Activation of peroxymonosulfate by sustainable biomass-based carbon nanotubes for controlling the spread of plant viruses in water environments.

With the increasing demand for water in hydroponic systems and agricultural irrigation, viral diseases have seriously affected the yield and quality of crops. By removing plant viruses in water environments, virus transmission can be prevented and agricultural production and ecosystems can be protected. But so far, there have been few reports on the removal of plant viruses in water environments. Herein, in this study, easily recyclable biomass-based carbon nanotubes catalysts were synthesized with varying metal activities to activate peroxymonosulfate (PMS). Among them, the magnetic 0.125Fe@NCNTs-1/PMS system showed the best overall removal performance against pepper mild mottle virus, with a 5.9 log10 removal within 1 min. Notably, the key reactive species in the 0.125Fe@NCNTs-1/PMS system is 1O2, which can maintain good removal effect in real water matrices (river water and tap water). Through RNA fragment analyses and label free analysis, it was found that this system could effectively cleave virus particles, destroy viral proteins and expose their genome. The capsid protein of pepper mild mottle virus was effectively decomposed where serine may be the main attacking sites by 1O2. Long viral RNA fragments (3349 and 1642 nt) were cut into smaller fragments (∼160 nt) and caused their degradation. In summary, this study contributes to controlling the spread of plant viruses in real water environment, which will potentially help protect agricultural production and food safety, and improve the health and sustainability of ecosystems.

Photothermal switch by gallic acid-calcium grafts synthesized by coordination chemistry for sequential treatment of bone tumor and regeneration.

The residual bone tumor and defects which is caused by surgical therapy of bone tumor is a major and important problem in clinicals. And the sequential treatment for irradiating residual tumor and repairing bone defects has wildly prospects. In this study, we developed a general modification strategy by gallic acid (GA)-assisted coordination chemistry to prepare black calcium-based materials, which combines the sequential photothermal therapy of bone tumor and bone defects. The GA modification endows the materials remarkable photothermal properties. Under the near-infrared (NIR) irradiation with different power densities, the black GA-modified bone matrix (GBM) did not merely display an excellent performance in eliminating bone tumor with high temperature, but showed a facile effect of the mild-heat stimulation to accelerate bone regeneration. GBM can efficiently regulate the microenvironments of bone regeneration in a spatial-temporal manner, including inflammation/immune response, vascularization and osteogenic differentiation. Meanwhile, the integrin/PI3K/Akt signaling pathway of bone marrow mesenchymal stem cells (BMSCs) was revealed to be involved in the effect of osteogenesis induced by the mild-heat stimulation. The outcome of this study not only provides a serial of new multifunctional biomaterials, but also demonstrates a general strategy for designing novel blacked calcium-based biomaterials with great potential for clinical use.

User Experience of a (Semi-) Automated Cognitive Phone-Based Assessment Within a Memory Clinic Population.

OBJECTIVE: We examined the user experience in different modalities (face-to-face, semi-automated phone-based, and fully automated phone-based) of cognitive testing in people with subjective cognitive decline and mild cognitive impairment. METHOD: A total of 67 participants from the memory clinic of the Maastricht University Medical Center+ participated in the study. The study consisted of cognitive tests in different modalities, namely, face-to-face, semi-automated phone-based guided by a researcher, and fully automated phone-based without the involvement of a researcher. After each assessment, a user experience questionnaire was administered, including questions about, for example, satisfaction, simplicity, and missing personal contact, on a seven-point Likert scale. Non-parametric tests were used to compare user experiences across different modalities. RESULTS: In all modalities, user experiences were rated above average. The face-to-face ratings were comparable to the ratings of the semi-automated phone-based assessment, except for the satisfaction and recommendation items, which were rated higher for the face-to-face assessment. The face-to-face assessment was preferred above the fully automated phone-based assessment on all items. In general, the semi- and fully automated phone-based assessments were comparable (simplicity, conceivability, quality of sound, visiting the hospital, and missing personal contact), while on all the other items, the semi-automated phone-based assessment was preferred. CONCLUSIONS: User experience was rated high within all modalities. Simplicity, conceivability, comfortability, and participation scores were comparable in the semi-automated phone-based and face-to-face assessment. Based on these findings and earlier research on validation of the semi-automated phone-based assessment, the semi-automated assessment could be useful for screening for clinical trials, and after more research, in clinical practice.

