Institute of Fundamental Technological Research

Hydrogels with multifunctional properties activated at specific times have gained significant attention in the biomedical field. As bacterial infections can cause severe complications that negatively impact wound repair, herein, we present the development of a stimuli-responsive, injectable, and in situ-forming hydrogel with antibacterial, self-healing, and drug-delivery properties. In this study, we prepared a Pluronic F-127 (PF127) and sodium alginate (SA)-based hydrogel that can be targeted to a specific tissue via injection. The PF127/SA hydrogel was incorporated with polymeric short-filaments (SFs) containing an anti-inflammatory drug – ketoprofen, and stimuli-responsive polydopamine (PDA) particles. The hydrogel, after injection, could be in situ gelated at the body temperature, showing great in vitro stability and self-healing ability after 4 h of incubation. The SFs and PDA improved the hydrogel injectability and compressive strength. The introduction of PDA significantly accelerated the KET release under near-infrared light exposure and extended its release validity period. The excellent composites’ photo-thermal performance led to antibacterial activity against representative Gram-positive and Gram-negative bacteria, resulting in 99.9% E. coli and S. aureus eradication after 10 min of NIR light irradiation. In vitro, fibroblast L929 cell studies confirmed the materials’ biocompatibility and paved the way toward further in vivo and clinical application of the system for chronic wound treatments.

In situ forming injectable hydrogels hold great potential for the treatment of irregular wounds. However, their practical applications were hindered by long gelation time, poor mechanical performance, and a lack of a natural extracellular matrix structure. Herein, amino-modified electrospun poly(lactic-co-glycolic acid) (APLGA) short fibers with uniform distribution were introduced into gelatin methacrylate/oxidized dextran (GM/ODex) hydrogels. In comparison with the fiber aggregation structure in the PLGA fiber-incorporated hydrogels, the hydrogels with APLGA fibers possessed a uniform porous structure. The highly dispersed APLGA short fibers accelerated the sol–gel phase transition of the hydrogel due to the formation of dynamic Schiff-base bonds between the fibers and hydrogels. Furthermore, in combination with UV-assisted crosslinking, a rapid gelation time of 90 s was achieved for the double-crosslinked hydrogels. The addition of APLGA short fibers as fillers and the formation of the double-crosslinking network enhanced the mechanical performance of the hydrogels. Furthermore, the fiber–hydrogel composites exhibited favorable injectability, excellent biocompatibility, and improved cell infiltration. In vivo assessment indicated that the GM/ODex-APLGA hydrogels successfully filled the full-thickness defects and improved wound healing. This work demonstrates a promising solution for the treatment of irregular wounds.

Purpose: To develop a deep learning-based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1ρ, and T2* parameters, which can be used to assess knee osteoarthritis (OA). Methods: Whole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1ρ-weighted MR images. Transfer learning was applied to develop 2D attention U-Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1ρ, T2* relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. Results: The models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists' manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1ρ, and T2* relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter-observer variability of 2 radiologists. Conclusion: The proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.

deep learning, menisci, osteoarthritis, quantitative MR, segmentation

Objectives: To assess the feasibility of using ultrasound (US) image features related to the median nerve echogenicity and shape for carpal tunnel syndrome (CTS) diagnosis. Methods: In 31 participants (21 healthy participants and 10 patients with CTS), US images were collected with a 30-MHz transducer from median nerves at the wrist crease in 2 configurations: a neutral position and with wrist extension. Various morphologic features, including the cross-sectional area (CSA), were calculated to assess the nerve shape. Carpal tunnel syndrome commonly results in loss of visualization of the nerve fascicular pattern on US images. To assess this phenomenon, we developed a nerve-tissue contrast index (NTI) method. The NTI is a ratio of average brightness levels of surrounding tissue and the median nerve, both calculated on the basis of a US image. The area under the curve (AUC) from a receiver operating characteristic curve analysis and t test were used to assess the usefulness of the features for differentiation of patients with CTS from control participants. Results: We obtained significant differences in the CSA and NTI parameters between the patients with CTS and control participants (P < .01), with the corresponding highest AUC values equal to 0.885 and 0.938, respectively. For the remaining investigated morphologic features, the AUC values were less than 0.685, and the differences in means between the patients and control participants were not statistically significant (P > .10). The wrist configuration had no impact on differences in average parameter values (P > .09). Conclusions: Patients with CTS can be differentiated from healthy individuals on the basis of the median nerve CSA and echogenicity. Carpal tunnel syndrome is not manifested in a change of the median nerve shape that could be related to circularity or contour variability.

