Homogenized contact in all-perovskite tandems using tailored 2D perovskite.

The fabrication of scalable all-perovskite tandem solar cells is considered an attractive route to commercialize perovskite photovoltaic modules1. However, The certified efficiency of 1-cm2 scale all-perovskite tandem solar cells lags behind their small-area (~0.1 cm2) counterparts2,3. This performance deficit originates from inhomogeneity in wide-bandgap (WBG) perovskite solar cells (PSCs) at a large scale. The inhomogeneity is known to be introduced at the bottom interface and within the perovskite bulk itself4,5. Here we uncover another crucial source for the inhomogeneity - the top interface formed during the deposition of the electron transport layer (ETL, C60). Meanwhile, the poor ETL interface is also a significant limitation of device performance. We address this issue by introducing a mixture of 4-fluorophenethylamine (F-PEA) and 4-trifluoromethyl-phenylammonium (CF3-PA) to create a tailored two-dimensional perovskite layer (TTDL), in which F-PEA forms a two-dimensional perovskite at the surface reducing contact losses and inhomogeneity, CF3-PA enhances charge extraction and transport. As a result, we demonstrate a high open-circuit voltage of 1.35 V and an efficiency of 20.5% in 1.77-eV WBG PSCs at a square centimeter scale. By stacking with a narrow-bandgap perovskite sub-cell, we report 1.05 cm2 all-perovskite tandem cells delivering 28.5% (certified 28.2%) efficiency, the highest among all reported so far. Our work showcases the importance of treating the top perovskite/ETL contact for upscaling perovskite solar cells.

Malnutrition is associated with severe outcome in elderly patients hospitalised with COVID-19.

Some studies have identified influencing factors of COVID-19 illness in elderly, such as underlying diseases, but research on the effect of nutritional status is still lacking. This study retrospectively examined the influence of nutritional status on the outcome of elderly COVID-19 inpatients. A retrospective analysis of the clinical data of 4241 COVID-19 patients who were admitted to a third-class hospital of Nanchang between November 1, 2022 and January 31, 2023 was conducted. Nutritional status was assessed using the prognostic nutritional index (PNI) and controlling nutritional status score (CONUT). The influence of nutritional status on the outcome of COVID-19 patients was determined through multivariate adjustment analysis, restrictive cubic spline, and receiver operating characteristic curve (ROC). Compared with mild/no malnutrition, severe malnutrition substantially increased the critical outcome of COVID-19. A linear relationship was observed between the odds ratio (OR) and PNI and CONUT (P > 0.05). The area under the ROC curve indicated that PNI was the better predictor. The optimal cutoff value of PNI was 38.04 (95%CI: 0.797 ~ 0.836, AUC = 0.817), with a sensitivity of 70.7% and a specificity of 79.6%. The critical illness of elderly COVID-19 patients shows a linear relationship with malnutrition at admission. The use of PNI to assess the prognosis of COVID-19 eldeely patients is reliable, highlighting the importance for doctors to closely pay attention to the nutritional status of COVID-19 patients. Focusing on nutritional status in clinical practice can effectively reduce the critical illness of elderly COVID-19 patients.

The relationship between the Barthel Index and stroke-associated pneumonia in elderly patients and factors of SAP.

