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The glymphatic system is a system of specialized perivascular spaces in the brain that facilitates removal of toxic waste solutes from the brain. Evaluation of glymphatic system function by means of magnetic resonance imaging (MRI) has thus far been largely focused on rodents because of the limitations of intrathecal delivery of gadolinium‐based contrast agents to humans. This review discusses MRI methods that can be employed clinically for glymphatic‐related measurements intended for early diagnosis, prevention, and the treatment of various neurological conditions. Although glymphatic system‐based MRI research is in its early stages, recent studies have identified promising noninvasive MRI markers associated with glymphatic system alterations in neurological diseases. However, further optimization in data acquisition, validation, and modeling are needed to investigate the glymphatic system within the clinical setting. [ABSTRACT FROM AUTHOR]

Food security is one of the most pressing concerns of our times. Despite the vast opportunities, there is a shortage of skilled professionals in the food, agriculture, and plant sciences. Although most programs focused on addressing this concern have targeted students, we have often overlooked another critical group--the teachers. To address this issue, we recruited high school teachers from the Pharr--San Juan--Alamo Independent School District (PSJA-ISD) in South Texas to participate in a workshop at the University of Texas Rio Grande Valley (UTRGV) and fostered their agricultural knowledge through boot camp-style intensive training by faculty members in food, agriculture, and plant sciences. Through this project, we seek to indirectly educate nearly 2,000 high school students and use these lesson plans as models for undergraduate education. Results from the teacher and student assistant evaluations show that our workshop structure and design is effective in serving the objectives and instigating interest among teachers who plan to incorporate these into their lesson plans. Student assistants involved in the project also describe it as an opportunity to improve their teaching and public speaking skills. Taken together, we suggest that in addition to planning student-centered activities, the teacher-centered activities should also take the center stage in our fight for better food security.

Rice straw lignin was extracted via alkaline hydrolysis and structurally characterized using FT-IR and 1H NMR spectra. Ethyl acetate extract of acid solubilized lignin was found to contain p-coumaric acid, ferulic acid and caffeic acid as major phenolic acids which were isolated and characterized using spectral data. Amides of isolated phenolic acids were synthesized by their reaction with propyl and butyl amines using microwave irradiation and analysed using spectral studies. Phenolic acids and amides were evaluated for their effect on pollen germination and tube growth in pumpkin. Pollen tube length was significantly increased with N-butyl-3-(3, 4-dihydroxyphenyl) acrylamide and N-butyl-3-(4-hydroxyphenyl) acrylamide at 5 ppm concentration than the control. These results could be utilised in increasing pollen tube length of Cucurbita pepo while making interspecific cross between C. moschata and C. pepo in order to transfer hull-less character of C. pepo to virus resistant C. moschata genotypes. [ABSTRACT FROM AUTHOR]

Sleep quality is a critical factor in human health and well-being, with implications for various physiological and psychological processes. Traditional methods of sleep data collection are often limited by the quality and reliability of the data due to issues such as recall bias and subjective interpretation. This research aims to propose a novel framework that objectively measures and evaluates sleep quality using smart thermostats equipped with motion sensors, providing noninvasive and effortless sleep monitoring. The study conducts a comprehensive analysis of sleep patterns, exploring the relationship between activity sensors and sleep quality. By analyzing behavioral characteristics, the study identifies periods or clusters of days that require attention in terms of health and stress levels. The approach ensures privacy, ease of access, and integrates environmental factors, enabling a comprehensive understanding of an individual’s sleep health. The findings suggest that this zero-effort technology can significantly enhance sleep monitoring at both individual and population levels, with implications for health monitoring, stress management, and personalized healthcare interventions. Future work will focus on expanding the data set, incorporating more variables, and integrating contextual data to further improve sleep quality analysis and support real-time health interventions. [ABSTRACT FROM AUTHOR]

In Canada, extreme heat occurrences present significant risks to public health, particularly for vulnerable groups like older individuals and those with preexisting health conditions. Accurately predicting indoor temperatures during these events is crucial for informing public health strategies and mitigating the adverse impacts of extreme heat. While current systems rely on outdoor temperature data, incorporating real-time indoor temperature estimations can significantly enhance decision-making and strengthen overall health system responses. Sensor-based technologies, such as ecobee smart thermostats installed in homes, enable effortless collection of indoor temperature and humidity data. This study evaluates the efficacy of deep learning models in predicting indoor temperatures during heat waves using smart thermostat data, to enhance public health responses. Utilizing ecobee smart thermostats, we analyzed indoor temperature trends and developed forecasting models. Our findings indicate the potential of integrating IoT and deep learning into health warning systems, enabling proactive interventions, and improving sustainable health care practices in extreme heat scenarios. This approach highlights the role of digital health innovations in creating the resilient and sustainable healthcare systems against climate-related health adversities. [ABSTRACT FROM AUTHOR]

Background:Most of the Veterans Administration (VA) population is either overweight or obese, which is a serious health concern. Medical weight management visits have traditionally occurred through in-person clinics. However, the COVID-19 pandemic forced care delivery to virtual platforms. Methods:We compared weight loss with in-person versus telephone-based medical weight management (lifestyle counseling coupled with pharmacotherapy) delivered by physician and nurse practitioner visits during the pandemic. We designed a program evaluation utilizing a naturalistic (pragmatic) observational study structure, including both newly enrolled and previously established participants in the Minneapolis VA MOVE! program between 2017 and 2021. A "transition" cohort (n = 74) received in-person care from March 2019 to March 2020, and then transitioned to virtual care. A "new start" virtual care cohort (n = 149) enrolled after March 2020 was compared to a separate historical group (n = 180) that received in-person care between January 2017 and December 2019. Weight loss was accessed over a 9-month period in both cohorts. Results:Mean weight loss over 9 months was −6.5 ± 18.2 and −2.5 ± 13.3 lbs in the in-person and virtual phases of the transition cohort, respectively, without significant difference between the two phases (p = 0.22). Mean weight loss over 9 months in the new start (virtual) cohort was −14.4 ± 17.0 lbs compared to −16.7 ± 21.0 lbs in the historical cohort, without significant difference between groups (p = 0.44). Conclusions:In our naturalistic study in a single-site VA clinic setting, weight loss with telephone-based medical weight management during the pandemic was comparable to in-person care. These findings are important for veterans living in rural and/or underserved areas. [ABSTRACT FROM AUTHOR]