BACKGROUND

Determination of serum bicarbonate (HCO3 - ) using arterial blood gas analysis in patients with diabetic ketoacidosis allows for the evaluation of the severity of the condition, determines whether HCO3 - therapy is required, and tracks the progression and resolution of the disease. Serum carbon dioxide combining power (CO2CP) from venous chemistry analysis has often been used as an indicator of metabolic acidosis. This study investigated the relationship between HCO3 - and CO2CP and developed an approximation formula for serum bicarbonate concentration using its predictor variables, as this may lessen the need to repeat arterial blood gas analysis or be used in settings in which blood gas analyzers are unavailable.

METHODOLOGY

This single-center, retrospective, cross-sectional study investigated a total of 77 patients diagnosed with diabetic ketoacidosis. Assessment of the bivariate correlations between serum HCO3 - and serum CO2CP as well as other potential predictor variables was done via Pearson’s correlation coefficient. Predictor variables that were significantly correlated with serum HCO3 - were identified and an approximation formula was developed by regression analysis. Evaluation of the correlation between the approximated HCO3 - value and the actual serum HCO3 - concentration was performed using correlation coefficient and residual statistics to assess agreement.

RESULTS

Serum CO2CP had significant correlation with serum HCO3 - (r = 0.768, p < 0.05). By multiple regression analysis, the following approximation formula was therefore expressed: HCO3 - = 12.682 + (0.612 x CO2CP) – [ketones] + (0.085 x BUN) - (0.026 x SGPT) – (1.23 x Creatinine) - (0.067 x Chloride). Examination of residuals revealed a mean of zero (0), indicating no significant difference between the actual and approximated levels of serum HCO3 -

CONCLUSION

The predictor variables included in the formula collectively contribute significantly to the approximation of serum HCO3 - . The approximated serum HCO3 - values also showed significant correlation with actual serum HCO3 - concentration; thus, the formula may be utilized to derive an approximation of serum bicarbonate concentration in patients with diabetic ketoacidosis.

Human ; Male ; Female ; Adolescent: 13-18 Yrs Old ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Attention ; Bicarbonates ; Carbon Dioxide ; Diabetic Ketoacidosis ; Ketosis ; Patients ; Carbon ; Power (psychology) ; Power, Psychological ; Serum

Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and intellectual developmental disorder (IDD), are highly prevalent and lack effective treatments, posing significant health challenges. These disorders are frequently comorbid with disruptions in sleep rhythms, and sleep-related indicators are often used to assess disease severity and treatment efficacy. Recent evidence has highlighted the crucial roles of circadian rhythm disturbances and circadian clock gene mutations in the pathogenesis of NDDs. This review focuses on the mechanisms by which circadian rhythm disruptions and circadian clock gene mutations contribute to cognitive, behavioral, and emotional disorders associated with NDDs, particularly through the dysregulation of dopamine system. Additionally, we discussed the potential of targeting the circadian system as novel therapeutic strategies for the treatment of NDDs.
Humans ; Neurodevelopmental Disorders/genetics* ; Attention Deficit Disorder with Hyperactivity/genetics* ; Circadian Rhythm/genetics* ; Autism Spectrum Disorder/genetics* ; Mutation ; Intellectual Disability/genetics* ; Circadian Clocks/physiology* ; Dopamine/metabolism*

