## Sources

1. [Potential Health Risks of Artificial Sweeteners](https://www.annualreviews.org/content/journals/10.1146/annurev-med-043024-012626?TRACK=RSS)
2. [Zonation, Zonation, Zonation: The Real Estate of the Liver](https://www.annualreviews.org/content/journals/10.1146/annurev-pathmechdis-042624-091820?TRACK=RSS)
3. [Navigating the Computational Landscape for Drug Repurposing](https://www.annualreviews.org/content/journals/10.1146/annurev-pharmtox-121924-042636?TRACK=RSS)
4. [Brain Nutrient Sensing: A Unifying Framework](https://www.annualreviews.org/content/journals/10.1146/annurev-physiol-042324-100329?TRACK=RSS)

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### **Brain Nutrient Sensing: A Unifying Framework | Clemence Blouet and Gary J. Schwartz** [1]

*   **Main Arguments:**
    *   The authors propose a unifying framework where **brain nutrient sensing is the central mediator for whole-body nutrient homeostasis**, ensuring survival, growth, and reproduction [2].
    *   Specialized brain cells (sentinels) monitor internal nutrient levels and meal-related signals to trigger adaptive physiological and behavioral responses to environmental fluctuations [2].
    *   **Maladaptive functioning** of these sensing pathways is a likely contributor to various pathophysiological states, including **cardiometabolic and neurodegenerative diseases** [2].

*   **Key Takeaways:**
    *   The framework shifts the perspective of brain nutrient sensing from being merely a regulator of food intake to a broad controller of **systemic physiology and behavior** [2].
    *   Specialized sensing occurs in specific regions like the **hypothalamus and the brainstem**, utilizing a diverse set of cell types including neurons, tanycytes, astrocytes, and pericytes [2-6].
    *   Targeting these neural pathways offers therapeutic potential for a range of conditions beyond just obesity management [2].

*   **Important Details:**
    *   **Glucose sensing** is a primary focus, with the brain monitoring both blood and brain glucose levels to regulate feeding and sympathoadrenal responses [3, 7].
    *   **Lipid sensing** involves the detection of circulating triglycerides and free fatty acids, influencing dopamine-associated behaviors and energy balance [8].
    *   **Amino acid sensing**, particularly of leucine, through specialized transporters in the hypothalamus, plays a role in maintaining energy and bone homeostasis [9-11].
    *   The framework examines how **circulating signals like leptin, ghrelin, and insulin** interact with nutrient-sensing circuits to rewire feeding pathways [3, 12].
    *   Recent advances in spatial multi-omics and high-resolution imaging have helped identify distinct neuronal and non-neuronal populations involved in these processes [2, 13].

### **Navigating the Computational Landscape for Drug Repurposing | Andrea Álvarez-Pérez, Lucía Prieto-Santamaría, Ana I. Casas, Joseph Loscalzo, and Alejandro Rodríguez-González** [14]

*   **Main Arguments:**
    *   **Drug repurposing**—finding new uses for existing drugs—is a highly efficient and attractive strategy to identify therapeutic candidates given the high cost and failure rate of traditional drug discovery [15, 16].
    *   **Data-driven computational approaches** are essential for navigating the complex landscape of drug-disease-target interactions to find these new opportunities [15].
    *   The integration of **artificial intelligence (AI) and large language models (LLMs)** is opening new horizons in identifying repurposing candidates by synthesizing vast amounts of biomedical data [15, 17].

*   **Key Takeaways:**
    *   Repurposing utilizes varied methodologies, including **molecular docking, network-based medicine, and omics data integration** [15].
    *   **Network pharmacology** is a significant paradigm, focusing on curing causal mechanisms within disease modules rather than just treating symptoms [18].
    *   The field faces challenges in establishing **mechanistic disease definitions** and finding corresponding association data, which are necessary for progress [19].

