Urine based near-infrared spectroscopy analysis reveals a noninvasive and convenient diagnosis method for cancers: a pilot study.

Background: The challenges in cancer diagnosis underline the need for continued research and development of new diagnostic tools and methods. This study aims to explore an effective, noninvasive, and convenient diagnostic tool using urine based near-infrared spectroscopy (NIRS) analysis combined with machine learning algorithm. Methods: Urine samples were collected from a total of 327 participants, including 181 cancer cases and 146 healthy controls. These participants were randomly spit into train set (n = 218) and test set (n = 109). NIRS analysis (4,000 ∼10,000 cm-1) was performed for each sample in both train and test sets. Five pretreatment methods, including Savitzky-Golay (SG) smoothing, multiplicative scatter correction (MSC), baseline removal (BSL) with fitting polynomials to be used as baselines, the first derivative (DERIV1), and the second derivative (DERIV2), and combination with "scaling" and "center", were investigated. Then partial least-squares (PLS) and linear support-vector machine (SVM) classification models were established, and prediction performance was evaluated in test set. Results: NIRS had greatly overlapping in peaks, and PCA analysis failed in separation between cancers and healthy controls. In modeling with urine based NIRS data, PLS model showed its highest prediction accuracy of 0.780, with DERIV2, "scaling" and "center" pretreatment, while linear SVM displayed its best prediction accuracy of 0.844, with raw NIRS. With optimization in SVM, the prediction accuracy could improve to 0.862, when the top 262 features were involved as variables. Discussion: This pilot study combining urine based NIRS analysis and machine learning is effective and convenient that might facilitate in cancer diagnosis, encouraging further evaluation with a large-size multi-center study.

Preoperative serum bilirubin is an independent prognostic factor for curatively resected esophageal squamous cell carcinoma.

PURPOSE: This study examines prognostic value of preoperative serum bilirubin, including unconjugated bilirubin (UCB), conjugated bilirubin (CB), and total bilirubin (TB), in esophageal squamous cell carcinoma (ESCC) patients who underwent curative resection. METHODS: Between May 2010 and December 2012, a total of 351 ESCC patients were retrospectively reviewed. All the patients underwent curative resection as their primary treatment. Clinicopathological features and overall survival (OS) rate were investigated. Kaplan-Meier curves were used to calculate the OS rate, and the prognostic factors were identified by Cox regression model. Besides, the potential inhibition effect of UCB on ESCC was investigated with both in vitro and in vivo models. RESULTS: The higher-level groups of UCB, CB, and TB demonstrated longer OS than their low counterparts, with hazard ratio (HR) values of 0.567 (95% CI: 0.424-0.759), 0.698 (95% CI: 0.522-0.933), and 0.602 (95% CI: 0.449-0.807), respectively. All three forms of bilirubin were identified as independent prognostic factors for patients with ESCC, and they were found to effectively stratify the survival risk of patients at TNM stage III. In vivo and in vitro models further confirmed the inhibitory effect of unconjugated bilirubin (UCB) on the proliferation of ESCC. CONCLUSION: The findings of our study have shed new light on the prognostic value and biological functions of bilirubin in relation to ESCC. These results may contribute to a better understanding of the underlying mechanisms involved in ESCC tumorigenesis and provide potential therapeutic pathways for treating ESCC.

Mitigation mechanism of zinc oxide nanoparticles on cadmium toxicity in tomato.

Cadmium (Cd) pollution seriously reduces the yield and quality of vegetables. Reducing Cd accumulation in vegetables is of great significance for improving food safety and sustainable agricultural development. Here, using tomato as the material, we analyzed the effect of foliar spraying with zinc oxide nanoparticles (ZnO NPs) on Cd accumulation and tolerance in tomato seedlings. Foliar spraying with ZnO NPs improved Cd tolerance by increasing photosynthesis efficiency and antioxidative capacity, while it reduced Cd accumulation by 40.2% in roots and 34.5% in leaves but increased Zn content by 33.9% in roots and 78.6% in leaves. Foliar spraying with ZnO NPs also increased the contents of copper (Cu) and manganese (Mn) in the leaves of Cd-treated tomato seedlings. Subsequent metabonomic analysis showed that ZnO NPs exposure alleviated the fluctuation of metabolic profiling in response to Cd toxicity, and it had a more prominent effect in leaves than in roots. Correlation analysis revealed that several differentially accumulated metabolites were positively or negatively correlated with the growth parameters and physiol-biochemical indexes. We also found that flavonoids and alkaloid metabolites may play an important role in ZnO NP-alleviated Cd toxicity in tomato seedlings. Taken together, the results of this study indicated that foliar spraying with ZnO NPs effectively reduced Cd accumulation in tomato seedlings; moreover, it also reduced oxidative damage, improved the absorption of trace elements, and reduced the metabolic fluctuation caused by Cd toxicity, thus alleviating Cd-induced growth inhibition in tomato seedlings. This study will enable us to better understand how ZnO NPs regulate plant growth and development and provide new insights into the use of ZnO NPs for improving growth and reducing Cd accumulation in vegetables.