The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.

BACKGROUND: Various machine learning (ML) models based on resting-state functional MRI (Rs-fMRI) have been developed to facilitate differential diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the diagnostic accuracy of such models remains understudied. Therefore, we conducted this systematic review and meta-analysis to explore the diagnostic accuracy of Rs-fMRI-based radiomics in differentiating MCI from AD. METHODS: PubMed, Embase, Cochrane, and Web of Science were searched from inception up to February 8, 2024, to identify relevant studies. Meta-analysis was conducted using a bivariate mixed-effects model, and sub-group analyses were carried out by the types of ML tasks (binary classification and multi-class classification tasks). FINDINGS: In total, 23 studies, comprising 5,554 participants were enrolled in the study. In the binary classification tasks (twenty studies), the diagnostic accuracy of the ML model for AD was 0.99 (95%CI: 0.34 ~ 1.00), with a sensitivity of 0.94 (95%CI: 0.89 ~ 0.97) and a specificity of 0.98 (95%CI: 0.95 ~ 1.00). In the multi-class classification tasks (six studies), the diagnostic accuracy of the ML model was 0.98 (95%CI: 0.98 ~ 0.99) for NC, 0.96 (95%CI: 0.96 ~ 0.96) for early mild cognitive impairment (EMCI), 0.97 (95%CI: 0.96 ~ 0.97) for late mild cognitive impairment (LMCI), and 0.95 (95%CI: 0.95 ~ 0.95) for AD. CONCLUSIONS: The Rs-fMRI-based ML model can be adapted to multi-class classification tasks. Therefore, multi-center studies with large samples are needed to develop intelligent application tools to promote the development of intelligent ML models for disease diagnosis.

[Classification of mild cognitive impairment and normal cognition using an automated voice-based testing application].

AIM: An easy-to-use tool that can detect cognitive decline in mild cognitive impairment (MCI) is required. In this study, we aimed to construct a machine learning model that discriminates between MCI and cognitively normal (CN) individuals using spoken answers to questions and speech features. METHODS: Participants of ≥50 years of age were recruited from the Silver Human Resource Center. The Japanese Version of the Mini-Mental State Examination (MMSE-J) and Clinical Dementia Rating (CDR) were used to obtain clinical information. We developed a research application that presented neuropsychological tasks via automated voice guidance and collected the participants' spoken answers. The neuropsychological tasks included time orientation, sentence memory tasks (immediate and delayed recall), and digit span memory-updating tasks. Scores and speech features were obtained from spoken answers. Subsequently, a machine learning model was constructed to classify MCI and CN using various classifiers, combining the participants' age, gender, scores, and speech features. RESULTS: We obtained a model using Gaussian Naive Bayes, which classified typical MCI (CDR 0.5, MMSE ≤26) and typical CN (CDR 0 and MMSE ≥29) with an area under the curve (AUC) of 0.866 (accuracy 0.75, sensitivity 0.857, specificity 0.712). CONCLUSIONS: We built a machine learning model that can classify MCI and CN using spoken answers to neuropsychological questions. Easy-to-use MCI detection tools could be developed by incorporating this model into smartphone applications and telephone services.

Depressive symptoms mediate the longitudinal relationships between sleep quality and cognitive functions among older adults with mild cognitive impairment: A cross-lagged modeling analysis.

Although it is generally recognized that sleep quality, depressive symptoms, and cognitive functions are related respectively, the main ambiguity comes from difficulties in determining their cause-effect relationships. The present study aimed to explore the longitudinal causation relationships among sleep quality, depressive symptoms, and cognitive functions in older people with mild cognitive impairment (MCI). A total of 134 patients from 24 communities in Ningbo City, Zhejiang Province, China with MCI were interviewed at baseline, while 124 of them were re-interviewed 2 months later, and 122 were re-interviewed 4 months later. The Patient Health Questionnaire-9, the Pittsburgh Sleep Quality Index and the Montreal Cognitive Assessment Scale were assessed in the interview. Cross-lagged models were tested to disentangle the relationships among sleep quality, depressive symptoms, and cognitive functions using structural equation modeling with latent variables on the four-mouth longitudinal data. The correlation coefficients between sleep quality and depressive symptoms were significant showing the stability across time points of assessment, while the correlation coefficient of cognitive function was not significant (r = 0.159, p > 0.05). The results of index of model fit indicated that the cross-lagged model was acceptable (CFI = 0.934, TLI = 0.899, RMSEA = 0.075, χ2/df = 1.684). The results of cross-lagged model analysis supported the complete mediating role of depressive symptoms in the association between sleep quality and cognitive functions, where worse sleep quality may lead to more severe depressive symptoms, which in turn leads to more severe cognitive decline. In Conclusion, sleep quality is significantly correlated with cognitive functions in patients with mild cognitive impairment, which association is fully mediated by depressive symptoms. Approaches addressing sleep quality and depressive symptoms are recommended and hold promise for the management of mild cognitive impairment.

