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Keratins are a class of intermediate filament proteins that can be obtained from numerous sources including human hair. Materials fabricated from keratins offer desirable characteristics as scaffolds for tissue engineering, including intrinsic cell adhesion sequences and tunable degradation kinetics. The capacity to create 3D printed constructs from keratin-based bio-inks generates unique opportunities for spatial control of scaffold physicochemical properties to direct scaffold functions in ways not readily achieved through other means. The aim of this study was to leverage the controllable rheological properties of keratin hydrogels to create a strategy for extrusion 3D printing of keratin bio-inks without the use of exogenous rheological modifiers, crosslinking agents, or photocurable resins. The rheological properties of keratin hydrogels were tuned by varying two parameters: (a) the ratio of keratose (obtained by oxidative extraction of keratin) to kerateine (obtained by reductive extraction of keratin); and (b) the weight percentage of total keratin protein in the gel. A computational model of the dispensing nozzle for a commercially available extrusion 3D printer was developed to calculate the needed pneumatic printing pressures based on the known rheological properties of the gels. Keratin hydrogel constructs, of varying keratose/kerateine ratios and total keratin weight percentages, were 3D printed in cylindrical geometries via extrusion 3D printing. Rheology and degradation studies showed that gels with greater relative kerateine content exhibited greater flow resistance and slower degradation kinetics when submerged in phosphate buffered saline solution at 37 °C, owing to the presence of cysteine residues in kerateine and the capability of forming disulfide bonds. Total keratin weight percentage was found to influence gel yield stress, with possible implications for tuning filament fidelity. Findings from this work support the use of keratose/kerateine ratio and total keratin weight percentage as handles for modulating rheological characteristics of keratin hydrogels to enhance printability and control scaffold properties., (© 2022 IOP Publishing Ltd.)

Objective . White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings. Approach . We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding. Main results . The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only. Significance . Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications., (© 2021 IOP Publishing Ltd.)

Objective: Hand movement is a crucial function for humans' daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI., Approach: Stereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination r 2 . According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding., Main Results: We find that a majority(L: [Formula: see text] 60%, R: [Formula: see text] 40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used., Significance: This is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combining PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.

Objective: The purpose of this study is to judge whether this combination method of multispectral image and convolutional neural network (CNN) method can be used to distinguish amnestic mild cognitive impairment (aMCI) with Type 2 diabetes mellitus (T2DM) and normal controls (NC) with T2DM effectively., Approach: In this study, the authors first combined EEG signals from aMCI patients with T2DM and NC with T2DM on five different frequency bands, including Theta, Alpha1, Alpha2, Beta1, and Beta2. Then, the authors converted these time series into a series of multispectral images. Finally, the images data were classified with the CNN method., Main Results: The classification effects of up to 89%, 91%, and 92% are obtained on the three combinations of frequency bands: Theta, Alpha1, and Alpha2; Alpha1, Alpha2, and Beta1; and Alpha2, Beta1, and Beta2. The spatial properties of EEG signals are highlighted, and its classification performance is found to be better than all the previous methods in the field of aMCI and T2DM diagnosis. The combination of multispectral images and CNN can be used as an effective biomarker for distinguishing the EEG signals in patients with aMCI and T2DM and in patients with NC with T2DM., Significance: The combined approach used in this paper provides a new perspective for the analysis of EEG signals in patients with aMCI and T2DM.

The aim of this study was to evaluate the in vitro osteogenic effects and in vivo new bone formation of three-dimensional (3D) printed alendronate (Aln)-releasing poly(caprolactone) (PCL) (Aln/PCL) scaffolds in rat tibial defect models. 3D printed Aln/PCL scaffolds were fabricated via layer-by-layer deposition. The fabricated Aln/PCL scaffolds had high porosity and an interconnected pore structure and showed sustained Aln release. In vitro studies showed that MG-63 cells seeded on the Aln/PCL scaffolds displayed increased alkaline phosphatase (ALP) activity and calcium content in a dose-dependent manner when compared with cell cultures in PCL scaffolds. In addition, in vivo animal studies and histologic evaluation showed that Aln/PCL scaffolds implanted in a rat tibial defect model markedly increased new bone formation and mineralized bone tissues in a dose-dependent manner compared to PCL-only scaffolds. Our results show that 3D printed Aln/PCL scaffolds are promising templates for bone tissue engineering applications.

Silver nanoparticles (Ag NPs) were synthesized rapidly in one pot via the Tollens reaction, in which quaternized chitosan (QCS) and rectorite (REC) acted as the reducing and stabilizing agent, while other chemical reducing and stabilizing agents and the surfactant were not included. X-ray diffraction, scanning electron microscopy and transmission electron microscopy results showed that spherical Ag NPs with uniform sizes were obtained, the layers of clay were peeled and thus exfoliated QCS/REC/Ag NP (QCRAg) nanocomposite was achieved. Moreover, Ag NPs dispersed well in the exfoliated nanocomposite matrix, some Ag NPs even entered into the interlayer of REC. QCRAg nanocomposites showed strong antimicrobial activity; the lowest minimum inhibitory concentration against Staphyloccocus aureus was only 0.0001% (w/v). The study reveals that the obtained QCRAg nanocomposites have great potential for biomedical applications.