High glycated albumin is associated with early neurological deterioration in patients with acute ischemic stroke.

BACKGROUND: Glycated albumin (GA) is an indicator of glycemic variability over the past 2-4 weeks and has suitable characteristics for predicting the prognosis of ischemic stroke during the acute phase. This study evaluated the association between early neurological deterioration (END) and GA values in patients with acute ischemic stroke (AIS). METHODS: We assessed consecutive patients with AIS between 2022 and 2023 at two large medical centers in Korea. END was defined as an increase of ≥ 2 in the total National Institutes of Health Stroke Scale (NIHSS) score or ≥ 1 in the motor NIHSS score within the first 72 h of admission. We evaluated various glycemic parameters including fasting glucose (mg/dL), hemoglobin A1c (%), and GA (%). RESULTS: In total, 531 patients with AIS were evaluated (median age: 69 years, male sex: 66.3%). In the multivariable logistic regression analysis, GA value was positively associated with END (adjusted odds ratio [aOR] = 3.24, 95% confidence interval [CI]: 1.10-9.50). Initial NIHSS score (aOR = 1.04, 95% CI: 1.01-1.08) and thrombolytic therapy (aOR = 2.06, 95% CI: 1.14-3.73) were also associated with END. In a comparison of the predictive power of glycemic parameters for END, GA showed a higher area under the curve value on the receiver operating characteristic curve than fasting glucose and hemoglobin A1c. CONCLUSIONS: High GA values were associated with END in patients with AIS. Furthermore, GA was a better predictor of END than fasting glucose or hemoglobin A1c.

Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.

BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at a high risk of fall. OBJECTIVE: We aimed to determine the most accurate clinical machine learning (ML) algorithm for predicting fall-related balance factors and identifying RAGT attributes. METHODS: We applied five ML algorithms- logistic regression, random forest, decision tree, support vector machine (SVM), and extreme gradient boosting (XGboost)- to a dataset of 105 post-stroke patients undergoing RAGT. The variables included the Berg Balance Scale score, walking speed, steps, hip and knee active torques, functional ambulation categories, Fugl- Meyer assessment (FMA), the Korean version of the Modified Barthel Index, and fall history. RESULTS: The random forest algorithm excelled (receiver operating characteristic area under the curve; AUC = 0.91) in predicting balance improvement, outperforming the SVM (AUC = 0.76) and XGboost (AUC = 0.71). Key determinants identified were knee active torque, age, step count, number of RAGT sessions, FMA, and hip torque. CONCLUSION: The random forest algorithm was the best prediction model for identifying fall-related balance and RAGT determinants, highlighting the importance of key factors for successful RAGT outcome performance in fall-related balance improvement.

Triglyceride-Related Parameters and Symptomatic Atherosclerotic Lesions in Patients With Ischemic Stroke.

Objective: Recently, evidence has suggested that the pathophysiology and risk factors of intracranial atherosclerosis (ICAS) differs from those of extracranial atherosclerosis (ECAS). In addition, novel parameters reflecting metabolic conditions, such as insulin resistance or atherogenic dyslipidemia, based on triglycerides (TG) and other biomarkers, have emerged. In this study, we evaluated the association between TG-related parameters and symptomatic cerebral atherosclerosis in patients with acute ischemic stroke resulting from large artery atherosclerosis (LAA). Methods: We assessed consecutive acute LAA-stroke patients between January 2010 and December 2020. Based on the radiological findings, we classified the relevant symptomatic arteries that caused the index stroke into LAA-ICAS and LAA-ECAS. As TG-related parameters, the atherogenic index of plasma (AIP) and TG-glucose (TyG) index were calculated according to the following formulas: AIP = log10 (TG Level/High-density Lipoprotein Cholesterol Level), TyG Index = Ln (TG Level × Glucose Level/2). Results: A total of 519 patients with LAA-stroke were evaluated. In multivariable logistic regression analysis to identify predictors of LAA-ICAS, AIP was significantly associated with LAA-ICAS (adjusted odds ratio [aOR], 3.60; 95% confidence interval [CI], 1.60-8.06). TyG index also showed a statistically significant relationship with LAA-ICAS (aOR, 1.60; 95% CI, 1.11-2.32). However, TG per se did not show a statistical association with LAA-ECAS. Conclusion: TG-related parameters were more closely associated with stroke by ICAS than by ECAS. The metabolic conditions reflected by the AIP or TyG index, rather than hypertriglyceridemia itself, may play a greater role in determining the relevant vessel causally involved in a stroke.

A comparison of random forest-based missing imputation methods for covariates in propensity score analysis.

Propensity score analysis (PSA) is a prominent method to alleviate selection bias in observational studies, but missing data in covariates is prevalent and must be dealt with during propensity score estimation. Through Monte Carlo simulations, this study evaluates the use of imputation methods based on multiple random forests algorithms to handle missing data in covariates: multivariate imputation by chained equations-random forest (Caliber), proximity imputation (PI), and missForest. The results indicated that PI and missForest outperformed other methods with respect to bias of average treatment effect regardless of sample size and missing mechanisms. A demonstration of these five methods with PSA to evaluate the effect of participation in center-based care on children's reading ability is provided using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Chronic ultraviolet irradiation induces memory deficits via dysregulation of the dopamine pathway.

