Urine osmolality assessment through the integration of urea hydrolysis and impedance measurement.

We present the development and validation of an impedance-based urine osmometer for accurate and portable measurement of urine osmolality. The urine osmolality of a urine sample can be estimated by determining the concentrations of the conductive solutes and urea, which make up approximately 94% of the urine composition. Our method utilizes impedance measurements to determine the conductive solutes and urea after hydrolysis with urease enzyme. We built an impedance model using sodium chloride (NaCl) and urea at various known concentrations. In this work, we validated the accuracy of the impedance-based urine osmometer by developing a proof-of-concept first prototype and an integrated urine dipstick second prototype, where both prototypes exhibit an average accuracy of 95.5 ± 2.4% and 89.9 ± 9.1%, respectively in comparison to a clinical freezing point osmometer in the hospital laboratory. While the integrated dipstick design exhibited a slightly lower accuracy than the first prototype, it eliminated the need for pre-mixing or manual pipetting. Impedance calibration curves for conductive and non-conductive solutes consistently yielded results for NaCl but underscored challenges in achieving uniform urease enzyme coating on the dipstick. We also investigated the impact of storing urine at room temperature for 24 hours, demonstrating negligible differences in osmolality values. Overall, our impedance-based urine osmometer presents a promising tool for point-of-care urine osmolality measurements, addressing the demand for a portable, accurate, and user-friendly device with potential applications in clinical and home settings.

2023 Korean Society of Echocardiography position paper for the diagnosis and management of valvular heart disease, part II: mitral and tricuspid valve disease.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases. This position paper focuses on the diagnosis and management of valvular heart diseases with referring to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee sought to reflect national data on the topic of valvular heart diseases published to date through a systematic literature search based on validity and relevance. In the part II of this article, we intend to present recommendations for diagnosis and treatment of mitral valve disease and tricuspid valve disease.

Rapid Plaque Progression Is Independently Associated With Hyperglycemia and Low HDL Cholesterol in Patients With Stable Coronary Artery Disease: A PARADIGM Study.

BACKGROUND: We assessed whether combinations of cardiometabolic risk factors independently predict coronary plaque progression (PP) and major adverse cardiovascular events in patients with stable coronary artery disease. METHODS: Patients with known or suspected stable coronary artery disease (60.9±9.3 years, 55.4% male) undergoing serial coronary computed tomography angiographies (≥2 years apart), with clinical characterization and follow-up (N=1200), were analyzed from the PARADIGM study (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging). Plaque volumes measured in coronary segments (≥2 mm in diameter) were summed to provide whole heart plaque volume (mm3) and percent atheroma volume (plaque volume/vessel volume×100; %) per patient at baseline and follow-up. Rapid PP was defined as a percent atheroma volume increase of ≥1.0%/y. Major adverse cardiovascular events included nonfatal myocardial infarction, death, and unplanned coronary revascularization. RESULTS: In an interscan period of 3.2 years (interquartile range, 1.9), rapid PP occurred in 341 patients (28%). At multivariable analysis, the combination of cardiometabolic risk factors defined as metabolic syndrome predicted rapid PP (odds ratio, 1.51 [95% CI, 1.12-2.03]; P=0.007) together with older age, smoking habits, and baseline percent atheroma volume. Among single cardiometabolic variables, high fasting plasma glucose (diabetes or fasting plasma glucose >100 mg/dL) and low HDL-C (high-density lipoprotein cholesterol; <40 mg/dL in males and <50 mg/dL in females) were independently associated with rapid PP, in particular when combined (odds ratio, 2.37 [95% CI, 1.56-3.61]; P<0.001). In a follow-up of 8.23 years (interquartile range, 5.92-9.53), major adverse cardiovascular events occurred in 201 patients (17%). At multivariable Cox analysis, the combination of high fasting plasma glucose with high systemic blood pressure (treated hypertension or systemic blood pressure >130/85 mm Hg) was an independent predictor of events (hazard ratio, 1.79 [95% CI, 1.10-2.90]; P=0.018) together with family history, baseline percent atheroma volume, and rapid PP. CONCLUSIONS: In patients with stable coronary artery disease, the combination of hyperglycemia with low HDL-C is associated with rapid PP independently of other risk factors, baseline plaque burden, and treatment. The combination of hyperglycemia with high systemic blood pressure independently predicts the worse outcome beyond PP. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02803411.

