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Crystal structures of agonist-bound human cannabinoid receptor CB1
Publisher Correction: Ultrasonographic evaluation of testicular and Pampiniform plexus characteristics in young bulls under different microclimatic conditions in a tropical environment
Formation of the structural-phase state of titanium-matrix composites reinforced with tic during synthesis and hot pressing, and their testing as ballistic-resistant (bulletproof) protective materials
Dietary composition modulate gut microbiota and related biomarkers in patients with chronic kidney disease
Inhibition of the lateral hypothalamus emboldens adult female spiny mice to huddle with an established group of novel peers
Habitat changes due to cultivation alter the genetic diversity and secondary metabolic changes of medicinal plants
Microglia regulate GABAergic neurogenesis in prenatal human brain through IGF1
Abstract GABAergic neurons are essential cellular components of neural circuits. Their abundance and diversity have increased significantly in the human brain, contributing to the expanded cognitive capacity of humans 1 . However, the developmental mechanism underlying the extended production of GABAergic neurons in the human brain remains elusive. Here we uncovered the microglial regulation of the sustained proliferation of GABAergic progenitors and neuroblasts in the human medial ganglionic eminence (hMGE). We showed that microglia are preferentially distributed in the proliferating zone and identified insulin-like growth factor 1 (IGF1) and its receptor IGR1R as the predicted top ligand–receptor pair underlying microglia–progenitor communication in the prenatal hMGE. Using our newly developed neuroimmune hMGE organoids, which mimic the hMGE cytoarchitecture and developmental trajectory, we demonstrated that microglia-derived IGF1 promotes progenitor proliferation and production of GABAergic neurons. Conversely, IGF1-neutralizing antibodies and IGF1 knockout human embryonic stem-cell-induced microglia abolish the induced microglia-mediated progenitor proliferation. Together, these findings revealed a previously unappreciated role of microglia-derived IGF1 in promoting the proliferation of neural progenitors and the development of GABAergic neurons in the human brain.
Dynamical study of different types of soliton solutions with bifurcation, chaos and sensitivity analysis to the non-linear coupled Schrödinger model
NADPH oxidase 2 inhibitor GSK2795039 exerts antiplatelet and antithrombotic activity
Abstract Reactive oxygen species (ROS) production by NADPH oxidase (NOX) is a key promoter of platelet activation, making NOX inhibition an attractive antiplatelet strategy. This study evaluated the effects of the novel NOX2 inhibitor, GSK2795039, on human platelet functional responses and ROS-mediated signaling pathways. GSK2795039 effectively suppressed both extracellular and intracellular ROS production in collagen-stimulated platelets. Consequently, GSK2795039 treatment significantly inhibited collagen-induced platelet aggregation, dense-granule ATP release, and α-granule P-selectin exposure. Furthermore, GSK2795039 attenuated tyrosine phosphorylation-mediated activation of spleen tyrosine kinase (Syk), linker for the activation of T cells (LAT), vav guanine nucleotide exchange factor 1 (Vav1), and Bruton tyrosine kinase (Btk) within the collagen receptor signaling pathway, leading to decreased phospholipase Cγ2 (PLCγ2) activation and calcium mobilization. GSK2795039 also inhibited collagen-induced integrin αIIbβ3 activation, associated with increased cyclic guanosine monophosphate (cGMP) levels and vasodilator-stimulated phosphoprotein (VASP) phosphorylation. Additionally, the inhibitor reduced collagen-induced p38 mitogen-activated protein kinase (MAPK) activation, which led to lower cytosolic phospholipase A2 (cPLA2) phosphorylation and thromboxane production. GSK2795039 further decreased extracellular signal regulated kinase 5 (ERK5) activation, thereby limiting procoagulant phosphatidylserine exposure. Notably, thrombus formation induced by platelet adhesion to collagen was abolished in the presence of GSK2795039. In vivo, GSK2795039 administration markedly inhibited arterial thrombosis. This is the first study to demonstrate that GSK2795039 suppresses collagen-induced ROS production, platelet activation, and thrombus formation, highlighting its potential as a therapeutic agent for thrombotic and cardiovascular diseases.
