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Objective To explore the factors influencing the severity of acute phase and short-term prognosis of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, and search for potential indicators for assessing disease severity and predicting prognosis. Methods A total of 60 patients with anti-NMDAR encephalitis admitted to West China Hospital, Sichuan University from January 2020 to July 2023 were included. According to the modified Rankin Scale (mRS) during the acute phase, they were divided into mild group (mRS ≤ 3 score, n = 25) and severe group (mRS > 3 score, n = 35), as well as good outcome group (mRS ≤ 3 score, n = 32) and poor outcome group (mRS > 3 score, n = 28) based on the mRS score after one month of treatment at our hospital. Univariate and multivariate Logistic regression analyses were used to determine the risk factors of anti-NMDAR encephalitis with severe condition during the acute phase and poor short - term prognosis. Results Logistic regression analysis showed consciousness disorder (OR = 8.975, 95%CI: 2.048-39.327; P = 0.004) and high level of neutrophil percentage-to-albumin ratio (NPAR; OR = 5.004, 95%CI: 1.138-22.011, P = 0.033) were the risk factors of anti-NMDAR encephalitis with severe condition during the acute phase, while high level of serum IgE was the protective factor (OR = 0.994, 95%CI: 0.989-0.999; P = 0.026). Female (OR = 4.380, 95%CI: 1.205-15.929; P = 0.025), consciousness disorder (OR = 5.493, 95%CI: 1.535-19.657; P = 0.009) and high level of NPAR (OR = 2.949, 95%CI:1.010-8.612; P = 0.048) were the risk factors of anti-NMDAR encephalitis with poor short-term prognosis. Conclusions Consciousness disorder, low serum IgE level and high NPAR are risk factors of anti-NMDAR encephalitis with severe condition during the acute phase, while female, consciousness disorder and high NPAR are risk factors of anti-NMDAR encephalitis with poor short-term prognosis. NPAR is a potential biological indicator for assessing disease severity during the acute phase and predicting short-term prognosis of patients with anti-NMDAR encephalitis.

In engineering, gelled acids play a crucial role in facilitating reactive flow phenomena across diverse mediums. This study undertakes a comprehensive numerical investigation into the reactive flow dynamics within fracture-vuggy carbonates, employing gelled acid as the agent. The mathematical model intricately couples thermal, hydrological, and chemical aspects to provide a holistic understanding of the process. Fractures are meticulously modeled using a pseudo-fracture approach, while vugs are delineated as highly porous matrix clusters. The simulation meticulously examines the influence of vugs and fractures, in conjunction with the rheological behavior of gelled acid, on the dissolution process. Our findings reveal that compared to hydrochloric acid, gelled acid is more effective in treating high-temperature carbonate rocks. A lower power law index induces a more pronounced response in shear stress, resulting in a more uniform dissolution pattern. Moreover, the presence of vugs and fractures exerts a significant impact on both the trajectory of wormhole growth and its penetration depth. As the length of fractures increases and their number multiplies, their dominant influence on the growth of wormholes becomes more pronounced. Furthermore, an abundance of fractures may attenuate the influence of vugs. This study highlights the importance of controlling the power-law index and understanding the complex interactions between fractures and vugs for reactive flow.

Background: Eye tracking (ET) is emerging as a promising early and objective screening method for autism spectrum disorders (ASD), but it requires more reliable metrics with enhanced sensitivity and specificity for clinical use. Methods: This study introduces a suite of novel ET metrics: Area of Interest (AOI) Switch Counts (ASC), Favorable AOI Shifts (FAS) along self-determined pathways, and AOI Vacancy Counts (AVC), applied to toddlers and preschoolers diagnosed with ASD. The correlation between these new ET metrics and Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) scores via linear regression and sensitivity and specificity of the cut-off scores were assessed to predict diagnosis. Results: Our findings indicate significantly lower FAS and ASC and higher AVC (P<0.05) in children with ASD compared to their non-ASD counterparts within this high-risk cohort; the significance was not seen in total fixation time neither pupil size (p > 0.05). Furthermore, FAS was negatively correlated with ADOS-2 total scores and social affect (SA) subscale (p < 0.05). Among these new ET metrics, AVC yielded the best sensitivity 88-100% and specificity 80-88% with cut off score 0.305-0.306, followed by FAS and ASC to separate ASD from non-ASD for diagnosis. Conclusions: This study confirms the utility of innovative ET metrics—FAS, AVC, and ASC—which exhibit markedly improved sensitivity and specificity, enhancing ASD screening and diagnostic processes. [ABSTRACT FROM AUTHOR]