Automated segmentation of MRI white matter hyperintensities in 8,421 patients with acute ischemic stroke.

BACKGROUND AND PURPOSE: To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction, which is complicated because stroke-related lesions can obscure WMH borders. We developed and validated deep learning algorithms to segment WMH lesions accurately in patients with cerebral infarction, using multisite datasets involving 8,421 patients with acute ischemic stroke. MATERIALS AND METHODS: We included 8,421 stroke patients from 9 centers in Korea. 2D UNet and SE-Unet models were trained using 2,408 FLAIR MRI from 3 hospitals and validated using 6,013 FLAIR MRIs from 6 hospitals. WMH segmentation performance was assessed by calculating DSC, correlation coefficient, and concordance correlation coefficient compared to a human-segmented gold standard. In addition, we obtained an uncertainty index that represents overall ambiguity in the voxel classification for WMH segmentation in each patient based on the Kullback-Leibler divergence. RESULTS: In the training dataset, the mean age was 67.4±13.0 years and 60.4% were men. The mean (95% CI) DSCs for Unet in internal testing and external validation were respectively 0.659 (0.649-0.669) and 0.710 (0.707-0.714), which were slightly lower than the reliability between humans (DSC=0.744; 95% CI=0.738-0.751; P=.031). Compared with the Unet, the SE-Unet demonstrated better performance, achieving a mean DSC of 0.675 (0.666-0.685; P<.001) in the internal testing and 0.722 (0.719-0.726; P<.001) in the external validation; moreover, it achieved high DSC values (ranging from 0.672 to 0.744) across multiple validation datasets. We observed a significant correlation between WMH volumes that were segmented automatically and manually for the Unet (r=0.917, P<.0001) and even stronger for the SE-Unet (r=0.933, P<.0001). The SE-Unet also attained a high concordance correlation coefficient (ranging from 0.841 to 0.956) in external test datasets. In addition, the uncertainty indices in the majority of patients (86%) in the external datasets were below 0.35, with an average DSC of 0.744 in these patients. CONCLUSIONS: We developed and validated deep learning algorithms to segment WMH in patients with acute cerebral infarction using the largest-ever MRI datasets. In addition, we showed that the uncertainty index can be used to identify cases where automatic WMH segmentation is less accurate and requires human review. ABBREVIATIONS: WMH = white matter hyperintensity; CNN = convolutional neural networks; SE = squeeze-and-excitation; KL = Kullback-Leibler; ReLU = rectified linear unit; LKW = last known well; mRS = modified Rankin Scale; NIHSS = National Institute of Health Stroke Scale; LAA = large artery atherosclerosis; SVO = small vessel occlusion; CE = cardioembolism.

Prediction Model for Chronological Weight Loss After Bariatric Surgery in Korean Patients.

Purpose: This study aimed to develop a predictive model for monitoring chronological weight loss during the early postoperative period following bariatric surgery in Korean patients with morbid obesity. Materials and Methods: The baseline characteristics and postoperative weight loss outcomes were collected for up to 24 months after surgery in patients who underwent sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB). The factors influencing weight loss outcomes were analyzed, and longitudinal percentile charts were plotted using quantile regression models adjusted for the identified independent factors. Results: The analysis included 491 and 274 patients who underwent SG and RYGB, respectively, of whom 225 (29.4%) were men. A positive association was found between the maximum percentage of total weight loss (%TWL) and female sex, body mass index (BMI) ≥40, and age <40 years. Among patients who reached nadir BMI or had at least 12 months of follow-up data (n=304), 7.6% exhibited inadequate weight loss (TWL <20%). The predictors of insufficient weight loss were older age (>40 years), male sex, and psychological problems. Centile charts were generated for the entire cohort, incorporating age, sex, and the type of procedure as covariates. Conclusion: The percentile charts proposed in the present study can assist surgeons and healthcare providers in gauging patients' progress toward their weight loss goals and determining the timing of adjunctive intervention in poor responders during early postoperative follow-up.

Altered Brain Glymphatic Function at Diffusion-Tensor MRI in Pre-cirrhotic Metabolic Dysfunction-Associated Fatty Liver Disease.

