The use of individual-based FDG-PET volume of interest in predicting conversion from mild cognitive impairment to dementia.

BACKGROUND: Based on a longitudinal cohort design, the aim of this study was to investigate whether individual-based 18F fluorodeoxyglucose positron emission tomography (18F-FDG-PET) regional signals can predict dementia conversion in patients with mild cognitive impairment (MCI). METHODS: We included 44 MCI converters (MCI-C), 38 non-converters (MCI-NC), 42 patients with Alzheimer's disease with dementia, and 40 cognitively normal controls. Data from annual cognitive measurements, 3D T1 magnetic resonance imaging (MRI) scans, and 18F-FDG-PET scans were used for outcome analysis. An individual-based FDG-PET approach was applied using seven volumes of interest (VOIs), Z transformed using a normal FDG-PET template. Hypometabolism was defined as a Z score < -2 of regional standard uptake value ratio. For the longitudinal cognitive test scores, generalized estimating equations were used. A linear mixed-effects model was used to compare the temporal impact of cortical hypometabolism and cortical thickness degeneration. RESULTS: The clinical follow-up period was 6.6 ± 3.8 years (range 3.1 to 16.0 years). The trend of cognitive decline could differentiate MCI-C from MCI-NC after 3 years of follow-up. In the baseline 18F-FDG-PET scan of the patients with MCI, medial temporal lobe (MTL; 94.7% sensitivity, 80.5% specificity) and posterior cingulate cortex (PCC; 89.5% sensitivity, 73.1% specificity) hypometabolism predicted conversion with high accuracy. 18F-FDG-PET hypometabolism preceded dementia conversion at an interval of 3.70 ± 1.68 years and was earlier than volumetric changes, with the exception of the MTL. CONCLUSIONS: Our finding supports the use of individual-based 18F-FDG-PET analysis to predict MCI conversion to dementia. Reduced FDG-PET metabolism in the MTL and PCC were strongly associated with future cognitive decline in the MCI-C group. Changes in 18F-FDG-PET occurred 1 to 8 years prior to conversion to dementia. Progressive hypometabolism in the PCC, precuneus and lateral temporal lobe, but not MTL, preceded MRI findings at the MCI stage.

Chronic kidney disease duration and suicide risk among maintenance hemodialysis patients in China.

Background: Our aim was to investigate the relationship between chronic kidney disease (CKD) duration and suicide risk among maintenance hemodialysis patients in China. Methods: Patients with end-stage renal disease (ESRD) who received MHD were enrolled. The demographic and disease characteristics of MHD patients were collected using a self-designed basic information questionnaire. The Suicide Risk Assessment Scale was used to assess suicide risk. Results: A total of 543 (40.8%) patients had suicide risk with Nurses' Global Assessment Scale for Suicide Risk scores ranging from 1 to 19 points. After adjusting for age, gender, disease conditions and mental state, the odds ratios of different CKD duration for suicide risk were 1.00, 2.02, 3.03 and 2.71, respectively (P for trend <.001). There were significant interactions between CKD duration and ESRD duration in relation to suicide risk (P for interaction <.001). There were also interactions between CKD duration and hemodialysis treatment duration, and suicide risk (P for interaction = .01). Patients with ESRD duration of ≤28 months or hemodialysis treatment duration of ≤24 months had the highest risk of suicide when the duration of CKD was 63-94 months, about 2-10 times higher than the other time groups. Conclusions: We found that CKD duration was associated with an increased risk of suicide in maintenance hemodialysis patients in China, independently of other risk factors. Early ESRD and maintenance hemodialysis were associated with suicide in CKD patients.

Comparative Study of Heavy Metal Blood Serum Level Between Organic and Conventional Farmers in Eastern Taiwan.

