The characteristics and environmental significance of BVOCs released by aquatic macrophytes.

Biogenic volatile organic compounds (BVOCs) emitted by plants serve crucial biological functions and potentially impact atmospheric environment and global carbon cycling. Despite their significance, BVOC emissions from aquatic macrophytes have been relatively understudied. In this study, for the first time we identified there were 68 major BVOCs released from 34 common aquatic macrophytes, and these compounds referred to alcohols, aldehydes, alkanes, alkenes, arenes, ethers, furans, ketones, phenol. For type of BVOC emissions from different life form and phylogenetic group of aquatic macrophytes, 34 of the 68 BVOCs from emergent and submerged macrophytes are classified into alkene and alcohol compounds, over 50% BVOCs from dicotyledon and monocotyledon belong to alcohol and arene compounds. Charophyte and pteridophyte emitted significantly fewer BVOCs than dicotyledon and monocotyledon, and each of them only released 12 BVOCs. These BVOCs may be of great importance for the growth and development of macrophytes, because many BVOCs, such as azulene, (E)-ß-farnesene, and dimethyl sulfide are proved to play vital roles in plant growth, defense, and information transmission. Our results confirmed that both life form and phylogenetic group of aquatic macrophytes had significantly affected the BVOC emissions form macrophytes, and suggested that the intricate interplay of internal and external factors that shape BVOC emissions from aquatic macrophytes. Thus, further studies are urgently needed to investigate the influence factors and ecological function of BVOCs released by macrophytes within aquatic ecosystem.

Responses of submerged macrophytes to different particle size microplastics and tetracycline co-pollutants at the community and population level.

Microplastics (MPs) and antibiotics are ubiquitous in aquatic ecosystems, and their accumulation and combined effects are considered emerging threats that may affect biodiversity and ecosystem function. The particle size of microplastics plays an important role in their combined effects with antibiotics. Submerged macrophytes are crucial in maintaining the health and stability of freshwater ecosystems. However, little is known about the combined effects of different particle size of MPs and antibiotics on freshwater plants, particularly their effects on submerged macrophyte communities. Thus, there is an urgent need to study their effects on the macrophyte communities to provide essential information for freshwater ecosystem management. In the present study, a mesocosm experiment was conducted to explore the effects of three particle sizes (5 µm, 50 µm, and 500 µm) of polystyrene-microplastics (PSMPs) (75 mg/L), tetracycline (TC) (50 mg/L), and their co-pollutants on interactions between Hydrilla verticillata and Elodea nuttallii. Our results showed that the effects of MPs are size-dependent on macrophytes at the community level rather than at the population level, and that small and medium sized MPs can promote the growth of the two test macrophytes at the community level. In addition, macrophytes at the community level have a stronger resistance to pollutant stress than those at the population level. Combined exposure to MPs and TC co-pollutants induces species-specific responses and antagonistic toxic effects on the physio-biochemical traits of submerged macrophytes. Our study provides evidence that MPs and co-pollutants not only affect the morphology and physiology at the population level but also the interactions between macrophytes. Thus, there are promising indications on the potential consequences of MPs and co-pollutants on macrophyte community structure, which suggests that future studies should focus on the effects of microplastics and their co-pollutants on aquatic macrophytes at the community level rather than only at the population level. This will improve our understanding of the profound effects of co-pollutants in aquatic environments on the structure and behavior of aquatic communities and ecosystems.

Enhanced denitrification performance of granular sludge for the treatment of waste brine from ion exchange resin process.

Ion exchange resin process is a widely used process in wastewater treatment plants, but its waste brine is characterized by high salinity and nitrate concentration, leading to costly treatment. This study innovatively explored the use of an up-flow anaerobic sludge bed (USB) for the treatment of waste brine from ion exchange resin process, following a pilot-scale ion exchange resin process. Specifically, the D890 ion exchange resin was employed for nitrate removal from secondary effluent, with resin regeneration using 4% NaCl solution. The USB was inoculated with anaerobic granular sludge and acclimated under various single-factor conditions, which revealed the optimal pH range of 6.5-9, salt concentration of 2%, hydraulic retention time of 12 h, C/N ratio of 3.3, and up-flow velocity of 1.5 m/h for reactor operation. This study provides a novel approach for the cost-effective treatment of waste brine from ion exchange resin process. The study found that the denitrification efficiency was highest when the NO3--N concentration was around 200 mg/L, with NO3--N and TN removal rates exceeding 95% and 90%, respectively, under optimal operating conditions. Characterization of the granular sludge during different phases of the operation revealed a significant increase in proteobacteria and gradually became the dominant species over time. This study presents a novel, cost-effective approach to treat waste brine from ion exchange resin process, and the long-term stable operation of the reactor offers a reliable option for resin regeneration wastewater treatment.