RATIONALE AND OBJECTIVES: To evaluate glymphatic function changes and their relationships with clinical features in patients with metabolic dysfunction-associated fatty liver disease (MAFLD), thereby facilitating early intervention before this disease progresses to cirrhosis. MATERIALS AND METHODS: A cross-sectional cohort of 46 pre-cirrhotic MAFLD patients and 30 age-, sex-, and education-matched controls was enrolled, with diffusion-tensor imaging (DTI) data, laboratory and neurocognitive scores collected. The DTI analysis along the perivascular space (DTI-ALPS) index was computed for qualifying glymphatic function. Generalized linear model and partial correlation analyses were applied to evaluate relationships between the ALPS index and clinical variables. RESULTS: MAFLD group exhibited a decreased ALPS index and increased diffusivity along the y-axis in the projection fiber compared to the controls. The altered ALPS index was associated with clock drawing test (CDT) score (3.931 [0.914, 6.947], P = 0.011) and was correlated with diastolic pressure level (r = -0.315, P = 0.033) in MAFLD group. The relationships of ALPS index with CDT score (6.263 [2.069, 10.458], P = 0.003) and diastolic pressure level (r = -0.518, P = 0.014) remained in the MAFLD with metabolic syndrome (MetS) group. Furthermore, the ALPS index was even associated with Auditory Verbal Learning Test-Immediate recall score (-23.853 [-45.417, -2.289], P = 0.030) in MAFLD with MetS group. CONCLUSION: MAFLD patients may have a glymphatic dysfunction prior to cirrhosis, and this alteration may be related to cognition and diastolic pressure. Glymphatic dysfunction has a more severe impact on cognition when MAFLD patient is accompanied by MetS.

Preclinical evaluation of [18F]FP-CIT, the radiotracer targeting dopamine transporter for diagnosing Parkinson's disease: pharmacokinetic and efficacy analysis.

BACKGROUND: N-(3-fluoropropyl)-2ß-carboxymethoxy-3ß-(4-iodophenyl) nortropane (FP-CIT), the representative cocaine derivative used in dopamine transporter imaging, is a promising biomarker, as it reflects the severity of Parkinson's disease (PD). 123I- and 18F-labeled FP-CIT has been used for PD diagnosis. However, preclinical studies evaluating [18F]FP-CIT as a potential diagnostic biomarker are scarce. Among translational research advancements from bench to bedside, translating preclinical findings into clinical practice is one-directional. The aim of this study is to employ a circular approach, beginning back from the preclinical stage, progressing to the supplementation of [18F]FP-CIT, and subsequently returning to clinical application. We investigated the pharmacokinetic properties of [18F]FP-CIT and its efficacy for PD diagnosis using murine models. RESULTS: Biodistribution, metabolite and excretion analyses were performed in mice and PD models were induced in rats using 6-hydroxydopamine (6-OHDA). The targeting efficiency of [18F]FP-CIT for the dopamine receptor was assessed through animal PET/CT imaging. Subsequently, correlation analysis was conducted between animal PET/CT imaging results and immunohistochemistry (IHC) targeting tyrosine hydroxylase. Rapid circulation was confirmed after [18F]FP-CIT injection. [18F]FP-CIT reached the highest uptake of 23.50 ± 12.46%ID/g in the striatum 1 min after injection, and it was rapidly excreted within 60 min. The major metabolic organs of [18F]FP-CIT were confirmed to be the intestines, liver, and kidneys. Its uptake in the intestine was approximately 5% ID/g. The uptake in the liver gradually increased, with excretion beginning after reaching a maximum after 60 min. The kidneys exhibited rapid elimination after 10 min. In the excretion study, rapid elimination was verified, with 21.46 ± 9.53% of the compound excreted within a 6 h period. Additionally, the efficacy of [18F]FP-CIT PET was demonstrated in the PD model, with a high correlation with IHC for both the absolute value (R = 0.803, p = 0.0017) and the ratio value (R = 0.973, p = 0.0011). CONCLUSIONS: This study fills the gap regarding insufficient preclinical studies on [18F]FP-CIT, including its ADME, metabolites, and efficiency. The pharmacological results, including accurate diagnosis, rapid circulation, and [18F]FP-CIT excretion, provide complementary evidence that [18F]FP-CIT can be used safely and efficiently to diagnose PD in clinics, although it is already used in clinics.

A Comparative Metagenomic Analysis of Specified Microorganisms in Groundwater for Non-Sterilized Pharmaceutical Products.

