Unveiling the interplay of AMPK/SIRT1/PGC-1α axis in brain health: Promising targets against aging and NDDs.

The escalating prevalence of neurodegenerative diseases (NDDs) within an aging global population presents a pressing challenge. The multifaceted pathophysiological mechanisms underlying these disorders, including oxidative stress, mitochondrial dysfunction, and neuroinflammation, remain complex and elusive. Among these, the AMPK/SIRT1/PGC-1α pathway emerges as a pivotal network implicated in neuroprotection against these destructive processes. This review sheds light on the potential therapeutic implications of targeting this axis, specifically emphasizing the promising role of flavonoids in mitigating NDD-related complications. Expanding beyond conventional pharmacological approaches, the exploration of non-pharmacological interventions such as exercise and calorie restriction (CR), coupled with the investigation of natural compounds, offers a beacon of hope. By strategically elucidating the intricate connections within these pathways, this review aims to pave the ways for novel multi-target agents and interventions, fostering a renewed optimism in the quest to combat and manage the debilitating impacts of NDDs on global health and well-being.

A patient safety knowledge graph supporting vaccine product development.

BACKGROUND: Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources. METHODS: We designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs). RESULTS: We import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster. CONCLUSIONS: Organizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., "Produce vaccine-X safety profile for adverse event-Y by country instead of age-range").

Alpha-Lipoic acid alleviates imidacloprid-induced neuro-behavioral deficits in rats via Nrf2/HO-1 pathway.

Imidacloprid (IMI), a widely used pesticide in agriculture and a potential food contaminant, poses significant health concerns. This study sought to comprehensively evaluate its neurotoxic effects while investigating the potential protective role of alpha-lipoic acid (ALA), a naturally occurring dietary antioxidant renowned for its capacity to combat oxidative stress, support cardiovascular health, and maintain optimal nerve function. In this study, 28 rats were divided evenly into four groups and administered oral treatments of corn oil, IMI, IMI + ALA, and ALA, respectively. The results of the study indicated that rats exposed to IMI exhibited significant neurobehavioral impairments, decreased levels of antioxidant enzymes and acetylcholinesterase activity, reduced expression of HO-1 and Nrf2, and increased levels of pro-inflammatory cytokines like IL-6 and TNF-α in their hippocampal tissues. Furthermore, histopathological analysis of the brain tissues, specifically cortex and hippocampus, from the IMI-treated group revealed varying degrees of neuronal degeneration. In contrast, rats co-administered ALA alongside IMI showed noticeable improvements in all the assessed toxicological parameters. This study underscores the vital significance of ALA as a potential therapeutic adjunct in mitigating the adverse neurobehavioral consequences of insecticide exposure. By harnessing the Nrf2/HO-1 pathway, ALA demonstrates its ability to shield against IMI-induced neurotoxicity, offering a promising avenue for enhancing public health and safety. As a result, our findings advocate for the incorporation of ALA as a daily dietary supplement to fortify resilience against oxidative stress-related neurobehavioral deficits linked to pesticide exposure, thereby advancing our understanding of neuroprotection strategies in the face of environmental challenges.

Unlocking the therapeutic potential of natural stilbene: Exploring pterostilbene as a powerful ally against aging and cognitive decline.

The therapeutic potential of natural stilbenes, with a particular focus on pterostilbene (PTE), has emerged as a promising avenue of research targeting age-associated conditions encompassing cardiovascular diseases (CVD), diabetes mellitus (DM), and cognitive decline. This comprehensive investigation delves into the intricate mechanisms through which PTE, a polyphenolic compound abundant in grapes and blueberries, exerts its advantageous effects as an anti-aging agent. Central to its action is the modulation of hallmark aging processes, including oxidative damage, inflammatory responses, telomere attrition, and cellular senescence. PTE's ability to effectively penetrate the blood-brain barrier amplifies its potential for safeguarding neural health, thereby facilitating the regulation of neuronal signalling cascades, synaptic plasticity, and mitochondrial functionality. Through engagement with sirtuin proteins, it orchestrates cellular resilience, longevity, and metabolic equilibrium. Encouraging findings from preclinical studies portray PTE as a robust candidate for counteracting age-linked cognitive decline, augmenting memory consolidation, and potentially ameliorating neurodegenerative maladies such as Alzheimer's disease (AD). The synthesis of current scientific insights accentuates the promising translational prospects of PTE as a potent, naturally derived therapeutic agent against cognitive impairments associated with aging. Consequently, these collective findings lay a solid groundwork for forthcoming clinical inquiries and innovative therapeutic interventions in this realm.

