Anaplastic Lymphoma Kinase (ALK) Inhibitors Show Activity in Colorectal Cancer With ALK Rearrangements: Case Series and Literature Review

Anaplastic lymphoma kinase (ALK) rearrangement is a well-known driver oncogene detected in approximately 5% of non-small cell lung cancer. However, ALK rearrangement is much less frequent in other solid tumors outside the lungs, such as colorectal cancer (CRC); thus, the optimal management of CRC with ALK rearrangements has yet to be established. In this report, we describe 2 cases of ALK-positive CRC, both of which benefited from ALK tyrosine kinase inhibitor (ALK-TKI) therapy. Case 1 was a postoperative patient with poorly differentiated colon adenocarcinoma, who was diagnosed with metastatic relapse shortly after surgery. Both fluorouracil, leucovorin, and oxaliplatin (FOLFOX) and bevacizumab combined with 5-fluorouracil, l-leucovorin, and irinotecan (FOLFIRI) proved ineffective against the disease. The patient was then treated with ensartinib, as the CAD-ALK fusion gene was detected by genomic analysis. The patient was initially treated with ensartinib monotherapy for 9 months, then with ensartinib combined with local radiotherapy and fruquintinib for another 4 months for isolated hilar hepatic lymph node metastasis. The patient experienced disease progression with an acquired ALK G1202R resistance mutation that responded well to lorlatinib. Case 2 involved a 72-year-old man with advanced colon cancer (pT4bN2aM1b, stage IV) harboring an EML4-ALK fusion. The patient underwent resection of the right colon tumor due to intestinal obstruction, but the disease continued to progress after 12 courses of FOLFIRI and bevacizumab chemotherapy. However, the patient responded remarkably well to alectinib. Our report emphasizes the importance of gene detection in the treatment of malignant tumors, and the significance of ALK mutations in CRC.

Biomass-Derived Carbon-Based Multicomponent Integration Catalysts for Electrochemical Water Splitting

Electrocatalytic water splitting powered by sustainable electricity is a crucial approach for the development of new generation green hydrogen technology. Biomass materials are abundant and renewable, and the application of catalysis can increase the value of some biomass waste and turn waste into fortune. Converting economical and resource-rich biomass into carbon-based multicomponent integrated catalysts (MICs) has been considered as one of the most promising ways to obtain inexpensive, renewable and sustainable electrocatalysts in recent years. In this review, recent advances in biomass-derived carbon-based MICs towards electrocatalytic water splitting are summarized, and the existing issues and key aspects in the development of these electrocatalysts are also discussed and prospected. The application of biomass-derived carbon-based materials will bring some new opportunities in the fields of energy, environment, and catalysis, as well as promote the commercialization of new nanocatalysts in the near future.

Antibacterial and Anticancer Activity, Acute Toxicity, and Solubility of Co-crystals of 5-Fluorouracil and Trimethoprim

5-Fluorouracil is mainly used for the treatment of tumors and has relatively high toxicity. Trimethoprim is a common broad-spectrum antibiotic agent with extremely poor water solubility. We hoped to solve these problems by synthesizing co-crystals (compound 1) of 5-fluorouracil and trimethoprim. Solubility tests showed that the solubility of compound 1 was improved compared to that of trimethoprim. In vitro anticancer activity tests of compound 1 showed higher activity against human breast cancer cells than 5-fluorouracil. Acute toxicity showed that its toxicity was much lower than that of 5-fluorouracil. In the test of anti-Shigella dysenteriae activity, compound 1 showed much stronger antibacterial activity than trimethoprim.

Biological Activity Predictions of Ligands Based on Hybrid Molecular Fingerprinting and Ensemble Learning

The biological activity predictions of ligands are an important research direction, which can improve the efficiency and success probability of drug screening. However, the traditional prediction method has the disadvantages of complex modeling and low screening efficiency. Machine learning is considered an important research direction to solve these traditional method problems in the near future. This paper proposes a machine learning model with high predictive accuracy and stable prediction ability, namely, the back propagation neural network cross-support vector regression model (BPCSVR). By comparing multiple molecular descriptors, MACCS fingerprint and ECFP6 fingerprint were selected as inputs, and the stable prediction ability of the model was improved by integrating multiple models and correcting similar samples. We used leave-one-out cross-validation on 3038 samples from six data sets. The coefficient of determination, root mean square error, and absolute error were used as the evaluation parameters. After comparing the multiclass models, the results show that the BPCSVR model has stable prediction ability in different data sets, and the prediction accuracy is higher than other comparison models.

Validation of Serotransferrin in the Serum as Candidate Biomarkers for the Diagnosis of Pulmonary Tuberculosis by Label-Free LC/MS

