Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution

Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images

Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.

A Novel Fit-Flexible Fluorescence Soft Imager: Tri-Sensing of Intensity, Fall-Time, and Life Profile

Time-resolved fluorescence imaging techniques, like confocal fluorescence lifetime imaging microscopy, are powerful photonic instrumentation tools of modern science with diverse applications, including: biology, medicine, and chemistry. However, complexities of the systems, both at specimen and device levels, cause difficulties in quantifying soft biomarkers. To address the problems, we first aim to understand and model the underlying photophysics of fluorescence decay curves. For this purpose, we provide a set of mathematical functions, called "life models", fittable with the real temporal recordings of histogram of photon counts. For each model, an equivalent electrical circuit, called a "life circuit", is derived for explaining the whole process. In confocal endomicroscopy, the components of excitation laser, specimen, and fluorescence-emission signal as the histogram of photon counts are modelled by a power source, network of resistor-inductor-capacitor circuitry, and multimetre, respectively. We then design a novel pixel-level temporal classification algorithm, called a "fit-flexible approach", where qualities of "intensity", "fall-time", and "life profile" are identified for each point. A model selection mechanism is used at each pixel to flexibly choose the best representative life model based on a proposed Misfit-percent metric. A two-dimensional arrangement of the quantified information detects some kind of structural information. This approach showed a potential of separating microbeads from lung tissue, distinguishing the tri-sensing from conventional methods. We alleviated by 7% the error of the Misfit-percent for recovering the histograms on real samples than the best state-of-the-art competitor. Codes are available online.

The use of fluorescence lifetime imaging (FLIM) for in situ microbial detection in complex mineral substrates

The utility of fluorescence lifetime imaging microscopy (FLIM) for identifying bacteria in complex mineral matrices was investigated. Baseline signals from unlabelled Bacillus subtilis and Euglena gracilis, and Bacillus subtilis labelled with SYTO 9 were obtained using two-photon excitation at 730, 750 and 800 nm, identifying characteristic lifetimes of photosynthetic pigments, unpigmented cellular autofluorescence, and SYTO 9. Labelled and unlabelled B. subtilis were seeded onto marble and gypsum samples containing endolithic photosynthetic cyanobacteria and the ability to distinguish cells from mineral autofluorescence and nonspecific dye staining was examined in parallel with ordinary multichannel confocal imaging. It was found that FLIM enabled discrimination of SYTO 9 labelled cells from background, but that the lifetime of SYTO 9 was shorter in cells on minerals than in pure culture under our conditions. Photosynthetic microorganisms were easily observed using both FLIM and confocal. Unlabelled, nonpigmented bacteria showed weak signals that were difficult to distinguish from background when minerals were present, though cellular autofluorescence consistent with NAD(P)H could be seen in pure cultures, and phasor analysis permitted detection on rocks. Gypsum and marble samples showed similar autofluorescence profiles, with little autofluorescence in the yellow-to-red range. Lifetime or time-gated imaging may prove a useful tool for environmental microbiology. LAY DESCRIPTION: The standard method of bacterial enumeration is to label the cells with a fluorescent dye and count them under high-power fluorescence microscopy. However, this can be difficult when the cells are embedded in soil and rock due to fluorescence from the surrounding minerals and dye binding to ambiguous features of the substrate. The use of fluorescence lifetime imaging (FLIM) can disambiguate these signals and allow for improved detection of bacteria in environmental samples.

Fibre-based fluorescence-lifetime imaging microscopy: a real-time biopsy guidance tool for suspected lung cancer

