The projection from the prelimbic cortex to the ventral tegmental area negatively regulates 5-HT-induced itch-scratching and positively regulates itch-related aversion in rats.

Direct and indirect evidence suggests that the prelimbic cortex (PrL) and the ventral tegmental area (VTA) are the key brain regions involved in the regulation of itch sensation and itch-related emotions. The PrL projects widely to various cortical and subcortical brain regions, with the VTA being one of the main targets of PrL descending projections. However, the differential roles of the PrL-VTA pathway in the regulation of itch sensation and itch-related emotion remain unclear.

Comparative study of Mg/Al-LDH and Mg/Fe-LDH on adsorption and loss control of 2,4-dichlorophenoxyacetic acid.

Low efficiency and high surface runoff of 2,4-dichlorophenoxyacetic acid (2,4-D) from agricultural field threaten crop yield severely. Layered double hydroxides (LDH) have shown promising adsorption properties for 2,4-D. However, the comparison of two environmentally friendly LDHs (i.

Proxy panels enable privacy-aware outsourcing of genotype imputation.

One of the major challenges in genomic data sharing is protecting participants' privacy in collaborative studies and in cases when genomic data are outsourced to perform analysis tasks, for example, genotype imputation services and federated collaborations genomic analysis. Although numerous cryptographic methods have been developed, these methods may not yet be practical for population-scale tasks in terms of computational requirements, rely on high-level expertise in security, and require each algorithm to be implemented from scratch. In this study, we focus on outsourcing of genotype imputation, a fundamental task that utilizes population-level reference panels, and develop protocols that rely on using "proxy panels" to protect genotype panels, whereas the imputation task is being outsourced at servers.

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To uniformly test and benchmark the secure evaluation of transformer-based models, we designed the iDASH24 homomorphic encryption track dataset. The dataset comprises a protein family classification model with a transformer architecture and an example dataset that is used to build and test the secure evaluation strategies. This dataset was used in the challenge period of iDASH24 Genomic Privacy Competition, where the teams designed secure evaluation of the classification model using a homomorphic encryption scheme.

Simulate Scientific Reasoning with Multiple Large Language Models: An Application to Alzheimer's Disease Combinatorial Therapy.

Motivation: This study aims to develop an AI-driven framework that leverages large language models (LLMs) to simulate scientific reasoning and peer review to predict efficacious combinatorial therapy when data-driven prediction is infeasible.

Results: Our proposed framework achieved a significantly higher accuracy (0.74) than traditional knowledge-based prediction (0.

iGTP: Learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.

Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein-protein interactions (PPI) between different biological states.

MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning for Computational Phenotyping.

Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications including Electronic Health Records (EHR) mining. PARAFAC2 and its variants have been proposed to address irregular tensors where one of the tensor modes is not aligned, e.g.

De-identification is not enough: a comparison between de-identified and synthetic clinical notes.

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes.

Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process.

Objectives: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

Generalizable self-supervised learning for brain CTA in acute stroke.

Acute stroke management involves rapid and accurate interpretation of CTA imaging data. However, generalizable models for multiple acute stroke tasks able to learn from unlabeled data do not exist. We propose a linear probed self-supervised contrastive learning utilizing 3D CTA images and the findings section of radiologists' reports for pretraining.

Size effect of ZIF-8 based nanocarrier pesticide delivery system on targeted release and insecticidal activity.

Background: Traditional chemical pesticides are easily lost by surface runoff and only small quantities reach the target, thus causing serious environmental pollution. In this work, dinotefuran@zeolitic imidazolate framework-8@polydopamine@zein (DNF@ZIF-8@PDA@zein), was constructed to deliver DNF with pH and enzyme double response of release, thereby achieving targeted release and efficient long-term pest control.

Results: DNF@ZIF-8@PDA@zein was synthesized with three hydrated diameters (249.

Federated Multiple Imputation for Variables that Are Missing Not At Random in Distributed Electronic Health Records.

Large electronic health records (EHR) have been widely implemented and are available for research activities. The magnitude of such databases often requires storage and computing infrastructure that are distributed at different sites. Restrictions on data-sharing due to privacy concerns have been another driving force behind the development of a large class of distributed and/or federated machine learning methods.

Clinical outcome-guided deep temporal clustering for disease progression subtyping.

Objective: Complex diseases exhibit heterogeneous progression patterns, necessitating effective capture and clustering of longitudinal changes to identify disease subtypes for personalized treatments. However, existing studies often fail to design clustering-specific representations or neglect clinical outcomes, thereby limiting the interpretability and clinical utility.

Method: We design a unified framework for subtyping longitudinal progressive diseases.

Co-transport of citrate-modified biochar nanoparticles and released plant-available silicon in saturated porous media: Effect of LMWOAs and solution chemistry.

Citrate-modified biochar nanoparticles (CBCNPs) represent a promising amendment with plant-available silicon (PASi) releasing capacity. However, the co-transport behavior with released PASi remain poorly understood. This study investigated their co-transport in saturated porous media under various solution chemistry and low molecular weight organic acids (LMWOAs).

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, and Artificial Intelligence to Interrogate the Metabolic Evolution of Glioblastoma.

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models.

Generative Pre-trained Transformer for Pediatric Stroke Research: A Pilot Study.

Background: Pediatric stroke is an important cause of morbidity in children. Although research can be challenging, large amounts of data have been captured through collaborative efforts in the International Pediatric Stroke Study (IPSS). This study explores the use of an advanced artificial intelligence program, the Generative Pre-trained Transformer (GPT), to enter pediatric stroke data into the IPSS.

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

Summary: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's "copilot.

Age-associated proteins explain the role of medial temporal lobe networks in Alzheimer's disease.

The structural connectivity (SC) of the medial temporal lobe and its associated cortical anterior temporal and posterior medial networks (MTL-AT-PM) is linked to pathologies and memory decline in Alzheimer's disease (AD). However, neuroimaging analyses cannot tell us how SC changes occur in AD at the molecular level and do not provide a means of intervening to slow/prevent pathology-related changes in MTL-AT-PM SC. The current study aimed to understand how and where AD-related changes occur within MTL-AT-PM using proteomics.

FedGMMAT: Federated generalized linear mixed model association tests.

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results.

The burden of diagnostic error in dentistry: A study on periodontal disease misclassification.

Background: Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case.

Microorganisms immobilized hydroxyethyl cellulose/luffa composite sponge for selective adsorption and biodegradation of oils in wastewater.

The highly efficient removal of oils such as oils or dyes from wastewater has aroused wide concern and is of great significance for clean production and environmental remediation. The synthesis of a novel aerogel (designated as HEC/LS) is reported herein, achieved through a sol-gel method followed by freeze-drying utilizing loofa and hydroxyethyl cellulose as the raw materials. The new HEC/LS aerogel exhibits excellent porosity and specific surface area, with a porosity of 88.