Engineering small MgAl-layered double hydroxide nanoparticles for enhanced gene delivery

In this paper we report an approach for engineering small MgAl-layered double hydroxide (sLDH) nanoparticles with the Z-average diameter of about 40 nm. This method first requires co-precipitation of magnesium and aluminum nitrate solution with sodium hydroxide in methanol, followed by LDH slurry collection and re-suspension in methanol. The methanol suspension is then heated in an autoclave, followed by separation via centrifugation and thorough washing with deionized water. The nanoparticles are finally dispersed in deionized water into homogeneous aqueous suspension after 4–6 day standing at room temperature. In general, sLDH nanoparticles have the Z-average size of 35–50 nm, the number-average size of 14–30 nm and the polydispersity index (PdI) of 0.19–0.25. The prepared sLDH suspension is stable for at least 1 month when stored at fridge (2–8 °C) or ambient (22–25 °C) temperature. Moreover, sLDH nanoparticles are found to carry higher payloads of small double stranded DNA (dsDNA). More excitedly, sLDH nanoparticles transfect dsDNA into HEK 293T cells with a 5 to 6-fold greater efficiency compared to the larger LDH particles (Z-average diameter of 110 nm)

The RCSB PDB information portal for structural genomics

The RCSB Protein Data Bank (PDB) offers online tools, summary reports and target information related to the worldwide structural genomics initiatives from its portal at . There are currently three components to this site: Structural Genomics Initiatives contains information and links on each structural genomics site, including progress reports, target lists, target status, targets in the PDB and level of sequence redundancy; Targets provides combined target information, protocols and other data associated with protein structure determination; and Structures offers an assessment of the progress of structural genomics based on the functional coverage of the human genome by PDB structures, structural genomics targets and homology models. Functional coverage can be examined according to enzyme classification, gene ontology (biological process, cell component and molecular function) and disease

Depression in veterans with Parkinson's disease: frequency, co-morbidity, and healthcare utilization

Objective To determine the frequency of depression in Parkinson's disease (PD) in routine clinical care, and to examine its association with co-morbid psychiatric and medical conditions and healthcare utilization. Methods Depression diagnoses and healthcare utilization data for all male veterans with PD age 55 or older seen in fiscal year 2002 ( n  = 41,162) were analyzed using Department of Veterans Affairs (VA) national databases. Frequencies of co-morbid disorders and healthcare utilization were determined for depressed and non-depressed patients; associations with depression were examined using multivariate logistic regression models. Results A depression diagnosis was recorded for 18.5% of PD patients, including major depression in 3.9%. Depression decreased in frequency and severity with increasing age. In multivariate logistic regression models, depressed patients had significantly greater psychiatric and medical co-morbidity, including dementia, psychosis, stroke, congestive heart failure, diabetes, and chronic obstructive pulmonary disease than non-depressed patients (all p  < 0.01). Depressed PD patients were also significantly more likely to have medical (OR = 1.34, 95% CI = 1.25–1.44) and psychiatric hospitalizations (OR = 2.14, 95% CI = 1.83–2.51), and had more outpatient visits ( p  < 0.01), than non-depressed PD patients in adjusted models. Conclusion Depression in PD in non-tertiary care settings may not be as common or as severe as that seen in specialty care, though these findings also may reflect under-recognition or diagnostic imprecision. The occurrence of depression in PD is associated with greater psychiatric and medical co-morbidity, and greater healthcare utilization. These findings suggest that screening for depression in PD is important and should be embedded in a comprehensive psychiatric, neuropsychological, and medical evaluation. Copyright © 2006 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56073/1/1712_ftp.pd

Bioactive Properties and Clinical Safety of a Novel Milk Protein Peptide

Background: Milk protein fractions and peptides have been shown to have bioactive properties. This preliminary study examined the potential mechanisms of action and clinical safety of novel milk protein peptide ( MP). Findings: A novel MP mixture inhibits the tyrosine kinase activity of epidermal growth factor receptor ( EGFR), vascular endothelial growth factor receptor 2 (VEGFR2), and insulin receptor (IR) with IC(50) of 9.85 mu M, 7.7 mu M, and 6.18 mu M respectively. In vitro, this multi-kinase inhibitor causes apoptosis in HT-29 colon cancer cells, and in a C. elegans worm study, showed a weak but significant increase in lifespan. A six week double-blind, placebo-controlled study involving 73 healthy volunteers demonstrated that the MP mixture is safe to consume orally. All clinical blood markers remained within normal levels and no clinically significant side effects were reported. There was some evidence of improved insulin sensitivity, neutrophil-to-lymphocyte ratio (NLR), and quality of life assessment of role of physical function. Conclusions: These data in combination with the observed in vitro anti-cancer properties warrant further clinical studies to investigate this MP mixture as a potential clinical nutrition intervention for improving the quality of life and clinical outcomes in cancer patients

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products. While these exceptional AIGC products are gaining increasing recognition and sparking enthusiasm among consumers, the questions regarding whether, when, and how these models might unintentionally reinforce existing societal stereotypes remain largely unaddressed. Motivated by recent advancements in language agents, here we introduce a novel agent architecture tailored for stereotype detection in text-to-image models. This versatile agent architecture is capable of accommodating free-form detection tasks and can autonomously invoke various tools to facilitate the entire process, from generating corresponding instructions and images, to detecting stereotypes. We build the stereotype-relevant benchmark based on multiple open-text datasets, and apply this architecture to commercial products and popular open source text-to-image models. We find that these models often display serious stereotypes when it comes to certain prompts about personal characteristics, social cultural context and crime-related aspects. In summary, these empirical findings underscore the pervasive existence of stereotypes across social dimensions, including gender, race, and religion, which not only validate the effectiveness of our proposed approach, but also emphasize the critical necessity of addressing potential ethical risks in the burgeoning realm of AIGC. As AIGC continues its rapid expansion trajectory, with new models and plugins emerging daily in staggering numbers, the challenge lies in the timely detection and mitigation of potential biases within these models

Detection and Retrieval of Multi-Layered Cloud Properties Using Satellite Data

Four techniques for detecting multilayered clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. A new technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other methods examined here use atmospheric sounding data (CO2-slicing, CO2), BTD, or microwave data. The CO2 and BTD methods are limited to optically thin cirrus over low clouds, while the MWR methods are limited to ocean areas only. This paper explores the use of the BTD and CO2 methods as applied to Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data taken from the Aqua satellite over ocean surfaces. Cloud properties derived from MODIS data for the Clouds and the Earth's Radiant Energy System (CERES) Project are used to classify cloud phase and optical properties. The preliminary results focus on a MODIS image taken off the Uruguayan coast. The combined MW visible infrared (MVI) method is assumed to be the reference for detecting multilayered ice-over-water clouds. The BTD and CO2 techniques accurately match the MVI classifications in only 51 and 41% of the cases, respectively. Much additional study is need to determine the uncertainties in the MVI method and to analyze many more overlapped cloud scenes