Self-Foldable Three-Dimensional Biointerfaces by Strain Engineering of Two-Dimensional Layered Materials on Polymers.

Two-dimensional layered materials (2DLMs) have received increasing attention for their potential in bioelectronics due to their favorable electrical, optical, and mechanical properties. The transformation of the planar structures of 2DLMs into complex 3D shapes is a key strategic step toward creating conformal biointerfaces with cells and applying them as scaffolds to simultaneously guide their growth to tissues and enable integrated bioelectronic monitoring. Using a strain-engineered self-foldable bilayer, we demonstrate the facile formation of predetermined 3D microstructures of 2DLMs with controllable curvatures, called microrolls.

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery.

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing.

Urgency Prediction for Medical Laboratory Tests Through Optimal Sparse Decision Tree: Case Study With Echocardiograms.

Background: In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care.

Identification of risk factors for suboptimal adherence in people living with HIV through measurement of medication possession ratio: a cross-sectional study.

The most significant progress in addressing the HIV/AIDS epidemic has been the development of antiretroviral therapy (ART). However, ensuring a high degree of treatment adherence is necessary to prevent resistance and disease progression. We conducted a cross-sectional study to evaluate adherence to ART through the calculation of the medication possession ratio (MPR) and to identify risk factors for suboptimal adherence in a cohort of HIV-positive patients receiving care at a Colombian healthcare institution across 16 cities.

Upper Limit on the Photoproduction Cross Section of the Spin-Exotic π_{1}(1600).

The spin-exotic hybrid meson π_{1}(1600) is predicted to have a large decay rate to the ωππ final state. Using 76.6  pb^{-1} of data collected with the GlueX detector, we measure the cross sections for the reactions γp→ωπ^{+}π^{-}p, γp→ωπ^{0}π^{0}p, and γp→ωπ^{-}π^{0}Δ^{++} in the range E_{γ}=8-10  GeV.

scHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.

Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.

Silence as epistemic agency in mania.

Silence is a byword for socially imposed harm in the burgeoning literature on epistemic injustice in psychiatry. While some silence is harmful and should be broken, this understanding of silence is untenably simplistic. Crucially, it neglects the possibility that silence can also play a constructive epistemic role in the lives of people with mental illness.

Epstein-Barr virus-associated smooth muscle tumor with multiple lesions in a patient with AIDS.

Epstein-Barr virus-associated smooth muscle tumors (EBV-SMTs) represent a rare category of soft tissue tumors that are predominantly seen in individuals with compromised immune systems. Pathologically, EBV-SMT has malignant potential because of its unpredictable nature. These tumors can manifest at various anatomical sites or even multiple lesions in different locations.

Aging with HIV and HIV-associated neurocognitive impairment.

Antiretroviral therapy (ART) is the most effective therapeutic intervention for HIV infection. With improved survival, comorbidities, including neuropsychiatric and HIV-associated neurocognitive impairment (NCI) are of increasing concern to aging people with HIV (PWH). The clinical features and the inter-individual variability of the aging process confound the elucidation of the diagnosis and underlying mechanisms of cognitive dysfunction in aging PWH.

Segmentation of coronary artery and calcification using prior knowledge based deep learning framework.

Background: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods have been proposed for the segmentation (SEG) of coronary artery and calcification, however, they often neglect leveraging related anatomical prior knowledge, resulting in low accuracy and instability.

An Unusual Meroterpenoid and Two New Steroids from Fungus Penicillium fellutanum and Their Bioactivities.

An unusual clathrate-type meroterpenoid isoatlantinone A (1), two new steroids acrocalysterols E (2) and F (3), together with fifteen known compounds (4-18) were separated from a plant-associated fungus Penicillium fellutanum. Their structures and absolute configurations were established based on spectroscopic data (NMR and HRESIMS), electronic circular dichroism (ECD) and modified Mosher's method. Notably, compound 1 represents an unusual highly oxygenated meroterpenoid derivative with a unique caged bioxatetracyclo-[6.

Worth your sweat: wearable microfluidic flow rate sensors for meaningful sweat analytics.

Wearable microfluidic sweat sensors could play a major role in the future of monitoring health and wellbeing. Sweat contains biomarkers to monitor health and hydration status, and it can provide information on drug intake, making it an interesting non-invasive alternative to blood. However, sweat is not created in excess, and this requires smart sweat collection strategies to handle small volumes.

Pathogen genomic surveillance and the AI revolution.

The unprecedented sequencing efforts during the COVID-19 pandemic paved the way for genomic surveillance to become a powerful tool for monitoring the evolution of circulating viruses. Herein, we discuss how a state-of-the-art artificial intelligence approach called protein language models (pLMs) can be used for effectively analyzing pathogen genomic data. We highlight examples of pLMs applied to predicting viral properties and evolution and lay out a framework for integrating pLMs into genomic surveillance pipelines.

Towards the Stable Chelation of Radioantimony(V) for Targeted Auger Theranostics.

Antimony-119 (119Sb) is one of the most attractive Auger-electron emitters identified to date, but it remains practically unexplored for targeted radiotherapy because no chelators have been identified to stably bind this metalloid in vivo. In a departure from current studies focused on chelator development for Sb(III), we explore the chelation chemistry of Sb(V) using the tris-catecholate ligand TREN-CAM. Through a combination of radiolabeling, spectroscopic, solid-state, and computational studies, the radiochemistry and structural chemistry of TREN-CAM with 1XX/natSb(V) were established.

pLM4CPPs: Protein Language Model-Based Predictor for Cell Penetrating Peptides.

Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification.

Cervical cancer stage distribution and survival outcomes in Africa: a systematic review and meta-analysis.

Objective: Cervical cancer is a leading cause of cancer-related deaths among women, with a disproportionate burden in sub-Saharan Africa. Understanding the cervical cancer stage and outcomes is crucial for developing effective interventions and reducing its burden. We aimed to undertake a systematic review and meta-analysis of cervical cancer stage distribution and survival outcomes in Africa.

Pattern Separation and Pattern Completion Within the Hippocampal Circuit During Naturalistic Stimuli.

Pattern separation and pattern completion in the hippocampus play a critical role in episodic learning and memory. However, there is limited empirical evidence supporting the role of the hippocampal circuit in these processes during complex continuous experiences. In this study, we analyzed high-resolution fMRI data from the "Forrest Gump" open-access dataset (16 participants) using a sliding-window temporal autocorrelation approach to investigate whether the canonical hippocampal circuit (DG-CA3-CA1-SUB) shows evidence consistent with the occurrence of pattern separation or pattern completion during a naturalistic audio movie task.

Performance of sentinel lymph node biopsy after neoadjuvant chemotherapy in clinically node positive breast cancer patients: systematic review and meta-analysis.

Background: The reliability of sentinel lymph node biopsy (SLNB) after neoadjuvant chemotherapy (NACT) in patients with initially node-positive breast cancer is still controversial. This meta-analysis is conducted to investigate the feasibility and accuracy of SLNB after NACT in patients with initially positive axillary nodes.

Methods: We conducted a literature search using Medline, PubMed, Embase, Central, and SCOPUS up until April 2021 for studies on the performance of SLNB following NACT.

MutualDTA: An Interpretable Drug-Target Affinity Prediction Model Leveraging Pretrained Models and Mutual Attention.

Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing methods often encounter several common issues: first, the data representations lack sufficient information; second, the extracted features are not comprehensive; and third, most methods lack interpretability when modeling drug-target binding.

The Picture of Health: Seeing the Health of the Chronically Challenged Through Van Gogh's Sunflowers as a Needed Revision of the Clinical Perspective.

This article identifies and offers a response to several problems that affect the quality of both clinical education and health care services. These matters are: that in clinical training and practice, health, as lived by patients (persons), is not properly considered, and is equated reductively with treating diseases/disorders; that health is seen through disease, and as restricted to a single model defined by an organism's meeting (or being returned to) biochemical or functional standards; that intellectual assumptions instilled in schools of Medicine and Psychology about realities pertaining to healthcare determine an understanding of chronic illness or life with chronic challenges focused on impairment and suffering, and not on the fuller experience of living with illness, disability or neuropsychological challenges that patients have as persons; that arts-based education reflects the same focus in understanding 'illness', and thus neglects giving attention to the creation of personal health states of those living with challenging or debilitating long-term conditions; that, consequently, the arts are instrumentalized to serve these predefined educational purposes, rather than allowed to inform clinical training through that which is intrinsic or more specific to them. As a way out of these limitations and as an illustration of how things could be done differently, Vincent Van Gogh's paintings of the Sunflowers are used as visual inspiration for how we could change the way we see, and construct new mental representations of 'health', 'chronic illness' or 'chronic challenges', 'patient as person' or even 'person as non-patient', 'the clinician's role' and 'the identity of clinical practice'.

Understanding the cost-utility of implementing HIV self-testing with digital-based supports.

Introduction: HIV self-testing (HIVST) is an innovative strategy that has been shown to increase uptake of HIV testing compared to conventional facility-based testing. HIVST implementation with digital-based supports may help facilitate testing accessibility and linkage to care after a reactive self-test. Economic evidence around community-based implementation of HIVST is growing; however, economic evidence around digital-based HIVST approaches remains limited.

Intramural pregnancy after intrauterine insemination in a nulligravid patient without previous uterine trauma, complicated by idiopathic thrombocytopenic purpura: A case report.

Intramural pregnancy (IMP) is an extremely rare form of ectopic pregnancy (EP), typically associated with previous uterine trauma, adenomyosis, or assisted reproductive technology (ART), such as embryo transfer (ET). Despite its potentially life-threatening nature, the absence of definitive preoperative diagnostic criteria for IMP complicates its early detection and management, especially in patients without known risk factors. Additionally, management becomes more challenging when there is an elevated risk of hemorrhage.

Interplay between conformational dynamics and substrate binding regulates enzymatic activity: a single-molecule FRET study.

Proteins often harness extensive motions of domains and subunits to promote their function. Deciphering how these movements impact activity is key for understanding life's molecular machinery. The enzyme adenylate kinase is an intriguing example for this relationship; it ensures efficient catalysis by large-scale domain motions that lead to the enclosure of the bound substrates ATP and AMP.

CTU Hornet 65 Niner: A network dataset of geographically distributed low-interaction honeypots.

This data article introduces a new network dataset created to help understand how geographical location impacts the quality, type, and amount of incoming network attacks received by honeypots. The dataset consists of 12.4 million network flows collected from nine low-interaction honeypots in nine cities across the world for 65 days, from April 29th to July 1st, 2024.