This study investigated the effects of Rehmanniae Radix Praeparata on striatal neuronal apoptosis in rats with attention deficit hyperactivity disorder(ADHD) based on the B-cell lymphoma-2(Bcl-2)/Bcl-2-associated X protein(Bax)/caspase-3 signaling pathway. Twenty-four 3-week-old male spontaneously hypertensive rats(SHR) were randomly divided into a model group, a methylphenidate group(2 mg·kg~(-1)·d~(-1)), and a Rehmanniae Radix Praeparata group(2.4 mg·kg~(-1)·d~(-1)). Age-matched male Wistar Kyoto(WKY) rats were used as the normal control group, with 8 rats in each group. The rats were administered by gavage for 28 days. Body weight and food intake were recorded for each group. The open field test and elevated plus maze test were used to assess hyperactivity and impulsive behaviors. Nissl staining was used to detect changes in striatal neurons and Nissl bodies. Terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL) fluorescence staining was used to detect striatal cell apoptosis. Western blot was employed to detect the expression levels of Bcl-2, Bax, and caspase-3 proteins in the striatum. The results showed that compared with the model group, Rehmanniae Radix Praeparata significantly reduced the total movement distance, average movement speed, and central area residence time in the open field test, and significantly reduced the ratio of open arm entries, open arm stay time, and head dipping in the elevated plus maze test. Furthermore, it increased the number of Nissl bodies in striatal neurons, significantly downregulated the apoptosis index, significantly increased Bcl-2 protein expression and the Bcl-2/Bax ratio, and reduced Bax and caspase-3 protein expression. In conclusion, Rehmanniae Radix Praeparata can reduce hyperactivity and impulsive behaviors in ADHD rats. Its mechanism may be related to the regulation of the Bcl-2/Bax/caspase-3 signaling pathway in the striatum, enhancing the anti-apoptotic capacity of striatal neurons.
Animals ; Male ; Apoptosis/drug effects* ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Caspase 3/genetics* ; Proto-Oncogene Proteins c-bcl-2/genetics* ; bcl-2-Associated X Protein/genetics* ; Rehmannia/chemistry* ; Attention Deficit Disorder with Hyperactivity/physiopathology* ; Signal Transduction/drug effects* ; Neurons/cytology* ; Rats, Inbred SHR ; Rats, Inbred WKY ; Humans ; Corpus Striatum/cytology* ; Plant Extracts

In audiovisual emotion recognition, representational learning is a research direction receiving considerable attention, and the key lies in constructing effective affective representations with both consistency and variability. However, there are still many challenges to accurately realize affective representations. For this reason, in this paper we proposed a cross-modal audiovisual recognition model based on a multi-head cross-attention mechanism. The model achieved fused feature and modality alignment through a multi-head cross-attention architecture, and adopted a segmented training strategy to cope with the modality missing problem. In addition, a unimodal auxiliary loss task was designed and shared parameters were used in order to preserve the independent information of each modality. Ultimately, the model achieved macro and micro F1 scores of 84.5% and 88.2%, respectively, on the crowdsourced annotated multimodal emotion dataset of actor performances (CREMA-D). The model in this paper can effectively capture intra- and inter-modal feature representations of audio and video modalities, and successfully solves the unity problem of the unimodal and multimodal emotion recognition frameworks, which provides a brand-new solution to the audiovisual emotion recognition.
Emotions ; Humans ; Attention ; Algorithms

The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can selectively enhance specific information. Building on this, the study introduces a dual-branch U-Net that integrates lateral inhibition and feedback-driven attention mechanisms. Noisy speech signals input into the first branch of the U-Net led to the selective feedback of time-frequency units with high confidence. The generated activation layer gradients, in conjunction with the lateral inhibition mechanism, were utilized to calculate attention maps. These maps were then concatenated to the second branch of the U-Net, directing the network's focus and achieving selective enhancement of auditory speech signals. The evaluation of the speech enhancement effect was conducted by utilising five metrics, including perceptual evaluation of speech quality. This method was compared horizontally with five other methods: Wiener, SEGAN, PHASEN, Demucs and GRN. The experimental results demonstrated that the proposed method improved speech signal enhancement capabilities in various noise scenarios by 18% to 21% compared to the baseline network across multiple performance metrics. This improvement was particularly notable in low signal-to-noise ratio conditions, where the proposed method exhibited a significant performance advantage over other methods. The speech enhancement technique based on lateral inhibition and feedback-driven attention mechanisms holds significant potential in auditory speech enhancement, making it suitable for clinical practices related to artificial cochleae and hearing aids.
Humans ; Attention/physiology* ; Speech Perception/physiology* ; Neural Networks, Computer ; Speech ; Noise ; Feedback