BACKGROUND: Stroke-associated pneumonia (SAP) is a common complication in stroke patients, and the Barthel Index (BI) is a well-established metric for assessing activities of daily living (ADL). However, the association between BI and SAP in acute ischemic stroke (AIS) patients remains unclear. This study aims to investigate the relationship between BI at admission and SAP, and explore the factors in AIS elderly patients. METHOD: Retrospective data were collected from ischemic stroke patients hospitalized at the Second Affiliated Hospital of Nanchang University between January 2018 and July 2021, including their basic demographic and laboratory test results. Restricted cubic spline regression, multivariate logistic regression analysis, and receiver operating characteristic (ROC) curve analysis were employed to investigate the relationship between BI and SAP. Additionally, the Shapley Additive exPlanations (SHAP) method was used to identify the factors influencing SAP. RESULTS: The study included 7,548 eligible stroke patients with a mean age of 75.1 ± 7.6 years, among which 41.14% were female. The SAP group demonstrated significantly lower BI compared to the non-SAP group (50.86 ± 35.60 vs. 75.27 ± 26.33, P < 0.001). Additionally, a conspicuous trend of decreasing SAP risk across the Q1-4 groups was observed (P < 0.001). The RCS analysis further confirmed a gradual reduction in SAP risk with increasing BI. Based on the clinical model, both the BI (NRI = 0.014, P = 0.005; IDI = 0.04, P < 0.001) and the NIHSS score (NRI = 0.09, P = 0.03; IDI = 0.025, P < 0.001) demonstrated additional predictive value for SAP. Multivariate logistic regression and SHAP analysis identified WBC, CONUT, TG, UA, and RBC levels, as well as the type of health insurance (urban employee basic medical insurance), as important independent predictors of SAP. CONCLUSION: BI at admission constitutes a risk factor for the onset of SAP in elderly patients with AIS, Compared to the NIHSS and mRS score, BI may be a more reliable and practical predictor of SAP.

Nanosized-Selenium-Application-Mediated Cadmium Toxicity in Aromatic Rice at Different Stages.

Cadmium (Cd) pollution restricts the rice growth and poses a threat to human health. Nanosized selenium (NanoSe) is a new nano material. However, the effects of NanoSe application on aromatic rice performances under Cd pollution have not been reported. In this study, a pot experiment was conducted with two aromatic rice varieties and a soil Cd concentration of 30 mg/kg. Five NanoSe treatments were applied at distinct growth stages: (T1) at the initial panicle stage, (T2) at the heading stage, (T3) at the grain-filling stage, (T1+2) at both the panicle initial and heading stages, and (T1+3) at both the panicle initial and grain-filling stages. A control group (CK) was maintained without any application of Se. The results showed that, compared with CK, the T1+2 and T1+3 treatments significantly reduced the grain Cd content. All NanoSe treatments increased the grain Se content. The grain number per panicle, 1000-grain weight, and grain yield significantly increased due to NanoSe application under Cd pollution. The highest yield was recorded in T3 and T1+3 treatments. Compared with CK, all NanoSe treatments increased the grain 2-acetyl-1-pyrroline (2-AP) content and impacted the content of pyrroline-5-carboxylic acid and 1-pyrroline which are the precursors in 2-AP biosynthesis. In conclusion, the foliar application of NanoSe significantly reduced the Cd content, increased the Se content, and improved the grain yield and 2-AP content of aromatic rice. The best amendment was applying NanoSe at both the panicle initial and grain-filling stages.

Adverse childhood experiences, marital status and depressive symptoms in later life among the Chinese middle-aged and older adults : the mediating role of marital status.

BACKGROUND: Many studies have shown that adverse childhood experiences (ACEs) lead to adverse social relations in middle-aged and older adults and harm physical and mental health, but few studies have focused on the impact of ACEs on marital status in middle-aged and older adults and the potential influence of marital status between ACEs and depressive symptoms. PURPOSE: This study aimed to analyze the effect of ACEs on marital status and depressive symptoms in the Chinese middle-aged and older adults, and to explore the mediating role of marital status in the association between ACEs and depressive symptoms in middle-aged and older adults. METHOD: This study used the China Health and Retirement Longitudinal Study (CHARLS) 2014 life history survey and 2015 and 2018 follow-up data to analyze, ten ACEs conditions and marital status were collected by questionnaire, using the Center for Epidemiological Studies Depression Scale (CESD-10) 10-item short form to assess depressive symptoms. The association between cumulative ACEs and marital status was assessed by constructing a multinomial logistic regression (MLR) model, as well as a binary logistic regression model to assess the association between ACEs and depressive symptoms. The mediating role of marital status in the association between ACEs and depressive symptoms was also assessed. RESULTS: A total of 10,246 individuals aged 45 years or older were included in the analysis. Compared to individuals who did not experience ACEs, those who experienced two or more ACEs had a higher risk of being unmarried (seperated/divorced/never married) (OR = 1.67, 95% CI=[1.10,2.51]) and a higher risk of depressive symptoms (OR = 1.66, 95% CI=[1.49,1.84]) in middle and old age. Unmarried status partially mediated the association of ACEs with depressive symptoms. CONCLUSION: Chinese middle-aged and older people who experienced two or more ACEs have higher risks of unmarried status and depressive symptoms, and unmarried status partially mediated the ACEs-depressive symptom association. These findings reveal the fact that we need to develop life-cycle public health strategies to reduce exposure to ACEs and society should give more attention to the marital status of older people, thereby reducing the risk of depression among middle-aged and older adults in China.

