Prefrontal intra-individual ERP variability and its asymmetry: exploring its biomarker potential in mild cognitive impairment

BACKGROUND

The worldwide trend of demographic aging highlights the progress made in healthcare, albeit with health challenges like Alzheimer's Disease (AD), prevalent in individuals aged 65 and above. Its early detection at the mild cognitive impairment (MCI) stage is crucial. Event-related potentials (ERPs) obtained by averaging EEG segments responded to repeated events are vital for cognitive impairment research. Consequently, examining intra-trial ERP variability is vital for comprehending fluctuations within psychophysiological processes of interest. This study aimed to investigate cognitive deficiencies and instability in MCI using ERP variability and its asymmetry from a prefrontal two-channel EEG device.

METHODS

In this study, ERP variability for both target and non-target responses was examined using the response variance curve (RVC) in a sample comprising 481 participants with MCI and 1,043 age-matched healthy individuals. The participants engaged in auditory selective attention tasks. Cognitive decline was assessed using the Seoul Neuropsychological Screening Battery (SNSB) and the Mini-Mental State Examination (MMSE). The research employed various statistical methods, including independent t-tests, and univariate and multiple logistic regression analyses. These analyses were conducted to investigate group differences and explore the relationships between neuropsychological test results, ERP variability and its asymmetry measures, and the prevalence of MCI.

RESULTS

Our results showed that patients with MCI exhibited unstable cognitive processing, characterized by increased ERP variability compared to cognitively normal (CN) adults. Multiple logistic regression analyses confirmed the association between ERP variability in the target and non-target responses with MCI prevalence, independent of demographic and neuropsychological factors.

DISCUSSION

The unstable cognitive processing in the MCI group compared to the CN individuals implies abnormal neurological changes and reduced and (or) unstable attentional maintenance during cognitive processing. Consequently, utilizing ERP variability measures from a portable EEG device could serve as a valuable addition to the conventional ERP measures of latency and amplitude. This approach holds significant promise for identifying mild cognitive deficits and neural alterations in individuals with MCI.

Executive Summary of 2023 International Conference of the Korean Dementia Association (IC-KDA 2023): A Report From the Academic Committee of the Korean Dementia Association

The Korean Dementia Association (KDA) has been organizing biennial international academic conferences since 2019, with the International Conference of the KDA (IC-KDA) 2023 held in Busan under the theme 'Beyond Boundaries: Advancing Global Dementia Solutions.' The conference comprised 6 scientific sessions, 3 plenary lectures, and 4 luncheon symposiums, drawing 804 participants from 35 countries. Notably, a Korea-Taiwan Joint Symposium addressed insights into Alzheimer's disease (AD). Plenary lectures by renowned scholars explored topics such as microbiome-related AD pathogenesis, social cognition in neurodegenerative diseases, and genetic frontotemporal dementia (FTD). On the first day, specific presentations covered subjects like the gut-brain axis and neuroinflammation in dementia, blood-based biomarkers in AD, and updates in AD therapeutics. The second day's presentations addressed recent issues in clinical neuropsychology, FTD cohort studies, and the pathogenesis of non-AD dementia. The Academic Committee of the KDA compiles lecture summaries to provide comprehensive understanding of the advanced dementia knowledge presented at IC-KDA 2023.

Implementation of an ultra-sensitive microwell-based electrochemical sensor for the detection of Alzheimer's disease

Alzheimer's Disease (AD) is one of the most common neurodegenerative disorders in elderly people. It is diagnosed by detecting amyloid beta (Aβ) protein in cerebrospinal fluid (CSF) obtained by lumbar puncture or through expensive positron emission tomography (PET) imaging. Although blood-based diagnosis of AD offers a less invasive and cost-effective alternative, the quantification of Aβ is technically challenging due to its low abundance in peripheral blood. To address this, we developed a compact yet highly sensitive microwell-based electrochemical sensor with a densely packed microelectrode array (20 by 20) for enhancing sensitivity. Employing microwells on the working and counter electrodes minimized the leakage current from the metallic conductors into the assay medium, refining the signal fidelity. We achieved a detection limit <10 fg/mL for Aβ by elevating the signal-to-noise ratio, thus capable of AD biomarker quantification. Moreover, the microwell structure maintained the performance irrespective of variations in bead number, indicative of the sensor's robustness. The sensor's efficacy was validated through the analysis of Aβ concentrations in plasma samples from 96 subjects, revealing a significant distinction between AD patients and healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.85. Consequently, our novel microwell-based electrochemical biosensor represents a highly sensitive platform for detecting scant blood-based biomarkers, including Aβ, offering substantial potential for advancing AD diagnostics.

Early onset diagnosis in Alzheimer's disease patients via amyloid-β oligomers-sensing probe in cerebrospinal fluid

Amyloid-β (Aβ) oligomers are implicated in the onset of Alzheimer's disease (AD). Herein, quinoline-derived half-curcumin-dioxaborine (Q-OB) fluorescent probe was designed for detecting Aβ oligomers by finely tailoring the hydrophobicity of the biannulate donor motifs in donor-π-acceptor structure. Q-OB shows a great sensing potency in dynamically monitoring oligomerization of Aβ during amyloid fibrillogenesis in vitro. In addition, we applied this strategy to fluorometrically analyze Aβ self-assembly kinetics in the cerebrospinal fluids (CSF) of AD patients. The fluorescence intensity of Q-OB in AD patients' CSF revealed a marked change of log (I/I0) value of 0.34 ± 0.13 (cognitive normal), 0.15 ± 0.12 (mild cognitive impairment), and 0.14 ± 0.10 (AD dementia), guiding to distinguish a state of AD continuum for early diagnosis of AD. These studies demonstrate the potential of our approach can expand the currently available preclinical diagnostic platform for the early stages of AD, aiding in the disruption of pathological progression and the development of appropriate treatment strategies.

A screening method for mild cognitive impairment in elderly individuals combining bioimpedance and MMSE

We investigated a screening method for mild cognitive impairment (MCI) that combined bioimpedance features and the Korean Mini-Mental State Examination (K-MMSE) score. Data were collected from 539 subjects aged 60 years or older at the Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center, A total of 470 participants were used for the analysis, including 318 normal controls and 152 MCI participants. We measured bioimpedance, K-MMSE, and the Seoul Neuropsychological Screening Battery (SNSB-II). We developed a multiple linear regression model to predict MCI by combining bioimpedance variables and K-MMSE total score and compared the model's accuracy with SNSB-II domain scores by the area under the receiver operating characteristic curve (AUROC). We additionally compared the model performance with several machine learning models such as extreme gradient boosting, random forest, support vector machine, and elastic net. To test the model performances, the dataset was divided into a training set (70%) and a test set (30%). The AUROC values of SNSB-II scores were 0.803 in both sexes, 0.840 for males, and 0.770 for females. In the combined model, the AUROC values were 0.790 (0.773) for males (and females), which were significantly higher than those from the model including MMSE scores alone (0.723 for males and 0.622 for females) or bioimpedance variables alone (0.640 for males and 0.615 for females). Furthermore, the accuracies of the combined model were comparable to those of machine learning models. The bioimpedance-MMSE combined model effectively distinguished the MCI participants and suggests a technique for rapid and improved screening of the elderly population at risk of cognitive impairment.