Convolutional neural networks (CNNs) are renowned for their excellent representation learning capabilities and have become a mainstream model for motor imagery based electroencephalogram (MI-EEG) signal classification. However, MI-EEG exhibits strong inter-individual variability, which may lead to a decline in classification performance. To address this issue, this paper proposes a classification model based on dynamic multi-scale CNN and multi-head temporal attention (DMSCMHTA). The model first applies multi-band filtering to the raw MI-EEG signals and inputs the results into the feature extraction module. Then, it uses a dynamic multi-scale CNN to capture temporal features while adjusting attention weights, followed by spatial convolution to extract spatiotemporal feature sequences. Next, the model further optimizes temporal correlations through time dimensionality reduction and a multi-head attention mechanism to generate more discriminative features. Finally, MI classification is completed under the supervision of cross-entropy loss and center loss. Experiments show that the proposed model achieves average accuracies of 80.32% and 90.81% on BCI Competition IV datasets 2a and 2b, respectively. The results indicate that DMSCMHTA can adaptively extract personalized spatiotemporal features and outperforms current mainstream methods.
Electroencephalography/methods* ; Humans ; Neural Networks, Computer ; Brain-Computer Interfaces ; Attention ; Signal Processing, Computer-Assisted ; Imagination/physiology* ; Algorithms

Most current medical image segmentation models are primarily built upon the U-shaped network (U-Net) architecture, which has certain limitations in capturing both global contextual information and fine-grained details. To address this issue, this paper proposes a novel U-shaped network model, termed the Multi-View U-Net (MUNet), which integrates self-attention and multi-view attention mechanisms. Specifically, a newly designed multi-view attention module is introduced to aggregate semantic features from different perspectives, thereby enhancing the representation of fine details in images. Additionally, the MUNet model leverages a self-attention encoding block to extract global image features, and by fusing global and local features, it improves segmentation performance. Experimental results demonstrate that the proposed model achieves superior segmentation performance in coronary artery image segmentation tasks, significantly outperforming existing models. By incorporating self-attention and multi-view attention mechanisms, this study provides a novel and efficient modeling approach for medical image segmentation, contributing to the advancement of intelligent medical image analysis.
Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Algorithms ; Attention ; Coronary Vessels/diagnostic imaging* ; Diagnostic Imaging/methods*

OBJECTIVES: To report the development, validation, and findings of the Multi-dimensional Attention Rating Scale (MARS), a self-report tool crafted to evaluate six-dimension attention levels. METHODS: The MARS was developed based on Classical Test Theory (CTT). Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests. Reliability was measured using Cronbach's alpha and test-retest reliability. Structural validity was explored using principal component analysis. Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test (THAT), the Attentional Control Scale (ACS), and the Attention Network Test (ANT). RESULTS: The MARS comprises 12 items spanning six distinct dimensions of attention: focused attention, sustained attention, shifting attention, selective attention, divided attention, and response inhibition.As assessed by six experts, the content validation index (CVI) was 0.95, the Cronbach's alpha for the MARS was 0.78, and the test-retest reliability was 0.81. Four factors were identified (cumulative variance contribution rate 68.79%). The total score of MARS was correlated positively with THAT (r = 0.60, P < 0.01) and ACS (r = 0.78, P < 0.01) and negatively with ANT's reaction time for alerting (r = -0.31, P = 0.049). CONCLUSIONS The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.
Humans ; Adult ; Male ; Attention/physiology* ; Female ; Middle Aged ; Reproducibility of Results ; Young Adult ; Psychometrics

