Crystallization Modulation and Holistic Passivation Enables Efficient Two-Terminal Perovskite/CuIn(Ga)Se2 Tandem Solar Cells.

Two-terminal (2-T) perovskite (PVK)/CuIn(Ga)Se2 (CIGS) tandem solar cells (TSCs) have been considered as an ideal tandem cell because of their best bandgap matching regarding to Shockley-Queisser (S-Q) limits. However, the nature of the irregular rough morphology of commercial CIGS prevents people from improving tandem device performances. In this paper, D-homoserine lactone hydrochloride is proven to improve coverage of PVK materials on irregular rough CIGS surfaces and also passivate bulk defects by modulating the growth of PVK crystals. In addition, the minority carriers near the PVK/C60 interface and the incompletely passivated trap states caused interface recombination. A surface reconstruction with 2-thiopheneethylammonium iodide and N,N-dimethylformamide assisted passivates the defect sites located at the surface and grain boundaries. Meanwhile, LiF is used to create this field effect, repelling hole carriers away from the PVK and C60 interface and thus reducing recombination. As a result, a 2-T PVK/CIGS tandem yielded a power conversion efficiency of 24.6% (0.16 cm2), one of the highest results for 2-T PVK/CIGS TSCs to our knowledge. This validation underscores the potential of our methodology in achieving superior performance in PVK/CIGS tandem solar cells.

Genetic association analyses highlight apolipoprotein B as a determinant of chronic kidney disease in patients with type 2 diabetes.

BACKGROUND: Blood lipid levels were associated with chronic kidney disease (CKD) in patients with type 2 diabetes (T2D), but the genetic basis and causal nature remains unclear. OBJECTIVE: This study aimed to investigate the relationships of lipids and their fractions with CKD in patients with T2D. METHODS: Our prospective analysis involved 8,607 White participants with T2D but no CKD at baseline from the UK Biobank. Five common lipid traits were included as exposures. Weighted genetic risk scores (GRSs) for these lipid traits were developed. The causal associations between lipid traits, as well as lipid fractions, and CKD were explored using linear or nonlinear Mendelian randomization (MR). The 10-year predicted probabilities of CKD were evaluated via integrating MR and Cox models. RESUTLS: Higher GRS of apolipoprotein B (ApoB) was associated with an increased CKD risk (HR[95 % CI]:1.07[1.02,1.13] per SD;P = 0.008) after adjusting for potential confounders. Linear MR indicated a positive association between genetically predicted ApoB levels and CKD (HR[95 % CI]:1.53[1.12,2.09];P = 0.008), but no evidence of associations was found between other lipid traits and CKD in T2D. Regarding 12 ApoB-contained lipid fractions, a significant causal association was found between medium very-low-density lipoprotein particles and CKD (HR[95 % CI]:1.16[1.02,1.32];P = 0.020). Nonlinear MR did not support nonlinearity in these causal associations. The 10-year probability curve showed that ApoB levels was positively associated with the risk of CKD in patients with T2D. CONCLUSION: Lower ApoB levels were causally associated with a reduced risk of CKD in patients with T2D, positioning ApoB as a potential therapeutic target for CKD prevention in this population.

Causal relationship between genetically determined plasma metabolites and stroke: A two sample Mendelian randomization study.

INTRODUCTION: This study investigates the causal relationship between plasma metabolites and stroke. METHOD: The primary analytical approach employed was the inverse variance weighted (IVW) method, complemented by the weighted median (WM) and MR Egger methods for Additionally, validation of the identified plasma metabolites was performed using the Steiger test and LD linkage disequilibrium score. Furthermore, the main results were confirmed through data from the UK Biobank. RESULT: The IVW analysis revealed the most notable negative association found in X-17335 levels (OR [95 % CI]: 0.82 [0.72, 0.94]). On the other hand, the strongest positive effect was seen in the 5'-homophase (AMP) to phase ratio (OR [95 % CI]: 1.17 [1.03, 1.32]). Moving on to the validation dataset, the most significant positive effect was observed in the 13 HODE+9-HODE levels (OR [95 % CI]: 0.996 [0.993, 0.999]), whereas the most significant negative effect was seen in the Dihydroxide levels (OR [95 % CI]: 1.004 [1.00, 1.007]). Notably, Alpha ketoglutarate levels exhibited strong causal effects in both datasets (OR 0.908 [0.841, 0.981], p = 0.0144). Enrichment analysis highlighted the association of Alpha ketoglutarate levels with five plasma metabolites in metabolic pathways relevant to stroke, specifically Arginine biosynthesis, Butanoate metabolism, Citrate cycle (TCA cycle), Alanine, aspartate, and glutamate metabolism, and Lipid acid metabolism, all linked to oxoglutaric acid. CONCLUSION: The discovery dataset showed the most significant positive effect of the 5'-homophase (AMP) to phase ratio, while the validation dataset revealed the most significant positive effect of the 13 HODE+9-HODE levels. Additionally, alpha ketoglutarate may offer a potential protective effect on stroke by influencing five metabolic pathways that intersect with Oxoglutaric acid during the progression of the condition.

