Clinical assessment and striatal dopaminergic activity in healthy controls and patients with Parkinson's disease: a Bayesian approach.

OBJECTIVES: We aimed to evaluate correlations between striatal dopamine transporter (DAT) uptake and clinical assessments in both patients with Parkinson's disease (PD) and healthy controls. METHODS: This study enrolled 193 healthy controls, and 581 patients with PD. They underwent various clinical assessments and 123I-FP-CIT SPECT scans. After reconstruction, attenuation correction, and normalization of SPECT images, counts were measured from the bilateral caudate and putamen, and the occipital cortex for reference. Count densities for each region were extracted and used to calculate striatal binding ratios (SBRs) for each striatal region. SBR is calculated as (target region/reference region)-1. After logarithmic transformation of striatal SBRs, we analyzed the effects of clinical assessments on striatal SBRs using Bayesian hierarchical modeling. RESULTS: MDS-UPDRS total score, part I, part II, part III, Epworth Sleepiness Scale, REM sleep behavior disorder screening questionnaire, SCOPA-AUT total score were negatively associated with striatal SBR in patients with PD. Also, HVLT recognition discrimination was positively associated with striatal SBR in both healthy controls and patients with PD. In healthy control, MDS-UPDRS part II, MOCA, SCOPA-AUT total score were positively associated with striatal SBR. CONCLUSION: We demonstrated that motor symptom, sleep disturbance, autonomic symptom, and cognition of patients with PD were associated with striatal dopaminergic activity. In healthy controls, motor symptoms, autonomic symptom, and cognition were associated with striatal dopaminergic activity, some of which showing the opposite direction with patients with PD. This result might provide new insight to underlying mechanism of dopamine system with motor and non-motor assessments.

Comparison of Adenosine Deaminase, C-reactive Protein, Uric Acid, and Rheumatoid Arthritis Levels in Patients With Rheumatoid Arthritis and Those Without Arthritis: A Review.

One of the hallmarks of rheumatoid arthritis (RA) is inflammation of the synovial membrane, and oxidative stress is a mediator of tissue damage. RA is characterized by persistent joint inflammation, which leads to pain, edema, and finally joint destruction. Numerous biochemical markers can cause RA because of their impact on systemic and local inflammation. Numerous biomarkers have been investigated for their potential application in the diagnosis and prognosis of RA. In this review article, we evaluate the role of RA factor or rheumatoid factor (RF), uric acid, C-reactive protein (CRP), and adenosine deaminases (ADAs) as biomarkers in patients with and without arthritis. Studies that analyze and compare the levels of uric acid, ADAs, CRP, and RF in patients with and without arthritis. Although recent research has shown higher levels of uric acid, ADA, CRP, and RA in patients with RF compared to healthy controls, these findings may indicate a role for these markers in reflecting inflammation and disease activity. In the metabolism of purines, the enzyme ADA is involved. The liver produces CRP, which is then released into the bloodstream. In inflammatory situations, there is a rise in CRP levels. This biomarker is frequently used for systemic inflammatory assessment in RA. The pathophysiology and severity of RA have both been connected to uric acid, which has historically been linked to gout. One particular biomarker for RA is RF. When compared to a healthy control group of individuals with arthritis, this review provides valuable insights into the diagnostic and prognostic use of uric acid, CRP, ADAs, and RF.

Diffusion tensor imaging metrics as natural markers of multiple sclerosis-induced brain disorders with a low Expanded Disability Status Scale score.

