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Background: ORIN1001, a first-in-class oral IRE1-α endoribonuclease inhibitor to block the activation of XBP1, is currently in clinical development for inhibiting tumor growth and enhancing the effect of chemical or targeted therapy. Early establishment of a population pharmacokinetic (PopPK) model could characterize the pharmacokinetics (PK) of ORIN1001 and evaluate the effects of individual-specific factors on PK, which will facilitate the future development of this investigational drug.Methods: Non-linear mixed effect model was constructed by Phoenix NLME software, utilizing the information from Chinese patients with advanced solid tumors in a phase I clinical trial (Register No. NCT05154201). Statistically significant PK covariates were screened out by a stepwise process. The final model, after validating by the goodness-of-fit plots, non-parametric bootstrap, visual predictive check and test of normalized prediction distribution errors, was further applied to simulate and evaluate the impact of covariates on ORIN1001 exposure at steady state up to 900 mg per day as a single agent.Results: A two-compartment model with first-order absorption (with lag-time)/elimination was selected as the best structural model. Total bilirubin (TBIL) and lean body weight (LBW) were considered as the statistically significant covariates on clearance (CL/F) of ORIN1001. They were also confirmed to exert clinically significant effects on ORIN1001 steady-state exposure after model simulation. The necessity of dose adjustments based on these two covariates remains to be validated in a larger population.Conclusion: The first PopPK model of ORIN1001 was successfully constructed, which may provide some important references for future research.

Introduction The prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially. The sequelae can be severe in older adults and interact with age-related conditions such as multimorbidity. Despite this, TBI research in older adults is sparse. Minder, an in-home monitoring system developed by the UK Dementia Research Institute Centre for Care Research and Technology, uses infrared sensors and a bed mat to passively collect sleep and activity data. Similar systems have been used to monitor the health of older adults living with dementia. We will assess the feasibility of using this system to study changes in the health status of older adults in the early period post-TBI.Methods and analysis The study will recruit 15 inpatients (>60 years) with a moderate-severe TBI, who will have their daily activity and sleep patterns monitored using passive and wearable sensors over 6 months. Participants will report on their health during weekly calls, which will be used to validate sensor data. Physical, functional and cognitive assessments will be conducted across the duration of the study. Activity levels and sleep patterns derived from sensor data will be calculated and visualised using activity maps. Within-participant analysis will be performed to determine if participants are deviating from their own routines. We will apply machine learning approaches to activity and sleep data to assess whether the changes in these data can predict clinical events. Qualitative analysis of interviews conducted with participants, carers and clinical staff will assess acceptability and utility of the system.Ethics and dissemination Ethical approval for this study has been granted by the London-Camberwell St Giles Research Ethics Committee (REC) (REC number: 17/LO/2066). Results will be submitted for publication in peer-reviewed journals, presented at conferences and inform the design of a larger trial assessing recovery after TBI.

Abstract Identifying mechanisms that control molecular function is a significant challenge in pharmaceutical science and molecular engineering. Here, we present a novel projection pursuit recurrent neural network to identify functional mechanisms in the context of iterative supervised machine learning for discovery-based design optimization. Molecular function recognition is achieved by pairing experiments that categorize systems with digital twin molecular dynamics simulations to generate working hypotheses. Feature extraction decomposes emergent properties of a system into a complete set of basis vectors. Feature selection requires signal-to-noise, statistical significance, and clustering quality to concurrently surpass acceptance levels. Formulated as a multivariate description of differences and similarities between systems, the data-driven working hypothesis is refined by analyzing new systems prioritized by a discovery-likelihood. Utility and generality are demonstrated on several benchmarks, including the elucidation of antibiotic resistance in TEM-52 beta-lactamase. The software is freely available, enabling turnkey analysis of massive data streams found in computational biology and material science.

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

Global downregulation of microRNAs (miRNAs) is a common feature of human tumors and has been shown to enhance cancer progression. Several components of the miRNA biogenesis machinery (XPO5, DICER and TRBP) have been shown to act as haploinsufficient tumor suppressors. How the deregulation of miRNA biogenesis promotes tumor development is not clearly understood. Here we show that loss of miRNA biogenesis increased resistance to endoplasmic reticulum (ER) stress-induced cell death. We observed that HCT116 cells with a DICER hypomorphic mutation (Exn5/Exn5) or where DICER or DROSHA were knocked down were resistant to ER stress-induced cell death. Extensive analysis revealed little difference in the unfolded protein response (UPR) of WT compared to Exn5/Exn5 HCT116 cells upon ER stress treatment. However, analysis of the intrinsic apoptotic pathway showed that resistance occurred upstream of the mitochondria. In particular, BAX activation and dissipation of mitochondrial membrane potential was attenuated, and there was altered expression of BCL-2 family proteins. These observations demonstrate a key role for miRNAs as critical modulators of the ER stress response. In our model, downregulation of miRNA biogenesis delays ER stress-induced apoptosis. This suggests that disrupted miRNA biogenesis may contribute to cancer progression by inhibiting ER stress-induced cell death.

A vital aspect of any professional education is the opportunity for students to engage in meaningful practical experiences. In pre-service teacher education in Australia, this vital teaching practice component has undergone challenges in recent years due to increasing student numbers (linked to the increasing demand for new teachers) and limited resources in university and school sectors. As such, initiatives to enhance the practical component of this professional degree have been sought. This paper details the methodology and outcomes associated with a pilot project that utilized asynchronous Web-based communication tools to facilitate mentoring and peer support through the teaching practice experience. Analysis of the online discussions and interviews with participants provides an indication of the nature of the interactions and the perceived value of the intervention, and informs the potential for larger-scale implementation.

With Yeats lcompare painters like El Greco or Van Gogh; his pictures smoulder on the canvas. They have vaster dimensions of thought and feeling, ranging from the primitive, the esoteric and the fantastic to the intensely personal. They have an order which is all their own and an evocative power that is terribly disturbing. As Alex Zwerdling says of the heroes of the Easter Rising

These guidelines are intended to assist instructors teaching conversation to Iraqi students. They are related to the understanding and production of the language of ordinary speech rather than to theoretical phonetics, although some aspects of practical phonetics form parts of the testing. The areas suggested for testing are as follows: a. Comprehension b. Fluency c. Correctness d. Pronunciation