Original and liposome-modified indocyanine green-assisted fluorescence study with animal models.

Medical diagnosis heavily relies on the use of bio-imaging techniques. One such technique is the use of ICG-based biological sensors for fluorescence imaging. In this study, we aimed to improve the fluorescence signals of ICG-based biological sensors by incorporating liposome-modified ICG. The results from dynamic light scattering and transmission electron microscopy showed that MLM-ICG was successfully fabricated with a liposome diameter of 100-300 nm. Fluorescence spectroscopy showed that MLM-ICG had the best properties among the three samples (Blank ICG, LM-ICG, and MLM-ICG), as samples immersed in MLM-ICG solution achieved the highest fluorescence intensity. The NIR camera imaging also showed a similar result. For the rat model, the best period for fluorescence tests was between 10 min and 4 h, where most organs reached their maximum fluorescence intensity except for the liver, which continued to rise. After 24 h, ICG was excreted from the rat's body. The study also analyzed the spectra properties of different rat organs, including peak intensity, peak wavelength, and FWHM. In conclusion, the use of liposome-modified ICG provides a safe and optimized optical agent, which is more stable and efficient than non-modified ICG. Incorporating liposome-modified ICG in fluorescence spectroscopy could be an effective way to develop novel biosensors for disease diagnosis.

Mapping soil available copper content in the mine tailings pond with combined simulated annealing deep neural network and UAV hyperspectral images.

Improper discharge of slag from mining will pollute the surrounding soil, thereby affecting the ecology and becoming an important global problem. The available copper (ACu) content in polluted soil is an important factor affecting plant growth and development. When investigating a large area of soil with ACu, manual sampling by points and inspection are mainly used, due to the heterogeneity of soil, the efficiency and accuracy are lower. The Unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor as a remote sensing technology is widely used in soil indicator monitoring because of its rapid and convenience. Meanwhile, using the relationship between soil organic matter and available copper has the potential to predict available copper. In this study, we selected the study area with tailings area in the Jianghan Plain of China and used a UAV equipped with a hyperspectral sensor to predict ACu and soil organic matter (SOM) in the soil with two datasets. Firstly, 74 soil samples were collected in the study area, and the ACu and SOM of the soil samples were determined. Second, a hyperspectral image of the study area is obtained using a UAV equipped with a hyperspectral sensor. Thirdly, we combine hyperspectral data with competitive adaptive reweighted sampling (CARS) to obtain feature bands and utilize simulated annealing deep neural network (SA-DNN) to generate estimation models. Finally, maps of the distribution of ACu and SOM in the area were generated using the model. In two datasets, the model of ACu with R2 values both are 0.89, and R2 on the model of SOM is 0.89 and 0.88. The results show that the combination of UAV hyperspectral imagery with the SA-DNN model has good performance in the prediction of organic matter and available copper, which is helpful for soil environmental monitoring.

Microplastics and co-pollutant with ciprofloxacin affect interactions between free-floating macrophytes.

Microplastic and antibiotic contamination are considered an increasing environmental problem in aquatic systems, while little is known about the impact of microplastics and co-pollutant with antibiotics on freshwater vascular plants, particularly the effects of interactions between macrophytes. Here, we performed a mesocosm experiment to evaluate the impact of polyethylene-microplastics and their co-pollutants with ciprofloxacin on the growth and physiological characteristics of Spirodela polyrhiza and Lemna minor and the interactions between these two macrophytes. Our results showed that microplastics alone cannot significantly influence fresh weight and specific leaf area of the two test free-floating macrophytes, but the effects on photosynthetic pigments, malondialdehyde, catalase and soluble sugar contents were species-specific. Ciprofloxacin can significant adverse effects on the growth and physiological traits of the two test macrophytes and microplastic mitigated the toxicity of ciprofloxacin on the two free-floating plants to a certain extent. In addition, our studies showed that microplastics and co-pollutants can influence relative yield and competitiveness of S. polyrhiza and L. minor by directly or indirectly influencing their physiology and growth. Therefore our findings suggest that species-specific sensibility to microplastic and its co-pollutant among free-floating macrophytes may influence macrophyte population dynamics and thereby community structure and ecosystem functioning. And microplastics altered other contaminant behaviours and toxicity, and may directly or indirectly influence macrophytes interactions and community structure. The present study is the first experimental study exploring the effects of microplastics alone and with their co-pollutants on interactions between free-floating macrophytes, which can provide basic theoretical guidance for improving the stability of freshwater ecosystems.

Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation.

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results: In both the whole-image and left- and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung regions had higher contributions to the classification of COVID-19 CXRs, whereas the left-lung regions had higher contributions to identifying normal CXRs. Conclusions: Overall, MI-CNNs could achieve higher accuracy with an increasing number of inputs (eg, 16-input MI-CNN). This approach could assist radiologists in identifying COVID-19 CXRs and in screening the critical regions related to COVID-19 classifications.

Mouthwash as a non-invasive method of indocyanine green delivery for near-infrared fluorescence dental imaging.

SIGNIFICANCE: X-ray imaging serves as the mainstream imaging in dentistry, but it involves risk of ionizing radiation. AIM: This study presents the feasibility of indocyanine green-assisted near-infrared fluorescence (ICG-NIRF) dental imaging with 785-nm NIR laser in the first (ICG-NIRF-I: 700 to 1000 nm) and second (ICG-NIRF-II: 1000 to 1700 nm) NIR wavelengths. APPROACH: Sprague Dawley rats with different postnatal days were used as animal models. ICG, as a fluorescence agent, was delivered to dental structures by subcutaneous injection (SC) and oral administration (OA). RESULTS: For SC method, erupted and unerupted molars could be observed from ICG-NIRF images at a short imaging time (<1 min). ICG-NIRF-II could achieve a better image contrast in unerupted molars at 24 h after ICG injection. The OA could serve as a non-invasive method for ICG delivery; it could also cause the glow-in-dark effect in unerupted molars. For erupted molars, OA can be considered as mouthwash and exhibits outstanding performance for delivery of ICG dye; erupted molar structures could be observed at a short imaging time (<1 min) and low ICG dose (0.05 mg / kg). CONCLUSIONS: Overall, ICG-NIRF with mouthwash could perform in-vivo dental imaging in two NIR wavelengths at a short time and low ICG dose.

Machine-learning-assisted spontaneous Raman spectroscopy classification and feature extraction for the diagnosis of human laryngeal cancer.

The early detection of laryngeal cancer significantly increases the survival rates, permits more conservative larynx sparing treatments, and reduces healthcare costs. A non-invasive optical form of biopsy for laryngeal carcinoma can increase the early detection rate, allow for more accurate monitoring of its recurrence, and improve intraoperative margin control. In this study, we evaluated a Raman spectroscopy system for the rapid intraoperative detection of human laryngeal carcinoma. The spectral analysis methods included principal component analysis (PCA), random forest (RF), and one-dimensional (1D) convolutional neural network (CNN) methods. We measured the Raman spectra from 207 normal and 500 tumor sites collected from 10 human laryngeal cancer surgical specimens. Random Forest analysis yielded an overall accuracy of 90.5%, sensitivity of 88.2%, and specificity of 92.8% on average over 10 trials. The 1D CNN demonstrated the highest performance with an accuracy of 96.1%, sensitivity of 95.2%, and specificity of 96.9% on average over 50 trials. In predicting the first three principal components (PCs) of normal and tumor data, both RF and CNN demonstrated high performances, except for the tumor PC2. This is the first study in which CNN-assisted Raman spectroscopy was used to identify human laryngeal cancer tissue with extracted feature weights. The proposed Raman spectroscopy feature extraction approach has not been previously applied to human cancer diagnosis. Raman spectroscopy, as assisted by machine learning (ML) methods, has the potential to serve as an intraoperative, non-invasive tool for the rapid diagnosis of laryngeal cancer and margin detection.

Paediatric lung injury due to accidental ingestion of meperfluthrin: a case report.