Non-invasive and effective differentiation along with determining the degree of deviations compared to the healthy cohort is important in the case of various brain disorders, including multiple sclerosis (MS). Evaluation of the effectiveness of diffusion tensor metrics (DTM) in 3T DTI for recording MS-related deviations was performed using a time-acceptable MRI protocol with unique comprehensive detection of systematic errors related to spatial heterogeneity of magnetic field gradients. In a clinical study, DTMs were acquired in segmented regions of interest (ROIs) for 50 randomly selected healthy controls (HC) and 50 multiple sclerosis patients. Identical phantom imaging was performed for each clinical measurement to estimate and remove the influence of systematic errors using the b-matrix spatial distribution in the DTI (BSD-DTI) technique. In the absence of statistically significant differences due to age in healthy volunteers and patients with multiple sclerosis, the existence of significant differences between groups was proven using DTM. Moreover, a statistically significant impact of spatial systematic errors occurs for all ROIs and DTMs in the phantom and for approximately 90 % in the HC and MS groups. In the case of a single patient measurement, this appears for all the examined ROIs and DTMs. The obtained DTMs effectively discriminate healthy volunteers from multiple sclerosis patients with a low mean score on the Expanded Disability Status Scale. The magnitude of the group differences is typically significant, with an effect size of approximately 0.5, and similar in both the standard approach and after elimination of systematic errors. Differences were also observed between metrics obtained using these two approaches. Despite a small alterations in mean DTMs values for groups and ROIs (1-3 %), these differences were characterized by a huge effect (effect size ∼0.8 or more). These findings indicate the importance of determining the spatial distribution of systematic errors specific to each MR scanner and DTI acquisition protocol in order to assess their impact on DTM in the ROIs examined. This is crucial to establish accurate DTM values for both individual patients and mean values for a healthy population as a reference. This approach allows for an initial reliable diagnosis based on DTI metrics.

Reduced plasma calcitonin gene-related peptide level identified in cluster headache: A prospective and controlled study.

BACKGROUND: The role of calcitonin gene-related peptide (CGRP) in the cyclic pattern of cluster headache is unclear. To acquire biological insight and to comprehend why only episodic cluster headache responds to CGRP monoclonal antibodies, we examined whether plasma CGRP changes between disease states (i.e. bout, remission and chronic) and controls. METHODS: The present study is a prospective case-control study. Participants with episodic cluster headache were sampled twice (bout and remission). Participants with chronic cluster headache and controls were sampled once. CGRP concentrations were measured in plasma with a validated radioimmunoassay. RESULTS: Plasma was collected from 201 participants diagnosed with cluster headache according to the International Classification of Headache Disorders, 3rd edition, and from 100 age- and sex-matched controls. Overall, plasma CGRP levels were significantly lower in participants with cluster headache compared to controls (p < 0.05). In episodic cluster headache, CGRP levels were higher in bout than in remission (mean difference: 17.1 pmol/L, 95% confidence interval = 9.8-24.3, p < 0.0001). CGRP levels in bout were not different from chronic cluster headache (p = 0.266). CONCLUSIONS: Plasma CGRP is unsuitable as a diagnostic biomarker of cluster headache or its disease states. The identified reduced CGRP levels suggest that CGRPs role in cluster headache is highly complex and future investigations are needed into the modulation of CGRP and its receptors.

A study of the roles of some immunological biomarkers in the diagnosis of rheumatoid arthritis.

Autoimmune rheumatoid arthritis (RA) is a systemic condition closely correlated with a variety of autoantibodies (Abs) that could be considered diagnostic and prognostic markers. The current research was designed to detect the diagnostic values for a number (n) of these auto-Abs in RA detection and to evaluate the accuracy of a combined diagnostic scheme. This prospective study was conducted between September 2021 and August 2022 and included 110 subjects with RA, 70 individuals with other autoimmune disorders as positive controls (PC), and 50 unrelated, apparently healthy individuals as healthy controls (HC). The eligibility criteria for all study groups were followed stringently. An enzyme-linked immunosorbent assay (ELISA) was employed to measure rheumatoid factors (RF), cyclic citrullinated peptide antibodies (CCP-Abs), mutated citrullinated vimentin antibodies (MCV-Abs), anti-perinuclear factor antibodies (APF-Abs), and anti-keratin antibodies (AKA). We calculated the specificity, sensitivity, and predictive values of all auto-Abs. Significantly higher levels of anti-CCP-Abs, anti-MCV-Abs, APF-Abs, and AKAs were reported in the RA patients compared to the HC and PC subjects. RF levels, however, were only statistically elevated when compared to the HC individuals. Anti-APF-Abs had a higher sensitivity rate (70.9%), and anti-CCP-Abs had a higher specificity rate (94.16%) compared to other auto-Abs, whereas the combined detection scheme revealed a higher sensitivity (81.81%) and excellent specificity (90.83%) compared to the two former auto-Abs. Anti-perinuclear factor-Ab was a highly sensitive test, and CCP-Ab was a surpassingly specific assay for identifying RA. Furthermore, the combined detection scheme is an essential serological approach for RA diagnosis and crucial in differentiating this disease from other autoimmune diseases, thus promoting early diagnosis and treatment.

Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest.

Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.

Diagnostic value of circular free DNA for colorectal cancer detection.

BACKGROUND: Minimally invasive or noninvasive, sensitive and accurate detection of colorectal cancer (CRC) is urgently needed in clinical practice. AIM: To identify a noninvasive, sensitive and accurate circular free DNA marker detected by digital polymerase chain reaction (dPCR) for the early diagnosis of clinical CRC. METHODS: A total of 195 healthy control (HC) individuals and 101 CRC patients (38 in the early CRC group and 63 in the advanced CRC group) were enrolled to establish the diagnostic model. In addition, 100 HC individuals and 62 patients with CRC (30 early CRC and 32 advanced CRC groups) were included separately to validate the model. CAMK1D was dPCR. Binary logistic regression analysis was used to establish a diagnostic model including CAMK1D and CEA. RESULTS: To differentiate between the 195 HCs and 101 CRC patients (38 early CRC and 63 advanced CRC patients), the common biomarkers CEA and CAMK1D were used alone or in combination to evaluate their diagnostic value. The area under the curves (AUCs) of CEA and CAMK1D were 0.773 (0.711, 0.834) and 0.935 (0.907, 0.964), respectively. When CEA and CAMK1D were analyzed together, the AUC was 0.964 (0.945, 0.982). In differentiating between the HC and early CRC groups, the AUC was 0.978 (0.960, 0.995), and the sensitivity and specificity were 88.90% and 90.80%, respectively. In differentiating between the HC and advanced CRC groups, the AUC was 0.956 (0.930, 0.981), and the sensitivity and specificity were 81.30% and 95.90%, respectively. After building the diagnostic model containing CEA and CAMK1D, the AUC of the CEA and CAMK1D joint model was 0.906 (0.858, 0.954) for the validation group. In differentiating between the HC and early CRC groups, the AUC was 0.909 (0.844, 0.973), and the sensitivity and specificity were 93.00% and 83.30%, respectively. In differentiating between the HC and advanced CRC groups, the AUC was 0.904 (0.849, 0.959), and the sensitivity and specificity were 93.00% and 75.00%, respectively. CONCLUSION: We built a diagnostic model including CEA and CAMK1D for differentiating between HC individuals and CRC patients. Compared with the common biomarker CEA alone, the diagnostic model exhibited significant improvement.

Consensus Definition of Blood Samples from the Subcategorized Normal Controls in the Korea Biobank Network.

A control group is defined as a group of people used for comparison. Depending on the type of study, it can be a group of healthy people or a group not exposed to risk factors. It is important to allow researchers to select the appropriate control participants. The Korea Biobank Project-sponsored biobanks are affiliated with the Korea Biobank Network (KBN), for which the National Biobank of Korea plays a central coordinating role among KBN biobanks. KBN organized several working groups to address new challenges and needs in biobanking. The "Normal Healthy Control Working Group" developed standardized criteria for three defined control groups, namely, normal, normal-plus, and disease-specific controls. Based on the consensus on the definition of a normal control, we applied the criteria for normal control participants to retrospective data. The main reason for exclusion from the "Normal-plus" group was blood test results beyond 5% of the reference range, including hypercholesterolemia. Subclassification of samples of normal controls by detailed criteria will help researchers select optimal normal controls for their studies.

Integration-segregation dynamics in functional networks of individuals diagnosed with schizophrenia.

Schizophrenia has been associated with dysfunction in information integration/segregation dynamics. One of the neural networks whose role has been most investigated in schizophrenia is the default mode network (DMN). In this study, we have explored the possible alteration of integration and segregation dynamics in individuals diagnosed with schizophrenia with respect to healthy controls, based on the study of the topological properties of the graphs derived from the functional connectivity between the nodes of the DMN in the resting state. Our results indicate that the patients show a diminution of the modularity of the DMN and a higher global efficiency, in sparse graphs. Our data emphasise the interest in studying temporal changes in network measures and are compatible with the hypothesis of randomization of functional networks in schizophrenia.

