Photoacoustic (PA) imaging with much deeper tissue penetration and better spatial resolution had been widely employed for the prevention and diagnosis of many diseases. In this study, a new type of hydrogen peroxide (H2O2)-activated photoacoustic nanoprobe [Mn-AH nanoscale coordination polymer nanodots (NCPs)] was successfully synthesized by a simple one-step method in water phase containing 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), horse radish peroxidase (HRP), and manganese ion (Mn2+). After modification by polyethylene glycol (PEG), Mn-AH NCPs exhibited excellent stability and biocompatibility for in vivo H2O2-responsive chromogenic assay with great specificity and sensitivity. In the presence of H2O2, colorless ABTS would be converted by HRP into the oxidized form with strong near-infrared (NIR) absorbance, enabling photoacoustic detection of endogenous H2O2. Using H2O2-activated Mn-AH NCPs, we have successfully performed PA imaging and H2O2 detection of subcutaneous murine colon CT26 tumor and deep-seated orthotopic bladder tumor. Due to the inherent Mn element existence inside the Mn-AH, this nanoprobe also serves as a good T1-weighted magnetic resonance imaging (MRI) contrast agent simultaneously. Lastly, after accomplishing its imaging functions, the Mn-AH NCPs could be cleared out from the body without any long-term toxicity, providing a new opportunity for cancer diagnosis and treatment.Photoacoustic (PA) imaging with much deeper tissue penetration and better spatial resolution had been widely employed for the prevention and diagnosis of many diseases. In this study, a new type of hydrogen peroxide (H2O2)-activated photoacoustic nanoprobe [Mn-AH nanoscale coordination polymer nanodots (NCPs)] was successfully synthesized by a simple one-step method in water phase containing 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), horse radish peroxidase (HRP), and manganese ion (Mn2+). After modification by polyethylene glycol (PEG), Mn-AH NCPs exhibited excellent stability and biocompatibility for in vivo H2O2-responsive chromogenic assay with great specificity and sensitivity. In the presence of H2O2, colorless ABTS would be converted by HRP into the oxidized form with strong near-infrared (NIR) absorbance, enabling photoacoustic detection of endogenous H2O2. Using H2O2-activated Mn-AH NCPs, we have successfully performed PA imaging and H2O2 detection of subcutaneous murine colon CT26 tumor and deep-seated orthotopic bladder tumor. Due to the inherent Mn element existence inside the Mn-AH, this nanoprobe also serves as a good T1-weighted magnetic resonance imaging (MRI) contrast agent simultaneously. Lastly, after accomplishing its imaging functions, the Mn-AH NCPs could be cleared out from the body without any long-term toxicity, providing a new opportunity for cancer diagnosis and treatment.