Numerous studies have indicated that organic fertilizers (OFer) might contain heavy metals (HMs) that present health risks to organic farmers (OFar). This study compared the concentrations of six HMs (Zn, Ni, Cd, Cu, Pb, Cr) in the blood of two distinct groups of farmers: 30 OFar from a designated organic area in eastern Taiwan, and 74 conventional farmers (CFar) from neighboring non-organic designated regions. The findings revealed that the OFar exhibited higher levels of Zn (1202.70 ± 188.74 µg/L), Cr (0.20 ± 0.09 µg/L), and Ni (2.14 ± 1.48 µg/L) in their blood compared to the CFar (988.40 ± 163.16 µg/L, 0.18 ± 0.15 µg/L, and 0.77 ± 1.23 µg/L), respectively. The disparities in Zn, Cr, and Ni levels were measured at 214.3 µg/L, 0.02 µg/L, and 1.37 µg/L, respectively. Furthermore, among the OFar, those who utilized green manures (GM) displayed significantly elevated blood levels of Zn (1279.93 ± 156.30 µg/L), Cr (0.24 ± 0.11 µg/L), and Ni (1.94 ± 1.38 µg/L) compared to individuals who exclusively employed chemical fertilizers (CFer) (975.42 ± 165.35 µg/L, 0.19 ± 0.16 µg/L, and 0.74 ± 1.20 µg/L), respectively. The differences in Zn, Cr, and Ni levels were measured at 304.51 µg/L, 0.05 µg/L, and 1.20 µg/L, respectively. As a result, OFar should be careful in choosing OFer and avoid those that may have heavy metal contamination.

Diet Pattern Analysis in Alzheimer's Disease Implicates Gender Differences in Folate-B12-Homocysteine Axis on Cognitive Outcomes.

BACKGROUND & AIMS: Low plasma B12 and folate levels or hyperhomocysteinemia are related to cognitive impairment. This study explores the relationships among diet pattern, blood folate-B12-homocysteine levels, and cognition measurement in Alzheimer's disease (AD) while exploring whether a gender effect may exist. METHODS: This cross-sectional study enrolled 592 AD patients (246 males, 346 females) and the demographic data, blood biochemical profiles, Mini-Mental State Examination (MMSE), and a Food Frequency Questionnaire (FFQ) for quantitative assessment of dietary frequency were collected. Structural Equation Modeling (SEM) was employed to explore the associations among dietary patterns, blood profiles, and cognition. A least absolute shrinkage and selection operator regression model, stratified by gender, was constructed to analyze the weighting of possible confounders. RESULTS: Higher MMSE scores were related to higher frequencies of coffee/tea and higher educational levels, body mass index, and younger age. The SEM model revealed a direct influence of dietary frequencies (skimmed milk, thin pork, coffee/tea) and blood profiles (homocysteine, B12, and folate) on cognitive outcomes. At the same time, the influence of dietary pattern on cognition was not mediated by folate-B12-homocysteine levels. In males, a direct influence on the MMSE is attributed to B12, while in females, homocysteine is considered a more critical factor. CONCLUSIONS: Dietary patterns and blood profiles are both associated with cognitive domains in AD, and there are gender differences in the associations of dietary patterns and the levels of B12 and homocysteine. To enhance the quality of dietary care and nutritional status for individuals with dementia, our study results still require future validations with multi-center and longitudinal studies.

Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy?

The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical-radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT.

Comparison of Outpatient Department-Referred and Self-Referred Patients in the Emergency Department.

BACKGROUND: Patients present to emergency departments (EDs) from a variety of backgrounds, which may help inform decision making. OBJECTIVE: This study investigated the clinical characteristics and outcomes of outpatient department (OPD)-referred patients and self-referred patients in the ED. METHODS: We selected nontrauma ED adult patients from a tertiary teaching hospital in Taiwan between August 1, 2020, and October 31, 2020. The acuity levels were determined by dichotomizing the triage classification scores. After propensity score matching, we compared the hospitalization, mortality, and length of ED stay of OPD-referred and self-referred patients. We categorized the patients into "emergency" or "urgent" subgroups according to their triage information and then analyzed the effects of different severity levels. Statistical significance was set at p < 0.05. RESULTS: A total of 564 OPD-referred and 11,959 self-referred patients were included. After propensity score matching, the OPD-referred patients (n = 564), compared with self-referred patients (n = 564), had a higher admission rate (49.8% vs. 28.9%; p < 0.001; odds ratio [OR] 2.44). Among the emergency subgroup patients, there was no significant difference between OPD-referred patients (n = 131) and self-referred patients (n = 138) regarding the admission rate (p = 0.257) or the mortality rate (p = 0.253). Among the urgent subgroup patients, OPD-referred patients (n = 433), compared with self-referred patients (n = 426), had a significantly higher admission rate (46.0% vs. 20.2%; p < 0.001; OR 3.36), but not mortality rate (2.1% vs. 0.5%; p = 0.064). Regarding the length of ED stay, OPD-referred and self-referred patients had a significant difference only in the "urgent and discharged" subgroup (5.8 vs. 2.3 h; p < 0.001). CONCLUSIONS: OPD-referred ED patients might have more severe and complex conditions and need comprehensive care management.

