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

OBJECTIVES: To investigate the effects of early life intervention with Bifidobacterium bifidum BD-1 (B. bifidum BD-1) on hyperactivity in a female mouse model of attention deficit hyperactivity disorder (ADHD) and explore the underlying mechanisms. METHODS: Eight newborn female Wistar-Kyoto (WKY) rats and 6 spontaneous hypertensive rats (SHRs) were gavaged with saline and another 6 SHRs were gavaged with B. bifidum BD-1 (10⁹ CFU) daily for 3 weeks. Open field test of the rats was conducted at 7 weeks, and fecal samples were collected at weaning (3 weeks) and at 7 weeks for 16S rRNA sequencing. Immunofluorescent staining was used to detect dopamine transporter (DAT) and tyrosine hydroxylase (Th) levels in the striatum and activated microglia in the prefrontal cortex. Treg cells in the mesenteric lymph nodes, spleen and blood were analyzed using flow cytometry. RESULTS: The SHRs traveled a significantly greater distance in open fields test than WKY rats, and this behavior was significantly attenuated by B. bifidum BD-1 intervention. The expression of DAT and Th in the striatum was significantly lower in the SHRs than in WKY rats, while B. bifidum BD-1 treatment obviously increased Th levels in the SHRs. B. bifidum BD-1 intervention significantly deceased the number of activated microglia and increased Treg cell counts in the spleen of SHRs. The treatment also enhanced α diversity in gut microbiota of the SHRs and resulted in a decreased Firmicutes/Bacteroidota ratio, more active Muribaculaceae growth, and suppression of Clostridia_UCG-014 proliferation. CONCLUSIONS Early life intervention with B. bifidum BD-1 alleviates hyperactivity in female SHRs by modulating the gut microbiota and peripheral immune response, suppressing neuroinflammation and improving dopaminergic system function. These findings provide evidence for early prevention strategies and support the development and application of psychobiotics for ADHD.
Animals ; Female ; Rats ; Rats, Inbred WKY ; Rats, Inbred SHR ; Attention Deficit Disorder with Hyperactivity/therapy* ; Bifidobacterium bifidum ; Probiotics/therapeutic use* ; Dopamine Plasma Membrane Transport Proteins/metabolism* ; Tyrosine 3-Monooxygenase/metabolism* ; Gastrointestinal Microbiome ; Disease Models, Animal

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*

Attention deficit hyperactivity disorder (ADHD), a prevalent neurodevelopmental disorder influenced by both genetic and environmental factors, remains poorly understood regarding how its polygenic risk score (PRS) impacts functional networks and symptomology. This study capitalized on data from 11,430 children in the Adolescent Brain Cognitive Development study to explore the interplay between PRS_ADHD, brain function, and behavioral problems, along with their interactive effects. The results showed that children with a higher PRS_ADHD exhibited more severe attention deficits and rule-breaking problems, and experienced sleep disturbances, particularly in initiating and maintaining sleep. We also identified the central executive network, default mode network, and sensory-motor network as the functional networks most associated with PRS and symptoms in ADHD cases, with potential mediating roles. Particularly, the impact of PRS_ADHD was enhanced in children experiencing heightened sleep disturbances, emphasizing the need for early intervention in sleep issues to potentially mitigate subsequent ADHD symptoms.
Humans ; Attention Deficit Disorder with Hyperactivity/physiopathology* ; Male ; Female ; Sleep Wake Disorders/physiopathology* ; Adolescent ; Child ; Brain/diagnostic imaging* ; Multifactorial Inheritance ; Genetic Predisposition to Disease

Attention is the cornerstone of effective functioning in a complex and information-rich world. While the neural activity of attention has been extensively studied in the cortex, the brain-wide neural activity patterns are largely unknown. In this study, we conducted a comprehensive analysis of neural activity across the mouse brain during attentional processing using EEG and c-Fos staining, utilizing hierarchical clustering and c-Fos-based functional network analysis to evaluate the c-Fos activation patterns. Our findings reveal that a wide range of brain regions are activated, notably in the high-order cortex, thalamus, and brain stem regions involved in advanced cognition and arousal regulation, with the central lateral nucleus of the thalamus as a strong hub, suggesting the crucial role of the thalamus in attention control. These results provide valuable insights into the neural network mechanisms underlying attention, offering a foundation for formulating functional hypotheses and conducting circuit-level testing.
Animals ; Attention/physiology* ; Mice ; Brain/physiology* ; Male ; Electroencephalography ; Reaction Time/physiology* ; Brain Mapping ; Mice, Inbred C57BL ; Choice Behavior/physiology* ; Proto-Oncogene Proteins c-fos/metabolism*

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