Biology subjects

Fallah, A.

Publications and source records attributed to Fallah, A..

8 recordsLinked to original sources

bioRxiv2026

A Protease-Cleavable iNOS-Inhibitor Polymeric Prodrug Designed for Controlled Modulation of Nitric Oxide

Inducible nitric oxide synthase (iNOS) is frequently overexpressed in inflammatory disorders and solid tumors, where sustained nitric oxide (NO) production promotes angiogenesis, tumor progression, and resistance to therapy. Despite promising preclinical results, the clinical translation of iNOS inhibitors remains limited by poor tumor selectivity, rapid systemic clearance, and off-target toxicities. To address these challenges, we developed a protease-responsive polymeric iNOS-inhibiting prodrug (ProCIP) designed for localized activation within protease-rich pathological microenvironments. ProCIP was synthesized from poly(ethylene glycol)-poly(L-glutamate) and functionalized with amidine-based iNOS inhibitory moieties. The resulting cationic polymer readily formed nanoscale polyionic complexes with anionic polymers or molecules. In cell-free assays, enzymatic activation of ProCIP resulted in a significant reduction in iNOS activity, whereas non-activated nanoparticles showed minimal inhibition. Cellular studies confirmed efficient nanoparticle uptake by RAW264.7 macrophages and revealed a significant reduction in intracellular NO levels in lipopolysaccharide-stimulated cells. These findings demonstrate that ProCIP enables protease-triggered iNOS inhibition and localized NO regulation, offering a promising strategy for improving the safety and efficacy of iNOS-targeted therapies in cancer and other inflammatory diseases.

pharmacology and toxicology↗

bioRxiv2026

Controlled delivery of iNOS antagonist, 1400W, for synergistic breast cancer therapy

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer that lacks effective targeted therapies and is frequently associated with chemotherapy resistance and immunosuppression. Inducible nitric oxide synthase (iNOS) is overexpressed in breast cancer and has been strongly correlated with poor clinical outcomes, owing to its role in promoting tumor progression, invasiveness, and resistance to therapy. Although highly selective iNOS inhibitors such as N-(3-(Aminomethyl)benzyl)acetamidine (1400W) exhibit considerable therapeutic promise, their clinical translation has been hindered by unfavorable pharmacokinetic properties. To overcome these limitations, we developed a pH-responsive nanoscale formulation based on Schiff base conjugation between 1400W and oxidized PEGylated alginate (OPA), in combination with ionic interactions. The resulting nanoparticles (NPs) exhibited efficient release of 1400W under acidic conditions and effectively suppressed nitric oxide (NO) production in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages. While the NPs alone did not induce significant cytotoxicity, they synergistically enhanced the anticancer efficacy of paclitaxel (PTX) in MDA-MB-231 TNBC cells, significantly inhibiting cell viability and migration. In addition, the NP-PTX combination markedly reduced endothelial tube formation in HUVECs, compared to PTX alone indicating potentiation of the anti-angiogenic activity of PTX. In conclusion, the pH-responsive NPs enables effective modulation of NO signaling and enhances the therapeutic activity of PTX in TNBC cells. These findings support the potential of iNOS-targeted nanomedicine as an adjuvant strategy for TNBC treatment and warrant further investigation using in vivo models to evaluate pharmacokinetics, tumor accumulation, and antitumor efficacy of the NPs.

pharmacology and toxicology↗

bioRxiv2023

PyHFO: Lightweight Deep Learning-poweredEnd-to-End High-Frequency Oscillations AnalysisApplication

In the context of epilepsy studies, intracranially-recorded interictal high-frequency oscillations (HFOs) in EEG signals are emerging as promising spatial neurophysiological biomarkers for epileptogenic zones. While significant efforts have been made in identifying and understanding these biomarkers, deep learning is carving novel avenues for biomarker detection and analysis. Yet, transitioning such methodologies to clinical environments is difficult due to the rigorous computational needs of processing EEG data via deep learning. This paper presents our development of an advanced end to end software platform, PyHFO, aimed at bridging this gap. PyHFO provides an integrated and user-friendly platform that includes time-efficient HFO detection algorithms such as short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors and deep learning models for artifact and HFO with spike classification. This application functions seamlessly on conventional computer hardware. Our platform has been validated to adeptly handle datasets from 10-minute EEG recordings captured via grid/strip electrodes in 19 patients. Through implementation optimization, PyHFO achieves speeds up to 50 times faster than the standard HFO detection method. Users can either employ our pre-trained deep learning model for their analyses or use their EEG data to train their model. As such, PyHFO holds great promise for facilitating the use of advanced EEG data analysis tools in clinical practice and large-scale research collaborations.

neuroscience↗

bioRxiv2022

An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation

Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets that can represent the complex tasks required for naturalistic speech decoding are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome various kinds of noise in the EEG signal and remain reliable across sessions and subjects without overfitting to a specific dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in forty consonant-vowel pairs, twenty real words, and twenty pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation targeted to inhibit or augment the EEG signal associated with specific articulatory processes.

neuroscience↗

bioRxiv2022

A wearable platform for closed-loop stimulation and recording of single- neuron and local field potential activity in freely-moving humans

Advances in technologies that can record and stimulate deep-brain activity in humans have led to impactful discoveries within the field of neuroscience and contributed to the development of novel therapies for neurological and psychiatric disorders. Further progress, however, has been hindered by device limitations in that recording of single-neuron activity during freely-moving behaviors in humans has not been possible. Additionally, implantable neurostimulation devices, currently approved for human use, have limited stimulation programmability and lack full-duplex bi-directional capability. Here, we developed a wearable bi-directional closed-loop neuromodulation system (Neuro-stack) and used it to record single-neuron and local field potential activity during stationary and ambulatory behavior in humans. Together with a highly flexible and customizable stimulation capability, the Neuro-stack provides an opportunity to investigate the neurophysiological basis of disease, develop improved responsive neuromodulation therapies, explore brain function during naturalistic behaviors in humans, and consequently, bridge decades of neuroscientific findings across species.