Advances in research on the effectiveness and mechanism of Traditional Chinese Medicine formulas for colitis-associated colorectal cancer.

Inflammatory bowel disease (IBD) can progress into colitis-associated colorectal cancer (CAC) through the inflammation-cancer sequence. Although the mechanism of carcinogenesis in IBD has not been fully elucidated, the existing research indicates that CAC may represent a fundamentally different pathogenesis pattern of colorectal cancer. At present, there is no proven safe and effective medication to prevent IBD cancer. In recent years, Chinese medicine extracts and Chinese medicine monomers have been the subject of numerous articles about the prevention and treatment of CAC, but their clinical application is still relatively limited. Traditional Chinese Medicine (TCM) formulas are widely applied in clinical practice. TCM formulas have demonstrated great potential in the prevention and treatment of CAC in recent years, although there is still a lack of review. Our work aimed to summarize the effects and potential mechanisms of TCM formulas for the prevention and treatment of CAC, point out the issues and limitations of the current research, and provide recommendations for the advancement of CAC research in the future. We discovered that TCM formulas regulated many malignant biological processes, such as inflammation-mediated oxidative stress, apoptosis, tumor microenvironment, and intestinal microecology imbalance in CAC, through a review of the articles published in databases such as PubMed, SCOPUS, Web of Science, Embase, and CNKI. Several major signal transduction pathways, including NF-κB, STAT3, Wnt/ß-catenin, HIF-1α, and Nrf2, were engaged. TCM formula may be a promising treatment candidate to control the colitis-cancer transformation, however further high-quality research is required.

Classification of multiple cancer types by combination of plasma-based near-infrared spectroscopy analysis and machine learning modeling.

BACKGROUND AND AIM: Near-infrared spectroscopy (NIRS) is a non-invasive and convenient tool, which gains features related to chemical components in biological samples. Machine learning (ML) has been popularized in medical diagnosis. This study aimed at investigating a novel cancer diagnosis strategy using NIRS data based ML modeling. METHODS: Plasma samples were collected from a total of 247 participants, including lung cancer, cervical cancer, nasopharyngeal cancer, and healthy control, and were randomly split into train set and test set. After performing NIRS analysis, the train dataset was utilized to train ML models, including partial least-squares (PLS), random forest (RF), gradient boosting machine (GBM), and support-vector machine (SVM). Subsequently, these models were tested for their prediction performance by the test set. RESULTS: All ML models demonstrated high prediction performance in differentiating cancers from controls, and SVM had high prediction accuracy for different types of cancers. SVM was considered as the most suitable model for its minimal computational cost and high accuracies for both binary and quaternary classification. CONCLUSIONS: This strategy coupling NIRS with ML is insightful that may aid in clinic cancer diagnosis, while further studies should test our results in a larger cohort with better representativeness.

Comparative physiological and metabolomic analyses reveal that Fe3O4 and ZnO nanoparticles alleviate Cd toxicity in tobacco.

BACKGROUND: Heavy metals repress tobacco growth and quality, and engineered nanomaterials have been used for sustainable agriculture. However, the underlying mechanism of nanoparticle-mediated cadmium (Cd) toxicity in tobacco remains elusive. RESULTS: Herein, we investigated the effects of Fe3O4 and ZnO nanoparticles (NPs) on Cd stress in tobacco cultivar 'Yunyan 87' (Nicotiana tabacum). Cd severely repressed tobacco growth, whereas foliar spraying with Fe3O4 and ZnO NPs promoted plant growth, as indicated by enhancing plant height, root length, shoot and root fresh weight under Cd toxicity. Moreover, Fe3O4 and ZnO NPs increased, including Zn, K and Mn contents, in the roots and/or leaves and facilitated seedling growth under Cd stress. Metabolomics analysis showed that 150 and 76 metabolites were differentially accumulated in roots and leaves under Cd stress, respectively. These metabolites were significantly enriched in the biosynthesis of amino acids, nicotinate and nicotinamide metabolism, arginine and proline metabolism, and flavone and flavonol biosynthesis. Interestingly, Fe3O4 and ZnO NPs restored 50% and 47% in the roots, while they restored 70% and 63% in the leaves to normal levels, thereby facilitating plant growth. Correlation analysis further indicated that these metabolites, including proline, 6-hydroxynicotinic acid, farrerol and quercetin-3-O-sophoroside, were significantly correlated with plant growth. CONCLUSIONS: These results collectively indicate that metal nanoparticles can serve as plant growth regulators and provide insights into using them for improving crops in heavy metal-contaminated areas.