Intrahospital transmission and infection control of Candida auris originating from a severely infected COVID-19 patient transferred abroad

BACKGROUND

Intrahospital spread of Candida auris, which survives tenaciously in many environments, can cause sustained colonization and infection. A large outbreak of C. auris was experienced in the intensive care units (ICUs) at the study hospital during the coronavirus disease 2019 (COVID-19) pandemic.

METHODS

The index patient with severe COVID-19, who was transferred from Vietnam in January 2022, developed C. auris candidaemia 10 days after hospitalization. From mid-June 2022 to January 2023, strengthened infection prevention and control (IPC) measures were implemented in three ICUs: (1) contact precautions and isolation (CPI) for C. auris-positive cases; (2) surveillance cultures including point-prevalence (N=718) for patients or close contacts or ICU-resident healthcare workers (HCWs); (3) intensive environmental disinfection with 10-fold diluted bleach; and (4) 2% chlorhexidine bathing for all ICU patients. Environmental cultures (ECx) on surfaces and shared objects (N=276) were conducted until early September 2022, when all ECx were negative.

RESULTS

Among 53 C. auris-positive patients between February 2022 and January 2023, invasive infections resulted in seven cases of candidaemia and one case of pneumonia. C. auris was isolated from reusable tympanic thermometers (TTMs) contaminated with earwax. The isolation rate of C. auris in ECx decreased from 6.8% in June 2022 to 2.0% in August 2022, and was no longer detected in TTMs. Colonization in HCWs was remarkably rare (0.5%). The number of C. auris-positive patients peaked in July (N=10) then decreased gradually. By January 2023, no C. auris were isolated in the ICU.

CONCLUSION

Aggressive IPC measures with CPI, ECx and surveillance, decontamination of TTMs, and bathing were effective in successfully controlling this C. auris outbreak.

Multi-Ethnic Norms for Volumes of Subcortical and Lobar Brain Structures Measured by Neuro I: Ethnicity May Improve the Diagnosis of Alzheimer's Disease1

Background

We previously demonstrated the validity of a regression model that included ethnicity as a novel predictor for predicting normative brain volumes in old age. The model was optimized using brain volumes measured with a standard tool FreeSurfer.

Objective

Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations.

Methods

Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59-89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers.

Results

In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001).

Conclusions

Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases.

An extremely energetic cosmic ray observed by a surface detector array

Cosmic rays are energetic charged particles from extraterrestrial sources, with the highest-energy events thought to come from extragalactic sources. Their arrival is infrequent, so detection requires instruments with large collecting areas. In this work, we report the detection of an extremely energetic particle recorded by the surface detector array of the Telescope Array experiment. We calculate the particle's energy as [Formula: see text] (~40 joules). Its arrival direction points back to a void in the large-scale structure of the Universe. Possible explanations include a large deflection by the foreground magnetic field, an unidentified source in the local extragalactic neighborhood, or an incomplete knowledge of particle physics.

Prefrontal event-related potential markers in association with mild cognitive impairment

Background

Alzheimer's disease (AD) is among the leading contributors of dementia globally with approximately 60-70% of its cases. Current research is focused on the mild cognitive impairment (MCI), which is associated with cognitive decline but does not disrupt routine activities. Event-related potential (ERP) research is essential in screening patients with MCI. Low-density channel electroencephalography (EEG) is frequently used due to its convenience, portability, and affordability, making it suitable for resource-constrained environments. Despite extensive research on neural biomarkers for cognitive impairment, there is a considerable gap in understanding the effects on early stages of cognitive processes, particularly when combining physiological and cognitive markers using portable devices. The present study aimed to examine cognitive shortfalls and behavioral changes in patients with MCI using prefrontal selective attention ERP recorded from a prefrontal two-channel EEG device.

Methods

We assessed cognitive decline using the Mini-Mental State Examination (MMSE) and the Seoul Neuropsychological Screening Battery (SNSB). We administered auditory selective attention tasks to 598 elderly participants, including those with MCI (160) and cognitively normal (CN) individuals (407). We conducted statistical analyses such as independent t-tests, Pearson's correlations, and univariate and multiple logistic regression analyses to assess group differences and associations between neuropsychological tests, ERP measures, behavioral measures, and MCI prevalence.

Results

Our findings revealed that patients with MCI demonstrated slower information-processing abilities, and exhibited poorer task execution, characterized by reduced accuracy, increased errors, and higher variability in response time, compared to CN adults. Multiple logistic regression analyses confirmed the association between some ERP and behavioral measures with MCI prevalence, independent of demographic and neuropsychological factors. A relationship was observed between neuropsychological scores, ERP, and behavioral measures.

Discussion

The slower information processing abilities, and poor task execution in the MCI group compared to the CN individuals suggests flawed neurological changes and reduced attentional maintenance during cognitive processing, respectively. Hence, the utilization of portable EEG devices to capture prefrontal selective attention ERPs, in combination with behavioral assessments, holds promise for the identification of mild cognitive deficits and neural alterations in individuals with MCI. This approach could potentially augment the traditional neuropsychological tests during clinical screening for MCI.

Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging

Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer's disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.

Multiancestry analysis of the HLA locus in Alzheimer's and Parkinson's diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson's disease (PD) and Alzheimer's disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues.

Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies

MOTIVATION

Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model.

RESULTS

Simulation analyses showed that weighting imputed phenotypes method increased the statistical power compared to ordinary logistic regression using only AD cases and controls. Applied to real-world data, the penalized logistic method had the highest AUC (0.96) for AD prediction and weighting imputed phenotypes method performed well in terms of power. We identified an association (P<5.0×10-8) of AD with several variants in the APOE region and rs143625563 in LMX1A. Our method, which allows the inclusion of individuals with mild cognitive impairment, improves the statistical power of GWAS for AD. We discovered a novel association with LMX1A.

AVAILABILITY AND IMPLEMENTATION

Simulation codes can be accessed at https://github.com/Junkkkk/wGEE_GWAS.

Automating Rey Complex Figure Test scoring using a deep learning-based approach: a potential large-scale screening tool for cognitive decline

BACKGROUND

The Rey Complex Figure Test (RCFT) has been widely used to evaluate the neurocognitive functions in various clinical groups with a broad range of ages. However, despite its usefulness, the scoring method is as complex as the figure. Such a complicated scoring system can lead to the risk of reducing the extent of agreement among raters. Although several attempts have been made to use RCFT in clinical settings in a digitalized format, little attention has been given to develop direct automatic scoring that is comparable to experienced psychologists. Therefore, we aimed to develop an artificial intelligence (AI) scoring system for RCFT using a deep learning (DL) algorithm and confirmed its validity.

