Improved Hole-Selective Contact Enables Highly Efficient and Stable FAPbBr3 Perovskite Solar Cells and Semitransparent Modules.

Self-assembled monolayers (SAMs) as the hole-selective contact have achieved remarkable success in iodine-based perovskite solar cells (PSCs), while their impact on bromine-based PSCs is limited due to the poor perovskite crystallization behavior and mismatched energy level alignment. Here, a highly efficient SAM of (2-(3,6-diiodo-9H-carbazol-9-yl)ethyl)phosphonic acid (I-2PACz) is employed to address these challenges in FAPbBr3-based PSCs. The incorporation of I atoms into I-2PACz not only releases tensile stress within FAPbBr3 perovskite, promoting oriented crystallization and minimizing defects through halogen-halogen bond, but also optimizes the energy levels alignment at hole-selective interface for enhanced hole extraction. Ultimately, a power conversion efficiency (PCE) of 11.14% is achieved, which stands among the highest reported value for FAPbBr3 PSCs. Furthermore, the semitransparent devices/modules exhibit impressive PCEs of 8.19% and 6.23% with average visible transmittance of 41.98% and 38.99%. Remarkably, after operating at maximum power point for 1000 h, the encapsulated device maintains 93% of its initial PCE. These results demonstrate an effective strategy for achieving high-performance bromine-based PSCs toward further applications.

AKT1 Promotes Tumorigenesis and Metastasis by Directly Phosphorylating Hexokinases.

The importance of protein kinase B (AKT) in tumorigenesis and development is well established, but its potential regulation of metabolic reprogramming via phosphorylation of the hexokinase (HK) isozymes remains unclear. There are two HK family members (HK1/2) and three AKT family members (AKT1/2/3), with varied distribution of AKTs exhibiting distinct functions in different tissues and cell types. Although AKT is known to phosphorylate HK2 at threonine 473, AKT-mediated phosphorylation of HK1 has not been reported. We examined direct binding and phosphorylation of HK1/2 by AKT1 and identified the phosphorylation modification sites using coimmunoprecipitation, glutathione pull-down, western blotting, and in vitro kinase assays. Regulation of HK activity through phosphorylation by AKT1 was also examined. Uptake of 2-[1,2-3H]-deoxyglucose and production of lactate were investigated to determine whether AKT1 regulates glucose metabolism by phosphorylating HK1/2. Functional assays, immunohistochemistry, and tumor experiments in mice were performed to investigate whether AKT1-mediated regulation of tumor development is dependent on its kinase activity and/or the involvement of HK1/2. AKT interacted with and phosphorylated HK1 and HK2. Serine phosphorylation significantly increased AKT kinase activity, thereby enhancing glycolysis. Mechanistically, the phosphorylation of HK1 at serine 178 (S178) by AKT significantly decreased the Km and enhanced the Vmax by interfering with the formation of HK1 dimers. Mutations in the AKT phosphorylation sites of HK1 or HK2 significantly abrogated the stimulatory characteristics of AKT on glycolysis, tumorigenesis, and cell migration, invasion, proliferation, and metastasis. HK1-S178 phosphorylation levels were significantly correlated with the occurrence and metastasis of different types of clinical tumors. We conclude that AKT not only regulates tumor glucose metabolism by directly phosphorylating HK1 and HK2, but also plays important roles in tumor progression, proliferation, and migration.

Causal association between plasma metabolites and neurodegenerative diseases.

