DAP12 deficiency alters microglia-oligodendrocyte communication and enhances resilience against tau toxicity.

Pathogenic tau accumulation fuels neurodegeneration in Alzheimer's disease (AD). Enhancing aging brain's resilience to tau pathology would lead to novel therapeutic strategies. DAP12 (DNAX-activation protein 12) is critically involved in microglial immune responses. Previous studies have showed that mice lacking DAP12 in tauopathy mice exhibit higher tau pathology but are protected from tau-induced cognitive deficits. However, the exact mechanism remains elusive. Our current study uncovers a novel resilience mechanism via microglial interaction with oligodendrocytes. Despite higher tau inclusions, Dap12 deletion curbs tau-induced brain inflammation and ameliorates myelin and synapse loss. Specifically, removal of Dap12 abolished tau-induced disease-associated clusters in microglia (MG) and intermediate oligodendrocytes (iOli), which are spatially correlated with tau pathology in AD brains. Our study highlights the critical role of interactions between microglia and oligodendrocytes in tau toxicity and DAP12 signaling as a promising target for enhancing resilience in AD.

DAP12 deficiency alters microglia-oligodendrocyte communication and enhances resilience against tau toxicity.

Pathogenic tau accumulation fuels neurodegeneration in Alzheimer's disease (AD). Enhancing aging brain's resilience to tau pathology would lead to novel therapeutic strategies. DAP12 (DNAX-activation protein 12) is critically involved in microglial immune responses. Previous studies have showed that mice lacking DAP12 in tauopathy mice exhibit higher tau pathology but are protected from tau-induced cognitive deficits. However, the exact mechanism remains elusive. Our current study uncovers a novel resilience mechanism via microglial interaction with oligodendrocytes. Despite higher tau inclusions, Dap12 deletion curbs tau-induced brain inflammation and ameliorates myelin and synapse loss. Specifically, removal of Dap12 abolished tau-induced disease-associated clusters in microglia (MG) and intermediate oligodendrocytes (iOli), which are spatially correlated with tau pathology in AD brains. Our study highlights the critical role of interactions between microglia and oligodendrocytes in tau toxicity and DAP12 signaling as a promising target for enhancing resilience in AD.

Robust Superhydrophobic Surfaces via the Sand-In Method.

Superhydrophobic surfaces have gained sustained attention because of their extensive applications in the fields of self-cleaning, anti-icing, and drag reduction systems. Water droplets must have large apparent contact angle (CA) (>150°) and small CA hysteresis (<10°) on these surfaces. However, previous research usually involves complex fabrication strategies to modify the surface wettability. It is also challenging to maintain the temporal and mechanical stability of the delicate surface textures. Here, we develop a one-step solvent-free sand-in method to fabricate robust superhydrophobic surfaces directly atop various substrates with an apparent CA up to ∼163.8° and hysteresis less than 5°. The water repellency can withstand 100 Scotch tape peeling tests and remain stable after being stored under ambient humid conditions in Houston, Texas, for 18 months or being heated at 130 °C in air for 24 h. The superhydrophobic surfaces have excellent anti-icing ability, including a ∼2.6× longer water freezing time and ∼40% smaller ice adhesion strength with the temperature as low as -35 °C. Since the surface layers are fabricated by sanding the substrates with the powder additives, the surface damage can be repaired by a direct re-sanding treatment with the same powder additives. Further sand-in condition screenings broaden surface wettability from hydrophilic to superhydrophobic. The sand-in method induces the surface modification and the formation of the tribofilm. Surface and materials characterizations reveal that both microstructures and nanoscale asperities of the tribofilms contribute to the robust superhydrophobic features of sanded surfaces.

Initial Experience with a COVID-19 Screening Chatbot Before Radiology Appointments.

Guidelines for COVID-19 issued by the Centers for Disease Control and Prevention prompted state and local governments to mandate safety measures for screening high-risk patient populations and for institutions to look for ways to limit human contact when possible. The aim of this study was to determine the feasibility of an automated communication system (chatbot) for COVID-19 screening before patients' radiology appointments and to describe patient experiences with the chatbot. We developed a chatbot for COVID-19 screening before outpatient radiology examination appointments and tested it in a pilot study from July 6 to August 31, 2020. The chatbot assessed the presence of any symptoms, exposure, and recent testing. User experience was assessed via a questionnaire based on a 5-point Likert scale. Multivariable logistic regression was performed to predict response rate. The chatbot COVID-19 screening SMS message was sent to 4687 patients. Of these patients, 2722 (58.1%) responded. Of the respondents, 46 (1.7%) reported COVID-19 symptoms; 34 (1.2%) had COVID-19 tests scheduled or pending. Of the 1965 nonresponders, authentication failed for 174 (8.8%), 1496 (76.1%) did not engage with the SMS message, and 251 (12.8%) timed out of the chatbot. The mean rating for the chatbot experience was 4.6. In a multivariable logistic regression model predicting response rate, English written-language preference independently predicted response (odds ratio, 2.71 [95% CI, 1.77-2.77]; P = .007). Age (P = 0.57) and sex (P = 0.51) did not predict response rate. SMS-based COVID-19 screening before scheduled radiology appointments was feasible. English written-language preference (not age or sex) was associated with higher response rate.

The impact of blood pressure on the risk of major bleeding complication after renal transplant biopsy.

