Injectable hydrogels activated with copper sulfide nanoparticles for enhancing spatiotemporal sterilization and osteogenesis in periodontal therapy.

Developing biomaterials capable of promoting bone regeneration in bacteria-infected sites is of utmost urgency for periodontal disease therapies. Here we produce a hybrid hydrogel by integrating CuS nanoparticles (CuSNPs), which could kill bacteria through photothermal therapy (PTT) triggered by a near infrared (NIR) light, and a gelatin methacryloyl (GelMA) hydrogel, which is injectable and biocompatible. Specifically, CuSNPs were precipitated by chitosan (CS) firstly, then grafted with methacrylic anhydride (MA) to form CuSNP@CS-MA, which was photo-crosslinked with GelMA to synthesize hybrid hydrogels (GelMA/CuSNP). The hybrid hydrogels exhibited a broad-spectrum antibacterial property that could be spatiotemprorally manipulated through applying a NIR light. Their mechanical properties were adjustable by controlling the concentration of CuSNPs, enabling the hydrogels to become more adapted to the oral diseases. Meanwhile, the hybrid hydrogels showed good cytocompatibility in vitro and improved hemostasis in vivo. Moreover, they accelerated alveolar osteogenesis and vascular genesis, successfully treating periodontis in four weeks in a rat model. GelMA/CuSNP hydrogels showed a broad-spectrum sterilization ability via PTT in vitro and outstanding antibacterial property in vivo, suggesting that the hybrid hydrogels could function in the challenging, bacteria-rich, oral environment. Such injectable hybrid hydrogels, capable of achieving both facilitated osteogenesis and NIR-inducible sterilization, represent a new biomaterial for treating periodontitis.

Real-time and quantitative protein detection via polyacrylamide gel electrophoresis and online intrinsic fluorescence imaging.

The detection of intrinsic protein fluorescence is a powerful tool for studying proteins in their native state. Thanks to its label-free and stain-free feature, intrinsic fluorescence detection has been introduced to polyacrylamide gel electrophoresis (PAGE), a fundamental and ubiquitous protein analysis technique, to avoid the tedious detection process. However, the reported methods of intrinsic fluorescence detection were incompatible with online PAGE detection or standard slab gel. Here, we fulfilled online intrinsic fluorescence imaging (IFI) of the standard slab gel to develop a PAGE-IFI method for real-time and quantitative protein detection. To do so, we comprehensively investigated the arrangement of the deep-UV light source to obtain a large imaging area compatible with the standard slab gel, and then designed a semi-open gel electrophoresis apparatus (GEA) to scaffold the gel for the online UV irradiation and IFI with low background noise. Thus, we achieved real-time monitoring of the protein migration, which enabled us to determine the optimal endpoint of PAGE run to improve the sensitivity of IFI. Moreover, online IFI circumvented the broadening of protein bands to enhance the separation resolution. Because of the low background noise and the optimized endpoint, we showcased the quantitative detection of bovine serum albumin (BSA) with a limit of detection (LOD) of 20 ng. The standard slab gel provided a high sample loading volume that allowed us to attain a wide linear range of 0.03-10 µg. These results indicate that the PAGE-IFI method can be a promising alternative to conventional PAGE and can be widely used in molecular biology labs.

Marker-Free Isoelectric Focusing Patterns for Identification of Meat Samples via Deep Learning.

Isoelectric focusing (IEF) is a powerful tool for resolving complex protein samples, which generates IEF patterns consisting of multiplex analyte bands. However, the interpretation of IEF patterns requires the careful selection of isoelectric point (pI) markers for profiling the pH gradient and a trivial process of pI labeling, resulting in low IEF efficiency. Here, we for the first time proposed a marker-free IEF method for the efficient and accurate classification of IEF patterns by using a convolutional neural network (CNN) model. To verify our method, we identified 21 meat samples whose IEF patterns comprised different bands of meat hemoglobin, myoglobin, and their oxygen-binding variants but no pI marker. Thanks to the high throughput and short assay time of the microstrip IEF, we efficiently collected 1449 IEF patterns to construct the data set for model training. Despite the absence of pI markers, we experimentally introduced the severe pH gradient drift into 189 IEF patterns in the data set, thereby omitting the need for profiling the pH gradient. To enhance the model robustness, we further employed data augmentation during the model training to mimic pH gradient drift. With the advantages of simple preprocessing, a rapid inference of 50 ms, and a high accuracy of 97.1%, the CNN model outperformed the traditional algorithm for simultaneously identifying meat species and cuts of meat of 105 IEF patterns, suggesting its great potential of being combined with microstrip IEF for large-scale IEF analyses of complicated protein samples.

A handheld contactless conductivity detector for monitoring the desalting of low-volume virus and cell samples.

Desalting of biosamples is crucial for analytical techniques intolerant to abundant salts. However, there is no simple tool to monitor the desalting of low-volume biosamples so far. Here we developed a handheld capacitively coupled contactless conductivity detector (hC4D) as a miniaturized device to measure the conductivity of 75 µL biosamples. Polyether-ether-ketone (PEEK) tubing was selected as the sample reservoir for sample loading via a pipette. Another pipetting of air pushed the sample solution out of the tubing to recollect the sample. Owing to the low sample consumption and easy sample recollection, hC4D is advantageous for testing expensive biosamples, such as viruses and cells. In addition, the whole process of sample injection, conductivity measurement, recollection, and calibration of conductivity can be completed within 1 min. To verify the feasibility of hC4D, we monitored the desalting progress of gel filtration (GF) of 200 µL blood samples, ultrafiltration (UF) of 300 µL virus samples, and dialysis of 7 mL cell samples. Three rounds of GF and UF completely removed the salts but led to poor sample recovery. In contrast, low concentrations of residual salts remained and better recovery was achieved after two rounds of GF and UF. We further utilized the hC4D to monitor the dialysis and tuned the salt concentration in the cell sample, such that we maintained the viability of cells in a low conductivity environment. These results indicated that hC4D is a promising tool for optimizing the desalting procedure of low-volume biosamples.