BACKGROUND: Establishing causal relationships between metabolic biomarkers and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) is a challenge faced by observational studies. In this study, our aim was to investigate the causal associations between plasma metabolites and neurodegenerative diseases using Mendelian Randomization (MR) methods. METHODS: We utilized genetic associations with 1400 plasma metabolic traits as exposures. We used large-scale genome-wide association study (GWAS) summary statistics for AD and PD as our discovery datasets. For validation, we performed repeated analyses using different GWAS datasets. The main statistical method employed was inverse variance-weighted (IVW). We also conducted enrichment pathway analysis for IVW-identified metabolites. RESULTS: In the discovered dataset, there are a total of 69 metabolites (36 negatively, 33 positively) potentially associated with AD, and 47 metabolites (24 negatively， 23 positively) potentially associated with PD. Among these, 4 significant metabolites overlap with significant metabolites (PIVW < 0.05)in the validation dataset for AD, and 1 metabolite overlaps with significant metabolites in the validation dataset for PD. Three metabolites serve as common potential metabolic markers for both AD and PD, including Tryptophan betaine, Palmitoleoylcarnitine (C16:1), and X-23655 levels. Further pathway enrichment analysis suggests that the SLC-mediated transmembrane transport pathway, involving tryptophan betaine and carnitine metabolites, may represent potential intervention targets for treating AD and PD. CONCLUSION: This study offers novel insights into the causal effects of plasma metabolites on degenerative diseases through the integration of genomics and metabolomics. The identification of metabolites and metabolic pathways linked to AD and PD enhances our comprehension of the underlying biological mechanisms and presents promising targets for future therapeutic interventions in AD and PD.

Global, regional, and national burden of trachea, bronchus, and lung cancer among women of reproductive age, 1990-2019.

BACKGROUND: Trachea, bronchus, and lung (TBL) cancer has demonstrated a discernible feminization and a tendency towards younger onset in recent decades. Therefore, our objective is to examine the most recent patterns in the worldwide prevalence of TBL among women of reproductive age on a global, regional, and national scale. METHODS: To assess the prevalence trends of TBL in women of reproductive age, we calculated the estimated annual percentage change (EAPC), age-standardized incidence rate (ASIR), age-standardized death rate (ASDR), and disability-adjusted life years (DALYs) for 204 countries and territories from 1990 to 2019. These calculations were based on the Global Burden of Disease (GBD) 2019 database. RESULTS: From 1990 to 2019, there was a global increase in the absolute number of incidence cases, deaths, and DALYs of TBL in women of reproductive age. However, the ASIR, ASDR, and age-standardized DALY rates were decreasing over this period, with EAPC of -0.77 (95 % confidence interval [CI]: -1.03 to -0.51), -1.08 (95 % CI: -1.34 to -0.82), and -1.10 (95 % CI: -1.36 to -0.84), respectively. This trend was observed even in regions with higher Socio-Demographic Index (SDI). East Asia consistently had the highest ASIR, ASDR, and age-standardized DALY rate, but there was a decreasing trend. Conversely, Eastern Sub-Saharan Africa displayed an increasing burden pattern. When examining countries individually, Monaco, Greenland, and Palau had the highest ASIR. Moreover, in most countries, the ASIR for TBL increased with age, particularly among women aged 35-49 years. CONCLUSIONS: Despite a global decline in ASIR, ASDR, and age-standardized DALY rates for TBL in women of reproductive age over the past three decades, there is still a troubling increase observed in low- and low-middle SDI regions. It is crucial to implement effective preventive and curative measures in these regions in order to address this concerning trend.

Methods and clinical biomarker discovery for targeted proteomics using Olink technology.

PURPOSE: This paper is to offer insights for designing research utilizing Olink technology to identify biomarkers and potential therapeutic targets for disease treatment. EXPERIMENTAL DESIGN: We discusses the application of Olink technology in oncology, cardiovascular, respiratory and immune-related diseases, and Outlines the advantages and limitations of Olink technology. RESULTS: Olink technology simplifies the search for therapeutic targets, advances proteomics research, reveals the pathogenesis of diseases, and ultimately helps patients develop precision treatments. CONCLUSIONS: Although proteomics technology has been rapidly developed in recent years, each method has its own disadvantages, so in the future research, more methods should be selected for combined application to verify each other.

Two-Dimensional Materials for Highly Efficient and Stable Perovskite Solar Cells.