In pharmaceutical manufacturing, ensuring product safety involves the detection and identification of microorganisms with human pathogenic potential, including Burkholderia cepacia complex (BCC), Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Staphylococcus aureus, Clostridium sporogenes, Candida albicans, and Mycoplasma spp., some of which may be missed or not identified by traditional culture-dependent methods. In this study, we employed a metagenomic approach to detect these taxa, avoiding the limitations of conventional cultivation methods. We assessed the groundwater microbiome's taxonomic and functional features from samples collected at two locations in the spring and summer. All datasets comprised 436-557 genera with Proteobacteria, Bacteroidota, Firmicutes, Actinobacteria, and Cyanobacteria accounting for > 95% of microbial DNA sequences. The aforementioned species constituted less than 18.3% of relative abundance. Escherichia and Salmonella were mainly detected in Hot Springs, relative to Jefferson, while Clostridium and Pseudomonas were mainly found in Jefferson relative to Hot Springs. Multidrug resistance efflux pumps and BlaR1 family regulatory sensor-transducer disambiguation dominated in Hot Springs and in Jefferson. These initial results provide insight into the detection of specified microorganisms and could constitute a framework for the establishment of comprehensive metagenomic analysis for the microbiological evaluation of pharmaceutical-grade water and other non-sterile pharmaceutical products, ensuring public safety.

Sparse reconstruction of sound field using pattern-coupled Bayesian compressive sensing.

Conventional near-field acoustic holography based on compressive sensing either does not fully exploit the underlying block-sparse structures of the signal or suffers from a mismatch between the actual and predefined block structure due to the lack of prior information about block partitions, resulting in poor accuracy in sound field reconstruction. In this paper, a pattern-coupled Bayesian compressive sensing method is proposed for sparse reconstruction of sound fields. The proposed method establishes a hierarchical Gaussian-Gamma probability model with a pattern-coupled prior based on the equivalent source method, transforming the sound field reconstruction problem into recovering the sparse coefficient vector of the equivalent source strengths within the compressive sensing framework. A set of hyperparameters is introduced to control the sparsity of each element in the sparse coefficient vector of the equivalent source strengths, where the sparsity of each element is determined by both its own hyperparameters and those of its immediate neighbors. This approach enables the promotion of block sparse solutions and achieves better performance in solving for the sparse coefficient vector of the equivalent source strengths without prior information of block partitions. The effectiveness and superiority of the proposed method in reconstructing sound fields are verified by simulations and experiments.

Air pollution and survival in patients with malignant mesothelioma and asbestos-related lung cancer: a follow-up study of 1591 patients in South Korea.

BACKGROUND: Despite significant advancements in treatments such as surgery, radiotherapy, and chemotherapy, the survival rate for patients with asbestos-related cancers remains low. Numerous studies have provided evidence suggesting that air pollution induces oxidative stress and inflammation, affecting acute respiratory diseases, lung cancer, and overall mortality. However, because of the high case fatality rate, there is limited knowledge regarding the effects of air pollution exposures on survival following a diagnosis of asbestos-related cancers. This study aimed to determine the effect of air pollution on the survival of patients with malignant mesothelioma and asbestos-related lung cancer. METHODS: We followed up with 593 patients with malignant mesothelioma and 998 patients with lung cancer identified as asbestos victims between 2009 and 2022. Data on five air pollutants-sulfur dioxide, carbon monoxide, nitrogen dioxide, fine particulate matter with a diameter < 10 µm, and fine particulate matter with a diameter < 2.5 µm-were obtained from nationwide atmospheric monitoring stations. Cox proportional hazard models were used to estimate the association of cumulative air pollutant exposure with patient mortality, while adjusting for potential confounders. Quantile-based g-computation was used to assess the combined effect of the air pollutant mixture on mortality. RESULTS: The 1-, 3-, and 5-year survival rates for both cancer types decreased with increasing exposure to all air pollutants. The estimated hazard ratios rose significantly with a 1-standard deviation increase in each pollutant exposure level. A quartile increase in the pollutant mixture was associated with a 1.99-fold increase in the risk of malignant mesothelioma-related mortality (95% confidence interval: 1.62, 2.44). For lung cancer, a quartile increase in the pollutant mixture triggered a 1.87-fold increase in the mortality risk (95% confidence interval: 1.53, 2.30). CONCLUSION: These findings support the hypothesis that air pollution exposure after an asbestos-related cancer diagnosis can negatively affect patient survival.