METHODS

A total of 6680 subjects were enrolled in the Gwangju Alzheimer's and Related Dementia cohort registry, Korea, from January 2015 to June 2021. We obtained 20,040 scanned images using three images per subject (copy, immediate recall, and delayed recall) and scores rated by 32 experienced psychologists. We trained the automated scoring system using the DenseNet architecture. To increase the model performance, we improved the quality of training data by re-examining some images with poor results (mean absolute error (MAE) [Formula: see text] 5 [points]) and re-trained our model. Finally, we conducted an external validation with 150 images scored by five experienced psychologists.

RESULTS

For fivefold cross-validation, our first model obtained MAE = 1.24 [points] and R-squared ([Formula: see text]) = 0.977. However, after evaluating and updating the model, the performance of the final model was improved (MAE = 0.95 [points], [Formula: see text] = 0.986). Predicted scores among cognitively normal, mild cognitive impairment, and dementia were significantly different. For the 150 independent test sets, the MAE and [Formula: see text] between AI and average scores by five human experts were 0.64 [points] and 0.994, respectively.

CONCLUSION

We concluded that there was no fundamental difference between the rating scores of experienced psychologists and those of our AI scoring system. We expect that our AI psychologist will be able to contribute to screen the early stages of Alzheimer's disease pathology in medical checkup centers or large-scale community-based research institutes in a faster and cost-effective way.

Reduced body cell mass and functions in lower extremities are associated with mild cognitive impairment and Alzheimer's dementia

This study examined the alterations of segmental body composition in individuals with Alzheimer's pathology (AD), including mild cognitive impairment (MCI) and dementia. A multifrequency bioimpedance analysis (BIA) was used to provide segmental water and impedance variables from 365 cognitively normal (CN), 123 MCI due to AD, and 30 AD dementia participants. We compared the BIA variables between the three groups, examined their correlations with neuropsychological screening test scores, and illustrate their 95% confidence RXc graphs. AD dementia participants were older, more depressive, and had worse cognitive abilities than MCI due to AD and CN participants. Although the BIA variables showed weak partial correlations with the cognitive test scores, we found patterns of an increasing water content in lean mass, increasing extra to intracellular water ratio, and decreasing reactance and phase angle in the lower extremities with effect sizes ranging from 0.26 to 0.51 in the groups of MCI and dementia due to AD compared with CN individuals. The RXc graphs upheld the findings with a significant displacement downward and toward the right, dominantly in the lower extremities. Individuals with AD pathology exhibit a reduced body cell mass or cell strength, an abnormal cellular water distribution, and an overhydration status in lean mass, especially in the lower extremities.

Multi-ancestry meta-analysis and fine-mapping in Alzheimer's disease

Genome-wide association studies (GWAS) of Alzheimer's disease are predominantly carried out in European ancestry individuals despite the known variation in genetic architecture and disease prevalence across global populations. We leveraged published GWAS summary statistics from European, East Asian, and African American populations, and an additional GWAS from a Caribbean Hispanic population using previously reported genotype data to perform the largest multi-ancestry GWAS meta-analysis of Alzheimer's disease and related dementias to date. This method allowed us to identify two independent novel disease-associated loci on chromosome 3. We also leveraged diverse haplotype structures to fine-map nine loci with a posterior probability >0.8 and globally assessed the heterogeneity of known risk factors across populations. Additionally, we compared the generalizability of multi-ancestry- and single-ancestry-derived polygenic risk scores in a three-way admixed Colombian population. Our findings highlight the importance of multi-ancestry representation in uncovering and understanding putative factors that contribute to risk of Alzheimer's disease and related dementias.

Probiotics that Ameliorate Cognitive Impairment through Anti-Inflammation and Anti-Oxidation in Mice

The gut-brain axis encompasses a bidirectional communication pathway between the gastrointestinal microbiota and the central nervous system. There is some evidence to suggest that probiotics may have a positive effect on cognitive function, but more research is needed before any definitive conclusions can be drawn. Inflammation-induced by lipopolysaccharide (LPS) may affect cognitive function. To confirm the effect of probiotics on oxidative stress induced by LPS, the relative expression of antioxidant factors was confirmed, and it was revealed that the administration of probiotics had a positive effect on the expression of antioxidant-related factors. After oral administration of probiotics to mice, an intentional inflammatory response was induced through LPS i.p., and the effect on cognition was confirmed by the Morris water maze test, nitric oxide (NO) assay, and interleukin (IL)-1β enzyme-linked immunosorbent assay performed. Experimental results, levels of NO and IL-1 β in the blood of LPS i.p. mice were significantly decreased, and cognitive evaluation using the Morris water maze test showed significant values in the latency and target quadrant percentages in the group that received probiotics. This proves that intake of these probiotics improves cognitive impairment and memory loss through anti-inflammatory and antioxidant mechanisms.

Saccadic eye movement variables as biomarkers for cognitive decline in elderly individuals

Alzheimer's disease (AD) is the leading cause of Dementia, and mild cognitive impairment (MCI) is often considered a precursor to the development of AD dementia and other types of Dementia. Biomarkers such as amyloid beta are specific and sensitive in identifying AD and can identify individuals who have biological evidence of the disease but have no symptoms, but clinicians and researchers may not easily use them on a large scale. Ocular biomarkers, such as those obtained through eye tracking (ET) technology, have the potential as a diagnostic tool due to their accuracy, affordability, and ease of use. In this study, we show that eye movement (EM) metrics from an interleaved Pro/Anti-saccade (PS/AS) ET task can differentiate between cognitively normal (CN) and MCI subjects and that the presence of Aβ brain deposits, a biomarker of AD, significantly affects performance on these tasks. Individuals with Aβ deposits (Aβ+) performed worse than those without (Aβ-). Our findings suggest that eye-tracking measurements may be a valuable tool for detecting amyloid brain pathology and monitoring changes in cognitive function in CN and MCI individuals over time.Clinical Relevance- The PS/AS paradigm, which measures saccadic eye movements, can accurately detect subtle cognitive impairments and changes in the brain associated with Alzheimer's disease in CN and MCI individuals. This makes it a valuable tool for identifying individuals at risk for cognitive decline and tracking changes in cognitive function over time.

Eye movement changes as an indicator of mild cognitive impairment

Background

Early identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data.

Methods

We collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics' odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC).

Results

LR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840.

Conclusion

Changes in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.

Heritability of cognitive abilities and regional brain structures in middle-aged to elderly East Asians

This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer's and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.

Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning

Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.

Gender differences in osteoporotic hip fractures in Sarawak General Hospital

INTRODUCTION

Osteoporosis and osteoporotic fracture pose a major public health problem in our ageing population, and particularly concerning is the increased morbidity and mortality associated with osteoporotic hip fractures. While overall diagnosis and treatment for osteoporosis have improved, osteoporosis in men remains underdiagnosed and undertreated. We aim to describe the difference in clinical characteristics between elderly men and women with osteoporotic hip fractures in Sarawak General Hospital.

MATERIALS AND METHODS

All patients diagnosed with osteoporotic hip fracture admitted to Sarawak General Hospital from June 2019 to March 2021 were recruited, and demographic data and clinical features were obtained.

