Introduction: Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy, with an incidence of 1–2 cases per million annually. Its occurrence during pregnancy is exceptionally uncommon, presenting significant diagnostic and management challenges due to overlapping physiological changes and concerns for both maternal and fetal outcomes. While most cases are hormonally functional, non-functioning ACC during pregnancy is particularly rare and may result in delayed diagnosis. Case: A 35-year-old Malay female was referred following ultrasonography for persistent back and left flank pain, which revealed a left adrenal incidentaloma and a concurrent 10-week intrauterine pregnancy. There were no clinical signs of hormone excess. Physical examination revealed a normotensive patient with a large, palpable left abdominal mass, without Cushingoid or virilizing features. Magnetic resonance imaging (MRI) demonstrated a 12.1 × 10.3 × 12.8 cm heterogeneous left adrenal mass with cystic and necrotic components, displacing adjacent structures. Hormonal evaluation was within normal limits: 24-hour urinary cortisol 320.2 nmol/24 hours (reference range [RR] 11.8–350), midnight salivary cortisol <3 and 4.2 nmol/L (RR <11.3), plasma metanephrine <0.2 nmol/L (RR <0.5), normetanephrine 0.6 nmol/L (RR <0.9), and 17-hydroxyprogesterone 223 ng/dL (RR <285), consistent with a non-functioning tumor. Following multidisciplinary consultation, the patient underwent open left adrenalectomy at 16 weeks’ gestation. Histopathological analysis confirmed ACC (Weiss score 8/9) without extra-adrenal extension. Surveillance MRI at 32 weeks demonstrated no recurrence or residual mass. At 40 weeks’ gestation, she delivered a healthy infant, and both mother and child remain well on follow-up. Conclusion Non-functioning ACC during pregnancy is rare and poses significant diagnostic challenges. Early imaging, comprehensive hormonal assessment, and timely surgical intervention during the second trimester are essential. Multidisciplinary management is crucial to optimizing maternal and fetal outcomes.
Female ; Pregnancy ; Heart Rate ; Neoplasms

BACKGROUND

Heart rate variability (HRV) is a common tool for assessing autonomic nervous system activity and monitoring training load in athletes. However, limited research has explored how HRV responds to different forms of resistance training, particularly in high-intensity intermittent sports like basketball and football.

This study aims to compare the acute HRV responses between neuromuscular and metabolic training in collegiate athletes involved in high-intensity intermittent sports.

A comparative cross-sectional study with a quasi-experimental crossover design will be employed.

Collegiate athletes will be randomly assigned to undergo both neuromuscular and metabolic training sessions with a one-week wash-out period in between. HRV data will be recorded using the Polar H10 chest strap during each session.

Descriptive statistics will summarize salient participant characteristics and HRV measurements. Inferential analysis will use paired t-tests or Wilcoxon signed-rank tests based on normality, assessed via the Kolmogorov-Smirnov test. All statistical analyses will be conducted using the IBM SPSS (ver.25) with a confidence interval set. at 95% and a critical α equal to 0.05.

Neuromuscular training is expected to elicit higher low-frequency (LF) power and an increased LF/HF ratio, reflecting greater sympathetic activation, while metabolic training is expected to show lower LF power and a decreased LF/HF ratio, indicating a more balanced autonomic response. These findings will offer insights into the differential autonomic impacts of these training modalities.

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*

Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
Humans ; Deep Learning ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Video Recording ; Face ; Monitoring, Physiologic/methods* ; Signal Processing, Computer-Assisted

Multi-task learning (MTL) has demonstrated significant advantages in the field of physiological signal measurement. This approach enhances the model's generalization ability by sharing parameters and features between similar tasks, even in data-scarce environments. However, traditional multi-task physiological signal measurement methods face challenges such as feature conflicts between tasks, task imbalance, and excessive model complexity, which limit their application in complex environments. To address these issues, this paper proposes an enhanced multi-scale spatiotemporal network (EMSTN) based on Eulerian video magnification (EVM), super-resolution reconstruction and convolutional multilayer perceptron. First, EVM is introduced in the input stage of the network to amplify subtle color and motion changes in the video, significantly improving the model's ability to capture pulse and respiratory signals. Additionally, a super-resolution reconstruction module is integrated into the network to enhance the image resolution, thereby improving detail capture and increasing the accuracy of facial action unit (AU) tasks. Then, convolutional multilayer perceptron is employed to replace traditional 2D convolutions, improving feature extraction efficiency and flexibility, which significantly boosts the performance of heart rate and respiratory rate measurements. Finally, comprehensive experiments on the Binghamton-Pittsburgh 4D Spontaneous Facial Expression Database (BP4D+) fully validate the effectiveness and superiority of the proposed method in multi-task physiological signal measurement.
Humans ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Face/physiology* ; Video Recording ; Facial Expression ; Heart Rate ; Algorithms