*   **Important Details:**
    *   The review highlights **AlphaFold 3** as a major advancement in predicting biomolecular interactions and protein structures [16].
    *   **Knowledge graphs** and algorithms like "DIAMOnD" help uncover disease-disease relationships and identify overlapping protein complexes within the human interactome [16, 18, 20].
    *   **Literature-based discovery** uses text mining of scientific papers and social media (for side effects) to generate new repurposing hypotheses [21].
    *   Case studies demonstrate the application of these methods in finding treatments for **COVID-19, Alzheimer’s disease, Parkinson’s disease, and various cancers** [16, 21-23].
    *   Financial and regulatory hurdles remain, especially regarding the repurposing of generic drugs [19].

### **Potential Health Risks of Artificial Sweeteners | Maria Effenberger and Herbert Tilg** [24]

*   **Main Arguments:**
    *   While widely used for weight management, artificial sweeteners (AS) may paradoxically **stimulate appetite and lead to increased caloric intake** [25].
    *   Consumption of AS is associated with **metabolic changes** that elevate the risk of obesity, type 2 diabetes, and cardiovascular diseases [25].
    *   Emerging evidence suggests that AS might negatively impact **cancer biology and the immune system** [25, 26].

*   **Key Takeaways:**
    *   Artificial sweeteners, including sucralose, aspartame, and saccharin, can **alter gut microbiota**, which is a key driver of glucose intolerance in humans [27].
    *   Specific sugar alcohols like **erythritol and xylitol** have been linked to an increased risk of **thrombosis and major adverse cardiovascular events** [28].
    *   The long-term safety of AS is not fully established, and more research is needed to understand their comprehensive impact on human health [25, 29].

*   **Important Details:**
    *   Preclinical studies show that high-dose **sucralose can negatively modulate T-cell responses**, potentially affecting autoimmunity and cancer immunity [26].
    *   Maternal consumption of AS during pregnancy has been linked in human studies to a higher body mass index in infants [11].
    *   Long-term sugar overconsumption (frequently replaced by AS) is also linked to persistence in ADHD-like phenotypes and neurocognitive deficits [29].
    *   The review notes that some AS, like aspartame, have been scrutinized for potential links to liver and lung cancers in animal models [26].
    *   The **World Health Organization (WHO)** has issued guidelines recommending against the use of non-sugar sweeteners for weight control or reducing the risk of noncommunicable diseases [30].

### **Zonation, Zonation, Zonation: The Real Estate of the Liver | Tyler M. Yasaka, Chang Kyung Kim, Vik Meadows, and Satdarshan P. Monga** [31]

*   **Main Arguments:**
    *   The liver lobule is not a homogeneous mass; instead, it exhibits **metabolic zonation**, a spatial organization where cells have distinct identities and functions based on their location [32].
    *   This spatial arrangement dictates gene and protein expression, allowing for a **division of labor** and the stepwise arrangement of complex metabolic pathways [32].
    *   Liver zonation extends beyond hepatocytes to include **nonparenchymal cells** like endothelial cells and hepatic stellate cells [32].

*   **Key Takeaways:**
    *   **Wnt/β-catenin signaling** is the primary driver of perivenous identity (Zone 3), while other signals like Hedgehog and oxygen gradients help define the different zones [33-35].
    *   Understanding zonation is critical for studying **liver injury, regeneration, and disease pathogenesis**, as different insults often target specific zones (e.g., acetaminophen injury in Zone 3) [32, 33].
    *   Spatial multi-omics technologies are revealing a highly nuanced "coordinate system" within the hepatic milieu [32].

*   **Important Details:**
    *   The lobule is conventionally divided into **three zones**: Zone 1 (periportal), Zone 2 (midlobular), and Zone 3 (perivenous/pericentral) [32, 36].
    *   **Zone 1** is typically associated with processes like **gluconeogenesis and fatty acid oxidation**, while **Zone 3** is more involved in **glycolysis, lipogenesis, and xenobiotic metabolism** [37].
    *   Diseases like **MASLD (metabolic dysfunction-associated steatotic liver disease)** and alcohol-related liver disease show zone-specific alterations in gene expression and lipid remodeling [38, 39].
    *   In **liver cancer**, tumor phenotypes often retain signatures of the zonation program from which they originated, with β-catenin-mutated tumors typically exhibiting a perivenous-like metabolic profile [40, 41].
    *   Zonation also influences liver regeneration, with different populations of hepatocytes and nonparenchymal cells responding to injury in a spatially defined manner [42, 43].