge bandwidth requirements and the amount of holographic data transmitted have always been one of the important factors limiting its application. In addition, the hardware cost of pixel array-based CCD or CMOS imaging is very high under high resolution or specific wavelength conditions. Accordingly, a single-pixel Fresnel incoherent correlation holography (SP-FINCH) compressed imaging method is proposed, which replaces pixel array detector with single-pixel detector and designs a Trumpet network to achieve low-cost and high-resolution imaging. Firstly, a modified FINCH imaging system is constructed and data acquisition is carried out using a single-pixel detector. Secondly, a Trumpet network is constructed to directly map the relationship between one-dimensional sampled data and two-dimensional image in an end-to-end manner. Moreover, by comparing the reconstructed images using neural network with that using commonly used single-pixel reconstruction methods, the results indicate that the proposed SP-FINCH compressed imaging method can significantly improve the quality of image reconstruction at lower sampling rate and achieve imaging without phase-shifting operation. The proposed method has been shown to be feasible and advantageous through numerical simulations and optical experiment results.

Mapping transcriptional heterogeneity and metabolic networks in fatty livers at single-cell resolution.

Non-alcoholic fatty liver disease is a heterogeneous disease with unclear underlying molecular mechanisms. Here, we perform single-cell RNA sequencing of hepatocytes and hepatic non-parenchymal cells to map the lipid signatures in mice with non-alcoholic fatty liver disease (NAFLD). We uncover previously unidentified clusters of hepatocytes characterized by either high or low srebp1 expression. Surprisingly, the canonical lipid synthesis driver Srebp1 is not predictive of hepatic lipid accumulation, suggestive of other drivers of lipid metabolism. By combining transcriptional data at single-cell resolution with computational network analyses, we find that NAFLD is associated with high constitutive androstane receptor (CAR) expression. Mechanistically, CAR interacts with four functional modules: cholesterol homeostasis, bile acid metabolism, fatty acid metabolism, and estrogen response. Nuclear expression of CAR positively correlates with steatohepatitis in human livers. These findings demonstrate significant cellular differences in lipid signatures and identify functional networks linked to hepatic steatosis in mice and humans.

Research progress in the risk factors and screening assessment of dysphagia in the elderly.

With the aging of the population, the incidence of dysphagia has gradually increased and become a major clinical and public health issue. Early screening of dysphagia in high-risk populations is crucial to identify the risk factors of dysphagia and carry out effective interventions and health management in advance. In this study, the current epidemiology, hazards, risk factors, preventive, and therapeutic measures of dysphagia were comprehensively reviewed, and a literature review of screening instruments commonly used globally was conducted, focusing on their intended populations, main indicators, descriptions, and characteristics. According to analysis and research in the current study, previous studies of dysphagia were predominantly conducted in inpatients, and there are few investigations and screenings on the incidence and influencing factors of dysphagia in the community-dwelling elderly and of dysphagia developing in the natural aging process. Moreover, there are no unified, simple, economical, practical, safe, and easy-to-administer screening tools and evaluation standards for dysphagia in the elderly. It is imperative to focus on dysphagia in the community-dwelling elderly, develop unified screening and assessment tools, and establish an early warning model of risks and a dietary structure model for dysphagia in the community-dwelling elderly.

Construction of disease-specific cytokine profiles by associating disease genes with immune responses.

The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response-usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING's high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an "Inflammation Score" that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery.

Transition Metal and N Doping on AlP Monolayers for Bifunctional Oxygen Electrocatalysts: Density Functional Theory Study Assisted by Machine Learning Description.

It is vital to search for highly efficient bifunctional oxygen evolution/reduction reaction (OER/ORR) electrocatalysts for sustainable and renewable clean energy. Herein, we propose a single transition-metal (TM)-based defective AlP system to validate bifunctional oxygen electrocatalysis by using the density functional theory (DFT) method. We found that the catalytic activity is enhanced by substituting two P atoms with two N atoms in the Al vacancy of the TM-anchored AlP monolayer. Specifically, the overpotential of OER(ORR) in Co- and Ni-based defective AlP systems is found to be 0.38 (0.25 V) and 0.23 V (0.39 V), respectively, showing excellent bifunctional catalytic performance. The results are further presented by establishing the volcano plots and contour maps according to the scaling relation of the Gibbs free-energy change of *OH, *O, and *OOH intermediates. The d-band center and the product of the number of d-orbital electrons and electronegativity of the TM atom are the ideal descriptors for this system. To investigate the activity origin of the OER/ORR process, we performed the machine learning (ML) algorithm. The result indicates that the number of TM-d electrons (Ne), the radius of TM atoms (rd), and the charge transfer of TM atoms (Qe) are the three primary descriptors characterizing the adsorption behavior. Our results can provide a theoretical guidance for designing highly efficient bifunctional electrocatalysts and pave a way for the DFT-ML hybrid method in catalysis research.