BACKGROUND: High levels of attention-deficit/hyperactivity disorder (ADHD) traits are associated with various outcomes, including depressive symptoms, functional impairment, and low self-esteem. Additionally, individuals with high levels of ADHD traits are reported to be more adversely affected by fear of coronavirus disease 2019 (COVID-19). The current study aimed to examine whether the association between ADHD traits and outcomes was partially mediated by fear of COVID-19 using mediation analysis. METHODS: A cross-sectional study was conducted in a sample of university students in medical-related faculties (n = 1,166). ADHD traits, fear of COVID-19, depressive symptoms, functional impairment, and self-esteem were assessed using the adult ADHD Self-Report Scale, Fear of COVID-19 Scale, K6 Scale, Sheehan Disability Scale, and Rosenberg Self-Esteem Scale, respectively. We used linear regression analysis and the Paramed command in Stata to analyze whether fear of COVID-19 mediated the association between ADHD traits and outcomes. RESULTS: ADHD traits were significantly associated with outcomes. Regarding the impact of fear of COVID-19, the results revealed a significant association between ADHD traits and fear of COVID-19, and between fear of COVID-19 and outcomes. The results of the mediation analyses showed that the association between ADHD traits and outcomes was partially mediated by the fear of COVID-19 (depressive symptoms: direct effect B = 1.029, 95% confidence interval [CI] 0.878, 1.181, indirect effect B = 0.021, 95% CI 0.002, 0.040; functional impairment: direct effect B = 0.786, 95% CI 0.593, 0.979, indirect effect B = 0.033, 95% CI 0.005, 0.060; self-esteem: direct effect B = -1.052, 95% CI -1.226, -0.878, indirect effect B = -0.024, 95% CI -0.046, -0.002). CONCLUSIONS Developing preventive measures against the adverse impacts of pandemics like COVID-19 will be particularly important for individuals with high levels of ADHD traits in future.
Humans ; COVID-19/psychology* ; Attention Deficit Disorder with Hyperactivity/epidemiology* ; Cross-Sectional Studies ; Self Concept ; Male ; Female ; Depression/epidemiology* ; Fear/psychology* ; Young Adult ; Universities ; Students/psychology* ; Adult ; SARS-CoV-2 ; Mediation Analysis ; Adolescent

BACKGROUND: Although serum carotene may contribute to dementia prevention, there is a lack of longitudinal evidence for early cognitive decline before dementia symptoms. The aim of this study was to examine whether serum carotene levels were associated with annually evaluated cognitive trajectories among the Japanese general population. METHODS: Among 581 baseline participants, 199 individuals (83 males; mean age [min, max], 62.7 [39, 90] years) who underwent cognitive assessments more than twice after baseline were analyzed. "Attention" levels were assessed using one- and three-target Digit Cancellation Tests (D-CAT1 and D-CAT3). "General cognitive ability" was assessed by the short version of Mini-Mental State Examination (SMMSE). Serum carotenes (α-carotene, β-carotene and lycopene) were measured by high-performance liquid chromatography. After the measurements, we calculated total carotene levels by summing up the levels of all measured carotene. Carotene levels were categorized into three groups for analysis (low: 0%-25%, middle: 25%-75%, and high: 75%-100%). A linear mixed model was used to estimate the slope of the D-CAT score trajectory and to compare it between three categories. RESULTS: Compared with the middle carotene group, decline of attention was faster in the D-CAT1 for low β-carotene (β = -3.48, p = 0.035), lycopene (β = -3.10, p = 0.062), and total carotene (β = -4.75, p = 0.003), but not for α-carotene (β = -2.60, p = 0.111). For the D-CAT3, decline of attention was faster in the group of low lycopene (β = -3.17, p = 0.002) and total carotene (β = -2.17, p = 0.037) compared with the middle carotene group, while no clear association for α-carotene (β = -0.67, p = 0.521) and β-carotene (β = -0.64, p = 0.639). There were no clear associations between serum carotene and the SMMSE score. CONCLUSIONS These findings suggest low levels of serum lycopene are associated with a decline of attention in the setting of the general population.
Humans ; Male ; Carotenoids/blood* ; Female ; Longitudinal Studies ; Middle Aged ; Japan ; Aged ; Adult ; Aged, 80 and over ; Attention ; Lycopene ; East Asian People