Learning deep features for dead and living breast cancer cell classification without staining

Pd(II) complexes bearing NNS pincer ligands: unveiling potent cytotoxicity against breast and pancreatic cancer

The continuously increasing rate of breast cancer is one of the major threats to female health worldwide. Recently, palladium complexes have emerged as impressive candidates with effective biocompatibility and anticancer activities. Hence, in the present study, we report a new series of palladium complexes bearing NNS pincer ligands for cytotoxicity studies. The reaction of thiophenol/4-chlorothiophenol/4-methylthiophenol/4-methoxythiophenol with 2-bromo-N-quinolin-8-yl-acetamide in the presence of sodium hydroxide in ethanol at 80 °C gave [C9H6N-NH-C(O)-CH2-S-Ar] [Ar = C6H5 (L1), C6H4Cl-4 (L2), C6H4Me-4 (L3), and C6H4-OMe-4 (L4)]. The corresponding reaction of L1-L4 with Na2PdCl4 in methanol at room temperature for 3 h resulted in complexes [(L1-H)PdCl] (C1), [(L2-H)PdCl] (C2), [(L3-H)PdCl] (C3), and [(L4-H)PdCl] (C4). All new compounds have been characterized by spectroscopic analyses. The structures of complexes C1, C3, and C4 have also been determined from single-crystal X-ray diffraction data. The cytotoxicities of L1-L4 and C1-C4 have been investigated for breast cancer 4T1 and pancreatic cancer MIA-PaCa-2 cells. The IC50 values for complexes C2 and C3 were observed to be comparable to or higher than those of cisplatin. The stressed morphology and cell death of cancerous cells were successfully observed through cellular morphology analysis and the assessment of cytoskeleton damage.

High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system

Accurate identification and classification of bone marrow (BM) nucleated cell morphology are crucial for the diagnosis of hematological diseases. However, the subjective and time-consuming nature of manual identification by pathologists hinders prompt diagnosis and patient treatment. To address this issue, we developed Morphogo, a convolutional neural network-based system for morphological examination. Morphogo was trained using a vast dataset of over 2.8 million BM nucleated cell images. Its performance was evaluated using 508 BM cases that were categorized into five groups based on the degree of morphological abnormalities, comprising a total of 385,207 BM nucleated cells. The results demonstrated Morphogo's ability to identify over 25 different types of BM nucleated cells, achieving a sensitivity of 80.95%, specificity of 99.48%, positive predictive value of 76.49%, negative predictive value of 99.44%, and an overall accuracy of 99.01%. In most groups, Morphogo cell analysis and Pathologists' proofreading showed high intragroup correlation coefficients for granulocytes, erythrocytes, lymphocytes, monocytes, and plasma cells. These findings further validate the practical applicability of the Morphogo system in clinical practice and emphasize its value in assisting pathologists in diagnosing blood disorders.

SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator

As a measure of cytotoxic potency, half-maximal inhibitory concentration (IC50) is the concentration at which a drug exerts half of its maximal inhibitory effect against target cells. It can be determined by various methods that require applying additional reagents or lysing the cells. Here, we describe a label-free Sobel-edge-based method, which we name SIC50, for the evaluation of IC50. SIC50 classifies preprocessed phase-contrast images with a state-of-the-art vision transformer and allows for the continuous assessment of IC50 in a faster and more cost-efficient manner. We have validated this method using four drugs and 1,536-well plates and also built a web application. We anticipate that this method will assist in the high-throughput screening of chemical libraries (e.g., small-molecule drugs, small interfering RNA [siRNA], and microRNA and drug discovery).

Nickel Complexes Bearing ONS Chelating Ligands: A Promising Contender for In Vitro Cytotoxicity Effects on Human Pancreatic Cancer MIA-PaCa-2 Cells

