Biology subjects

Alavi, M.

Publications and source records attributed to Alavi, M..

5 recordsLinked to original sources

bioRxiv2026

Rhpn2 promotes zebrafish melanoma development and aggressiveness in vivo

Melanoma, one of the most metastatic and multidrug resistant cancer, is the first leading cause of death from skin cancer. This complex disease requires identification of additional cooperating events that contribute to progression, invasion and metastasis to reinforce therapeutics. RhoGTPases play key roles in cancer development and metastasis. Rhophilin-2 (RHPN2), a Rho effector, is amplified in various human cancers and its role in melanoma remains unexplored. Here, we combined knock-down experiments in human melanoma cells, with knock-out and overexpression experiments in zebrafish to uncover the roles of RHPN2 in melanoma development. We show that in human melanoma cells RHPN2 contributes to growth, and to clonogenic, migratory and invasive properties of the cells. Using NRASQ61L and BRAFV600E zebrafish models, we provide the first in vivo evidence that Rhpn2 promotes melanoma onset and development. Histological analysis of the Rhpn2 deficient tumors showed decreased cellular density and absence of primary cilia structures at the invasive tumor/stroma borders. Transcriptomic profiling of the Rhpn2-KO melanoma revealed increased expression of the IFN1-responsive genes and modulation of genes involved in lipid metabolism and cilia function. Together these findings position RHPN2 as a modulator of melanoma, offering new perspectives in considering it as a target to impair the development of the tumor.

cancer biology↗

bioRxiv2022

Identifiability analysis and noninvasive online estimation of the first-order neural activation dynamics in the brain with transcranial magnetic stimulation

BackgroundNeurons demonstrate very distinct nonlinear activation dynamics, influenced by the neuron type, morphology, ion channel expression, and various other factors. The measurement of the activation dynamics can identify the neural target of stimulation and detect deviations, e.g., for diagnosis. This paper describes a tool for closed-loop sequential parameter estimation (SPE) of the activation dynamics through transcranial magnetic stimulation (TMS). The proposed SPE method operates in real time, selects ideal stimulus parameters, detects and processes the response, and concurrently estimates the input-output (IO) curve and the first-order approximation of the activated neural target. ObjectiveTo develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS. MethodFirst, identifiability of an integrated model of the first-order neural activation dynamics and IO curve is assessed, demonstrating that at least two IO curves need to be acquired with different pulse widths. Then, a two-stage SPE method is proposed. It estimates the IO curve by using Fisher information matrix (FIM) optimization in the first stage and subsequently estimates the membrane time constant as well as the coupling gain in the second stage. The procedure continues in a sequential manner until a stopping rule is satisfied. ResultsThe results of 73 simulation cases confirm the satisfactory estimation of the membrane time constant and coupling gain with average absolute relative errors (AREs) of 6.2% and 5.3%, respectively, with an average of 344 pulses (172 pulses for each IO curve or pulse width). The method estimates the IO curves lower and upper plateaus, mid-point, and slope with average AREs of 0.2%, 0.7%, 0.9%, and 14.5%, respectively. The conventional time constant estimation method based on the strength-duration (S-D) curve leads to 33.3% ARE, which is 27.0% larger than 6.2% ARE obtained through the proposed real-time FIM-based SPE method in this paper. ConclusionsSPE of the activation dynamics requires acquiring at least two IO curves with different pulse widths, which needs a controllable TMS (cTMS) device with adjustable pulse duration. SignificanceThe proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

neuroscience↗

bioRxiv2022

Closed-loop and automatic tuning of pulse amplitude and width in EMG-guided controllable transcranial magnetic stimulation (cTMS)

This paper proposes a tool for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the membrane time constant and input-output curve in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). A normalized depolarization factor is defined which separates the optimization of the pulse amplitude and width. Then, the pulse amplitude is chosen by the maximization of the Fisher information matrix (FIM), while the pulse width is chosen by the maximization of the normalized depolarization factor. The simulation results confirm satisfactory estimation. The results show that the normalized depolarization factor maximization can identify the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane.

neuroscience↗

bioRxiv2022

Formalism for the sequential estimation of neural membrane time constant and input-output curve towards selective and closed-loop transcranial magnetic stimulation

ObjectiveTo obtain a formalism for real-time concurrent sequential estimation of neural membrane time constant and input-output (IO) curve with transcranial magnetic stimulation (TMS). ApproachFirst, the neural membrane response and depolarization factor, which leads to motor evoked potentials (MEPs) with TMS are analytically computed and discussed. Then, an integrated model is developed which combines the neural membrane time constant and input-output curve. Identifiability of the proposed integrated model is discussed. A condition is derived, which assures estimation of the proposed integrated model. Finally, sequential parameter estimation (SPE) of the neural membrane time constant and IO curve is described through closed-loop optimal sampling and open-loop uniform sampling TMS. Without loss of generality, this paper focuses on a specific case of commercialized TMS pulse shapes. The proposed formalism and SPE method are directly applicable to other pulse shapes. Main resultsThe results confirm satisfactory estimation of the membrane time constant and IO curve parameters. By defining a stopping rule based on five times consecutive convergence of the estimation parameters with a tolerances of 0.01, the membrane time constant and IO curve parameters are estimated with 82 TMS pulses with absolute relative estimation errors (AREs) of less than 4% with the optimal sampling SPE method. At this point, the uniform sampling SPE method leads to AREs up to 16%. The uniform sampling method does not satisfy the stopping rule due to the large estimation variations. SignificanceThis paper provides a tool for real-time closed-loop SPE of the neural time constant and IO curve, which can contribute novel insights in TMS studies. SPE of the membrane time constant enables selective stimulation, which can be used for advanced brain research, precision medicine and personalized medicine.

neuroscience↗

bioRxiv2021

Input-Output Slope Curve Estimation in Neural Stimulation Based on Optimal Sampling Principles

This paper discusses some of the practical limitations and issues, which exist for the input-output (IO) slope curve estimation (SCE) in neural, brain and spinal, stimulation techniques. The drawbacks of the SCE techniques by using existing uniform sampling and Fisher-information-based optimal IO curve estimation (FO-IOCE) methods are elaborated. A novel IO SCE technique is proposed with a modified sampling strategy and stopping rule which improve the SCE performance compared to these methods. The effectiveness of the proposed IO SCE is tested on 1000 simulation runs in transcranial magnetic stimulation (TMS), with a realistic model of motor evoked potentials (MEPs). The results show that the proposed IO SCE method successfully satisfies the stopping rule, before reaching the maximum number of TMS pulses in 79.5% of runs, while the estimation based on the uniform sampling technique never converges and satisfies the stopping rule. At the time of successful termination, the proposed IO SCE method decreases the 95th percentile (mean value in the parentheses) of the absolute relative estimation errors (AREs) of the slope curve parameters up to 7.45% (2.2%), with only 18 additional pulses on average compared to that of the FO-IOCE technique. It also decreases the 95th percentile (mean value in the parentheses) of the AREs of the IO slope curve parameters up to 59.33% (16.71%), compared to that of the uniform sampling method. The proposed IO SCE also identifies the peak slope with higher accuracy, with the 95th percentile (mean value in the parentheses) of AREs reduced by up to 9.96% (2.01%) compared to that of the FO-IOCE method, and by up to 46.29% (13.13%) compared to that of the uniform sampling method.

bioinformatics↗