BACKGROUND: It is common for children to accidentally ingest chemical drugs with different degrees of toxicity. Meperfluthrin is a highly effective and easy-to-use pyrethroid pesticide with low toxicity. It is widely used in electric mosquito coils. This type of electric mosquito coil is used in daily life, which increases the chance of exposure among children and, consequently, may lead to accidental ingestion. There are only few reports of meperfluthrin poisoning causing lung injury in children. We report a rare clinical case of lung injury wherein a child ingested meperfluthrin orally. CASE PRESENTATION: We report the case of a 1-year-old boy who accidentally swallowed an electric mosquito coil containing meperfluthrin and developed cough and fever. The patient's parents observed him swallowing the electric mosquito coil (Qiangshou®). Although he was stopped, the child had already swallowed approximately 10 ml of the liquid. According to the instructions, it contained 9 mg/ml of meperfluthrin, thus, it was assumed that he ingested meperfluthrin at a dose of approximately 90 mg. Computed tomography (CT) of his lungs showed uneven brightness in both lungs with multiple spots, scaly shadows, and mesh. Density of the shadows indicated lung parenchymal and interstitial lung disease. Lung tidal function tests indicated obstructive ventilation dysfunction. After evaluation and treatment, his cough drastically reduced, his fever disappeared, and his lung CT findings showed improvement. Therefore, accidental ingestion of meperfluthrin led to acute lung injury in a paediatric patient. Because of prompt treatment, his lung lesions recovered well. CONCLUSIONS: Meperfluthrin causes airway mucosal damage and hypersensitivity. Lung CT and lung tidal function measurements can be used to monitor changes in the condition. Presently, there is a lack of specific detoxification drugs for meperfluthrin poisoning. Thus, the focus of treatment is to protect the airway mucosa and reduce inflammatory reactions.

Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization.

Pancreatic cancer is the deadliest cancer type with a five-year survival rate of less than 9%. Detection of tumor margins plays an essential role in the success of surgical resection. However, histopathological assessment is time-consuming, expensive, and labor-intensive. We constructed a lab-designed, hand-held Raman spectroscopic system that could enable intraoperative tissue diagnosis using convolutional neural network (CNN) models to efficiently distinguish between cancerous and normal pancreatic tissue. To our best knowledge, this is the first reported effort to diagnose pancreatic cancer by CNN-aided spontaneous Raman scattering with a lab-developed system designed for intraoperative applications. Classification based on the original one-dimensional (1D) Raman, two-dimensional (2D) Raman images, and the first principal component (PC1) from the principal component analysis on the 2D image, could all achieve high performance: the testing sensitivity, specificity, and accuracy were over 95%, and the area under the curve approached 0.99. Although CNN models often show great success in classification, it has always been challenging to visualize the CNN features in these models, which has never been achieved in the Raman spectroscopy application in cancer diagnosis. By studying individual Raman regions and by extracting and visualizing CNN features from max-pooling layers, we identified critical Raman peaks that could aid in the classification of cancerous and noncancerous tissues. 2D Raman PC1 yielded more critical peaks for pancreatic cancer identification than that of 1D Raman, as the Raman intensity was amplified by 2D Raman PC1. To our best knowledge, the feature visualization was achieved for the first time in the field of CNN-aided spontaneous Raman spectroscopy for cancer diagnosis. Based on these CNN feature peaks and their frequency at specific wavenumbers, pancreatic cancerous tissue was found to contain more biochemical components related to the protein contents (particularly collagen), whereas normal pancreatic tissue was found to contain more lipids and nucleic acid (particularly deoxyribonucleic acid/ribonucleic acid). Overall, the CNN model in combination with Raman spectroscopy could serve as a useful tool for the extraction of key features that can help differentiate pancreatic cancer from a normal pancreas.

Spatial patterns of leaf δ13C and δ15N of aquatic macrophytes in the arid zone of northwestern China.

Analysis of stable isotope composition is an important tool in research on plant physiological ecology. However, large-scale patterns of leaf-stable isotopes for aquatic macrophytes have received considerably less attention. In this study, we examined the spatial pattern of stable isotopes of carbon (δ13C) and nitrogen (δ15N) of macrophytes leaves collected across the arid zone of northwestern China (approximately 2.4 × 106 km2) and attempted to illustrate its relationship with environmental factors (i.e., temperature, precipitation, potential evapotranspiration, sediment total carbon and nitrogen). Our results showed that the mean values of the leaf δ13C and δ15N in the macrophytes sampled from the arid zone were -24.49‰ and 6.82‰, respectively, which were far less depleted than those measured of terrestrial plants. The order of averaged leaf δ13C from different life forms was as follows: submerged > floating-leaved > emergent. Additionally, our studies indicated that the values of foliar δ13C values of all the aquatic macrophytes were only negatively associated with precipitation, but the foliar δ15N values were mainly associated with temperature, precipitation, and potential evapotranspiration. Therefore, we speculated that water-relation factors are the leaf δ13C determinant of macrophytes in the arid zone of northwestern China, and the main factors affecting leaf δ15N values are the complex combination of water and energy factors.

