Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.

Artificial intelligence (AI) has significantly impacted various fields, including oncology. This comprehensive review examines the current applications and future prospects of AI in lung cancer research and treatment. We critically analyze the latest AI technologies and their applications across multiple domains, including genomics, transcriptomics, proteomics, metabolomics, immunomics, microbiomics, radiomics, and pathomics in lung cancer research.

Beyond symptom reduction: Veterans' goals for posttraumatic stress disorder treatment.

Despite a varied selection of available trauma-focused evidence-based psychotherapies (TF-EBPs) for posttraumatic stress disorder (PTSD), few veterans receive a full course of an evidence-based treatment. A better understanding of and alignment with veterans' PTSD treatment goals could be one way to improve treatment engagement and adherence, consistent with veteran-oriented care within the U.S.

DNA-damage orchestrates self-renewal and differentiation via reciprocal p53 family and Hippo/Wnt/TGF-β pathway activation in embryonic stem cells.

The mechanism by which DNA-damage affects self-renewal and pluripotency remains unclear. DNA damage and repair mechanisms have been largely elucidated in mutated cancer cells or simple eukaryotes, making valid interpretations on early development difficult. Here we show the impact of ionizing irradiation on the maintenance and early differentiation of mouse embryonic stem cells (ESCs).

Current Progress and Future Directions in Non-Alzheimer's Disease Tau PET Tracers.

Alzheimer's disease (AD) and non-AD tauopathies are dominant public health issues driven by several factors, especially in the aging population. The discovery of first-generation radiotracers, including [F]FDDNP, [C]PBB3, [F]flortaucipir, and the [F]THK series, for the in vivo detection of tauopathies has marked a significant breakthrough in the fields of neuroscience and radiopharmaceuticals, creating a robust new category of labeled compounds: tau positron emission tomography (PET) tracers. Subsequently, other tau PET tracers with improved binding properties have been developed using various chemical scaffolds to target the three-repeat/four-repeat (3R/4R) tau folds in AD.

Data and AI-driven synthetic binding protein discovery.

Synthetic binding proteins (SBPs) are a class of protein binders that are artificially created and do not exist naturally. Their broad applications in tackling challenges of research, diagnostics, and therapeutics have garnered significant interest. Traditional protein engineering is pivotal to the discovery of SBPs.

Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 2.

Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions.

Implementing brief behavioral treatment for insomnia in Department of Veterans Affairs Primary Care Mental Health Integration clinics: Reach outcomes from a hybrid type 3 effectiveness-implementation trial.

Chronic insomnia is one of the most common health problems among veterans and can significantly impact health, function, and quality of life. Brief behavioral treatment for insomnia (BBTI), an adaptation of cognitive behavioral therapy for insomnia (CBT-I), was developed to help increase access to care outside of specialty settings. However, training providers alone is rarely sufficient, and implementation strategies are needed for successful uptake, adoption, and sustainable delivery of care.

Revisiting the potential of natural products in antimycobacterial therapy: advances in drug discovery and semisynthetic solutions.

Natural products have been pivotal in treating mycobacterial infections with early antibiotics such as streptomycin, forming the foundation of tuberculosis therapy. However, the emergence of multidrug-resistant and extensively drug-resistant Mycobacterium species has intensified the need for novel antimycobacterial agents. In this review, we revisit the historical contributions of natural products to antimycobacterial drug discovery and highlight recent advances in the field.

Gut Microbiota-Derived Hyocholic Acid Enhances Type 3 Immunity and Protects Against Salmonella enterica Serovar Typhimurium in Neonatal Rats.

This study investigates how microbiome colonization influences the development of intestinal type 3 immunity in neonates. The results showed that reduced oxygen levels in the small intestine of neonatal rats induced by Saccharomyces boulardii accelerated microbiome colonization and type 3 immunity development, which protected against Salmonella enterica serovar Typhimurium infection. Microbiome maturation increased the abundance of microbiome-encoded bile salt hydrolase (BSH) genes and hyocholic acid (HCA) levels.

A Milk Extracellular Vesicle-Based Nanoplatform Enhances Combination Therapy Against Multidrug-Resistant Bacterial Infections.

