AAV-mediated hepatic LPL expression ameliorates severe hypertriglyceridemia and acute pancreatitis in Gpihbp1 deficient mice and rats.

GPIHBP1 plays an important role in the hydrolysis of triglyceride (TG) lipoproteins by lipoprotein lipases (LPLs). However, Gpihbp1 knockout mice did not develop hypertriglyceridemia (HTG) during the suckling period but developed severe HTG after weaning on a chow diet. It has been postulated that LPL expression in the liver of suckling mice may be involved. To determine whether hepatic LPL expression could correct severe HTG in Gpihbp1 deficiency, liver-targeted LPL expression was achieved via intravenous administration of the adeno-associated virus (AAV)-human LPL gene, and the effects of AAV-LPL on HTG and HTG-related acute pancreatitis (HTG-AP) were observed. Suckling Gpihbp1-/- mice with high hepatic LPL expression did not develop HTG, whereas Gpihbp1-/- rat pups without hepatic LPL expression developed severe HTG. AAV-mediated liver-targeted LPL expression dose-dependently decreased plasma TG levels in Gpihbp1-/- mice and rats, increased post-heparin plasma LPL mass and activity, decreased mortality in Gpihbp1-/- rat pups, and reduced the susceptibility and severity of both Gpihbp1-/- animals to HTG-AP. However, the muscle expression of AAV-LPL had no significant effect on HTG. Targeted expression of LPL in the liver showed no obvious adverse reactions. Thus, liver-targeted LPL expression may be a new therapeutic approach for HTG-AP caused by GPIHBP1 deficiency.

Comparing Causal Bayesian Networks Estimated from Data.

The knowledge of the causal mechanisms underlying one single system may not be sufficient to answer certain questions. One can gain additional insights from comparing and contrasting the causal mechanisms underlying multiple systems and uncovering consistent and distinct causal relationships. For example, discovering common molecular mechanisms among different diseases can lead to drug repurposing. The problem of comparing causal mechanisms among multiple systems is non-trivial, since the causal mechanisms are usually unknown and need to be estimated from data. If we estimate the causal mechanisms from data generated from different systems and directly compare them (the naive method), the result can be sub-optimal. This is especially true if the data generated by the different systems differ substantially with respect to their sample sizes. In this case, the quality of the estimated causal mechanisms for the different systems will differ, which can in turn affect the accuracy of the estimated similarities and differences among the systems via the naive method. To mitigate this problem, we introduced the bootstrap estimation and the equal sample size resampling estimation method for estimating the difference between causal networks. Both of these methods use resampling to assess the confidence of the estimation. We compared these methods with the naive method in a set of systematically simulated experimental conditions with a variety of network structures and sample sizes, and using different performance metrics. We also evaluated these methods on various real-world biomedical datasets covering a wide range of data designs.

Comprehensive characterization of pathogenic synovial fluid extracellular vesicles from knee osteoarthritis.

Synovial fluid (SF) extracellular vesicles (EVs) play a pathogenic role in osteoarthritis (OA). However, the surface markers, cell and tissue origins, and effectors of these EVs are largely unknown. We found that SF EVs contained 692 peptides that were positively associated with knee radiographic OA severity; 57.4% of these pathogenic peptides were from 46 proteins of the immune system, predominantly the innate immune system. CSPG4, BGN, NRP1, and CD109 are the major surface markers of pathogenic SF EVs. Genes encoding surface marker CSPG4 and CD109 were highly expressed by chondrocytes from damaged cartilage, while VISG4, MARCO, CD163 and NRP1 were enriched in the synovial immune cells. The frequency of CSPG4+ and VSIG4+ EV subpopulations in OA SF was high. We conclude that pathogenic SF EVs carry knee OA severity-associated proteins and specific surface markers, which could be developed as a new source of diagnostic biomarkers or therapeutic targets in OA.

Z-Scheme Modulated Charge Transfer on InVO4 @ZnIn2 S4 for Durable Overall Water Splitting.

The charge transfer within heterojunction is crucial for the efficiency and stability of photocatalyst for overall water splitting (OWS). Herein, InVO4 nanosheets have been employed as a support for the lateral epitaxial growth of ZnIn2 S4 nanosheets to produce hierarchical InVO4 @ZnIn2 S4 (InVZ) heterojunctions. The distinct branching heterostructure facilitates active site exposure and mass transfer, further boosting the participation of ZnIn2 S4 and InVO4 for proton reduction and water oxidation, respectively. The unique Z-scheme modulated charge transfer, visualized by simulation and in situ analysis, has been proved to promote the spatial separation of photoexcited charges and strengthen the anti-photocorrosion capability of InVZ. The optimized InVZ heterojunction presents improved OWS (153.3 µmol h-1  g-1 for H2 and 76.9 µmol h-1  g-1 for O2 ) and competitive H2 production (21090 µmol h-1  g-1 ). Even after 20 times (100 h) of cycle experiment, it still holds more than 88% OWS activity and a complete structure.

Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs.

BACKGROUND: We aimed to identify unmet treatment needs for improving social and occupational functioning in early schizophrenia using a data-driven causal discovery analysis. METHODS: Demographic, clinical, and psychosocial measures were obtained for 276 participants from the Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) trial at baseline and 6-months, along with measures of social and occupational functioning from the Quality of Life Scale. The Greedy Fast Causal Inference algorithm was used to learn a partial ancestral graph modeling causal relationships across baseline variables and 6-month functioning. Effect sizes were estimated using a structural equation model. Results were validated in an independent dataset (N = 187). RESULTS: In the data-generated model, greater baseline socio-affective capacity was a cause of greater baseline motivation [Effect size (ES) = 0.77], and motivation was a cause of greater baseline social and occupational functioning (ES = 1.5 and 0.96, respectively), which in turn were causes of their own 6-month outcomes. Six-month motivation was also identified as a cause of occupational functioning (ES = 0.92). Cognitive impairment and duration of untreated psychosis were not direct causes of functioning at either timepoint. The graph for the validation dataset was less determinate, but otherwise supported the findings. CONCLUSIONS: In our data-generated model, baseline socio-affective capacity and motivation are the most direct causes of occupational and social functioning 6 months after entering treatment in early schizophrenia. These findings indicate that socio-affective abilities and motivation are specific high-impact treatment needs that must be addressed in order to promote optimal social and occupational recovery.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks.

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Reward versus motoric activations in nucleus accumbens core of rats during Pavlovian conditioning.

The nucleus accumbens (NAc) core plays an important role in processing of events related to food reward, such as conditioned cues, approach or consumption. Nonetheless, there is lack of clarity regarding whether NAc core processes these separable events differently. We used the high temporal and spatial resolution of single unit recording with trial-by-trial video analysis to examine firing during three distinct categories termed cue, approach and consumption in a Pavlovian task. We had three goals. First, we sought to precisely define task-related behaviour in terms of distinct phases, in order to compare neural activity between motorically matched behaviours. We found that cue-evoked firing did not distinguish between trials on which animals initiated an approach versus ones on which they did not. Firing associated with consumption was greater than firing associated with motorically similar uncued head entry, indicating that previously reported decreases in NAc core firing during consumption relative to approach or baseline may reflect differences in motor behaviour. Secondly, we assessed changes in firing over the course of training. We found that NAc core neurons acquired a response to the tone cue within three sessions but did not change further across 10 total sessions. Thirdly, we correlated individual neuron firing during a given event with its firing during the same event on subsequent sessions. We found substantial variation in processing of cue and approach but not consumption, indicating that a given neuron may process certain events differently from session to session, while maintaining more stable processing of appetitive reward.

Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection.

OBJECTIVES: Sepsis remains a leading and preventable cause of hospital utilization and mortality in the United States. Despite updated guidelines, the optimal definition of sepsis as well as optimal timing of bundled treatment remain uncertain. Identifying patients with infection who benefit from early treatment is a necessary step for tailored interventions. In this study, we aimed to illustrate clinical predictors of time-to-antibiotics among patients with severe bacterial infection and model the effect of delay on risk-adjusted outcomes across different sepsis definitions. DESIGN: A multicenter retrospective observational study. SETTING: A seven-hospital network including academic tertiary care center. PATIENTS: Eighteen thousand three hundred fifteen patients admitted with severe bacterial illness with or without sepsis by either acute organ dysfunction (AOD) or systemic inflammatory response syndrome positivity. MEASUREMENTS AND MAIN RESULTS: The primary exposure was time to antibiotics. We identified patient predictors of time-to-antibiotics including demographics, chronic diagnoses, vitals, and laboratory results and determined the impact of delay on a composite of inhospital death or length of stay over 10 days. Distribution of time-to-antibiotics was similar across patients with and without sepsis. For all patients, a J-curve relationship between time-to-antibiotics and outcomes was observed, primarily driven by length of stay among patients without AOD. Patient characteristics provided good to excellent prediction of time-to-antibiotics irrespective of the presence of sepsis. Reduced time-to-antibiotics was associated with improved outcomes for all time points beyond 2.5 hours from presentation across sepsis definitions. CONCLUSIONS: Antibiotic timing is a function of patient factors regardless of sepsis criteria. Similarly, we show that early administration of antibiotics is associated with improved outcomes in all patients with severe bacterial illness. Our findings suggest identifying infection is a rate-limiting and actionable step that can improve outcomes in septic and nonseptic patients.