Dietary intake of potassium, vitamin E, and vitamin C emerges as the most significant predictors of cardiovascular disease risk in adults.

Prediction models were developed to assess the risk of cardiovascular disease (CVD) based on micronutrient intake, utilizing data from 90,167 UK Biobank participants. Four machine learning models were employed to predict CVD risk, with performance evaluation metrics including area under the receiver operating characteristic curve (AUC), accuracy, recall, specificity, and F1-score. The eXtreme Gradient Boosting (XGBoost) model was utilized to rank the importance of 11 micronutrients in cardiovascular health. Results indicated that vitamin E, calcium, vitamin C, and potassium intake were associated with a reduced risk of CVD. The XGBoost model demonstrated the highest performance with an AUC of 0.952, highlighting potassium, vitamin E, and vitamin C as key predictors of CVD risk. Subgroup analysis revealed a stronger correlation between calcium intake and CVD risk in older adults and those with higher BMI, while vitamin B6 intake showed a link to CVD risk in women. Overall, the XGBoost model emphasized the significance of potassium, vitamin E, and vitamin C intake as primary predictors of CVD risk in adults, with age, sex, and BMI potentially influencing the importance of micronutrient intake in predicting CVD risk.

Synchronized crystallization in tin-lead perovskite solar cells.

Tin-lead halide perovskites with a bandgap near 1.2 electron-volt hold great promise for thin-film photovoltaics. However, the film quality of solution-processed Sn-Pb perovskites is compromised by the asynchronous crystallization behavior between Sn and Pb components, where the crystallization of Sn-based perovskites tends to occur faster than that of Pb. Here we show that the rapid crystallization of Sn is rooted in its stereochemically active lone pair, which impedes coordination between the metal ion and Lewis base ligands in the perovskite precursor. From this perspective, we introduce a noncovalent binding agent targeting the open metal site of coordinatively unsaturated Sn(II) solvates, thereby synchronizing crystallization kinetics and homogenizing Sn-Pb alloying. The resultant single-junction Sn-Pb perovskite solar cells achieve a certified power conversion efficiency of 24.13 per cent. The encapsulated device retains 90 per cent of the initial efficiency after 795 h of maximum power point operation under simulated one-sun illumination.

Identification of Blood Biomarkers in Ischemic Stroke by Integrated Analysis of Metabolomics and Proteomics.

We aimed to uncover the pathological mechanism of ischemic stroke (IS) using a combined analysis of untargeted metabolomics and proteomics. The serum samples from a discovery set of 44 IS patients and 44 matched controls were analyzed using a specific detection method. The same method was then used to validate metabolites and proteins in the two validation sets: one with 30 IS patients and 30 matched controls, and the other with 50 IS patients and 50 matched controls. A total of 105 and 221 differentially expressed metabolites or proteins were identified, and the association between the two omics was determined in the discovery set. Enrichment analysis of the top 25 metabolites and 25 proteins in the two-way orthogonal partial least-squares with discriminant analysis, which was employed to identify highly correlated biomarkers, highlighted 15 pathways relevant to the pathological process. One metabolite and seven proteins exhibited differences between groups in the validation set. The binary logistic regression model, which included metabolite 2-hydroxyhippuric acid and proteins APOM_O95445, MASP2_O00187, and PRTN3_D6CHE9, achieved an area under the curve of 0.985 (95% CI: 0.966-1) in the discovery set. This study elucidated alterations and potential coregulatory influences of metabolites and proteins in the blood of IS patients.

Olink Proteomics-Based Exploration of Immuno-Oncology-Related Biomarkers Leading to Lung Adenocarcinoma Progression.

INTRODUCTION: It is crucial to investigate the distinct proteins that contribute to the advancement of lung cancer. MATERIAL AND METHODS: We analyzed the expression levels of 92 immuno-oncology-related proteins in 96 pairs of lung adenocarcinoma tissue samples using Olink proteomics. The differentially expressed proteins (DEPs) were successively screened in tumor and paraneoplastic groups, early and intermediate-late groups by a nonparametric rank sum test, and the distribution and expression levels of DEPs were determined by volcano and heat maps, etc., and the area under the curve was calculated. RESULTS: A total of 24 DEPs were identified in comparisons between tumor and paracancerous tissues. Among them, interleukin-8 (IL8) and chemokine (C-C motif) ligand 20 (CCL20) as potential markers for distinguishing tumor tissues. Through further screening, it was found that interleukin-6 (IL6) and vascular endothelial growth factor A (VEGFA) may be able to lead to tumor progression through the JaK-STAT signaling pathway, Toll-like receptor signaling pathway and PI3K/AKT signaling pathway. Interestingly, our study revealed a down-regulation of IL6 and VEGFA in tumor tissues compared to paracancerous tissues. CONCLUSIONS: IL8 + CCL20 (AUC: 0.7056) have the potential to differentiate tumor tissue from paracancerous tissue; IL6 + VEGFA (AUC: 0.7531) are important protein markers potentially responsible for tumor progression.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.