RESULTS

There were 140 patients with osteoporotic hip fracture, and 40 were men (28.6%). The mean age for males was 74.1 ± 9.5 years, while the mean age for females was 77.4 ± 9.1 years (p=0.06). The types of fracture consisted of neck of femur=78, intertrochanteric=61 and subtrochanteric=1. More men were active smokers (15% vs 1%, p<0.001). There were 20 men with secondary osteoporosis (50%), while 13 women (13%) had secondary osteoporosis (p<0.001). The causes of secondary osteoporosis among the men were hypogonadism, COPD, glucocorticoid-induced osteoporosis, renal disease, androgen deprivation therapy, thyroid disorder, prostate cancer and previous gastrectomy. There were two deaths among the men and four deaths among the women during the inpatient and 3 months follow-up period. There was no statistical significance between the mortality rates between male patients (5%) and female patients (4%) (p=0.55).

CONCLUSION

There were more females with osteoporotic hip fractures, and there were significantly more males with secondary osteoporotic hip fractures.

Comparing the efficacy of different methods of faecal microbiota transplantation via oral capsule, oesophagogastroduodenoscopy, colonoscopy, or gastric tube

BACKGROUND

The increasing prevalence of multidrug-resistant organism (MDRO) carriage poses major challenges to medicine as healthcare costs increase. Recently, faecal microbiota transplantation (FMT) has been discussed as a novel and effective method for decolonizing MDRO.

AIM

To compare the efficacy of different FMT methods to optimize the success rate of decolonization in patients with MDRO carriage.

METHODS

This prospective cohort study enrolled patients with MDRO carriages from 2018 to 2021. Patients underwent FMT via one of the following methods: oral capsule, oesophagogastroduodenoscopy (EGD), colonoscopy, or gastric tube.

FINDINGS

A total of 57 patients underwent FMT for MDRO decolonization. The colonoscopy group required the shortest time for decolonization, whereas the EGD group required the longest (24.9 vs 190.4 days, P = 0.022). The decolonization rate in the oral capsule group was comparable to that in the EGD group (84.6% vs 85.7%, P = 0.730). An important clinical factor associated with decolonization failure was antibiotic use after FMT (odds ratio = 6.810, P = 0.008). All four groups showed reduced proportions of MDRO species in microbiome analysis after FMT.

CONCLUSION

Compared to other conventional methods, the oral capsule is an effective FMT method for patients who can tolerate an oral diet. The discontinuation of antibiotics after FMT is a key factor in the success of decolonization.

Prefrontal EEG slowing, synchronization, and ERP peak latency in association with predementia stages of Alzheimer's disease

Background

Early screening of elderly individuals who are at risk of dementia allows timely medical interventions to prevent disease progression. The portable and low-cost electroencephalography (EEG) technique has the potential to serve it.

Objective

We examined prefrontal EEG and event-related potential (ERP) variables in association with the predementia stages of Alzheimer's disease (AD).

Methods

One hundred elderly individuals were recruited from the GARD cohort. The participants were classified into four groups according to their amyloid beta deposition (A+ or A-) and neurodegeneration status (N+ or N-): cognitively normal (CN; A-N-, n = 27), asymptomatic AD (aAD; A + N-, n = 15), mild cognitive impairment (MCI) with AD pathology (pAD; A+N+, n = 16), and MCI with non-AD pathology (MCI(-); A-N+, n = 42). Prefrontal resting-state eyes-closed EEG measurements were recorded for five minutes and auditory ERP measurements were recorded for 8 min. Three variables of median frequency (MDF), spectrum triangular index (STI), and positive-peak latency (PPL) were employed to reflect EEG slowing, temporal synchrony, and ERP latency, respectively.

Results

Decreasing prefrontal MDF and increasing PPL were observed in the MCI with AD pathology. Interestingly, after controlling for age, sex, and education, we found a significant negative association between MDF and the aAD and pAD stages with an odds ratio (OR) of 0.58. Similarly, PPL exhibited a significant positive association with these AD stages with an OR of 2.36. Additionally, compared with the MCI(-) group, significant negative associations were demonstrated by the aAD group with STI and those in the pAD group with MDF with ORs of 0.30 and 0.42, respectively.

Conclusion

Slow intrinsic EEG oscillation is associated with MCI due to AD, and a delayed ERP peak latency is likely associated with general cognitive impairment. MCI individuals without AD pathology exhibited better cortical temporal synchronization and faster EEG oscillations than those with aAD or pAD.

Significance

The EEG/ERP variables obtained from prefrontal EEG techniques are associated with early cognitive impairment due to AD and non-AD pathology. This result suggests that prefrontal EEG/ERP metrics may serve as useful indicators to screen elderly individuals' early stages on the AD continuum as well as overall cognitive impairment.

DeepParcellation: A novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians

Accurate parcellation of cortical regions is crucial for distinguishing morphometric changes in aged brains, particularly in degenerative brain diseases. Normal aging and neurodegeneration precipitate brain structural changes, leading to distinct tissue contrast and shape in people aged >60 years. Manual parcellation by trained radiologists can yield a highly accurate outline of the brain; however, analyzing large datasets is laborious and expensive. Alternatively, newly-developed computational models can quickly and accurately conduct brain parcellation, although thus far only for the brains of Caucasian individuals. To develop a computational model for the brain parcellation of older East Asians, we trained magnetic resonance images of dimensions 256 × 256 × 256 on 5,035 brains of older East Asians (Gwangju Alzheimer's and Related Dementia) and 2,535 brains of Caucasians. The novel N-way strategy combining three memory reduction techniques inception blocks, dilated convolutions, and attention gates was adopted for our model to overcome the intrinsic memory requirement problem. Our method proved to be compatible with the commonly used parcellation model for Caucasians and showed higher similarity and robust reliability in older aged and East Asian groups. In addition, several brain regions showing the superiority of the parcellation suggest that DeepParcellation has a great potential for applications in neurodegenerative diseases such as Alzheimer's disease.

Segmental bioelectrical impedance analysis for Korean older population with cold pattern

Objective

This study examined the association of whole-body composition and segmental bioimpedance variables with cold pattern (CP) in different sexes.

Methods

We assigned 667 older individuals to a CP group (n = 488) and a non-CP group (n = 179) by using an eight-item self-administered questionnaire. Seven body composition variables and three pairs of segmental bioimpedance variables for the upper and lower extremities, which were obtained from a segmental multifrequency bioimpedance analyzer, were employed to investigate their association with CP. Participants’ characteristics were first described. Then we compared the selected body composition and bioimpedance variables between the CP and non-CP groups. Finally, their association with CP was investigated using univariate and multivariate regression analyses. All analyses were performed separately for women and men.