Tunable Schottky Barrier and Interfacial Electronic Properties in Graphene/ZnSe Heterostructures.

With a direct bandgap, two-dimensional (2D) ZnSe is a promising semiconductor material in photoelectric device fields. In this work, based on first-principles methods, we theoretically studied the modulation of the Schottky barrier height (SBH) by applying horizontal and vertical strains on graphene/ZnSe heterojunction. The results show that the inherent electronic properties of graphene and ZnSe monolayers are both well-conserved because of the weak van der Waals (vdW) forces between two sublayers. Under horizontal strain condition, the n(p)-type SBH decreases from 0.56 (1.62) eV to 0.21 (0.78) eV. By changing the interlayer distance in the range of 2.8 Å to 4.4 Å, the n(p)-type SBH decreases (increases) from 0.88 (0.98) eV to 0.21 (1.76) eV. These findings prove the SBH of the heterojunction to be tuned effectively, which is of great significance to optoelectronic devices, especially in graphene/ZnSe-based nano-electronic and optoelectronic devices.

Distinct clinical phenotypes for Crohn's disease derived from patient surveys.

BACKGROUND: Defining clinical phenotypes provides opportunities for new diagnostics and may provide insights into early intervention and disease prevention. There is increasing evidence that patient-derived health data may contain information that complements traditional methods of clinical phenotyping. The utility of these data for defining meaningful phenotypic groups is of great interest because social media and online resources make it possible to query large cohorts of patients with health conditions. METHODS: We evaluated the degree to which patient-reported categorical data is useful for discovering subclinical phenotypes and evaluated its utility for discovering new measures of disease severity, treatment response and genetic architecture. Specifically, we examined the responses of 1961 patients with inflammatory bowel disease to questionnaires in search of sub-phenotypes. We applied machine learning methods to identify novel subtypes of Crohn's disease and studied their associations with drug responses. RESULTS: Using the patients' self-reported information, we identified two subpopulations of Crohn's disease; these subpopulations differ in disease severity, associations with smoking, and genetic transmission patterns. We also identified distinct features of drug response for the two Crohn's disease subtypes. These subtypes show a trend towards differential genotype signatures. CONCLUSION: Our findings suggest that patient-defined data can have unplanned utility for defining disease subtypes and may be useful for guiding treatment approaches.

Safety and long-term outcomes of early gastric cardiac cancer treated with endoscopic submucosal dissection in 499 Chinese patients.

AIMS: Early gastric cardiac cancer (EGCC) has a low risk of lymph node metastasis with the potential for endoscopic therapy. We aimed to evaluate the short- and long-term outcomes of endoscopic submucosal dissection (ESD)-resected EGCCs in a large cohort of Chinese patients and compare endoscopic and clinicopathologic features between EGCC and early gastric non-cardiac cancer (EGNC). METHODS: We retrospectively studied 512 EGCCs in 499 consecutive patients and 621 EGNCs in 555 consecutive patients between January 2011 and March 2018 at our center. We investigated clinicopathological characteristics of EGCC tumors, ESD treatment results, adverse events, and postresection patient survival. RESULTS: Compared with EGNC patients, EGCC patients were significantly older (average age: 66 years versus 62 years, p < 0.001). The percentage of the gross 0-IIc pattern was higher in EGCCs (46.1%) than in EGNCs (41.5%), while the frequency of the 0-IIa pattern was lower in EGCCs (14.9%) than in EGNCs (22.4%) (p = 0.001). Compared with EGNCs, EGCCs showed smaller size, deeper invasion, fewer ulcerated or poorly differentiated tumors, but more cases with gastritis cystica profunda. The prevalence of ESD-related complications was higher in EGCCs (6.1%) than in EGNCs (2.3%) (p = 0.001). In EGCCs, the disease-specific survival rate was significantly higher in patients of the noncurative resection group with surgery (100%), compared with that (93.9%) without surgery (p < 0.001). CONCLUSION: Clinicopathological characteristics were significantly different between EGCCs and EGNCs. ESD is a safe and effective treatment option with favorable outcomes for patients with EGCC. Additional surgery improved survival in patients with noncurative ESD resection.

Homology modeling of TMPRSS2 yields candidate drugs that may inhibit entry of SARS-CoV-2 into human cells.

