OBJECTIVE: To analyze the disease burden of pelvic fractures at the global, regional, and national levels from 1990 to 2023 using data from the 2023 Global Burden of Disease Study (GBD), and to predict the disease burden through 2050. METHODS: Leveraging data from the GBD 2023, this study investigated the disease burden of pelvic fractures across 204 countries and regions. Assessment indicators included incidence rate, prevalence rate, and years lived with disability (YLDs). The Joinpoint regression model was employed to analyze trends in pelvic fracture burden from 1990 to 2023, while the average annual percentage change (AAPC) was used to quantify this temporal trend. The relationship between the socio-demographic index and pelvic fracture burden was evaluated. Furthermore, the long short-term memory (LSTM) model was applied to predict trends in pelvic fracture burden through 2050. RESULTS: In 2023, the estimated number of new pelvic fracture cases globally was 7 479 884 [95% uncertainty interval ( UI): 5 293 401-10 611 876], representing a 42.74% increase from 1990. In the same year, the number of prevalent pelvic fracture cases and YLDs were 23 007 508 (95% UI: 21 021 518-25 327 165) and 3 909 228 person-years (95% UI: 2 725 498-5 194 385), respectively. Additionally, age-standardized rates exhibited an opposing downward trend. Significant disparities in the disease burden of pelvic fractures were identified across different age groups, genders, and social contexts. According to predictions from the LSTM model, the global age-standardized incidence rate (ASIR) of pelvic fractures will be approximately 88.44 per 100 000 persons by 2050, while the total number of incident cases will rise to 8 547 095. CONCLUSION Although the overall incidence rate, prevalence rate, and YLDs of pelvic fractures have exhibited an upward trend over the past three decades, the ASIR, age-standardized prevalence rate (ASPR), and age-standardized years of life lost rate (ASYR) have shown a downward trend. It is predicted that over the upcoming 26-year period, the age-standardized rate of disease burden due to pelvic fractures will further decrease, while the number of incident cases and prevalent cases will continue to exhibit an upward trend. Formulating more targeted disease prevention strategies is critical to addressing disparities across genders, regions, and other dimensions, and to mitigating the burden of pelvic fractures.
Humans ; Fractures, Bone/epidemiology* ; Pelvic Bones/injuries* ; Male ; Female ; Middle Aged ; Adult ; Incidence ; Prevalence ; Aged ; Global Burden of Disease/trends* ; Global Health ; Adolescent ; Cost of Illness ; Young Adult ; Forecasting ; Disability-Adjusted Life Years ; Memory, Short-Term

Rhythm, as a prominent characteristic of auditory experiences such as speech and music, is known to facilitate attention, yet its contribution to working memory (WM) remains unclear. Here, human participants temporarily retained a 12-tone sequence presented rhythmically or arrhythmically in WM and performed a pitch change-detection task. Behaviorally, while having comparable accuracy, rhythmic tone sequences showed a faster response time and lower response boundaries in decision-making. Electroencephalographic recordings revealed that rhythmic sequences elicited enhanced non-phase-locked beta-band (16 Hz-33 Hz) and theta-band (3 Hz-5 Hz) neural oscillations during sensory encoding and WM retention periods, respectively. Importantly, the two-stage neural signatures were correlated with each other and contributed to behavior. As beta-band and theta-band oscillations denote the engagement of motor systems and WM maintenance, respectively, our findings imply that rhythm facilitates auditory WM through intricate oscillation-based interactions between the motor and auditory systems that facilitate predictive attention to auditory sequences.
Humans ; Memory, Short-Term/physiology* ; Male ; Beta Rhythm/physiology* ; Female ; Theta Rhythm/physiology* ; Young Adult ; Auditory Perception/physiology* ; Adult ; Electroencephalography ; Acoustic Stimulation ; Reaction Time/physiology* ; Brain/physiology* ; Attention/physiology*

