Transparent Recombination Layers Design and Rational Characterizations for Efficient Two-Terminal Perovskite-Based Tandem Solar Cells.

Two-terminal (2T) perovskite-based tandem solar cells (TSCs) arouse burgeoning interest in breaking the Shockley-Queisser (S-Q) limit of single-junction solar cells by combining two subcells with different bandgaps. However, the highest certified efficiency of 2T perovskite-based TSCs (33.9%) lags behind the theoretical limit (42-43%). A vital challenge limiting the development of 2T perovskite-based TSCs is the transparent recombination layers/interconnecting layers (RLs) design between two subcells. To improve the performance of 2T perovskite-based TSCs, RLs simultaneously fulfill the optical loss, contact resistance, carrier mobility, stress management, and conformal coverage requirements. In this review, the definition, functions, and requirements of RLs in 2T perovskite-based TSCs are presented. The insightful characterization methods applicable to RLs, which are inspiring for further research on the RLs both in 2T perovskite-based two-junction and multi-junction TSCs, are also highlighted. Finally, the key factors that currently limit the performance enhancement of RLs and the future directions that should be continuously focused on are summarized.

Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trials.

Background: The use of artificial intelligence (AI) in detecting colorectal neoplasia during colonoscopy holds the potential to enhance adenoma detection rates (ADRs) and reduce adenoma miss rates (AMRs). However, varied outcomes have been observed across studies. Thus, this study aimed to evaluate the potential advantages and disadvantages of employing AI-aided systems during colonoscopy. Methods: Using Medical Subject Headings (MeSH) terms and keywords, a comprehensive electronic literature search was performed of the Embase, Medline, and the Cochrane Library databases from the inception of each database until October 04, 2023, in order to identify randomized controlled trials (RCTs) comparing AI-assisted with standard colonoscopy for detecting colorectal neoplasia. Primary outcomes included AMR, ADR, and adenomas detected per colonoscopy (APC). Secondary outcomes comprised the poly missed detection rate (PMR), poly detection rate (PDR), and poly detected per colonoscopy (PPC). We utilized random-effects meta-analyses with Hartung-Knapp adjustment to consolidate results. The prediction interval (PI) and I2 statistics were utilized to quantify between-study heterogeneity. Moreover, meta-regression and subgroup analyses were performed to investigate the potential sources of heterogeneity. This systematic review and meta-analysis is registered with PROSPERO (CRD42023428658). Findings: This study encompassed 33 trials involving 27,404 patients. Those undergoing AI-aided colonoscopy experienced a significant decrease in PMR (RR, 0.475; 95% CI, 0.294-0.768; I2 = 87.49%) and AMR (RR, 0.495; 95% CI, 0.390-0.627; I2 = 48.76%). Additionally, a significant increase in PDR (RR, 1.238; 95% CI, 1.158-1.323; I2 = 81.67%) and ADR (RR, 1.242; 95% CI, 1.159-1.332; I2 = 78.87%), along with a significant increase in the rates of PPC (IRR, 1.388; 95% CI, 1.270-1.517; I2 = 91.99%) and APC (IRR, 1.390; 95% CI, 1.277-1.513; I2 = 86.24%), was observed. This resulted in 0.271 more PPCs (95% CI, 0.144-0.259; I2 = 65.61%) and 0.202 more APCs (95% CI, 0.144-0.259; I2 = 68.15%). Interpretation: AI-aided colonoscopy significantly enhanced the detection of colorectal neoplasia detection, likely by reducing the miss rate. However, future studies should focus on evaluating the cost-effectiveness and long-term benefits of AI-aided colonoscopy in reducing cancer incidence. Funding: This work was supported by the Heilongjiang Provincial Natural Science Foundation of China (LH2023H096), the Postdoctoral research project in Heilongjiang Province (LBH-Z22210), the National Natural Science Foundation of China's General Program (82072640) and the Outstanding Youth Project of Heilongjiang Natural Science Foundation (YQ2021H023).

Development and validation of prognostic nomograms based on De Ritis ratio and clinicopathological features for patients with stage II/III colorectal cancer.

