Green analytical chemistry (GAC) focuses on mitigating the adverse effects of analytical activities on human safety, human health, and environment. In addition to the 12 principles of GAC, proper GAC tools should be developed and employed to assess the greenness of different analytical assays. The 15 widely used GAC metrics, i.e., national environmental methods index (NEMI), advanced NEMI, assessment of green profile (AGP), chloroform-oriented toxicity estimation scale (ChlorTox Scale), Analytical Eco-Scale, Green Certificate Modified Eco-Scale, analytical method greenness score (AMGS), green analytical procedure index (GAPI), ComplexGAPI, red-green-blue (RGB) additive color model, RGB 12 algorithm, analytical greenness calculator (AGREE), AGREE preparation (AGREEprep), HEXAGON, and blue applicability grade index (BAGI), are selected as the typical tools. This article comprehensively presents and elucidates the principles, characteristics, merits, and demerits of 15 widely used GAC tools. This review is helpful for researchers to use the current GAC metrics to assess the environmental sustainability of analytical assays.

Cell toxicity may result in organ dysfunction and cause severe health problem. Recent studies revealed many toxicants may induced the over production of Nitric oxide, reactive oxygen species and the subsequent oxidative stress, cause cell toxicity. Mitochondrion dysfunction maybe the subsequent consequence of oxidative stress and has been recognized as another contributing factor in cell toxicity. Besides, oxidative products induced by some toxicants may also produce the compounds that damage cell DNA, leading to toxicity. Especially, the significance of nanoparticle induced cell toxicity was disclosed recently and attract more concern. The mechanism mainly includes inflammation, oxidative stress and DNA damage. On the other side, some biomarkers of cell toxicity including autophagy, cytokines, miRNA has been identified. The understanding of these phenomenon may enable us to clarify the cell toxicity mechanism then contribute to cell toxicity protection, disease treatment and drug side effect prevention.

Background: Kaiso has been identified as a new member of the POZ-zinc finger family of transcription factors that are implicated in development and cancer. Although controversy still exists, Kaiso is supposed to be involved in human cancer. However, there is limited information regarding the clinical significance of cytoplasmic/nuclear Kaiso in human lung cancer. Methods: In this study, immunohistochemical studies were performed on 20 cases of normal lung tissues and 294 cases of non-small cell lung cancer (NSCLC), including 50 cases of paired lymph node metastases and 88 cases with complete follow-up records. Three lung cancer cell lines showing primarily nuclear localization of Kaiso were selected to examine whether roles of Kaiso in cytoplasm and in nucleus are identical. Nuclear Kaiso was down-regulated by shRNA technology or addition a specific Kaiso antibody in these cell lines. The proliferative and invasive abilities were evaluated by MTT and Matrigel invasive assay, transcription of Kaiso's target gene matrilysin was detected by RT-PCR. Results: Kaiso was primarily expressed in the cytoplasm of lung cancer tissues. Overall positive cytoplasmic expression rate was 63.61% (187/294). The positive cytoplasmic expression of Kaiso was higher in advanced TNM stages (III+IV) of NSCLC, compared to lower stages (I+II) (p = 0.019). A correlation between cytoplasmic Kaiso expression and lymph node metastasis was found (p = 0.003). In 50 paired cases, cytoplasmic expression of Kaiso was 78.0% (41/50) in primary sites and 90.0% (45/50) in lymph node metastases (p = 0.001). The lung cancer-related 5-year survival rate was significantly lower in patients who were cytoplasmic Kaiso-positive (22.22%), compared to those with cytoplasmic Kaiso-negative tumors (64.00%) (p = 0.005). Nuclear Kaiso staining was seen in occasional cases with only a 5.10% (15/294) positive rate and was not associated with any clinicopathological features of NSCLC. Furthermore, after the down-regulation of the nuclear expresses Kaiso in vitro, both proliferative and invasive abilities of three cancer cell lines were significantly enhanced, along with the up-regulation of Kaiso target gene, matrilysin. Conclusion: Our data suggest cytoplasmic Kaiso expression is associated with poor prognosis of NSCLC and various subcellular localizations of Kaiso may play differential biological roles in NSCLC.

Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framework integrating pretreatment CT scans and H&E-stained biopsy images, for improved decision-making regarding neoadjuvant chemotherapy. We have constructed and tested iSCLM using retrospective data from 2,387 patients across 10 medical centers and evaluated its discriminative ability in a prospective cohort (132 patients; ChiCTR2300068917). iSCLM achieves areas under receiver operating characteristic curves of 0.846-0.876 across different test cohorts. Computed tomography (CT) and pathological attention heatmaps from Shapley additive explanations and global sort pooling illustrate additional benefits for capturing morphological features through supervised contrastive learning. Specifically, pathological top-ranked tiles exhibit decreased distances to tumor-invasive borders and increased inflammatory cell infiltration in responders compared with non-responders. Moreover, CD11c expression is elevated in responders. The developed interpretable model at the molecular pathology level accurately predicts chemotherapy efficacy.

Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: a multi-parameter prediction model Fuying Hu, Haihua Huang, Yunyan Jiang, Minxiang Feng, Hao Wang, Min Tang, Yi Zhou, Xianhua Tan, Yalan Liu, Chen Xu, Ning Ding, Chunxue Bai, Jie Hu, Dawei Yang, Yong Zhang