Results

Both women and men exhibited a comparable mean age in the CP and non-CP groups; however, women with CP had significantly lower blood pressures, whereas men with CP showed a higher proportion of osteoarthritis than those without CP. Compared with the non-CP group, individuals with CP exhibited significantly smaller body sizes indicated by shorter height and smaller weight, lower body mass index, and smaller volume-to-body surface area ratio in both sexes. After controlling for age, height, weight, and other covariates, we found significant reductions in body lean mass such as fat-free mass and body cell mass, basal metabolic rate per unit mass, total body water, and intra-to-extracellular water ratio in the CP group. With regard to segmental bioimpedance analysis, the resistance ratios and phase angles in the upper and lower extremities yield significant associations with CP incidence, as demonstrated by the odds ratio (95% confidence interval) of 1.72 (1.16–2.57), 1.69 (1.18–2.48), 0.60 (0.40–0.89), and 0.57 (0.39–0.82), respectively. However, these results did not emerge in men.

Conclusion

Abnormal cellular water distribution and deterioration in body cell mass and/or cell strength are associated with CP prevalence, regardless of age, height, weight. These findings are similar in the upper and lower extremities and are more pronounced in women. The abovementioned patterns may be considered effective indicators for identifying CP in the older adult population.

Can hippocampal subfield measures supply information that could be used to improve the diagnosis of Alzheimer's disease?

The diagnosis of Alzheimer's disease (AD) needs to be improved. We investigated if hippocampal subfield volume measured by structural imaging, could supply information, so that the diagnosis of AD could be improved. In this study, subjects were classified based on clinical, neuropsychological, and amyloid positivity or negativity using PET scans. Data from 478 elderly Korean subjects grouped as cognitively unimpaired β-amyloid-negative (NC), cognitively unimpaired β-amyloid-positive (aAD), mild cognitively impaired β-amyloid-positive (pAD), mild cognitively impaired-specific variations not due to dementia β-amyloid-negative (CIND), severe cognitive impairment β-amyloid-positive (ADD+) and severe cognitive impairment β-amyloid-negative (ADD-) were used. NC and aAD groups did not show significant volume differences in any subfields. The CIND did not show significant volume differences when compared with either the NC or the aAD (except L-HATA). However, pAD showed significant volume differences in Sub, PrS, ML, Tail, GCMLDG, CA1, CA4, HATA, and CA3 when compared with the NC and aAD. The pAD group also showed significant differences in the hippocampal tail, CA1, CA4, molecular layer, granule cells/molecular layer/dentate gyrus, and CA3 when compared with the CIND group. The ADD- group had significantly larger volumes than the ADD+ group in the bilateral tail, SUB, PrS, and left ML. The results suggest that early amyloid depositions in cognitive normal stages are not accompanied by significant bilateral subfield volume atrophy. There might be intense and accelerated subfield volume atrophy in the later stages associated with the cognitive impairment in the pAD stage, which subsequently could drive the progression to AD dementia. Early subfield volume atrophy associated with the β-amyloid burden may be characterized by more symmetrical atrophy in CA regions than in other subfields. We conclude that the hippocampal subfield volumetric differences from structural imaging show promise for improving the diagnosis of Alzheimer's disease.

SPIN90 Deficiency Ameliorates Amyloid β Accumulation by Regulating APP Trafficking in AD Model Mice

Alzheimer’s disease (AD), a common form of dementia, is caused in part by the aggregation and accumulation in the brain of amyloid β (Aβ), a product of the proteolytic cleavage of amyloid precursor protein (APP) in endosomes. Trafficking of APP, such as surface-intracellular recycling, is an early critical step required for Aβ generation. Less is known, however, about the molecular mechanism regulating APP trafficking. This study investigated the mechanism by which SPIN90, along with Rab11, modulates APP trafficking, Aβ motility and accumulation, and synaptic functionality. Brain Aβ deposition was lower in the progeny of 5xFAD-SPIN90KO mice than in 5xFAD-SPIN90WT mice. Analysis of APP distribution and trafficking showed that the surface fraction of APP was locally distinct in axons and dendrites, with these distributions differing significantly in 5xFAD-SPIN90WT and 5xFAD-SPIN90KO mice, and that neural activity-driven APP trafficking to the surface and intracellular recycling were more actively mobilized in 5xFAD-SPIN90KO neurons. In addition, SPIN90 was found to be cotrafficked with APP via axons, with ablation of SPIN90 reducing the intracellular accumulation of APP in axons. Finally, synaptic transmission was restored over time in 5xFAD-SPIN90KO but not in 5xFAD-SPIN90WT neurons, suggesting SPIN90 is implicated in Aβ production through the regulation of APP trafficking.

Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes

Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.

Segmental Bioimpedance Variables in Association With Mild Cognitive Impairment

Objective

To examine the changes in body composition, water compartment, and bioimpedance in mild cognitive impairment (MCI) individuals.

Methods

We obtained seven whole-body composition variables and seven pairs of segmental body composition, water compartment, and impedance variables for the upper and lower extremities from the segmental multi-frequency bioelectrical impedance analysis (BIA) of 939 elderly participants, including 673 cognitively normal (CN) people and 266 individuals with MCI. Participants’ characteristics, anthropometric information, and the selected BIA variables were described and statistically compared between the CN participants and those with MCI. The correlations between the selected BIA variables and neuropsychological tests such as the Korean version of the Mini-Mental State Examination and Seoul Neuropsychological Screening Battery – Second Edition were also examined before and after controlling for age and sex. Univariate and multivariate logistic regression analyses with estimated odds ratios (ORs) were conducted to investigate the associations between these BIA variables and MCI prevalence for different sexes.

Results

Participants with MCI were slightly older, more depressive, and had significantly poorer cognitive abilities when compared with the CN individuals. The partial correlations between the selected BIA variables and neuropsychological tests upon controlling for age and sex were not greatly significant. However, after accounting for age, sex, and the significant comorbidities, segmental lean mass, water volume, resistance, and reactance in the lower extremities were positively associated with MCI, with ORs [95% confidence interval (CI)] of 1.33 (1.02–1.71), 1.33 (1.03–1.72), 0.76 (0.62–0.92), and 0.79 (0.67–0.93), respectively; with presumably a shift of water from the intracellular area to extracellular space. After stratifying by sex, resistance and reactance in lower extremities remained significant only in the women group.

Conclusion

An increase in segmental water along with segmental lean mass and a decrease in body cell strength due to an abnormal cellular water distribution demonstrated by reductions in resistance and reactance are associated with MCI prevalence, which are more pronounced in the lower extremities and in women. These characteristic changes in BIA variables may be considered as an early sign of cognitive impairment in the elderly population.

Trends in body mass index changes among Korean adolescents between 2005-2020, including the COVID-19 pandemic period: a national representative survey of one million adolescents

OBJECTIVE

The impact of the coronavirus disease 2019 (COVID-19) pandemic on weight gain in children and adolescents remains unknown. We aimed to identify an estimated 15-year trend in mean body mass index (BMI) changes and prevalence of obesity and overweight among Korean adolescents from 2005 to 2020, including the period of the COVID-19 pandemic.

PATIENTS AND METHODS

We analyzed data taken from a nationwide survey (Korea Youth Risk Behavior Survey), between 2005 and 2020. Representative samples of one million Korean adolescents aged 13-18 years (n=1,057,885) were examined. The 15-year trends in mean BMI and proportion of obesity or overweight, and the changes due to the COVID-19 pandemic were analyzed.