Probing the Dark Sector with Nuclear Transition Photons.

Here we present world-leading sensitivity to light (<170 MeV) dark matter (DM) using beam-dump experiments. Dark sector particles produced during pion decay at accelerator beam dumps can be detected via scattering in neutrino detectors. The decay of nuclei excited by the inelastic scattering of DM is an unexploited channel which has significantly better sensitivity than similar searches using the elastic scattering channel. We show that this channel is a powerful probe of DM by demonstrating sensitivity to the thermal relic abundance benchmark in a scalar DM dark-photon portal model. This is achieved through the use of existing data, obtained by the KARMEN experiment over two decades ago, which allow us to set world-leading constraints on this model over a wide mass range. With experimental improvements planned for the future, this technique will be able to probe the thermal relic benchmark for fermionic DM across a wide mass range.

Role of GLP-1 receptor agonist in diabetic cardio-renal disorder: Recent updates of clinical and pre-clinical evidence.

Cardiovascular complications and renal disease is the growing cause of mortality in patients with diabetes. The subversive complications of diabetes such as hyperglycemia, hyperlipidemia and insulin resistance lead to an increase in the risk of myocardial infarction (MI), stroke, heart failure (HF) as well as chronic kidney disease (CKD). Among the commercially available anti-hyperglycemic agents, incretin-based medications appear to be safe and effective in the treatment of type 2 diabetes mellitus (T2DM) and associated cardiovascular and renal disease. Glucagon-like peptide 1 receptor agonists (GLP-1RAs) have been shown to be fruitful in reducing HbA1c, blood glucose, lipid profile, and body weight in diabetic patients. Several preclinical and clinical studies revealed the safety, efficacy, and preventive advantages of GLP-1RAs against diabetes-induced cardiovascular and kidney disease. Data from cardio-renal outcome trials had highlighted that GLP-1RAs protected people with established CKD from significant cardiovascular disease, lowered the likelihood of hospitalization for heart failure (HHF), and lowered all-cause mortality. They also had a positive effect on people with end-stage renal disease (ESRD) and CKD. Beside clinical outcomes, GLP-1RAs reduced oxidative stress, inflammation, fibrosis, and improved lipid profile pre-clinically in diabetic models of cardiomyopathy and nephropathy that demonstrated the cardio-protective and reno-protective effect of GLP-1RAs. In this review, we have focused on the recent clinical and preclinical outcomes of GLP-1RAs as cardio-protective and reno-protective agents as GLP-1RAs medications have been demonstrated to be more effective in treating T2DM and diabetes-induced cardiovascular and renal disease than currently available treatments in clinics, without inducing hypoglycemia or weight gain.

Oxymatrine and insulin resistance: Focusing on mechanistic intricacies involve in diabetes associated cardiomyopathy via SIRT1/AMPK and TGF-ß signaling pathway.

Cardiomyopathy (CDM) and related morbidity and mortality are increasing at an alarming rate, in large part because of the increase in the number of diabetes mellitus cases. The clinical consequence associated with CDM is heart failure (HF) and is considerably worse for patients with diabetes mellitus, as compared to nondiabetics. Diabetic cardiomyopathy (DCM) is characterized by structural and functional malfunctioning of the heart, which includes diastolic dysfunction followed by systolic dysfunction, myocyte hypertrophy, cardiac dysfunctional remodeling, and myocardial fibrosis. Indeed, many reports in the literature indicate that various signaling pathways, such as the AMP-activated protein kinase (AMPK), silent information regulator 1 (SIRT1), PI3K/Akt, and TGF-ß/smad pathways, are involved in diabetes-related cardiomyopathy, which increases the risk of functional and structural abnormalities of the heart. Therefore, targeting these pathways augments the prevention as well as treatment of patients with DCM. Alternative pharmacotherapy, such as that using natural compounds, has been shown to have promising therapeutic effects. Thus, this article reviews the potential role of the quinazoline alkaloid, oxymatrine obtained from the Sophora flavescensin CDM associated with diabetes mellitus. Numerous studies have given a therapeutic glimpse of the role of oxymatrine in the multiple secondary complications related to diabetes, such as retinopathy, nephropathy, stroke, and cardiovascular complications via reductions in oxidative stress, inflammation, and metabolic dysregulation, which might be due to targeting signaling pathways, such as AMPK, SIRT1, PI3K/Akt, and TGF-ß pathways. Thus, these pathways are considered central regulators of diabetes and its secondary complications, and targeting these pathways with oxymatrine might provide a therapeutic tool for the diagnosis and treatment of diabetes-associated cardiomyopathy.

