Efficient Inverted Perovskite Photovoltaics Through Surface State Manipulation.

Inverted perovskite solar cells (PSCs) are considered as the most promising avenue for the commercialization of PSCs due to their potential inherent stability. However, suboptimal interface contacts between electron transport layer (ETL) (such as C60 ) and the perovskite absorbing layer within inverted PSCs always result in reduced efficiency and poor stability. Herein, a surface state manipulation strategy has been developed by employing a highly electronegative 4-fluorophenethylamine hydrochloride (p-F-PEACl) to effectively address the issue of poor interface contacts in the inverted PSCs. The p-F-PEACl demonstrates a robust interaction with perovskite film through bonding of amino group and Cl- with I- and Pb2+ ions in the perovskite, respectively. As such, the surface defects of perovskite film can be significantly reduced, leading to suppressed non-radiative recombination. Moreover, p-F-PEACl also plays a dual role in enhancing the surface potential and improving energy-level alignment at the interfaces between the perovskite and C60 carrier transport layer, which directly contributes to efficient charge extraction. Finally, the open-circuit voltage (Voc ) of devices increases from 1.104 V to 1.157 V, leading to an overall efficiency improvement from 22.34% to 24.78%. Furthermore, the p-F-PEACl-treated PSCs also display excellent stability.

Corynebacterium glutamicum cell factory design for the efficient production of cis, cis-muconic acid.

Cis, cis-muconic acid (MA) is widely used as a key starting material in the synthesis of diverse polymers. The growing demand in these industries has led to an increased need for MA. Here, we constructed recombinant Corynebacterium glutamicum by systems metabolic engineering, which exhibit high efficiency in the production of MA. Firstly, the three major degradation pathways were disrupted in the MA production process. Subsequently, metabolic optimization strategies were predicted by computational design and the shikimate pathway was reconstructed, significantly enhancing its metabolic flux. Finally, through optimization and integration of key genes involved in MA production, the recombinant strain produced 88.2 g/L of MA with the yield of 0.30 mol/mol glucose in the 5 L bioreactor. This titer represents the highest reported titer achieved using glucose as the carbon source in current studies, and the yield is the highest reported for MA production from glucose in Corynebacterium glutamicum. Furthermore, to enable the utilization of more cost-effective glucose derived from corn straw hydrolysate, we subjected the strain to adaptive laboratory evolution in corn straw hydrolysate. Ultimately, we successfully achieved MA production in a high solid loading of corn straw hydrolysate (with the glucose concentration of 83.56 g/L), resulting in a titer of 19.9 g/L for MA, which is 4.1 times higher than that of the original strain. Additionally, the glucose yield was improved to 0.33 mol/mol. These provide possibilities for a greener and more sustainable production of MA.

Insight into the Feasibility of Fatty Acyl Chlorides with 10-18 Carbons for the Ball-Milling Synthesis of Thermoplastic Cellulose Esters.

Fatty acid cellulose esters (FACE) are common cellulose-based thermoplastics, and their thermoplasticity is determined by both the contents and the lengths of the side chains. Herein, various FACE were synthesized by the ball-milling esterification of cellulose and fatty acyl chlorides containing 10-18 carbons, and their structures and thermoplasticity were thoroughly studied. The results showed that FACE with high degrees of substitution (DS) and low melting flow temperatures (Tf) were achieved as the chain lengths of the fatty acyl chlorides were reduced. In particular, a cellulose decanoate with a DS of 1.85 and a Tf of 186 °C was achieved by feeding 3 mol of decanoyl chloride per mole anhydroglucose units of cellulose. However, cellulose stearate (DS = 1.53) synthesized by the same protocols cannot melt even at 250 °C. More interestingly, the fatty acyl chlorides with 10 and 12 carbons resulted in FACE with superior toughness (elongation at break up to 94.4%). In contrast, due to their potential crystallization of the fatty acyl groups with 14-18 carbons, the corresponding FACE showed higher tensile strength and Young's modulus than the others. This study provides some theoretical basis for the mechanochemical synthesis of thermoplastic FACE with designated properties.

Stacking-dependent interlayer magnetic interactions in CrSe2.

Recently, CrSe2, a new ferromagnetic van der Waals two-dimensional material, was discovered to be highly stable under ambient conditions, making it an attractive candidate for fundamental research and potential device applications. Here, we study the interlayer interactions of bilayer CrSe2using first-principles calculations. We demonstrate that the interlayer interaction depends on the stacking structure. The AA and AB stackings exhibit antiferromagnetic (AFM) interlayer interactions, while the AC stacking exhibits ferromagnetic (FM) interlayer interaction. Furthermore, the interlayer interaction can be further tuned by tensile strain and charge doping. Specifically, under large tensile strain, most stacking structures exhibit FM interlayer interactions. Conversely, under heavy electron doping, all stacking structures exhibit AFM interlayer interactions. These findings are useful for designing spintronic devices based on CrSe2.

Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep-learning features of primary lesions and peripheral lymph nodes.

Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In our study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76 ± 0.08 and 0.91 ± 0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine.

Size and number of lymph nodes were risk factors of recurrence in stage II colorectal cancer.

BACKGROUND: Size and number of lymph nodes (LNs) were reported to be associated with the prognosis of stage II colorectal cancer (CRC). The purpose of this study was to determine the prognostic role of the size of LNs (SLNs) measured by computer tomography (CT) and the number of retrieved LNs (NLNs) in the relapse-free survival (RFS) and overall survival (OS) among stage II CRC patients. METHODS: Consecutive patients diagnosed with stage II CRC at Fudan University Shanghai Cancer Center (FUSCC) from January 2011 to December 2015 were reviewed, and 351 patients were randomly divided into two cohorts for cross-validation. The optimal cut-off values were obtained using X-tile program. Kaplan-Meier curves and Cox regression analyses were conducted for the two cohorts. RESULTS: Data from 351 stage II CRC patients were analyzed. The cut-off values for SLNs and NLNs were 5.8 mm and 22, respectively, determined by the X-tile in the training cohort. In the validation cohort, Kaplan-Meier curves demonstrated SLNs (P = 0.0034) and NLNs (P = 0.0451) were positively correlated with RFS but not with OS. The median follow-up time in the training cohort and the validation cohort were 60.8 months and 61.0 months respectively. Univariate and multivariate analysis revealed that both SLNs (training cohort: Hazard Ratio (HR) = 2.361, 95% Confidence interval (CI): 1.044-5.338, P = 0.039; validation cohort: HR = 2.979, 95%CI: 1.435-5.184, P = 0.003) and NLNs (training cohort: HR = 0.335, 95%CI: 0.113-0.994, P = 0.049; validation cohort: HR = 0.375, 95%CI: 0.156-0.900, P = 0.021) were independent prognostic factors for RFS whereas not for OS. CONCLUSION: SLNs and NLNs are independent prognostic factors for patients with stage II CRC. Patients with SLNs > 5.8 mm and NLNs ≤ 22 are apt to have higher risk of recurrence.

Polar metals in strain-engineered KNbO3/CaNbO3 superlattices: a first-principles study.

Polar metals have generated significant interest since the ferroelectric-like structural transition in metallic LiOsO3 was discovered. Herein, we report on a strain-modulated polar metal in the ferroelectric/metal superlattice of 1 : 1 KNbO3/CaNbO3. Using first-principles calculations, we have investigated the structural distortions, including polar distortions and octahedral rotations, and layer-by-layer electronic structures in the KNbO3/CaNbO3 superlattice under different epitaxial strains. Along the stacking direction, the superlattice has almost parallel polar displacements under compressive strain, whereas both in-plane and out-of-plane antiferroelectric-like polar displacements are robust under intermediate strain, which is connected to the octahedral tilting pattern and interlayer electron transfer. In addition, the in-plane polar distortions are enhanced by tensile strains and have a sudden increase at 4% tensile strain. The metallicity is mainly contributed by d electrons from Nb atoms. And orbital-resolved electron distributions in each layer show that d-orbital splitting is related not only to the epitaxial strain but also to the direction of polar displacements. Our results suggest an efficient way to tune polar distortions as well as local metallicity via epitaxial strains in the superlattice.

Reservoir Lithology Identification Based on Multicore Ensemble Learning and Multiclassification Algorithm Based on Noise Detection Function.

Reservoir lithology identification is an important part of well logging interpretation. The accuracy of identification affects the subsequent exploration and development work, such as reservoir division and reserve prediction. Correct reservoir lithology identification has important geological significance. In this paper, the wavelet threshold method will be used to preliminarily reduce the noise of the curve, and then the MKBoost-MC model will be used to identify the reservoir lithology. It is found that the prediction accuracy of MKBoost-MC is higher than that of the traditional SVM algorithm, and though the operation of MKBoost-MC takes a long time, the speed of MKBoost-MC reservoir lithology identification is much higher than that of manual processing. The accuracy of MKBoost-MC for reservoir lithology recognition can reach the application standard. For the unbalanced distribution of lithology types, the MKBoost-MC algorithm can be effectively suppressed. Finally, the MKBoost-MC reservoir lithology identification method has good applicability and practicality to the lithology identification problem.