Perovskite solar cells (PSCs) offer low costs and high power conversion efficiency. However, the lack of long-term stability, primarily stemming from the interfacial defects and the susceptible metal electrodes, hinders their practical application. In the past few years, two-dimensional (2D) materials (e.g., graphene and its derivatives, transitional metal dichalcogenides, MXenes, and black phosphorus) have been identified as a promising solution to solving these problems because of their dangling bond-free surfaces, layer-dependent electronic band structures, tunable functional groups, and inherent compactness. Here, recent progress of 2D material toward efficient and stable PSCs is summarized, including its role as both interface materials and electrodes. We discuss their beneficial effects on perovskite growth, energy level alignment, defect passivation, as well as blocking external stimulus. In particular, the unique properties of 2D materials to form van der Waals heterojunction at the bottom interface are emphasized. Finally, perspectives on the further development of PSCs using 2D materials are provided, such as designing high-quality van der Waals heterojunction, enhancing the uniformity and coverage of 2D nanosheets, and developing new 2D materials-based electrodes.

Doping with KBr to Achieve High-Performance CsPbBr3 Semitransparent Perovskite Solar Cells.

Wide-bandgap semitransparent perovskite photovoltaics are emerging as one of the ideal candidates for building-integrated photovoltaics (BIPV). However, surface defects in inorganic CsPbBr3 perovskite prepared by vapor deposition severely limit the optoelectronic performance of perovskite solar cells. To address this issue, a strategy of doping a trace amount of KBr into perovskite by vapor deposition is adopted, effectively improving the quality of the film, reducing surface defect concentration, and enhancing the transportation and extraction of charge carriers. Simultaneously, fully physical vapor deposition technology is employed to fabricate perovskite solar cells with an average visible light transmittance of 44%. These devices exhibited an ultrahigh open-circuit voltage of 1.55 V and a superior power conversion efficiency (PCE) of 7.28%, demonstrating excellent moisture and heat resistance. Moreover, the corresponding 5 cm × 5 cm modules achieve a PCE of 5.35% with great thermal insulation capability. This work provides an approach for fabricating highly efficient all-inorganic perovskite solar cells with high average visible light transmittance, demonstrating new insights into their application in building-integrated photovoltaics.

Effects of exogenous glycine betaine on growth and development of tomato seedlings under cold stress.

Low temperature is a type of abiotic stress affecting the tomato (Solanum lycopersicum) growth. Understanding the mechanisms and utilization of exogenous substances underlying plant tolerance to cold stress would lay the foundation for improving temperature resilience in this important crop. Our study is aiming to investigate the effect of exogenous glycine betaine (GB) on tomato seedlings to increase tolerance to low temperatures. By treating tomato seedlings with exogenous GB under low temperature stress, we found that 30 mmol/L exogenous GB can significantly improve the cold tolerance of tomato seedlings. Exogenous GB can influence the enzyme activity of antioxidant defense system and ROS levels in tomato leaves. The seedlings with GB treatment presented higher Fv/Fm value and photochemical activity under cold stress compared with the control. Moreover, analysis of high-throughput plant phenotyping of tomato seedlings also supported that exogenous GB can protect the photosynthetic system of tomato seedlings under cold stress. In addition, we proved that exogenous GB significantly increased the content of endogenous abscisic acid (ABA) and decreased endogenous gibberellin (GA) levels, which protected tomatoes from low temperatures. Meanwhile, transcriptional analysis showed that GB regulated the expression of genes involved in antioxidant capacity, calcium signaling, photosynthesis activity, energy metabolism-related and low temperature pathway-related genes in tomato plants. In conclusion, our findings indicated that exogenous GB, as a cryoprotectant, can enhance plant tolerance to low temperature by improving the antioxidant system, photosynthetic system, hormone signaling, and cold response pathway and so on.

Branched-chain amino acids and the risks of dementia, Alzheimer's disease, and Parkinson's disease.

