Biology subjects

Fukasawa, M.

Publications and source records attributed to Fukasawa, M..

4 recordsLinked to original sources

bioRxiv2026

Large-scale dimensional behavioral profiling dissociates fear memory from locomotor confounds in mice: The necessity of baseline-normalized metrics

Fear conditioning is widely used to assess associative memory in mice, yet percent freezing conflates memory with baseline locomotor and anxiety-related traits. A systematic survey of recent studies (2020-2025) found that fewer than 1% statistically integrate locomotor activity into freezing analyses. Here, we address this gap using a large-scale dataset of >10,000 mice across >160 comparisons, including genetic mutations, pharmacological interventions and aging, tested in 15 standardized behavioral paradigms. Conventional freezing scores covaried strongly with general locomotor activity, obscuring memory-related phenotypes. Multiple factor analysis identified two principal behavioral dimensions, locomotor activity and learning/memory: conventional freezing aligned with the locomotor dimension, whereas freezing subtraction and the activity suppression ratio mapped onto the memory dimension and improved detection of synaptic plasticity phenotypes. These analyses show that baseline locomotor normalization is essential for interpreting fear conditioning as a memory assay and provide an open framework for selecting and reporting locomotor-normalized metrics.

neuroscience↗

bioRxiv2023

Overcoming antibody-resistant SARS-CoV-2 variants with bispecific antibodies constructed using non-neutralizing antibodies

A current challenge is the emergence of SARS-CoV-2 variants, such as BQ.1.1 and XBB.1.5, that can evade immune defenses, thereby limiting antibody drug effectiveness. Emergency-use antibody drugs, including the widely effective bebtelovimab, are losing their benefits. One potential approach to address this issue are bispecific antibodies which combine the targeting abilities of two antibodies with distinct epitopes. We engineered neutralizing bispecific antibodies in the IgG-scFv format from two initially non-neutralizing antibodies, CvMab-6 (which binds to the receptor-binding domain [RBD]) and CvMab-62 (targeting a spike protein S2 subunit epitope adjacent to the known anti-S2 antibody epitope). Furthermore, we created a bispecific antibody by incorporating the scFv of bebtelovimab with our anti-S2 antibody, demonstrating significant restoration of effectiveness against bebtelovimab-resistant BQ.1.1 variants. This study highlights the potential of neutralizing bispecific antibodies, which combine existing less effective anti-RBD antibodies with anti-S2 antibodies, to revive the effectiveness of antibody therapeutics compromised by immune-evading variants.

microbiology↗

bioRxiv2020

Identification of characteristic genomic markers in human hepatoma Huh7 and Huh7.5.1-8 cell lines

The human hepatoma-derived Huh7 cell line and its derivatives (Huh7.5 and Huh7.5.1) have been widely used as a convenient experimental substitute for primary hepatocytes. In particular, these cell lines represent host cells suitable for propagating the hepatitis C virus (HCV) in vitro. The Huh7.5.1-8 cell line, a subline of Huh7.5.1, can propagate HCV more efficiently than its parental cells. To provide genomic information for cells quality control, we performed whole-genome sequencing of Huh7 and Huh7.5.1-8 and identified their characteristic genomic deletions, some of which are applicable to an in-house test for cell authentication. Among the genes related to HCV infection and replication, 53 genes were found to carry missense or loss-of-function mutations likely specific to the Huh7 and/or Huh7.5.1-8. Eight genes, including DDX58 (RIG-I), BAX, EP300, and SPP1 (osteopontin), contained mutations observed only in Huh7.5.1-8 or mutations with higher frequency in Huh7.5.1-8. These mutations might be relevant to phenotypic differences between the two cell lines and may also serve as genetic markers to distinguish Huh7.5.1-8 cells from the ancestral Huh7 cells.

genomics↗

bioRxiv2019

Deep learning-based imaging classification identified cingulate island sign in dementia with Lewy bodies

The differentiation of dementia with Lewy bodies (DLB) from Alzheimers disease (AD) using brain perfusion single photon emission tomography is important but has been a challenge because these conditions have common features. The cingulate island sign (CIS) is the most recently identified specific feature of DLB for a differential diagnosis. The present study aimed to examine the usefulness of deep learning-based imaging classification for the diagnoses of DLB and AD. We also investigated whether CIS was focused by the deep convolutional neural network (CNN) during differentiation.\n\nBrain perfusion single photon emission tomography images were acquired from 80 patients each with DLB and with AD and 80 individuals with normal cognition (NL). The CNN was trained on brain surface perfusion images. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN for visualization of the features that the trained CNN focused on.\n\nBinary classifications between DLB and NL, DLB and AD and AD and NL were 94.69%, 87.81% and 94.38% accurate, respectively. The CIS ratios closely correlated with softmax output scores for DLB-AD discrimination (DLB/AD scores). The Grad-CAM highlighted CIS in the DLB discrimination. Visualization of learning process by guided Grad-CAM revealed that CIS became more focused by the CNN as the training progressed. DLB/AD score was significantly associated with three core-features of DLB.\n\nDeep learning-based imaging classification was useful not only for objective and accurate differentiation of DLB from AD but also for predicting clinical features of DLB. The CIS was identified as a specific feature during DLB classification. The visualization of specific features and learning process could have important implications for the potential of deep learning to discover new imaging features.

neuroscience↗