Quantitative assessment and monitoring of microplastics and nanoplastics distributions and lipid metabolism in live zebrafish using hyperspectral stimulated Raman scattering microscopy.

Microplastics (MP) and nanoplastics (NP) pollutions pose a rising environmental threat to humans and other living species, given their escalating presence in essential resources that living subjects ingest and/or inhale. Herein, to elucidate the potential health implications of MP/NP, we report for the first time by using label-free hyperspectral stimulated Raman scattering (SRS) imaging technique developed to quantitatively monitor the bioaccumulation and metabolic toxicity of MP/NP within live zebrafish larvae during their early developmental stages. Zebrafish embryos are exposed to environmentally related concentrations (3-60 µg/ml) of polystyrene (PS) beads with two typical sizes (2 µm and 50 nm). Zebrafish are administered isotope-tagged fatty acids through microinjection and dietary intake for in vivo tracking of lipid metabolism dynamics. In vivo 3D quantitative vibrational imaging of PS beads and intrinsic biomolecules across key zebrafish organs reveals that gut and liver are the primary target organs of MP/NP, while only 50 nm PS beads readily aggregate and adhere to the brain and blood vessels. The 50 nm PS beads are also found to induce more pronounced hepatic inflammatory response compared to 2 µm counterparts, characterized by increased biogenesis of lipid droplets and upregulation of arachidonic acid detected in zebrafish liver. Furthermore, Raman-tagged SRS imaging of fatty acids uncovers that MP/NP exposure significantly reduces yolk lipid utilization and promotes dietary lipid storage in zebrafish, possibly associated with developmental delays and more pronounced food dilution effects in zebrafish larvae exposed to 2 µm PS beads. The hyperspectral SRS imaging in this work shows that MP/NP exposure perturbs the development and lipid metabolism in zebrafish larvae, furthering the understanding of MP/NP ingestions and consequent toxicity in different organs in living species.

LRFNet: A deep learning model for the assessment of liver reserve function based on Child-Pugh score and CT image.

BACKGROUND AND OBJECTIVE: Liver reserve function should be accurately evaluated in patients with hepatic cellular cancer before surgery to evaluate the degree of liver tolerance to surgical methods. Meanwhile, liver reserve function is also an important indicator for disease analysis and prognosis of patients. Child-Pugh score is the most widely used liver reserve function evaluation and scoring system. However, this method also has many shortcomings such as poor accuracy and subjective factors. To achieve comprehensive evaluation of liver reserve function, we developed a deep learning model to fuse bimodal features of Child-Pugh score and computed tomography (CT) image. METHODS: 1022 enhanced abdomen CT images of 121 patients with hepatocellular carcinoma and impaired liver reserve function were retrospectively collected. Firstly, CT images were pre-processed by de-noising, data amplification and normalization. Then, new branches were added between the dense blocks of the DenseNet structure, and the center clipping operation was introduced to obtain a lightweight deep learning model liver reserve function network (LRFNet) with rich liver scale features. LRFNet extracted depth features related to liver reserve function from CT images. Finally, the extracted features are input into a deep learning classifier composed of fully connected layers to classify CT images into Child-Pugh A, B and C. Precision, Specificity, Sensitivity, and Area Under Curve are used to evaluate the performance of the model. RESULTS: The AUC by our LRFNet model based on CT image for Child-Pugh A, B and C classification of liver reserve function was 0.834, 0.649 and 0.876, respectively, and with an average AUC of 0.774, which was better than the traditional clinical subjective Child-Pugh classification method. CONCLUSION: Deep learning model based on CT images can accurately classify Child-Pugh grade of liver reserve function in hepatocellular carcinoma patients, provide a comprehensive method for clinicians to assess liver reserve function before surgery.

Pharmacokinetics and Safety of Single-Dose Amphotericin B Colloidal Dispersion in Healthy Chinese Subjects and Population Pharmacokinetic/Pharmacodynamic Analysis to Inform Clinical Efficacy in Invasive Infections Caused by Candida albicans.

