A Biomimetic Adhesive and Robust Janus Patch with Anti-Oxidative, Anti-Inflammatory, and Anti-Bacterial Activities for Tendon Repair.

Early stage oxidative stress, inflammatory response, and infection after tendon surgery are highly associated with the subsequent peritendinous adhesion formation, which may diminish the quality and function of the repaired tendon. Although various anti-inflammatory and/or antibacterial grafts have been proposed to turn the scale, most of them suffer from the uncertainty of drug-induced adverse effects, low mechanical strength, and tissue adhesiveness. Here, inspired by the tendon anatomy and pathophysiology of adhesion development, an adhesive and robust dual-layer Janus patch is developed, whose inner layer facing the operated tendon is a multifunctional electrospun hydrogel patch (MEHP), encircled further by a poly-l-lactic acid (PLLA) fibrous outer layer facing the surrounding tissue. Specifically, MEHP is prepared by gelatin methacryloyl (GelMA) and zinc oxide (ZnO) nanoparticles, which are co-electrospun first and then treated by tannic acid (TA). The inner MEHP exhibits superior mechanical performance, adhesion strength, and outstanding antioxidation, anti-inflammation, and antibacterial properties, and it can adhere to the injury site offering a favorable microenvironment for tendon regeneration. Meanwhile, the outer PLLA acts as a physical barrier that prevents extrinsic cells and tissues from invading the defect site, reducing peritendinous adhesion formation. This work presents a proof-of-concept of a drug-free graft with anisotropic adhesive and biological functions to concert the healing phases of injured tendon by alleviating incipient inflammation and oxidative damage but supporting tissue regeneration and reducing tendon adhesion in the later phase of repair and remodeling. It is envisioned that this Janus patch could offer a promising strategy for safe and efficient tendon therapy.

Sonoelastography for Testicular Tumor Identification: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.

The objective of this review was to summarize the applications of sonoelastography in testicular tumor identification and inquire about their test performances. Two authors independently searched English journal articles and full conference papers from CINAHL, Embase, IEEE Xplore®, PubMed, Scopus, and Web of Science from inception and organized them into a PIRO (patient, index test, reference test, outcome) framework. Eleven studies (n = 11) were eligible for data synthesis, nine of which (n = 9) utilized strain elastography and two (n = 2) employed shear-wave elastography. Meta-analyses were performed on the distinction between neoplasm (tumor) and non-neoplasm (non-tumor) from four study arms and between malignancy and benignity from seven study arms. The pooled sensitivity of classifying malignancy and benignity was 86.0% (95%CI, 79.7% to 90.6%). There was substantial heterogeneity in the classification of neoplasm and non-neoplasm and in the specificity of classifying malignancy and benignity, which could not be addressed by the subgroup analysis of sonoelastography techniques. Heterogeneity might be associated with the high risk of bias and applicability concern, including a wide spectrum of testicular pathologies and verification bias in the reference tests. Key technical obstacles in the index test were manual compression in strain elastography, qualitative observation of non-standardized color codes, and locating the Regions of Interest (ROI), in addition to decisions in feature extractions. Future research may focus on multiparametric sonoelastography using deep learning models and ensemble learning. A decision model on the benefits-risks of surgical exploration (reference test) could also be developed to direct the test-and-treat strategy for testicular tumors.

A Comprehensive Assessment Protocol for Swallowing (CAPS): Paving the Way towards Computer-Aided Dysphagia Screening.

Dysphagia is one of the most common problems among older adults, which might lead to aspiration pneumonia and eventual death. It calls for a feasible, reliable, and standardized screening or assessment method to prompt rehabilitation measures and mitigate the risks of dysphagia complications. Computer-aided screening using wearable technology could be the solution to the problem but is not clinically applicable because of the heterogeneity of assessment protocols. The aim of this paper is to formulate and unify a swallowing assessment protocol, named the Comprehensive Assessment Protocol for Swallowing (CAPS), by integrating existing protocols and standards. The protocol consists of two phases: the pre-test phase and the assessment phase. The pre-testing phase involves applying different texture or thickness levels of food/liquid and determining the required bolus volume for the subsequent assessment. The assessment phase involves dry (saliva) swallowing, wet swallowing of different food/liquid consistencies, and non-swallowing (e.g., yawning, coughing, speaking, etc.). The protocol is designed to train the swallowing/non-swallowing event classification that facilitates future long-term continuous monitoring and paves the way towards continuous dysphagia screening.

Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review.

Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.

Forefoot Function after Hallux Valgus Surgery: A Systematic Review and Meta-Analysis on Plantar Load Measurement.

While hallux valgus (HV) surgeries are useful for correcting skeletal alignment problems, their effects on plantar load, which reflects forefoot functions, are less understood. The objective of this study is to conduct a systematic review and meta-analysis on the plantar load change after HV surgeries. A systematic search of Web of Science, Scopus, PubMed, CENTRAL, EMBASE, and CINAHL was performed. Studies that assessed the pre- and post-operative plantar pressure of HV patients undergoing surgeries and reported load-related parameters over the hallux, medial metatarsal, and/or central metatarsal regions were included. Studies were appraised by using the modified NIH quality assessment tool for before-after study. Studies suitable for meta-analysis were pooled with the random-effects model, using the standardized mean difference of the before-after parameters as an effect measure. Twenty-six studies containing 857 HV patients and 973 feet were included for the systematic review. Meta-analysis was conducted on 20 of them, and most studies did not favor HV surgeries. Overall, HV surgeries reduced the plantar load over the hallux region (SMD -0.71, 95% CI, -1.15 to -0.26), indicating that forefoot function worsened after surgeries. For the other five outcomes, the overall estimates were not statistically significant, indicating that surgeries did not improve them either. There was substantial heterogeneity among the studies, which in most cases could not be resolved by pre-planned subgroup analyses by surgical classification, year of publication, median age of patients, and length of follow-up. Sensitivity analysis removing lower-quality studies showed that the load integrals (impulse) over the central metatarsal region significantly increased (SMD 0.27, 95% CI, 0 to 0.53), indicating that surgeries increased the risk of transfer metatarsalgia. There is no solid evidence that HV surgeries could improve forefoot functions from a biomechanical point perspective. Currently available evidence even suggests that surgeries might reduce the plantar load over the hallux and adversely affect push-off function. The reasons behind and the effectiveness of alternative surgical methods warrant further investigation.

A Multilayered Mesoporous Gold Nanoarchitecture for Ultraeffective Near-Infrared Light-Controlled Chemo/Photothermal Therapy for Cancer Guided by SERS Imaging.

Surface-enhanced Raman scattering (SERS) imaging has emerged as a promising tool for guided cancer diagnosis and synergistic therapies, such as combined chemotherapy and photothermal therapy (chemo-PTT). Yet, existing therapeutic agents often suffer from low SERS sensitivity, insufficient photothermal conversion, or/and limited drug loading capacity. Herein, a multifunctional theragnostic nanoplatform consisting of mesoporous silica-coated gold nanostar with a cyclic Arg-Gly-Asp (RGD)-coated gold nanocluster shell (named RGD-pAS@AuNC) is reported that exhibits multiple "hot spots" for pronouncedly enhanced SERS signals and improved near-infrared (NIR)-induced photothermal conversion efficiency (85.5%), with a large capacity for high doxorubicin (DOX) loading efficiency (34.1%, named RGD/DOX-pAS@AuNC) and effective NIR-triggered DOX release. This nanoplatform shows excellent performance in xenograft tumor model of HeLa cell targeting, negligible cytotoxicity, and good stability both in vitro and in vivo. By SERS imaging, the optimal temporal distribution of injected RGD/DOX-pAS@AuNCs at the tumor site is identified for NIR-triggered local chemo-PTT toward the tumor, achieving ultraeffective therapy in tumor cells and tumor-bearing mouse model with 5 min of NIR irradiation (0.5 W cm-2 ). This work offers a promising approach to employing SERS imaging for effective noninvasive tumor treatment by on-site triggered chemo-PTT.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

A reliability and validity study for Scolioscan: a radiation-free scoliosis assessment system using 3D ultrasound imaging.