Heat Waves Alter Macrophyte-Derived Detrital Nutrients Release under Future Climate Warming Scenarios.

In addition to a rise in global air and water mean temperatures, extreme climate events such as heat waves are increasing in frequency, intensity, and duration in many regions of the globe. Developing a mechanistic understanding of the impacts of heat waves on key ecosystem processes and how they differ from just an increase in mean temperatures is therefore of utmost importance for adaptive management against effects of global change. However, little is known about the impact of extreme events on freshwater ecosystem processes, particularly the decomposition of macrophyte detritus. We performed a mesocosm experiment to evaluate the impact of warming and heat waves on macrophyte detrital decomposition, applied as a fixed increment (+4 °C) above ambient and a fluctuating treatment with similar energy input, ranging from 0 to 6 °C above ambient (i.e., simulating heat waves). We showed that both warming and heat waves significantly accelerate dry mass loss of the detritus and carbon (C) release but found no significant differences between the two heated treatments on the effects on detritus dry mass loss and C release amount. This suggests that moderate warming indirectly enhanced macrophyte detritus dry mass loss and C release mainly by the amount of energy input rather than by the way in which warming was provided (i.e., by a fixed increment or in heat waves). However, we found significantly different amounts of nitrogen (N) and phosphorus (P) released between the two warming treatments, and there was an asymmetric response of N and P release patterns to the two warming treatments, possibly due to species-specific responses of decomposers to short-term temperature fluctuations and litter quality. Our results conclude that future climate scenarios can significantly accelerate organic matter decomposition and C, N, and P release from decaying macrophytes, and more importantly, there are asymmetric alterations in macrophyte-derived detrital N and P release dynamic. Therefore, future climate change scenarios could lead to alterations in N/P ratios in the water column via macrophyte decomposition processes and ultimately affect the structure and function of aquatic ecosystems, especially in the plankton community.

Detection and analysis of enamel cracks by ICG-NIR fluorescence dental imaging.

Cracked teeth are the third most common cause of tooth loss, but there is no reliable imaging tool for the diagnosis of cracks. Here, we demonstrate the feasibility of indocyanine green near-infrared fluorescence (ICG-NIRF) dental imaging for the detection of enamel cracks and enamel-dentin cracks in vitro in the first (ICG-NIRF-I, 700-950 nm) and second (ICG-NIRF-II, 950-1700 nm) imaging windows with transmission excitation light, and compared ICG-NIRF with conventional NIR illumination-II (NIRi-II) and X-ray imaging. Dentin cracks were detected by CT scan, while most enamel cracks, undetectable under X-ray imaging, were clearly visible in NIR images. We found that ICG-NIRF-II detected cracks more effectively than NIRi-II, and that light orientation is an important factor for crack detection: an angled exposure obtained better image contrast of cracks than parallel exposure, as it created a shadow under the crack. Crack depth could be evaluated from the crack shadow in ICG-NIRF and NIRi-II images; from this shadow we could determine crack depth and discriminate enamel-dentin cracks from craze lines. Cracks could be observed clearly from ICG-NIRF images with 1-min ICG tooth immersion, although longer ICG immersion produced images with greater contrast. Overall, our data show that ICG-NIRF dental imaging is a useful tool for diagnosing cracked teeth at an early stage.

[Species richness and influencing factors of aquatic plant in the Kaidu River Basin, Xinjiang, China]. / æ–°ç–†å¼€éƒ½æ²³æµåŸŸæ°´ç”Ÿæ¤ç‰©å¤šæ ·æ€§åŠå ¶å½±å“å› ç´ .

River is a continuous, flowing, unique, and complete ecosystem. Studies on the aquatic macrophyte richness and its influencing factors were important for river ecology study. In this study, species richness, main aquatic plant community types and their quantitative characteristics, and the influencing factors of aquatic macrophyte in the Kaidu River Basin, Xinjiang, were examined by field investigation. We followed the water-energy hypothesis and habitat heterogeneity hypothesis to explain the geographical pattern of species diversity in the basin. The results showed that there were 71 species of aquatic plants belonging to 24 families and 39 genera in the Kaidu River Basin. The aquatic macrophyte communities could be divided into 10 main community types by cluster analysis, among which Phragmites australis association had the highest species richness, and Typha angustifolia association and Ceratophyllum demersum association had the lowest species richness. Shannon index of the aquatic macrophyte community of Kaidu River Basin was significantly negatively correlated with water pH. Simpson index was significantly negatively correlated with pH and longitude, and significantly positively correlated with altitude. The aquatic plant community types in the basin were mainly affected by altitude, water depth, and water temperature. The species diversity changed little in altitude and longtitude. The water-energy dynamic hypothesis and habitat heterogeneity hypothesis together explained 31.4% of the richness patterns of macrophytes, indicating low exploratory power.