Gentiana macrophylla flavonoids from Tibetan medicine decreases circ_0059665 to alleviate the progression of non-small cell lung cancer under hypoxia.

BACKGROUND: Gentiana macrophylla Pall. is a traditional Tibetan medicinal herb possessing antinociceptive and anti-inflammatory activities. Circular RNAs (circRNAs) have been identified to be involved in the tumorigenesis of non-small cell lung cancer (NSCLC). Here, this study focused on investigating the function and mechanism of Gentiana macrophylla flavonoids (GF) and circ_0059665 in NSCLC progression. METHODS: The contents of mRNA and protein were detected using qRT-PCR and western blotting analysis. Cell proliferative and invasive abilities were evaluated by cell counting kit-8, EdU, colony formation and transwell assays, respectively. M2 macrophage polarization was analyzed by flow cytometry. RESULTS: GF treatment suppressed NSCLC cell proliferation, invasion and M2 macrophage polarization under hypoxic conditions. Circ_0059665 was highly expressed in NSCLC tissues and cells. Its expression was increased under hypoxic conditions but was reduced following GF treatment. Furthermore, circ_0059665 overexpression reversed the anticancer effects of GF on NSCLC cells under hypoxic conditions. Mechanistically, circ_0059665 acted as a sponge for miR-512-5p to regulate NOVA2 expression. Hypoxia decreased miR-512-5p levels, and increased NOVA2 levels in NSCLC cells, while these tendencies were abolished after GF treatment. Circ_0059665 silencing inhibited NSCLC cell proliferation, invasion and M2 macrophage polarization in hypoxic environments, which were counteracted by NOVA2 overexpression. Moreover, NOVA2 upregulation reversed the suppressive effects of GF on NSCLC cells with hypoxia treatment. In addition, GF impeded NSCLC tumor growth in vivo via suppressing circ_0059665. CONCLUSION: GF treatment in hypoxic environments suppressed NSCLC cell proliferation, invasion and M2 macrophage polarization via the circ_0059665/miR-512-5p/NOVA2 axis.

Intravenous immunoglobulin for treatment of hospitalized COVID-19 patients: an evidence mapping and meta-analysis.

BACKGROUND: The clinical efficacy and safety of intravenous immunoglobulin (IVIg) treatment for COVID-19 remain controversial. This study aimed to map the current status and gaps of available evidence, and conduct a meta-analysis to further investigate the benefit of IVIg in COVID-19 patients. METHODS: Electronic databases were searched for systematic reviews/meta-analyses (SR/MAs), primary studies with control groups, reporting on the use of IVIg in patients with COVID-19. A random-effects meta-analysis with subgroup analyses regarding study design and patient disease severity was performed. Our outcomes of interest determined by the evidence mapping, were mortality, length of hospitalization (days), length of intensive care unit (ICU) stay (days), number of patients requiring mechanical ventilation, and adverse events. RESULTS: We included 34 studies (12 SR/MAs, 8 prospective and 14 retrospective studies). A total of 5571 hospitalized patients were involved in 22 primary studies. Random-effects meta-analyses of very low to moderate evidence showed that there was little or no difference between IVIg and standard care or placebo in reducing mortality (relative risk [RR] 0.91; 95% CI 0.78-1.06; risk difference [RD] 3.3% fewer), length of hospital (mean difference [MD] 0.37; 95% CI - 2.56, 3.31) and ICU (MD 0.36; 95% CI - 0.81, 1.53) stays, mechanical ventilation use (RR 0.92; 95% CI 0.68-1.24; RD 2.8% fewer), and adverse events (RR 0.98; 95% CI 0.84-1.14; RD 0.5% fewer) of patients with COVID-19. Sensitivity analysis using a fixed-effects model indicated that IVIg may reduce mortality (RR 0.76; 95% CI 0.60-0.97), and increase length of hospital stay (MD 0.68; 95% CI 0.09-1.28). CONCLUSION: Very low to moderate certainty of evidence indicated IVIg may not improve the clinical outcomes of hospitalized patients with COVID-19. Given the discrepancy between the random- and fixed-effects model results, further large-scale and well-designed RCTs are warranted.