The most rapid path to discovering treatment options for the novel coronavirus SARS-CoV-2 is to find existing medications that are active against the virus. We have focused on identifying repurposing candidates for the transmembrane serine protease family member II (TMPRSS2), which is critical for entry of coronaviruses into cells. Using known 3D structures of close homologs, we created seven homology models. We also identified a set of serine protease inhibitor drugs, generated several conformations of each, and docked them into our models. We used three known chemical (non-drug) inhibitors and one validated inhibitor of TMPRSS2 in MERS as benchmark compounds and found six compounds with predicted high binding affinity in the range of the known inhibitors. We also showed that a previously published weak inhibitor, Camostat, had a significantly lower binding score than our six compounds. All six compounds are anticoagulants with significant and potentially dangerous clinical effects and side effects. Nonetheless, if these compounds significantly inhibit SARS-CoV-2 infection, they could represent a potentially useful clinical tool.

A deep learning framework to predict binding preference of RNA constituents on protein surface.

Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins.

Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site.

Taxanes are a family of natural products with a broad spectrum of anticancer activity. This activity is mediated by interaction with the taxane site of beta-tubulin, leading to microtubule stabilization and cell death. Although widely used in the treatment of breast cancer and other malignancies, existing taxane-based therapies including paclitaxel and the second-generation docetaxel are currently limited by severe adverse effects and dose-limiting toxicity. To discover taxane site modulators, we employ a computational binding site similarity screen of > 14,000 drug-like pockets from PDB, revealing an unexpected similarity between the estrogen receptor and the beta-tubulin taxane binding pocket. Evaluation of nine selective estrogen receptor modulators (SERMs) via cellular and biochemical assays confirms taxane site interaction, microtubule stabilization, and cell proliferation inhibition. Our study demonstrates that SERMs can modulate microtubule assembly and raises the possibility of an estrogen receptor-independent mechanism for inhibiting cell proliferation.

Computational analysis of kinase inhibitor selectivity using structural knowledge.

Motivation: Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling. Results: Here, we present a KinomeFEATURE database for kinase binding site similarity search by comparing protein microenvironments characterized using diverse physiochemical descriptors. Initial selectivity prediction of 15 known kinase inhibitors achieved an >90% accuracy and demonstrated improved performance in comparison to commonly used kinase inhibitor selectivity prediction methods. Additional kinase ATP binding site similarity assessment (120 binding sites) identified 55 kinases with significant promiscuity and revealed unexpected inhibitor cross-activities between PKR and FGFR2 kinases. Kinome-wide selectivity profiling of 11 kinase drug candidates predicted novel as well as experimentally validated off-targets and suggested structural mechanisms of kinase cross-activities. Our study demonstrated potential utilities of our approach for large-scale kinase inhibitor selectivity profiling that could contribute to kinase drug development and safety assessment. Availability and implementation: The KinomeFEATURE database and the associated scripts for performing kinase pocket similarity search can be downloaded from the Stanford SimTK website (https://simtk.org/projects/kdb). Supplementary information: Supplementary data are available at Bioinformatics online.

Micropapillary early gastric carcinoma with distinct clinicopathological features, high risk for lymph node metastasis, and dismal prognosis: a multicenter clinicopathological study of 29 cases identified in 1890 early gastric carcinoma radical gastrectomies.

Clinicopathology and risk factors of lymph node metastasis (LNM) in micropapillary early (pT1) gastric carcinoma (MEGC) remain elusive because of the extreme rarity. In this multicenter study, we investigated 1890 consecutive radical resections of early gastric carcinoma diagnosed with the World Health Organization criteria and identified 29 (1.5%) MEGC cases with a small (≥5%) micropapillary component. MEGC showed a male predominance (male-to-female ratio, 21:8). Most (93.1%; 27/29) tumors invaded submucosa. Lymphovascular invasion was detected in 14 (48.3%) of 29 cases. LNM was found in 13 cases (44.8%; 11 identified with a routine hematoxylin-eosin stain and 2 additional cases with a positive pancytokeratin immunostain). Overall, independent risk factors for LNM in early gastric carcinoma included patient age of 62 years or less, female sex, noncardiac location, ulcerative pattern, tumor size of greater than 2 cm, submucosal invasion, Lauren diffuse type, lymphovascular invasion, and MEGC. In MEGC, advanced pathologic stages were demonstrated in 6 (20.7%) of 29 cases. The 5-year overall survival rate of MEGC patients was 58.6%. Submucosal invasion, lymphovascular invasion, and LNM were significantly more frequent in the MEGC group than in the non-MEGC groups. Advanced pathologic stages were significantly more common in MEGC than in nonmicropapillary Lauren intestinal- but not diffuse-type early gastric carcinomas. In conclusion, MEGC demonstrated a high propensity for lymphovascular invasion, LNM with advanced stages, and dismal prognosis.

PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development.

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

The Pioglitazone Trek via Human PPAR Gamma: From Discovery to a Medicine at the FDA and Beyond.

For almost two decades, pioglitazone has been prescribed primarily to prevent and treat insulin resistance in some type 2 diabetic patients. In this review, we trace the path to discovery of pioglitazone as a thiazolidinedione compound, the glitazone tracks through the regulatory agencies, the trek to molecular agonism in the nucleus and the binding of pioglitazone to the nuclear receptor PPAR gamma. Given the rise in consumption of pioglitazone in T2D patients worldwide and the increased number of clinical trials currently testing alternate medical uses for this drug, there is also merit to some reflection on the reported adverse effects. Going forward, it is imperative to continue investigations into the mechanisms of actions of pioglitazone, the potential of glitazone drugs to contribute to unmet needs in complex diseases associated with the dynamics of adaptive homeostasis, and also the routes to minimizing adverse effects in every-day patients throughout the world.

Low risk of lymph node metastasis in 495 early gastric cardiac carcinomas: a multicenter clinicopathologic study of 2101 radical gastrectomies for early gastric carcinoma.

Clinical decision-making on endoscopic vs. surgical resection of early gastric cardiac carcinoma remains challenging because of uncertainty on risk of lymph node metastasis. The aim of this multicenter study was to investigate risk factors of lymph node metastasis in early gastric cardiac carcinoma. Guided with the World Health Organization diagnostic criteria, we studied 2101 radical resections of early gastric carcinoma for risk factors associated with lymph node metastasis, including tumor location, gross pattern, size, histology type, differentiation, invasion depth, lymphovascular, and perineural invasion. We found that the risk of lymph node metastasis was significantly lower in early gastric cardiac carcinomas (6.7%, 33/495), compared with early gastric non-cardiac carcinomas (17.1%, 275/1606) (p < 0.0001). In early gastric cardiac carcinoma, no lymph node metastasis was identified in intramucosal carcinoma (0/193) and uncommon types of carcinomas (0/24), irrespective of the gross pattern, size, histologic type, differentiation, and invasion depth. Ulceration, size > 3 cm, and submucosal invasion were not significant independent risk factors for lymph node metastasis. In 33 early gastric cardiac carcinomas with lymph node metastasis, either lymphovascular invasion or poor differentiation was present in 16 (48.5%) cases and together in six cases. By multivariate analysis, independent risk factors of lymph node metastasis in early gastric cardiac carcinoma included lymphovascular invasion (Odds Ratio (OR): 7.6, 95% Confidence Interval (CI): 2.8-20.2) (p < 0.0001) and poor differentiation (OR: 6.0, 95% CI: 1.4-25.9) (p < 0.05). In conclusion, lymph node metastasis was not identified in early gastric cardiac intramucosal carcinoma and uncommon types of carcinoma. The risk of lymph node metastasis was also significantly lower in tumors with submucosal invasion, especially for cases without lymphovascular invasion or poor differentiation. These results lend support to the role of endoscopic therapy in the treatment of patients with early gastric cardiac carcinoma.

Biological and functional relevance of CASP predictions.

Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.

Mn-complex modified NaDyF4:Yb@NaLuF4:Yb,Er@polydopamine core-shell nanocomposites for multifunctional imaging-guided photothermal therapy.

Upconversion nanoparticles (UCNPs) have been used as building blocks in the construction of multimodal contrast agents for theranostics, that is, the combination of diagnostics and therapies. Upconversion luminescence (UCL) imaging is widely used for cell and tissue imaging while magnetic resonance imaging (MRI) is well-suited for in vivo imaging. Photothermal therapy (PTT) using (near-infrared) NIR laser radiation constitutes a minimally invasive alternative to the conventional surgical treatment of cancer. As such, multifunctional Mn-complex modified NaDyF4:Yb@NaLuF4:Yb,Er@polydopamine (PDA) nanocomposites (Dy@Lu@PDA-Mn) were synthesized, which was based on the fact that Yb3+ and Er3+ induce UCL, Dy3+ and Mn2+ interferes with T2 and T1 in MRI, and PDA strongly absorbs in the NIR region for PTT. Through tri-modal UCL, T1-, T2-weighted MRI guiding, an outstanding photothermal therapeutic efficacy with tumour elimination was obtained in a mice breast cancer model, by injection of Dy@Lu@PDA-Mn upon laser irradiation. A strategy for multimodal imaging-guided PTT was formulated and the potential of multifunctional nanostructures for use in cancer theranostics was demonstrated.