PURPOSE: Amphotericin B colloidal dispersion (ABCD) is a less toxic formulation of amphotericin B for the treatment of invasive fungal infections. The pharmacokinetic (PK) profile and safety of a generic ABCD were investigated after a single dose (0.5 to 1.5 mg/kg) administered as an intravenous infusion in 30 healthy Chinese subjects. METHODS: PK data from healthy Chinese male subjects were applied for developing a population PK model to predict the PK profiles of standard doses (3 or 4 mg/kg) in patients. A 5000-time Monte Carlo simulation of AUC0-24/MIC target was implemented to determine the probability of target attainment (PTA) and cumulative fraction of response (CFR) under standard doses. FINDINGS: The PK profiles of intravenous administration of ABCD were best described by a 3-compartmental model with a time-varying clearance and a dose-dependent volume of distribution in the peripheral compartment. PK/pharmacodynamic (PK/PD) analysis revealed that 3 or 4 mg/kg ABCD once a day resulted in favorable CRF (>98%) with 2-log reduction of Candida albicans. A high PTA (>90%) was achieved at MIC ≤2 mg/L for the dosing regimen of ABCD 3 mg/kg and 4 mg/kg for MIC ≤4 mg/L. IMPLICATIONS: PK/PD analysis indicated that a favorable efficacy of ABCD could be reached at a dose of 3 or 4 mg/kg once daily for 14 to 28 days to treat invasive fungal infections caused by C albicans. ClinicalTrials.gov identifier: NCT03577509.

Regulatory utility of pharmacometrics in the development and evaluation of antimicrobial agents and its recent progress in China.

Pharmacometrics is an emerging science that interprets drug, disease, and trial information in a mathematical fashion to inform and facilitate efficient drug development and/or regulatory decisions. Pharmacometrics study is increasingly adopted in the regulatory review of new antimicrobial agents. We summarized the 31 antimicrobial agents approved by the US Food and Drug Administration (FDA) and the 26 antimicrobial agents approved by European Medicines Agency (EMA) from January 2001 to May 2019. We also reviewed recent examples of utilizing pharmacometrics to support antimicrobial agent's registration in China, including modeling and simulation methods, effects of internal/external factors on pharmacokinetic (PK) parameters, safety and efficacy evaluation in terms of exposure-response analysis, refinement of the wording of product labeling and package leaflet, and possible postmarketing clinical trial. Ongoing communication among regulator, academia, and industry regarding pharmacometrics is encouraged to streamline and facilitate the development of new antimicrobial agents. The industry can maximize its benefit in drug development through continued pharmacometrics education/training.

Deep Learning-Guided Fiberoptic Raman Spectroscopy Enables Real-Time In Vivo Diagnosis and Assessment of Nasopharyngeal Carcinoma and Post-treatment Efficacy during Endoscopy.

In this work, we develop a deep learning-guided fiberoptic Raman diagnostic platform to assess its ability of real-time in vivo nasopharyngeal carcinoma (NPC) diagnosis and post-treatment follow-up of NPC patients. The robust Raman diagnostic platform is established using innovative multi-layer Raman-specified convolutional neural networks (RS-CNN) together with simultaneous fingerprint and high-wavenumber spectra acquired within sub-seconds using a fiberoptic Raman endoscopy system. We have acquired a total of 15,354 FP/HW in vivo Raman spectra (control: 1761; NPC: 4147; and post-treatment (PT): 9446) from 888 tissue sites of 418 subjects (healthy control: 85; NPC: 82; and PT: 251) during endoscopic examination. The optimized RS-CNN model provides an overall diagnostic accuracy of 82.09% (sensitivity of 92.18% and specificity of 73.99%) for identifying NPC from control and post-treatment patients, which is superior to the best diagnosis performance (accuracy of 73.57%; sensitivity of 89.74%; and specificity of 58.10%) using partial-least-squares linear-discriminate-analysis, proving the robustness and high spectral information sensitiveness of the RS-CNN model developed. We further investigate the saliency map of the best RS-CNN models using the correctly predicted Raman spectra. The specific Raman signatures that are related to the cancer-associated biomolecular variations (e.g., collagens, lipids, and nucleic acids) are uncovered in the map, validating the diagnostic capability of RS-CNN models to correlate with biomolecular signatures. Deep learning-based Raman spectroscopy is a powerful diagnostic tool for rapid screening and surveillance of NPC patients and can also be deployed for longitudinal follow-up monitoring of post-treatment NPC patients to detect early cancer recurrences in the head and neck.

Diabetes Treatment Is Associated With Better Cognitive Function: The Age Disparity.

Background: Diabetes mellitus (DM) is a recognised risk factor for cognitive dysfunction. The purpose of this study was to explore the relationship between active treatment for DM and cognitive function in middle-aged (< 60 years) and older adults (≥60 years), respectively. Methods: A total of 13,691 participants (58.55 ± 9.64 years, 47.40% of men) from the Chinese Health and Retirement Longitudinal Study (CHARLS) were included. The participants were classified into three groups according to whether or not they have diabetes and to their diabetes treatment status: diabetes-free, treated-diabetes and untreated-diabetes, in which the diabetes-free group was regarded as reference specially. Cognitive function was assessed by two interview-based measurements for mental intactness and episodic memory. Results: Compared with the participants in the diabetes-free group, the older participants in the treated-diabetes group had better performance in terms of mental intactness (ß = 0.37, 95% CI = 0.04-0.70). No significant association was observed in the middle-aged participants. In the subgroup analyses, the lower cognitive score was only observed in people without depression, who had never smoked and drunk, and with a normal weight (body mass index: 18.5-23.9 kg/m2). Conclusion: The cognitive function of actively treated diabetic patients was better than that of patients without diabetes, but the improvement was significant only in elderly people. Depression, smoking, drinking, and an abnormal weight may attenuate this effect.