Salidroside suppresses the multiple oncogenic activates and immune escape of lung adenocarcinoma through the circ_0009624-mediated PD-L1 pathway.

BACKGROUND: Lung adenocarcinoma (LUAD) is a fatal malignancy all over the world. Salidroside (SAL) is an active component extracted from Rhodiola rosea that has been reported to exert antitumor activity against several human cancers, containing lung adenocarcinoma (LUAD). The purpose of this study was to explore the effect and underlying mechanism of SAL in LUAD. METHODS: Cell viability, proliferation, migration, and invasion were measured using cell counting kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), and transwell assays. Effects of LUAD cells on the cytotoxicity, percentage, and death of CD8+ cells were detected using lactate dehydrogenase (LDH) and flow cytometry assays. Programmed cell death ligand 1 (PD-L1) protein level was examined using western blot. Circ_0009624, enolase 1 (ENO1), and PD-L1 levels were determined using real-time quantitative polymerase chain reaction (RT-qPCR). The biological role of SAL on LUAD tumor growth was assessed using the xenograft tumor model in vivo. RESULTS: SAL restrained LUAD cell proliferation, migration, invasion, and immune escape in vitro via modulating PD-L1. Circ_0009624 expression was increased in LUAD. Applying SAL repressed circ_0009624 and PD-L1 expression in LUAD cells. SAL treatment hindered suppressed various oncogenic activates and immune escape of LUAD cells by regulating the circ_0009624/PD-L1 pathway. SAL blocked LUAD xenograft growth in vivo. CONCLUSION: Applying SAL might constrain malignant phenotypes and immune escape of LUAD cells partially through the circ_0009624-mediated PD-L1 pathway, providing a novel insight for LUAD treatment.

Corrigendum: Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.

[This corrects the article DOI: 10.3389/fonc.2023.1105100.].

Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.

Purpose: To compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs). Methods: A retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models. Result: In the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275). Conclusion: Our study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model.

Untargeted metabolomics analysis assisted by signal selection for comprehensively identifying metabolites of new psychoactive substances: 4-MeO-α-PVP as an example.

New psychoactive substances (NPS) have been rapidly emerged as legal alternatives to controlled drugs, which raised severe public health issue. The detection and monitoring of its intake by complete metabolic profiling is an urgent and vital task. Untargeted metabolomics approach has been applied for several NPS metabolites studies. Although the number of such works is relatively limited but with a rapidly growing need. The present study aimed to propose a procedure that includes liquid chromatography high-resolution mass spectrometry (LC-HRMS) analysis and a signal selection software, MetaboFinder, programed as a web tool. The comprehensive metabolites profile of one kind of NPS, 4-methoxy-α-pyrrolidinovalerophenone (4-MeO-α-PVP), was studied by using this workflow. In this study, two different concentrations of 4-MeO-α-PVP along with as blank sample were incubated with human liver S9 fraction for the conversion to their metabolites and followed by LC-MS analysis. After retention time alignment and feature identification, 4640 features were obtained and submitted to statistical analysis for signal selection by using MetaboFinder. A total of 50 features were considered as 4-MeO-α-PVP metabolite candidates showing significant changes (p < 0.00001 and fold change >2) between the two investigated groups. Targeted LC-MS/MS analysis was conducted focusing on these significantly expressed features. Assisted by chemical formula determination according to high mass accuracy and in silico MS2 fragmentation prediction, 19 chemical structure identifications were achieved. Among which, 8 metabolites have been reported derived from 4-MeO-α-PVP in a previous literature while 11 novel 4-MeO-α-PVP metabolites were identified by using our strategy. Further in vivo animal experiment confirmed that 18 compounds were 4-MeO-α-PVP metabolites, which demonstrated the feasibility of our strategy for screening the 4-MeO-α-PVP metabolites. We anticipate that this procedure may support and facilitate traditional metabolism studies and potentially being applied for routine NPS metabolites screening.

