Machine learning for the prediction of cognitive impairment in older adults.

Objective: The purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm. Methods: The complete data of 2,226 participants aged 60-80 years were extracted from the 2011-2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application. Results: The study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application. Conclusions: ML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly.

Associations between dietary and blood inflammatory indices and their effects on cognitive function in elderly Americans.

Objective: To determine the correlations between dietary and blood inflammation indices in elderly Americans and their effects on cognitive function. Methods: This research extracted data from the 2011-2014 National Health and Nutrition Examination Survey for 2,479 patients who were ≥60 years old. Cognitive function was assessed as a composite cognitive function score (Z-score) calculated from the results of the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, the Animal Fluency test, and the Digit Symbol Substitution Test. We used a dietary inflammatory index (DII) calculated from 28 food components to represent the dietary inflammation profile. Blood inflammation indicators included the white blood cell count (WBC), neutrophil count (NE), lymphocyte count (Lym), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), neutrophil-albumin ratio (NAR), systemic immune-inflammation index [SII, calculated as (peripheral platelet count) × NE/Lym], and systemic inflammatory response index [SIRI, calculated as (monocyte count) × NE/Lym]. WBC, NE, Lym, NLR, PLR, NAR, SII, SIRI, and DII were initially treated as continuous variables. For logistic regression, WBC, NE, Lym, NLR, PLR, NAR, SII, and SIRI were divided into quartile groups, and DII was divided into tertile groups. Results: After adjusting for covariates, WBC, NE, NLR, NAR, SII, SIRI, and DII scores were markedly higher in the cognitively impaired group than in the normal group (p < 0.05). DII was negatively correlated with the Z-score when combined with WBC, NE, and NAR (p < 0.05). After adjusting for all covariates, DII was positively correlated with SII in people with cognitive impairment (p < 0.05). Higher DII with NLR, NAR, SII, and SIRI all increased the risk of cognitive impairment (p < 0.05). Conclusion: DII was positively correlated with blood inflammation indicators, and higher DII and blood inflammation indicators increased the risk of developing cognitive impairment.

An efficient and multifunctional S-scheme heterojunction photocatalyst constructed by tungsten oxide and graphitic carbon nitride: Design and mechanism study.

The design of multifunctional photocatalyst with strong redox performance is the key to achieve sustainable utilization of solar energy. In this study, an elegant S-scheme heterojunction photocatalyst was constructed between metal-free graphitic carbon nitride (g-C3N4) and noble-metal-free tungsten oxide (W18O49). As-established S-scheme heterojunction photocatalyst enabled multifunctional photocatalysis behavior, including hydrogen production, degradation (Rhodamine B) and bactericidal (Escherichia coli) properties, which represented extraordinary sustainability. Finite-difference time-domain (FDTD) simulations manifested that the integration of double-layer hollow g-C3N4 nanotubes with W18O49 nanowires could expand the light harvesting ability. Demonstrated by density functional theory (DFT) calculations and electron spin resonance (ESR) measurements, the S-scheme heterojunction not only promoted the separation of carriers, but also improved the redox ability of the catalyst. This work provides a theoretical basis for enhancing the photocatalytic performances and broadening the application field of photocatalysis.

Effectiveness of Response Inhibition Training and Its Long-Term Effects in Healthy Adults: A Systematic Review and Meta-Analysis.

Objective: This study aims to evaluate the effectiveness and long-term effects of response inhibition training as a therapeutic approach in healthy adults. Methods: The PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal Database (VIP) were searched for studies. Data on the improvement of Cognitive function and its long-term effect were extracted by two authors independently. The pooled data were meta-analyzed using a random-effects model, and the quality of each eligible study was assessed by The Cochrane Collaboration's tool. Results: Nine articles were included. 1 of the articles included 2 trials, so 10 eligible trials (response inhibition training group vs. control group) were identified. A total of 490 patients were included. Response inhibition training has beneficial effects on improving cognitive function in healthy adults compared to control treatment (SMD, -0.93; 95% CI, -1.56 to -0.30; Z = 2.88, P = 0.004), the subgroup analysis results showed that either GNG training alone (SMD, -2.27; 95% CI, -3.33 to -1.21; Z = 4.18, P < 0.0001) or the combination of both SST and GNG significantly improved cognitive function in healthy adults (SMD, -0.94; 95% CI, -1.33 to -0.56; Z = 4.80, P < 0.0001), whereas SST training alone did not have such an effect (SMD, -0.15; 95% CI, -0.76 to 0.47; Z = 0.47, P = 0.64). But its long-term effects are not significant (SMD, -0.29; 95% CI, -0.68 to 0.10; Z = 1.45, P = 0.15). The subgroup analysis results showed that neither GNG training alone (SMD, -0.25; 95% CI, -0.75 to 0.24; Z = 0.99, P = 0.32) nor SST training alone (SMD, 0.03; 95% CI, -0.42 to 0.48; Z = 0.14, P = 0.89) could improve the cognitive function of healthy adults in the long term. In contrast, the combination of both training (SMD, -0.95; 95% CI, -1.46 to -0.45; Z = 3.68, P = 0.0002) can have long-term effects on the improvement of cognitive function in healthy adults. Conclusion: The findings of our study indicate that response inhibition training can improve the cognitive function of healthy adults and that more RCTs need to be conducted to validate their usefulness in clinical cases.

