Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms.

Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping.

Ovarian cancer detection has traditionally relied on a multistep process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology.

Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping.

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology.

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming and subjective and requires extensive expertise.

The PaCO2/FiO2 ratio as outcome predictor in SARS-COV-2 related pneumonia: a retrospective study.

Background And Aim: Respiratory failure in SARS-CoV-2 patients is characterized by the presence of hypoxemia and hypocapnia without relevant dyspnea. To date, the use of respiratory parameters other than PaO2/FiO2 ratio to stratify the risk of worsening of these patients has not been sufficiently studied.  Aim of this work was to evaluate whether the ratio between partial pressure levels of carbon dioxide (PaCO2) and the fraction of inspired oxygen (FiO2) measured at emergency department (ED) admission is predictive of the clinical course of patients suffering from SARS-CoV-2 pneumonia.