Lung cancer is the most common cause of cancer-related deaths worldwide. Early detection improves outcomes, however, existing sampling techniques are associated with suboptimal diagnostic yield and procedure-related complications. Autofluorescence-based fluorescence-lifetime imaging microscopy (FLIM), a technique which measures endogenous fluorophore decay rates, may aid identification of optimal biopsy sites in suspected lung cancer. Our fibre-based fluorescence-lifetime imaging system, utilising 488 nm excitation, which is deliverable via existing diagnostic platforms, enables real-time visualisation and lifetime analysis of distal alveolar lung structure. We evaluated the diagnostic accuracy of the fibre-based fluorescence-lifetime imaging system to detect changes in fluorescence lifetime in freshly resected ex vivo lung cancer and adjacent healthy tissue as a first step towards future translation. The study compares paired non-small cell lung cancer (NSCLC) and non-cancerous tissues with gold standard diagnostic pathology to assess the performance of the technique. Paired NSCLC and non-cancerous lung tissues were obtained from thoracic resection patients (N=21). A clinically compatible 488 nm fluorescence-lifetime endomicroscopy platform was used to acquire simultaneous fluorescence intensity and lifetime images. Fluorescence lifetimes were calculated using a computationally-lightweight, rapid lifetime determination method. Fluorescence lifetime was significantly reduced in ex vivo lung cancer, compared with non-cancerous lung tissue [mean ± standard deviation (SD), 1.79±0.40 vs. 2.15±0.26 ns, P<0.0001], and fluorescence intensity images demonstrated distortion of alveolar elastin autofluorescence structure. Fibre-based fluorescence-lifetime imaging demonstrated good performance characteristics for distinguishing lung cancer, from adjacent non-cancerous tissue, with 81.0% sensitivity and 71.4% specificity. Our novel fibre-based fluorescence-lifetime imaging system, which enables label-free imaging and quantitative lifetime analysis, discriminates ex vivo lung cancer from adjacent healthy tissue. This minimally invasive technique has potential to be translated as a real-time biopsy guidance tool, capable of optimising diagnostic accuracy in lung cancer.

Applications of machine learning in time-domain fluorescence lifetime imaging: a review

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

Fibre-optic based exploration of lung cancer autofluorescence using spectral fluorescence lifetime

Fibre-optic based time-resolved fluorescence spectroscopy (TRFS) is an advanced spectroscopy technique that generates sample-specific spectral-temporal signature, characterising variations in fluorescence in real-time. As such, it can be used to interrogate tissue autofluorescence. Recent advancements in TRFS technology, including the development of devices that simultaneously measure high-resolution spectral and temporal fluorescence, paired with novel analysis methods extracting information from these multidimensional measurements effectively, provide additional insight into the underlying autofluorescence features of a sample. This study demonstrates, using both simulated data and endogenous fluorophores measured bench-side, that the shape of the spectral fluorescence lifetime, or fluorescence lifetimes estimated over high-resolution spectral channels across a broad range, is influenced by the relative abundance of underlying fluorophores in mixed systems and their respective environment. This study, furthermore, explores the properties of the spectral fluorescence lifetime in paired lung tissue deemed either abnormal or normal by pathologists. We observe that, on average, the shape of the spectral fluorescence lifetime at multiple locations sampled on 14 abnormal lung tissue, compared to multiple locations sampled on the respective paired normal lung tissue, shows more variability; and, while not statistically significant, the average spectral fluorescence lifetime in abnormal tissue is consistently lower over every wavelength than the normal tissue.

Combined fluorescence lifetime and surface topographical imaging of biological tissue

In this work a combined fluorescence lifetime and surface topographical imaging system is demonstrated. Based around a 126 × 192 time resolved single photon avalanche diode (SPAD) array operating in time correlated single-photon counting (TCSPC) mode, both the fluorescence lifetime and time of flight (ToF) can be calculated on a pixel by pixel basis. Initial tests on fluorescent samples show it is able to provide 4 mm resolution in distance and 0.4 ns resolution in lifetime. This combined modality has potential biomedical applications such as surgical guidance, endoscopy, and diagnostic imaging. The system is demonstrated on both ovine and human pulmonary tissue samples, where it offers excellent fluorescence lifetime contrast whilst also giving a measure of the distance to the sample surface.

Fluorescence Lifetime Measurements and Analyses: Protocols Using Flow Cytometry and High-Throughput Microscopy

This chapter focuses on applications and protocols that involve the measurement of the fluorescence lifetime as an informative cytometric parameter. The timing of fluorescence decay has been well-studied for cell counting, sorting, and imaging. Therefore, provided herein is an overview of the techniques used, how they enhance cytometry protocols, and the modern techniques used for lifetime analysis. The background and theory behind fluorescence decay kinetic measurements in cells is first discussed followed by the history of the development of time-resolved flow cytometry. These sections are followed by a review of applications that benefit from the quantitative nature of fluorescence lifetimes as a photophysical trait. Lastly, perspectives on the modern ways in which the fluorescence lifetime is scanned at high throughputs which include high-speed microscopy and machine learning are provided.