Gray matter reserve determines glymphatic system function in young-onset Alzheimer's disease: Evidenced by DTI-ALPS and compared with age-matched controls.

BACKGROUND: The diffusion tensor imaging analysis along the perivascular space (ALPS)-index can be used to model the glymphatic system in vivo. AIM: This study explores putative mechanisms between prediction of ALPS-index and cognitive outcomes in young-onset Alzheimer's disease (YOAD) and age-matched controls (CTLs) and analyzes whether the link was mediated by the integrity of ALPS-index-anchored cerebral gray matter (GM). METHODS: We enrolled 130 patients with YOAD and 137 CTLs. All participants underwent three-dimensional T1 -weighted MRI, diffusion tensor imaging and cognitive tests. We constructed GM regions correlated with the ALPS-index in the YOAD and CTL groups. For the GM regions significantly correlated with the ALPS-index and cognitive measures, we extracted a 4-mm radius sphere. In the YOAD and CTL groups, we used mediator analysis to explore the ALPS-index as predictor, GM partitions as mediators, and significant cognitive test scores as outcomes. RESULTS: Patient group had significantly lower ALPS-index. The ALPS-index was associated with GM volume in the cerebellar gray, dorsolateral prefrontal, thalamus, superior frontal, amygdala and hippocampus, and these coherent regions coincided with those showing GM atrophy in the YOAD group. Mediation analysis of the YOAD group suggested that the relationships between the ALPS-index and cognitive performance were fully mediated by the integrity of ALPS-index coherent GM areas. DISCUSSION: Reserved GM mediates the link between the glymphatic system and cognition. Our findings suggest that GM integrity rather than the glymphatic system could serve as a direct cognitive test scores predictor in patients with YOAD.

Sexuality and Quality of Life in Eastern Taiwan People With Schizophrenia.

OBJECTIVE: Patients with schizophrenia are living at the border of society and their sexuality is often neglected. The aim of the study is to explore the association among The Brief Psychiatric Rating Scale (BPRS), quality of life (QoL), Taiwanese Depression Questionnaire, and Sexual Desire Inventory in people with schizophrenia (PwS). METHODS: This study used a cross-sectional design with 277 psychiatric inpatients. A descriptive analysis, difference analysis, and logistic regression model were presented to identify relevant variables that may affect the probability of good QoL. RESULTS: The study showed that male PwS had higher scores of standard deviation (SD) than females in PwS. The study also showed that smoking, early illness onset age, and shorter illness duration demonstrated a significantly higher SD. The logistic regression analysis showed that BPRS, depression, and SD significantly affected the probability of QoL. By structural equation model, SD would be positively correlated with mental status and SD would indirectly influence QoL. CONCLUSION: Our results showed psychological and sociological factors interactions may contribute to the QoL and SD for PwS. This study also demonstrated a close relationship between SD, depression, and BPRS. These factors may predict the probability of good life quality for the PwS.

High-precision early warning system for rice cadmium accumulation risk assessment.

Rapid global industrialization has resulted in widespread cadmium contamination in agricultural soils and products. A considerable proportion of rice consumers are exposed to Cd levels above the provisional safe intake limit, raising widespread environmental concerns on risk management. Therefore, a generalized approach is urgently needed to enable correct evaluation and early warning of cadmium contaminants in rice products. Combining big data and computer science together, this study developed a system named "SMART Cd Early Warning", which integrated 4 modules including genotype-to-phenotype (G2P) modelling, high-throughput sequencing, G2P prediction and rice Cd contamination risk assessment, for rice cadmium accumulation early warning. This system can rapidly assess the risk of rice cadmium accumulation by genotyping leaves at seeding stage. The parameters including statistical methods, population size, training population-testing population ratio, SNP density were assessed to ensure G2P model exhibited superior performance in terms of prediction precision (up to 0.76 ± 0.003) and computing efficiency (within 2 h). In field trials of cadmium-contaminated farmlands in Wenling and Fuyang city, Zhejiang Province, "SMART Cd Early Warning" exhibited superior capability for identification risk rice varieties, suggesting a potential of "SMART Cd Early-Warning system" in OsGCd risk assessment and early warning in the age of smart.