Correction of Familial LCAT Deficiency by AAV-hLCAT Prevents Renal Injury and Atherosclerosis in Hamsters-Brief Report.

[Figure: see text].

Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage.

Objective: A tiered trauma team activation system allocates resources proportional to patients' needs based upon injury burden. Previous trauma hospital-triage models are limited to predicting Injury Severity Score which is based on > 10% all-cause in-hospital mortality, rather than need for emergent intervention within 6 hours (NEI-6). Our aim was to develop a novel prediction model for hospital-triage that utilizes criteria available to the EMS provider to predict NEI-6 and the need for a trauma team activation.Methods: A regional trauma quality collaborative was used to identify all trauma patients ≥ 16 years from the American College of Surgeons-Committee on Trauma verified Level 1 and 2 trauma centers. Logistic regression and random forest were used to construct two predictive models for NEI-6 based on clinically relevant variables. Restricted cubic splines were used to model nonlinear predictors. The accuracy of the prediction model was assessed in terms of discrimination.Results: Using data from 12,624 patients for the training dataset (62.6% male; median age 61 years; median ISS 9) and 9,445 patients for the validation dataset (62.6% male; median age 59 years; median ISS 9), the following significant predictors were selected for the prediction models: age, gender, field GCS, vital signs, intentionality, and mechanism of injury. The final boosted tree model showed an AUC of 0.85 in the validation cohort for predicting NEI-6.Conclusions: The NEI-6 trauma triage prediction model used prehospital metrics to predict need for highest level of trauma activation. Prehospital prediction of major trauma may reduce undertriage mortality and improve resource utilization.

Transcriptional, secondary metabolic, and antioxidative investigations elucidate the rapid response mechanism of Pontederia cordata to cadmium.

Pontederia cordata is previously demonstrated a cadmium (Cd) tolerant plant, and also a candidate for the phytoremediation of heavy-metal-contaminated wetlands. A hydroponic experiment was used to investigate variations in photosynthetic gas exchange parameters, antioxidative activities, chlorophyll and secondary metabolite contents, and transcriptome in leaves of the plant exposed to 0.44 mM Cd2+ for 0 h, 24 h, and 48 h. Under Cd2+ exposure for 24 h, the plant presented a favorable photosynthesis by maintaining relatively higher antioxidant activity. Cd2+ exposure for 48 h accelerated membrane peroxidation, declined photosynthetic pigment content, and increased polyphenol oxidase activity, thus interfering with photosynthesis. The phenylpropane pathway served as a chemical rather than physical defense against Cd2+ in the plant leaves. A total of 20,998, 4743, and 4413 differentially expressed genes (DEGs) were identified in the groups of 0 h vs 24 h, 0 h vs 48 h, and 24 h vs 48 h, respectively. The primary metabolic pathways of the DEGs were mainly enriched in nitrogen metabolism, starch and sucrose metabolism, fructose and mannose metabolism, as well as pentose-phosphate pathway, contributing to a stable cell structure and function. Flavonoid biosynthesis directly or indirectly played an antioxidative role against Cd2+ in the leaves. Forty-nine transcription factor (TF) families were identified, and 8 TF families were shared among the three groups. The present study provides a theoretical foundation for investigating tolerance mechanisms of wetland plants to Cd stress in terms of secondary metabolism and transcriptional regulation.

Extracting Pathway-level Signatures from Proteogenomic Data in Breast Cancer Using Independent Component Analysis.

Recent advances in the multi-omics characterization necessitate knowledge integration across different data types that go beyond individual biomarker discovery. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.

Bariatric Surgery is Associated With Decreased Progression of Nonalcoholic Fatty Liver Disease to Cirrhosis: A Retrospective Cohort Analysis.

