Identification of DAPI-stained normal, inflammatory, and carcinoma hepatic cells based on hyperspectral microscopy

Synergistic Anticancer Effects of Silibinin and Sulforaphane: Targeting Gastric Cancer via PI3K/AKT and ERK1/2 MAPK Pathway Inhibition and Molecular Docking Insights

In the current period of pharmaceutical discovery, herbal remedies have shown to be an unmatched supply of anticancer medications. By changing the tumor microenvironment and several signaling pathways, plants and their byproducts through analogs have an important part in the therapy for carcinoma. The current investigation assessed the effectiveness of inhibiting the development of gastric cancer cells in HGC-27 cells by attenuating the PI3K/AKT and ERK 1/2 MAPK signaling pathways using the natural medicines silibinin (SIL) and sulforaphane (SFN) complemented by molecular docking analysis. After being exposed to various doses of SIL and SFN (SIL+SFN) for 24 h (0-50 µM), the cells were evaluated for multiple studies. The MTT assay was used to examine the combo that SIL+SFN induced cytotoxicity. ROS was assessed by DCFH-DA staining. Apoptotic changes were investigated, and MMP levels in HGC-27 cells were investigated utilizing the proper fluorescent staining techniques. Flow cytometry and western blot analysis were used to evaluate the protein profiles of cell survival, cell cycle, proliferation, and apoptosis. The molecular docking was conducted with Autodock Vina (v1.5.6). The docking results were analyzed using BIOVIA Discovery Studio Visualizer to identify key interactions. The relative cytotoxicity of SIL and SFN was found to be approximately 24.96 and 28.79 μM, correspondingly, according to the findings. After a 24-h incubation period, the combination of SIL and SFN generates significant cytotoxicity in HGC-27 cells, with an IC50 of 15.43 μM. Furthermore, HGC-27 cells administered SIL and SFN simultaneously exhibited elevated apoptotic signals and significant ROS production. Molecular docking demonstrated strong binding affinities between the compounds and the target proteins, supporting their potential mechanisms of action. Therefore, the combination usage of SIL + SFN has been viewed as a chemotherapeutic drug since it prevents the synthesis of PI3K/AKT and ERK 1/2 MAPK mediated control of cell growth and cell cycle-regulating proteins. To utilize them commercially conducting more in vivo research in the near future will be necessary to ascertain how well the co-treatment triggers apoptosis.

Exosomes-encapsulated biomimetic polydopamine carbon dots with dual-targeting effect alleviate motor and non-motor symptoms of Parkinson's disease via anti-neuroinflammation

Currently, the clinical drugs for Parkinson's disease (PD) only focus on motor symptoms, while non-motor symptoms like depression are usually neglected. Even though, the efficacy of existing neurotherapeutic drugs is extremely poor which is due to the blood brain barrier (BBB). Therefore, a biomimetic polydopamine carbon dots (PDA C-dots) at 2-4 nm was synthesized, while exosomes from macrophages were applied to encapsulate PDA C-dots for improving their BBB-crossing ability and inflammation-targeting effect. Importantly, the prepared PDA C-dots@Exosomes (PEs) significantly alleviated both motor and non-motor symptoms of PD mice. Further mechanism research revealed that PEs eliminated oxidant stress and alleviated neuroinflammation to restore the injured neurons. The content of α-syn was markedly reduced, and the neural viability was dramatically improved on the areas of substantia nigra, striata, and prefrontal cortex. In summary, this work reported a mild synthetic approach to produce a kind of PDA C-dots, which had a fantastic neuroprotective effect. After being encapsulated with exosomes of macrophages, the obtained PEs could be utilized as a neuroprotective drug with great penetration ability of BBB and targeting ability into inflammatory zone. The great therapeutic effect on both motor and non-motor symptoms of PD indicates that PEs could become a promising drug for PD treatment.

Depside and depsidone-rich hydroalcoholic extract, resourced from the lichen Parmelinella wallichiana (Taylor) Elix & Hale selectively restricts Non-Small Cell Lung Cancer by modulating p53, FOXO1 and PALLADIN genes