BACKGROUND: Radiographic evaluation for patients with scoliosis using Cobb method is the current gold standard, but radiography has radiation hazards. Several groups have recently demonstrated the feasibility of using 3D ultrasound for the evaluation of scoliosis. Ultrasound imaging is radiation-free, comparatively more accessible, and inexpensive. However, a reliable and valid 3D ultrasound system ready for clinical scoliosis assessment has not yet been reported. Scolioscan is a newly developed system targeted for scoliosis assessment in clinics by using coronal images of spine generated by a 3D ultrasound volume projection imaging method. The aim of this study is to test the reliability of spine deformity measurement of Scolioscan and its validity compared to the gold standard Cobb angle measurements from radiography in adolescent idiopathic scoliosis (AIS) patients. METHODS: Prospective study divided into two stages: 1) Investigation of intra- and inter- reliability between two operators for acquiring images using Scolioscan and among three raters for measuring spinal curves from those images; 2) Correlation between the Cobb angle obtained from radiography by a medical doctor and the spine curve angle obtained using Scolioscan (Scolioscan angle). The raters for ultrasound images and the doctors for evaluating radiographic images were mutually blinded. The two stages of tests involved 20 (80 % females, total of 26 angles, age of 16.4 ± 2.7 years, and Cobb angle of 27.6 ± 11.8°) and 49 (69 % female, 73 angles, 15.8 ± 2.7 years and 24.8 ± 9.7°) AIS patients, respectively. Intra-class correlation coefficients (ICC) and Bland-Altman plots and root-mean-square differences (RMS) were employed to determine correlations, which interpreted based on defined criteria. RESULTS: We demonstrated a very good intra-rater and intra-operator reliability for Scolioscan angle measurement with ICC larger than 0.94 and 0.88, respectively. Very good inter-rater and inter-operator reliability was also demonstrated, with both ICC larger than 0.87. For the thoracic deformity measurement, the RMS were 2.5 and 3.3° in the intra- and inter-operator tests, and 1.5 and 3.6° in the intra- and inter-rater tests, respectively. The RMS differences were 3.1, 3.1, 1.6, 3.7° in the intra- and inter-operator and intra- and inter-rater tests, respectively, for the lumbar angle measurement. Moderate to strong correlations (R(2) > 0.72) were observed between the Scolioscan angles and Cobb angles for both the thoracic and lumbar regions. It was noted that the Scolioscan angle slightly underestimated the spinal deformity in comparison with Cobb angle, and an overall regression equation y = 1.1797x (R(2) = 0.76) could be used to translate the Scolioscan angle (x) to Cobb angle (y) for this group of patients. The RMS difference between Scolioscan angle and Cobb angle was 4.7 and 6.2°, with and without the correlation using the overall regression equation. CONCLUSIONS: We showed that Scolioscan is reliable for measuring coronal deformity for patients with AIS and appears promising in screening large numbers of patients, for progress monitoring, and evaluation of treatment outcomes. Due to it being radiation-free and relatively low-cost, Scolioscan has potential to be widely implemented and may contribute to reducing radiation dose during serial monitoring.

Three-dimensional elastography for cervical lymph node volume measurements: a study to investigate feasibility, accuracy and reliability.

This study investigated the feasibility of using three-dimensional (3-D) elastography in measuring cervical lymph node volume and compared the accuracy and reliability of 3-D elastography and 3-D grayscale ultrasound in measurement of ill-defined cervical nodes. Eighteen porcine lymph nodes from the neck were embedded in tissue-mimicking phantoms and scanned with the two ultrasound techniques. Ultrasound measurements were compared with the volume determined by water-displacement method to evaluate measurement accuracy. Inter-observer reproducibility and intra-observer repeatability of measurements were evaluated. Four patients with enlarged neck nodes were included to evaluate intra-observer repeatability of ultrasound measurements. Results demonstrated that lymph nodes that appeared ill-defined on grayscale ultrasound showed well-defined boundaries on elastography. 3-D elastography has higher measurement accuracy (84.2%), reproducibility (intraclass correlation coefficient, ICC = 0.909) and repeatability (ICC = 0.964-0.988) than does 3-D grayscale ultrasound (62.2%, 0.777 and 0.863-0.906 respectively). As a conclusion, 3-D elastography is accurate and reliable in volume measurement of ill-defined lymph nodes and has potential for accurate assessment of lymph node volume.