Utilizing deep learning for automated detection of oral lesions: A multicenter study.

OBJECTIVES: We aim to develop a YOLOX-based convolutional neural network model for the precise detection of multiple oral lesions, including OLP, OLK, and OSCC, in patient photos. MATERIALS AND METHODS: We collected 1419 photos for model development and evaluation, conducting both a comparative analysis to gauge the model's capabilities and a multicenter evaluation to assess its diagnostic aid, where 24 participants from 14 centers across the nation were invited. We further integrated this model into a mobile application for rapid and accurate diagnostics. RESULTS: In the comparative analysis, our model overperformed the senior group (comprising three most experienced experts with more than 10 years of experience) in macro-average recall (85 % vs 77.5 %), precision (87.02 % vs 80.29 %), and specificity (95 % vs 92.5 %). In the multicenter model-assisted diagnosis evaluation, the dental, general, and community hospital groups showed significant improvement when aided by the model, reaching a level comparable to the senior group, with all macro-average metrics closely aligning or even surpassing with those of the latter (recall of 78.67 %, 74.72 %, 83.54 % vs 77.5 %, precision of 80.56 %, 76.42 %, 85.15 % vs 80.29 %, specificity of 92.89 %, 91.57 %, 94.51 % vs 92.5 %). CONCLUSION: Our model exhibited a high proficiency in detection of oral lesions, surpassing the performance of highly experienced specialists. The model can also help specialists and general dentists from dental and community hospitals in diagnosing oral lesions, reaching the level of highly experienced specialists. Moreover, our model's integration into a mobile application facilitated swift and precise diagnostic procedures.

Cu-Catalyzed Amination of Base-Sensitive Aryl Bromides and the Chemoselective N- and O-Arylation of Amino Alcohols.

We report a general and functional-group-tolerant method for the Cu-catalyzed amination of base-sensitive aryl bromides including substrates possessing acidic functional groups and small five-membered heteroarenes. The results presented herein substantially expand the scope of Cu-catalyzed C-N coupling reactions. The combination of L8, an anionic N1,N2-diarylbenzene-1,2-diamine ligand, along with the mild base NaOTMS leads to the formation of a stable yet reactive catalyst that resists deactivation from coordination to heterocycles or charged intermediates. This system enables the use of low catalyst and ligand loadings. Exploiting the differences in nucleophile deprotonation in C-O and C-N coupling reactions catalyzed by Cu·L8 we developed a method to chemoselectively N- and O-arylate a variety of amino alcohol substrates. Employing NaOt-Bu as the base resulted exclusively in C-O coupling when the amino alcohols featured primary alcohols and more hindered amines or aniline groups. Utilizing NaOTMS enabled the ability to override the steric-based selectivity of these reactions completely and exclusively promoted C-N coupling regardless of the structure of the amino alcohol. The ability to invert the observed chemoselectivity is distinct from previously described methods that require protecting group manipulations or rely entirely on steric effects to control reactivity. These results substantially improve the scope of Cu-catalyzed C-N coupling reactions using N1,N2-diarylbenzene-1,2-diamine ligands and introduce a new chemoselective method to arylate amino alcohols.

Mitral Annular Tissue Velocity Predicts Survival in Patients With Primary Mitral Regurgitation.