Association between blood-based protein biomarkers and brain MRI in the Alzheimer's disease continuum: a systematic review.

Blood-based biomarkers (BBM) are becoming easily detectable tools to reveal pathological changes in Alzheimer's disease (AD). A comprehensive and up-to-date overview of the association between BBM and brain MRI parameters is not available. This systematic review aimed to summarize the literature on the associations between the main BBM and MRI markers across the clinical AD continuum. A systematic literature search was carried out on PubMed and Web of Science and a total of 33 articles were included. Hippocampal volume was positively correlated with Aß42 and Aß42/Aß40 and negatively with Aß40 plasma levels. P-tau181 and p-tau217 concentrations were negatively correlated with temporal grey matter volume and cortical thickness. NfL levels were negatively correlated with white matter microstructural integrity, whereas GFAP levels were positively correlated with myo-inositol values in the posterior cingulate cortex/precuneus. These findings highlight consistent associations between various BBM and brain MRI markers even in the pre-clinical and prodromal stages of AD. This suggests a possible advantage in combining multiple AD-related markers to improve accuracy of early diagnosis, prognosis, progression monitoring and treatment response.

Dual-Modal Near-Infrared Organic Nanoparticles: Integrating Mild Hyperthermia Phototherapy with Fluorescence Imaging.

Purpose: Our study seeks to develop dual-modal organic-nanoagents for cancer therapy and real-time fluorescence imaging, followed by their pre-clinical evaluation on a murine model. Integrating NIR molecular imaging with nanotechnology, our aim is to improve outcomes for early-stage cutaneous melanoma by offering more effective and less invasive methods. This approach has the potential to enhance both photothermal therapy (PTT) and Sentinel Lymph Node Biopsy (SLNB) procedures for melanoma patients. Methods: NIR-797-isothiocyanate was encapsulated in poly(D,L-lactide-co-glycolide) acid (PLGA) nanoparticles (NPs) using a two-step protocol, followed by thorough characterization, including assessing loading efficiency, fluorescence stability, and photothermal conversion. Biocompatibility and cellular uptake were tested in vitro on melanoma cells, while PTT assay, with real-time thermal monitoring, was performed in vivo on tumor-bearing mice under irradiation with an 808 nm laser. Finally, ex vivo fluorescence microscopy, histopathological assay, and TEM imaging were performed. Results: Our PLGA NPs, with a diameter of 270 nm, negative charge, and 60% NIR-797 loading efficiency, demonstrated excellent stability and fluorescence properties, as well as efficient light-to-heat conversion. In vitro studies confirmed their biocompatibility and cellular internalization. In vivo experiments demonstrated their efficacy as photothermal agents, inducing mild hyperthermia with temperatures reaching up to 43.8 °C. Ex vivo microscopy of tumor tissue confirmed persistent NIR fluorescence and uniform distribution of the NPs. Histopathological and TEM assays revealed early apoptosis, immune cell response, ultrastructural damage, and intracellular material debris resulting from combined NP treatment and irradiation. Additionally, TEM analyses of irradiated zone margins showed attenuated cellular damage, highlighting the precision and effectiveness of our targeted treatment approach. Conclusion: Specifically tailored for dual-modal NIR functionality, our NPs offer a novel approach in cancer PTT and real-time fluorescence monitoring, signaling a promising avenue toward clinical translation.

Production and characterization of low-density silicon nitride reinforced zinc nanocomposite coatings on mild steel for applications in marine and automotive industries.