Social versus nonsocial visual cues of trustworthiness uniquely influence trust related behavior and memory
Abstract Trust-related impressions and behavior are influenced by behavioral and non-behavioral (e.g., visual) cues. Similarly, nonsocial cues of trustworthiness (e.g., color cues like red or green, symbols such as warning signs or checkmarks) can influence behaviors associated with trust and distrust in memory and choice tasks. It is unclear, however, whether such social and nonsocial visual cues of trustworthiness have similar effects on behavior over iterative experiences and on post-task associative memory. To address these gaps, the Multi-Round Trust Game was used to determine effects of visual cues of trustworthiness on trust-related decision-making in social versus nonsocial economic game contexts. “Trustees” were represented by visual cues of trustworthiness (i.e., trustworthy and untrustworthy-looking faces, respectively). Social and nonsocial visual cues with comparable perceived trustworthiness yielded similar biasing effects on initial investments and investments over repeated interactions. However, social relative to nonsocial cues differentially influenced investment behavior with relatively more subtle visual cue differences (e.g., nonemotional facial characteristics versus positive/negative scenes). Additionally, social and nonsocial post-game memory was generally more distinctive, particularly for trustees who had shown untrustworthy behavior during the game. Extending Attribution and Betrayal Aversion theories, these findings suggest that while social and nonsocial visual cues can have similar effects on trust-related cognition and behavior, social relative to nonsocial cues affect behavior via more subtle visual differences, and their influence on post-interaction memory exceeds effects on trust-related behavior. These results highlight the privileged status of social information in dual-process models and support the critical role of intuitive evaluability in shaping behavior and memory across diverse trust-relevant contexts.
Cocaine chemogenetics blunts drug-seeking by synthetic physiology
Abstract Chemical feedback is ubiquitous in physiology but is challenging to study without perturbing basal functions. One example is addictive drugs, which elicit a positive-feedback cycle of drug-seeking and ingestion by acting on the brain to increase dopamine signalling1–3. However, interfering with this process by altering basal dopamine also adversely affects learning, movement, attention and wakefulness4. Here, inspired by physiological control systems, we developed a highly selective synthetic physiology approach to interfere with the positive-feedback cycle of addiction by installing a cocaine-dependent opposing signalling process into this body–brain signalling loop. We used protein engineering to create cocaine-gated ion channels that are selective for cocaine over other drugs and endogenous molecules. Expression of an excitatory cocaine-gated channel in the rat lateral habenula, a brain region that is normally inhibited by cocaine, suppressed cocaine self-administration without affecting food motivation. This artificial cocaine-activated chemogenetic process reduced the cocaine-induced extracellular dopamine rise in the nucleus accumbens. Our results show that cocaine chemogenetics is a selective approach for countering drug reinforcement by clamping dopamine release in the presence of cocaine. In the future, chemogenetic receptors could be developed for additional addictive drugs or hormones and metabolites, which would facilitate efforts to probe their neural circuit mechanisms using a synthetic physiology approach. As these chemogenetic ion channels are specific for cocaine over natural rewards, they may also offer a route towards gene therapies for cocaine addiction.
Unified artificial intelligence framework for modeling pollution dynamics and sustainable remediation in environmental chemistry
Abstract Environmental pollution involves complex nonlinear interactions, chemical variability, and physical constraints that challenge traditional models. A unified artificial intelligence framework integrating Graph Neural Networks, Generative Adversarial Networks, Reinforcement Learning, Green Chemistry optimization, and Physics Informed Neural Networks with embedded Darcy’s law and a hybrid AI physics model. This framework simulates contaminant transport, generates climate scenarios, and optimizes sustainable remediation strategies across four calibrated environmental scenarios. Validation employed four synthetic environmental scenarios with parameters calibrated from documented PFAS contamination studies, representing controlled algorithm development prior to field deployment (noise sigma 1.5 to 4.0 mg per liter; seasonal amplitude 0.1 to 0.3; trend 0 to 0.1 mg per liter per day). The hybrid AI physics model achieved 89% predictive accuracy on synthetic validation datasets with literature-calibrated parameters, outperforming traditional (65%), pure AI (78%), and physics-only (72%) approaches under controlled synthetic conditions. Graph Neural Networks captured complex spatiotemporal patterns (R² > 0.89), while Reinforcement Learning optimization improved simulated treatment efficiency from 62.3% to 89.7% in synthetic remediation scenarios. Green Chemistry optimization modeling suggested supercritical carbon dioxide and ionic liquids as potentially efficient solvents, with model-predicted efficiencies of 88% to 92% and relative toxicity scores between 1.8 and 2.1 units (where lower scores indicate reduced environmental impact). Physics Informed Neural Networks reduced physics loss from approximately 1.2 to 0.03 ± 0.005, achieving convergence at a total loss of 0.08 ± 0.01 over 50 training epochs on synthetic datasets. The framework demonstrated computational scalability from 80 to 5000 synthetic records and estimated deployment feasibility within a 22-month timeline under optimal conditions, contingent upon successful field validation. SHAP and LIME analyses indicated natural attenuation, particularly the decay process, as the most influential model feature, contributing a mean SHAP value of 0.34 ± 0.08, consistent with expected physical processes. This framework contributes to accurate, interpretable, and sustainable pollution modeling.