Choosing the source of healthy controls for studies on myeloid malignancies: all bone marrow cells are created equal, but some are more equal than others.

Bone marrow samples from discarded femoral heads are often used as healthy controls in studies investigating the in vitro characteristics of cells from patients with hematologic malignancies. Since patient samples are usually derived from iliac crest aspirates, this carries the risk that the properties of the cells from both sources might be different due to the site and method of harvesting. Comparing BM cells from iliac crest aspirates and femoral heads from age-matched healthy donors, we show that, while mesenchymal stromal cells have indistinguishable properties between both sources, hematopoietic stem and progenitor cells (HSPC) from femoral heads show a considerable proliferative advantage in vitro. These data therefore suggest that experiments comparing leukemic cells from the iliac crest to healthy HSPC obtained from femoral heads should be interpreted with caution.

Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study.

Background: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC). Methods: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362. Findings: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers. Interpretation: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG). Funding: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).

Asymmetry of Gray- and White-Matter Volume and Metabolites in the Central-Vestibular System in Healthy Individuals.

This study was designed to determine whether there was an asymmetry of structure and neurochemical activity of the interhemispheric vestibular-cortical system between healthy individuals and patients with vestibular failure. Previous studies have identified differences in gray-matter-volume (GMV) and white-matter-volume (WMV) asymmetry in the central-vestibular system and in concentrations of brain metabolites in the parietal lobe 2 (PO2) between patients with vestibulopathy and healthy controls. However, a comparison of the left and right sides in the healthy controls has not been made conclusively. This study included 23 healthy right-handed volunteers, and was carried out between March 2016 and March 2020. A three-dimensional T1-weighted image was used to calculate the GMV and WMV of the central-vestibular network on both sides, and proton magnetic resonance spectroscopy (H1MRS) was employed to analyze the brain metabolites in the PO2 area. The relative ratios of N-acetylaspartate (NAA)/tCr, tNAA/tCr, glycerophosphocholine (GPC)/tCr, Glx/tCr, and myo-inositol/tCr were quantified from the proton-MRS data. GMV and WMV differed significantly between the right and left vestibular-cortical regions. The GMVs of the right PO2, caudate, insula, and precuneus were significantly higher than those of the same locations on the left side; however, in the Rolandic operculum, the GMV on the left was significantly higher than on the right. In the PO2, Rolandic operculum, thalamus, and insula, the WMV on the left side was higher than on the right side of the corresponding location. However, the right caudate and precuneus WMV were higher than the left at the same location. In the H1MRS study, the Glx/tCr and GPC/tCr ratios on the left side were significantly higher than on the right. In comparison, the NAA/tCr and tNAA/tCr ratios showed contrasting results. The NAA/tCr ratio (r = -0.478, p = 0.021), tNAA/tCr ratio (r = -0.537, p = 0.008), and Glx/tCr ratio (r = -0.514, p = 0.012) on the right side showed a significant negative correlation with the participants' age. There was no relationship between GMV and metabolites on either side. Brain structure and concentrations of brain metabolites related to the vestibular system may differ between the two hemispheres in healthy individuals. Therefore, the asymmetry of the central-vestibular system should be considered when performing imaging.

Sensory Processing in Children and Adolescents with Attention Deficit Hyperactivity Disorder.

OBJECTIVE: The aim is to assess the sensory processing difficulties in children and adolescents with ADHD. METHODS: In all, 38 ADHD children of the age group 6-14 years and 34 age- and gender-matched typically developing controls were included in the study. Sensory processing was assessed on Child Sensory Profile-2. The child behavior checklist and Weiss functional impairment rating scale were applied to assess behavioral problems and functional impairments, respectively. RESULTS: A significantly higher sensory processing difficulties were seen in children with ADHD than typically developing controls. There were positive correlations between the scores of Child sensory profile 2 with internalizing (with Sensitivity p = .036, Avoiding p = .001, and Auditory p = .029) and externalizing T scores (with Seeking p = .031, Movement p = .025, and Visual p = .018) of CBCL and also with scores of Weiss functional impairment rating scale (with Seeking p = .001, Sensitivity p = .019, and Registration p = .045). CONCLUSIONS: Sensory problems were common in children with ADHD and add to the functional impairments.