Bioinformatics analysis on differentially expressed genes between colorectal adenoma and colorectal adenocarcinoma.

BACKGROUND: Colorectal adenoma (CRA) is the main cause of the progression of Colorectal adenocarcinoma (COAD). Therefore, it is very important to accurately reveal its developmental mechanism. METHODS: Differential expression genes (DEGs) in three microarray datasets were screened using GEO and GEO2R. R packages were used for gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) path enrichment analysis. Hub genes screened by STRING, Cytoscape and CytoHubba were used. R was used for DEGs of hub genes, and Gene Expression Profiling Interactive Analysis (GEPIA2) database was used for prognostic Analysis. R-packet were used to analyze tumor pathology, tumour, lymph-nodes, and metastases (TNM) staging, enrichment, immune invasion and prognosis. RESULTS: Among the 66 genes, including 36 up-regulated and 30 down-regulated genes. Survival analysis showed that COL1A1, COL5A2, COL5A1 and secreted protein acidic and rich in cysteine (SPARC) were associated with disease-free survival in patients. The four genes were related to tumor pathological stage, TNM stage and immune invasion. COL1A1 and COL5A2 were highly expressed in chromatin modification and cellular senescence. Low expression of COL5A1 and SPARC was significantly enriched in neutrophil degranulation and Wp VegfavegFR2 signaling pathways. CONCLUSIONS: Obviously, these four key genes can serve as important targets for early diagnosis, treatment, immunity and prognosis of CRA to COAD.

Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting.

As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM process. In situ monitoring is a vital technique to detect the defects in advance, which is expected to reduce the defects. This work proposed a method that combined acoustic signals with a deep learning algorithm to monitor the spatter behaviors. The acoustic signals were recorded by a microphone and the spatter information was collected by a coaxial high-speed camera simultaneously. The signals were divided into two types according to the number and intensity of spatter during the SLM process with different combinations of processing parameters. Deep learning models, one-dimensional Convolutional Neural Network (1D-CNN), two-dimensional Convolutional Neural Network (2D-CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were trained to establish the relationships between the acoustic signals and characteristics of spatter. After K-fold verification, the highest classification confidence of models is 85.08%. This work demonstrates that it is feasible to use acoustic signals in monitoring the spatter defect during the SLM process. It is possible to use cheap and simple microphones instead of expensive and complicated high-speed cameras for monitoring spatter behaviors.

A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer.

BACKGROUND: Accurate lymph node metastasis (LNM) prediction in colorectal cancer (CRC) patients is of great significance for treatment decision making and prognostic evaluation. We aimed to develop and validate a clinical-radiomics nomogram for the individual preoperative prediction of LNM in CRC patients. METHODS: We enrolled 766 patients (458 in the training set and 308 in the validation set) with clinicopathologically confirmed CRC. We included nine significant clinical risk factors (age, sex, preoperative carbohydrate antigen 19-9 (CA19-9) level, preoperative carcinoembryonic antigen (CEA) level, tumor size, tumor location, histotype, differentiation and M stage) to build the clinical model. We used analysis of variance (ANOVA), relief and recursive feature elimination (RFE) for feature selection (including clinical risk factors and the imaging features of primary lesions and peripheral lymph nodes), established classification models with logistic regression analysis and selected the respective candidate models by fivefold cross-validation. Then, we combined the clinical risk factors, primary lesion radiomics features and peripheral lymph node radiomics features of the candidate models to establish combined predictive models. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, decision curve analysis (DCA) and a nomogram were used to evaluate the clinical usefulness of the model. RESULTS: The clinical-primary lesion radiomics-peripheral lymph node radiomics model, with the highest AUC value (0.7606), was regarded as the candidate model and had good discrimination and calibration in both the training and validation sets. DCA demonstrated that the clinical-radiomics nomogram was useful for preoperative prediction in the clinical environment. CONCLUSION: The present study proposed a clinical-radiomics nomogram with a combination of clinical risk factors and radiomics features that can potentially be applied in the individualized preoperative prediction of LNM in CRC patients.

Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection.

