Chemical & Biomedical Imaging最新文献

The development of peptide-based, radiometal-labeled PET imaging agents has seen an increase in attention due to the favorable properties the peptide backbone exhibits. These include high selectivity and affinity to proteins and cells directly linked to various types of cancers. In addition, rapid clearance from circulation and low toxicity allow for unique approaches to engineering a viable peptide-based imaging agent. Utilizing peptides as the backbone allows for various modifications to improve metabolic stability, target cell affinity, and image quality and imaging capabilities and reduce toxicity. Select radiolabeled peptides have already been FDA approved, with many more in late-stage trials. This review summarizes the current state of the radiometal-labeled PET peptide imaging field as well as explores methods used by researchers to modify peptides, concluding with a look at the future of peptide-based therapy and diagnostics.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.

Correlation signal processing of optical three-dimensional (x, y, t) data can produce super-resolution images. The second-order cross-correlation function XC ₂ has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters. Here, we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles. We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) analysis to use cross-correlation as a postprocessing computational technique to extract both dynamic and structural information on particle diffusion in nanoscale structures simultaneously. Cross-correlation maintains the same super-resolution as auto-correlation while also increasing the sampling rates to reduce aliasing for spatial information in both simulated and experimental data. Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.

Correlation signal processing of optical three-dimensional (x, y, t) data can produce super-resolution images. The second-order cross-correlation function XC₂ has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters. Here, we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles. We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) analysis to use cross-correlation as a postprocessing computational technique to extract both dynamic and structural information on particle diffusion in nanoscale structures simultaneously. Cross-correlation maintains the same super-resolution as auto-correlation while also increasing the sampling rates to reduce aliasing for spatial information in both simulated and experimental data. Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.

Elemental imaging in laser-induced breakdown spectroscopy is usually performed by placing laser shots adjacent to each other on the sample surface without spatial overlap. Seeing that signal intensity is directly related to the amount of ablated material, this restricts either spatial resolution (for a given excitation efficiency) or sensitivity (when reducing the laser spot size). The experimental applicability of a concept involving the spatial overlapping of shots on the sample surface is investigated and compared to the conventional approach. By systematic choice of spacing between laser shots, spatial resolution can be improved to the single digit micrometer range for a given laser spot size. Signal intensity is found to be linearly dependent on the area ablated per shot, facilitating larger signal-to-background ratios with increased spot sizes. Owing to this, the presented approach is also employed to enhance signal intensity, while preserving spatial resolution. The applicability of the method is explored by analyzing samples with distinct thickness of the surface layer, allowing for the assessment of the concept’s suitability for different sample types.

We demonstrate low-frequency interferometric impulsive stimulated Raman scattering (ISRS) imaging with high robustness to distortions by optical scattering. ISRS is a pump-probe coherent Raman spectroscopy that can capture Raman vibrational spectra. Recording of ISRS spectra requires isolation of a probe pulse from the pump pulse. While this separation is simple in nonscattering specimens, such as liquids, scattering leads to significant pump pulse contamination and prevents the extraction of a Raman spectrum. We introduce a robust method for ISRS microscopy that works in complex scattering samples. High signal-to-noise ISRS spectra are obtained even when the pump and probe pulses pass through many scattering layers.

We demonstrate low-frequency interferometric impulsive stimulated Raman scattering (ISRS) imaging with high robustness to distortions by optical scattering. ISRS is a pump–probe coherent Raman spectroscopy that can capture Raman vibrational spectra. Recording of ISRS spectra requires isolation of a probe pulse from the pump pulse. While this separation is simple in nonscattering specimens, such as liquids, scattering leads to significant pump pulse contamination and prevents the extraction of a Raman spectrum. We introduce a robust method for ISRS microscopy that works in complex scattering samples. High signal-to-noise ISRS spectra are obtained even when the pump and probe pulses pass through many scattering layers.

Measuring chemical concentrations at the surface of implanted medical devices is important for elucidating the local biochemical environment, especially during implant infection. Although chemical indicator dyes enable chemical measurements in vitro, they are usually ineffective when measuring through tissue because the background obscures the dye signal and scattering dramatically reduces the spatial resolution. X-ray excited luminescent chemical imaging (XELCI) is a recent imaging modality which overcomes these limitations using a focused X-ray beam to excite a small spot of red light on scintillator-coated medical implants with well-defined location (because X-rays are minimally scattered) and low background. A spectrochemical indicator film placed over the scintillator layer, e.g., a polymer film containing pH-indicator dyes, absorbs some of the luminescence according to the local chemical environment, and this absorption is then detected by measuring the light intensity/spectrum passing through the tissue. A focused X-ray beam is used to scan point-by-point with a spatial resolution mainly limited by the X-ray beam width with minimum increase from X-ray absorption and scattering in the tissue. X-ray resolution, implant surface specificity, and chemical sensitivity are the three key features of XELCI. Here, we study spatial resolution using optically absorptive targets. For imaging a series of lines, the 20–80% knife-edge resolution was ∼285 (±15) μm with no tissue and 475 ± 18 and 520 ± 34 μm, respectively, through 5 and 10 mm thick tissue. Thus, doubling the tissue depth did not appreciably change the spatial resolution recorded through the tissue. This shows the promise of XELCI for submillimeter chemical imaging through tissue.