MALSU1-mediated regulation of mitochondrial function governs proliferation and doxorubicin resistance in triple-negative breast cancer cells.

Triple-negative breast cancer (TNBC) poses a formidable challenge in oncology due to its aggressive nature and limited treatment options. Although doxorubicin, a widely used chemotherapeutic agent, shows efficacy in TNBC treatment, acquired resistance remains a significant obstacle. Our study explores the role of MALSU1, a regulator of mitochondrial translation, in TNBC and its impact on cell proliferation and doxorubicin resistance. We observed increased MALSU1 expression in TNBC, correlating with poor patient prognosis. MALSU1 knockdown in TNBC cells significantly reduced proliferation, indicating its pivotal role in sustaining cell growth. Mechanistically, MALSU1 depletion resulted in decreased activities of mitochondrial respiratory chain complexes, cellular ATP levels, and mitochondrial respiration. Notably, exogenous addition of normal mitochondria restored proliferation and mitochondrial respiration in MALSU1-depleted TNBC cells. Importantly, MALSU1 knockdown enhanced the sensitivity of doxorubicin-resistant TNBC cells to doxorubicin treatment. Furthermore, pharmacological inhibition of mitochondrial translation using tigecycline and chloramphenicol mimicked the effects of MALSU1 knockdown, suggesting mitochondrial translation as a potential therapeutic target. Taken together, our findings not only elucidate the intricate role of MALSU1 in TNBC biology and doxorubicin resistance but also lay the groundwork for future investigations targeting MALSU1 and/or mitochondrial translation as a promising avenue for developing innovative therapeutic strategies against TNBC.

Unsupervised OCT image despeckling with ground-truth- and repeated-scanning-free features.

Optical coherence tomography (OCT) can resolve biological three-dimensional tissue structures, but it is inevitably plagued by speckle noise that degrades image quality and obscures biological structure. Recently unsupervised deep learning methods are becoming more popular in OCT despeckling but they still have to use unpaired noisy-clean images or paired noisy-noisy images. To address the above problem, we propose what we believe to be a novel unsupervised deep learning method for OCT despeckling, termed Double-free Net, which eliminates the need for ground truth data and repeated scanning by sub-sampling noisy images and synthesizing noisier images. In comparison to existing unsupervised methods, Double-free Net obtains superior denoising performance when trained on datasets comprising retinal and human tissue images without clean images. The efficacy of Double-free Net in denoising holds significant promise for diagnostic applications in retinal pathologies and enhances the accuracy of retinal layer segmentation. Results demonstrate that Double-free Net outperforms state-of-the-art methods and exhibits strong convenience and adaptability across different OCT images.

PYCR3 modulates mtDNA copy number to drive proliferation and doxorubicin resistance in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) poses significant challenges in treatment due to its aggressive nature and limited therapeutic targets. Understanding the underlying molecular mechanisms driving TNBC progression and chemotherapy resistance is imperative for developing effective therapeutic strategies. Thus, in this study, we aimed to elucidate the role of pyrroline-5-carboxylate reductase 3 (PYCR3) in TNBC pathogenesis and therapeutic response. We observed that PYCR3 is significantly upregulated in TNBC specimens compared to normal breast tissues, correlating with a poorer prognosis in TNBC patients. Knockdown of PYCR3 not only suppresses TNBC cell proliferation but also reverses acquired resistance of TNBC cells to doxorubicin, a commonly used chemotherapeutic agent. Mechanistically, we identified the mitochondrial localization of PYCR3 in TNBC cells and demonstrated its impact on TNBC cell proliferation and sensitivity to doxorubicin through the regulation of mtDNA copy number and mitochondrial respiration. Importantly, Selective reduction of mtDNA copy number using the mtDNA replication inhibitor 2', 3'-dideoxycytidine effectively recapitulates the phenotypic effects observed in PYCR3 knockout, resulting in decreased TNBC cell proliferation and the reversal of doxorubicin resistance through apoptosis induction. Thus, our study underscores the clinical relevance of PYCR3 and highlight its potential as a therapeutic target in TNBC management. By elucidating the functional significance of PYCR3 in TNBC, our findings contribute to a deeper understanding of TNBC biology and provide a foundation for developing novel therapeutic strategies aimed at improving patient outcomes.