Solvent engineering for scalable fabrication of perovskite/silicon tandem solar cells in air.

Perovskite/silicon tandem solar cells hold great promise for realizing high power conversion efficiency at low cost. However, achieving scalable fabrication of wide-bandgap perovskite (~1.68 eV) in air, without the protective environment of an inert atmosphere, remains challenging due to moisture-induced degradation of perovskite films. Herein, this study reveals that the extent of moisture interference is significantly influenced by the properties of solvent. We further demonstrate that n-Butanol (nBA), with its low polarity and moderate volatilization rate, not only mitigates the detrimental effects of moisture in air during scalable fabrication but also enhances the uniformity of perovskite films. This approach enables us to achieve an impressive efficiency of 29.4% (certified 28.7%) for double-sided textured perovskite/silicon tandem cells featuring large-size pyramids (2-3 µm) and 26.3% over an aperture area of 16 cm2. This advance provides a route for large-scale production of perovskite/silicon tandem solar cells, marking a significant stride toward their commercial viability.

Effects of 3-year organic farming management on soil antibiotic resistant genes and virulence factors in a double rice cropping system.

Investigating the antibiotic resistance genes (ARGs) and virulence factors (VFs) within soil microbial communities is crucial for understanding microbial ecology and the evolution of antibiotic resistance. However, the study of ARGs, VFs, and their predominant microbial hosts in soils under varying rice production management practices remains largely underexplored. To this end, a three-year field experiment was conducted under organic management within a double rice cropping system in South China. The study revealed that, in contrast to conventional management (CK), organic farming practices did not significantly alter the total reads of ARGs and VFs. However, there was a notable alteration in the ARGs abundance at the antibiotic class level, such as an increase (P < 0.05) in the abundance of Multidrug ARGs (by 1.7 %) and a decrease (P < 0.05) in Rifamycin (by 17.5 %) and Fosfomycin ARGs (by 15.3 %). Furthermore, a significant shift in VFs was observed under organic farming compared to CK, characterized by an increase (P < 0.05) in offensive VFs and a decrease (P < 0.05) in nonspecific VFs and the regulation of virulence-associated genes. Key microbial taxa identified as influencing ARGs and VFs in the tested soil samples, e.g., Proteobacteria. The findings highlight the need for more detailed attention to soil ecology within organic rice production systems in South China, particularly concerning the significant alterations observed in ARGs and VFs.

Machine learning model for predicting stroke recurrence in adult stroke patients with moyamoya disease and factors of stroke recurrence.