BACKGROUND: Metabolic derangements and systemic inflammation are related to the progression of colorectal cancer (CRC) and the prognoses of these patients. The survival of stage II and III CRC patients existed considerable heterogeneity highlighting the urgent need for new prediction models. This study aimed to develop and validate prognostic nomograms based on preoperative serum liver enzyme as well as evaluate the clinical utility. METHODS: A total of 4014 stage II/III primary CRC patients pathologically diagnosed from January 2007 to December 2013 were included in this study. These patients were randomly divided into a training set (n = 2409) and a testing set (n = 1605). Univariate and multivariate Cox analyses were used to select the independent factors for predicting overall survival (OS) and disease-free survival (DFS) of stage II/III CRC patients. Next, nomograms were constructed and validated to predict the OS and DFS of individual CRC patients. The clinical utility of nomograms, tumor-node-metastasis (TNM), and the American Joint Committee on Cancer (AJCC) system was evaluated using time-dependent ROC and decision curve analyses. RESULTS: Among seven preoperative serum liver enzyme markers, aspartate aminotransferase-to-alanine aminotransferase ratio (De Ritis ratio) was identified as an independent factor for predicting both OS and DFS of stage II/III CRC patients. The nomograms incorporated De Ritis ratio and significant clinicopathological features achieved good accuracy in terms of OS and DFS prediction, with C-index of 0.715 and 0.692, respectively. The calibration curve showed good agreement between prediction by nomogram and actual observation. The results of time-dependent ROC and decision curve analyses suggested that the nomograms had improved discrimination and greater clinical benefits compared with TNM and AJCC staging. CONCLUSIONS: De Ritis ratio was an independent predictor in predicting both the OS and DFS of patients with stage II/III CRC. Nomograms based on De Ritis ratio and clinicopathological features showed better clinical utility, which is expected to help clinicians develop appropriate individual treatment strategies for patients with stage II /III CRC.

A new S-scheme heterojunction of 1D ZnGa2O4/ZnO nanofiber for efficient photocatalytic degradation of TC-HCl.

One-dimensional shaped ZnGa2O4, ZnO and ZnGa2O4/ZnO nanofibers were successfully prepared by electrostatic spinning technique and the photocatalytic degradation performance of tetracycline hydrochloride (TC-HCl) were studied. It was found that the S-scheme heterojunction formed in the ZnGa2O4/ZnO could greatly reduce the recombination of the photogenerated carriers and therefore improve the photocatalytic performance. By optimizing the ratio of the ZnGa2O4 and ZnO, the largest degradation rate could reach 0.0573 min-1, which was 20 times of the self-degradation rate of TC-HCl. It was verified that the h+ played the key role in the reactive groups for the high performance decomposition of TC-HCl by capture experiments. This work provides a new method for the highly efficient photocatalytic degradation of TC-HCl.

Exploring biomarkers for prognosis and neoadjuvant chemosensitivity in rectal cancer: Multi-omics and ctDNA sequencing collaboration.

Introduction: This study aimed to identified the key genes and sequencing metrics for predicting prognosis and efficacy of neoadjuvant chemotherapy (nCT) in rectal cancer (RC) based on genomic DNA sequencing in samples with different origin and multi-omics association database. Methods: We collected 16 RC patients and obtained DNA sequencing data from cancer tissues and plasma cell-free DNA before and after nCT. Various gene variations were analyzed, including single nucleotide variants (SNV), copy number variation (CNV), tumor mutation burden (TMB), copy number instability (CNI) and mutant-allele tumor heterogeneity (MATH). We also identified genes by which CNV level can differentiate the response to nCT. The Cancer Genome Atlas database and the Clinical Proteomic Tumor Analysis Consortium database were used to further evaluate the specific role of therapeutic relevant genes and screen out the key genes in multi-omics levels. After the intersection of the screened genes from differential expression analysis, survival analysis and principal components analysis dimensionality reduction cluster analysis, the key genes were finally identified. Results: The genes CNV level of principal component genes in baseline blood and cancer tissues could significantly distinguish the two groups of patients. The CNV of HSP90AA1, EGFR, SRC, MTOR, etc. were relatively gained in the better group compared with the poor group in baseline blood. The CNI and TMB was significantly different between the two groups. The increased expression of HSP90AA1, EGFR, and SRC was associated with increased sensitivity to multiple chemotherapeutic drugs. The nCT predictive score obtained by therapeutic relevant genes could be a potential prognostic indicator, and the combination with TMB could further refine prognostic prediction for patients. After a series of analysis in multi-omics association database, EGFR and HSP90AA1 with significant differences in multiple aspects were identified as the key predictive genes related to prognosis and the sensitivity of nCT. Discussion: This work revealed that effective combined application and analysis in multi-omics data are critical to search for predictive biomarkers. The key genes EGFR and HSP90AA1 could serve as an effective biomarker to predict prognose and neoadjuvant chemosensitivity.

