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ObjectiveTo identify whether the amplitude of low-frequency fluctuations (ALFF) analysis has the potential to serve as a biological marker to detect alcohol-induced spontaneous brain activities and distinguish the patients with alcohol dependence from the healthy subjects.MethodsWe utilized the ALFF analysis to report on the alcohol-induced spontaneous brain activities in 29 patients with alcohol dependence (9 female, 20 male) relative to 29 status-matched healthy subjects (11 female, 18 male). Receiver operating characteristic curve was used to test the ability of the ALFF analysis in discriminating the patients with alcohol dependence from the healthy subjects. Pearson correlation was used to evaluate the relationships between the signal value of those ALFF differences in brain areas and behavioral characteristics.ResultsAlcohol-induced brain differences located in the right inferior parietal lobule and right supplementary motor area with significant higher ALFF values, and in the left precuneus and bilateral cerebellum posterior lobe with lower ALFF values. The movement-related areas were significantly correlated with each other (P<0.05). Receiver operating characteristic curve revealed good area under the curve values (mean, 0.86±0.079; 0.774–0.951) of the ALFF differences in those specific brain areas, as well as high degree of sensitivities (mean, 80.84%±14.01% or 80%±14.56%; 62.5%–100%) and specificities (mean, 83.32%±9.31%; 70.8%–95.8% or 84.16%±8%; 75%–95.8%).ConclusionThe ALFF analysis may serve as a biological indicator to detect the spontaneous brain activities in patients with alcohol dependence. The prefrontal–parietal–cerebellar circuit appears to be disturbed by long-term alcoholism in patients with alcohol dependence.

Background: Acquired dysphagia is common in patients with tracheal intubation and neurological disease, leading to increased mortality. This study aimed to ascertain the risk factors and develop a prediction model for acquired dysphagia in patients after neurosurgery.Methods: A multicenter prospective observational study was performed on 293 patients who underwent neurosurgery. A standardized swallowing assessment was performed bedside within 24 h of extubation, and logistic regression analysis with a best subset selection strategy was performed to select predictors. A nomogram model was then established and verified.Results: The incidence of acquired dysphagia in our study was 23.2% (68/293). Among the variables, days of neurointensive care unit (NICU) stay [odds ratio (OR), 1.433; 95% confidence interval (CI), 1.141–1.882; P = 0.005], tracheal intubation duration (OR, 1.021; CI, 1.001–1.062; P = 0.175), use of a nasogastric feeding tube (OR, 9.131; CI, 1.364–62.289; P = 0.021), and Acute Physiology and Chronic Health Evaluation (APACHE)-II C score (OR, 1.709; CI, 1.421–2.148; P &lt; 0.001) were selected as risk predictors for dysphagia and included in the nomogram model. The area under the receiver operating characteristic curve was 0.980 (CI, 0.965–0.996) in the training set and 0.971 (0.937–1) in the validation set, with Brier scores of 0.045 and 0.056, respectively.Conclusion: Patients who stay longer in the NICU, have a longer duration of tracheal intubation, require a nasogastric feeding tube, and have higher APACHE-II C scores after neurosurgery are likely to develop dysphagia. This developed model is a convenient and efficient tool for predicting the development of dysphagia.

Background Risk factors that predispose the development of severe community-acquired pneumonia (CAP) among pediatric CAP patients of different age ranges are yet to be identified. Methods We retrospectively analyzed pediatric in-patients (< 6 years old) diagnosed with CAP in our hospital. We subdivided patients into four age groups (< 6 months, 6 months-1 year, 1–2 years, and 2–6 years). Their medical records, including demographic information, clinical features, laboratory findings, and chest radiographic reports, were reviewed and collected for further analysis. Univariate logistic regression analysis and stepwise regression analysis were applied to identify risk factors associated with severe CAP and ICU admission for overall patients and age-stratified subgroups. Results A total of 20,174 cases were initially included. Among them, 3309 (16.40%) cases were identified as severe CAP, and 2824 (14.00%) cases required ICU admission. Potential risk factors for severe CAP and ICU admission identified by univariate analysis included younger age, rural residency, premature birth, low birth weight (LBW), formula feeding, congenital heart disease (CHD), history of pneumonia or neonatal jaundice, patients with other health issues, certain symptoms (manifesting wheezing, dyspnea, cyanosis, but have no cough or fever), abnormal laboratory findings (abnormal levels of white blood cells, albumin, and C-reactive protein and RSV infection), and chest X-ray (odds ratio [OR] > 1 for all). CHD, low albumin, proteinuria, abnormal chest x-ray were independent risks factors across different age groups, whereas birth or feeding history, history of pneumonia, cyanosis or dyspnea on admission, and RSV infection were independent risk factors for only younger kids (< 1 year), and wheezing was an independent risk factor only for older children (2–5 years old). Conclusions Risk factors predicting disease severity among children hospitalized with CAP vary with age. Risk factor stratification of pediatric CAP based on age-specific risk factors can better guide clinical practice. Trial registration This study has been registered in China, with the registration number being ChiCTR2000033019.