Assessment of nutrient and heavy metal contamination in surface sediments of the Xiashan stream, eastern Guangdong Province, China.

In this study, nutrient and heavy metal contamination in surface sediments of the Xiashan stream were investigated. Determining pollution degree of nutrient and heavy metal were the main objectives of this investigation. For this reason, the concentrations of total nitrogen (TN), total phosphorus (TP), seven heavy metals (Cu, Zn, Pb, Cd, Cr, Ni, Hg), and metalloid (As) were analyzed at 55 sampling sites. The mean TN concentration in surface sediments was 5.007 g/kg, while the mean TP concentration was 0.385 g/kg. Based on the sediment quality guideline (SQGs) and background values of Chinese soil and sediment, the majority of the mean TN concentrations in surface sediments were higher than their background values, while the TP concentrations were different from those observed for TN. For heavy metal concentrations in surface sediments, most of sampling stations were higher than their background values. The mean geo-accumulation (Igeo) indices for heavy metals were ranked as follows: Cd > Hg > Cu > Zn > Pb > Ni > Cr > As. The potential ecological risk index (RI) indicated heavy metal contamination level in Xiashan stream was very high ecological risk. According to Igeo and RI values, heavy metals especially Cd and Hg are influenced significantly by anthropogenic activities. Cd and Hg are not only as pollutant but also considerable contributor to ecological risk. Multivariate statistical investigations showed that there is a significant and positive correlation between Pb, As, and Cd. Cu, Ni, and Cr have similar characteristic and therefore probably originated from the same sources. Suggested by the results, it is necessary to control nitrogen and heavy metal contamination caused by human activities in the study area.

Development of a beveled fiber-optic confocal Raman probe for enhancing in vivo epithelial tissue Raman measurements at endoscopy.

We report on the development of a beveled fiber-optic confocal Raman probe coupled with a ball lens for enhancing in vivo epithelial tissue Raman measurements at endoscopy. Our Monte Carlo simulations show that by selecting a proper fiber-ball lens distance and beveled angle of collection fibers, the confocal Raman probe design can be optimized for maximizing shallower tissue Raman measurements in epithelial tissue; in addition, the ratio of epithelium to stromal Raman photons collected using an optimized confocal Raman probe is approximately 19-fold higher than that using a volume-type Raman probe. Further experiments confirm that the confocal Raman endoscopic probe developed is in favor of probing superficial tissue Raman signals from a two-layer tissue phantom as well as esophagus tissue in vivo during endoscopy. This work suggests the great potential of applying the beveled fiber-optic confocal Raman probe for improving in vivo diagnosis of precancer occurring in epithelial tissue at endoscopy.

[Research on biometric method of heart sound signal based on GMM].

OBJECTIVE: Extraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal. METHODS: Firstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm. RESULTS: The comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM. CONCLUSION: Those results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.

Assessment of liver steatosis and fibrosis in rats using integrated coherent anti-Stokes Raman scattering and multiphoton imaging technique.

We report the implementation of a unique integrated coherent anti-Stokes Raman scattering (CARS), second-harmonic generation (SHG), and two-photon excitation fluorescence (TPEF) microscopy imaging technique developed for label-free monitoring of the progression of liver steatosis and fibrosis generated in a bile duct ligation (BDL) rat model. Among the 21 adult rats used in this study, 18 rats were performed with BDL surgery and sacrificed each week from weeks 1 to 6 (n = 3 per week), respectively; whereas 3 rats as control were sacrificed at week 0. Colocalized imaging of the aggregated hepatic fats, collagen fibrils, and hepatocyte morphologies in liver tissue is realized by using the integrated CARS, SHG, and TPEF technique. The results show that there are significant accumulations of hepatic lipid droplets and collagen fibrils associated with severe hepatocyte necrosis in BDL rat liver as compared to a normal liver tissue. The volume of normal hepatocytes keeps decreasing and the fiber collagen content in BDL rat liver follows a growing trend until week 6; whereas the hepatic fat content reaches a maximum in week 4 and then appears to stop growing in week 6, indicating that liver steatosis and fibrosis induced in a BDL rat liver model may develop at different rates. This work demonstrates that the integrated CARS and multiphoton microscopy imaging technique has the potential to provide an effective means for early diagnosis and detection of liver steatosis and fibrosis without labeling.

