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Optoacoustically induced auditory brainstem responses in the mouse model enhanced through an absorbing film
Significance: Optoacoustic stimulation offers an alternative stimulation strategy for the hearing organ. To serve as the base for a novel auditory prosthesis, the optoacoustic stimulation must be biocompatible and energy-saving. Aim: Enhancing the efficiency of optoacoustic stimulation while reducing the energy input in a suited animal model. Approach: Optoacoustically induced auditory brainstem responses (oABRs) were recorded after the pulsed laser irradiation of the tympanic membrane (TM) in mice. The results were compared with the ABRs induced through acoustic click stimulation. In addition, self-adhesive absorbing films were applied on the TM before the optoacoustic stimulation to investigate their effect on the resulting ABRs.Results: Using an absorbing film on the TM during optical stimulation led to considerably enhanced oABR wave I amplitude values compared with the stimulation of the bare TM. When using our stimulation strategy, we induced oABR waves in the 50% to 60% range of the acoustical stimulation reached with 80-dB SPL click stimuli. Conclusions: The mouse model can be used for certain developmental work for an optoacoustic auditory prosthesis. Using absorbing films on the TM during optical stimulation considerably enhances oABR wave I amplitude. Optimization of the stimulation strategy could further enhance the efficiency within biocompatibility margins.
FLIM data analysis based on Laguerre polynomial decomposition and machine-learning
Significance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components.
Looking for a perfect match: multimodal combinations of Raman spectroscopy for biomedical applications
Raman spectroscopy has shown very promising results in medical diagnostics by providing label-free and highly specific molecular information of pathological tissue ex vivo and in vivo. Nevertheless, the high specificity of Raman spectroscopy comes at a price, i.e., low acquisition rate, no direct access to depth information, and limited sampling areas. However, a similar case regarding advantages and disadvantages can also be made for other highly regarded optical modalities, such as optical coherence tomography, autofluorescence imaging and fluorescence spectroscopy, fluorescence lifetime microscopy, second-harmonic generation, and others. While in these modalities the acquisition speed is significantly higher, they have no or only limited molecular specificity and are only sensitive to a small group of molecules. It can be safely stated that a single modality provides only a limited view on a specific aspect of a biological specimen and cannot assess the entire complexity of a sample. To solve this issue, multimodal optical systems, which combine different optical modalities tailored to a particular need, become more and more common in translational research and will be indispensable diagnostic tools in clinical pathology in the near future. These systems can assess different and partially complementary aspects of a sample and provide a distinct set of independent biomarkers. Here, we want to give an overview on the development of multimodal systems that use RS in combination with other optical modalities to improve the diagnostic performance.