Mitofusin 1 and 2 overexpression reduces AßO-mediated ER stress and apoptosis in N2a APPswe cells.

Alzheimer's disease (AD) is the most common neurodegenerative disorder, and amyloid beta oligomers (AßO), which are pathological markers of AD, are known to be highly toxic. AßO increase mitochondrial dysfunction, which is accompanied by a decrease in mitochondrial fusion. Although mitofusin (Mfn) 1 and Mfn2 are mitochondrial fusion proteins, Mfn2 is known to regulate endoplasmic reticulum (ER) function, as it is located in the ER. Several studies have shown that AßO exacerbates ER stress, however, the exact mechanism requires further elucidation. In this study, we used mouse neuroblastoma cells stably overexpressing the amyloid precursor protein (APP) with the Swedish mutation (N2a APPswe cells) to investigate the role of Mfn in ER stress. Our results revealed that  amyloid beta (Aß) caused cellular toxicity in N2a APPswe cells, upregulated ER stress-related proteins, and promoted ER expansion. The AßO-mediated ER stress was reduced when Mfn1 and Mfn2 were overexpressed. Moreover, Mfn1 and Mfn2 overexpressed resulted in reduced apoptosis of N2a APPswe cells. In conclusion, our results indicate that both Mfn1 and Mfn2 reduce ER stress and apoptosis. Our data provide a foundation for future studies on the roles of Mfn1 and Mfn2 in the molecular mechanisms underlying AßO-mediated ER stress and the pathogenesis of AD.

Machine learning-based prediction of cerebral oxygen saturation based on multi-modal cerebral oximetry data.

This study develops machine learning-based algorithms that facilitate accurate prediction of cerebral oxygen saturation using waveform data in the near-infrared range from a multi-modal oxygen saturation sensor. Data were obtained from 150,000 observations of a popular cerebral oximeter, Masimo O3™ regional oximetry (Co., United States) and a multi-modal cerebral oximeter, Votem (Inc., Korea). Among these observations, 112,500 (75%) and 37,500 (25%) were used for training and test sets, respectively. The dependent variable was the cerebral oxygen saturation value from the Masimo O3™ (0-100%). The independent variables were the time of measurement (0-300,000 ms) and the 16-bit decimal amplitudes values (infrared and red) from Votem (0-65,535). For the right part of the forehead, the root mean square error of the random forest (0.06) was much smaller than those of linear regression (1.22) and the artificial neural network with one, two or three hidden layers (2.58). The result was similar for the left part of forehead, that is, random forest (0.05) vs logistic regression (1.22) and the artificial neural network with one, two or three hidden layers (2.97). Machine learning aids in accurately predicting of cerebral oxygen saturation, employing the data from a multi-modal cerebral oximeter.

Phosphate level predicts mortality in acute kidney injury patients undergoing continuous kidney replacement therapy and has a U-shaped association with mortality in patients with high disease severity: a multicenter retrospective study.

BACKGROUND: This study investigated the association between serum phosphate level and mortality in acute kidney injury (AKI) patients undergoing continuous kidney replacement therapy (CKRT) and evaluated whether this association differed according to disease severity. METHODS: Data from eight tertiary hospitals in Korea were retrospectively analyzed. The patients were classified into four groups (low, normal, high, and very high) based on their serum phosphate level at baseline. The association between serum phosphate level and mortality was then analyzed, with further subgroup analysis being conducted according to disease severity. RESULTS: Among the 3,290 patients identified, 166, 955, 1,307, and 862 were in the low, normal, high, and very high phosphate groups, respectively. The 90-day mortality rate was 63.9% and was highest in the very high group (76.3%). Both the high and very high groups showed a significantly higher 90-day mortality rate than did the normal phosphate group (high: hazard ratio [HR], 1.35, 95% confidence interval [CI], 1.21-1.51, p < 0.001; very high: HR, 2.01, 95% CI, 1.78-2.27, p < 0.001). The low group also exhibited a higher 90-day mortality rate than did the normal group among those with high disease severity (HR, 1.47; 95% CI, 1.09-1.99; p = 0.01) but not among those with low disease severity. CONCLUSION: High serum phosphate level predicted increased mortality in AKI patients undergoing CKRT, and low phosphate level was associated with increased mortality in patients with high disease severity. Therefore, serum phosphate levels should be carefully considered in critically ill patients with AKI.