OBJECT: The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence. METHODS: The data of this retrospective study originated from the database of JiangXi Province Medical Big Data Engineering & Technology Research Center. Moreover, the information of MMD patients admitted to the second affiliated hospital of Nanchang university from January 1st, 2007 to December 31st, 2019 was acquired. A total of 661 patients from January 1st, 2007 to February 28th, 2017 were covered in the training set, while the external validation set comprised 284 patients that fell into a scope from March 1st, 2017 to December 31st, 2019. First, the information regarding all the subjects was compared between the training set and the external validation set. The key influencing variables were screened out using the Lasso Regression Algorithm. Furthermore, the models for predicting stroke recurrence in 1, 2, and 3 years after the initial stroke were built based on five different machine learning algorithms, and all models were externally validated and then compared. Lastly, the CatBoost model with the optimal performance was explained using the SHapley Additive exPlanations (SHAP) interpretation model. RESULT: In general, 945 patients suffering from MMD were recruited, and the recurrence rate of acute stroke in 1, 2, and 3 years after the initial stroke reached 11.43%(108/945), 18.94%(179/945), and 23.17%(219/945), respectively. The CatBoost models exhibited the optimal prediction performance among all models; the area under the curve (AUC) of these models for predicting stroke recurrence in 1, 2, and 3 years was determined as 0.794 (0.787, 0.801), 0.813 (0.807, 0.818), and 0.789 (0.783, 0.795), respectively. As indicated by the results of the SHAP interpretation model, the high Suzuki stage, young adults (aged 18-44), no surgical treatment, and the presence of an aneurysm were likely to show significant correlations with the recurrence of stroke in adult stroke patients subjected to MMD. CONCLUSION: In adult stroke patients suffering from MMD, the CatBoost model was confirmed to be effective in stroke recurrence prediction, yielding accurate and reliable prediction outcomes. High Suzuki stage, young adults (aged 18-44 years), no surgical treatment, and the presence of an aneurysm are likely to be significantly correlated with the recurrence of stroke in adult stroke patients subjected to MMD.

Regulation of 2-Acetyl-1-pyrroline Content in Fragrant Rice under Different Temperatures at the Grain-Filling Stage.

2-Acetyl-1-pyrroline (2-AP) is a key volatile organic compound in fragrant rice aroma. However, the effects of temperature on 2-AP biosynthesis in fragrant rice and its regulation mechanism have been rarely reported. In the present study, three fragrant rice varieties were used as plant materials, and four temperature treatments during the grain-filling stage, i.e., (T1) 22/17 °C, (T2) 27/22 °C, (T3) 32/27 °C, and (T4) 37/32 °C, were adopted. The results showed that grain contents of 2-AP, proline, and γ-aminobutyric acid (GABA) significantly (P < 0.05) increased with decreased temperature, while the lowest and highest 2-AP contents were recorded in the T4 and T1 treatments, respectively. Higher pyrroline-5-carboxylic acid (P5C) content was recorded in low-temperature treatments (T1 and T2) than in high-temperature treatments (T3 and T4). The transcript levels of genes BADH2, PRODH, and OAT significantly (P < 0.05) decreased with decreased temperature. Lower transcript levels of genes P5CR, P5CS2, DAO2, DAO4, and DAO5 were recorded in low-temperature treatments (T1 and T2) than in high-temperature treatments (T3 and T4). In conclusion, low temperature increased 2-AP content and high temperature decreased 2-AP content in fragrant rice. We deduced that temperature regulated 2-AP biosynthesis through the metabolism of proline and GABA.

Automatic Recognition and Quantification Feeding Behaviors of Nursery Pigs Using Improved YOLOV5 and Feeding Functional Area Proposals.

A novel method is proposed based on the improved YOLOV5 and feeding functional area proposals to identify the feeding behaviors of nursery piglets in a complex light and different posture environment. The method consists of three steps: first, the corner coordinates of the feeding functional area were set up by using the shape characteristics of the trough proposals and the ratio of the corner point to the image width and height to separate the irregular feeding area; second, a transformer module model was introduced based on YOLOV5 for highly accurate head detection; and third, the feeding behavior was recognized and counted by calculating the proportion of the head in the located feeding area. The pig head dataset was constructed, including 5040 training sets with 54,670 piglet head boxes, and 1200 test sets, and 25,330 piglet head boxes. The improved model achieves a 5.8% increase in the mAP and a 4.7% increase in the F1 score compared with the YOLOV5s model. The model is also applied to analyze the feeding pattern of group-housed nursery pigs in 24 h continuous monitoring and finds that nursing pigs have different feeding rhythms for the day and night, with peak feeding periods at 7:00-9:00 and 15:00-17:00 and decreased feeding periods at 12:00-14:00 and 0:00-6:00. The model provides a solution for identifying and quantifying pig feeding behaviors and offers a data basis for adjusting the farm feeding scheme.

Homogeneous crystallization and buried interface passivation for perovskite tandem solar modules.