Alpha and gamma EEG coherence during on-task and mind wandering states in schizophrenia.

OBJECTIVE: Electroencephalographic (EEG) coherence is one of the most relevant physiological measures used to detect abnormalities in patients with schizophrenia. The present study applies a task-related EEG coherence approach to understand cognitive processing in patients with schizophrenia and healthy controls. METHODS: EEG coherence for alpha and gamma frequency bands was analyzed in a group of patients with schizophrenia and a group of healthy controls during the performance of an ecological task of sustained attention. We compared EEG coherence when participants presented externally directed cognitive states (On-Task) and when they presented cognitive distraction episodes (Mind-Wandering). RESULTS: Results reflect cortical differences between groups (higher coherence for schizophrenia in the frontocentral and fronto-temporal regions, and higher coherence for healthy-controls in the postero-central regions), especially in the On-Task condition for the alpha band, compared to Mind-Wandering episodes. Few individual differences in gamma coherence were found. CONCLUSIONS: The current study provides evidence of neurophysiological differences underlying different cognitive states in schizophrenia and healthy controls. SIGNIFICANCE: Differences between groups may reflect inhibitory processes necessary for the successful processing of information, especially in the alpha band, given its role in cortical inhibition processes. Patients may activate compensatory inhibitory mechanisms when performing the task, reflected in increased coherence in fronto-temporal regions.

Cross-sectional and longitudinal comparison of health-related quality of life and mental well-being between persons with and without post COVID-19 condition.

Background: Still little is known about the impact of post COVID-19 condition (PC) on health-related quality of life (HRQOL) and mental well-being. We compared participants with PC with three groups: an acute COVID-19 infection (AC) only, at least one chronic condition (CC) but no COVID-19, or no condition at all, healthy (PH). Between these disease groups, we also estimated and compared HRQOL and mental well-being change over time. Methods: Participants from six countries (Greece, Italy, Netherlands, Sweden, United Kingdom and United States) completed two web-based questionnaires (T1 = April-May 2020 and T2 = April-June 2022). Primary outcomes were HRQOL, measured by EQ-5D-5L and EQ VAS, and mental well-being (measured by World Health Organisation-Five (WHO-5) Well-Being Index, Patient Health Questionnaire (PHQ)-9 and General Anxiety Disorder (GAD)-7). All analyses were stratified by the disease groups. Results: In total, 4,999 participants filled out both surveys: 240 were in PC, 107 in AC, 1798 in CC and 2,854 in PH. At T2, the mean EQ-5D-5L index values for the PC, AC, CC and PH groups were 0.70, 0.73, 0.75 and 0.92 (p < .001), respectively. Mean EQ VAS scores were 66, 65, 68 and 81 (p < .001), respectively. Poor mental well-being, depression and anxiety mean values were highest in the PC group (47.7; 9.1; 7.4), followed by the AC group (51.1; 7.7; 5.7), CC group (56.1; 5.2; 4.2) and the PH group (65.6; 2.8; 2.5), respectively (p < .001 between groups). Over time, HRQOL deteriorated in all groups, apart from the PH group. We observed the largest deterioration in the CC (EQ-5D-5L index: Δ0.03, p < .001) and AC group (EQ VAS: Δ6.3, p < .001). For the mental well-being outcomes, deterioration for WHO-5 and PHQ-9 were largest in the AC group (Δ4.8, p = .016; Δ-1.3, p = .012). Rates for GAD-7 improved for the PH and CC groups (PH: Δ1.27, CC: Δ0.56, p < .001). Conclusions: In the cross-sectional analysis, participants with PC had the worst HRQOL and mental well-being compared to the other groups. In terms of change since the start of the COVID-19 pandemic, HRQOL and mental well-being deterioration was highest among AC participants and had a lower impact among PC participants, most likely due to pre-existing chronic disease.