carpal tunnel syndrome, cross-sectional area, echogenicity, median nerve, morphologic features, ultrasound

Purpose: Quantitative imaging methods could improve diagnosis of rotator cuff degeneration, but the capability of quantitative MR and US imaging parameters to detect alterations in collagen is unknown. The goal of this study was to assess quantitative MR and US imaging measures for detecting abnormalities in collagen using an in vitro model of tendinosis with biochemical and histological correlation. Method: 36 pieces of supraspinatus tendons from 6 cadaveric donors were equally distributed into 3 groups (2 subjected to different concentrations of collagenase and a control group). Ultrashort echo time MR and US imaging measures were performed to assess changes at baseline and after 24 h of enzymatic digestion. Biochemical and histological measures, including brightfield, fluorescence, and polarized microscopy, were used to verify the validity of the model and were compared with quantitative imaging parameters. Correlations between the imaging parameters and biochemically measured digestion were analyzed. Results: Among the imaging parameters, macromolecular fraction (MMF), adiabatic T1p, T2*, and backscatter coefficient (BSC) were useful in differentiating between the extent of degeneration among the 3 groups. MMF strongly correlated with collagen loss (r=-0.81; 95% confidence interval [CI]: -0.90,-0.66), while the adiabatic T1p (r = 0.66; CI: 0.42,0.81), T2* (r = 0.58; CI: 0.31,0.76), and BSC (r = 0.51; CI: 0.22,0.72) moderately correlated with collagen loss. Conclusions: MMF, adiabatic T1p, and T2* measured and US BSC can detect alterations in collagen. Of the quantitative MR and US imaging measures evaluated, MMF showed the highest correlation with collagen loss and can be used to assess rotator cuff degeneration.

rotator cuff tendon, tendinopathy, quantitative MRI, UTE, quantitative ultrasound

We investigate the usefulness of quantitative ultrasound and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30-MHz probe. Next, the nerves were extracted to prepare histology sections. Eighty-five fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin and ultrasound data to calculate the backscatter coefficient (–24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 db/cm-MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83), as well as B-mode texture features obtained via the gray-level co-occurrence matrix algorithm. Significant Spearman rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R = –0.68), entropy (R = –0.51) and several texture features. Our study indicates that quantitative ultrasound may potentially provide information on structural components of nerve fascicles.

nerve, quantitative ultrasound, high frequency, histology, pattern recognition, texture analysis

We investigated the use of high-frequency quantitative ultrasound (QUS) and B-mode texture features to characterize ulnar and median nerve fascicles using a clinical scanner (Vevo MD) and a 30-MHz center-frequency probe. US correlation with histology was first investigated in the ulnar nerve in situ in cadaveric specimens. 85 fascicles were matched in B-mode images and the histology sections. Collagen and myelin concentrations were quantified from trichrome labeling, and backscatter coefficient (-24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 dB/cm MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83) were calculated from ultrasound data. B-mode texture features were obtained via the gray-level co-occurrence matrix algorithm. Combined collagen and myelin concentration were significantly correlated with the backscatter coefficient (R = -0.68), entropy (R = -0.51), and several texture features. For the median nerve, we measured backscatter and morphology in 10 patients with carpal tunnel syndrome and 21 healthy volunteers. Significant differences (<0.01) between patients and controls and AUC 0.89–0.94 for QUS biomarkers were observed. Our study indicates that QUS may potentially provide useful information on structural components of even very small nerves (2 × 4 mm) and fascicles for diagnosing and monitoring injury, and surgical planning.