Multiway Decomposition Followed by Reconvolution of Fluorescence Time Decay Data

The use of time-resolved emission spectroscopy (TRES) is increasing as these instruments become more available, and the prices are decreasing, especially with the development of cheaper LED light sources. In this article, we propose a new methodology for analyzing TRES data. It combines two existing methods: PARAFAC and reconvolution. PARAFAC is a soft modeling curve resolution technique which has been extensively applied to steady-state fluorescence data, and reconvolution is the most common method for fitting TRES data. The proposed method is compared to two well-established methods of analyzing these data, namely, global reconvolution and tail fitting. In addition, we compare our approach with the SLICING method proposed in 2021 by Devos et al. which is also based on a soft model, but does not include the reconvolution step. All of these methods follow the assumption that the measured fluorescence signal is a linear combination of the underlying fluorophores. The comparison is based on a measured TRES data set with a mixture of three fluorophores and two sets of simulated data sets with up to four fluorophores. The results show that global fitting works well as long as the signal-to-noise ratio (SNR) is high (more than 15 dB), independent of the spacing between the emission peak maxima. SLICING does not give as good estimates of the time decay, mainly due to the challenge of defining the tail. Our proposed method gives robust and accurate results, outperforming the other techniques in cases with broad instrument response functions and high noise levels with SNRs down to 5 dB.

Phasor identifier: A cloud-based analysis of phasor-FLIM data on Python notebooks

This paper introduces an innovative approach utilizing Google Colaboratory for the versatile analysis of phasor fluorescence lifetime imaging microscopy (FLIM) data collected from various samples (e.g., cuvette, cells, tissues) and in various input file formats. In fact, phasor-FLIM widespread adoption has been hampered by complex instrumentation and data analysis requirements. We mean to make advanced FLIM analysis more accessible to researchers through a cloud-based solution that 1) harnesses robust computational resources, 2) eliminates hardware limitations, and 3) supports both CPU and GPU processing. We envision a paradigm shift in FLIM data accessibility and potential, aligning with the evolving field of artificial intelligence-driven FLIM analysis. This approach simplifies FLIM data handling and opens doors for diverse applications, from studying cellular metabolism to investigating drug encapsulation, benefiting researchers across multiple domains. The comparative analysis of freely distributed FLIM tools highlights the unique advantages of this approach in terms of adaptability, scalability, and open-source nature.

Macroscopic temporally and spectrally resolved fluorescence imaging enhanced by laser-wavelength multiplexing

We present a laser scanning system for macroscopic samples that records fully resolved decay curves in individual pixels, resolves the images in 16 wavelength channels, and records simultaneously at several laser wavelengths. By using confocal detection, the system delivers images that are virtually free of lateral scattering and out-of-focus haze. Image formats can be up to 256 × 256 pixels and up to 1024 time channels. We demonstrate the performance of the system both on model experiments with fluorescent micro-beads and on the tumor model in the living mice.

Delineation and detection of breast cancer using novel label-free fluorescence

BACKGROUND

Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence.

METHODS

Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440-460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples.

RESULTS

Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis.

CONCLUSIONS

This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC.

Kernelizing: A way to increase accuracy in trilinear decomposition analysis of multiexponential signals

The unmixing of multiexponential decay signals into monoexponential components using soft modelling approaches is a challenging task due to the strong correlation and complete window overlap of the profiles. To solve this problem, slicing methodologies, such as PowerSlicing, tensorize the original data matrix into a three-way data array that can be decomposed based on trilinear models providing unique solutions. Satisfactory results have been reported for different types of data, e.g., nuclear magnetic resonance or time-resolved fluorescence spectra. However, when decay signals are described by only a few sampling (time) points, a significant degradation of the results can be observed in terms of accuracy and precision of the recovered profiles. In this work, we propose a methodology called Kernelizing that provides a more efficient way to tensorize data matrices of multiexponential decays. Kernelizing relies on the invariance of exponential decays, i.e., when convolving a monoexponential decaying function with any positive function of finite width (hereafter called "kernel"), the shape of the decay (determined by the characteristic decay constant) remains unchanged and only the preexponential factor varies. The way preexponential factors are affected across the sample and time modes is linear, and it only depends on the kernel used. Thus, using kernels of different shapes, a set of convolved curves can be obtained for every sample, and a three-way data array generated, for which the modes are sample, time and kernelizing effect. This three-way array can be afterwards analyzed by a trilinear decomposition method, such as PARAFAC-ALS, to resolve the underlying monoexponential profiles. To validate this new approach and assess its performance, we applied Kernelizing to simulated datasets, real time-resolved fluorescence spectra collected on mixtures of fluorophores and fluorescence-lifetime imaging microscopy data. When the measured multiexponential decays feature few sampling points (down to fifteen), more accurate trilinear model estimates are obtained than when using slicing methodologies.