The effects of ultraviolet (UV) radiation on brain function have previously been investigated; however, the specific neurotransmitter-mediated mechanisms responsible for UV radiation-induced neurobehavioral changes remain elusive. In this study, we aimed to explore the mechanisms underlying UV radiation-induced neurobehavioral changes. In a mouse model, we observed that UV irradiation of the skin induces deficits in hippocampal memory, synaptic plasticity, and adult neurogenesis, as well as increased dopamine levels in the skin, adrenal glands, and brain. Chronic UV exposure altered the expression of genes involved in dopaminergic neuron differentiation. Furthermore, chronic peripheral dopamine treatments resulted in memory deficits. Systemic administration of a dopamine D1/D5 receptor antagonist reversed changes in memory, synaptic plasticity, adult neurogenesis, and gene expression in UV-irradiated mice. Our findings provide converging evidence that chronic UV exposure alters dopamine levels in the central nervous system and peripheral organs, including the skin, which may underlie the observed neurobehavioral shifts, such as hippocampal memory deficits and impaired neurogenesis. This study underscores the importance of protection from UV exposure and introduces the potential of pharmacological approaches targeting dopamine receptors to counteract the adverse neurological impacts of UV exposure.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings. AIM: This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI. METHODS: This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance. RESULTS: Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results. CONCLUSION: This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

Outcomes of non-vitamin K oral anticoagulants for secondary prevention in ischemic stroke with atrial fibrillation.

Previous studies have rarely investigated the role of non-vitamin K oral anticoagulants (NOAC) and warfarin in the secondary prevention of ischemic stroke patients with nonvalvular atrial fibrillation (NVAF). In this study, we compared the effectiveness and safety of NOAC and warfarin for secondary prevention in Korean ischemic stroke patients with NVAF. Based on the Korean National Health Insurance Service Database, this study included 21,064 oral anticoagulants-naïve acute ischemic stroke patients with NVAF between July 2015 and June 2019. The main study outcomes included ischemic stroke, systemic embolism, major bleeding, and death. During the observational periods, NOAC users had a significantly decreased risk of ischemic stroke + systemic embolism (adjusted hazard ratio [aHR] 0.86; 95% confidence interval [CI] 0.78-0.95), ischemic stroke (aHR 0.89; 95% CI 0.81-0.99), major bleeding (aHR 0.78; 95% CI 0.68-0.89), and all-cause death (aHR 0.87; 95% CI 0.81-0.93). Standard-dose NOAC users had a lower risk of ischemic stroke, systemic embolism, and major bleeding events than warfarin users. In contrast, low-dose NOAC users did not differ in risk from warfarin users for all outcomes. In conclusion, NOACs were associated with a lower risk of secondary thromboembolic events and bleeding complications in Korean ischemic stroke patients with NVAF than warfarin.

SHP2 regulates GluA2 tyrosine phosphorylation required for AMPA receptor endocytosis and mGluR-LTD.

Posttranslational modifications regulate the properties and abundance of synaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors that mediate fast excitatory synaptic transmission and synaptic plasticity in the central nervous system. During long-term depression (LTD), protein tyrosine phosphatases (PTPs) dephosphorylate tyrosine residues in the C-terminal tail of AMPA receptor GluA2 subunit, which is essential for GluA2 endocytosis and group I metabotropic glutamate receptor (mGluR)-dependent LTD. However, as a selective downstream effector of mGluRs, the mGluR-dependent PTP responsible for GluA2 tyrosine dephosphorylation remains elusive at Schaffer collateral (SC)-CA1 synapses. In the present study, we find that mGluR5 stimulation activates Src homology 2 (SH2) domain-containing phosphatase 2 (SHP2) by increasing phospho-Y542 levels in SHP2. Under steady-state conditions, SHP2 plays a protective role in stabilizing phospho-Y869 of GluA2 by directly interacting with GluA2 phosphorylated at Y869, without affecting GluA2 phospho-Y876 levels. Upon mGluR5 stimulation, SHP2 dephosphorylates GluA2 at Y869 and Y876, resulting in GluA2 endocytosis and mGluR-LTD. Our results establish SHP2 as a downstream effector of mGluR5 and indicate a dual action of SHP2 in regulating GluA2 tyrosine phosphorylation and function. Given the implications of mGluR5 and SHP2 in synaptic pathophysiology, we propose SHP2 as a promising therapeutic target for neurodevelopmental and autism spectrum disorders.

Heat conduction in live tissue during radiofrequency electrosurgery.