The increasing occurrence of infections caused by multidrug-resistant (MDR) bacteria drives the need for new antibacterial drugs. Due to the current lack of antibiotic discovery and development, new strategies to fight MDR bacteria are urgently needed. Efforts to develop new antibiotic adjuvants to increase the effectiveness of existing antibiotics and design delivery systems are essential to address this issue.

Rethinking Biomedical Titanium Alloy Design: A Review of Challenges from Biological and Manufacturing Perspectives.

Current biomedical titanium alloys have been repurposed from other industries, which has contributed to several biologically driven implant failure mechanisms. This review highlights the added value that may be gained by building an appreciation of implant biological responses at the onset of alloy design. Specifically, the fundamental mechanisms associated with immune response, angiogenesis, osseointegration and the potential threat of infection are discussed, including how elemental selection can modulate these pivotal systems.

Selenium promotes neural development through the regulation of GPX4 and SEPP1 in an iPSC-derived neuronal model.

Selenium (Se) is incorporated into selenoproteins in the form of selenocysteine, which has biological functions associated with neural development. Unfortunately, the specific roles and mechanisms of selenoproteins at different stages of neuronal development are still unclear. Therefore, in this study, we successfully established a neuronal model derived from induced pluripotent stem cells (iPSC-iNeuron) and used Se nanoparticles (SeNPs@LNT) with high bioavailability to intervene at different stages of neural development in iPSC-iNeuron model.

PyNetCor: a high-performance Python package for large-scale correlation analysis.

The development of multi-omics technologies has generated an abundance of biological datasets, providing valuable resources for investigating potential relationships within complex biological systems. However, most correlation analysis tools face computational challenges when dealing with these high-dimensional datasets containing millions of features. Here, we introduce pyNetCor, a fast and scalable tool for constructing correlation networks on large-scale and high-dimensional data.

Bridging silicon and carbon worlds with digital twins and on-chip systems in drug discovery.

This perspective discusses the convergence of digital twin (DT) technology and on-the-chip systems as pivotal innovations in precision medicine, substantially advancing drug discovery. DT leverages extensive health data to create dynamic virtual patient models, enabling predictive insights and optimized treatment strategies. Concurrently, on-the-chip systems from the Carbon world replicate human biological processes on microfluidic platforms, providing detailed insights into disease mechanisms and pharmacological interactions.

Investigating Aging-Related Endometrial Dysfunction Using Endometrial Organoids.

Ageing of the endometrium is a critical factor that affects reproductive health, yet its intricate mechanisms remain poorly explored. In this study, we performed transcriptome profiling and experimental verification of endometrium and endometrial organoids from young and advanced age females, to elucidate the underlying mechanisms and to explore novel treatment strategies for endometrial ageing. First, we found that age-associated decline in endometrial functions including fibrosis and diminished receptivity, already exists in reproductive age.

The Changing Landscape of Materials Discovery.

In this perspective, we will discuss the impact of some of the most recent advancements in materials discovery, particularly focusing on the role of robotics, artificial intelligence, and self-driving laboratories, as well as their implications for the Swiss research landscape. While it seems timely to aim for broad, revolutionary breakthroughs in this field, we argue that more incremental steps - such as, for example, fully automatic grinding of solid powders or fully automated Rietveld refinements - may have a more significant impact on materials discovery, at least in the short run. In the center of these considerations is how small, interdisciplinary groups can drive significant progress by contributing targeted innovations, such as e.

Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment.

Computational approaches using theoretical calculations and data scientific methods have become increasingly important in materials science and technology, with the development of relevant methodologies and algorithms, the availability of large materials data, and the enhancement of computer performance. As reviewed herein, we have developed computational methods for the design and prediction of inorganic materials with a particular focus on the exploration of semiconductors and dielectrics. High-throughput first-principles calculations are used to systematically and accurately predict the local atomic and electronic structures of polarons, point defects, surfaces, and interfaces, as well as bulk fundamental properties.

Mobile App-Facilitated Collaborative Palliative Care Intervention for Critically Ill Older Adults: A Randomized Clinical Trial.

Importance: Few person-centered, scalable models of collaborative intensive care unit (ICU) clinician-palliative care specialist care exist.

Objective: To evaluate the effect of a collaborative palliative care intervention compared to usual care among family members of patients in the ICU.

Design, Setting, And Participants: This parallel-group randomized clinical trial with patient-level randomization was conducted between April 2021 and September 2023.