Background: We aimed to examine the association between blood levels of Branched-chain amino acids (BCAAs) - specifically isoleucine, leucine, and valine - and the susceptibility to three neurodegenerative disorders: dementia, Alzheimer's disease (AD), and Parkinson's disease (PD). Methods: Based on data from the UK Biobank, a Cox proportional hazard regression model and a dose-response relationship were used to analyze the association between BCAAs and the risks of dementia, AD, and PD. We also generated a healthy lifestyle score and a polygenic risk score. Besides, we conducted a sensitivity analysis to ensure the robustness of our findings. Results: After adjusting for multiple covariates, blood concentrations of isoleucine, leucine, and valine were significantly associated with a reduced risk of dementia and AD. This association remained robust even in sensitivity analyses. Similarly, higher levels of isoleucine and leucine in the blood were found to be associated with an increased risk of PD, but this positive correlation could potentially be explained by the presence of covariates. Further analysis using a dose-response approach revealed that a blood leucine concentration of 2.14 mmol/L was associated with the lowest risk of dementia. Conclusion: BCAAs have the potential to serve as a biomarker for dementia and AD. However, the specific mechanism through which BCAAs are linked to the development of dementia, AD, and PD remains unclear and necessitates additional investigation.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank.

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Improving Sedum plumbizincicola genetic transformation with the SpGRF4-SpGIF1 gene and the self-excision CRE/LoxP system.

MAIN CONCLUSION: S. plumbizincicola genetic transformation was optimized using a self-excision molecular-assisted transformation system by integrating the SpGRF4/SpGIF1 gene with XVE and Cre/loxP. Sedum plumbizincicola, despite being an excellent hyperaccumulator of cadmium and zinc with significant potential for soil pollution phytoremediation on farmland, has nonetheless trailed behind other major model plants in genetic transformation technology. In this study, different explants and SpGRF4-SpGIF1 genes were used to optimize the genetic transformation of S. plumbizincicola. We found that petiole and stem segments had higher genetic transformation efficiency than cluster buds. Overexpression of SpGRF4-SpGIF1 could significantly improve the genetic transformation efficiency and shorten the period of obtaining regenerated buds. However, molecular assistance with overexpression of SpGRF4-SpGIF1 leads to abnormal morphology, resulting in plant tissue enlargement and abnormal growth. Therefore, we combined SpGRF4-SpGIF1 with XVE and Cre/loxP to obtain DNA autocleavage transgenic plants induced by estradiol, thereby ensuring normal growth in transgenic plants. This study optimized the S. plumbizincicola genetic transformation system, improved the efficiency of genetic transformation, and established a self-excision molecular-assisted transformation system. This work also established the basis for studying S. plumbizincicola gene function, and for S. plumbizincicola breeding and germplasm innovation.

A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases.

BACKGROUND AND AIMS: A machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson's disease (PD), and Alzheimer's disease (AD). MATERIALS AND METHODS: The eXtreme Gradient Boosting (XGBoost) algorithm was used to construct a metabolic biomarker-based model (metabolic model), a clinical risk factor-based model (clinical model), and a combined model for the prediction of the three NLDs. Risk discrimination (c-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index values were determined for each model. RESULTS: The results indicate that incorporation of metabolic biomarkers into the clinical model afforded a model with improved performance in the prediction of dementia, AD, and PD, as demonstrated by NRI values of 0.159 (0.039-0.279), 0.113 (0.005-0.176), and 0.201 (-0.021-0.423), respectively; and IDI values of 0.098 (0.073-0.122), 0.070 (0.049-0.090), and 0.085 (0.068-0.101), respectively. CONCLUSION: The performance of the model based on circulating NMR spectroscopy-detected metabolic biomarkers was better than that of the clinical model in the prediction of dementia, AD, and PD.

Causal association of blood lipids with all-cause and cause-specific mortality risk: a Mendelian randomization study.