Predictive molecular markers for the treatment with immune checkpoint inhibitors in colorectal cancer.

Colorectal cancer is one of the most common malignant tumors and, hence, has become one of the most important public health issues in the world. Treatment with immune checkpoint inhibitors (ICIs) successfully improves the survival rate of patients with melanoma, non-small-cell lung cancer, and other malignancies, and its application in metastatic colorectal cancer is being actively explored. However, a few patients develop drug resistance. Predictive molecular markers are important tools to precisely screen patient groups that can benefit from treatment with ICIs. The current article focused on certain important predictive molecular markers for ICI treatment in colorectal cancer, including not only some of the mature molecular markers, such as deficient mismatch repair (d-MMR), microsatellite instability-high (MSI-H), tumor mutational burden (TMB), programmed death-ligand-1 (PD-L1), tumor immune microenvironment (TiME), and tumor-infiltrating lymphocytes (TILs), but also some of the novel molecular markers, such as DNA polymerase epsilon (POLE), polymerase delta 1 (POLD1), circulating tumor DNA (ctDNA), and consensus molecular subtypes (CMS). We have reviewed these markers in-depth and presented the results from certain important studies, which suggest their applicability in CRC and indicate their advantages and disadvantages. We hope this article is helpful for clinicians and researchers to systematically understand these markers and can guide the treatment of colorectal cancer.

A panel of differentially methylated regions enable prognosis prediction for colorectal cancer.

We aim to identify a panel of differentially methylated regions (DMRs) for predicting survival outcomes for patients with CRC from the TCGA (n = 393). Four DMRs (MUC12, TBX20, CHN2, and B3GNT7) were selected as candidate prognostic markers for CRC. The prediction potential of selected DMRs was validated by the targeted bisulfite sequencing method in an independent cohort with 251 Chinese CRC patients. DMR methylation scores (DMSs) were constructed to evaluate the prognosis of CRC. Results of the validation cohort confirmed that higher DMSs were associated with poor overall survival (OS) of CRC, with hazard ratio (HR) value ranged from 1.445 to 2.698 in multivariable Cox models. Patients in the high prognostic index (high-PI) group showed a markedly unfavorable prognosis compared to the low-PI group in both TCGA discovery cohort (HR = 3.508, 95%CI: 2.196-5.604, P < 0.001) and independent validation cohort (HR = 1.912, 95%CI: 1.258-2.907, P = 0.002).

Prognostic Inflammatory Index Based on Preoperative Peripheral Blood for Predicting the Prognosis of Colorectal Cancer Patients.

Host inflammation is a critical component of tumor progression and its status can be indicated by peripheral blood cell counts. We aimed to construct a comprehensively prognostic inflammatory index (PII) based on preoperative peripheral blood cell counts and further evaluate its prognostic value for patients with colorectal cancer (CRC). A total of 9315 patients with stage II and III CRC from training and external validation cohorts were included. The PII was constructed by integrating all the peripheral blood cell counts associated with prognosis in the training cohort. Cox analyses were performed to evaluate the association between PII and overall survival (OS) and disease-free survival (DFS). In the training cohort, multivariate Cox analyses indicated that high OS-PII (>4.27) was significantly associated with worse OS (HR: 1.330, 95% CI: 1.189-1.489, p < 0.001); and high DFS-PII (>4.47) was significantly associated with worse DFS (HR: 1.366, 95% CI: 1.206-1.548, p < 0.001). The prognostic values of both OS-PII and DFS-PII were validated in the external validation cohort. The nomograms achieved good accuracy in predicting both OS and DFS. Time-dependent ROC analyses showed that both OS-PII and DFS-PII have a stable prognostic performance at various follow-up times. The prognostic value of tumor-node-metastasis staging could be enhanced by combining it with either OS-PII or DFS-PII. We demonstrated that PIIs are independent prognostic predictors for CRC patients, and the nomograms based on PIIs can be recommended for personalized survival prediction of patients with CRC.