Mini-Mental State Examination and Montreal Cognitive Assessment as Tools for Following Cognitive Changes in Alzheimer's Disease Neuroimaging Initiative Participants.

BACKGROUND: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are two commonly used cognitive screening and diagnostic tools. OBJECTIVE: Our goal was to assess their efficacy for monitoring cognitive changes, as well as the correlation between the two tests. METHODS: At baseline, participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were divided into four groups based on their cognitive diagnoses: healthy control (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). MMSE or MoCA scores were compared among the four groups using an analysis of variance (ANOVA) model with repeated measures with post-hoc Bonferroni correction. For those participants who had both MMSE and MoCA assessments done, a Pearson correlation analysis was performed between the two assessments for each visit. RESULTS: The MMSE scores were significantly different among the four groups at baseline, which was true for each of the three annual follow-up visits. By contrast, the MoCA scores were not significantly different between HC and EMCI groups at either baseline or any of the follow-up visits. For participants with a diagnosis of LMCI, the cognitive performance deteriorated in a linear manner 12 months after the baseline, which was independent of MMSE or MoCA. At last, the MMSE scores were moderately related to MoCA scores, which got stronger along with the time of follow-up. CONCLUSION: MMSE and MoCA are comparable as cognitive assessment tools to monitor cognitive changes. In addition, the measurements of MMSE and MoCA are moderately correlated for the follow-up visits.

Role of cytochrome P450 for vitamin D metabolisms in patients with neurodegenerative disorders.

Introduction: We previously reported lower serum 25-hydroxyvitamin D concentrations in patients with Alzheimer's disease (AD), Parkinson's disease (PD) and Multiple system atrophy (MSA) compared to healthy controls (HC), whereas 1,25-di-hydroxyvitamin D levels were solely lower in MSA patients. We investigate serum concentrations of P450 involved in Vitamin D(VD) hydroxylation to clarify the responsible hydroxylase for the low serum concentrations of VD metabolites. Methods: A total of 79 individuals were enrolled including 20 HC, 20 AD, 19 PD and 20 MSA patients. The serum concentrations of P450 involved in VD hydroxylation were assayed by ELISA. The data were analyzed by the nonparametric Kruskal-Wallis test between groups. Results: Though CYP2R1 and CYP27A1 mediate 25-hydroxylation for VD, CYP2R1 is the main hydroxylase, and CYP27A1 is also involved in VD synthesis. CYP2R1 concentrations showed no differences among groups, while lower CYP27A1 concentrations were found in PD (p < 0.05) and MSA (p < 0.005) compared to HC and differences between AD and MSA (p < 0.05), however no differences between PD and MSA. CYP27B1 is the main 1α-hydroxylase for 25-hydroxyvitamin D and showed differences between HC and PD (p < 0.05), between HC and MSA (p < 0.005) and between PD and MSA (p = 0.055). CYP24A1, which inactivate 1,25-di-hydroxyvitamin D, showed no differences among groups. Conclusions: CYP27A1 might affect VD synthesis and cause low 25-hydroxyvitamin D levels in AD, PD and MSA patients. Low 1,25-di-hydroxyvitamin D levels in MSA patients might be caused by impaired feedback mediated by CYP27B1.

Nasal Dysbiosis in Cutaneous T-Cell Lymphoma Is Characterized by Shifts in Relative Abundances of Non-Staphylococcus Bacteria.