STK32C modulates doxorubicin resistance in triple-negative breast cancer cells via glycolysis regulation.

Understanding the mechanisms underlying doxorubicin resistance in triple-negative breast cancer (TNBC) holds paramount clinical significance. In our study, we investigate the potential of STK32C, a little-explored kinase, to impact doxorubicin sensitivity in TNBC cells. Our findings reveal elevated STK32C expression in TNBC specimens, associated with unfavorable prognosis in doxorubicin-treated TNBC patients. Subsequent experiments highlighted that STK32C depletion significantly augmented the sensitivity of doxorubicin-resistant TNBC cells to doxorubicin. Mechanistically, we unveiled that the cytoplasmic subset of STK32C plays a pivotal role in mediating doxorubicin sensitivity, primarily through the regulation of glycolysis. Furthermore, the kinase activity of STK32C proved to be essential for its mediation of doxorubicin sensitivity, emphasizing its role as a kinase. Our study suggests that targeting STK32C may represent a novel therapeutic approach with the potential to improve doxorubicin's efficacy in TNBC treatment.

High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention.

Wet age-related macular degeneration (AMD) is the leading cause of visual impairment and vision loss in the elderly, and optical coherence tomography (OCT) enables revolving biotissue three-dimensional micro-structure widely used to diagnose and monitor wet AMD lesions. Many wet AMD segmentation methods based on deep learning have achieved good results, but these segmentation results are two-dimensional, and cannot take full advantage of OCT's three-dimensional (3D) imaging characteristics. Here we propose a novel deep-learning network characterizing multi-scale and cross-channel feature extraction and channel attention to obtain high-accuracy 3D segmentation results of wet AMD lesions and show the 3D specific morphology, a task unattainable with traditional two-dimensional segmentation. This probably helps to understand the ophthalmologic disease and provides great convenience for the clinical diagnosis and treatment of wet AMD.

Toward Ground-Truth Optical Coherence Tomography via Three-Dimensional Unsupervised Deep Learning Processing and Data.

Optical coherence tomography (OCT) can perform non-invasive high-resolution three-dimensional (3D) imaging and has been widely used in biomedical fields, while it is inevitably affected by coherence speckle noise which degrades OCT imaging performance and restricts its applications. Here we present a novel speckle-free OCT imaging strategy, named toward-ground-truth OCT ( t GT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed t GT-OCT utilizes an unsupervised 3D-convolution deep-learning network trained using random 3D volumetric data to distinguish and separate speckle from real structures in 3D imaging volumetric space; moreover, t GT-OCT effectively further reduces speckle noise and reveals structures that would otherwise be obscured by speckle noise while preserving spatial resolution. Results derived from different samples demonstrated the high-quality speckle-free 3D imaging performance of t GT-OCT and its advancement beyond the previous state-of-the-art. The code is available online: https://github.com/Voluntino/tGT-OCT.

High-precision Drosophila heart segmentation and dynamic cardiac parameter measurement for optogenetics-OCT-based cardiac function research.

Drosophila model has been widely used to study cardiac functions, especially combined with optogenetics and optical coherence tomography (OCT) that can continuously acquire mass cross-sectional images of the Drosophila heart in vivo over time. It's urgent to quickly and accurately obtain dynamic Drosophila cardiac parameters such as heartbeat rate for cardiac function quantitative analysis through these mass cross-sectional images of the Drosophila heart. Here we present a deep-learning method that integrates U-Net and generative adversarial network architectures while incorporating residually connected convolutions for high-precision OCT image segmentation of Drosophila heart and dynamic cardiac parameter measurements for optogenetics-OCT-based cardiac function research. We compared our proposed network with the previous approaches and our segmentation results achieved the accuracy of intersection over union and Dice similarity coefficient higher than 98%, which can be used to better quantify dynamic heart parameters and improve the efficiency of Drosophila-model-based cardiac research via the optogenetics-OCT-based platform.