Biology subjects

Kazemian, A.

Publications and source records attributed to Kazemian, A..

3 recordsLinked to original sources

bioRxiv2026

Simulation-guided non-thermal low-intensity ultrasound reprograms the tumor immune microenvironment and engages systemic antitumor immunity in a syngeneic orthotopic mouse model of breast cancer

IntroductionTherapeutic ultrasound has been extensively studied in ablative and sonodynamic contexts, leaving the intrinsic bioactivity of continuous non-thermal low-intensity ultrasound (LIU) largely uncharacterized. ObjectivesTo characterize the tumor biological and immunomodulatory effects of non-thermal continuous LIU in complementary in vitro and in vivo breast cancer models, underpinned by a standardized exposure platform characterized through finite element simulations and experimental validation. MethodsAcoustic and thermal fields were characterized and optimized using in silico simulations and validated against hydrophone and temperature measurements to ensure homogeneous, non-thermal exposure (1MHz, 1W/cm2, 100% duty cycle). 4T07 murine mammary carcinoma spheroids received 20min LIU treatment, and metabolic activity, apoptosis, and intracellular stress-associated markers were assessed. In a syngeneic orthotopic 4T07 mammary carcinoma model in BALB/c mice, up to six LIU treatment cycles were administered; tumor growth, survival, histopathology, immunohistochemistry, bulk tumor RNA sequencing, spleen volume and plasma cytokine profiles were assessed. ResultsIn vitro and intratumoral temperatures remained within the physiological range ([≤]39{degrees}C) throughout exposure. In spheroids, LIU reduced ATP content by more than 40% and significantly increased apoptotic, Hsp70 and Hsp90 cell fractions. In vivo, cyclic LIU slowed tumor growth, increased intratumoral necrosis, and significantly prolonged time to humane endpoint compared to untreated controls. LIU promoted early intratumoral myeloid cell infiltration and shifted the tumor transcriptome (2,573 differentially expressed genes), with enrichment in gene sets associated with immunogenic cell death, pattern-recognition, inflammatory, and innate and adaptive immune programs and downregulation of pro-tumorigenic pathways. LIU enriched the transcriptional signatures of M1 macrophage polarization and, notably, B-cell compartment engagement, which has not previously been reported for standalone continuous mechanical ultrasound. LIU significantly attenuated tumor-associated splenomegaly and elevated plasma IL-1, TNF-, and IL-10. ConclusionThese results establish a reproducible preclinical platform and provide a hypothesis-generating mechanistic basis for evaluating LIU as an adjunct to immune checkpoint blockade. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/743931v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@31d366org.highwire.dtl.DTLVardef@12df6aborg.highwire.dtl.DTLVardef@9d91adorg.highwire.dtl.DTLVardef@c72b8a_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2026

Distinct yet neighboring neural populations encode past, future, and surrounding speech context in the human temporal lobe

Context is critical for both human and artificial speech comprehension systems. While the role of preceding context in speech processing has been well documented, the neural mechanisms supporting the integration of subsequent input -- phonemes and words that occur in the future -- remain poorly understood. Here, we leverage advances in artificial speech systems to model the contribution of different sources of context on the neural encoding of speech in the human brain. For neural encoding, context-informed but not context-uninformed speech model embeddings explain unique variance in human neural activity beyond acoustics, including in early speech processing regions. In particular, model embeddings informed by past, future, and surrounding context explain activity in distinct intracranial electrodes. These electrodes are left-lateralised, and spatially intermixed in the temporal lobe. We find that beyond-word context is crucial for the representational quality of speech model embeddings, and in particular for the encoding of abstract linguistic information. Our finding that spatially neighboring yet distinct neural populations in the temporal lobe encode representations shaped by different contextual sources (past, future, and surrounding input) provides key insight into the neural circuitry that integrates multiple forms of contextual information. Furthermore, our results may inform the downstream use of self-supervised speech representations in language technology tasks, and in models of speech comprehension in the human brain.

neuroscience↗

bioRxiv2024

Convolutional architectures are cortex-aligned de novo

What underlies the emergence of cortex-aligned representations in deep neural network models of vision? Earlier work suggested that shared architectural constraints were a major factor, but the success of widely varied architectures after pre-training raises critical questions about the importance of architectural constraints. Here we show that in wide networks with minimal training, architectural inductive biases have a prominent role. We examined networks with varied architectures but no pre-training and quantified their ability to predict image representations in the visual cortices of monkeys and humans. We found that cortex-aligned representations emerge in convolutional architectures that combine two key manipulations of dimensionality: compression in the spatial domain, through pooling, and expansion in the feature domain by increasing the number of channels. We further show that the inductive biases of convolutional architectures are critical for obtaining performance gains from feature expansion--dimensionality manipulations were relatively ineffective in other architectures and in convolutional models with targeted lesions. Our findings suggest that the architectural constraints of convolutional networks are sufficiently close to the constraints of biological vision to allow many aspects of cortical visual representation to emerge even before synaptic connections have been tuned through experience.

animal behavior and cognition↗