In today's automotive, marine and petrochemical industries, the desire for lightweight materials has increased. Hence, necessitating the production of components with low density. In this work, lightweight Zn-Si3N4 coatings were developed by including Si3N4 in the zinc matrix. The optimal coatings were produced on steel samples at 45 °C and varied Si3N4 particles and voltages following ASTM A53/A53M standard. The deterioration (corrosion) property i.e. corrosion rate (CR) and current density (jocorr) of the uncoated (control) and coated samples were examined in 0.5 M of sulphuric acid using a potentiodynamic polarization technique following ASTM G3/G102 standard. The microstructure of the samples was studied via the SEM micrographs and XRD patterns, while the wear performance resistance (following ASTM G99 standard) and electrical conductivity of the samples were examined with a pin-on-disc tribometer and ammeter-voltmeter. The corrosion experiment indicated that the uncoated mild steel specimen possessed a CR of 12.345 mm year-1 and jocorr of 1060 µA/cm2, while the CR and jcorr of the coated samples ranged from 2.6793 to 4.7975 mm year-1 and 231-413 µA/cm2, respectively. The lower CR and jcorr values of the coated specimens, relative to the coated sample showed that the coatings possessed superior passivation ability in the test medium. The SEM micrographs of the samples showed refined morphology, while the XRD patterns revealed high peak intensity crystals such as Zn4SiN, ZnNSi, Zn4N and Zn2NSi, which could be beneficial to the mechanical properties and corrosion resistance of the steel. Moreover, the wear resistance study indicated that the COF of the uncoated sample ranged from 0.1 to 0.5, while those for coated specimens ranged from 0.05 to 0.35. Similarly, the uncoated steel exhibited a wear volume (WV) of 0.00508 mm3, while the WV of the coated specimens ranged from 0.00266 to 0.0028 mm3, indicating the existence of high strengthening mechanisms between the interface of the protecting device and the steel. Also, the electrical conductivity of the mild steel sample reduced from 12.97 Ω-1cm-1 to 0.64 Ω-1cm-1, indicating that the electrical resistivity of the steel was enhanced by the coatings.

Neuropsychiatric symptoms: Risk factor or disease marker? A study of structural imaging biomarkers of Alzheimer's disease and incident cognitive decline.

Neuropsychiatric symptoms (NPS) are risk factors for Alzheimer's disease (AD) but can also manifest secondary to AD pathology. Mild behavioral impairment (MBI) refers to later-life emergent and persistent NPS that may mark early-stage AD. To distinguish MBI from NPS that are transient or which represent psychiatric conditions (non-MBI NPS), we investigated the effect of applying MBI criteria on NPS associations with AD structural imaging biomarkers and incident cognitive decline. Data for participants (n = 1273) with normal cognition (NC) or mild cognitive impairment (MCI) in the National Alzheimer's Coordinating Center Uniform Data Set were analyzed. NPS status (MBI, non-MBI NPS) was derived from the Neuropsychiatric Inventory Questionnaire and psychiatric history. Normalized measures of bilateral hippocampal (HPC) and entorhinal cortex (EC) volume, and AD meta-region of interest (ROI) mean cortical thickness were acquired from T1-weighted magnetic resonance imaging scans. Multivariable linear and Cox regressions examined NPS associations with imaging biomarkers and incident cognitive decline, respectively. MBI was associated with lower volume and cortical thickness in all ROIs in both NC and MCI, except for EC volume in NC. Non-MBI NPS were only associated with lower HPC volume in NC. Although both of the NPS groups showed higher hazards for MCI/dementia than No NPS, MBI participants showed more rapid decline. Although both types of NPS were linked to HPC atrophy, only NPS that emerged and persisted in later-life, consistent with MBI criteria, were related to AD neurodegenerative patterns beyond the HPC. Moreover, MBI predicted faster progression to dementia than non-MBI NPS.

How Mild Is the Mild Long COVID? A Comprehensive Neuropsychological Assessment of Patients with Cognitive Complaints.