Scalable fabrication of all-perovskite tandem solar cells is challenging because the narrow-bandgap subcells made of mixed lead-tin (Pb-Sn) perovskite films suffer from nonuniform crystallization and inferior buried perovskite interfaces. We used a dopant from Good's list of biochemical buffers, aminoacetamide hydrochloride, to homogenize perovskite crystallization and used it to extend the processing window for blade-coating Pb-Sn perovskite films and to selectively passivate defects at the buried perovskite interface. The resulting all-perovskite tandem solar module exhibited a certified power conversion efficiency of 24.5% with an aperture area of 20.25 square centimeters.

Effects of paclobutrazol seed priming on seedling quality, photosynthesis, and physiological characteristics of fragrant rice.

BACKGROUND: Paclobutrazol is widely used in the agricultural field. This study investigated the effects of seed priming with different concentrations of paclobutrazol on seedling quality, 2-acetyl-1-pyrroline (2-AP, a key aroma component of fragrant rice) biosynthesis, and related physiological and biochemical indicators in fragrant rice seedlings. RESULTS: The experiment is being conducted at the College of Agriculture, South China Agricultural University. In the experiment, three concentrations of paclobutrazol (Pac 1: 20 mg·L-1; Pac 2: 40 mg·L-1; Pac 3: 80 mg·L-1) were used to initiate the treatment of fragrant rice seeds, while water treatment was used as a control (CK). The results showed that compared with CK, paclobutrazol treatment reduced plant height, increased stem diameter, and increased fresh and dry weight of aromatic rice seedlings. Moreover, paclobutrazol treatment also increased the seedlings' photosynthetic pigment content and net photosynthetic rate. CONCLUSIONS: This study demonstrates that paclobutrazol primarily increases the content of proline by reducing the content of glutamate and down-regulating the expression of P5CS2, thereby promoting the conversion of proline to the aromatic substance 2-AP. Under the appropriate concentration of paclobutrazol (40 mg·L-1~80 mg·L-1), the seedling quality, stress resistance, and aroma of fragrant rice can be improved.

Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia.

PURPOSE: To explore the predictive value of radiomics in predicting stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients and construct a prediction model based on clinical features and DWI-MRI radiomics features. METHODS: Univariate and multivariate logistic regression analyses were used to identify the independent clinical predictors for SAP. Pearson correlation analysis and the least absolute shrinkage and selection operator with ten-fold cross-validation were used to calculate the radiomics score for each feature and identify the predictive radiomics features for SAP. Multivariate logistic regression was used to combine the predictive radiomics features with the independent clinical predictors. The prediction performance of the SAP models was evaluated using receiver operating characteristics (ROC), calibration curves, decision curve analysis, and subgroup analyses. RESULTS: Triglycerides, the neutrophil-to-lymphocyte ratio, dysphagia, the National Institutes of Health Stroke Scale (NIHSS) score, and internal carotid artery stenosis were identified as clinically independent risk factors for SAP. The radiomics scores in patients with SAP were generally higher than in patients without SAP (P < 0. 05). There was a linear positive correlation between radiomics scores and NIHSS scores, as well as between radiomics scores and infarct volume. Infarct volume showed moderate performance in predicting the occurrence of SAP, with an AUC of 0.635. When compared with the other models, the combined prediction model achieved the best area under the ROC (AUC) in both training (AUC = 0.859, 95% CI 0.759-0.936) and validation (AUC = 0.830, 95% CI 0.758-0.896) cohorts (P < 0.05). The calibration curves and decision curve analysis further confirmed the clinical value of the nomogram. Subgroup analysis showed that this nomogram had potential generalization ability. CONCLUSION: The addition of the radiomics features to the clinical model improved the prediction of SAP in AIS patients, which verified its feasibility.

Reducing Perovskite/C60 Interface Losses via Sequential Interface Engineering for Efficient Perovskite/Silicon Tandem Solar Cell.