Alzheimer's disease (AD) is the most common degenerative disease of the nervous system. Studies have found that the 40 Hz pulsed magnetic field has the effect of improving cognitive ability in AD, but the mechanism of action is not clear. In this study, APP/PS1 double transgenic AD model mice were used as the research object, the water maze was used to group dementia, and 40 Hz/10 mT pulsed magnetic field stimulation was applied to AD model mice with different degrees of dementia. The behavioral indicators, mitochondrial samples of hippocampal CA1 region and electrocardiogram signals were collected from each group, and the effects of 40 Hz pulsed magnetic field on mouse behavior, mitochondrial kinetic indexes and heart rate variability (HRV) parameters were analyzed. The results showed that compared with the AD group, the loss of mitochondrial crest structure was alleviated and the mitochondrial dynamics related indexes were significantly improved in the AD + stimulated group ( P < 0.001), sympathetic nerve excitation and parasympathetic nerve inhibition were improved, and the spatial cognitive memory ability of mice was significantly improved ( P < 0.05). The preliminary results of this study show that 40 Hz pulsed magnetic field stimulation can improve the mitochondrial structure and mitochondrial kinetic homeostasis imbalance of AD mice, and significantly improve the autonomic neuromodulation ability and spatial cognition ability of AD mice, which lays a foundation for further exploring the mechanism of ultra-low frequency magnetic field in delaying the course of AD disease and realizing personalized neurofeedback therapy for AD.
Animals ; Heart Rate/physiology* ; Mice ; Alzheimer Disease/therapy* ; Mice, Transgenic ; Mitochondrial Dynamics/radiation effects* ; Magnetic Field Therapy/methods* ; Magnetic Fields ; Disease Models, Animal ; Mitochondria ; Male ; Maze Learning ; Cognition ; Dementia/therapy*

Exercise intervention is an important non-pharmacological intervention for various diseases, and establishing precise exercise load assessment techniques can improve the quality of exercise intervention and the efficiency of disease prevention and control. Based on data collection from wearable devices, this study conducts nonlinear optimization and empirical verification of the original "Fitness-Fatigue Model". By constructing a time-varying attenuation function and specific coefficients, this study develops an optimized mathematical model that reflects the nonlinear characteristics of training responses. Thirteen participants underwent 12 weeks of moderate-intensity continuous cycling, three times per week. For each training session, external load (actual work done) and internal load (heart rate variability index) data were collected for each individual to conduct a performance comparison between the optimized model and the original model. The results show that the optimized model demonstrates a significantly improved overall goodness of fit and superior predictive ability. In summary, the findings of this study can support dynamic adjustments to participants' training programs and aid in the prevention and control of chronic diseases.
Humans ; Wearable Electronic Devices ; Exercise/physiology* ; Models, Theoretical ; Heart Rate/physiology* ; Exercise Therapy

Heart rate variability time and frequency indices are widely used in functional assessment for autonomic nervous system (ANS). However, this method merely analyzes the effect of cardiac dynamics, overlooking the effect of cardio-pulmonary interplays. Given this, the present study proposes a novel cardiopulmonary coupling (CPC) algorithm based on cross-wavelet transform to quantify cardio-pulmonary interactions, and establish an assessment system for ANS aging effects using wearable electrocardiogram (ECG) and respiratory monitoring devices. To validate the superiority of the proposed method under nonstationary and low signal-to-noise ratio conditions, simulations were first conducted to demonstrate the performance strength of the proposed method to the traditional one. Next, the proposed CPC algorithm was applied to analyze cardiac and respiratory data from both elderly and young populations, revealing that young populations exhibited significantly stronger couplings in the high-frequency band compared with their elderly counterparts. Finally, a CPC assessment system was constructed by integrating wearable devices, and additional recordings from both elderly and young populations were collected by using the system, completing the validation and application of the aging effect assessment algorithm and the wearable system. In conclusion, this study may offers methodological and system support for assessing the aging effects on the ANS.
Humans ; Autonomic Nervous System/physiology* ; Algorithms ; Aging/physiology* ; Electrocardiography/methods* ; Heart Rate/physiology* ; Wavelet Analysis ; Aged ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices

BACKGROUND: Neck cooling is a practical method for preventing heat-related illness, however, its effectiveness in general workers is not well established. This study aimed to assess the effects of neck cooling on core body temperature and other physiological markers during exercise in a hot environment. METHODS: This randomized crossover trial was conducted from November 2023 to April 2024 at the Shared-Use Research Center at UOEH. Fourteen healthy adult males participated in the study under two conditions: with neck cooling (COOL) and without neck cooling (CON). All participants completed both conditions, and the order of condition assignment was determined by a random draw. Participants first rested for 10 minutes in a 28.0 °C, 50% relative humidity environment, followed by a rest in a 35.0 °C, 50% relative humidity environment for another 10 minutes. In the COOL condition, participants wore a neck cooler containing 1,200 g of ice while exercising at 50% Heart Rate Reserve on a bicycle ergometer for 20 minutes. Afterward, they rested for 15 minutes in the hot environment while still wearing the cooler. MAIN OUTCOME MEASURES: Core body temperature (rectal and esophageal), forehead skin temperature, and heart rate were continuously monitored and compared using a mixed model. Estimated sweat volume was calculated based on changes in body weight before and after the experiment. RESULTS: At the end of the rest period, no significant differences were observed between the COOL and CON conditions in rectal temperature (37.76 ± 0.18 °C versus 37.75 ± 0.24 °C, p = 0.9493), esophageal temperature (37.75 ± 0.30 °C versus 37.76 ± 0.23 °C, p = 0.7325), forehead skin temperature (36.87 ± 0.29 °C versus 36.88 ± 0.27 °C, p = 0.2160), or heart rate (104.18 ± 7.56 bpm versus 107.52 ± 7.40 bpm, p = 0.1035). Estimated sweat loss was similar between conditions (578 ± 175 g for CON versus 572 ± 242 g for COOL, p = 0.5066). While more participants felt cooler in the COOL condition, RPE showed no significant difference. CONCLUSION Neck cooling did not significantly affect core temperature or perceived exertion. Maintaining close contact with the skin at sufficiently low temperatures or utilizing cooling methods that prevent excessive negative feedback may be necessary to enhance the effectiveness of neck cooling.
Humans ; Male ; Cross-Over Studies ; Exercise/physiology* ; Adult ; Neck/physiology* ; Hot Temperature/adverse effects* ; Young Adult ; Body Temperature ; Heart Rate ; Skin Temperature ; Body Temperature Regulation ; Cold Temperature

