Auditory Hallucinations in Borderline Personality Disorder and Schizophrenia: A Quantitative Comparison Using Patient Records.

The phenomenological differences in auditory hallucinations between schizophrenia and borderline personality disorder (BPD) are unclear in the existing literature, in part due to underpowered studies and heterogeneous research populations that do not represent those in the acute clinical setting. This study addresses this by using patient records to compare auditory hallucinations at the point of clinical psychiatric assessment for 341 unique patients, 165 with BPD and 176 with schizophrenia. Patients with BPD were found to have more subjectively distressing and objectively negative hallucinations, as well as more command hallucinations.

Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers' physiological measurements and mouth level exposure.

Pharmacokinetic (PK) studies can provide essential information on abuse liability of nicotine and tobacco products but are intrusive and must be conducted in a clinical environment. The objective of the study was to explore whether changes in plasma nicotine levels following use of an e-cigarette can be predicted from real time monitoring of physiological parameters and mouth level exposure (MLE) to nicotine before, during, and after e-cigarette vaping, using wearable devices. Such an approach would allow an -effective pre-screening process, reducing the number of clinical studies, reducing the number of products to be tested and the number of blood draws required in a clinical PK study Establishing such a prediction model might facilitate the longitudinal collection of data on product use and nicotine expression among consumers using nicotine products in their normal environments, thereby reducing the need for intrusive clinical studies while generating PK data related to product use in the real world.

Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling.

Imputation machine learning (ML) surpasses traditional approaches in modeling toxicity data. The method was tested on an open-source data set comprising approximately 2500 ingredients with limited and data obtained from the OECD QSAR Toolbox. By leveraging the relationships between different toxicological end points, imputation extracts more valuable information from each data point compared to well-established single end point methods, such as ML-based Quantitative Structure Activity Relationship (QSAR) approaches, providing a final improvement of up to around 0.

Isolated pulmonary valve endocarditis in a patient without structural heart disease or a history of intravenous drug use.

We present a case of a febrile patient in his 70s who was found to have isolated native pulmonary valve vegetations on echocardiography, and on blood cultures. Of note, our patient had none of the typical risk factors associated with this rare form of endocarditis previously described in only a handful of case reports.

Potential for redistribution of post-moult habitat for Eudyptes penguins in the Southern Ocean under future climate conditions.

Anthropogenic climate change is resulting in spatial redistributions of many species. We assessed the potential effects of climate change on an abundant and widely distributed group of diving birds, Eudyptes penguins, which are the main avian consumers in the Southern Ocean in terms of biomass consumption. Despite their abundance, several of these species have undergone population declines over the past century, potentially due to changing oceanography and prey availability over the important winter months.

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, , by targeting ATP4, an essential ion pump on the parasite surface. The structure of ATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of ATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds.

Imputation of sensory properties using deep learning.

Predicting the sensory properties of compounds is challenging due to the subjective nature of the experimental measurements. This testing relies on a panel of human participants and is therefore also expensive and time-consuming. We describe the application of a state-of-the-art deep learning method, Alchemite™, to the imputation of sparse physicochemical and sensory data and compare the results with conventional quantitative structure-activity relationship methods and a multi-target graph convolutional neural network.

Use of Digital Technology among Adolescents Attending Schools in Bissau, Guinea-Bissau.

Digital technology plays an important role in achieving many of the Sustainable Development Goals. However, access is uneven, with 80% of those in high-income countries being online compared to 20% of those in the 47 least developed countries. This study aimed to describe and analyse adolescents' access to and usage of digital technology in Guinea-Bissau and its implications.

Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data.

Contemporary deep learning approaches still struggle to bring a useful improvement in the field of drug discovery because of the challenges of sparse, noisy, and heterogeneous data that are typically encountered in this context. We use a state-of-the-art deep learning method, Alchemite, to impute data from drug discovery projects, including multitarget biochemical activities, phenotypic activities in cell-based assays, and a variety of absorption, distribution, metabolism, and excretion (ADME) endpoints. The resulting model gives excellent predictions for activity and ADME endpoints, offering an average increase in of 0.

Accurate characterization of the IFITM locus using MiSeq and PacBio sequencing shows genetic variation in Galliformes.

Background: Interferon inducible transmembrane (IFITM) proteins are effectors of the immune system widely characterized for their role in restricting infection by diverse enveloped and non-enveloped viruses. The chicken IFITM (chIFITM) genes are clustered on chromosome 5 and to date four genes have been annotated, namely chIFITM1, chIFITM3, chIFITM5 and chIFITM10. However, due to poor assembly of this locus in the Gallus Gallus v4 genome, accurate characterization has so far proven problematic.

Effect of mechanical ventilation on cytokine response to intratracheal lipopolysaccharide.

Background: Mechanical ventilation may cause lung injury through the excitation of an inflammatory response and the release of mediators, such as cytokines. The authors tested the hypothesis that intratracheal lipopolysaccharide amplifies the cytokine response to mechanical ventilation.

Methods: Rat lungs were intratracheally instilled with lipopolysaccharide followed by ex vivo mechanical ventilation for 2 h with low tidal volume of 7 ml/kg with 3 cm H2O positive end-expiratory pressure (PEEP), high tidal volume of 40 ml/kg with zero PEEP, medium tidal volume of 15 ml/kg with 3 cm H2O PEEP, or medium tidal volume and zero PEEP.

Intercellular adhesion molecule-1 mediates cellular cross-talk between parenchymal and immune cells after lipopolysaccharide neutralization.

The mechanisms by which parenchymal cells interact with immune cells, particularly after removal of LPS, remain unknown. Lung explants from rats, mice deficient in the TNF gene, or human lung epithelial A549 cells were treated with LPS and washed, before naive alveolar macrophages, bone marrow monocytes, or PBMC, respectively, were added to the cultures. When the immune cells were cocultured with LPS-challenged explants or A549 cells, TNF production was greatly enhanced.

Ventilator-associated lung injury.

Mechanical ventilation is indispensable in support of patients with respiratory failure who are critically ill. However, use of this technique has adverse effects, including increased risk of pneumonia, impaired cardiac performance, and difficulties associated with sedation and paralysis. Moreover, application of pressure to the lung, whether positive or negative, can cause damage known as ventilator-associated lung injury (VALI).