BACKGROUND AND OBJECTIVES: Early diastolic mitral annular tissue (e') velocity is a commonly used marker of left ventricular (LV) diastolic function. This study aimed to investigate the prognostic implications of e' velocity in patients with mitral regurgitation (MR). METHODS: This retrospective cohort study included 1,536 consecutive patients aged <65 years with moderate or severe chronic primary MR diagnosed between 2009 and 2018. The primary and secondary outcomes were all-cause and cardiovascular mortality, respectively. According to the current guidelines, the cut-off value of e' velocity was defined as 7 cm/s. RESULTS: A total of 404 individuals were enrolled (median age, 51.0 years; 64.1% male; 47.8% severe MR). During a median 6.0-year follow-up, there were 40 all-cause mortality and 16 cardiovascular deaths. Multivariate analysis revealed a significant association between e' velocity and all-cause death (adjusted hazard ratio [aHR], 0.770; 95% confidence interval [CI], 0.634-0.935; p=0.008) and cardiovascular death (aHR, 0.690; 95% CI, 0.477-0.998; p=0.049). Abnormal e' velocity (≤7 cm/s) independently predicted all-cause death (aHR, 2.467; 95% CI, 1.170-5.200; p=0.018) and cardiovascular death (aHR, 5.021; 95% CI, 1.189-21.211; p=0.028), regardless of symptoms, LV dimension and ejection fraction. Subgroup analysis according to sex, MR severity, mitral valve replacement/repair, and symptoms, showed no significant interactions. Including e' velocity in the 10-year risk score improved reclassification for mortality (net reclassification improvement [NRI], 0.154; 95% CI, 0.308-0.910; p<0.001) and cardiovascular death (NRI, 1.018; 95% CI, 0.680-1.356; p<0.001). CONCLUSIONS: In patients aged <65 years with primary MR, e' velocity served as an independent predictor of all-cause and cardiovascular deaths.

Impact of Aeroallergen Sensitization on Chronic Rhinosinusitis.

PURPOSE: This study investigated the impact of aeroallergens on the development and progression of chronic rhinosinusitis (CRS), with a focus on the specific associations between aeroallergens and CRS according to allergen type, number, and extent of sensitization. METHODS: The medical records of 256 CRS patients were retrospectively analyzed. All were divided into nonallergic, house dust mite (HDM)-allergic, pollen-allergic, and double allergic groups via specific immunoglobulin E (IgE) testing. Clinical characteristics, computed tomography (CT) scores, olfactory functions, and demographic data were compared. Correlation analysis was performed to explore the relationships between the extent of allergen sensitization and CRS severity. Binary logistic regression analysis was used to identify risk factors for hyposmia and anosmia. RESULTS: The allergic group exhibited higher total CT scores than the nonallergic group (P = 0.001). Sensitivity to HDM or pollen allergens alone was not significantly associated with increased CRS severity. No significant differences were observed between the effects of HDM and pollen allergens on CRS severity. However, the double allergic group exhibited significantly higher CT scores (P < 0.001, < 0.001, and 0.003) than the other groups. Although the prevalence rates of anosmia and hyposmia were notably higher in the double allergic group, the difference was not statistically significant. The maximum specific IgE levels to HDM and pollen allergens positively correlated with the CT scores (P = 0.001 and 0.001, respectively). CONCLUSIONS: Allergen sensitization, particularly to multiple common allergens, contributed to CRS severity. CRS patients sensitized to both HDM and pollen allergens tended to experience the diminished olfactory function. These findings underscore the importance of considering the allergen sensitization pattern when assessing CRS severity and its potential progression.

Locus coeruleus activation contributes to masseter muscle overactivity induced by chronic restraint stress in mice.

It is commonly accepted that exposure to stress may cause overactivity in the orofacial muscles, leading to consistent muscle pain, which is the main symptom of temporomandibular disorders. The central neural mechanism underlying this process, however, remains unclear. The locus coeruleus is considered to play an important role in stress-related behavioral changes. Therefore, the present study was designed to examine the role of locus coeruleus neurons in masseter overactivity induced by stress. C57BL/6 mice were subjected to chronic restraint stress for 14 days to establish an animal model. The behavioral changes and the electromyography of the masseter muscle in mice were measured. The expression of Fos in locus coeruleus was observed by immunofluorescence staining to assess neuronal activation. A chemogenetic test was used to inhibit locus coeruleus neuronal activity, and the behavioral changes and electromyography of the masseter muscle were observed again. The results exhibited that chronic restraint stress could induce anxiety-like behavior, overactivity of the masseter muscle, and significant activation of locus coeruleus neurons in mice. Furthermore, inhibition of noradrenergic neuron activity within the locus coeruleus could alleviate stress-induced anxiety behavior and masseter muscle overactivity. Activation of noradrenergic neurons in locus coeruleus induced by stress may be one of the central regulatory mechanisms for stress-induced anxiety-like behaviors and overactivity of masseter muscles.

Extrahepatic malignancies and antiviral drugs for chronic hepatitis B: A nationwide cohort study.