Optimal imaging windows of indocyanine green-assisted near-infrared dental imaging with rat model and its comparison to X-ray imaging.

In this study, we used rat animal model to compare the efficiency of indocyanine green (ICG)-assisted dental near-infrared fluorescence imaging with X-ray imaging, and we optimized the imaging window for both unerupted and erupted molars. The results show that the morphology of the dental structures was observed clearly from ICG-assisted dental images (especially through the endoscope). A better image contrast was easily acquired at the short imaging windows (<10 minutes) for unerupted and erupted molars. For unerupted molars, there is another optimized imaging window (48-96 hours) with a prominent glow-in-the-dark effect: only the molars remain bright. This study also revealed that the laser ablation of dental follicles can disrupt the molar development, and our method is able to efficiently detect laser-treated molars and acquire the precise morphology. Thus, ICG-assisted dental imaging has the potential to be a safer and more efficient imaging modality for the real-time diagnosis of dental diseases.

Improvement of cell deposition by self-absorbent capability of freeze-dried 3D-bioprinted scaffolds derived from cellulose material-alginate hydrogels.

Cell-laden printing is the most commonly used approach in 3D bioprinting. One of the major drawbacks of cell-laden printing is that cell viability is highly affected by the extrusion pressure and shear force in the printing process. We present a new cell-deposition method by using the superabsorbent capability of 3D printed scaffolds with four ink formations: 20:10 nanocrystal/alginate (NCA 20/10), 20:10 nanofiber/alginate (NFA 20/10), 20:02 nanocrystal/alginate (NCA 20/02) and 20:02 nanofiber/alginate (NFA 20/02). Limited pores were observed from the surface of inherent NCA and NFA scaffolds, which may limit the numbers of cells to enter into the scaffolds. Therefore, we designed a dual-porous (DP) structure to connect the inherent pores (IPs) to the scaffold surface. Due to these porous structures, NCA and NFA scaffolds exhibit an excellent capability to absorb cell suspension, which may be used for depositing cells to 3D-printed scaffolds, namely self-absorbent (SA) deposition. Compared to the conventional top-loading (TL) method, the SA method had more uniform cell distributions in the entire 3D-printed scaffolds and higher efficiency of cell deposition. For the TL method, DP scaffold exhibited a more uniform cell distribution, which may provide a better microenvironment for the cells in comparison to the IP scaffold. For both cell loading methods, a rapid increase of cell number was observed in the first 4 days of culture in the 3D-printed NCA and NFA structures. NFA 20/02 exhibits the best cell viability compared to the other three inks. In conclusion, the SA method may serve as a new approach for loading cells in cell-free 3D-bioprinting, and DP design could improve the efficiency of the cell deposition.

Indocyanine-green-assisted near-infrared dental imaging - the feasibility of in vivo imaging and the optimization of imaging conditions.

X-ray-based imaging, including computed tomography, plays a crucial role in the diagnosis and surgery of impacted teeth that affects over 25% of the human population. But the greatest disadvantage of this technique is ionizing radiation risk to the patients. Here we describe a completely ionizing-radiation-free in vivo near-infrared (NIR) fluoresence dental imaging with indocyanine green (ICG) agent that has rarely been applied in dental imaging. Our method can acquire dental structure images within a short period (only 10 minutes after injection) without ionizing radiation risk. NIR enables the observation of dental structures that are not distinguishable under visible conditions. At prolonged 72 hours, only molar regions remained highlighted; the contrast between molar regions and surrounding tissues was prominent; this is particularly useful for in vivo dental imaging. Using the quantitative spectral analysis, we found the peak wavelengths of ICG fluorescence shifted along with the injection time: the peak wavelength shifted 8 nm (from 819 nm to 811 nm) in 0~72 hours. The injection methods of tail vein v.s. intradermal injections caused ~3 nm shift. ICG-assisted NIR fluorescence imaging can serve as a useful tool for in vivo real-time diagnosis in dental clinics and surgeries without ionizing radiation risk.