RESULTS

The data of 1,057,885 Korean adolescents were analyzed (mean age: 14.98 years; females, 48.4%). The estimated weighted mean BMI was 20.5 kg/m2 [95% confidence interval (CI), 20.4-20.5] from 2005 to 2008 and 21.5 kg/m2 (95% CI, 21.4-21.6) in 2020 (during the COVID-19 pandemic). Although the 15-year trend of mean BMI gradually increased, the change in mean BMI before and during the pandemic significantly lessened (βdiff, -0.027; 95% CI, -0.028 to -0.026). The 15-year (2005-2020) trend changes in the prevalence of obesity and overweight were similar (obesity prevalence from 2005-2008, 3.2%; 95% CI, 3.1-3.3 vs. obesity prevalence in 2020, 8.6%; 95% CI, 8.2-9.0; βdiff, -0.309; 95% CI, -0.330 to -0.288).

CONCLUSIONS

The 15-year trend of overall mean BMI and obesity and overweight prevalence demonstrated a significant increase; however, its slope decreased during the pandemic. These landmark results suggest the need for the development of precise strategies to prevent pediatric obesity and overweight during the COVID-19 pandemic.

COVID-19 susceptibility and clinical outcomes in autoimmune inflammatory rheumatic diseases (AIRDs): a systematic review and meta-analysis

OBJECTIVE

This meta-analysis aims to assess the susceptibility to and clinical outcomes of COVID-19 in autoimmune inflammatory rheumatic disease (AIRD) and following AIRD drug use.

MATERIALS AND METHODS

We included observational and case-controlled studies assessing susceptibility and clinical outcomes of COVID-19 in patients with AIRD as well as the clinical outcomes of COVID-19 with or without use of steroids and conventional synthetic disease-modifying antirheumatic drugs (csDMARDs).

RESULTS

Meta-analysis including three studies showed that patients with AIRD are not more susceptible to COVID-19 compared to patients without AIRD or the general population (OR: 1.11, 95% CI: 0.58 to 2.14). Incidence of severe outcomes of COVID-19 (OR: 1.34, 95% CI: 0.76 to 2.35) and COVID-19 related death (OR: 1.21, 95% CI: 0.68 to 2.16) also did not show significant difference. The clinical outcomes of COVID-19 among AIRD patients with and without csDMARD or steroid showed that both use of steroid (OR: 1.69, 95% CI: 0.96 to 2.98) or csDMARD (OR: 1.35, 95% CI: 0.63 to 3.08) had no effect on clinical outcomes of COVID-19.

CONCLUSIONS

AIRD does not increase susceptibility to COVID-19, not affecting the clinical outcome of COVID-19. Similarly, the use of steroids or csDMARDs for AIRD does not worsen the clinical outcome.

Clinical characteristics and treatments of multi-system inflammatory syndrome in children: a systematic review

OBJECTIVE

Multisystem inflammatory syndrome in children (MIS-C) can occur in association with coronavirus disease 2019 (COVID-19). It is not easy to differentiate MIS-C from severe COVID-19 or Kawasaki disease based on symptoms. The aim of this study was to describe the clinical and laboratory characteristics of MIS-C.

PATIENTS AND METHODS

We searched PubMed/Medline for case series and reports of MIS-C published until June 20, 2020. From a total of nine articles involving 45 cases, various clinical and laboratory data were extracted. Each target case was evaluated by using different diagnostic criteria.

RESULTS

The average age at onset of MIS-C was 8.6 years. In 80% of cases, the age of patients ranged from 5 to 15 years. Fever (100%) and shock (82%) were the most common presenting symptoms. Sixty percent of cases met the diagnostic criteria for typical or atypical Kawasaki disease. Biomarkers indicative of inflammation, coagulopathy, or cardiac injury were characteristically elevated as follows: ferritin (mean: 1,061 ng/mL), CRP (217 mg/L), ESR (69 mm/hr), IL-6 (214.8 pg/mL), TNFα (63.4 pg/mL), D-dimer (3,220 ng/mL), PT (15.5 s), troponin I (1,006 ng/L), and BNP (12,150 pg/mL). Intravenous immunoglobulin was administered in all target cases, and inotropic agents were commonly used as well. No case of death was observed.

CONCLUSIONS

This study demonstrated that MIS-C is a serious condition that presents with fever, rash, as well as cardiovascular and gastrointestinal symptoms. Although it is challenging to differentiate MIS-C from Kawasaki disease or severe COVID-19, initiation of appropriate treatments through early diagnosis is warranted.

Deep Learning-Based Multilevel Classification of Alzheimer’s Disease Using Non-invasive Functional Near-Infrared Spectroscopy

The timely diagnosis of Alzheimer’s disease (AD) and its prodromal stages is critically important for the patients, who manifest different neurodegenerative severity and progression risks, to take intervention and early symptomatic treatments before the brain damage is shaped. As one of the promising techniques, functional near-infrared spectroscopy (fNIRS) has been widely employed to support early-stage AD diagnosis. This study aims to validate the capability of fNIRS coupled with Deep Learning (DL) models for AD multi-class classification. First, a comprehensive experimental design, including the resting, cognitive, memory, and verbal tasks was conducted. Second, to precisely evaluate the AD progression, we thoroughly examined the change of hemodynamic responses measured in the prefrontal cortex among four subject groups and among genders. Then, we adopted a set of DL architectures on an extremely imbalanced fNIRS dataset. The results indicated that the statistical difference between subject groups did exist during memory and verbal tasks. This presented the correlation of the level of hemoglobin activation and the degree of AD severity. There was also a gender effect on the hemoglobin changes due to the functional stimulation in our study. Moreover, we demonstrated the potential of distinguished DL models, which boosted the multi-class classification performance. The highest accuracy was achieved by Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) using the original dataset of three hemoglobin types (0.909 ± 0.012 on average). Compared to conventional machine learning algorithms, DL models produced a better classification performance. These findings demonstrated the capability of DL frameworks on the imbalanced class distribution analysis and validated the great potential of fNIRS-based approaches to be further contributed to the development of AD diagnosis systems.

Prevalence of allergic diseases among Korean adolescents during the COVID-19 pandemic: comparison with pre-COVID-19 11-year trends

OBJECTIVE

During the coronavirus disease 2019 (COVID-19) pandemic, emergency department utilization and hospitalization rates for allergic diseases declined and the severity of allergies among admitted patients was low. This study aimed to determine the prevalence of allergic diseases among adolescents and the changes in trend during the COVID-19 pandemic compared with those during the preceding 11 years.

SUBJECTS AND METHODS

We analyzed data from the nationwide web-based self-report Korea Youth Risk Behavior Survey. From 2009 to 2020, adolescents aged 13-18 years participated in the survey. The survey period was divided into pre-pandemic Periods I (2009-2011), II (2012-2014), III (2015-2017), and IV (2018-2019) and the pandemic period (Period V, 2020). The current prevalence of asthma, allergic rhinitis, atopic dermatitis, allergic morbidity (having at least one of the three conditions) and changes in the prevalence before and during the COVID-19 pandemic were analyzed.