The global impact of the Coronavirus Disease (COVID-19) pandemic has extended beyond physical health, leading to widespread mental health issues. Beyond respiratory symptoms, there is a growing concern about long-term cognitive effects, particularly in individuals who experienced mild cases of the infection. We aimed to investigate the neuropsychological aspects of long-term COVID-19 in non-hospitalized adults compared with a control group. This cross-sectional study included 42 participants, 22 individuals with a history of mild COVID, and 20 healthy controls. The participants were recruited from the community and underwent a comprehensive neuropsychological assessment. Participants from the mild COVID group reported cognitive symptoms persisting for an average of 203.86 days and presented a higher frequency of psychological treatment history (81.8%) compared with the control group (25.0%). History of anxiety disorders was more prevalent in the mild COVID group (63.6%) than in the control group (20.0%). Significant reductions in verbal working memory were observed in the mild COVID group. Levels of anxiety were found to have a significant impact on difficulties with visual recognition memory. This study reveals important neuropsychological alterations in individuals following mild COVID-19, emphasizing executive functions deficits. Our findings underscore the persistence of these deficits even in non-hospitalized cases, suggesting potential inflammatory mechanisms in the central nervous system. The study highlights the need for comprehensive assessments and targeted interventions to address the diverse cognitive impacts on individuals recovering from COVID-19.

Enhancing Alzheimer's Disease Diagnosis and Care by Focusing on Plasma Biomarkers for Identifying Mild Cognitive Impairment.

Biomarkers that accurately identify mild cognitive impairment (MCI) are of greater importance for Alzheimer's disease (AD) management and treatment. On the other hand, blood-based biomarkers are not only more practical but also less invasive than the common cerebrospinal fluid biomarkers. In their report in the Journal of Alzheimer's Disease, Wang and collaborators identified 67 upregulated and 220 downregulated long noncoding RNAs (lncRNAs). They further demonstrated that 4 of these lncRNAs could discriminate MCI from cognitively healthy individuals. Apart from their significance as potential biomarkers for MCI diagnosis, these lncRNAs can offer additional information on the cellular mechanisms of AD pathology.

The effect of multi-component exercise intervention in older people with Parkinson's disease and mild cognitive impairment: A randomized controlled study.

A multimodal exercise training program might be the best way to improve motor and cognitive function in patients with Parkinson's disease (PD), but this has yet to be fully proven in PD patients with mild cognitive impairment (MCI). This study aims to examine the feasibility and effectiveness of a theory-based, multi-component exercise intervention in older people with PD-MCI. Participants were randomized into an intervention group (n=23) and an active control group (n=23), receiving the theory-based multi-component exercise intervention and Parkinson's health exercises, respectively. All participants performed 60-minute exercise training sessions three times a week over a 12-week period. The retention rate at post-intervention was 95.7% (42/46) for the entire cohort. The attendance rates were 99.6% in the intervention group and 99.5% in the control group. No adverse events occurred. The intervention group showed significantly greater improvements than the control group in global cognitive function, executive function, physical motor function, balance and gait, depression, and quality of life. This study indicates that the theory-based multi-component exercise intervention demonstrates high feasibility in promoting exercise adherence and is an effective treatment option for older adults with PD-MCI.

Effectiveness of brain magnetic resonance imaging in the early diagnosis and characterization of dementias; a systematic review. / Efectividad de la resonancia magnética cerebral en el diagnóstico precoz y caracterización de las demencias: revisión sistemática.

INTRODUCTION: Magnetic resonance imaging (MRI) is a frequently used test in the diagnosis of dementia. The objective was to evaluate its effectiveness for the early diagnosis of dementia in patients with mild cognitive impairment (MCI). MATERIAL AND METHODS: Original studies were selected from systematic reviews between 2011 and 2021, according to PRISMA 2020 criteria. QUADAS-2 and GRADE tools were used, and a meta-analysis was performed. RESULTS: Final selection of 23 articles. Patient selection and index test had a high probability of bias. The certainty of the evidence was very low. In the hippocampus, sensitivity was 0.62 (95%CI 0.48-0.79) and specificity 0.70 (95%CI 0.55-0.80). In the temporal lobe, sensitivity was 0.65 (range 0.45) and specificity 0.69 (range 0.32). CONCLUSIONS: There is insufficient evidence to recommend routine brain MRI for the early diagnosis of dementia in patients with MCI.

Conversion between the Rowland Universal Dementia Assessment Scale and Mini-Mental State Examination test scores in majority and minority populations.