Dyslipidemia has long been implicated in elevating mortality risk; yet, the precise associations between lipid traits and mortality remained undisclosed. Our study aimed to explore the causal effects of lipid traits on both all-cause and cause-specific mortality. One-sample Mendelian randomization (MR) with linear and nonlinear assumptions was conducted in a cohort of 407,951 European participants from the UK Biobank. Six lipid traits, consisting of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and lipoprotein(a), were included to investigate the causal associations with mortality. Two-sample MR was performed to replicate the association between each lipid trait and all-cause mortality. Univariable MR results showed that genetically predicted higher ApoA1 was significantly associated with a decreased all-cause mortality risk (HR[95% CI]:0.93 [0.89-0.97], P value = 0.001), which was validated by the two-sample MR analysis. Higher lipoprotein(a) was associated with an increased risk of all-cause mortality (1.03 [1.01-1.04], P value = 0.002). Multivariable MR confirmed the direct causal effects of ApoA1 and lipoprotein(a) on all-cause mortality. Meanwhile, nonlinear MR found no evidence for nonlinearity between lipids and all-cause mortality. Our examination into cause-specific mortality revealed a suggestive inverse association between ApoA1 and cancer mortality, a significant positive association between lipoprotein(a) and cardiovascular disease mortality, and a suggestive positive association between lipoprotein(a) and digestive disease mortality. High LDL-C was associated with an increased risk of cardiovascular disease mortality but a decreased risk of neurodegenerative disease mortality. The findings suggest that implementing interventions to raise ApoA1 and decrease lipoprotein(a) levels may improve overall health outcomes and mitigate cancer and digestive disease mortality.

Reinforcing self-assembly of hole transport molecules for stable inverted perovskite solar cells.

Power conversion efficiencies (PCEs) of inverted perovskite solar cells (PSCs) have been improved by the use of a self-assembled monolayer (SAM) hole transport layer. Long-term stability of PSCs requires keeping the SAM compact under the perovskite layer during operation. We found that strong polar solvents in the perovskite precursor desorb the SAM if it is anchored on substrates by hydrogen-bonded, rather than covalently bonded, hydroxyl groups. We used atomic-layer deposition to create an indium tin oxide substrate with a fully covalent hydroxyl-covered surface for SAM anchoring, as well as a SAM with a trimethoxysilane group that exhibited strong tridentate anchoring to the substrate. The resulting PSCs achieved PCEs of 24.8 (certified 24.6) and 23.2% with aperture areas of 0.08 and 1.01 square centimeters, respectively. The devices retained 98.9 and 98.2% of the initial PCE after 1000 hours damp-heat test and operation in maximum power point tracking at 85°C for 1200 hours under standard illumination, respectively.

Dietary protein sources, genetics, and cardiovascular disease incidence.

BACKGROUND: To explore the potential correlation between the amount and source of dietary protein and cardiovascular disease (CVD), as well as the potential impact of genetic susceptibility on these connections. METHODS: We performed a prospective analysis of 98,224 participants from the UK. We measured dietary protein intake using two 24-hour dietary recall interviews. To analyze the data, we used multivariable-adjusted Cox regression models and restricted cubic spline models. Additionally, we calculated weighted genetic risk scores. RESULTS: A total of 8818 new cases of CVD were documented, which included 4076 cases of coronary artery disease (CAD) and 1126 cases of stroke. The study found a J-shaped association (p nonlinearity = 0.005) between CVD risk and the percentage of energy obtained from consuming plant protein. Higher intake of plant protein and whole protein was associated with a decreased risk of CVD. On the other hand, larger intakes of animal protein was linked to a higher occurrence of CAD. Additionally, increased intake of plant protein was also linked to a lower incidence of stroke. Replacing 5 % of animal protein-based energy intake with plant protein-based energy intake resulted in a 5 % decrease in CVD risk. LIMITATIONS: There remains an effect of residual confounders. CONCLUSION: The consumption of larger amounts of plant protein, whole protein, and nut protein was found to be associated with a lower risk of CVD events. Conversely, higher intakes of animal protein was associated with an increased risk of CAD events. Furthermore, replacing 5 % of energy intake from animal protein with energy intake from plant protein was found to reduce the risk of CVD by 5 %.

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke.