This study aimed to identify secreted protein biomarkers in serum from the label-free LC/MS proteomics of neutrophils in pulmonary tuberculosis (TB) patients for the diagnosis biomarkers of TB label-free LC/MS. The proteomic profiles of neutrophils from 15 active TB patients and 15 healthy controls (HCs) were analyzed using label-free LC/MS. We identified 358 differentially expressed proteins preliminarily, including 279 up-regulated proteins and 79 down-regulated proteins. Thirty-eight differentially expressed secreted proteins involved in the progress of platelet degranulation between TB patients and HCs were focused. Of these, serotransferrin (TRF), alpha-2-macroglobulin (AMG), alpha-1-antitrypsin (AAT), alpha-1-acid glycoprotein 1 (AAG), alpha-1-acid glycoprotein 2 (AGP2), and alpha-1B-glycoprotein (A1BG) were selected for further verification in the serum of additional 134 TB patients and 138 HCs by nephelometry and ELISA in the training set. Statistically significant differences of TRF (P < 0.0001), AAT (P < 0.0001), AAG (P < 0.0001), AGP2 (P < 0.0001), and A1BG (P = 0.0003) were observed. The serum concentration of TRF was down-regulated in TB patients compared with healthy controls, which was coincident with the proteomics results. An additional validation of TRF was performed in an independent cohort of patients with active TB (n = 46), patients with lung cancer (n = 37), 20 HCs, and patients with pneumonia (n = 35) in the test set by nephelometry. The serum expression levels of TRF in the TB patients showed lower levels compared with those in patients with pneumonia (P = 0.0125), lung cancer (P = 0.0005), HCs (P < 0.0001), and the non-TB controls (P < 0.0001). Furthermore, the AUC value of TRF was 0.647 with 90.22% sensitivity and 42.86% specificity in discriminating the TB group from the pneumonia group, 0.702 with 93.48% sensitivity and 47.16% specificity in discriminating the TB group from the lung cancer group, 0.894 with 91.30% sensitivity and 71.62% specificity in discriminating the TB group from all HCs, and 0.792 with 91.30% sensitivity and 58.90% specificity in discriminating the TB group from the non-TB controls. This study obtained the proteomic profiles of neutrophils in the TB patients and HCs, which contribute to a better understanding of the pathogenesis molecules existing in the neutrophils of pulmonary tuberculosis and provide candidate biomarkers for the diagnosis of pulmonary tuberculosis.

Molecular Dynamics Analysis of 6H-SiC Subsurface Damage by Nanofriction

To investigate the subsurface damage of 6H-SiC nanofriction, this paper uses molecular dynamics analysis to analyze the loading process of friction 6H-SiC surfaces, thus providing an in-depth analysis of the formation mechanism of subsurface damage from microscopic crystal structure deformation characteristics. This paper constructs a diamond friction 6H-SiC nanomodel, combining the radial distribution function, dislocation extraction method, and diamond identification method with experimental analysis to verify the dislocation evolution process, stress distribution, and crack extension to investigate the subsurface damage mechanism. During the friction process, the kinetic and potential energies as well as the temperature of the 6H-SiC workpiece basically tend to rise, accompanied by the generation of dislocated lumps and cracks on the sides of the 6H-SiC workpiece. The stresses generated by friction during the plastic deformation phase lead to dislocations in the vicinity of the diamond tip friction, and the process of dislocation nucleation expansion is accompanied by energy exchange. Dislocation formation is found to be the basis for crack generation, and cracks and peeled blocks constitute the subsurface damage of 6H-SiC workpieces by diamond identification methods. Friction experiments validate microscopic crystal changes against macroscopic crack generation, which complements the analysis of the damage mechanism of the simulated 6H-sic nanofriction subsurface.

Bismuth Nanoclusters/Porous Carbon Composite: A Facile Ratiometric Electrochemical Sensing Platform for Pb2+ Detection with High Sensitivity and Selectivity

In this work, a ratiometric electrochemical sensor was constructed for the detection of Pb²⁺ based on a bismuth nanocluster-anchored porous activated biochar (BiNCs@AB) composite. BiNCs with loose structure and AB with abundant oxygen-containing functional groups are favorable for Pb²⁺ adsorption and preconcentration; meanwhile, porous AB provides more mass transfer pathways and increases electronic and ion diffusion coefficients, realizing high sensitivity for Pb²⁺ detection. At the same time, BiNCs were proposed as an inner reference for ratiometric electrochemical detection, which could greatly enhance the determination accuracy. Under optimized experimental conditions, the anodic peak current ratio between Pb²⁺ and BiNCs exhibited a good linear relationship with the concentration from 3.0 ng/L to 1.0 mg/L. The detection limit can be detected down to 1.0 ng/L. Furthermore, the proposed sensor demonstrated good reproducibility, stability, and interference resistance, as well as satisfactory recoveries for the detection of Pb²⁺ in real samples.

Autophagy and Hsp70 activation alleviate oral epithelial cell death induced by food-derived hypertonicity

Stable intracellular and intercellular osmolarity is vital for all physiological processes. Although it is the first organ that receives food, the osmolarity around the mouth epithelium has never been systematically investigated. We found that oral epithelial cells are a population of ignored cells routinely exposed to hypertonic environments mainly composed of saline, glucose, etc. in vivo after chewing food. By using cultured oral epithelial cells as an in vitro model, we found that the hypotonic environments caused by both high NaCl and high glucose induced cell death in a dose- and time-dependent manner. Transcriptomics revealed similar expression profiles after high NaCl and high glucose stimulation. Most of the common differentially expressed genes were enriched in "mitophagy" and "autophagy" according to KEGG pathway enrichment analysis. Hypertonic stimulation for 1 to 6 h resulted in autophagosome formation. The activation of autophagy protected cells from high osmolarity-induced cell death. The activation of Hsp70 by the pharmacological activator handelin significantly improved the cell survival rate after hypertonic stimulation. The protective role of Hsp70 activation was partially dependent on autophagy activation, indicating a crosstalk between Hsp70 and autophagy in hypertonic stress response. The extract of the handelin-containing herb Chrysanthemum indicum significantly protected oral epithelial cells from hypertonic-induced death, providing an inexpensive way to protect against hypertonic-induced oral epithelial damage. In conclusion, the present study emphasized the importance of changes in osmolarity in oral health for the first time. The identification of novel compounds or herbal plant extracts that can activate autophagy or HSPs may contribute to oral health and the food industry.