Introduction: Early identification of Autism Spectrum Disorder (ASD) is critical for effective intervention. Restricted interests (RIs), a subset of repetitive behaviors, are a prominent but underutilized domain for early ASD diagnosis. This study aimed to identify objective biomarkers for ASD by integrating electroencephalography (EEG) and eye-tracking (ET) to analyze toddlers' visual attention and cortical responses to RI versus neutral interest (NI) objects. Methods: The study involved 59 toddlers aged 2-4 years, including 32 with ASD and 27 non-ASD controls. Participants underwent a 24-object passive viewing paradigm, featuring RI (e.g., transportation items) and NI objects (e.g., balloons). ET metrics (fixation time and pupil size) and EEG time-frequency (TF) power in theta (4-8 Hz) and alpha (8-13 Hz) bands were analyzed. Statistical methods included logistic regression models to assess the predictive potential of combined EEG and ET biomarkers. Results: Toddlers with ASD exhibited significantly increased fixation times and pupil sizes for RI objects compared to NI objects, alongside distinct EEG patterns with elevated theta and reduced alpha power in occipital regions during RI stimuli. The multimodal logistic regression model, incorporating EEG and ET metrics, achieved an area under the curve (AUC) of 0.75, demonstrating robust predictive capability for ASD. Discussion: This novel integration of ET and EEG metrics highlights the potential of RIs as diagnostic markers for ASD. The observed neural and attentional distinctions underscore the utility of multimodal biomarkers for early diagnosis and personalized intervention strategies. Future work should validate findings across broader age ranges and diverse populations. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to determine whether type 2 diabetes (T2D) is an independent risk factor for sleep disorders in the elderly and explore the possible intestinal flora factors of sleep disorders combined with T2D in this population. Methods: All hospitalized patients with sleep disorders aged ≥65 years between June and November 2023 were retrospectively analyzed, and they were divided into a sleep disorder group (n = 134) and a control group (n = 109). The logistic regression method was utilized to clarify the causal relationship between T2D and sleep disorders. For stool analyses, 42 patients were randomly extracted, which included the control group (n = 14), diabetes group (n = 14), and elderly patients with sleep disorders combined with the T2D group (ESdD) (n = 14). The composition feature of intestinal flora and metabolomics in the ESdD group was described through high‐throughput 16S rDNA sequencing and nontargeted analysis based on liquid chromatography–mass spectrometry. Results: Gender, body mass index (BMI), T2D, intestinal discomfort, and anxiety depression were independent risk factors for sleep disorders in the elderly. Notably, older individuals with T2D were 3.3 times more likely to experience sleep disorders than normal individuals. Compared with the control group, the ESdD group had decreased relative abundance of Barnesiella and Marvinbryantia, with 47 metabolites upregulated and 53 metabolites downregulated. The ESdD group showed a decrease in Lachnospiraceae_UCG_010, with 62 metabolites upregulated and 43 metabolites downregulated, compared with the diabetes group. Conclusions: Diabetes is an independent risk factor for sleep disorders in the elderly patients. Variations in intestinal flora and metabolism significantly influence the onset and progression of the ESdD group. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement. [ABSTRACT FROM AUTHOR]

Juvenile hormones (JHs) play a crucial role in regulating development and reproduction in insects. Most insects predominantly synthesize JH III, which typically involves esterification followed by epoxidation, lepidopteran insects use a pathway of epoxidation followed by esterification. Although hemipteran insects have JH III and JH skipped bisepoxide III (JH SB3), the synthesis pathway and key epoxidases remain unclear. This study was conducted on Aphis craccivora, and demonstrated that corpora allata, microsomes, Ac‐CYP15C1, and Ac‐JHAMT catalyze JH III production in vitro, establishing the pathway of epoxidation followed by esterification. These findings were further confirmed through RNA interference and molecular docking. The presence of JH III and JH SB3 in A. craccivora was identified, and their synthesis pathway was elucidated as follows: Ac‐CYP15C1 oxidizes farnesic acid to JH A, followed by methylation to JH III by Ac‐JHAMT, possibly providing an epoxidation site on the second carbon for JH SB3. This alteration may significantly contribute to the differentiation and functional diversification of JH types in insects. [ABSTRACT FROM AUTHOR]