Glomerular crescents are associated with the risk of type 2 diabetic kidney disease progression: a retrospective cohort study.

BACKGROUND: Diabetic kidney disease (DKD) stands as the predominant cause of chronic kidney disease and end-stage kidney disease. Its diverse range of manifestations complicates the treatment approach for patients. Although kidney biopsy is considered the gold standard for diagnosis, it lacks precision in predicting the progression of kidney dysfunction. Herein, we addressed whether the presence of glomerular crescents is linked to the outcomes in patients with biopsy-confirmed type 2 DKD. METHODS: We performed a retrospective evaluation, involving 327 patients diagnosed with biopsy-confirmed DKD in the context of type 2 diabetes, excluding cases with other glomerular diseases, from nine tertiary hospitals. Hazard ratios (HRs) were calculated using a Cox regression model to assess the risk of kidney disease progression, defined as either ≥ 50% decrease in estimated glomerular filtration rates or the development of end-stage kidney disease, based on the presence of glomerular crescents. RESULTS: Out of the 327 patients selected, ten patients had glomerular crescents observed in their biopsied tissues. Over the follow-up period (median of 19 months, with a maximum of 18 years), the crescent group exhibited a higher risk of kidney disease progression than the no crescent group, with an adjusted HR of 2.82 (1.32-6.06) (P = 0.008). The presence of heavy proteinuria was associated with an increased risk of developing glomerular crescents. CONCLUSION: The presence of glomerular crescents is indeed linked to the progression of type 2 DKD. Therefore, it is important to determine whether there is an additional immune-mediated glomerulonephritis requiring immunomodulation, and it may be prudent to monitor the histology and repeat a biopsy.

Developmental self-reactivity determines pathogenic Tc17 differentiation potential of naive CD8+ T cells in murine models of inflammation.

The differentiation of naive CD8+ T cells into effector cells is important for establishing immunity. However, the effect of heterogeneous naive CD8+ T cell populations is not fully understood. Here, we demonstrate that steady-state naive CD8+ T cells are composed of functionally heterogeneous subpopulations that differ in their ability to differentiate into type 17 cytotoxic effector cells (Tc17) in a context of murine inflammatory disease models, such as inflammatory bowel disease and graft-versus-host disease. The differential ability of Tc17 differentiation is not related to T-cell receptor (TCR) diversity and antigen specificity but is inversely correlated with self-reactivity acquired during development. Mechanistically, this phenomenon is linked to differential levels of intrinsic TCR sensitivity and basal Suppressor of Mothers Against Decapentaplegic 3 (SMAD3) expression, generating a wide spectrum of Tc17 differentiation potential within naive CD8+ T cell populations. These findings suggest that developmental self-reactivity can determine the fate of naive CD8+ T cells to generate functionally distinct effector populations and achieve immense diversity and complexity in antigen-specific T-cell immune responses.

Prognostic factors for clinical outcomes after arthroscopic treatment of traumatic central tears of the triangular fibrocartilage complex.

Aims: The study aimed to assess the clinical outcomes of arthroscopic debridement and partial excision in patients with traumatic central tears of the triangular fibrocartilage complex (TFCC), and to identify prognostic factors associated with unfavourable clinical outcomes. Methods: A retrospective analysis was conducted on patients arthroscopically diagnosed with Palmer 1 A lesions who underwent arthroscopic debridement and partial excision from March 2009 to February 2021, with a minimum follow-up of 24 months. Patients were assessed using the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire, Mayo Wrist Score (MWS), and visual analogue scale (VAS) for pain. The poor outcome group was defined as patients whose preoperative and last follow-up clinical score difference was less than the minimal clinically important difference of the DASH score (10.83). Baseline characteristics, arthroscopic findings, and radiological factors (ulnar variance, MRI, or arthrography) were evaluated to predict poor clinical outcomes. Results: A total of 114 patients were enrolled in this study, with a mean follow-up period of 29.8 months (SD 14.4). The mean DASH score improved from 36.5 (SD 21.5) to 16.7 (SD 14.3), the mean MWS from 59.7 (SD 17.9) to 79.3 (SD 14.3), and the mean VAS pain score improved from 5.9 (SD 1.8) to 2.2 (SD 2.0) at the last follow-up (all p < 0.001). Among the 114 patients, 16 (14%) experienced poor clinical outcomes and ten (8.8%) required secondary ulnar shortening osteotomy. Positive ulnar variance was the only factor significantly associated with poor clinical outcomes (p < 0.001). Positive ulnar variance was present in 38 patients (33%); among them, eight patients (21%) required additional operations. Conclusion: Arthroscopic debridement alone appears to be an effective and safe initial treatment for patients with traumatic central TFCC tears. The presence of positive ulnar variance was associated with poor clinical outcomes, but close observation after arthroscopic debridement is more likely to be recommended than ulnar shortening osteotomy as a primary treatment.