OBJECTIVE: This study sought to compare trends in the development of cirrhosis between patients with NAFLD who underwent bariatric surgery and a well-matched group of nonsurgical controls. SUMMARY OF BACKGROUND DATA: Patients with NAFLD who undergo bariatric surgery generally have improvements in liver histology. However, the long-term effect of bariatric surgery on clinically relevant liver outcomes has not been investigated. METHODS: From a large insurance database, patients with a new NAFLD diagnosis and at least 2 years of continuous enrollment before and after diagnosis were identified. Patients with traditional contraindications to bariatric surgery were excluded. Patients who underwent bariatric surgery were identified and matched 1:2 with patients who did not undergo bariatric surgery based on age, sex, and comorbid conditions. Kaplan-Meier analysis and Cox proportional hazards modeling were used to evaluate differences in progression from NAFLD to cirrhosis. RESULTS: A total of 2942 NAFLD patients who underwent bariatric surgery were identified and matched with 5884 NAFLD patients who did not undergo surgery. Cox proportional hazards modeling found that bariatric surgery was independently associated with a decreased risk of developing cirrhosis (hazard ratio 0.31, 95% confidence interval 0.19-0.52). Male gender was associated with an increased risk of cirrhosis (hazard ratio 2.07, 95% confidence interval 1.31-3.27). CONCLUSIONS: Patients with NAFLD who undergo bariatric surgery are at a decreased risk for progression to cirrhosis compared to well-matched controls. Bariatric surgery should be considered as a treatment strategy for otherwise eligible patients with NAFLD. Future bariatric surgery guidelines should include NAFLD as a comorbid indication when determining eligibility.

Multiple predictively equivalent risk models for handling missing data at time of prediction: With an application in severe hypoglycemia risk prediction for type 2 diabetes.

The presence of missing data at the time of prediction limits the application of risk models in clinical and research settings. Common ways of handling missing data at the time of prediction include measuring the missing value and employing statistical methods. Measuring missing value incurs additional cost, whereas previously reported statistical methods results in reduced performance compared to when all variables are measured. To tackle these challenges, we introduce a new strategy, the MMTOP algorithm (Multiple models for Missing values at Time Of Prediction), which does not require measuring additional data elements or data imputation. Specifically, at model construction time, the MMTOP constructs multiple predictively equivalent risk models utilizing different risk factor sets. The collection of models are stored and to be queried at prediction time. To predict an individual's risk in the presence of incomplete data, the MMTOP selects the risk model based on measurement availability for that individual from the collection of predictively equivalent models and makes the risk prediction with the selected model. We illustrate the MMTOP with severe hypoglycemia (SH) risk prediction based on data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. We identified 77 predictively equivalent models for SH with cross-validated c-index of 0.77 ± 0.03. These models are based on 77 distinct risk factor sets containing 12-17 risk factors. In terms of handling missing data at the time of prediction, the MMTOP outperforms all four tested competitor methods and maintains consistent performance as the number of missing variables increase.

Human transposon insertion profiling: Analysis, visualization and identification of somatic LINE-1 insertions in ovarian cancer.

Mammalian genomes are replete with interspersed repeats reflecting the activity of transposable elements. These mobile DNAs are self-propagating, and their continued transposition is a source of both heritable structural variation as well as somatic mutation in human genomes. Tailored approaches to map these sequences are useful to identify insertion alleles. Here, we describe in detail a strategy to amplify and sequence long interspersed element-1 (LINE-1, L1) retrotransposon insertions selectively in the human genome, transposon insertion profiling by next-generation sequencing (TIPseq). We also report the development of a machine-learning-based computational pipeline, TIPseqHunter, to identify insertion sites with high precision and reliability. We demonstrate the utility of this approach to detect somatic retrotransposition events in high-grade ovarian serous carcinoma.

Pontederia cordata, an ornamental aquatic macrophyte with great potential in phytoremediation of heavy-metal-contaminated wetlands.

Pontederia cordata can tolerate heavy metal toxicity and possesses great potential for phytoremediation of heavy-metal-contaminated wetlands, yet how it copes with heavy metal stress has still not been determined. Hydroponic experiments were used to assess the effects of various levels of Cd2+ on the photosynthesis and activity of redox-regulatory systems in the plant leaves, and we also sought to elucidate the tolerance mechanism of the plant to Cd2+ by investigating Cd2+ enrichment characteristics and chemical forms. The plant can manage a low cadmium concentration (≤0.04 mM) with relatively stable biomass and photosynthetic performance. Cd2+ at the highest concentration (0.44 mM) decreased superoxide dismutase and peroxidase activities by 37.17% and 93.29%, respectively. Similar trends were demonstrated in the contents of ascorbic acid, carotenoids, lutein, glutathione, and non-protein thiol, as well as phytochelation in the leaves, exacerbating membrane peroxidation despite the significantly increased catalase activity observed. Moreover, the highest Cd2+ concentration disturbed the biosynthesis of chlorophyll precursors in the leaves, reduced chlorophyll a and b, as well as total chlorophyll contents by 60.47%, 67.47%, and 68.12%, respectively, which inhibited photosynthesis, leading to a decline in biomass. Compared with maximum quantum efficiency (Fv/Fm) and the potential activity (Fv/Fo) of photosystem II, the performance index for energy conservation from photons absorbed by PSII to the reduction of intersystem electron acceptors (PIabs), and of PSI end acceptors (PItotal), can indicate Cd2+ toxicity to the photosynthetic apparatus in the leaves. 49.95%-76.90% of the Cd2+ was sequestered in the plant roots, restraining translocation from roots to shoots, which is considered a tolerance mechanism, probably resulting from disturbed transpiration in leaves and increased Cd2+ content with low activity. Pontederia cordata is a candidate plant for phytoremediation of heavy-metal -contaminated wetlands.

