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Development of a radiomic model for cervical cancer staging based on pathologically verified, retrospective metastatic lymph node data

Zhang B, Liu L, Meng D, Kue CS

Acta Radiol, 2024 Dec;65(12):1548-1559.
PMID: 39569554 DOI: 10.1177/02841851241291931

BACKGROUND: Cervical cancer is a major cause of morbidity and mortality among gynecological malignancies. Diagnostic imaging of lymph node (LN) metastasis for prognosis and staging is used; however, the accuracy in classifying the stage needs to improve.
PURPOSE: To examine the accuracy of AI-based radiomics in diagnosis, prognosis assessment and predicting the diagnostic value of radiomics for pelvic LN metastasis in cervical cancer patients.
MATERIAL AND METHODS: The study included 118 female patients with 660 LNs and 118 merged LNs. Four imaging histology models-decision tree, random forest, logistic regression, and support vector machine (SVM)-were created in this study. The imaging histology features were extracted from both the independent and merged LN groups. The AUC values for the test sets and the training sets of the four imaging histology models were compared for the independent LN group and the merged LN group. The DeLong test was used to compare the models.
RESULT: The imaging histology prediction model developed in the merged LN group outperformed the independent LN group in terms of test set AUC (0.668 vs. 0.535 for decision tree, 0.841 vs. 0.627 for logistic regression, 0.785 vs. 0.637 for random forest, 0.85 vs. 0.648 for SVM) and accuracy (0.754 vs. 0.676 for decision tree, 0.780 vs. 0.671 for random forest, 0.848 vs. 0.685 for logistic regression, 0.822 vs. 0.657 for SVM).
CONCLUSION: The constructed SVM imaging histology model for the merged LN group might be advantageous in predicting pelvic LN metastasis in cervical cancer.
Potential of Producing Flavonoids Using Cyanobacteria As a Sustainable Chassis

Jin H, Wang Y, Zhao P, Wang L, Zhang S, Meng D, et al.

J Agric Food Chem, 2021 Oct 27;69(42):12385-12401.
PMID: 34649432 DOI: 10.1021/acs.jafc.1c04632

Numerous plant secondary metabolites have remarkable impacts on both food supplements and pharmaceuticals for human health improvement. However, higher plants can only generate small amounts of these chemicals with specific temporal and spatial arrangements, which are unable to satisfy the expanding market demands. Cyanobacteria can directly utilize CO2, light energy, and inorganic nutrients to synthesize versatile plant-specific photosynthetic intermediates and organic compounds in large-scale photobioreactors with outstanding economic merit. Thus, they have been rapidly developed as a "green" chassis for the synthesis of bioproducts. Flavonoids, chemical compounds based on aromatic amino acids, are considered to be indispensable components in a variety of nutraceutical, pharmaceutical, and cosmetic applications. In contrast to heterotrophic metabolic engineering pioneers, such as yeast and Escherichia coli, information about the biosynthesis flavonoids and their derivatives is less comprehensive than that of their photosynthetic counterparts. Here, we review both benefits and challenges to promote cyanobacterial cell factories for flavonoid biosynthesis. With increasing concerns about global environmental issues and food security, we are confident that energy self-supporting cyanobacteria will attract increasing attention for the generation of different kinds of bioproducts. We hope that the work presented here will serve as an index and encourage more scientists to join in the relevant research area.
Fulltext Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage

Pszczolkowski S, Law ZK, Gallagher RG, Meng D, Swienton DJ, Morgan PS, et al.

Comput Biol Med, 2019 Mar;106:126-139.
PMID: 30711800 DOI: 10.1016/j.compbiomed.2019.01.022

BACKGROUND: Spontaneous intracerebral haemorrhage (SICH) is a common condition with high morbidity and mortality. Segmentation of haematoma and perihaematoma oedema on medical images provides quantitative outcome measures for clinical trials and may provide important markers of prognosis in people with SICH.
METHODS: We take advantage of improved contrast seen on magnetic resonance (MR) images of patients with acute and early subacute SICH and introduce an automated algorithm for haematoma and oedema segmentation from these images. To our knowledge, there is no previously proposed segmentation technique for SICH that utilises MR images directly. The method is based on shape and intensity analysis for haematoma segmentation and voxel-wise dynamic thresholding of hyper-intensities for oedema segmentation.
RESULTS: Using Dice scores to measure segmentation overlaps between labellings yielded by the proposed algorithm and five different expert raters on 18 patients, we observe that our technique achieves overlap scores that are very similar to those obtained by pairwise expert rater comparison. A further comparison between the proposed method and a state-of-the-art Deep Learning segmentation on a separate set of 32 manually annotated subjects confirms the proposed method can achieve comparable results with very mild computational burden and in a completely training-free and unsupervised way.
CONCLUSION: Our technique can be a computationally light and effective way to automatically delineate haematoma and oedema extent directly from MR images. Thus, with increasing use of MR images clinically after intracerebral haemorrhage this technique has the potential to inform clinical practice in the future.
Fulltext Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1

Klionsky DJ, Abdel-Aziz AK, Abdelfatah S, Abdellatif M, Abdoli A, Abel S, et al.

Autophagy, 2021 Jan;17(1):1-382.
PMID: 33634751 DOI: 10.1080/15548627.2020.1797280

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.