In this paper, optical coherence tomography (OCT) and surface-enhanced Raman spectroscopy (SERS) were used to characterize normal knee joint (NKJ) tissue and knee osteoarthritis (KOA) tissue ex vivo. OCT images show that there is a clear hierarchical structure in NKJ tissue, including surface layer, transitional layer, radiation layer and cartilage matrix calcification layer tissue structure, while the hierarchical structure of KOA tissue is not clear and unevenly distributed, and the pathological tissues at different stages also show significant differences. SERS shows that NKJ tissue and mild osteoarthritic knee cartilage (MiKOA) tissue have strong characteristic Raman peaks at 964, 1073 (1086), 1271, 1305, 1442, 1660 and 1763cm?1. Compared with the Raman spectrum of NKJ tissue, the Raman characteristic peaks of MiKOA tissue have some shifts, moving from 1073cm?1 to 1086cm?1 and from 1542cm?1 to 1442cm?1. There is a characteristic Raman peak of 1271cm?1 in MiKOA tissue, but not in NKJ tissue. Compared with NKJ tissue, severely degenerated cartilage (SdKOA) tissues show some new SERS peaks at 1008, 1245, 1285, 1311 and 1321cm?1, which are not seen in SERS spectra of NKJ tissue. Principal component analysis (PCA) was used to analyze the Raman spectra of 1245–1345cm?1 region. The results show that PCA can distinguish NKJ, MiKOA and SdKOA tissues and the accuracy is about 90%. These results indicate that OCT can clearly distinguish NKJ, MiKOA, moderate osteoarthritic knee cartilage (MoKOA) and SdKOA tissue, while SERS can provide further judgment basis. The results also prove that the contents of protein and polysaccharide in knee tissue are changed during the pathological process of knee tissue, which is the cause of pain caused by poor friction in knee joint during movement.In this paper, optical coherence tomography (OCT) and surface-enhanced Raman spectroscopy (SERS) were used to characterize normal knee joint (NKJ) tissue and knee osteoarthritis (KOA) tissue ex vivo. OCT images show that there is a clear hierarchical structure in NKJ tissue, including surface layer, transitional layer, radiation layer and cartilage matrix calcification layer tissue structure, while the hierarchical structure of KOA tissue is not clear and unevenly distributed, and the pathological tissues at different stages also show significant differences. SERS shows that NKJ tissue and mild osteoarthritic knee cartilage (MiKOA) tissue have strong characteristic Raman peaks at 964, 1073 (1086), 1271, 1305, 1442, 1660 and 1763cm?1. Compared with the Raman spectrum of NKJ tissue, the Raman characteristic peaks of MiKOA tissue have some shifts, moving from 1073cm?1 to 1086cm?1 and from 1542cm?1 to 1442cm?1. There is a characteristic Raman peak of 1271cm?1 in MiKOA tissue, but not in NKJ tissue. Compared with NKJ tissue, severely degenerated cartilage (SdKOA) tissues show some new SERS peaks at 1008, 1245, 1285, 1311 and 1321cm?1, which are not seen in SERS spectra of NKJ tissue. Principal component analysis (PCA) was used to analyze the Raman spectra of 1245–1345cm?1 region. The results show that PCA can distinguish NKJ, MiKOA and SdKOA tissues and the accuracy is about 90%. These results indicate that OCT can clearly distinguish NKJ, MiKOA, moderate osteoarthritic knee cartilage (MoKOA) and SdKOA tissue, while SERS can provide further judgment basis. The results also prove that the contents of protein and polysaccharide in knee tissue are changed during the pathological process of knee tissue, which is the cause of pain caused by poor friction in knee joint during movement.

This work demonstrates a smartphone-based automated fluorescence analysis system (SAFAS) for point-of-care testing (POCT) of Hg(II). This system consists of three modules. The smartphone module is used to provide an excitation light source, and to collect and analyze fluorescent images. The dark box module is applied to integrate the desired optical elements and offers a dark environment. The cost of the integrated dark box mainly includes the upper cover, box body, lower bottom, fixture and some optical elements which is about $109. The chip module is used for fluorescence sensing, which is composed of an upper plate, bottom plate and cloth-based chip. Due to the integration of multiple smartphone functions, the SAFAS eliminates the need for additional power sources, light sources and analysis systems. The dark box and upper and bottom plates are made by 3D printer. The cloth-based chip (about $0.005 for each chip) is fabricated using the wax screen-printing technique, with no need for expensive and complex fabrication equipments. To our knowledge, the cloth-based microfluidic fluorescence detection method combined with smartphone functions is first reported. By using optimal conditions, the designed system can realize the quantitative detection of Hg(II), which has a linear range of 0.001–100μgmL?1 and a detection limit of 0.5ngmL?1. Additionally, the SAFAS has been successfully applied for detecting Hg(II) in actual water samples, with recoveries of 100.1%–111%, RSDs of 3.88%–9.74%, and fast detection time of about 1 min. Obviously, the proposed SAFAS has the advantages of high sensitivity, wide dynamic range, acceptable reproducibility, good stability and low cost. Therefore, it is believed that the presented SAFAS has great potential to perform the POCT of Hg(II) in different water samples.This work demonstrates a smartphone-based automated fluorescence analysis system (SAFAS) for point-of-care testing (POCT) of Hg(II). This system consists of three modules. The smartphone module is used to provide an excitation light source, and to collect and analyze fluorescent images. The dark box module is applied to integrate the desired optical elements and offers a dark environment. The cost of the integrated dark box mainly includes the upper cover, box body, lower bottom, fixture and some optical elements which is about $109. The chip module is used for fluorescence sensing, which is composed of an upper plate, bottom plate and cloth-based chip. Due to the integration of multiple smartphone functions, the SAFAS eliminates the need for additional power sources, light sources and analysis systems. The dark box and upper and bottom plates are made by 3D printer. The cloth-based chip (about $0.005 for each chip) is fabricated using the wax screen-printing technique, with no need for expensive and complex fabrication equipments. To our knowledge, the cloth-based microfluidic fluorescence detection method combined with smartphone functions is first reported. By using optimal conditions, the designed system can realize the quantitative detection of Hg(II), which has a linear range of 0.001–100μgmL?1 and a detection limit of 0.5ngmL?1. Additionally, the SAFAS has been successfully applied for detecting Hg(II) in actual water samples, with recoveries of 100.1%–111%, RSDs of 3.88%–9.74%, and fast detection time of about 1 min. Obviously, the proposed SAFAS has the advantages of high sensitivity, wide dynamic range, acceptable reproducibility, good stability and low cost. Therefore, it is believed that the presented SAFAS has great potential to perform the POCT of Hg(II) in different water samples.