Wide-bandgap (WBG) perovskite solar cells hold tremendous potential for realizing efficient tandem solar cells. However, nonradiative recombination and carrier transport losses occurring at the perovskite/electron-selective contact (e.g. C60 ) interface present significant obstacles in approaching their theoretical efficiency limit. To address this, a sequential interface engineering (SIE) strategy that involves the deposition of ethylenediamine diiodide (EDAI2 ) followed by sequential deposition of 4-Fluoro-Phenethylammonium chloride (4F-PEACl) is implemented. The SIE technique synergistically narrows the conduction band offset and reduces recombination velocity at the perovskite/C60 interface. The best-performing WBG perovskite solar cell (1.67 eV) delivers a power conversion efficiency (PCE) of 21.8% and an impressive open-circuit voltage of 1.262 V. Moreover, through integration with double-textured silicon featuring submicrometer pyramid structures, a stabilized PCE of 29.6% is attained for a 1 cm2 monolithic perovskite/silicon tandem cell (certified PCE of 29.0%).

Scalable Solution-Processed Hybrid Electron Transport Layers for Efficient All-Perovskite Tandem Solar Modules.

All-perovskite tandem solar cells offer the potential to surpass the Shockley-Queisser (SQ) limit efficiency of single-junction solar cells while maintaining the advantages of low-cost and high-productivity solution processing. However, scalable solution processing of electron transport layer (ETL) in p-i-n structured perovskite solar subcells remains challenging due to the rough perovskite film surface and energy level mismatch between ETL and perovskites. Here, scalable solution processing of hybrid fullerenes (HF) with blade-coating on both wide-bandgap (≈1.80 eV) and narrow-bandgap (≈1.25 eV) perovskite films in all-perovskite tandem solar modules is developed. The HF, comprising a mixture of fullerene (C60 ), phenyl C61 butyric acid methyl ester, and indene-C60 bisadduct, exhibits improved conductivity, superior energy level alignment with both wide- and narrow-bandgap perovskites, and reduced interfacial nonradiative recombination when compared to the conventional thermal-evaporated C60 . With scalable solution-processed HF as the ETLs, the all-perovskite tandem solar modules achieve a champion power conversion efficiency of 23.3% (aperture area = 20.25 cm2 ). This study paves the way to all-solution processing of low-cost and high-efficiency all-perovskite tandem solar modules in the future.

Heterojunction formed via 3D-to-2D perovskite conversion for photostable wide-bandgap perovskite solar cells.

Light-induced halide segregation constrains the photovoltaic performance and stability of wide-bandgap perovskite solar cells and tandem cells. The implementation of an intermixed two-dimensional/three-dimensional heterostructure via solution post-treatment is a typical strategy to improve the efficiency and stability of perovskite solar cells. However, owing to the composition-dependent sensitivity of surface reconstruction, the conventional solution post-treatment is suboptimal for methylammonium-free and cesium/bromide-enriched wide-bandgap PSCs. To address this, we develop a generic three-dimensional to two-dimensional perovskite conversion approach to realize a preferential growth of wider dimensionality (n ≥ 2) atop wide-bandgap perovskite layers (1.78 eV). This technique involves depositing a well-defined MAPbI3 thin layer through a vapor-assisted two-step process, followed by its conversion into a two-dimensional structure. Such a two-dimensional/three-dimensional heterostructure enables suppressed light-induced halide segregation, reduced non-radiative interfacial recombination, and facilitated charge extraction. The wide-bandgap perovskite solar cells demonstrate a champion power conversion efficiency of 19.6% and an open-circuit voltage of 1.32 V. By integrating with the thermal-stable FAPb0.5Sn0.5I3 narrow-bandgap perovskites, our all-perovskite tandem solar cells exhibit a stabilized PCE of 28.1% and retain 90% of the initial performance after 855 hours of continuous 1-sun illumination.

Efficient All-Perovskite Tandem Solar Cells with Low-Optical-Loss Carbazolyl Interconnecting Layers.