neuroscience↗

bioRxiv2021

Exploratory methods for high-performance EEG speech decoding

State-of-the-art technologies in neural speech decoding utilize data collected from microwires or microarrays implanted directly into the cerebral cortex. Yet as a tool accessible only to individuals with implanted electrodes, speech decoding from devices of this nature is severely limited in its implementation, and cannot be considered a viable solution for widespread application. Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%), but only from very small classification tasks, with more complex tasks typically yielding a limited (20-50%) classification accuracy. We propose a novel combination of technologies in which transcranial magnetic stimulation (TMS) is first applied to augment the neural signals of interest, producing a greater signal-to-noise ratio in the EEG data. Next, delay differential analysis (DDA) - a cutting-edge computational method based on nonlinear dynamics - is implemented to capture the widest range of information available in the neural signal, by incorporating both linear and nonlinear dynamics.

neuroscience↗

bioRxiv2021

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach

Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, visual verification of HFOs is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish HFOs generated from the epileptogenic zone (epileptogenic HFOs: eHFOs) from those generated from other areas (non-epileptogenic HFOs: non-eHFOs). To address these issues, we constructed a deep learning (DL)-based algorithm using HFO events from chronic intracranial electroencephalogram (iEEG) data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: 1) replicate human expert annotation of artifacts and HFOs with or without spikes, and 2) discover eHFOs by designing a novel weakly supervised model (HFOs from the resected brain regions are initially labeled as eHFOs, and those from the preserved brain regions as non-eHFOs). The "purification power" of DL is then used to automatically relabel the HFOs to distill eHFOs. Using 12,958 annotated HFO events from 19 patients, the model achieved 96.3% accuracy on artifact detection (F1 score = 96.8%) and 86.5% accuracy on classifying HFOs with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the DL-based algorithm trained from 84,602 HFO events from nine patients who achieved seizure-freedom after resection, the majority of such DL-discovered eHFOs were found to be HFOs with spikes (78.6%, p < 0.001). While the resection ratio of detected HFOs (number of resected HFOs/number of detected HFOs) did not correlate significantly with post-operative seizure freedom (the area under the curve [AUC]=0.76, p=0.06), the resection ratio of eHFOs positively correlated with post-operative seizure freedom (AUC=0.87, p=0.01). We discovered that the eHFOs had a higher signal intensity associated with ripple (80-250 Hz) and fast ripple (250-500 Hz) bands at the HFO onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-eHFOs. We then designed perturbations on the input of the trained model for non-eHFOs to determine the models decision-making logic. The model probability significantly increased towards eHFOs by the artificial introduction of signals in the inverted T-shaped frequency bands (mean probability increase: 0.285, p < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, p < 0.001). With this DL-based framework, we reliably replicated HFO classification tasks by human experts. Using a reverse engineering technique, we distinguished eHFOs from others and identified salient features of eHFOs that aligned with current knowledge. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/458385v1_ufig1.gif" ALT="Figure 1"> View larger version (81K): org.highwire.dtl.DTLVardef@39977dorg.highwire.dtl.DTLVardef@108b182org.highwire.dtl.DTLVardef@1bdf864org.highwire.dtl.DTLVardef@fe9e57_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Human Astrocytes Exhibit Tumor Microenvironment-, Age-, and Sex-Related Transcriptomic Signatures

Astrocytes are dynamic cells with important roles in brain function and neurological disease. There are notable species differences between human astrocytes and commonly used animal models. However, changes of the molecular attributes of human astrocytes across disease states, sex, and age are largely unknown, which is a barrier in understanding human astrocyte biology and its potential involvement in neurological diseases. To better understand the properties of human astrocytes, we acutely purified astrocytes from the cerebral cortices of over 40 humans across various ages, sexes, and disease states. We performed RNA sequencing to generate transcriptomic profiles of these astrocytes and identified genes associated with these biological variables. Here, we identified a robust transcriptomic signature of human astrocytes in the tumor-surrounding microenvironment, including upregulation of proliferation processes, along with downregulation of genes involved in ionic homeostasis and synaptic function, suggesting involvement of peri-tumor astrocytes in tumor-associated neural circuit dysfunction. In aging, we also found downregulation of synaptic regulators and upregulation of markers of astrocyte reactivity, while in maturation we identified changes in ionic transport with implications for calcium signaling. In addition, we identified some of the first transcriptomic evidence of sexual dimorphism in human cortical astrocytes, which has implications for observed sex differences across many neurological disorders. Overall, genes involved in synaptic function exhibited dynamic changes in multiple conditions. This data provides powerful new insights into human astrocyte biology in several biologically relevant states, that will aid in generating novel testable hypotheses about homeostatic and reactive astrocytes in humans. Significance StatementAstrocytes are an abundant class of cells playing integral roles in the central nervous system. Astrocyte dysfunction is implicated in a variety of human neurological diseases. Yet our knowledge of astrocytes is largely based on mouse studies. Direct knowledge of human astrocyte biology remains limited. Here, we present transcriptomic profiles of human cortical astrocytes, and we identified molecular differences associated with age, sex, and disease state. We found changes suggesting involvement of peritumor astrocytes in tumor-associated neural circuit dysfunction, aging-associated decline in astrocyte-synapse interactions, ionic transport changes with brain maturation, and some of the first evidence of sexual dimorphism in human astrocytes. These data provide necessary insight into human astrocyte biology that will improve our understanding of human disease.

neuroscience↗