INTRODUCTION: Despite the Rowland Universal Dementia Assessment Scale (RUDAS) having significant advantages as a cognitive screening tool, particularly for minority populations, the Mini-Mental State Examination (MMSE) test is the most widely used test for cognitive screening in Alzheimer's disease (AD). This study aimed to develop a conversion table to predict MMSE scores from observed RUDAS scores, allowing an easy-to-use method to compare both screening tests. METHODS: The equipercentile equating method was used to develop the conversion table using a training sample consisting of cognitively intact participants and individuals with early-stage AD. The resulting conversion table was validated in two samples, comprising participants from majority and minority populations assessed in Spanish. RESULTS: The conversion table demonstrated excellent reliability with intraclass correlation coefficients of.92 in both validation samples. CONCLUSION: This study provides a conversion table between RUDAS and MMSE scores, improving the comparability of these cognitive screening tools for use in clinical and research purposes.

Bifidobacterium pseudonumeratum W112 alleviated depressive and liver injury symptoms induced by chronic unpredictable mild stress via gut-liver-brain axis.

Introduction: Several studies indicated that depression is associated with liver injury. The role of probiotics in alleviating depression is focused on improving the abnormalities of the central nervous system through the gut-brain axis, while the effect on liver injury is still unclear. The aim of this study was to elucidate the potential link between the antidepressant effect of a potential probiotic strain Bifidobacterium pseudocatenulatum W112 and its effect on alleviating liver injury. Methods: The 4-week-old Kunming mice were exposed to chronic stress for 4 weeks to establish a depression model. Results: The depression-like behavior and related biomakers in chronic unpredictable mild stress (CUMS) mice were altered by supplemented with W112 for 2 weeks. Meanwhile, the modulation effect of W112 the gut microbiota in CUMS mice also result in an increase in the abundance of beneficial bacteria and a decrease in the abundance of harmful bacteria. Significantly, liver injury was observed in CUMS model mice. W112 improved liver injury by reducing AST/ALT in serum. Quantitative PCR results indicated that the mechanism of action of W112 in ameliorating liver injury was that the altered gut microbiota affected hepatic phospholipid metabolism and bile acid metabolism. Discussion: In short, W112 could significantly improve the depressive and liver injury symptoms caused by CUMS. The gut-liver-brain axis is a potential connecting pathway between the antidepressant effects of W112 and its alleviation of liver injury.

Transcranial direct current stimulation and neuronal functional connectivity in MCI: role of individual factors associated to AD.

Background: Alzheimer's disease (AD) encompasses a spectrum that may progress from mild cognitive impairment (MCI) to full dementia, characterized by amyloid-beta and tau accumulation. Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic option, but its efficacy in relation to individual genetic and biological risk factors remains underexplored. Objective: To evaluate the effects of a two-week anodal tDCS regimen on the left dorsolateral prefrontal cortex, focusing on functional connectivity changes in neural networks in MCI patients resulting from various possible underlying disorders, considering individual factors associated to AD such as amyloid-beta deposition, APOE ϵ4 allele, BDNF Val66Met polymorphism, and sex. Methods: In a single-arm prospective study, 63 patients with MCI, including both amyloid-PET positive and negative cases, received 10 sessions of tDCS. We assessed intra- and inter-network functional connectivity (FC) using fMRI and analyzed interactions between tDCS effects and individual factors associated to AD. Results: tDCS significantly enhanced intra-network FC within the Salience Network (SN) and inter-network FC between the Central Executive Network and SN, predominantly in APOE ϵ4 carriers. We also observed significant sex*tDCS interactions that benefited inter-network FC among females. Furthermore, the effects of multiple modifiers, particularly the interaction of the BDNF Val66Met polymorphism and sex, were evident, as demonstrated by increased intra-network FC of the SN in female Met non-carriers. Lastly, the effects of tDCS on FC did not differ between the group of 26 MCI patients with cerebral amyloid-beta deposition detected by flutemetamol PET and the group of 37 MCI patients without cerebral amyloid-beta deposition. Conclusions: The study highlights the importance of precision medicine in tDCS applications for MCI, suggesting that individual genetic and biological profiles significantly influence therapeutic outcomes. Tailoring interventions based on these profiles may optimize treatment efficacy in early stages of AD.

Association between type of menopause and mild cognitive impairment: The REDLINC XII study.

