A MIL series metal‒organic framework (MOF), MIL-100(Fe), was successfully synthesized at the nanoscale and fully characterized by TEM, TGA, XRD, FTIR, DLS, and BET. A toxicological assessment was performed using two different cell lines: human normal liver cells (HL-7702) and hepatocellular carcinoma (HepG2). In vitro cytotoxicity of MIL-100(Fe) was evaluated by the MTT assay, LDH releasing rate assay, DAPI staining, and annexin V/PI double staining assay. The safe dose of MIL-100(Fe) was 80 μg/mL. It exhibited good biocompatibility, low cytotoxicity, and high cell survival rate (HL-7702 cells' viability >85.97%, HepG2 cells' viability >91.20%). Therefore, MIL-100(Fe) has a potential application as a drug carrier.

In Vitro Toxicity Study of a Porous Iron(III) Metal‒Organic Framework.

Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.

/pdf/cell-morphology-based-machine-learning-models-for-human-cell-3fplty2dh7.pdf

Cell morphology-based machine learning models for human cell state classification.

Resveratrol (RSV) has been reported to exhibit cytotoxic activity in multiple types of malignant cells; however, the mechanisms underlying the antitumor effects of RSV in non‑small‑cell lung cancer (NSCLC) cells remain undetermined. Combining bioinformatics analysis with experimental validation, the present study aimed to examine the effects of RSV on the apoptosis and autophagy of A549 NSCLC cells, and to determine the potential underlying molecular mechanisms. Bioinformatics analysis was used to determine the differentially expressed genes (DEGs) and identify the enriched biological functions and pathways associated with these DEGs following RSV treatment. Cell viability was determined by MTT assay, and flow cytometry and TUNEL assay were used to evaluate cell apoptosis. Monodansylcadaverine staining combined with a transmission electron microscope were used to evaluate the extent of autophagy. The expression levels of apoptosis‑, autophagy‑, or pathway‑associated molecular markers were measured by reverse transcription‑quantitative PCR and/or western blot analysis. By bioinformatics analysis, a total of 1,031 DEGs were identified in the RSV‑treated A549 cells, which were enriched in apoptosis‑, or autophagy‑related biological functions and the p53 signaling pathway. In validation experiments, RSV significantly reduced cell viability and initiated apoptosis, with an increase in the number of apoptotic cells; it also upregulated cleaved caspase‑3 expression and Bax expression, and downregulated the Bcl‑2 expression levels. Additionally, there was an increase in the accumulation of green dot‑like structures, indicative of autophagic vesicles, observed under a fluorescence microscope, and an increase in the presence of autophagic vacuoles observed using a transmission electron microscope following RSV treatment. Furthermore, the expression levels of the autophagy‑related proteins, LC3‑II/LC3‑I and Beclin‑1, were increased and p62 expression was decreased. 3‑methyladenine (3‑MA), an inhibitor of autophagy, partially reversed the RSV‑induced cytotoxic effects, but did not significantly alter the number of apoptotic cells. RSV elevated the p53 levels and decreased the phosphorylated (p‑)Mdm2 and p‑Akt levels. Pifithrin‑α, an inhibitor of p53, partially reduced RSV‑induced apoptosis and autophagy. On the whole, the results of the present study demonstrated that RSV initiates the apoptosis and autophagic death of A549 cells via the activation of the p53 signaling pathway, further highlighting the potential of RSV for the treatment of NSCLC.

/pdf/resveratrol-modulates-the-apoptosis-and-autophagic-death-of-2xaxixjjur.pdf

Resveratrol modulates the apoptosis and autophagic death of human lung adenocarcinoma A549 cells via a p53‑dependent pathway: Integrated bioinformatics analysis and experimental validation

Background Targeted prodrug has various applications as drug formulation for tumor therapy. Therefore, amphoteric small-molecule prodrug combined with nanoscale characteristics for the self-assembly of the nano-drug delivery system (DDS) is a highly interesting research topic. Methods and results In this study, we developed a prodrug self-assembled nanoplatform, 2-glucosamine-fluorescein-5(6)-isothiocyanate-glutamic acid-paclitaxel (2DA-FITC-PTX NPs) by integration of targeted small molecule and nano-DDS with regular structure and perfect targeting ability. 2-glucosamine (DA) and paclitaxel were conjugated as the targeted ligand and anti-tumor chemotherapy drug by amino acid group. 2-DA molecular structure can enhance the targeting ability of prodrug-based 2DA-FITC-PTX NPs and prolong retention time, thereby reducing the toxicity of normal cell/tissue. The fluorescent dye FITC or near-infrared fluorescent dye ICG in prodrug-based DDS was attractive for in vivo optical imaging to study the behavior of 2DA-FITC-PTX NPs. In vitro and in vivo results proved that 2DA-FITC-PTX NPs exhibited excellent targeting ability, anticancer activity, and weak side effects. Conclusion This work demonstrates a new combination of nanomaterials for chemotherapy and may promote prodrug-based DDS clinical applications in the future.

/pdf/targeted-prodrug-based-self-assembled-nanoparticles-for-3zz1jsxprj.pdf

Targeted Prodrug-Based Self-Assembled Nanoparticles for Cancer Therapy.

https://www.researchsquare.com/article/rs-44290/v1.pdf?c=1631860997000

5-Fluorouracil enhances the chemosensitivity of gastric cancer to TRAIL via inhibition of the MAPK pathway.

This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques

A label-free cell flow sorting method based on a polarization diffraction imaging flow cytometer comprises the following steps: establishing a convolutional neural network classification model; training and examining the established convolutional neural network classification model according to the types of cells to be sorted; calling FPGA programming software to generate the FPGA code of the convolutional neural network classification model; writing the FPGA code into an FPGA chip in a real-time image recognition unit; and sorting the cells. The label-free cell flow sorting method based on the polarization diffraction imaging flow cytometer can quickly can be used to rapidly obtain an label-free cell diffraction image and recognize the cell type corresponding the diffraction image in realtime, and the cells are timely sorted by a micro-fluidic device to achieve the purpose of classification collection of unlabeled cells.

Label-free cell flow sorting method based on polarization diffraction imaging flow cytometer

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Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques

Label-free cell flow sorting method based on polarization diffraction imaging flow cytometer