In Silico Trials
In Silico Trials - Related Videos
01:28
The Trial Balance
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2025
A trial balance is a fundamental financial statement that provides a snapshot of a company's accounts and their balances at a specific point in time. It serves as an essential tool for verifying the accuracy of financial records by ensuring that the total debits equal the total credits. Typically prepared at the end of an accounting period, a trial balance lists all ledger accounts in the order they appear, with debit balances in the left column and credit balances in the right column. The two...
01:16
Clinical Trials
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2023
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias. There are four phases in a clinical trial. A phase one...
17:37
Large-scale Recording of Neurons by Movable Silicon Probes in Behaving Rodents
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2012
We describe methods for large-scale recording of multiple single units and local field potential in behaving rodents with silicon probes. Drive fabrication, probe attachment to the drive and probe implantation processes are illustrated in sufficient details for easy replication.
06:53
Fabrication and Optimization of Type II Silicon Clathrate Films
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2025
The two-thermal annealing method enables the fabrication of thin films of type II semiconductor silicon clathrate. Post-synthesis treatments, including thermal pressing and reactive ion etching, were performed to improve the material properties. This approach provides an accessible pathway for fabricating tunable clathrate films suitable for next-generation electronic and photonic devices.
09:46
Fabrication and Characterization of High-Q Silicon Nitride Membrane Resonators
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This article traces the life cycle of a silicon nitride membrane resonator, from design through fabrication to characterization.
FAQs
Virtual patient populations represent biological variability within a computer-based experiment, allowing an intervention to be evaluated across differing physiological or disease conditions. This approach can reveal patterns that may not appear in a single modeled case, including patient characteristics associated with improved or poorer responses. The resulting comparisons support treatment optimization and more focused hypothesis testing.
Each component contributes a different part of the prediction. Physiological data represent relevant biological conditions, computational algorithms process relationships within the simulation, and disease or device models describe how the intervention interacts with the biological system. Integrating these elements allows researchers to estimate effects under defined conditions rather than examining the treatment in isolation.
Validation is important because the usefulness of a simulation depends on whether its predictions are sufficiently credible for the intended purpose. Validated models can complement conventional trials by providing additional evidence about treatment or device behavior. They do not remove the need for laboratory or clinical studies, but they can strengthen interpretation and help prioritize questions for further testing.
A typical workflow begins by defining the treatment, device, intervention, biological system, and conditions to be examined. Researchers then combine appropriate physiological data with computational algorithms and disease or device models, generate virtual patient cases, and compare predicted outcomes across those cases. The results can guide design decisions, treatment adjustments, safety assessment, or later experimental studies.
In bioengineering, these simulations support several stages of development. Teams can examine device designs, evaluate how treatments may affect biological systems, optimize intervention parameters, assess safety, and test hypotheses before laboratory or clinical work. Because multiple conditions can be explored computationally, the method helps identify promising options and potential concerns earlier in the development process.
The simulations can indicate how an intervention is expected to affect a biological system under specified conditions and whether outcomes may differ among patient characteristics. They can also help narrow the most informative questions for subsequent studies. In this role, In Silico Trials may reduce development time, costs, and reliance on some animal or human experiments while remaining complementary to conventional evidence.