Background Menstrual cycle-related physiological variations represent a complex, multifaceted phenomenon with significant implications for female work performance and cardiovascular function. This study aimed to systematically evaluate the influence of menstrual cycle phases on cardiac efficiency and work performance among young women, utilizing a comprehensive bicycle ergometric assessment methodology. The research sought to quantify physiological variations during mid-follicular and mid-luteal phases, providing nuanced insights into hormonal dynamics and performance metrics. Methodology A prospective observational study was conducted among 100 young women volunteers aged 18-25 years in Chennai, Tamil Nadu, India. Participants underwent standardized bicycle ergometer testing during two distinct menstrual cycle phases: mid-follicular (seventh day) and mid-luteal (21st day). A bicycle ergometer (KH-695, Viva Fitness Company, New Delhi, India) was employed to measure energy expenditure, work performance, and cardiac efficiency. Subjects initially underwent a five-minute resting period, with baseline pulse rate and blood pressure recorded. Participants then performed cycling at a 2 kg resistance for a maximum of five minutes, with pulse rates monitored during post-exercise recovery intervals. Cardiac efficiency was calculated using a comprehensive formula incorporating exercise duration and post-exercise pulse rates, while work done was determined through precise mechanical measurements. Results Statistical analysis revealed significant physiological variations across menstrual cycle phases. Cardiac efficiency demonstrated a remarkable increase from 79.98 (SD ± 17.618) in the mid-follicular phase to 112.58 (SD ± 13.086) in the mid-luteal phase, with 95 out of 100 participants exhibiting enhanced performance (Z-statistic = -8.625, p = 0.000). Total work done similarly showed substantial improvements, increasing from 185.77 (SD ± 35.82) to 242.97 (SD ± 31.275), with 97 observations indicating superior luteal phase performance (Z-statistic = -8.374, p = 0.000). Notably, work done per minute remained consistently stable across both phases, suggesting an intrinsic physiological adaptation mechanism. The Wilcoxon signed-rank test confirmed statistically significant differences in cardiac efficiency and total work done, highlighting the complex interplay between hormonal fluctuations and physiological performance. Conclusions The study demonstrates significant menstrual cycle phase-related variations in cardiac efficiency and work performance, providing crucial insights into female physiological adaptability and underscoring the importance of personalized performance management strategies across different reproductive cycle stages.
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http://dx.doi.org/10.7759/cureus.78216 | DOI Listing |
J Med Eng Technol
March 2025
College of Basic Medical, North China University of Science and Technology, Tangshan, China.
Cardiovascular diseases (CVDs) significantly impact athletes, impacting the heart and blood vessels. This article introduces a novel method to assess CVD in athletes through an artificial neural network (ANN). The model utilises the mutual learning-based artificial bee colony (ML-ABC) algorithm to set initial weights and proximal policy optimisation (PPO) to address imbalanced classification.
View Article and Find Full Text PDFMagn Reson Med
March 2025
Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Purpose: To evaluate the feasibility of interleaved Na/H cardiac MRI at 7 T using H parallel transmission (pTx) pulses.
Methods: A combined setup consisting of a Na volume coil and two H transceiver arrays was employed and the transmit and receive characteristics were compared in vitro with those of the uncombined radiofrequency coils. Furthermore, the implemented interleaved Na/H pTx sequence was validated in phantom measurements and applied to four healthy subjects.
JMIR Cancer
March 2025
College of Public Health, Imam Abdulrahman bin Faisal University, Dammam, SA.
Background: Artificial intelligence (AI) is a revolutionary upcoming tool yet to be fully integrated into several healthcare sectors, including medical imaging. AI can transform how medical imaging is conducted and interpreted, especially in cardio-oncology.
Objective: This study aims to systematically review the available literature on the use of AI in cardio-oncology imaging to predict cardiotoxicity and describe the possible improvement of different imaging modalities that can be achieved if AI is successfully deployed to routine practice.
Nat Commun
March 2025
State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China.
Activation of endogenous full-length utrophin, a dystrophin homolog, presents an attractive therapeutic strategy for Duchenne muscular dystrophy (DMD), regardless of mutation types and loci. However, current dCas9-based activators are too large for efficient adeno-associated virus delivery, and the feasibility and durability of such treatments remain unclear. Here, we develop a muscle-targeted utrophin activation system using the compact dCasMINI-VPR system, termed MyoAAV-UA.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
March 2025
Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Munich, Germany.
Purpose: Due to new advances in molecular and imaging biomarkers, a biological classification of Parkinson's disease (PD) called SyNeurGe (Hoglinger et al. Lancet Neurol 2024;23:191-204) has been proposed for research use recently. [I]ioflupane dopamine transporter single-photon-emission-computed tomography (DaT-SPECT) and cardiac [I]meta-iodobenzylguanidine (MIBG) scintigraphy are included in this biological classification scheme together with 2-[F]fluoro-2-deoxy-D-glucose (FDG-PET) as central imaging biomarkers for the assessment of dopaminergic function, cardiac sympathetic denervation, and metabolic patterns in brain.
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