OBJECTIVES: To identify a CT-based radiomics nomogram for survival prediction in patients with resected pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 220 patients (training cohort n = 147; validation cohort n = 73) with PDAC were enrolled. A total of 300 radiomics features were extracted from CT images. And the least absolute shrinkage and selection operator algorithm were applied to select features and develop a radiomics score (Rad-score). The radiomics nomogram was constructed by multivariate regression analysis. Nomogram discrimination, calibration, and clinical usefulness were evaluated. The association of the Rad-score and recurrence pattern in PDAC was evaluated. RESULTS: The Rad-score was significantly associated with PDAC patient's disease-free survival (DFS) and overall survival (OS) (both p < 0.001 in two cohorts). Incorporating the Rad-score into the radiomics nomogram resulted in better performance of the survival prediction than that of the clinical model and TNM staging system. In addition, the radiomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. There was no association between the Rad-score and recurrence pattern. CONCLUSIONS: The radiomics nomogram integrating the Rad-score and clinical data provided better prognostic prediction in resected PDAC patients, which may hold great potential for guiding personalized care for these patients. The Rad-score was not a predictor of the recurrence pattern in resected PDAC patients. KEY POINTS: • The Rad-score developed by CT radiomics features was significantly associated with PDAC patients' prognosis. • The radiomics nomogram integrating the Rad-score and clinical data has value to permit non-invasive, low-cost, and personalized evaluation of prognosis in PDAC patients. • The radiomics nomogram outperformed clinical model and the TNM staging system in terms of survival estimation.

Hexagonal rare-earth manganites and ferrites: a review of improper ferroelectricity, magnetoelectric coupling, and unusual domain walls.

Hexagonal rare-earth manganites and ferrites are well-known improper ferroelectrics with low-temperature antiferromagnetism/weak ferromagnetism. In recent decades, new multi-functional device concepts and applications have provoked the exploration for multiferroics which simultaneously possess ferroelectric and magnetic orders. As a promising platform for multiferroicity, hexagonal manganites and ferrites are attracting great research interest among the fundamental scientific and technological communities. Moreover, the novel type of vortex-like ferroelectric domain walls are locked to the antiphase structural domain walls, providing an extra degree of freedom to tune the magnetoelectric coupling and other properties such as conductance. Here, we summarize the main experimental achievements and up-to-date theoretical understanding of the ferroelectric, magnetic, and magnetoelectric properties, as well as the intriguing domain patterns in hexagonal rare-earth manganites and ferrites. Recent work on non-stoichiometric compounds will also be briefly introduced.

Large negative thermal expansion in non-perovskite lead-free ferroelectric Sn2P2S6.

Functional materials showing both negative thermal expansion (NTE) and physical performance, such as ferroelectricity and magnetism, have been extensively explored in the past decade. However, among ferroelectrics a remarkable NTE was only found in perovskite-type PbTiO3-based compounds. In this work, a large NTE of -4.7 × 10(-5) K(-1) is obtained in the non-perovskite lead-free ferroelectric Sn2P2S6 from 243 K to TC (338 K). Structure refinements and first-principle calculations reveal the effects of the Sn(ii) 5s-S 3p interaction on spontaneous polarization and its correlation with NTE. Then the mechanism of spontaneous volume ferroelectrostriction (SVFS) is verified and it could well elucidate the nature of NTE in ferroelectric Sn2P2S6. This is the first case to demonstrate the unusual NTE behavior by SVFS in a non-perovskite lead-free ferroelectric material.

Factors influencing public participation behavior relating to government microblogs on COVID-19 updates.

Introduction: During the global COVID-19 pandemic, densely populated megacities engaged in active international exchanges have faced the most severe impacts from both the disease and the associated infodemic. This study examines the factors influencing public participation behavior on government microblogs in these megacities during the pandemic. It guides megacities in disseminating epidemic information, promoting knowledge on epidemic prevention, managing public opinion, and addressing related matters. Methods: Utilizing the elaboration likelihood model's central and peripheral routes, drawing on an empirical analysis of 6,677 epidemic-related microblogs from seven Chinese megacities, this study analyses the influence mechanisms influencing public participation behavior and reveals the regulatory role of confirmed case numbers. Meanwhile,a qualitative comparative analysis examines and discusses diferent confgurations of ixn fuential factors. Results: The study reveals that microblog content richness demonstrates a U-shaped impact on public participation behavior. Conversely, content interaction, content length, and the number of fans positively impact participation, while update frequency has a negative impact. Additionally, the number of new confrmed cases positively regulates the impact of microblog content and publisher characteristics on public participation behavior. Public participation behavior also varies based on publishing time and content semantic features. This study further revealed the different confgurations of influential factors by QCA method. Conclusion: This study reveals the impact mechanism of the microblog content and publisher characteristics on public participation behavior. It also demonstrates the regulatory role of newly confrmed cases in the way content and publishers' characteristics influence public participation behavior. This study is of great significance for the operation of government microblogs, the release of emergency information, and the promotion of public participation.

A sensitive CRISPR/Cas12a-assisted fluorescent aptasensor for rapid detection of food allergens.

