Dual Network Hydrogels Based on PRP and SA Promote the Retention Rate and Vascularization of Transplanted Fat.

BACKGROUND: Autologous adipose tissue often experiences ischemia and hypoxia after transplantation, leading to low retention rates and unstable operative impacts due to necrotic absorption. Platelet-rich plasma (PRP) can enhance fat regeneration and increase the fat retention rate after transplantation. However, the quick release of growth factors (GFs) in PRP decreases therapeutic efficiency. This study aimed to achieve a slow release of PRP to promote fat retention. METHODS: We prepared a dual-network hydrogel (DN gel) based on FDA-approved PRP and sodium alginate (SA) through a simple "one-step" activation process. In vivo study, adipose tissue with saline (control group), SA gel (SA gel group), PRP gel (PRP gel group), and DN gel (DN gel group) was injected subcutaneously into the dorsum of nude mice. At 4 and 12 weeks after injection, tissues were assessed for volume and weight. Hematoxylin and eosin staining (HE) and immunofluorescence staining were performed for histological assessment. RESULTS: DN gel exhibits long-lasting growth factor effects, surpassing conventional clinical PRP gel regarding vascularization potential. In fat transplantation experiments, DN gel demonstrated improved vascularization of transplanted fat and increased retention rates, showing promise for clinical applications. CONCLUSIONS: DN gel-assisted lipofilling can significantly improve the retention rate and quality of transplanted fat. DN gel-assisted lipofilling, which is considered convenient, is a promising technique to improve neovascularization and fat survival. NO LEVEL ASSIGNED: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Self-Healing, Laminated, and Low Resistance NH3 Sensor Based on 6,6',6″-(Nitrilotris(benzene-4,1-diyl))tris(5-phenylpyrazine-2,3-dicarbonitrile) Sensing Material Operating at Room Temperature.

With the rapid development of the concept of the Internet of Things (IoT), gas sensors with the function of simulating the human sense of smell became irreplaceable as a key element. Among them, ammonia (NH3) sensors played an important role in respiration tests, environmental monitoring, safety, and other fields. However, the fabrication of the high-performance device with high stability and resistance to mechanical damages was still a challenge. In this work, polyurethane (PU) with excellent self-healing ability was applied as the substrate, and the sensor was designed from new sensitive material design and device structure optimization, through applying the organic molecule with groups which could absorb NH3 and the laminated structure to shorten the electronic transmission path to achieve a low resistance state and favorable sensing properties. Accordingly, a room temperature flexible NH3 sensor based on 6,6',6″-(nitrilotris(benzene-4,1-diyl))tris(5-phenylpyrazine-2,3-dicarbonitrile) (TPA-3DCNPZ) was successfully developed. The device could self-heal by means of a thermal evaporation assisted method. It exhibited a detection limit of 1 ppm at 98% relative humidity (RH), as well as great stability, selectivity, bending flexibility, and self-healing properties. The improved NH3 sensing performance under high RH was further investigated by complex impedance plots (CIPs) and density functional theory (DFT), attributing to the enhanced adsorption of NH3. The TPA-3DCNPZ based NH3 sensors proved to have great potential for application on simulated exhaled breath to determine the severity of kidney diseases and the progress of treatment. This work also provided new ideas for the construction of high-performance room temperature NH3 sensors.

Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization.

Background: Cutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs' potential prognostic value in CM has not been identified. Methods: The RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The performance of this prognostic model was assessed by the receiver operating characteristic curves. A nomogram was constructed and verified by calibration. Furthermore, we also performed sub-group K-M analysis to explore the 5 lncRNAs' expression in different clinical stages. Function enrichment had been analyzed by GSEA and ssGSEA. In addition, qRT-PCR was performed to verify the five lncRNAs' expression level in CM cell line (A2058 and A375) and normal keratinocyte cell line (HaCaT). Results: We constructed a prognostic model based on five necroptosis-associated lncRNAs (AC245041.1, LINC00665, AC018553.1, LINC01871, and AC107464.3) and divided patients into high-risk group and low-risk group depending on risk scores. A predictive nomogram had been built to be a prognostic indicator to clinical factors. Functional enrichment analysis showed that immune functions had more relationship and immune checkpoints were more activated in low-risk group than that in high-risk group. Thus, the low-risk group would have a more sensitive response to immunotherapy. Conclusion: This risk score signature could be used to divide CM patients into low- and high-risk groups, and facilitate treatment strategy decision making that immunotherapy is more suitable for those in low-risk group, providing a new sight for CM prognostic evaluation.

Induction of Strong Long-Lived Room-Temperature Phosphorescence of N-Phenyl-2-naphthylamine Molecules by Confinement in a Crystalline Dibromobiphenyl Matrix.

The design and preparation of metal-free organic materials that exhibit room-temperature phosphorescence (RTP) is a very attractive topic owing to potential applications in organic optoelectronic devices. Herein, we present a facile approach to efficient and long-lived organic RTP involving the doping of N-phenylnaphthalen-2-amine (PNA) or its derivatives into a crystalline 4,4'-dibromobiphenyl (DBBP) matrix. The resulting materials showed strong and persistent RTP emission with a quantum efficiency of approximately 20 % and a lifetime of a few to more than 100 milliseconds. Bright white dual emission containing blue fluorescence and yellowish-green RTP from the PNA-doped DBBP crystals was also confirmed by Commission Internationale de l'Eclairage (CIE) coordinates of (x=0.29-0.31, y=0.38-0.41).