OBJECTIVE: To evaluate the association between type of menopause (spontaneous or surgical) and mild cognitive impairment (MCI). STUDY DESIGN: This study was a cross-sectional, observational, and sub-analytical investigation conducted within gynecological consultations across nine Latin American countries. METHOD: We assessed sociodemographic, clinical, and anthropometric data, family history of dementia, and the presence of MCI using the Montreal Cognitive Assessment (MoCA) tool. RESULTS: The study involved 1185 postmenopausal women with a mean age of 55.3 years and a body mass index of 26.4 kg/m2. They had an average of 13.3 years of education, and 37 % were homemakers. Three hundred ninety-nine experienced menopause before 40, including 136 with surgical menopause (bilateral oophorectomy). Out of the 786 women who experienced menopause at 40 or more years, 110 did so due to bilateral oophorectomy. There were no differences in MoCA scores among women who experienced menopause before or after the age of 40. However, lower MoCA scores were observed in women with surgical menopause than in those with spontaneous menopause (23.8 ± 4.9 vs. 25.0 ± 4.3 points, respectively, p < 0.001). Our logistic regression model with clustering of patients within countries found a significant association between MCI and surgical menopause (OR 1.47, 95 % CI: 1.01-2.16), use (ever) of menopausal hormone therapy (OR 0.33, 95 % CI: 0.21-0.50), and having >12 years of education (OR 0.21, 95 % CI: 0.14-0.30). CONCLUSION: When comparing women who experience spontaneous menopause over the age of 40 with those who undergo it before this age, there was no observed increased risk of developing MCI, while those with surgical menopause, independent of age, are more prone to cognitive decline. Women who have ever used menopausal hormone therapy have a lower MCI risk. Further research is warranted to delve deeper into this topic.

Development of a diagnostic model for detecting mild cognitive impairment in young and middle-aged patients with obstructive sleep apnea: a prospective observational study.

Objectives: Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition linked to the accelerated onset of mild cognitive impairment (MCI). However, the prevalence of undiagnosed MCI among OSA patients is high and attributable to the complexity and specialized nature of MCI diagnosis. Timely identification and intervention for MCI can potentially prevent or delay the onset of dementia. This study aimed to develop screening models for MCI in OSA patients that will be suitable for healthcare professionals in diverse settings and can be effectively utilized without specialized neurological training. Methods: A prospective observational study was conducted at a specialized sleep medicine center from April 2021 to September 2022. Three hundred and fifty consecutive patients (age: 18-60 years) suspected OSA, underwent the Montreal Cognitive Assessment (MoCA) and polysomnography overnight. Demographic and clinical data, including polysomnographic sleep parameters and additional cognitive function assessments were collected from OSA patients. The data were divided into training (70%) and validation (30%) sets, and predictors of MCI were identified using univariate and multivariate logistic regression analyses. Models were evaluated for predictive accuracy and calibration, with nomograms for application. Results: Two hundred and thirty-three patients with newly diagnosed OSA were enrolled. The proportion of patients with MCI was 38.2%. Three diagnostic models, each with an accompanying nomogram, were developed. Model 1 utilized body mass index (BMI) and years of education as predictors. Model 2 incorporated N1 and the score of backward task of the digital span test (DST_B) into the base of Model 1. Model 3 expanded upon Model 1 by including the total score of digital span test (DST). Each of these models exhibited robust discriminatory power and calibration. The C-statistics for Model 1, 2, and 3 were 0.803 [95% confidence interval (CI): 0.735-0.872], 0.849 (95% CI: 0.788-0.910), and 0.83 (95% CI: 0.763-0.896), respectively. Conclusion: Three straightforward diagnostic models, each requiring only two to four easily accessible parameters, were developed that demonstrated high efficacy. These models offer a convenient diagnostic tool for healthcare professionals in diverse healthcare settings, facilitating timely and necessary further evaluation and intervention for OSA patients at an increased risk of MCI.

Predicting the Progression of Mild Cognitive Impairment to Alzheimer's Dementia Using Recurrent Neural Networks With a Series of Neuropsychological Tests.

BACKGROUND AND PURPOSE: The prevalence of Alzheimer's dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression. METHODS: Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer's Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline. RESULTS: The experimental results confirmed that the Preclinical Alzheimer's Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916. CONCLUSIONS: Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.