Working memory is an executive memory process that includes encoding, maintenance, and retrieval. These processes can be modulated by transcranial alternating current stimulation (tACS) with sinusoidal waves. However, little is known about the impact of the rate of current change on working memory. In this study, we aimed to investigate the effects of two types of tACS with different rates of current change on working memory performance and brain activity. We applied a randomized, single-blind design and divided 81 young participants who received triangular wave tACS, sinusoidal wave tACS, or sham stimulation into three groups. Participants performed n-back tasks, and electroencephalograms were recorded before, during, and after active or sham stimulation. Compared to the baseline, working memory performance (accuracy and response time) improved after stimulation under all stimulation conditions. According to drift-diffusion model analysis, triangular wave tACS significantly increased the efficiency of non-target information processing. In addition, compared with sham conditions, triangular wave tACS reduced alpha power oscillations in the occipital lobe throughout the encoding period, while sinusoidal wave tACS increased theta power in the central frontal region only during the later encoding period. The brain network connectivity results showed that triangular wave tACS improved the clustering coefficient, local efficiency, and node degree intensity in the early encoding stage, and these parameters were positively correlated with the non-target drift rate and decision starting point. Our findings on how tACS modulates working memory indicate that triangular wave tACS significantly enhances brain network connectivity during the early encoding stage, demonstrating an improvement in the efficiency of working memory processing. In contrast, sinusoidal wave tACS increased the theta power during the later encoding stage, suggesting its potential critical role in late-stage information processing. These findings provide valuable insights into the potential mechanisms by which tACS modulates working memory.
Humans ; Memory, Short-Term/physiology* ; Male ; Female ; Young Adult ; Transcranial Direct Current Stimulation/methods* ; Brain/physiology* ; Adult ; Electroencephalography ; Single-Blind Method

Social working memory (SWM)-the ability to maintain and manipulate social information in the brain-plays a crucial role in social interactions. However, research on SWM is still in its infancy and is often treated as a unitary construct. In the present study, we propose that SWM can be conceptualized as having two relatively independent components: "externally oriented SWM" (e-SWM) and "internally oriented SWM" (i-SWM). To test this external-internal hypothesis, participants were tasked with memorizing and ranking either facial expressions (e-SWM) or personality traits (i-SWM) associated with images of faces. We then examined the neural correlates of these two SWM components and their functional roles in empathy. The results showed distinct activations as the e-SWM task activated the postcentral and precentral gyri while the i-SWM task activated the precuneus/posterior cingulate cortex and superior frontal gyrus. Distinct multivariate activation patterns were also found within the dorsal medial prefrontal cortex in the two tasks. Moreover, partial least squares analyses combining brain activation and individual differences in empathy showed that e-SWM and i-SWM brain activities were mainly correlated with affective empathy and cognitive empathy, respectively. These findings implicate distinct brain processes as well as functional roles of the two types of SWM, providing support for the internal-external hypothesis of SWM.
Humans ; Memory, Short-Term/physiology* ; Male ; Female ; Empathy/physiology* ; Young Adult ; Magnetic Resonance Imaging ; Adult ; Brain/diagnostic imaging* ; Brain Mapping ; Facial Expression ; Social Behavior ; Facial Recognition/physiology* ; Social Perception ; Personality/physiology*

The prefrontal cortex and hippocampus may support sequential working memory beyond episodic memory and spatial navigation. This stereoelectroencephalography (SEEG) study investigated how the dorsolateral prefrontal cortex (DLPFC) interacts with the hippocampus in the online processing of sequential information. Twenty patients with epilepsy (eight women, age 27.6 ± 8.2 years) completed a line ordering task with SEEG recordings over the DLPFC and the hippocampus. Participants showed longer thinking times and more recall errors when asked to arrange random lines clockwise (random trials) than to maintain ordered lines (ordered trials) before recalling the orientation of a particular line. First, the ordering-related increase in thinking time and recall error was associated with a transient theta power increase in the hippocampus and a sustained theta power increase in the DLPFC (3-10 Hz). In particular, the hippocampal theta power increase correlated with the memory precision of line orientation. Second, theta phase coherences between the DLPFC and hippocampus were enhanced for ordering, especially for more precisely memorized lines. Third, the theta band DLPFC → hippocampus influence was selectively enhanced for ordering, especially for more precisely memorized lines. This study suggests that theta oscillations may support DLPFC-hippocampal interactions in the online processing of sequential information.
Adult ; Female ; Humans ; Young Adult ; Epilepsy ; Hippocampus ; Memory, Short-Term ; Mental Recall ; Prefrontal Cortex ; Theta Rhythm ; Male