Handheld wide-field fluorescence lifetime imaging system based on a distally mounted SPAD array

In this work a handheld Fluorescent Lifetime IMaging (FLIM) system based on a distally mounted < 2 mm² 128 × 120 single photon avalanche diode (SPAD) array operating over a > 1 m long wired interface is demonstrated. The head of the system is ∼4.5 cm x 4.5 cm x 4.5 cm making it suitable for hand-held ex vivo applications. This is, to the best of the authors' knowledge, the first example of a SPAD array mounted on the distal end of a handheld FLIM system in this manner. All existing systems to date use a fibre to collect and relay fluorescent light to detectors at the proximal end of the system. This has clear potential biological and biomedical applications. To demonstrate this, the system is used to provide contrast between regions of differing tissue composition in ovine kidney samples, and between healthy and stressed or damaged plant leaves. Additionally, FLIM videos are provided showing that frame rates of > 1 Hz are achievable. It is thus an important step in realising an in vivo miniaturized chip-on-tip FLIM endoscopy system.

Light-sheet autofluorescence lifetime imaging with a single-photon avalanche diode array

Significance

Fluorescence lifetime imaging microscopy (FLIM) of the metabolic co-enzyme nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] is a popular method to monitor single-cell metabolism within unperturbed, living 3D systems. However, FLIM of NAD(P)H has not been performed in a light-sheet geometry, which is advantageous for rapid imaging of cells within live 3D samples.

Aim

We aim to design, validate, and demonstrate a proof-of-concept light-sheet system for NAD(P)H FLIM.

Approach

A single-photon avalanche diode camera was integrated into a light-sheet microscope to achieve optical sectioning and limit out-of-focus contributions for NAD(P)H FLIM of single cells.

Results

An NAD(P)H light-sheet FLIM system was built and validated with fluorescence lifetime standards and with time-course imaging of metabolic perturbations in pancreas cancer cells with 10 s integration times. NAD(P)H light-sheet FLIM in vivo was demonstrated with live neutrophil imaging in a larval zebrafish tail wound also with 10 s integration times. Finally, the theoretical and practical imaging speeds for NAD(P)H FLIM were compared across laser scanning and light-sheet geometries, indicating a 30 × to 6 × acquisition speed advantage for the light sheet compared to the laser scanning geometry.

Conclusions

FLIM of NAD(P)H is feasible in a light-sheet geometry and is attractive for 3D live cell imaging applications, such as monitoring immune cell metabolism and migration within an organism.

Development of a high-speed line-scanning fluorescence lifetime imaging microscope for biological imaging

We report the development of a novel line-scanning microscope capable of acquiring high-speed time-correlated single-photon counting (TCSPC)-based fluorescence lifetime imaging microscopy (FLIM) imaging. The system consists of a laser-line focus, which is optically conjugated to a 1024 × 8 single-photon avalanche diode (SPAD)-based line-imaging complementary metal-oxide semiconductor (CMOS), with 23.78 µm pixel pitch at 49.31% fill factor. Incorporation of on-chip histogramming on the line-sensor enables acquisition rates 33 times faster than our previously reported bespoke high-speed FLIM platforms. We demonstrate the imaging capability of the high-speed FLIM platform in a number of biological applications.

Tri-mode optical biopsy probe with fluorescence endomicroscopy, Raman spectroscopy, and time-resolved fluorescence spectroscopy

We present an endoscopic probe that combines three distinct optical fibre technologies including: A high-resolution imaging fibre for optical endomicroscopy, a multimode fibre for time-resolved fluorescence spectroscopy, and a hollow-core fibre with multimode signal collection cores for Raman spectroscopy. The three fibers are all enclosed within a 1.2 mm diameter clinical grade catheter with a 1.4 mm end cap. To demonstrate the probe's flexibility we provide data acquired with it in loops of radii down to 2 cm. We then use the probe in an anatomically accurate model of adult human airways, showing that it can be navigated to any part of the distal lung using a commercial bronchoscope. Finally, we present data acquired from fresh ex vivo human lung tissue. Our experiments show that this minimally invasive probe can deliver real-time optical biopsies from within the distal lung - simultaneously acquiring co-located high-resolution endomicroscopy and biochemical spectra.