In this paper, we propose a method to model radiofrequency electrosurgery to capture the phenomena at higher temperatures and present the methods for parameter estimation. Experimental data taken from our surgical trials performed on in vivo porcine liver show that a non-Fourier Maxwell-Cattaneo-type model can be suitable for this application when used in combination with an Arrhenius-type model that approximates the energy dissipation in physical and chemical reactions. The resulting model structure has the advantage of higher accuracy than existing ones, while reducing the computation time required.

Changes in dementia treatment patterns associated with changes in the National Policy in South Korea among patients with newly diagnosed Alzheimer's disease between 2011 and 2017: results from the multicenter, retrospective CAPTAIN study.

BACKGROUND: The South Korean government has been actively involved in plans to combat dementia, implementing a series of national strategies and plans since 2008. In July 2014, eligibility for mandatory long-term care insurance (LTCI) was extended to people with dementia enabling access to appropriate long-term care including the cognitive function training program and home nursing service. This study aimed to investigate changes in treatment patterns for Alzheimer's disease (AD) between July 2011 and June 2017 which spanned the 2014 revision. METHODS: This multicenter, retrospective, observational study of patients with newly diagnosed AD analyzed electronic medical records from 17 general hospitals across South Korea. Based on their time of AD diagnosis, subjects were categorized into Cohort 1 (1 July 2011 to 30 June 2014) and Cohort 2 (1 July 2014 to 30 June 2017). RESULTS: Subjects (N=3,997) divided into Cohorts 1 (n=1,998) and 2 (n=1,999), were mostly female (66.4%) with a mean age of 84.4 years. Cohort 1 subjects were significantly older (P<0.0001) and had a lower number of comorbidities (P=0.002) compared with Cohort 2. Mean Mini-Mental State Examination (MMSE) scores in Cohorts 1 and 2 at the time of AD diagnosis or start of initial treatment were 16.9 and 17.1, respectively (P=0.2790). At 1 year, mean MMSE scores in Cohorts 1 and 2 increased to 17.9 and 17.4, respectively (P=0.1524). Donepezil was the most frequently administered medication overall (75.0%), with comparable rates between cohorts. Rates of medication persistence were ≥98% for acetylcholinesterase inhibitor or memantine therapy. Discontinuation and switch treatment rates were significantly lower (49.7% vs. 58.0%; P<0.0001), and mean duration of initial treatment significantly longer, in Cohort 2 vs. 1 (349.3 vs. 300.2 days; P<0.0001). CONCLUSIONS: Comparison of cohorts before and after revision of the national LTCI system for dementia patients found no significant difference in mean MMSE scores at the time of AD diagnosis or start of initial treatment. The reduction in the proportion of patients who discontinued or changed their initial treatment, and the significant increase in mean duration of treatment, were observed following revision of the LTCI policy which enabled increased patient access to long-term care.

Gene expression and characterization of 2-keto-3-deoxy-gluconate kinase, a key enzyme in the modified Entner-Doudoroff pathway of Serratia marcescens KCTC 2172

We cloned 2-keto-3-deoxy-gluconate kinase (KDGK), which catalyzes the phosphorylation of 2-keto-3-deoxygluconate (KDG) to 2-keto-3-deoxy-6-phophogluconate (KDPG) from Serratia marcescens KCTC 2172. The nucleotide sequence revealed a single open reading frame containing 1,208 bp and encoding for 309 amino acids, with a molecular weight of 33,993 Da. The enzyme was purified via GST affinity chromatography. The putative KdgT binding site was detected upstream of the initial codon. The KDG kinase utilized 2-ketogluconate (KG) and KDG as substrates. The optimal temperature and pH for KDGK activity were 50ºC and 8.0, respectively.

Solubilization of insoluble inorganic phosphate by Burkholderia cepacia DA23 isolated from cultivated soil

A mineral phosphate solubilizing bacterium, Burkholderia cepacia DA23 has been isolated from cultivated soils. Phosphate-solubilizing activities of the strain against three types of insoluble phosphate were quantitatively determined. When 3 percent of glucose concentration was used for carbon source, the strain had a marked mineral phosphate-solubilizing activity. Mineral phosphate solubilization was directly related to the pH drop by the strain. Analysis of the culture medium by high pressure liquid chromatography identified gluconic acid as the main organic acid released by Burkholderia cepacia DA23. Gluconic acid production was apparently the result of the glucose dehydrogenase activity and glucose dehydrogenase was affected by phosphate regulation.

Uma bactéria capaz de solubilizar fosfato mineral, Burkholderia cepacea DA23, foi isolada de solo cultivado. A capacidade dessa bactéria solubilizar o fosfato de três tipos de fosfato insolúvel foi quantificada. Quando foi utilizada glicose a 3 por cento como fonte de carbono, a bactéria apresentou uma intensa atividade solubilizante de fosfato, sendo a solubilização diretamente relacionada com a queda de pH causada pela bactéria. A análise do meio de cultura por cromatografia líquida de alta pressão indicou o ácido glicônico como principal ácido produzido por Burkholderia cepacea DA23. Aparentemente, a produção de ácido glicônico foi causada pela atividade da glicose desidrogenase. A enzima foi afetada pela regulação do fosfato.