The non-specificity of contemporary cancer therapeutics has enticed us to develop safer, anticancer alternatives from natural resources. Lichens are unique natural entities which have long been neglected for explorations in cancer therapy, despite their vast potential. Our present study aims to investigate the anti-cancer potential of a wild lichen Parmelinella wallichiana. The anti-proliferative efficacy of the lichen extracts were screened through MTT assay against a panel of cell lines and the potent hydroalcoholic extract was selected for further evaluation against the most sensitive lung-cancer cell line A549 by implementing a wide range of microscopic and flow cytometric applications. The observations suggest that the extract could selectively induce apoptosis by augmenting ROS and disrupting the mitochondrial membrane potentiality. It was also found that the lichen-induced apoptosis was regulated by two crucial tumor suppressor genes, FOXO1, and p53, along with cell cycle inhibitor p21 which ultimately resulted in robust apoptosis through the up-regulation of pro-apoptotic BAX expression. Moreover, the extract also restricted the cancer progression by down-regulating the PALLADIN expression. Further, an LC-MS-based metabolomic profile highlighted a number of depsides, depsidones and dibenzofurans, which included atranorin, physodalic acid, salazinic acid, constictic acid and usnic acid. Then, an in silico docking with these lichen-derived metabolites against the PI3Kα receptor predicted these compounds has a binding affinity close to a standard PI3Kα inhibitor copanlisib. The study concludes that the extract restricts lung cancer possibly through the PI3Kα/FOXO1 axis and thus Parmelinella wallichiana represents a potential resource for anti-lung cancer drug development in future.

Rapid and robust bacterial species identification using hyperspectral microscopy and gram staining techniques

Gram staining can classify bacterial species into two large groups based on cell wall differences. Our study revealed that within the same gram group (gram-positive or gram-negative), subtle cell wall variations can alter staining outcomes, with the peptidoglycan layer and lipid content significantly influencing this effect. Thus, bacteria within the same group can also be differentiated by their spectra. Using hyperspectral microscopy, we identified six species of intestinal bacteria with 98.1% accuracy. Our study also demonstrated that selecting the right spectral band and background calibration can enhance the model's robustness and facilitate precise identification of varying sample batches. This method is suitable for analyzing bacterial community pathologies.

SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers

We present the development of SpeCamX, a mobile application that enables an unmodified smartphone into a multispectral imager. Multispectral imaging provides detailed spectral information about objects or scenes, but its accessibility has been limited due to its specialized requirements for the device. SpeCamX overcomes this limitation by utilizing the RGB photographs captured by smartphones and converting them into multispectral images spanning a range of 420 to 680 nm without a need for internal modifications or external attachments. The app also includes plugin functions for extracting medical information from the resulting multispectral data cube. In a clinical study, SpeCamX was used to implement an augmented smartphone bilirubinometer, predicting blood bilirubin levels (BBL) with superior performance in accuracy, efficiency and stability compared to default smartphone cameras. This innovative technology democratizes multispectral imaging, making it accessible to a wider audience and opening new possibilities for both medical and non-medical applications.

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma

BACKGROUND AND AIMS

Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens.

METHODS

HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model.

RESULTS

A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei.

CONCLUSIONS

An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel

Liver cancer is one of the most frequently diagnosed and fatal cancers worldwide, with hepatocellular carcinoma (HCC) being the most common primary liver cancer. Hundreds of studies involving thousands of patients have now been analysed across different cancer types, including HCC, regarding the effects of immune infiltrates on the prognosis of cancer patients. However, for these analyses, an unambiguous delineation of the cancer area is paramount, which is difficult due to the strong heterogeneity and considerable inter-operator variability induced by qualitative visual assessment and manual assignment. Nowadays, however, multiplex analyses allow the simultaneous evaluation of multiple protein markers, which, in conjunction with recent machine learning approaches, may offer great potential for the objective, enhanced identification of cancer areas with further in situ analysis of prognostic immune parameters. In this study, we, therefore, used an exemplary five-marker multiplex immunofluorescence panel of commonly studied markers for prognosis (CD3 T, CD4 T helper, CD8 cytotoxic T, FoxP3 regulatory T, and PD-L1) and DAPI to assess which analytical approach is best suited to combine morphological and immunohistochemical data into a cancer score to identify the cancer area that best matches an independent pathologist’s assignment. For each cell, a total of 68 individual cell features were determined, which were used as input for 4 different approaches for computing a cancer score: a correlation-based selection of individual cell features, a MANOVA-based selection of features, a multilayer perceptron, and a convolutional neural network (a U-net). Accuracy was used to evaluate performance. With a mean accuracy of 75%, the U-net was best capable of identifying the cancer area. Although individual cell features showed a strong heterogeneity between patients, the spatial representations obtained with the computed cancer scores delineate HCC well from non-cancer liver tissues. Future analyses with larger sample sizes will help to improve the model and enable direct, in-depth investigations of prognostic parameters, ultimately enabling precision medicine.