BACKGROUND/AIMS: Chronic hepatitis B (CHB) is related to an increased risk of extrahepatic malignancy (EHM), and antiviral treatment is associated with an incidence of EHM comparable to controls. We compared the risks of EHM and intrahepatic malignancy (IHM) between entecavir (ETV) and tenofovir disoproxil fumarate (TDF) treatment. METHODS: Using data from the National Health Insurance Service of Korea, this nationwide cohort study included treatment-naïve CHB patients who initiated ETV (n=24,287) or TDF (n=29,199) therapy between 2012 and 2014. The primary outcome was the development of any primary EHM. Secondary outcomes included overall IHM development. E-value was calculated to assess the robustness of results to unmeasured confounders. RESULTS: The median follow-up duration was 5.9 years, and all baseline characteristics were well balanced after propensity score matching. EHM incidence rate differed significantly between within versus beyond 3 years in both groups (P<0.01, Davies test). During the first 3 years, EHM risk was comparable in the propensity score-matched cohort (5.88 versus 5.84/1,000 person-years; subdistribution hazard ratio [SHR]=1.01, 95% confidence interval [CI]=0.88-1.17, P=0.84). After year 3, however, TDF was associated with a significantly lower EHM incidence compared to ETV (4.92 versus 6.91/1,000 person-years; SHR=0.70, 95% CI=0.60-0.81, P<0.01; E-value for SHR=2.21). Regarding IHM, the superiority of TDF over ETV was maintained both within (17.58 versus 20.19/1,000 person-years; SHR=0.88, 95% CI=0.81-0.95, P<0.01) and after year 3 (11.45 versus 16.20/1,000 person-years; SHR=0.68, 95% CI=0.62-0.75, P<0.01; E-value for SHR=2.30). CONCLUSION: TDF was associated with approximately 30% lower risks of both EHM and IHM than ETV in CHB patients after 3 years of antiviral therapy.

Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy.

Background: Current risk stratification strategies for patients with hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies. Objectives: The authors aimed to establish machine learning (ML)-based models to discriminate major cardiovascular events in patients with HCM. Methods: We enrolled consecutive HCM patients from 2 tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (MACE), including all-cause death, admission for heart failure (HF-adm), and stroke. The best model was selected for each outcome using the area under the receiver operating characteristic curve (AUROC) with 20-fold cross-validation. After testing in the external validation cohort, the relative importance of features in discriminating each outcome was determined using the SHapley Additive exPlanations (SHAP) method. Results: In total, 2,111 patients with HCM (age 61.4 ± 13.6 years; 67.6% men) were analyzed. During the median 4.0 years of follow-up, MACE occurred in 341 patients (16.2%). Among the 4 ML models, the logistic regression model achieved the best AUROC of 0.800 (95% CI: 0.760-0.841) for MACE, 0.789 (95% CI: 0.736-0.841) for all-cause death, 0.798 (95% CI: 0.736-0.860) for HF-adm, and 0.807 (95% CI: 0.754-0.859) for stroke. The discriminant ability of the logistic regression model remained excellent when applied to the external validation cohort for MACE (AUROC = 0.768), all-cause death (AUROC = 0.750), and HF-adm (AUROC = 0.806). The SHAP analysis identified left atrial diameter and hypertension as important variables for all outcomes of interest. Conclusions: The proposed ML models incorporating various phenotypes from patients with HCM accurately discriminated adverse cardiovascular events and provided variables with high importance for each outcome.

Copper-Nanoparticle-Decorated Hydrothermal Carbonaceous Carbon-Polydimethylsiloxane Nanocomposites: Unveiling Potential in Simultaneous Light-Driven Interfacial Water Evaporation and Power Generation.

This study introduces a hydrothermal synthesis method that uses glucose and Cu2+ ions to create a Cu-nanoparticle (NP)-decorated hydrothermal carbonaceous carbon hybrid material (Cu-HTCC). Glucose serves both as a reducing agent, efficiently transforming Cu2+ ions into elemental Cu nanostructures, and as a precursor for HTCC microstructures. An enhanced plasmon-induced electric field resulting from Cu NPs supported on microstructure matrices, coupled with a distinctive localized π-electronic configuration in the hybrid material, as confirmed by X-ray photoelectron spectroscopic analysis, lead to the heightened optical absorption in the visible-near-infrared range. Consequently, flexible nanocomposites of Cu-HTCC/PDMS and Cu-HTCC@PDMS (PDMS = polydimethylsiloxane) are designed as 2 and 3D structures, respectively, that exhibit broad-spectrum solar absorption. These composites promise efficient photo-assisted thermoelectric power generation and water evaporation, demonstrating commendable mechanical stability and flexibility. Notably, the Cu-HTCC@PDMS composite sponge simultaneously exhibits commendable efficiency in both water evaporation (1.47 kg m-2 h-1) and power generation (32.1 mV) under 1 sunlight illumination. These findings unveil new possibilities for innovative photothermal functional materials in diverse solar-driven applications.