Cognitive Decline Related to Diet Pattern and Nutritional Adequacy in Alzheimer's Disease Using Surface-Based Morphometry.

Dietary pattern (DP) results in nutrition adequacy and may influence cognitive decline and cortical atrophy in Alzheimer's disease (AD). The study explored DP in 248 patients with AD. Two neurobehavioral assessments (intervals 13.4 months) and two cortical thickness measurements derived from magnetic resonance images (intervals 26.5 months) were collected as outcome measures. Reduced rank regression was used to assess the groups of DPs and a linear mixed-effect model to explore the cortical neurodegenerative patterns. At screening, underweight body mass index (BMI) was related to significant higher lipid profile, impaired cognitive function, smaller cortical thickness, lower protein DP factor loading scores and the non-spouse caregiver status. Higher mini-mental state examination (MMSE) scores were related to the DP of coffee/tea, compared to the lipid/sugar or protein DP group. The underweighted-BMI group had faster cortical thickness atrophy in the pregenual and lateral temporal cortex, while the correlations between cortical thickness degeneration and high HbA1C or low B12 and folate levels were localized in the medial and lateral prefrontal cortex. The predictive model suggested that factors related to MMSE score were related to the caregiver status. In conclusion, normal or overweight BMI, coffee/tea DP group and living with a spouse were considered as protective factors for better cognitive outcomes in patients with AD. The influence of glucose, B12 and folate on the cortical degeneration was spatially distinct from the pattern of AD degeneration.

Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study.

INTRODUCTION: Bacteremia is a common but life-threatening infectious disease. However, a well-defined rule to assess patient risk of bacteremia and the urgency of blood culture is lacking. The aim of this study is to establish a predictive model for bacteremia in septic patients using available big data in the emergency department (ED) through logistic regression and other machine learning (ML) methods. MATERIAL AND METHODS: We conducted a retrospective cohort study at the ED of National Cheng Kung University Hospital in Taiwan from January 2015 to December 2019. ED adults (≥18 years old) with systemic inflammatory response syndrome and receiving blood cultures during the ED stay were included. Models I and II were established based on logistic regression, both of which were derived from support vector machine (SVM) and random forest (RF). Net reclassification index was used to determine which model was superior. RESULTS: During the study period, 437,969 patients visited the study ED, and 40,395 patients were enrolled. Patients diagnosed with bacteremia accounted for 7.7% of the cohort. The area under the receiver operating curve (AUROC) in models I and II was 0.729 (95% CI, 0.718-0.740) and 0.731 (95% CI, 0.721-0.742), with Akaike information criterion (AIC) of 16,840 and 16,803, respectively. The performance of model II was superior to that of model I. The AUROC values of models III and IV in the validation dataset were 0.730 (95% CI, 0.713-0.747) and 0.705 (0.688-0.722), respectively. There is no statistical evidence to support that the performance of the model created with logistic regression is superior to those created by SVM and RF. DISCUSSION: The advantage of the SVM or RF model is that the prediction model is more elastic and not limited to a linear relationship. The advantage of the LR model is that it is easy to explain the influence of the independent variable on the response variable. These models could help medical staff identify high-risk patients and prevent unnecessary antibiotic use. The performance of SVM and RF was not inferior to that of logistic regression. CONCLUSIONS: We established models that provide discrimination in predicting bacteremia among patients with sepsis. The reported results could inspire researchers to adopt ML in their development of prediction algorithms.

Body Mass Index and the Risk of Atrial Fibrillation: A Mendelian Randomization Study.