Combining wide-band gap (WBG) and narrow-band gap (NBG) perovskites with interconnecting layers (ICLs) to construct monolithic all-perovskite tandem solar cell is an effective way to achieve high power conversion efficiency (PCE). However, optical losses from ICLs need to be further reduced to leverage the full potential of all-perovskite tandem solar cells. Here, metal oxide nanocrystal layers anchored with carbazolyl hole-selective-molecules (CHs), which exhibit much lower optical loss, is employed to replace poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT : PSS) as the hole transporting layers (HTLs) in lead-tin (Pb-Sn) perovskite sub-cells and ICLs in all-perovskite tandem solar cells. Optically transparent indium tin oxide nanocrystals (ITO NCs) layers are employed to enhance anchoring of CHs, while a mixture of two CHs is adopted to tune the surface energy-levels of ITO NCs. The optimized mixed Pb-Sn NBG perovskite solar cells demonstrate a high PCE of 23.2 %, with a high short-circuit current density (Jsc ) of 33.5 mA cm-2 . A high PCE of 28.1 % is further obtained in all-perovskite tandem solar cells, with the highest Jsc of 16.7 mA cm-2 to date. Encapsulated tandem solar cells maintain 90 % of their reference point after 500 h of operation at the maximum power point (MPP) under 1-Sun illumination.

Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China.

INTRODUCTION: Stroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to identify patients who are at high risk of stroke recurrence. This study is positioned to develop and validate a prediction model using radiomics data and machine learning methods to identify the risk of stroke recurrence in patients with acute ischaemic stroke (AIS). METHODS AND ANALYSIS: A total of 1957 patients with AIS will be needed. Enrolment at participating hospitals will continue until the required sample size is reached, and we will recruit as many participants as possible. Multiple indicators including basic clinical data, image data, laboratory data, CYP2C19 genotype and follow-up data will be assessed at various time points during the registry, including baseline, 24 hours, 7 days, 1 month, 3 months, 6 months, 9 months and 12 months. The primary outcome was stroke recurrence. The secondary outcomes were death events, prognosis scores and adverse events. Imaging images were processed using deep learning algorithms to construct a programme capable of automatically labelling the lesion area and extracting radiomics features. The machine learning algorithms will be applied to integrate cross-scale, multidimensional data for exploratory analysis. Then, an ischaemic stroke recurrence prediction model of the best performance for patients with AIS will be established. Calibration, receiver operating characteristic and decision curve analyses will be evaluated. ETHICS AND DISSEMINATION: This study has received ethical approval from the Medical Ethics Committee of the Second Affiliated Hospital of Nanchang University (medical research review No.34/2021), and informed consent will be obtained voluntarily. The research findings will be disseminated through publication in journals and presented at conferences. TRIAL REGISTRATION NUMBER: ChiCTR2200055209.

Comparison of the Predictive Value of Inflammatory Biomarkers for the Risk of Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke.

Purpose: To investigate the predictive value of various inflammatory biomarkers in patients with acute ischemic stroke (AIS) and evaluate the relationship between stroke-associated pneumonia (SAP) and the best predictive index. Patients and Methods: We calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS), and prognostic index (PI). Variables were selectively included in the logistic regression analysis to explore the associations of NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI with SAP. We assessed the predictive performance of biomarkers by analyzing receiver operating characteristic (ROC) curves. We further used restricted cubic splines (RCS) to investigate the association. Next, we conducted subgroup analyses to investigate whether specific populations were more susceptible to NLR. Results: NLR, PLR, MLR, SIRI, SII, GPS, mGPS, and PI increased significantly in SAP patients, and PNI was significantly decreased. After adjustment for potential confounders, the association of inflammatory biomarkers with SAP persisted. NLR showed the most favorable discriminative performance and was an independent risk factor predicting SAP. The RCS showed an increasing nonlinear trend of SAP risk with increasing NLR. The AUC of the combined indicator of NLR and C-reactive protein (CRP) was significantly higher than those of NLR and CRP alone (DeLong test, P<0.001). Subgroup analyses suggested good generalizability of the predictive effect. Conclusion: NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI can predict the occurrence of SAP. Among the indices, the NLR was the best predictor of SAP occurrence. It can therefore be used for the early identification of SAP.

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics.

Purpose: This study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS). Methods: The MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models. Results: Twenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively. Conclusion: The ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model.