The immune response is considered essential for pathology of ischemic stroke (IS), but it remains unclear which immune response-related proteins exhibit altered expression in IS patients. Here, we used Olink proteomics to examine the expression levels of 92 immune response-related proteins in the sera of IS patients (n = 88) and controls (n = 88), and we found that 59 of these proteins were differentially expressed. Feature variables were screened from the differentially expressed proteins by the least absolute shrinkage and selection operator (LASSO) and the random forest and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. Ultimately, we identified six potential protein biomarkers of IS, namely, MASP1, STC1, HCLS1, CLEC4D, PTH1R, and PIK3AP1, and established a logistic regression model that used these proteins to diagnose IS. The AUCs of the models in the internal validation and the test set were 0.962 (95% confidence interval (CI): 0.895-1.000) and 0.954 (95% CI: 0.884-1.000), respectively, and the same protein detection method was performed in an external independent validation set (AUC: 0.857 (95% CI: 0.801-0.913)). These proteins may play a role in immune regulation via the C-type lectin receptor signaling pathway, the PI3K-AKT signaling pathway, and the B-cell receptor signaling pathway.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a serious threat worldwide. Although early screening is suggested to be the most effective method to prevent and control CRC, the current situation of early screening for CRC is still not optimistic. In China, the incidence of CRC in the Yangtze River Delta region is increasing dramatically, but few studies have been conducted. Therefore, it is necessary to develop a simple and efficient early screening model for CRC. AIM: To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC. METHODS: Data of 64448 participants obtained from Ningbo Hospital, China between 2014 and 2017 were retrospectively analyzed. The cohort comprised 64448 individuals, of which, 530 were excluded due to missing or incorrect data. Of 63918, 7607 (11.9%) individuals were considered to be high risk for CRC, and 56311 (88.1%) were not. The participants were randomly allocated to a training set (44743) or validation set (19175). The discriminatory ability, predictive accuracy, and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic (ROC) curves and calibration curves and by decision curve analysis. Finally, the model was validated internally using a bootstrap resampling technique. RESULTS: Seven variables, including demographic, lifestyle, and family history information, were examined. Multifactorial logistic regression analysis revealed that age [odds ratio (OR): 1.03, 95% confidence interval (CI): 1.02-1.03, P < 0.001], body mass index (BMI) (OR: 1.07, 95%CI: 1.06-1.08, P < 0.001), waist circumference (WC) (OR: 1.03, 95%CI: 1.02-1.03 P < 0.001), lifestyle (OR: 0.45, 95%CI: 0.42-0.48, P < 0.001), and family history (OR: 4.28, 95%CI: 4.04-4.54, P < 0.001) were the most significant predictors of high-risk CRC. Healthy lifestyle was a protective factor, whereas family history was the most significant risk factor. The area under the curve was 0.734 (95%CI: 0.723-0.745) for the final validation set ROC curve and 0.735 (95%CI: 0.728-0.742) for the training set ROC curve. The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population. CONCLUSION: The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age, BMI, WC, lifestyle, and family history exhibited high accuracy.

Corrigendum to "Differences in primary and secondary stroke prevention strategies for Chinese men and women" [Prev. Med. Rep. 33 (2023) 102219].

[This corrects the article DOI: 10.1016/j.pmedr.2023.102219.].

Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications.

Invasive brain-computer interfaces (BCIs) have the capability to simultaneously record discrete signals across multiple scales, but how to effectively process and analyze these potentially related signals remains an open challenge. This article introduces an innovative approach that merges modern control theory with spiking neural networks (SNNs) to bridge the gap among multiscale discrete information. Specifically, the macroscopic point-to-point trajectory is formulated as an optimal control problem with fixed terminal time and state, and it is iteratively solved using the direct dynamic programming (DDP) algorithm. Additionally, SNN is utilized to simulate microscale neural activities in the premotor cortex, employing the product of the weighted adjacency matrix and the mesoscale firing rate to approximate the macroscopic trajectory. The error between actual macroscale behavior and the preceding approximation is then used to update the weighted adjacency matrix through the recursive least square (RLS) method. Analysis and simulation of various tasks, including low-dimensional point-to-point tasks, high-dimensional complex Lorenz systems, and center-out-and-back tasks, verify the feasibility and interpretability of our method in processing multiscale signals ranging from spiking neurons to motion trajectory through the integration of SNN and control theory.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension.

INTRODUCTION: The burden of stroke in patients with hypertension is very high, and its prediction is critical. OBJECTIVES: We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension. METHODS: This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension. RESULTS: Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957). CONCLUSIONS: We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.