Although observational studies have shown positive associations between body mass index (BMI) and the risk of atrial fibrillation (AF), the causal relationship is still uncertain owing to the susceptibility to confounding and reverse causation. This study aimed to examine the potential causality of BMI on AF by conducting a two-sample Mendelian randomization (TSMR) study. METHODS: The independent genetic variants associated with BMI (n = 303) at the genome-wide significant level were derived as instrumental variables (IV) from the Genetic Investigation of Anthropometric Traits (GIANT) consortium consisting of 681,275 individuals of European ancestry. We then derived the outcome data from a GWAS meta-analysis comprised of 60,620 cases and 970,216 controls of European ancestry. The TSMR analyses were performed in five methods, namely inverse variance weighted (IVW) method, MR-Egger regression, the weighted median estimator (WME), the generalized summary data-based Mendelian randomization (GSMR), and the robust adjusted profile score (RAPS), to investigate whether BMI was causally associated with the risk of AF. RESULTS: We found a genetically determined 1-standard deviation (SD) increment of BMI causally increased a 42.5% risk of AF (OR = 1.425; 95% CI, 1.346 to 1.509) based on the IVW method, which was consistent with the results of MR-Egger regression, WME, GSMR, as well as RAPS. The Mendelian randomization assumptions did not seem to be violated. CONCLUSION: This study provides evidence that higher BMI causally increased the risk of AF, suggesting control of BMI and obesity for prevention of AF.

Atrial fibrillation burden and the risk of stroke: A systematic review and dose-response meta-analysis.

BACKGROUND: The increased stroke risk associated with atrial fibrillation (AF) burden exceeding 5 min is a matter of debate. In addition, the potential linear or nonlinear relationship between AF burden and stroke risk has been largely unexplored. AIM: To determine the association between AF burden > 5 min and the increased risk of stroke and explore the potential dose-response relationship between these two factors. METHODS: Sixteen studies from six databases with 53141 subjects (mean age 65 years) were included. Fifteen studies were observational studies, and one was a randomized controlled trial study. The potential nonlinear dose-response association was characterized using a restricted cubic splines regression model. AF burden for each 1 h and 2 h was associated with an increased risk of stroke. Trial sequential analysis with a random-effect model was used to evaluate the robustness of the evidence from the included 16 studies. RESULTS: AF burden > 5 min was associated with an increased risk of clinical AF [adjusted risk ratio (RR) = 4.18, 95% confidence interval (CI): 2.26-7.74]. However, no association was found with an increased risk of all-cause mortality (adjusted RR = 1.55, 95%CI: 0.87-2.75). Patients with AF burden > 5 min had an increased risk of stroke (adjusted RR = 2.49, 95%CI: 1.79-3.47). Moreover, a dose-response analysis showed that the increased stroke risk was paralleled by an increase in AF burden at a rate of 2.0% per hour (P nonlinear = 0.656, RR = 1.02, 95%CI: 1.01-1.03). Trial sequential analysis provided robust evidence of the association between AF burden > 5 min and an increased risk of stroke. CONCLUSION: AF burden was a significant risk factor for clinical AF and future stroke. A significant linear association was documented between increased AF burden and risk of future stroke.

Characterization of a novel arsenite long-distance transporter from arsenic hyperaccumulator fern Pteris vittata.

Pteris vittata is an arsenic (As) hyperaccumulator that can accumulate several thousand mg As kg-1 DW in aboveground biomass. A key factor for its hyperaccumulation ability is its highly efficient As long-distance translocation system. However, the underlying molecular mechanisms remain unknown. We isolated PvAsE1 through the full-length cDNA over-expression library of P. vittata and characterized it through a yeast system, RNAi gametophytes and sporophytes, subcellular-location and in situ hybridization. Phylogenomic analysis was conducted to estimate the appearance time of PvAsE1. PvAsE1 was a plasma membrane-oriented arsenite (AsIII) effluxer. The silencing of PvAsE1 reduced AsIII long-distance translocation in P. vittata sporophytes. PvAsE1 was structurally similar to solute carrier (SLC)13 proteins. Its transcripts could be observed in parenchyma cells surrounding the xylem of roots. The appearance time was estimated at c. 52.7 Ma. PvAsE1 was a previously uncharacterized SLC13-like AsIII effluxer, which may contribute to AsIII long-distance translocation via xylem loading. PvAsE1 appeared late in fern evolution and might be an adaptive subject to the selection pressure at the Cretaceaou-Paleogene boundary. The identification of PvAsE1 provides clues for revealing the special As hyperaccumulation characteristics of P. vittata.