Food allergens elicit abnormal immune system responses among allergic individuals and sensitive detection for allergenic ingredient is greatly significant. To address this need, a novel fluorescent aptasensor, assisted by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), have been developed for food allergens. In this study, aptamer offers distinctive recognition capabilities in binding specific targets, while CRISPR-associated-12a protein (Cas12a) holds precise cis-cleavage for cutting fluorescent signal probes. Notably, the utilization of Cas12a cis-cleavage activity, rather than trans-cleavage, eliminates the necessity for additional fluorescent probes, thus reducing interference between substances and enhancing sensitivity. Throughout the process, complementary DNA (cDNA) plays a crucial dual role in target recognition conversion and signal presentation, representing a key challenge and innovative aspect of this study. To evaluate the performance of the aptasensor, lysozyme (LYS) is employed as a representative model target of food allergens. Under optimal conditions, the developed aptasensor could achieve an exceptional low limit of detection (LOD) of 6.10 pM with a dynamic detection range of 10 pM-320 pM. The aptasensor demonstrates high selectivity and great recovery rates. This strategy yields promising outcomes, holding the potential to serve as a valuable reference for various food allergens detection.

Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database.

BACKGROUND: Mitral valve disorder (MVD) stands as the most prevalent valvular heart disease. Presently, a comprehensive clinical index to predict mortality in MVD remains elusive. The aim of our study is to construct and assess a nomogram for predicting the 28-day mortality risk of MVD patients. METHODS: Patients diagnosed with MVD were identified via ICD-9 code from the MIMIC-III database. Independent risk factors were identified utilizing the LASSO method and multivariate logistic regression to construct a nomogram model aimed at predicting the 28-day mortality risk. The nomogram's performance was assessed through various metrics including the area under the curve (AUC), calibration curves, Hosmer-Lemeshow test, integrated discriminant improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). RESULTS: The study encompassed a total of 2771 patients diagnosed with MVD. Logistic regression analysis identified several independent risk factors: age, anion gap, creatinine, glucose, blood urea nitrogen level (BUN), urine output, systolic blood pressure (SBP), respiratory rate, saturation of peripheral oxygen (SpO2), Glasgow Coma Scale score (GCS), and metastatic cancer. These factors were found to independently influence the 28-day mortality risk among patients with MVD. The calibration curve demonstrated adequate calibration of the nomogram. Furthermore, the nomogram exhibited favorable discrimination in both the training and validation cohorts. The calculations of IDI, NRI, and DCA analyses demonstrate that the nomogram model provides a greater net benefit compared to the Simplified Acute Physiology Score II (SAPSII), Acute Physiology Score III (APSIII), and Sequential Organ Failure Assessment (SOFA) scoring systems. CONCLUSION: This study successfully identified independent risk factors for 28-day mortality in patients with MVD. Additionally, a nomogram model was developed to predict mortality, offering potential assistance in enhancing the prognosis for MVD patients. It's helpful in persuading patients to receive early interventional catheterization treatment, for example, transcatheter mitral valve replacement (TMVR), transcatheter mitral valve implantation (TMVI).

Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke.

Background and objectives: Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients. Methods: A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient' demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants' prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables. Results: Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900. Conclusion: These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.

Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study.

Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760-0.838), 0.797 (95% CI, 0.733-0.860), 0.754 (95% CI, 0.678-0.829), and 0.727 (95% CI, 0.641-0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan-Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.

Highly Efficient Production of Cellulosic Ethanol from Poplar Using an Optimal C6/C5 Co-Fermentation Strain of Saccharomyces cerevisiae.

Cellulosic ethanol is the key technology to alleviate the pressure of energy supply and climate change. However, the ethanol production process, which is close to industrial production and has a high saccharification rate and ethanol yield, still needs to be developed. This study demonstrates the effective conversion of poplar wood waste into fuel-grade ethanol. By employing a two-step pretreatment using sodium chlorite (SC)-dilute sulfuric acid (DSA), the raw material achieved a sugar conversion rate exceeding 85% of the theoretical value. Under optimized conditions, brewing yeast co-utilizing C6/C5 enabled a yield of 35 g/L ethanol from 10% solid loading delignified poplar hydrolysate. We increased the solid loading to enhance the final ethanol concentration and optimized both the hydrolysis and fermentation stages. With 20% solid loading delignified poplar hydrolysate, the final ethanol concentration reached 60 g/L, a 71.4% increase from the 10% solid loading. Our work incorporates the pretreatment, enzymatic hydrolysis, and fermentation stages to establish a simple, crude poplar waste fuel ethanol process, expanding the range of feedstocks for second-generation fuel ethanol production.