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One of the most widely accepted benefits of enhanced physical activity is an improvement in the symptoms of depression, including the facilitation of decision making. Up until now, these effects have been shown in rodents and humans only. Little is known about their evolutionary origin or biological basis, and the underlying cellular mechanisms also remain relatively elusive. Here, we demonstrate for the first time that preceding motor activity accelerates decision making in an invertebrate, the pond snail Lymnaea stagnalis To investigate decision making in a novel environment, snails, which normally live in water, were placed on a flat dry surface to simulate the potentially threatening consequence of being in an arid environment. This stimulus initiated two distinct phases in snail behaviour: slow circular movements, followed by intense locomotion in a chosen direction. The first phase was prolonged when the test arena was symmetrically lit, compared with one with an apparent gradient of light. However, forced muscular locomotion for 2 h prior to the test promoted the transition from random circular motions to a directional crawl, accompanied by an increase in crawling speed but with no effect on the choice of direction. Intense locomotion for 2 h also produced a strong excitatory effect on the activity of serotonergic neurons in L. stagnalis Our results suggest that the beneficial effects of physical exercise on cognitive performance in mammals might have deep roots in evolution, granting the opportunity to unravel the origins of such effects at the single-neuron and network levels.
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http://dx.doi.org/10.1242/jeb.146837 | DOI Listing |
Knee Surg Relat Res
September 2025
Florida Orthopaedic Institute, Gainesville, FL, 32607, USA.
Background: A clear understanding of minimal clinically important difference (MCID) and substantial clinical benefit (SCB) is essential for effectively implementing patient-reported outcome measurements (PROMs) as a performance measure for total knee arthroplasty (TKA). Since not achieving MCID and SCB may reflect suboptimal surgical benefit, the primary aim of this study was to use machine learning to predict patients who may not achieve the threshold-based outcomes (i.e.
View Article and Find Full Text PDFGenome Biol
September 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
Background: Soil salinization represents a critical global challenge to agricultural productivity, profoundly impacting crop yields and threatening food security. Plant salt-responsive is complex and dynamic, making it challenging to fully elucidate salt tolerance mechanism and leading to gaps in our understanding of how plants adapt to and mitigate salt stress.
Results: Here, we conduct high-resolution time-series transcriptomic and metabolomic profiling of the extremely salt-tolerant maize inbred line, HLZY, and the salt-sensitive elite line, JI853.
J Assist Reprod Genet
September 2025
Department of Gynecology, Pingxiang Maternal and Child Health Hospital, PingXiang, Jiangxi, China.
Objective: This study aimed to identify key predictors of uterine fibroid (UF) recurrence following laparoscopic myomectomy (LM) in reproductive-age women and to construct a predictive nomogram to support individualized clinical decision-making.
Methods: This retrospective cohort study included 459 women who underwent LM. Recurrence of UFs and risk of recurrence were analyzed.
Bariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.
View Article and Find Full Text PDFJ Gen Intern Med
September 2025
University of Colorado School of Medicine, 1890 N Revere Ct, Third Floor, Mail Stop F443, Aurora, CO, 80045, USA.
Background: The SHARE Approach Model and training curriculum was developed by the Agency for Healthcare Research and Quality (AHRQ) to teach clinicians practicing in diverse settings how to engage in more effective Shared Decision Making (SDM).
Objective: To determine the effectiveness of the SHARE Approach at improving SDM in practices located across Colorado, USA.
Design: A longitudinal study with pre- and post-intervention observations.