With the widespread use of electrical equipment, cognitive functions such as working memory (WM) could be severely affected when people are exposed to 50 Hz electromagnetic fields (EMF) for long term. However, the effects of EMF exposure on WM and its neural mechanism remain unclear. In the present paper, 15 rats were randomly assigned to three groups, and exposed to an EMF environment at 50 Hz and 2 mT for a different duration: 0 days (control group), 24 days (experimental group I), and 48 days (experimental group II). Then, their WM function was assessed by the T-maze task. Besides, their local field potential (LFP) in the media prefrontal cortex (mPFC) was recorded by the in vivo multichannel electrophysiological recording system to study the power spectral density (PSD) of θ and γ oscillations and the phase-amplitude coupling (PAC) intensity of θ-γ oscillations during the T-maze task. The results showed that the PSD of θ and γ oscillations decreased in experimental groups I and II, and the PAC intensity between θ and high-frequency γ (hγ) decreased significantly compared to the control group. The number of days needed to meet the task criterion was more in experimental groups I and II than that of control group. The results indicate that long-term exposure to EMF could impair WM function. The possible reason may be the impaired communication between different rhythmic oscillations caused by a decrease in θ-hγ PAC intensity. This paper demonstrates the negative effects of EMF on WM and reveals the potential neural mechanisms from the changes of PAC intensity, which provides important support for further investigation of the biological effects of EMF and its mechanisms.
Humans ; Rats ; Animals ; Memory, Short-Term/physiology* ; Electromagnetic Fields/adverse effects* ; Prefrontal Cortex ; Cognition

Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive impairment, with the predominant clinical diagnosis of spatial working memory (SWM) deficiency, which seriously affects the physical and mental health of patients. However, the current pharmacological therapies have unsatisfactory cure rates and other problems, so non-pharmacological physical therapies have gradually received widespread attention. Recently, a novel treatment using 40 Hz light flicker stimulation (40 Hz-LFS) to rescue the cognitive function of model animals with AD has made initial progress, but the neurophysiological mechanism remains unclear. Therefore, this paper will explore the potential neural mechanisms underlying the modulation of SWM by 40 Hz-LFS based on cross-frequency coupling (CFC). Ten adult Wistar rats were first subjected to acute LFS at frequencies of 20, 40, and 60 Hz. The entrainment effect of LFS with different frequency on neural oscillations in the hippocampus (HPC) and medial prefrontal cortex (mPFC) was analyzed. The results showed that acute 40 Hz-LFS was able to develop strong entrainment and significantly modulate the oscillation power of the low-frequency gamma (lγ) rhythms. The rats were then randomly divided into experimental and control groups of 5 rats each for a long-term 40 Hz-LFS (7 d). Their SWM function was assessed by a T-maze task, and the CFC changes in the HPC-mPFC circuit were analyzed by phase-amplitude coupling (PAC). The results showed that the behavioral performance of the experimental group was improved and the PAC of θ-lγ rhythm was enhanced, and the difference was statistically significant. The results of this paper suggested that the long-term 40 Hz-LFS effectively improved SWM function in rats, which may be attributed to its enhanced communication of different rhythmic oscillations in the relevant neural circuits. It is expected that the study in this paper will build a foundation for further research on the mechanism of 40 Hz-LFS to improve cognitive function and promote its clinical application in the future.
Humans ; Adult ; Rats ; Animals ; Memory, Short-Term/physiology* ; Rats, Wistar ; Neurodegenerative Diseases ; Hippocampus ; Prefrontal Cortex