Background: Lung cancer is one of the most common malignant tumors worldwide. Currently, effective screening methods for early lung cancer are still scarce. Breath analysis provides a promising method for the pre-screening or early screening of lung cancer. Isoprene is a potential and important breath biomarker of lung cancer. Material and Methods: To investigate the clinical value of isoprene for diagnosing lung cancer patients, a cavity ringdown spectroscopy (CRDS) based near-real time, sensitive analysis method of breath isoprene is developed in our lab. In this paper, 92 breath samples from lung cancer patients, 17 breath samples from patients with benign lesions, and 107 breath samples from healthy people were collected. Results: Research indicates that breath isoprene concentration is significantly higher in healthy individuals (221.3±122.2ppbv) than in patients with lung cancer (112.0±36.6ppbv) and benign lung lesions (127.9±41.2ppbv). The result of Receiver Operating Characteristic (ROC) curve suggests that the concentration of isoprene is meaningful for the diagnosis of lung cancer (AUC=0.822, sensitivity=63.6%, specificity=90.2%, P0.01). Conclusion: This study demonstrates that the CRDS breath isoprene analysis system can effectively analyze a large sample of human breath isoprene, and preliminarily confirms the use of breath isoprene as a biomarker for lung diseases.Background: Lung cancer is one of the most common malignant tumors worldwide. Currently, effective screening methods for early lung cancer are still scarce. Breath analysis provides a promising method for the pre-screening or early screening of lung cancer. Isoprene is a potential and important breath biomarker of lung cancer. Material and Methods: To investigate the clinical value of isoprene for diagnosing lung cancer patients, a cavity ringdown spectroscopy (CRDS) based near-real time, sensitive analysis method of breath isoprene is developed in our lab. In this paper, 92 breath samples from lung cancer patients, 17 breath samples from patients with benign lesions, and 107 breath samples from healthy people were collected. Results: Research indicates that breath isoprene concentration is significantly higher in healthy individuals (221.3±122.2ppbv) than in patients with lung cancer (112.0±36.6ppbv) and benign lung lesions (127.9±41.2ppbv). The result of Receiver Operating Characteristic (ROC) curve suggests that the concentration of isoprene is meaningful for the diagnosis of lung cancer (AUC=0.822, sensitivity=63.6%, specificity=90.2%, P0.01). Conclusion: This study demonstrates that the CRDS breath isoprene analysis system can effectively analyze a large sample of human breath isoprene, and preliminarily confirms the use of breath isoprene as a biomarker for lung diseases.

Photoacoustic imaging (PAI) has been developed, and photoacoustic computed tomography (PACT) is widely used for in vivo tissue and mouse imaging. Simulated annealing (SA) algorithm solves optimization problems, and compressed sensing (CS) recovers sparse signals from undersampled measurements. We aim to develop an advanced sparse imaging framework for PACT, which invloves the use of SA to find an optimal sparse array element distribution and CS to perform sparse imaging. PACT reconstructions were performed using a dummy and porcine liver phantoms. Compared to traditional sparse reconstruction algorithms, the proposed method recovers signals using few ultrasonic transducer elements, enabling high-speed, low-cost PACT for practical application.Photoacoustic imaging (PAI) has been developed, and photoacoustic computed tomography (PACT) is widely used for in vivo tissue and mouse imaging. Simulated annealing (SA) algorithm solves optimization problems, and compressed sensing (CS) recovers sparse signals from undersampled measurements. We aim to develop an advanced sparse imaging framework for PACT, which invloves the use of SA to find an optimal sparse array element distribution and CS to perform sparse imaging. PACT reconstructions were performed using a dummy and porcine liver phantoms. Compared to traditional sparse reconstruction algorithms, the proposed method recovers signals using few ultrasonic transducer elements, enabling high-speed, low-cost PACT for practical application.