Trends in Dual Antiplatelet Therapy of Aspirin and Clopidogrel and Outcomes in Ischemic Stroke Patients Noneligible for POINT/CHANCE Trial Treatment.

BACKGROUND: Recent clinical trials established the benefit of dual antiplatelet therapy with aspirin and clopidogrel (DAPT-AC) in early-presenting patients with minor ischemic stroke. However, the impact of these trials over time on the use and outcomes of DAPT-AC among the patients with nonminor or late-presenting stroke who do not meet the eligibility criteria of these trials has not been delineated. METHODS AND RESULTS: In a multicenter stroke registry, this study examined yearly changes from April 2008 to August 2022 in DAPT-AC use for stroke patients ineligible for CHANCE/POINT (Clopidogrel in High-Risk Patients with Acute Nondisabling Cerebrovascular Events/Platelet-Oriented Inhibition in New TIA and Minor Ischemic Stroke) clinical trials due to National Institutes of Health Stroke Scale >4 or late arrival beyond 24 hours of onset. A total of 32 118 patients (age, 68.1±13.1 years; male, 58.5%) with National Institutes of Health Stroke Scale of 4 (interquartile range, 1-7) were analyzed. In 2008, DAPT-AC was used in 33.0%, other antiplatelets in 62.7%, and no antiplatelet in 4.3%. The frequency of DAPT-AC was relatively unchanged through 2013, when the CHANCE trial was published, and then increased steadily, reaching 78% in 2022, while other antiplatelets decreased to 17.8% in 2022 (Ptrend<0.001). From 2011 to 2022, clinical outcomes nonsignificantly improved, with an average relative risk reduction of 2%/y for the composite of stroke, myocardial infarction, and all-cause mortality, both among patients treated with DAPT-AC and patients treated with other antiplatelets. CONCLUSIONS: Use of DAPT-AC in stroke patients with stroke ineligible for recent DAPT clinical trials increased markedly and steadily after CHANCE publication in 2013, reaching deployment in nearly 4 of every 5 patients by 2022. The secondary prevention in patients with ischemic stroke seems to be gradually improving, possibly due to the enhancement of risk factor control.

Abnormalities in spontaneous brain activity and functional connectivity are associated with cognitive impairments in children with type 1 diabetes mellitus.

BACKGROUND: It remains unclear whether alterations in brain function occur in the early stage of pediatric type 1 diabetes mellitus(T1DM). We aimed to examine changes in spontaneous brain activity and functional connectivity (FC) in children with T1DM using resting-state functional magnetic resonance imaging (rs-fMRI), and to pinpoint potential links between neural changes and cognitive performance. METHODS: In this study, 22 T1DM children and 21 age-, sex-matched healthy controls underwent rs-fMRI. The amplitude of low frequency fluctuations (ALFF) and seed-based FC analysis were performed to examine changes in intrinsic brain activity and functional networks in T1DM children. Partial correlation analyses were utilized to explore the correlations between ALFF values and clinical parameters. RESULTS: The ALFF values were significantly lower in the lingual gyrus (LG) and higher in the left medial superior frontal gyrus (MSFG) in T1DM children compared to controls. Subsequent FC analysis indicated that the LG had decreased FC with bilateral inferior occipital gyrus, and the left MSFG had decreased FC with right precentral gyrus, right inferior parietal gyrus and right postcentral gyrus in children with T1DM. The ALFF values of LG were positively correlated with full-scale intelligence quotient and age at disease onset in T1DM children, while the ALFF values of left MSFG were positively correlated with working memory scores. CONCLUSION: Our findings revealed abnormal spontaneous activity and FC in brain regions related to visual, memory, default mode network, and sensorimotor network in the early stage of T1DM children, which may aid in further understanding the mechanisms underlying T1DM-associated cognitive dysfunction.