Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer

Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%-4% and 7-8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models' performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations.

A low-field ceramic magnet design for magnetic resonance

We describe the design of a low-field portable magnet, based on two ceramic magnets, separated by a distance, with their magnetic poles aligned to create a large homogeneous region with a field strength of 425 gauss. Ceramic magnets are an uncommon choice compared to Neodymium Iron Boron magnets for low-field magnetic resonance but are preferable for our purposes to create a homogeneous region at lower field strength. The low cost of large ceramic magnets results in an inexpensive design with a large measurement volume. The magnets rest in a 3D-printed structure, which allows for the magnets to be moved by hand so the experimentalist has control over the field topology. To test the utility of the design, we explored an Overhauser dynamic nuclear polarization experiment with an aqueous solution of 4-Hydroxy-TEMPO. We also explored a simple flow measurement employing the ceramic magnets at a 6-degree pitch, creating a 14.6 gauss/cm constant gradient.

Diagnosis of Alzheimer Disease and Tauopathies on Whole-Slide Histopathology Images Using a Weakly Supervised Deep Learning Algorithm

Neuropathologic assessment during autopsy is the gold standard for diagnosing neurodegenerative disorders. Neurodegenerative conditions, such as Alzheimer disease (AD) neuropathological change, are a continuous process from normal aging rather than categorical; therefore, diagnosing neurodegenerative disorders is a complicated task. We aimed to develop a pipeline for diagnosing AD and other tauopathies, including corticobasal degeneration (CBD), globular glial tauopathy, Pick disease, and progressive supranuclear palsy. We used a weakly supervised deep learning-based approach called clustering-constrained-attention multiple-instance learning (CLAM) on the whole-slide images (WSIs) of patients with AD (n = 30), CBD (n = 20), globular glial tauopathy (n = 10), Pick disease (n = 20), and progressive supranuclear palsy (n = 20), as well as nontauopathy controls (n = 21). Three sections (A: motor cortex; B: cingulate gyrus and superior frontal gyrus; and C: corpus striatum) that had been immunostained for phosphorylated tau were scanned and converted to WSIs. We evaluated 3 models (classic multiple-instance learning, single-attention-branch CLAM, and multiattention-branch CLAM) using 5-fold cross-validation. Attention-based interpretation analysis was performed to identify the morphologic features contributing to the classification. Within highly attended regions, we also augmented gradient-weighted class activation mapping to the model to visualize cellular-level evidence of the model's decisions. The multiattention-branch CLAM model using section B achieved the highest area under the curve (0.970 ± 0.037) and diagnostic accuracy (0.873 ± 0.087). A heatmap showed the highest attention in the gray matter of the superior frontal gyrus in patients with AD and the white matter of the cingulate gyrus in patients with CBD. Gradient-weighted class activation mapping showed the highest attention in characteristic tau lesions for each disease (eg, numerous tau-positive threads in the white matter inclusions for CBD). Our findings support the feasibility of deep learning-based approaches for the classification of neurodegenerative disorders on WSIs. Further investigation of this method, focusing on clinicopathologic correlations, is warranted.

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides

Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all thyroid cancers arising from follicular cells. Fine needle aspiration cytology (FNAC) is a non-invasive method regarded as the most cost-effective and accurate diagnostic method of choice in diagnosing PTC. Identification of BRAF (V600E) mutation in thyroid neoplasia may be beneficial because it is specific for malignancy, implies a worse prognosis, and is the target for selective BRAF inhibitors. To the authors' best knowledge, this is the first automated precision oncology framework effectively predict BRAF (V600E) immunostaining result in thyroidectomy specimen directly from Papanicolaou-stained thyroid fine-needle aspiration cytology and ThinPrep cytological slides, which is helpful for novel targeted therapies and prognosis prediction. The proposed deep learning (DL) framework is evaluated on a dataset of 118 whole slide images. The results show that the proposed DL-based technique achieves an accuracy of 87%, a precision of 94%, a sensitivity of 91%, a specificity of 71% and a mean of sensitivity and specificity at 81% and outperformed three state-of-the-art deep learning approaches. This study demonstrates the feasibility of DL-based prediction of critical molecular features in cytological slides, which not only aid in accurate diagnosis but also provide useful information in guiding clinical decision-making in patients with thyroid cancer. With the accumulation of data and the continuous advancement of technology, the performance of DL systems is expected to be improved in the near future. Therefore, we expect that DL can provide a cost-effective and time-effective alternative tool for patients in the era of precision oncology.