Deep learning-assisted co-registration of full-spectral autofluorescence lifetime microscopic images with H&E-stained histology images

Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsupervised image-to-image translation network that significantly improves the success of the co-registration using a conventional optimisation-based regression network, applicable to autofluorescence lifetime images at different emission wavelengths. A preliminary blind comparison by experienced researchers shows the superiority of our method on co-registration. The results also indicate that the approach is applicable to various image formats, like fluorescence in-tensity images. With the registration, stitching outcomes illustrate the distinct differences of the spectral lifetime across an unstained tissue, enabling macro-level rapid visual identification of lung cancer and cellular-level characterisation of cell variants and common types. The approach could be effortlessly extended to lifetime images beyond this range and other staining technologies.

Review of the British Thoracic Society Winter Meeting 2021, 24-26 November 2021

The Winter Meeting of the British Thoracic Society (BTS) is a platform for the latest clinical and scientific research in respiratory medicine. This review summarises the key symposia and presentations from the BTS Winter Meeting 2021 held online due to the COVID-19 pandemic.

Fibroblast Activation Protein Specific Optical Imaging in Non-Small Cell Lung Cancer

Fibroblast activation protein (FAP) is a cell surface propyl-specific serine protease involved in the regulation of extracellular matrix. Whilst expressed at low levels in healthy tissue, upregulation of FAP on fibroblasts can be found in several solid organ malignancies, including non-small cell lung cancer, and chronic inflammatory conditions such as pulmonary fibrosis and rheumatoid arthritis. Their full role remains unclear, but FAP expressing cancer associated fibroblasts (CAFs) have been found to relate to a poor prognosis with worse survival rates in breast, colorectal, pancreatic, and non-small cell lung cancer (NSCLC). Optical imaging using a FAP specific chemical probe, when combined with clinically compatible imaging systems, can provide a readout of FAP activity which could allow disease monitoring, prognostication and potentially stratify therapy. However, to derive a specific signal for FAP any sequence must retain specificity over closely related endopeptidases, such as prolyl endopeptidase (PREP), and be resistant to degradation in areas of active inflammation. We describe the iterative development of a FAP optical reporter sequence which retains FAP specificity, confers resistance to degradation in the presence of activated neutrophil proteases and demonstrates clinical tractability ex vivo in NSCLC samples with an imaging platform.

Improved Protoporphyrin IX-Guided Neurosurgical Tumor Detection with Frequency-Domain Fluorescence Lifetime Imaging

Precise intraoperative brain tumor visualization supports surgeons in achieving maximal safe resection. In this sense, improved prognosis in patients with high-grade gliomas undergoing protoporphyrin IX fluorescence-guided surgery has been demonstrated. Phase fluorescence lifetime imaging in the frequency-domain has shown promise to distinguish weak protoporphyrin IX fluorescence from competing endogenous tissue fluorophores, thus allowing for brain tumor detection with high sensitivity. In this work, we show that this technique can be further improved by minimizing the crosstalk of autofluorescence signal contributions when only detecting the fluorescence emission above 615 nm. Combining fluorescence lifetime and spectroscopic measurements on a set of 130 ex vivo brain tumor specimens (14 low- and 56 high-grade gliomas, 39 meningiomas and 21 metastases) coherently substantiated the resulting increase of the fluorescence lifetime with respect to the detection band employed in previous work. This is of major interest for obtaining a clear-cut distinction from the autofluorescence background of the physiological brain. In particular, the median fluorescence lifetime of low- and high-grade glioma specimens lacking visual fluorescence during surgical resection was increased from 4.7 ns to 5.4 ns and 2.9 ns to 3.3 ns, respectively. While more data are needed to create statistical evidence, the coherence of what was observed throughout all tumor groups emphasized that this optimization should be taken into account for future studies.

Fluorescence Thermometer: Intermediation of the Fontal Temperature and Light

The rapid advance of thermal materials and fluorescence spectroscopy has extensively promoted micro-scale fluorescence thermometry development in recent years. Based on the advantages of fast response, high sensitivity, simple operation,...