OBJECTIVE: To propose a semi-supervised epileptic seizure prediction model (ST-WGAN-GP-Bi-LSTM) to enhance the prediction performance by improving time-frequency analysis of electroencephalogram (EEG) signals, enhancing the stability of the unsupervised feature learning model and improving the design of back-end classifier. METHODS: Stockwell transform (ST) of the epileptic EEG signals was performed to locate the time-frequency information by adaptive adjustment of the resolution and retaining the absolute phase to obtain the time-frequency inputs. When there was no overlap between the generated data distribution and the real EEG data distribution, to avoid failure of feature learning due to a constant JS divergence, Wasserstein GAN was used as a feature learning model, and the cost function based on EM distance and gradient penalty strategy was adopted to constrain the unsupervised training process to allow the generation of a high-order feature extractor. A temporal prediction model was finally constructed based on a bi-directional long short term memory network (Bi-LSTM), and the classification performance was improved by obtaining the temporal correlation between high-order time-frequency features. The CHB-MIT scalp EEG dataset was used to validate the proposed patient-specific seizure prediction method. RESULTS: The AUC, sensitivity, and specificity of the proposed method reached 90.40%, 83.62%, and 86.69%, respectively. Compared with the existing semi-supervised methods, the propose method improved the original performance by 17.77%, 15.41%, and 53.66%. The performance of this method was comparable to that of a supervised prediction model based on CNN. CONCLUSION The utilization of ST, WGAN-GP, and Bi-LSTM effectively improves the prediction performance of the semi-supervised deep learning model, which can be used for optimization of unsupervised feature extraction in epileptic seizure prediction.
Humans ; Memory, Short-Term ; Seizures/diagnosis* ; Electroencephalography

OBJECTIVE: To select variables related to mortality risk of stroke patients in intensive care unit (ICU) through long short-term memory (LSTM) with attention mechanisms and Logistic regression with L1 norm, and to construct mortality risk prediction model based on conventional Logistic regression with important variables selected from the two models and to evaluate the model performance. METHODS: Medical Information Mart for Intensive Care (MIMIC)-Ⅳ database was retrospectively analyzed and the patients who were primarily diagnosed with stroke were selected as study population. The outcome was defined as whether the patient died in hospital after admission. Candidate predictors included demogra-phic information, complications, laboratory tests and vital signs in the initial 48 h after ICU admission. The data were randomly divided into a training set and a test set for ten times at a ratio of 8 ∶2. In training sets, LSTM with attention mechanisms and Logistic regression with L1 norm were constructed to select important variables. In the test sets, the mean importance of variables of ten times was used as a reference to pick out the top 10 variables in each of the two models, and then these variables were included in conventional Logistic regression to build the final prediction model. Model evaluation was based on the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. And the model performance was compared with the forward Logistic regression model which hadn't conducted variable selection previously. RESULTS: A total of 2 755 patients with 2 979 ICU admission records were included in the analysis, of which 526 recorded deaths. The AUC of Logistic regression model with L1 norm was statistically better than that of LSTM with attention mechanisms (0.819±0.031 vs. 0.760±0.018, P < 0.001). Age, blood glucose, and blood urea nitrogen were at the top ten important variables in both of the two models. AUC, sensitivity, specificity, and accuracy of Logistic regression models were 0.85, 85.98%, 71.74% and 74.26%, respectively. And the final prediction model was superior to forward Logistic regression model. CONCLUSION The variables selected by Logistic regression with L1 norm and LSTM with attention mechanisms had good prediction performance, which showed important implications on the mortality prediction of stroke patients in ICU.
Critical Care ; Humans ; Intensive Care Units ; Logistic Models ; Memory, Short-Term ; Prognosis ; ROC Curve ; Retrospective Studies ; Stroke

Working memory is an important foundation for advanced cognitive function. The paper combines the spatiotemporal advantages of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the neurovascular coupling mechanism of working memory. In the data analysis, the convolution matrix of time series of different trials in EEG data and hemodynamic response function (HRF) and the blood oxygen change matrix of fNIRS are extracted as the coupling characteristics. Then, canonical correlation analysis (CCA) is used to calculate the cross correlation between the two modal features. The results show that CCA algorithm can extract the similar change trend of related components between trials, and fNIRS activation of frontal pole region and dorsolateral prefrontal lobe are correlated with the delta, theta, and alpha rhythms of EEG data. This study reveals the mechanism of neurovascular coupling of working memory, and provides a new method for fusion of EEG data and fNIRS data.
Electroencephalography/methods* ; Memory, Short-Term ; Neurovascular Coupling/physiology* ; Prefrontal Cortex ; Spectroscopy, Near-Infrared/methods*