Solutions to the MiniBooNE Anomaly from New Physics in Charged Meson Decays.

We point out that production of new bosons by charged meson decays can greatly enhance the sensitivity of beam-focused accelerator-based experiments to new physics signals. This enhancement arises since the charged mesons are focused and their three-body decays do not suffer from helicity suppression in the same way as their usual two-body decays. As a realistic application, we attempt to explain the MiniBooNE low energy excess utilizing this overlooked mechanism, uniquely realizing dark-sector interpretations as plausible solutions to the excess. As proof of the principle, we consider two well-motivated classes of dark-sector models, models of vector-portal dark matter and models of long-lived (pseudo)scalar. We argue that the model parameter values to accommodate the excess are consistent with existing limits and that they can be tested at current and future accelerator-based neutrino experiments.

Inside the diabetic brain: Insulin resistance and molecular mechanism associated with cognitive impairment and its possible therapeutic strategies.

Type 2 diabetes mellitus (T2DM) the most prevalent metabolic disease that has evolved into a major public health issue. Concerning about its secondary complications, a growing body of evidence links T2DM to cognitive impairment and neurodegenerative disorders. The underlying pathology behind this secondary complication disease is yet to be fully known. Nonetheless, they are likely to be associated with poor insulin signaling as a result of insulin resistance. We have combed through a rising body of literature on insulin signaling in the normal and diabetic brains along with various factors like insulin resistance, hyperglycemia, obesity, oxidative stress, neuroinflammation and Aß plaques which can act independently or synergistically to link T2DM with cognitive impairments. Finally, we explored several pharmacological and non-pharmacological methods in the hopes of accelerating the rational development of medications for cognitive impairment in T2DM by better understanding these shared pathways.

Potential role of IP3/Ca2+ signaling and phosphodiesterases: Relevance to neurodegeneration in Alzheimer's disease and possible therapeutic strategies.

Despite large investments by industry and governments, no disease-modifying medications for the treatment of patients with Alzheimer's disease (AD) have been found. The failures of various clinical trials indicate the need for a more in-depth understanding of the pathophysiology of AD and for innovative therapeutic strategies for its treatment. Here, we review the rational for targeting IP3 signaling, cytosolic calcium dysregulation, phosphodiesterases (PDEs), and secondary messengers like cGMP and cAMP, as well as their correlations with the pathophysiology of AD. Various drugs targeting these signaling cascades are still in pre-clinical and clinical trials which support the ideas presented in this article. Further, we describe different molecular mechanisms and medications currently being used in various pre-clinical and clinical trials involving IP3/Ca+2 signaling. We also highlight various isoforms, as well as the functions and pharmacology of the PDEs broadly expressed in different parts of the brain and attempt to unravel the potential benefits of PDE inhibitors for use as novel medications to alleviate the pathogenesis of AD.

Similarity and consistency assessment of three major online drug-drug interaction resources.

AIMS: The aim of this study was to explore the level of agreement on drug-drug interaction (DDI) information listed in three major online drug information resources (DIRs) in terms of: (1) interacting drug pairs; (2) severity rating; (3) evidence rating; and (4) clinical management recommendations. METHODS: We extracted information from the British National Formulary (BNF), Thesaurus and Micromedex. Following drug name normalisation, we estimated the overlap of the DIRs in terms of DDI. We annotated clinical management recommendations either manually, where possible, or through application of a machine learning algorithm. RESULTS: The DIRs contained 51 481 (BNF), 38 037 (Thesaurus) and 65 446 (Micromedex) drug pairs involved in DDIs. The number of common DDIs across the three DIRs was 6970 (13.54% of BNF, 18.32% of Thesaurus and 10.65% of Micromedex). Micromedex and Thesaurus overall showed higher levels of similarity in their severity ratings, while the BNF agreed more with Micromedex on the critical severity ratings and with Thesaurus on the least significant ones. Evidence rating agreement between BNF and Micromedex was generally poor. Variation in clinical management recommendations was also identified, with some categories (i.e., Monitor and Adjust dose) showing higher levels of agreement compared to others (i.e., Use with caution, Wash-out, Modify administration). CONCLUSIONS: There is considerable variation in the DDIs included in the examined DIRs, together with variability in categorisation of severity and clinical advice given. DDIs labelled as critical were more likely to appear in multiple DIRs. Such variability in information could have deleterious consequences for patient safety, and there is a need for harmonisation and standardisation.