Early induction intensification with cladribine, cytarabine, and mitoxantrone (CLAM) in AML patients treated with the DAC induction regimen: a prospective, non-randomized, phase II study of the Polish Adult Leukemia Group (PALG)

We present the results of a prospective, non-randomized phase 2 trial in which 253 AML patients (pts) under 60 years old received DAC (Daunorubicin + AraC + Cladribine) as first induction followed by CLAM (Cladribine + AraC + Mitoxantrone) as early second induction on day 16 based on bone marrow (BM) blasts on day 14 (D14). The CR/CRi rate after a single course of DAC was 83% for pts with D14 BM blasts less than 10%. Forty-six pts had >10% BM blasts on D14, of whom 35 received CLAM with rates of CR/CRi 60% and early death (ED) 23%. The remaining 11 pts were not fit to receive CLAM, with rates of CR/CRi 28%, PR 18%, and ED 18%. Median OS was 7.2 versus 7.5 months, respectively. The overall CR/CRi rate was 77% after the first induction, with final CR/CRi rate 80% after DAC reinduction for pts who achieved PR with initial DAC course. CLAM used as early second induction might improve CR/CRi rates for younger AML pts with poor early response to DAC induction, but may be associated with higher mortality.

The Leu-Arg-Glu (LRE) adhesion motif in proteins of the neuromuscular junction with special reference to proteins of the carboxylesterase/cholinesterase family

Short linear motifs confer evolutionary flexibility on proteins as they can be added with relative ease allowing the acquisition of new functions. Such motifs may mediate a variety of signalling functions. The adhesion-mediating Leu-Arg-Glu (LRE) motif is enriched in laminin beta 2, and has been observed in other proteins, including members of the carboxylesterase/cholinesterase family. It acts as a stop signal for growing axons in the developing neuromuscular junction, binding to the voltage-gated calcium channel. In this bioinformatic analysis, we have investigated the presence of the motif in proteins of the neuromuscular junction, and have also examined its structural position and potential for ligand interaction, as well as phylogenetic conservation, in the carboxylesterase/cholinesterase family. The motif was observed to occur with a significantly higher frequency than expected in the UniProt/Swiss-Prot database, as well as in four individual species (human, mouse, Caenorhabditis elegans and Drosophila melanogaster). Examination of its presence in neuromuscular junction proteins showed it to be enriched in certain proteins of the synaptic basement membrane, including laminin, agrin, acetylcholinesterase and tenascin. A highly significant enrichment was observed in cytoskeletal proteins, particularly intermediate filament proteins and members of the spectrin family. In the carboxylesterase/cholinesterase family, the motif was observed in four conserved positions in the protein structure. It is present in the majority of mammalian acetylcholinesterases, as well as acetylcholinesterases from electric fish and a number of invertebrates. In insects, it is present in the ace-2, rather than in the synaptic ace-1, enzyme. It is also observed in the cholinesterase-like adhesion molecules (neuroligins, neurotactin and glutactin). It is never seen in butyrylcholinesterases, which do not mediate cell adhesion. In conclusion, the significant enrichment of the motif in certain classes of protein, as well as its conserved presence and structural positioning in one protein family, suggests that it has specific functions both in cell adhesion in the neuromuscular junction and in maintaining the structural integrity of the cytoskeleton.

Complete genome sequence of Candidatus Ruthia magnifica

The hydrothermal vent clam Calyptogena magnifica (Bivalvia: Mollusca) is a member of the Vesicomyidae. Species within this family form symbioses with chemosynthetic Gammaproteobacteria. They exist in environments such as hydrothermal vents and cold seeps and have a rudimentary gut and feeding groove, indicating a large dependence on their endosymbionts for nutrition. The C. magnifica symbiont, Candidatus Ruthia magnifica, was the first intracellular sulfur-oxidizing endosymbiont to have its genome sequenced (Newton et al. 2007). Here we expand upon the original report and provide additional details complying with the emerging MIGS/MIMS standards. The complete genome exposed the genetic blueprint of the metabolic capabilities of the symbiont. Genes which were predicted to encode the proteins required for all the metabolic pathways typical of free-living chemoautotrophs were detected in the symbiont genome. These include major pathways including carbon fixation, sulfur oxidation, nitrogen assimilation, as well as amino acid and cofactor/vitamin biosynthesis. This genome sequence is invaluable in the study of these enigmatic associations and provides insights into the origin and evolution of autotrophic endosymbiosis.