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1.
Cannabis Cannabinoid Res ; 8(4): 623-633, 2023 08.
Article in English | MEDLINE | ID: mdl-35647939

ABSTRACT

Background: The endocannabinoid system (ECS) plays a key physiological role in bladder function and it has been suggested as a potential target for relieving lower urinary tract symptoms (LUTSs). Whereas most studies indicate that activating the ECS has some beneficial effects on the bladder, some studies imply the opposite. In this study, we investigated the therapeutic potential of peripheral cannabinoid-1 receptor (CB1R) blockade in a mouse model for LUTSs. Materials and Methods: To this end, we used the cyclophosphamide (CYP; 300 mg/kg, intraperitoneal)-induced cystitis model of bladder dysfunction, in which 12-week-old, female C57BL/6 mice were treated with the peripherally restricted CB1R antagonist, JD5037 (3 mg/kg), or vehicle for three consecutive days. Bladder dysfunction was assessed using the noninvasive voiding spot assay (VSA) as well as the bladder-to-body weight (BW) ratio and gene and protein expression levels; ECS tone was assessed at the end of the study. Results: Peripheral CB1R blockade significantly ameliorated the severity of CYP-induced cystitis, manifested by reduced urination events measured in the VSA and an increased bladder-to-BW ratio. Moreover, JD5037 normalized CYP-mediated bladder ECS tone imbalance by affecting both the expression of CB1R and the endocannabinoid levels. These effects were associated with the ability of JD5037 to reduce CYP-induced inflammatory response, manifested by a reduction in levels of the proinflammatory cytokine, tumor necrosis factor alpha (TNFα), in the bladder and serum. Conclusions: Collectively, our results highlight the therapeutic relevance of peripheral CB1R blockade in ameliorating CYP-induced cystitis; they may further support the preclinical development and clinical use of peripherally restricted CB1R antagonism for treatment of LUTSs.


Subject(s)
Cannabinoids , Cystitis , Mice , Animals , Female , Endocannabinoids , Receptors, Cannabinoid , Mice, Inbred C57BL , Cystitis/chemically induced , Cystitis/drug therapy , Cystitis/metabolism , Cannabinoids/adverse effects
2.
Nature ; 590(7844): 67-73, 2021 02.
Article in English | MEDLINE | ID: mdl-33536657

ABSTRACT

Fundamental mathematical constants such as e and π are ubiquitous in diverse fields of science, from abstract mathematics and geometry to physics, biology and chemistry1,2. Nevertheless, for centuries new mathematical formulas relating fundamental constants have been scarce and usually discovered sporadically3-6. Such discoveries are often considered an act of mathematical ingenuity or profound intuition by great mathematicians such as Gauss and Ramanujan7. Here we propose a systematic approach that leverages algorithms to discover mathematical formulas for fundamental constants and helps to reveal the underlying structure of the constants. We call this approach 'the Ramanujan Machine'. Our algorithms find dozens of well known formulas as well as previously unknown ones, such as continued fraction representations of π, e, Catalan's constant, and values of the Riemann zeta function. Several conjectures found by our algorithms were (in retrospect) simple to prove, whereas others remain as yet unproved. We present two algorithms that proved useful in finding conjectures: a variant of the meet-in-the-middle algorithm and a gradient descent optimization algorithm tailored to the recurrent structure of continued fractions. Both algorithms are based on matching numerical values; consequently, they conjecture formulas without providing proofs or requiring prior knowledge of the underlying mathematical structure, making this methodology complementary to automated theorem proving8-13. Our approach is especially attractive when applied to discover formulas for fundamental constants for which no mathematical structure is known, because it reverses the conventional usage of sequential logic in formal proofs. Instead, our work supports a different conceptual framework for research: computer algorithms use numerical data to unveil mathematical structures, thus trying to replace the mathematical intuition of great mathematicians and providing leads to further mathematical research.

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