A reference set of clinically relevant adverse drug-drug interactions.

The accurate and timely detection of adverse drug-drug interactions (DDIs) during the postmarketing phase is an important yet complex task with potentially major clinical implications. The development of data mining methodologies that scan healthcare databases for drug safety signals requires appropriate reference sets for performance evaluation. Methodologies for establishing DDI reference sets are limited in the literature, while there is no publicly available resource simultaneously focusing on clinical relevance of DDIs and individual behaviour of interacting drugs. By automatically extracting and aggregating information from multiple clinical resources, we provide a scalable approach for generating a reference set for DDIs that could support research in postmarketing safety surveillance. CRESCENDDI contains 10,286 positive and 4,544 negative controls, covering 454 drugs and 179 adverse events mapped to RxNorm and MedDRA concepts, respectively. It also includes single drug information for the included drugs (i.e., adverse drug reactions, indications, and negative drug-event associations). We demonstrate usability of the resource by scanning a spontaneous reporting system database for signals of DDIs using traditional signal detection algorithms.

Axionlike Particles at Future Neutrino Experiments: Closing the Cosmological Triangle.

Axionlike particles (ALPs) provide a promising direction in the search for new physics, while a wide range of models incorporate ALPs. We point out that future neutrino experiments, such as DUNE, possess competitive sensitivity to ALP signals. The high-intensity proton beam impinging on a target can not only produce copious amounts of neutrinos, but also cascade photons that are created from charged particle showers stopping in the target. Therefore, ALPs interacting with photons can be produced (often energetically) with high intensity via the Primakoff effect and then leave their signatures at the near detector through the inverse Primakoff scattering or decays to a photon pair. Moreover, the high-capability near detectors allow for discrimination between ALP signals and potential backgrounds, improving the signal sensitivity further. We demonstrate that a DUNE-like detector can explore a wide range of parameter space in ALP-photon coupling g_{aγ} vs ALP mass m_{a}, including some regions unconstrained by existing bounds; the "cosmological triangle" will be fully explored and the sensitivity limits would reach up to m_{a}∼3-4 GeV and down to g_{aγ}∼10^{-8} GeV^{-1}.

A small sustained increase in NOD1 abundance promotes ligand-independent inflammatory and oncogene transcriptional responses.

Small, genetically determined differences in transcription [expression quantitative trait loci (eQTLs)] are implicated in complex diseases through unknown molecular mechanisms. Here, we showed that a small, persistent increase in the abundance of the innate pathogen sensor NOD1 precipitated large changes in the transcriptional state of monocytes. A ~1.2- to 1.3-fold increase in NOD1 protein abundance resulting from loss of regulation by the microRNA cluster miR-15b/16 lowered the threshold for ligand-induced activation of the transcription factor NF-κB and the MAPK p38. An additional sustained increase in NOD1 abundance to 1.5-fold over basal amounts bypassed this low ligand concentration requirement, resulting in robust ligand-independent induction of proinflammatory genes and oncogenes. These findings reveal that tight regulation of NOD1 abundance prevents this sensor from exceeding a physiological switching checkpoint that promotes persistent inflammation and oncogene expression. Furthermore, our data provide insight into how a quantitatively small change in protein abundance can produce marked changes in cell state that can serve as the initiator of disease.

Inverse Primakoff Scattering as a Probe of Solar Axions at Liquid Xenon Direct Detection Experiments.

We show that XENON1T and future liquid xenon (LXe) direct detection experiments are sensitive to axions through the standard g_{aγ}aFF[over ˜] operators due to inverse-Primakoff scattering. This previously neglected channel significantly improves the sensitivity to the axion-photon coupling, with a reach extending to g_{aγ}∼10^{-10} GeV^{-1} for axion masses up to a keV, thereby extending into the region of heavier QCD axion models. This result modifies the couplings required to explain the XENON1T excess in terms of solar axions, opening a large region of g_{aγ}-m_{a} parameter space that is not ruled out by the CAST helioscope experiment and reducing the tension with the astrophysical constraints. We explore the sensitivity to solar axions for future generations of LXe detectors that can exceed future helioscope experiments, such as IAXO, for a large region of parameter space.