OBJECTIVE: To observe the effects of Yisui Yangxin moxibustion (moxibustion for benefiting the marrow and nourishing the heart) in sequential trial on blood pressure (BP), perimenopausal symptoms, cardiovascular function and heart rate variability (HRV) in prehypertension in perimenopausal women. METHODS: The eligible female patients of prehypertension of perimenopausal period were collected according to the inclusion criteria. Based on the requirements of open two-way qualitative response sequential trial, the sample size was not set in advance, and the matched pair design was used for random division into a moxibustion group and a health education group. The patients of the two groups were treated in pairs, and the analysis was immediately performed after the end of treatment for each pair, and the sequential trial analysis (STA) was diagrammed. The health education group provided the health education for prehypertension to the patients according to 2018 Revised Chinese Hypertension Prevention and Treatment Guidelines. In the moxibustion group, beside the health education for prehypertension, the non-suppurative moxibustion was delivered at Baihui (GV20), Guanyuan (CV4) and bilateral Neiguan (PC6) and Zusanli (ST36), once every two days, 3 treatments per week and for 4 consecutive weeks. Before treatment, in 2 and 4 weeks of treatment, as well as in 1-month follow-up after treatment, BP was measured in each group, separately. Before treatment and in 4 weeks of treatment, the observation was performed in the score of the modified Kupperman scale, and Pittsburgh sleep quality index (PSQI) score, the indexes of cardiovascular function (stroke volume [SV], cardiac output per minute [CO], stroke index [SI], cardiac index [CI], left ventricular effective pumping force [VPE], left ventricular energy efficiency [EWK], arterial compliance [AC], left ventricular ejection resistance [VER], blood viscosity (N), and microcirculation half update rate [MHR]), and heart rate variability (HRV) indexes (low frequency [LF], high frequency [HF], LF/HF, standard deviation of NN interval [SDNN], root mean square of successive differences in adjacent NN intervals [RMSSD]). RESULTS: Based on sequential test line, when the trial was performed to the 17th pair, and the test line touched the upper limit U, meaning the results of the moxibustion group was superior to the health education group, thus, the trial stopped immediately. In 2 and 4 weeks of treatment and in follow-up, the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were lower when compared with those before treatment in the moxibustion group (P<0.05); SBP at each time point and DBP in 2 and 4 weeks of treatment in the moxibustion group were lower than those of the health education group (P<0.05). After treatment, the score of the modified Kupperman scale was reduced when compared with that before treatment in the moxibustion group (P<0.05). The score of the modified Kupperman scale and PSQI in the moxibustion group were lower than those of the health education group after treatment (P<0.05). After treatment, SV, CO, SI, CI and EWK were increased in comparison with the indexes before treatment in the moxibustion group (P<0.05), and VER and N were dropped (P<0.05). After treatment, SV, CO, SI, CI and EWK in the moxibustion group were higher than those of the health education group (P<0.05), and VER and N were lower (P<0.05). After treatment, in the moxibustion group, LF, HF, SDNN and RMSSD were increased in comparison with those before treatment (P<0.05), and LF/HF was declined (P<0.05). In the health education group, after treatment, LF, HF, SDNN and RMSSD decreased when compared with those before treatment (P<0.05). After treatment, LF, HF, SDNN and RMSSD in the moxibustion group were higher than those of the health education group (P<0.05), and LF/HF was lower (P<0.05). The total effective rate was 94.1% (16/17) in the moxibustion group, higher than that (41.2%, 7/17) in the health education group (P<0.05). CONCLUSION Moxibustion can reduce SBP and DBP in prehypertension of perimenopausal women, alleviate perimenopausal symptoms, improve cardiac function and adjust the overall balance of cardiac autonomic nerves.
Humans ; Female ; Middle Aged ; Moxibustion ; Perimenopause ; Blood Pressure ; Prehypertension/prevention & control* ; Heart Rate ; Acupuncture Points ; Adult