PURPOSE: To assess the impact of elevated blood pressure on the rate of major hemorrhagic complication after renal transplant biopsy. METHODS: Pre-procedural systolic (SBP), diastolic (SBP), and mean arterial (MAP) blood pressure for consecutive patients undergoing US-guided renal transplant biopsies from 08/01/2015 to 7/31/2017 were retrospectively recorded. Patients who had a major bleeding complication were identified. The risk of complication as a function of SBP, DBP, and MAP was statistically analyzed, with significance set at p < 0.05. RESULTS: Of 1689 biopsies, there were 10 bleeding complications (10/1689, 0.59%). There was no statistically significant difference between biopsies with complication compared to those without complication based on SBP (p = 0.351), DBP (p = 0.088), or MAP (p = 0.132). Using risk dichotomization criteria, the odds ratio for hemorrhagic complication when the patient had SBP ≥ 180 mmHg and DBP ≥ 95 mmHg was 75.63 (95% CI 6.87-516.8, p = 0.002). CONCLUSION: The rate of hemorrhagic complication from renal transplant biopsy is low, and there is no statistically significant threshold for increased biopsy risk based on SBP, DBP, or MAP alone. The risk of complication was significantly higher only when both the SBP is ≥ 180 mmHg and DBP is ≥ 95 mmHg.

An open label, multicenter clinical trial that investigated the efficacy and safety of leuprorelin treatment of central precocious puberty in Chinese children.

BACKGROUND: Leuprorelin is an analog of gonadotropin-releasing hormone that is used for the therapy of central precocious puberty (CPP). The aims of this prospective, open label, multicenter clinical trial were to establish its efficacy and safety during long-term use. METHODS: Patients, who were all children, were treated with 1.88 to 3.75 mg leuprorelin subcutaneously once every 4 weeks for a total of 96 weeks between 2015 and 2018. The primary endpoint was the rate of occurrence of adverse events (AEs) and the secondary endpoint was no progression in the Tanner stage or regression by week 96 compared to baseline. RESULTS: A total of 307 CPP patients, 305 (99.3%) females and 2 males (0.7%), completed the 96-weeks of treatment. Due to limited data for male patients, they are not discussed in the efficacy results. Treatment-emergent AEs (TEAEs) were reported for 252 (82.1%) patients, mostly (79.5%) being mild or moderate and only 33 (10.7%) of patients experienced TEAEs related to leuprorelin therapy. The most frequent (>2%) drug-related TEAEs were injection site induration (4.6%, 14/307) and vaginal bleeding (2.3%, 7/305). After treatment, 83.5% of patients had regression or no progression in the Tanner stage (95% confidence interval: 78.68%, 87.62%) and the majority had decreased gonadotropin-releasing hormone-stimulated peak luteinizing hormone and follicle-stimulating hormone concentrations, as well as reduced sex hormone concentrations and a reduction in the bone age/chronological age ratio compared to baseline. CONCLUSIONS: The trial revealed that CPP was effectively treated in most patients who received leuprorelin for nearly 2 years. Any drug-related AEs were reported with low incidence (<5%) and were consistent with the known safety profile of leuprorelin. TRIAL REGISTRATION: The trial was registered at ClinicalTrials.gov (registration number: NCT02427958).

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.

Clinical longitudinal evaluation of COVID-19 patients and prediction of organ-specific recovery using artificial intelligence.

Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems. To greatly enhance the speed and utility of our model, we applied an artificial intelligence method to segment and classify regions on CT imaging, from which interpretable data could be directly fed into the predictive machine learning model for overall recovery. Across all organ systems we achieved validation set area under the receiver operator characteristic curve (AUC) values for organ-specific recovery ranging from 0.80 to 0.89, and significant overall recovery prediction in Kaplan-Meier analyses. This demonstrates that the synergistic use of an artificial intelligence (AI) framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

Laser-Induced Graphene Triboelectric Nanogenerators.

Triboelectric nanogenerators (TENGs) show exceptional promise for converting wasted mechanical energy into electrical energy. This study investigates the use of laser-induced graphene (LIG) composites as an exciting class of triboelectric materials in TENGs. Infrared laser irradiation is used to convert the surfaces of the two carbon sources, polyimide (PI) and cork, into LIG. This gives the bilayer composite films the high conductivity associated with LIG and the triboelectric properties of the carbon source. A LIG/PI composite is used to fabricate TENGs based on conductor-to-dielectric and metal-free dielectric-to-dielectric device geometries with open-circuit voltages >3.5 kV and peak power >8 mW. Additionally, a single sheet of PI is converted to a metal-free foldable TENG. The LIG is also embedded within a PDMS matrix to form a single-electrode LIG/PDMS composite TENG. This single-electrode TENG is highly flexible and stretchable and was used to generate power from mechanical contact with skin. The LIG composites present a class of triboelectric materials that can be made from naturally occurring and synthetic carbon sources.

Bovine Lactoferrampin, Human Lactoferricin, and Lactoferrin 1-11 Inhibit Nuclear Translocation of HIV Integrase.

This study aimed to investigate fragments derived from human and bovine lactoferrins for ability to inhibit nuclear translocation of HIV-1 integrase. It was shown that human lactoferricin, human lactoferrin 1-11, and bovine lactoferrampin reduced nuclear distribution of HIV-1 integrase. Bovine lactoferrampin could inhibit both the activity and nuclear translocation of HIV-1 integrase. Human lactoferrampin, bovine lactoferricin, and bovine lactoferrin 1-11 had no effect on HIV-1 integrase nuclear translocation. Human lactoferrampin which inhibited the activity of integrase did not prevent its nuclear translocation. Human lactoferricin and lactoferrin 1-11 did not inhibit HIV-1 integrase nuclear translocation despite their ability to attenuate the enzyme activity. The discrepancy between the findings on reduction of HIV-1 activity and inhibition of nuclear translocation of HIV-1 integrase was due to the different mechanisms involved. A similar reasoning can also be applied to the different inhibitory potencies of the milk peptides on different HIV enzymes, i.e., nuclear translocation.