Explaining the XENON1T Excess with Luminous Dark Matter.

We show that the excess in electron recoil events seen by the XENON1T experiment can be explained by a relatively low-mass luminous dark matter candidate. The dark matter scatters inelastically in the detector (or the surrounding rock) to produce a heavier dark state with a ∼2-3 keV mass splitting. This heavier state then decays within the detector, producing a peak in the electron recoil spectrum that is a good fit to the observed excess. We comment on the ability of future direct detection experiments to differentiate this model from other "beyond the standard model" scenarios and from possible tritium backgrounds, including the use of diurnal modulation, multichannel signals, etc., as possible distinguishing features of this scenario.

New Directions for Axion Searches via Scattering at Reactor Neutrino Experiments.

Searches for pseudoscalar axionlike-particles (ALPs) typically rely on their decay in beam dumps or their conversion into photons in haloscopes and helioscopes. We point out a new experimental direction for ALP probes via their production by the intense gamma ray flux available from megawatt-scale nuclear reactors at neutrino experiments through Primakoff-like or Compton-like channels. Low-threshold detectors in close proximity to the core will have visibility to ALP decays and inverse Primakoff and Compton scattering, providing sensitivity to the ALP-photon and ALP-electron couplings. We find that the sensitivity to these couplings at the ongoing MINER and various other reactor based neutrino experiments, e.g., CONNIE, CONUS, ν-cleus, etc., exceeds existing limits set by laboratory experiments and, for the ALP-electron coupling, we forecast the world's best laboratory-based constraints over a large portion of the sub-MeV ALP mass range.

Dark Matter Signals from Timing Spectra at Neutrino Experiments.

We propose a novel strategy to search for new physics in timing spectra at low-energy neutrino experiments using a pulsed beam, envisioning the situation in which a new particle comes from the decay of its heavier partner with a finite particle width. The timing distribution of events induced by the dark matter (DM) candidate particle scattering at the detector may populate in a relatively narrow range, forming a "resonancelike" shape. Because of this structural feature, the signal may be isolated from the backgrounds, in particular when the backgrounds are uniformly distributed in energy and time. For proof of the principle, we investigate the discovery potential for DM from the decay of a dark photon in the ongoing COHERENT experiment and show the exciting prospects for exploring the associated parameter space with this experiment. We analyze the existing CsI detector data with a timing cut and an energy cut, and we find, for the first time, an excess in the timing distribution that can be explained by such DM. We compare the sensitivity to the kinetic mixing parameter (ε) for current and future COHERENT experiments with the projected limits from LDMX and DUNE.

Searching for Beyond the Standard Model Physics with COHERENT Energy and Timing Data.

We search for beyond the standard model physics by combining COHERENT Collaboration energy and timing data. Focusing on light, ≲GeV mediators, we find the data favor a ∼10-1000 MeV mediator, as compared to the standard model best fit at the ≲2σ level. The best-fit coupling range is g∼10^{-5}-10^{-3}. The timing data provide statistical information on the neutrino flavor distributions that is not attainable from the nuclear recoil energies alone. This result accounts for uncertainty in the effective size of the neutron distribution, and highlights the power of including uncertainties on experimental backgrounds, nuclear structure, and beyond the standard model physics.

IFN-mediated negative feedback supports bacteria class-specific macrophage inflammatory responses.

Despite existing evidence for tuning of innate immunity to different classes of bacteria, the molecular mechanisms used by macrophages to tailor inflammatory responses to specific pathogens remain incompletely defined. By stimulating mouse macrophages with a titration matrix of TLR ligand pairs, we identified distinct stimulus requirements for activating and inhibitory events that evoked diverse cytokine production dynamics. These regulatory events were linked to patterns of inflammatory responses that distinguished between Gram-positive and Gram-negative bacteria, both in vitro and after in vivo lung infection. Stimulation beyond a TLR4 threshold and Gram-negative bacteria-induced responses were characterized by a rapid type I IFN-dependent decline in inflammatory cytokine production, independent of IL-10, whereas inflammatory responses to Gram-positive species were more sustained due to the absence of this IFN-dependent regulation. Thus, disparate triggering of a cytokine negative feedback loop promotes tuning of macrophage responses in a bacteria class-specific manner and provides context-dependent regulation of inflammation dynamics.