RESULTS

Data of 787,043 participants were analyzed after weighting the study population (mean age, 15.1 years; males, 52.3%). The prevalence of asthma, allergic rhinitis, atopic dermatitis, and allergic morbidity was 2.1%, 18.4%, 6.8%, and 23.6%, respectively. The prevalence of allergic morbidity increased between Periods I and IV but declined significantly from Periods IV to V. From Periods I to IV, the prevalence of asthma decreased, the prevalence of allergic rhinitis increased, and the prevalence of atopic dermatitis remained unchanged. During Period V, the prevalence of all three conditions decreased.

CONCLUSIONS

It is necessary to update management measures and develop relevant policies in response to the altered prevalence of allergic diseases since the outbreak of COVID-19.

Factors Associated with Body Weight Gain among Korean Adults during the COVID-19 Pandemic

Background

Obesity is of grave concern as a comorbidity of coronavirus disease 2019 (COVID-19). We examined the factors associated with weight gain among Korean adults during the COVID-19 pandemic.

Methods

We conducted an online survey of 1,000 adults (515 men and 485 women aged 20-59 years) in March 2021. Multivariable logistic regression analysis was performed to evaluate the factors associated with weight gain. The analysis was adjusted for sex, age, region, depressive mood, anxiety, eating out, late-night meals, alcohol consumption, exercise, sleep disturbance, meal pattern, subjective body image, comorbidities, marital status, living alone, and income.

Results

After adjusting for confounding variables, the odds for weight gain increased in the group aged 20-34 years compared with the group aged 50-59 years (1.82; 95% confidence interval [CI], 1.01-3.32). Women were more associated with the risk of weight gain compared with men. The odds for weight gain increased in the lack of exercise group compared with the exercise group (4.89; 95% CI, 3.09-7.88). The odds for weight gain increased in the eating-out and late-night meal groups compared with that in the groups not eating out and not having late-night meals. Individuals watching a screen for 3-6 hr/day were more associated with the risk of weight gain compared with those who rarely watched a screen. The odds for weight gain increased in participants who considered themselves obese compared with those who did not consider themselves obese.

Conclusion

A healthy diet and regular physical activity tend to be the best approach to reduce obesity, a risk factor for COVID-19.

Rapid antigen test during a COVID-19 outbreak in a private hospital in Hong Kong

INTRODUCTION

In response to two nosocomial clusters of coronavirus disease 2019 (COVID-19) in our hospital, we adopted a series of strict infection control measures, including regular rapid antigen test (RAT) screening for high-risk patients, visitors, and healthcare workers. We evaluated the diagnostic performance of a locally developed RAT, the INDICAID COVID-19 Rapid Antigen Test (Phase Scientific, Hong Kong), using respiratory samples from both symptomatic and asymptomatic individuals.

METHODS

Real-time reverse-transcription polymerase chain reaction (rRT-PCR)-confirmed deep throat saliva (DTS) and pooled nasopharyngeal swab and throat swab (NPS/TS) samples collected from 1 November to 30 November 2020 were tested by INDICAID. Screening RATs were performed on asymptomatic healthcare workers during a 16-week period (1 December 2020 to 22 March 2021).

RESULTS

In total, 20 rRT-PCR-confirmed samples (16 DTS, four pooled NPS/TS) were available for RAT. Using the original sample, RAT results were positive in 17/20 samples, indicating 85% sensitivity (95% confidence interval [CI]=62.11%-96.79%). Negative RAT results were associated with higher cycle threshold (Ct) values. For samples with Ct values <25, the sensitivity was 100%. Of the 49 801 RATs collected from healthcare workers, 33 false positives and one rRT-PCR-confirmed case were detected. The overall specificity was 99.93% (95% CI=99.91%-99.95%). The positive and negative predictive values were 2.94% (95% CI=2.11%-4.09%) and 100%, respectively.

CONCLUSION

The INDICAID COVID-19 RAT demonstrated good sensitivity for specimens with high viral loads and satisfactory specificity for low-risk, asymptomatic healthcare workers.

Sire pedigree error estimation and sire verification of the Taiwan dairy cattle population by using SNP markers

Information regarding the correct pedigree of and relationship between animals is useful for managing dairy breeding, reducing inbreeding, estimating breeding value, and establishing correct breeding programs. Additionally, the successful implementation of progeny testing is crucial for improving the genetics of dairy cattle, which depends on the availability of correct pedigree information. Incorrect pedigree information leads to bias in bull evaluation. In this study, Neogen GeneSeek Genomic Profiler (GGP) 50K SNP chips were used to identify and verify the sire of Taiwanese Holstein dairy cattle and analyze the reasons that lead to incorrect sire records. Samples were collected from 2,059 cows of 36 dairy farms, and the pedigree information was provided by breeders. The results of sire verification can be divided into three categories: submitted unconfirmed sire, submitted confirmed sire, and incorrectly submitted verified sire. Data on the sires of 1,323 (64.25%) and 572 (27.78%) dairy cows were verified and discovered, respectively. Sires of 1,895 (92.03%) dairy cattle were identified, which showed that the paternal pedigree of dairy cattle could be discovered and verified through genetic testing. An error-like analysis revealed that the data of 37 sires were incorrectly recorded because the bull's NAAB code number was incorrectly entered into the insemination records: for 19 sires, the wrong bull was recorded because the frozen semen of a bull placed in the wrong storage tank was used, 6 had no sire records, and for 12 sires, the NAAB code of the correct bull was recorded but with a wrong stud code, marketing code, or unique number for the stud or breed. To reduce recorded sire error rates by at least 27.78%, automated identification of the mated bull must be adopted to reduce human error and improve dairy breeding management on dairy farms.

Two Opposing Roles of SARS-CoV-2 RBD-Reactive Antibodies in Pre-Pandemic Plasma Samples From Elderly People in ACE2-Mediated Pseudovirus Infection

A novel coronavirus designated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged and caused an outbreak of unusual viral pneumonia. Several reports have shown that cross-reactive antibodies against SARS-CoV-2 also exist in people unexposed to this virus. However, the neutralizing activity of cross-reactive antibodies is controversial. Here, we subjected plasma samples from SARS-CoV-2-unexposed elderly Korean people (n = 119) to bead-based IgG antibody analysis. SARS-CoV-2 S1 subunit-reactive IgG antibody analysis detected positive signals in some samples (59 of 119, 49.6%). SARS-CoV-2 receptor-binding domain (RBD)-reactive antibody levels were most significantly correlated with human coronavirus-HKU1 S1 subunit-reactive antibody levels. To check the neutralizing activity of plasma samples, the SARS-CoV-2 spike pseudotype neutralizing assay was used. However, the levels of cross-reactive antibodies did not correlate with neutralizing activity. Instead, SARS-CoV-2 pseudovirus infection was neutralized by some RBD-reactive plasma samples (n = 9, neutralization ≥ 25%, P ≤ 0.05), but enhanced by other RBD-reactive plasma samples (n = 4, neutralization ≤ -25%, P ≤ 0.05). Interestingly, the blood plasma groups with enhancing and neutralizing effects had high levels of SARS-CoV-2 RBD-reactive antibodies than the plasma group that had no effect. These results suggest that some SARS-CoV-2 RBD-reactive antibodies from pre-pandemic elderly people exert two opposing functions during SARS-CoV-2 pseudovirus infection. In conclusion, preformed RBD-reactive antibodies may have two opposing functions, namely, protecting against and enhancing viral infection. Analysis of the epitopes of preformed antibodies will be useful to elucidate the underlying mechanism.