The nasal microbiome of patients with cutaneous T-cell lymphoma (CTCL) remains unexplored despite growing evidence connecting nasal bacteria to skin health and disease. Nasal swabs from 45 patients with CTCL (40 with mycosis fungoides, 5 with Sézary syndrome) and 20 healthy controls from the same geographical region (Chicago Metropolitan Area, Chicago, IL) were analyzed using sequencing of 16S ribosomal RNA and tuf2 gene amplicons. Nasal α-diversity did not differ between mycosis fungoides/Sézary syndrome and healthy controls (Shannon index, genus level, P = 0.201), but distinct microbial communities were identified at the class (R2 = 0.104, P = 0.023) and order (R2 = 0.0904, P = 0.038) levels. Increased relative abundance of the genera Catenococcus, Vibrio, Roseomonas, Acinetobacter, and unclassified Clostridiales was associated with increased skin disease burden (P < 0.005, q < 0.05). Performed to accurately resolve nasal Staphylococcus at the species level, tuf2 gene amplicon sequencing revealed no significant differences between mycosis fungoides/Sézary syndrome and healthy controls. Although S. aureus has been shown to worsen CTCL through its toxins, no increase in the relative abundance of this taxon was observed in nasal samples. Despite the lack of differences in Staphylococcus, the CTCL nasal microbiome was characterized by shifts in numerous other bacterial taxa. These data add to our understanding of the greater CTCL microbiome and provide context for comprehending nasal-skin and host‒tumor‒microbial relationships.

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures.

Alongside the currently used nasal swab testing, the COVID-19 pandemic situation would gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a large-scale, and with real time answers. A novel approach for COVID-19 assessment is adopted here, discriminating negative subjects versus positive or recovered subjects. The scope is to identify potential discriminating features, highlight mid and short-term effects of COVID on the voice and compare two custom algorithms. A pool of 310 subjects took part in the study; recordings were collected in a low-noise, controlled setting employing three different vocal tasks. Binary classifications followed, using two different custom algorithms. The first was based on the coupling of boosting and bagging, with an AdaBoost classifier using Random Forest learners. A feature selection process was employed for the training, identifying a subset of features acting as clinically relevant biomarkers. The other approach was centered on two custom CNN architectures applied to mel-Spectrograms, with a custom knowledge-based data augmentation. Performances, evaluated on an independent test set, were comparable: Adaboost and CNN differentiated COVID-19 positive from negative with accuracies of 100% and 95% respectively, and recovered from negative individuals with accuracies of 86.1% and 75% respectively. This study highlights the possibility to identify COVID-19 positive subjects, foreseeing a tool for on-site screening, while also considering recovered subjects and the effects of COVID-19 on the voice. The two proposed novel architectures allow for the identification of biomarkers and demonstrate the ongoing relevance of traditional ML versus deep learning in speech analysis.

Increased Serum Trimethylamine N-Oxide Level in Type 2 Diabetic Patients with Mild Cognitive Impairment.

Purpose: Trimethylamine N-oxide (TMAO) is a metabolite of phosphatidylcholine in red meat and other diets, which is associated with cardiovascular and other diseases. The aim of this study is to evaluate the associations of serum TMAO with mild cognitive impairment (MCI) in the Chinese type 2 diabetes mellitus (T2DM) population. Materials and Methods: A total of 253 hospitalized T2DM patients and 150 healthy controls were included in this cross-sectional study. Montreal Cognitive Assessment (MoCA) assessed the cognition function, and the 253 T2DM patients were divided into 74 subjects with MCI and 179 with non-MCI. Demographic data and biochemical test results were evaluated. Serum TMAO level was measured by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Results: A higher serum TMAO level was observed in T2DM patients compared with the healthy controls (P < 0.001). Among all T2DM patients, the MCI group (n = 74) showed higher serum TMAO levels than the non-MCI group. Spearman correlation test showed that TMAO levels were significantly positively correlated with age (r = 0.147, P = 0.019), body mass index (BMI) (r = 0.153, P = 0.015), diabetes duration (r = 0.160, P = 0.011), HbA1c (r = 0.138, P = 0.029), triglyceride (TG) (r = 0.138, P = 0.029), creatinine (r = 0.184, p = 0.003), hs-CRP (r = 0.243, P < 0.001), and were negatively correlated with HDL-C (r = -0.144, P = 0.022), BDNF (r = -0.165, p = 0.009), and MoCA (r = -0.386, P < 0.001) score (all P < 0.05). Multivariable Logistic regression identified high serum TMAO level as a significant independent factor of MCI in the T2DM patients (OR = 1.404, 95% CI = 1.255-1.571; P < 0.001). Conclusion: Our study showed that T2DM patients with MCI have elevated serum TMAO levels.