Discovering and identifying New York heart association classification from electronic health records.

BACKGROUND: Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure (HF) patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. Though NYHA is rarely stored in EHR structured data, such information is often documented in unstructured clinical notes. METHODS: We accessed HF patients' data in a local EHR system and identified potential sources of NYHA, including local diagnosis codes, procedures, and clinical notes. We further investigated and compared the performances of rule-based versus machine learning-based natural language processing (NLP) methods to identify NYHA class from clinical notes. RESULTS: Of the 36,276 patients with a diagnosis of HF or a CRT implant, 19.2% had NYHA class mentioned at least once in their EHR. While NYHA class existed in descriptive fields association with diagnosis codes (31%) or procedure codes (2%), the richest source of NYHA class was clinical notes (95%). A total of 6174 clinical notes were matched with hospital-specific custom NYHA class diagnosis codes. Machine learning-based methods outperformed a rule-based method. The best machine-learning method was a random forest with n-gram features (F-measure: 93.78%). CONCLUSIONS: NYHA class is documented in different parts in EHR for HF patients and the documentation rate is lower than expected. NLP methods are a feasible way to extract NYHA class information from clinical notes.

In Situ Hot-Spot Assembly as a General Strategy for Probing Single Biomolecules.

Single-molecule detection using surface-enhanced Raman spectroscopy (SERS) has attracted increasing attention in chemical and biomedical analysis. However, it remains a major challenge to probe single biomolecules by means of SERS hot spots owing to the small volume of hot spots and their random distribution on substrates. We here report an in situ hot-spot assembly method as a general strategy for probing single biomolecules. As a proof-of-concept, this proposed strategy was successfully used for the detection of single microRNA-21 (miRNA-21, a potential cancer biomarker) at the single-cell level, showing great capability in differentiating the expression of miRNA-21 in single cancer cells from normal cells. This approach was further extended to single-protein detection. The versatility of the strategy opens an exciting avenue for single-molecule detection of biomarkers of interest and thus holds great promise in a variety of biological and biomedical applications.

Prussian Blue as a Highly Sensitive and Background-Free Resonant Raman Reporter.

We report a surprising discovery that Prussian blue (PB) can be employed as a highly sensitive and background-free resonant Raman reporter. Conventional Raman reporters show multiple spectral bands in the fingerprint region, which are generally overlapped with those from dominant endogenous biomolecules, and are thus difficult to be separated. Herein, we found that PB only possesses a strong and sharp single-band in the cellular Raman-silent region, where no Raman signals from biological species were observed. Therefore, the Raman spectra from PB and endogenous biomolecules are completely resolved without resorting to complicated spectral unmixing. Moreover, PB holds a strong UV-vis absorption band between 500 and 900 nm, which is resonant with the incident detection lasers, providing extremely high sensitivity. Through assembly of PB onto plasmonic cores, a new surface-enhanced resonance Raman scattering (SERRS) probe was achieved with a high signal-to-background ratio (SBR). We demonstrated the performance of the PB-based SERRS tags for high-sensitivity immunoassay and cancer cell imaging.

Interference-Free Surface-Enhanced Raman Scattering Tags for Single-Cell Molecular Imaging with a High Signal-to-Background Ratio.

An interference-free surface-enhanced Raman scattering tag is constructed to profile the expression of cancer biomarkers at the single-cell level. The Raman tags present a strong and sharp peak in the cellular Raman-silent region, significantly diminishing the background interference. Moreover, the reporters are embedded in the layered gold nanoparticles, avoiding desorption and enzymatic degradation in physiological conditions.