Comparison of the new-Poisoning Mortality Score and the Modified Early Warning Score for predicting in-hospital mortality in patients with acute poisoning.

INTRODUCTION: The evaluation of acute poisoning is challenging due to varied toxic substances and clinical presentations. The new-Poisoning Mortality Score was recently developed to assess patients with acute poisoning and showed good performance in predicting in-hospital mortality. The objective of this study is to externally validate the performance of the new-Poisoning Mortality Score and to compare it with the Modified Early Warning Score. METHODS: This retrospective analysis used data from the 2019-2020 Injury Surveillance Cohort, established by the Korea Center for Disease Control and Prevention, to perform external validation of the new-Poisoning Mortality Score. The statistical performances of the new-Poisoning Mortality and Modified Early Warning Scores were assessed and compared in terms of discrimination and calibration. Discrimination analysis involved metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. For calibration analysis, the Hosmer-Lemeshow goodness-of-fit test was utilized and calibration curves for each score were generated to elucidate the relationship between observed and predicted mortalities. RESULTS: This study analysed 16,570 patients with acute poisoning. Significant differences were observed between survivors and those who died in-hospital, including age, sex, and vital signs. The new-Poisoning Mortality Score showed better performance over the Modified Early Warning Score in predicting in-hospital mortality, in terms of the area under the receiver operating characteristic curve (0.947 versus 0.800), sensitivity (0.863 versus 0.667), specificity (0.912 versus 0.817), and accuracy (0.911 versus 0.814). When evaluated through calibration curves, the new-Poisoning Mortality Score showed better concordance between predicted and observed mortalities. In subgroup analyses, the score system consistently showed strong performance, excelling particularly in substances with high mortality indices and remaining superior in all substances as a group. CONCLUSIONS: Our study has helped to validate the new-Poisoning Mortality Score as an effective tool for predicting in-hospital mortality in patients with acute poisoning in the emergency department. The score system demonstrated superior performance over the Modified Early Warning Score in various metrics. Our findings suggest that the new-Poisoning Mortality Score can contribute to the enhancement of clinical decision-making and patient management.

Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning.

Xerostomia may be accompanied by changes in salivary flow rate and the incidence increases in elderly. We aimed to use machine learning algorithms, to identify significant predictors for the presence of xerostomia. This study is the first to predict xerostomia with salivary flow rate in elderly based on artificial intelligence. In a cross-sectional study, 829 patients with oral discomfort were enrolled, and six features (sex, age, unstimulated and stimulated salivary flow rates (UFR and SFR, respectively), number of systemic diseases, and medication usage) were used in four machine learning algorithms to predict the presence of xerostomia. The incidence of xerostomia increased with age. The SFR was significantly higher than the UFR, and the UFR and SFR were significantly correlated. The UFR, but not SFR, decreased with age significantly. In patients more than 60 years of age, the UFR had a significantly higher predictive accuracy for xerostomia than the SFR. Using machine learning algorithms with tenfold cross-validation, the prediction accuracy increased significantly. In particular, the prediction accuracy of the multilayer perceptron (MLP) algorithm that combined UFR and SFR data was significantly better than either UFR or SFR individually. Moreover, when sex, age, number of systemic diseases, and number of medications were added to the MLP model, the prediction accuracy increased from 56 to 68%.