The Unprecedented Biodegradable Polyzwitterion: A Removal-Free Patch for Accelerating Infected Diabetic Wound Healing.

Zwitterionic polymers have emerged as an important class of biomaterials to construct wound dressings and antifouling coatings over the past decade due to their excellent hydrophilicity. However, all the reported zwitterionic polymers as wound dressings are nondegradable because of noncleavable carbon─carbon bonding backbones, and must be removed periodically after treatment to avoid hypoxia in the wound, thus leading to potential secondary injury. In this work, a biodegradable polyzwitterion patch is fabricated for the first time by ring-opening polymerization of carboxybetaine dithiolane (CBDS), which is self-crosslinked via inter-amide hydrogen bonds and zwitterionic dipole-dipole interactions on the side chains. The unprecedented polyCBDS (PCBDS) patch demonstrates enough ductility owing to the intermolecular physical interactions to fully cover irregular wounds, also showing excellent biodegradability and antifouling performance resulted from the existence of disulfide bonds and carboxybetaine groups. Besides, the PCBDS degradation-induced released CBDS owns potent antioxidant and antibacterial activities. This PCBDS patch is used as a diabetic wound dressing, inhibiting bacterial adhesion on the external surface, and its degradation products can exactly kill bacteria and scavenge excessive reactive oxygen species (ROS) at the wound site to regulate local microenvironment, including regulation of cytokine express and macrophage polarization, accelerating infected diabetic wound repair, and also avoiding the potential secondary injury.

Potential Linkage between Heavy Metal Pollution Risk Assessment and Dissolved Organic Matter Spectra in the WWTPs-River Integrated Area-Case Study from Ashi River.

Direct sewage discharge can cause severe damage to the water environment of the river. However, the impacts of dissolved organic matter (DOM) in the discharge on the original pattern of DOM and the distribution of heavy metals (HMs) in the river are little known. How to monitor such areas in a long-term and systematic manner also needs to be urgently addressed. In this paper, we characterized the DOM of the sediments in the WWTPs (wastewater treatment plants)-river integrated zone by ultraviolet-visible absorption spectroscopy (UV-vis), three-dimensional excitation-emission matrix (3D-EEM) combined with parallel factor (PARAFAC) method. The effects of WWTP on receiving waters were investigated, and the potential link between DOM and HM pollution was explored. Hg (Igeo: 3.94 ± 0.65; EF: 44.83 ± 31.11), Cd (Igeo: 1.81 ± 0.69; EF: 8.02 ± 2.97), Cu (Igeo: 1.61 ± 0.83; EF: 6.85 ± 2.37), Zn (Igeo: 1.55 ± 0.54; EF: 7.24 ± 3.58), and Ni (Igeo: 1.46 ± 0.56; EF: 6.12 ± 1.99) in rivers were the primary risk sources of HM. The combined pollution risk indicates that the WWTPs-river integrated area is in a high pollution risk state. Moreover, α(254) has a significant correlation with pollution indicators and can be used as a proxy indicator. These results help to understand better the impact of WWTPs on receiving water bodies and the potential connection between DOM and HM pollution and provide new ideas for monitoring the water environment in highly polluted areas.

Potential linkage between WWTPs-river-integrated area pollution risk assessment and dissolved organic matter spectral index.

The direct discharge of wastewater can cause severe damage to the water environment of the surface water. However, the influence of dissolved organic matter (DOM) present in wastewater on the allocation of DOM, nitrogen (N), and phosphorus (P) in rivers remains largely unexplored. Addressing the urgent need to monitor areas affected by direct wastewater discharge in a long-term and systematic manner is crucial. In this paper, the DOM of overlying water and sediment in the WWTPs-river-integrated area was characterized by ultraviolet-visible absorption spectroscopy (UV-vis), three-dimensional excitation-emission matrix combined with parallel factor (PARAFAC) method. The effects of WWTPs on receiving waters were investigated, and the potential link between DOM and N, P pollution was explored. The pollution risk was fitted and predicted using a spectral index. The results indicate that the improved water quality index (IWQI) is more suitable for the WWTPs-river integration zone. The DOM fraction in this region is dominated by humic-like matter, which is mainly influenced by WWTPs drainage as well as microbial activities. The DOM fractions in sediment and overlying water were extremely similar, but fluorescence intensity possessed more significant spatial differences. The increase in humic-like matter facilitates the production and preservation of P and also inhibits nitrification, thus affecting the N cycle. There is a significant correlation between DOM fraction, fluorescence index, and N, P. Fluorescence index (FI) fitting of overlying water DOM predicted IWQI and trophic level index, and a(254) fitting of sediment DOM predicted nitrogen and phosphorus pollution risk (FF) with good results. These results contribute to a better understanding of the impact of WWTPs on receiving waters and the potential link between DOM and N and P pollution and provide new ideas for monitoring the water environment in highly polluted areas.

Emotion Recognition of Subjects With Hearing Impairment Based on Fusion of Facial Expression and EEG Topographic Map.

Emotion analysis has been employed in many fields such as human-computer interaction, rehabilitation, and neuroscience. But most emotion analysis methods mainly focus on healthy controls or depression patients. This paper aims to classify the emotional expressions in individuals with hearing impairment based on EEG signals and facial expressions. Two kinds of signals were collected simultaneously when the subjects watched affective video clips, and we labeled the video clips with discrete emotional states (fear, happiness, calmness, and sadness). We extracted the differential entropy (DE) features based on EEG signals and converted DE features into EEG topographic maps (ETM). Next, the ETM and facial expressions were fused by the multichannel fusion method. Finally, a deep learning classifier CBAM_ResNet34 combined Residual Network (ResNet) and Convolutional Block Attention Module (CBAM) was used for subject-dependent emotion classification. The results show that the average classification accuracy of four emotions recognition after multimodal fusion achieves 78.32%, which is higher than 67.90% for facial expressions and 69.43% for EEG signals. Moreover, visualization by the Gradient-weighted Class Activation Mapping (Grad-CAM) of ETM showed that the prefrontal, temporal and occipital lobes were the brain regions closely related to emotional changes in individuals with hearing impairment.