Fluorescence microscopy has become an essential tool for biologists, to visualize the dynamics of intracellular structures with specific labeling. Quantitatively measuring the dynamics of moving objects inside the cell is pivotal for understanding of the underlying regulatory mechanism. Protein-containing vesicles are involved in various biological processes such as material transportation, organelle interaction, and hormonal regulation, whose dynamic characteristics are significant to disease diagnosis and drug screening. Although some algorithms have been developed for vesicle tracking, most of them have limited performance when dealing with images with low resolution, poor signal-to-noise ratio (SNR) and complicated motion. Here, we proposed a novel deep learning-based method for intracellular vesicle tracking. We trained the U-Net for vesicle localization and motion classification, with demonstrates great performance in both simulated datasets and real biological samples. By combination with fan-shaped tracker (FsT) we have previously developed, this hybrid new algorithm significantly improved the performance of particle tracking with the function of subsequently automated vesicle motion classification. Furthermore, its performance was further demonstrated in analyzing with vesicle dynamics in different temperature, which achieved reasonable outcomes. Thus, we anticipate that this novel method would have vast applications in analyzing the vesicle dynamics in living cells.Fluorescence microscopy has become an essential tool for biologists, to visualize the dynamics of intracellular structures with specific labeling. Quantitatively measuring the dynamics of moving objects inside the cell is pivotal for understanding of the underlying regulatory mechanism. Protein-containing vesicles are involved in various biological processes such as material transportation, organelle interaction, and hormonal regulation, whose dynamic characteristics are significant to disease diagnosis and drug screening. Although some algorithms have been developed for vesicle tracking, most of them have limited performance when dealing with images with low resolution, poor signal-to-noise ratio (SNR) and complicated motion. Here, we proposed a novel deep learning-based method for intracellular vesicle tracking. We trained the U-Net for vesicle localization and motion classification, with demonstrates great performance in both simulated datasets and real biological samples. By combination with fan-shaped tracker (FsT) we have previously developed, this hybrid new algorithm significantly improved the performance of particle tracking with the function of subsequently automated vesicle motion classification. Furthermore, its performance was further demonstrated in analyzing with vesicle dynamics in different temperature, which achieved reasonable outcomes. Thus, we anticipate that this novel method would have vast applications in analyzing the vesicle dynamics in living cells.

Early diagnosis of liver cancer plays a significant role in reducing its high mortality. In this preliminary study, the feasibility of using serum surface-enhanced Raman spectroscopy (SERS) to identify liver cancer was studied. Serum samples were obtained from liver cancer patients and healthy controls. The differences between the SERS spectra of pre-operation and post-operation of liver cancer patients were also analyzed. The general shape and trend of SERS spectra of health control and liver cancer patients were similar. Multivariate analysis, e.g., PLS-SVM, might be useful for the discrimination of serum SERS spectra of pre-operation and post-operation.Early diagnosis of liver cancer plays a significant role in reducing its high mortality. In this preliminary study, the feasibility of using serum surface-enhanced Raman spectroscopy (SERS) to identify liver cancer was studied. Serum samples were obtained from liver cancer patients and healthy controls. The differences between the SERS spectra of pre-operation and post-operation of liver cancer patients were also analyzed. The general shape and trend of SERS spectra of health control and liver cancer patients were similar. Multivariate analysis, e.g., PLS-SVM, might be useful for the discrimination of serum SERS spectra of pre-operation and post-operation.

In this paper, we propose a new fluorescence emission difference microscopy (FED) technique based on polarization modulation. An electro-optical modulator (EOM) is used to switch the excitation beam between the horizontal and vertical polarization states at a high frequency, which leads to solid- and donut-shaped beams after spatial light modulation. Experiment on the fluorescent nanoparticles demonstrates that the proposed method can achieve ～λ∕4 spatial resolution. Using the proposed system, the dynamic imaging of subcellular structures in living cells over time is achieved.In this paper, we propose a new fluorescence emission difference microscopy (FED) technique based on polarization modulation. An electro-optical modulator (EOM) is used to switch the excitation beam between the horizontal and vertical polarization states at a high frequency, which leads to solid- and donut-shaped beams after spatial light modulation. Experiment on the fluorescent nanoparticles demonstrates that the proposed method can achieve ～λ∕4 spatial resolution. Using the proposed system, the dynamic imaging of subcellular structures in living cells over time is achieved.