Half-ball lens couples a beveled fiber probe for depth-resolved spectroscopy: Monte Carlo simulations.

In this study, we propose a beveled fiber-optic probe coupled with a half-ball lens for improving the depth-resolved fluorescence measurements of epithelial tissue. The Monte Carlo (MC) simulation results show that for a given excitation-collection fiber separation, the probe design with a bevel-angled collection fiber is more sensitive to detect fluorescence photons emitted from the shallow layer of tissue, whereas the flat-tip collection fiber is in favor of probing fluorescence photons originating from deeper tissue areas. This compact half-ball lens-beveled fiber probe design has the potential to facilitate the depth-resolved fluorescence detection of epithelial tissue.

Beveled fiber-optic probe couples a ball lens for improving depth-resolved fluorescence measurements of layered tissue: Monte Carlo simulations.

In this study, we evaluate the feasibility of designing a beveled fiber-optic probe coupled with a ball lens for improving depth-resolved fluorescence measurements of epithelial tissue using Monte Carlo (MC) simulations. The results show that by using the probe configuration with a beveled tip collection fiber and a flat tip excitation fiber associated with a ball lens, discrimination of fluorescence signals generated in different tissue depths is achievable. In comparison with a flat-tip collection fiber, the use of a large bevel angled collection fiber enables a better differentiation between the shallow and deep tissue layers by changing the excitation-collection fiber separations. This work suggests that the beveled fiber-optic probe coupled with a ball lens has the potential to facilitate depth-resolved fluorescence measurements of epithelial tissues.

Monte Carlo simulation of cutaneous reflectance and fluorescence measurements--the effect of melanin contents and localization.

Melanin content and distribution in skin were studied by examining a patient with white, brown and blue skin tones expressed on skin affected by vitiligo. Both diffuse reflectance and autofluorescence spectra of the three distinction skin sites were measured and compared. Monte Carlo simulations were then performed to help explain the measured spectral differences. The modeling is based on a six-layer skin optical model established from published skin optical parameters and by adding melanin content into different locations in the model skin. Both the reflectance and fluorescence spectra calculated by Monte Carlo (MC) simulation were approximately in agreement with experimental results. The study suggests that: (1) trichrome vitiligo skin may be an ideal in vivo model for studying the effect of skin melanin content and distribution on skin spectroscopy properties. (2) Based on the skin optical model and MC simulation, the content and distribution of melanin in skin, or other component of skin could be simulated and predicted. (3) Both reflectance and fluorescence spectra provided information about superficial skin structures but fluorescence spectra are capable of providing information from deeper cutaneous structures. (4) The research method, including the spectral ratio method, the method of adding and modifying the melanin content in skin optical models, and MC simulation could be applied in other non-invasive optical studies of the skin.

Spectroscopic assessment of dermal melanin using blue vitiligo as an in vivo model.

BACKGROUND: Spectroscopic methods have been used to analyze in vivo melanin in the past but the specific effect of melanin depth on autofluorescence and reflectance spectroscopy has not been determined. In patients with blue vitiligo, three distinctive clinicopathologic patterns are present: (1) normal skin with normal epidermal melanin pigmentation (2), skin of blue vitiligo with dermal melanin pigmentation, and (3) tissue of regular vitiligo with no melanin pigmentation. Blue vitiligo may thus serve as an in vivo model to assess dermal pigment using spectroscopic techniques. OBJECTIVES: To evaluate the reflectance and autofluorescence spectra of a patient with blue vitiligo in order to assess the effect of melanin pigmentation and its localization on the optical properties of the skin. METHODS: The blue-gray, normal and depigmented lesions of a patient with blue vitiligo were analyzed using reflectance and fluorescent spectroscopy. The condition was likely induced by a phototoxic reaction in a patient with pre-existing vitiligo. These data were then correlated to the histologic and electron microscopic findings present in the various types of lesions. RESULTS: Reflectance spectroscopy detected little difference in spectral shape between skin sites affected by blue vitiligo vs. vitiligo. Autofluorescence spectroscopy detected an apparent difference between the two types of lesions, with the blue-gray lesions (blue vitiligo) showing lower fluorescence intensity and spectral maximum position red-shifted compared with regular vitiligo, whereas regular vitiligo showed more intense hemoglobin absorption than the blue vitiligo. CONCLUSIONS: Dermal melanin present in blue vitiligo can be well characterized by autofluorescence spectroscopy, while little difference in reflectance spectral shape exists between vitiligo and blue vitiligo. Thus, autofluorescence spectroscopy may better identify deeper structures in skin tissue, such as melanin, than reflectance spectroscopy.

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues.

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.