The highly chronic human pancreatic cancer cell is one of the major reasons for cancerous death. Nickel complexes are recently gaining interest in anticancer activities on different types of cancer cells. Hence, in this study, we synthesized and characterized a series of ONS donor ligands [2-HO-C₆H₄-CH═N-(C₆H₄)-SH] (L1), [2-OH-3-OMe-C₆H₃-CH═N-(C₆H₄)-SH] (L2), [2-OH-3,5-(C(Me)₃)₂-C₆H₂-CH═N-(C₆H₄)-SH] (L3), [2-OH-C₆H₄-CH═N-(C₆H₄)-SMe] (L4), [2-OH-3-OMe-C₆H₃-CH═N-(C₆H₄)-SMe] (L5), [2-OH-3,5-(C(Me)₃)₂-C₆H₂-CH═N-(C₆H₄)-SMe] (L6) and their Ni(II) metal complexes [(MeOH)Ni(L1-L1-4H)] (1), [(MeOH)Ni(L2-L2-4H)] (2), [(MeOH)Ni(L3-L3-4H)] (3), [(L4-H)₂Ni] (4), [(L5-H)₂Ni] (5), and [(L6-H)₂Ni] (6). The single-crystal X-ray diffraction data of complexes 1 and 4 were collected to elucidate the geometry around the metal center. The anticancer activity of complexes 1-6 was investigated on human pancreatic cancer cell line MIA-PaCa-2, which revealed that complexes 4 and 6 were the most significantly effective in decreasing the cell viability of cancer cells at the lowest dose. The structure parameters obtained from single-crystal X-ray diffraction data are found to be in good agreement with the data from density functional theory and Hirshfeld surface analysis for complex 1.

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

Ectoderm-derived frontal bone mesenchymal stem cells promote traumatic brain injury recovery by alleviating neuroinflammation and glutamate excitotoxicity partially via FGF1

Background

Traumatic brain injury (TBI) leads to cell and tissue impairment, as well as functional deficits. Stem cells promote structural and functional recovery and thus are considered as a promising therapy for various nerve injuries. Here, we aimed to investigate the role of ectoderm-derived frontal bone mesenchymal stem cells (FbMSCs) in promoting cerebral repair and functional recovery in a murine TBI model.

Methods

A murine TBI model was established by injuring C57BL/6 N mice with moderate-controlled cortical impact to evaluate the extent of brain damage and behavioral deficits. Ectoderm-derived FbMSCs were isolated from the frontal bone and their characteristics were assessed using multiple differentiation assays, flow cytometry and microarray analysis. Brain repairment and functional recovery were analyzed at different days post-injury with or without FbMSC application. Behavioral tests were performed to assess learning and memory improvements. RNA sequencing analysis, immunofluorescence staining, and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) were used to examine inflammation reaction and neural regeneration. In vitro co-culture analysis and quantification of glutamate transportation were carried out to explore the possible mechanism of neurogenesis and functional recovery promoted by FbMSCs.

Results

Ectoderm-derived FbMSCs showed fibroblast like morphology and osteogenic differentiation capacity. FbMSCs were CD105, CD29 positive and CD45, CD31 negative. Different from mesoderm-derived MSCs, FbMSCs expressed the ectoderm-specific transcription factor Tfap2β. TBI mice showed impaired learning and memory deficits. Microglia and astrocyte activation, as well as neural damage, were significantly increased post-injury. FbMSC application ameliorated the behavioral deficits of TBI mice and promoted neural regeneration. RNA sequencing analysis showed that signal pathways related to inflammation decreased, whereas those related to neural activation increased. Immunofluorescence staining and qRT-PCR data revealed that microglial activation and astrocyte polarization to the A1 phenotype were suppressed by FbMSC application. In addition, FGF1 secreted from FbMSCs enhanced glutamate transportation by astrocytes and alleviated the cytotoxic effect of excessive glutamate on neurons.

Conclusions

Ectoderm-derived FbMSC application significantly alleviated neuroinflammation, brain injury, and excitatory toxicity to neurons, improved cognition and behavioral deficits in TBI mice. Therefore, ectoderm-derived FbMSCs could be ideal therapeutic candidates for TBI which mostly affect cells from the same embryonic origins as FbMSCs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-03032-6.

Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining

To facilitate rapid determination of cellular viability caused by the inhibitory effect of drugs, numerical deep learning algorithms was used for unlabeled cell culture images captured by a light microscope as input. In this study, A549, HEK293, and NCI-H1975 cells were cultured, each of which have different molecular shapes and levels of drug responsiveness to doxorubicin (DOX). The microscopic images of these cells following exposure to various concentrations of DOX were trained with the measured value of cell viability using a colorimetric cell proliferation assay. Convolutional neural network (CNN) models for the study cells were constructed using augmented image data; the predicted cell viability using CNN models was compared to the cell viability measured by colorimetric assay. The linear relationship coefficient (r²) between measured and predicted cell viability was determined as 0.94–0.95 for the three cell types. In addition, the measured and predicted IC50 values were not statistically different. When drug responsiveness was estimated using allogenic models that were trained with a different cell type, the correlation coefficient decreased to 0.004085–0.8643. Our models could be applied to label-free cells to conduct rapid and large-scale tests while minimizing cost and labor, such as high-throughput screening for drug responsiveness.

A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment

A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low- degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology- and phototherapy-based cancer treatment.