Plasma protein biomarker model for screening Alzheimer disease using multiple reaction monitoring-mass spectrometry

Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. Yet, the complexity of diagnosing AD and measuring its invasiveness poses an obstacle. To this end, blood-based biomarkers could mitigate the inconveniences that impede an accurate diagnosis. We developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring-mass spectrometry, a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control, asymptomatic AD, prodromal AD), and AD dementia. Multimarker models were developed using 10 protein biomarkers and apolipoprotein E genotypes for amyloid beta and 10 biomarkers with Korean Mini-Mental Status Examination (K-MMSE) score for predicting Alzheimer disease progression. The accuracies for the AD classification model and AD progression monitoring model were 84.9% (95% CI 82.8 to 87.0) and 79.1% (95% CI 77.8 to 80.5), respectively. The models were more accurate in diagnosing AD, compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD.

Diagnostic Blood Biomarkers in Alzheimer’s Disease

Potential biomarkers for Alzheimer’s disease (AD) include amyloid β1–42 (Aβ1–42), t-Tau, p-Tau181, neurofilament light chain (NFL), and neuroimaging biomarkers. Their combined use is useful for diagnosing and monitoring the progress of AD. Therefore, further development of a combination of these biomarkers is essential. We investigated whether plasma NFL/Aβ1–42 can serve as a plasma-based primary screening biomarker reflecting brain neurodegeneration and amyloid pathology in AD for monitoring disease progression and early diagnosis. We measured the NFL and Aβ1–42 concentrations in the CSF and plasma samples and performed correlation analysis to evaluate the utility of these biomarkers in the early diagnosis and monitoring of AD spectrum disease progression. Pearson’s correlation analysis was used to analyse the associations between the fluid biomarkers and neuroimaging data. The study included 136 participants, classified into five groups: 28 cognitively normal individuals, 23 patients with preclinical AD, 22 amyloid-negative patients with amnestic mild cognitive impairment, 32 patients with prodromal AD, and 31 patients with AD dementia. With disease progression, the NFL concentrations increased and Aβ1–42 concentrations decreased. The plasma and CSF NFL/Aβ1–42 were strongly correlated (r = 0.558). Plasma NFL/Aβ1–42 was strongly correlated with hippocampal volume/intracranial volume (r = 0.409). In early AD, plasma NFL/Aβ1–42 was associated with higher diagnostic accuracy than the individual biomarkers. Moreover, in preclinical AD, plasma NFL/Aβ1–42 changed more rapidly than the CSF t-Tau or p-Tau181 concentrations. Our findings highlight the utility of plasma NFL/Aβ1–42 as a non-invasive plasma-based biomarker for early diagnosis and monitoring of AD spectrum disease progression.

Tonicity-responsive enhancer-binding protein promotes diabetic neuroinflammation and cognitive impairment via upregulation of lipocalin-2

Background

Diabetic individuals have increased circulating inflammatory mediators which are implicated as underlying causes of neuroinflammation and memory deficits. Tonicity-responsive enhancer-binding protein (TonEBP) promotes diabetic neuroinflammation. However, the precise role of TonEBP in the diabetic brain is not fully understood.

Methods

We employed a high-fat diet (HFD)-only fed mice or HFD/streptozotocin (STZ)-treated mice in our diabetic mouse models. Circulating TonEBP and lipocalin-2 (LCN2) levels were measured in type 2 diabetic subjects. TonEBP haploinsufficient mice were used to investigate the role of TonEBP in HFD/STZ-induced diabetic mice. In addition, RAW 264.7 macrophages were given a lipopolysaccharide (LPS)/high glucose (HG) treatment. Using a siRNA, we examined the effects of TonEBP knockdown on RAW264 cell’ medium/HG-treated mouse hippocampal HT22 cells.

Results

Circulating TonEBP and LCN2 levels were higher in experimental diabetic mice or type 2 diabetic patients with cognitive impairment. TonEBP haploinsufficiency ameliorated the diabetic phenotypes including adipose tissue macrophage infiltrations, neuroinflammation, blood–brain barrier leakage, and memory deficits. Systemic and hippocampal LCN2 proteins were reduced in diabetic mice by TonEBP haploinsufficiency. TonEBP (+ / −) mice had a reduction of hippocampal heme oxygenase-1 (HO-1) expression compared to diabetic wild-type mice. In particular, we found that TonEBP bound to the LCN2 promoter in the diabetic hippocampus, and this binding was abolished by TonEBP haploinsufficiency. Furthermore, TonEBP knockdown attenuated LCN2 expression in lipopolysaccharide/high glucose-treated mouse hippocampal HT22 cells.

Conclusions

These findings indicate that TonEBP may promote neuroinflammation and cognitive impairment via upregulation of LCN2 in diabetic mice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-021-02331-8.

Suicide associated with COVID-19 infection: an immunological point of view

OBJECTIVE

Coronavirus disease 2019 (COVID-19) is a pandemic and leading cause of death. Beyond the deaths directly caused by the virus and the suicides related to the psychological response to the dramatic changes as socioeconomic related to the pandemic, there might also be suicides related to the inflammatory responses of the infection. Infection induces inflammation as a cytokine storm, and there is an increasing number of studies that report a relationship between infection and suicide.

MATERIALS AND METHODS

We searched the World Health Organization status report and the PubMed database for keywords (COVID-19, suicide, infection, inflammation, cytokines), and reviewed five cytokine pathways between suicide and inflammation using two meta-analyses and two observational studies starting from November 31, 2020, focusing on the relationship between suicide and inflammation by infection. First, we discussed existing evidence explaining the relationship between suicidal behaviors and inflammation. Second, we summarized the inflammatory features found in COVID-19 patients. Finally, we highlight the potential for these factors to affect the risk of suicide in COVID-19 patients.

RESULTS

Patients infected with COVID-19 have high amounts of IL-1β, IFN-γ, IP10, and MCP1, which may lead to Th1 cell response activation. Also, Th2 cytokines (e.g., IL-4 and IL-10) were increased in COVID-19 infection. In COVID-19 patients, neurological conditions, like headache, dizziness, ataxia, seizures, and others have been observed.

CONCLUSIONS

COVID-19 pandemic can serve as a significant environmental factor contributing directly to increased suicide risk; the role of inflammation by an infection should not be overlooked.