High-performance reversible aqueous Zinc-Ion battery based on Zn2+ pre-intercalation alpha-manganese dioxide nanowires/carbon nanotubes.

Rechargeable aqueous zinc ion batteries (ZIBs) have attracted more and more attention due to the advantages of high safety, low cost, and environmental friendly in recent years. However, the lack of high-performance cathode materials and uncertain reaction mechanisms hinder the large-scale application of ZIBs. Herein, a 1D structure interlaced by ZnxMnO2 nanowires and carbon nanotubes is synthesized as cathode material for ZIB. The ZnxMnO2/CNTs cathode exhibits excellent specific capacity of 400 mAh g-1 at 100 mA g-1 and outstanding long-cycle stability (with a capacity retention of 93% after 100 cycles at 1000 mA g-1), which indicates the Zn2+ pre-intercalation and composite carbon nanotubes can effectively change the storage space of the tunnel structure and increase the electron transmission rate. In addition, the energy storage mechanism of the highly reversible co-insertion of H+ and Zn2+ is further elaborated. This work has enlightenment and promotion for the future research of ZIBs cathode materials. Moreover, the simple preparation method, low cost and excellent performance of ZnxMnO2/CNTs cathode material provide a new way for the practical application of ZIBs.

Electrospun Zn Doped CuFe2O4 Nanofibers with Enhanced Photo Fenton-Like Catalytic Activity.

One dimensional Zn doped CuFe2O4 spinel ferrite nanofibers were successfully prepared via a facile electrospinning method followed by two different calcination routes. The results showed that the as-prepared nanofibers through two-step calcination exhibited more uniform size distribution in diameter compared with those calcined by one-step method. X-ray diffraction (XRD) results indicated that with the increase of Zn content the position of diffraction peaks of Zn doped CuFe2O4 slightly shift towards lower 2θ angle because the ionic sizes of the Zn2+ (0.74 Å) is larger than that of Cu2+ (0.69 Å). Fourier transform infrared spectroscopy (FTIR) results showed that with increasing Zn content the position of vibrational band (590 cm-1) shifted towards the smaller wavenumber. Generally, photo-generated carriers increased with the increasing of Zn content. The photo Fenton-like catalytic results revealed that the doping of Zn facilitated the enhancement of degradation efficiency of catalysts. Additionally, 10 at.% Zn doped CuFe2O4 exhibited the best photo Fenton-like catalytic activity and the degradation efficiency of Rhodamine B (RhB) could reach 100% in 40 min. Finally, the enhancement of photo Fenton-like catalytic mechanism of the Zn doped CuFe2O4 nanofibers was mainly attributed to actived spinel structure lattice by Zn doping, which allows more Cu2+ and Fe3+ ions are involved in the photo Fenton-like catalytic reaction.

Regenerated WO2.72 nanowires with superb fast and selective adsorption for cationic dye: Kinetics, isotherm, thermodynamics, mechanism.

Hydrothermally prepared nonstoichiometry tungsten oxide (WO2.72) nanowires possess super selective adsorption performance for methylene blue (MB). The effects of dye concentration, contact time, pH and temperature on the adsorption properties of WO2.72 were investigated. The experimental results indicated that WO2.72 nanowires have a high adsorption capacity (547.32 mg/g) under neutral pH and a fast adsorption rate (adsorbing 100% within 6 min) for MB dye. The kinetic data showed good correlation coefficient (R2 > 0.99) for the pseudo-second-order. The equilibrium data suggesting the monolayer coverage of adsorbate as it fits well with the Langmuir isotherm model (R2 > 0.99). The energy functions of chemical thermodynamics revealed the adsorbate transport across the phase boundary is spontaneous and exothermic. Electrostatic absorption was proposed as the main adsorption mechanisms. Calcination treatment was utilized to decompose MB adsorbed on WO2.72 and regenerate WO2.72. The adsorption efficiency of WO2.72 nanowires can still reach 86.54% after recycling five times, which indicated that as-obtained WO2.72 nanowires are a very promising high-efficiency adsorbent for practical purification of dyeing waste water.