Refining postoperative monitoring of recurrent laryngeal nerve injury in esophagectomy patients through transcutaneous laryngeal ultrasonography.

BACKGROUND: Recurrent laryngeal nerve injury (RLNI) leading to vocal cord paralysis (VCP) is a significant complication following minimally invasive esophagectomy (MIE) with upper mediastinal lymphadenectomy. Transcutaneous laryngeal ultrasonography (TLUSG) has emerged as a non-invasive alternative to endoscopic examination for evaluating vocal cord function. Our study aimed to assess the diagnostic value of TLUSG in detecting RLNI by evaluating vocal cord movement after MIE. METHODS: This retrospective study examined 96 patients with esophageal cancer who underwent MIE between January 2021 and December 2022, using both TLUSG and endoscopy. RESULTS: VCP was observed in 36 out of 96 patients (37.5%). The incidence of RLNI was significantly higher on the left side than the right (29.2% vs. 5.2%, P < 0.001). Postoperative TLUSG showed a sensitivity and specificity of 88.5% (31/35) and 86.5% (45/52), respectively, with an AUC of 0.869 (P < 0.001, 95% CI 0.787-0.952). The percentage agreement between TLUSG and endoscopy in assessing VCP was 87.4% (κ = 0.743). CONCLUSIONS: TLUSG is a highly effective screening tool for VCP, given its high sensitivity and specificity. This can potentially eliminate the need for unnecessary endoscopies in about 80% of patients who have undergone MIE.

Potential diagnostic value of quantitative superb microvascular imaging in premalignant and malignant cervical lesions.

Objective: The purpose of this study was to assess the diagnostic efficacy of the vascular index (VI) on superb microvascular imaging (SMI) in distinguishing normal uterine cervical epithelium, high-grade cervical intraepithelial neoplasia (CIN), and cervical cancer. Methods: The retrospective study included women with pathology-confirmed CIN or cervical cancer, who underwent transvaginal ultrasound and SMI between April 2021 and October 2022. The SIM manifestations of normal cervix and cervical lesions were reviewed. SIM were measured and converted into vascular index (VI) which compared between cervical lesions and control groups. We have retrospectively compared ultrasound features of cervical lesions and characteristics of patients. Measurement reliability was evaluated by intra class correlation coefficient (ICC). Results: A total of 235 consecutive females were enrolled, comprising 38 with high-grade CIN, 96 with cervical cancer, and 101 with a normal uterine cervix. The microvascular architecture exhibited significant variations between premalignant and malignant cervical lesions. Branch-like patterns were predominantly observed in high-grade CIN, while crab claw-like and fireball-like patterns were more commonly associated with cervical cancer. The median VI of cervical cancer (34.7 ± 10.3) was significantly higher than that of high-grade CIN (17.6 ± 4.2) (P < 0.001). Moreover, the VI values of cervical cancer differed significantly among different FIGO stages and pathological types (P < 0.001 and P = 0.003, respectively). The VI demonstrated superior diagnostic performance for cervical lesions compared to vascular patterns (AUC = 0.974 and 0.969, respectively). Using a cut-off value of 25.5, the VI yielded a sensitivity of 82.3% and a specificity of 99.3% for cervical lesion detection. Conclusions: The SMI parameter (VI) exhibited a significantly higher value in cervical cancer compared to high-grade CIN, with a high level of agreement among observers. These findings suggest that quantitative SMI holds promise as an imaging technique for the detection and characterization of cervical lesions.

Enzyme-Immobilized Metal-Organic Frameworks: From Preparation to Application.

As a class of widely used biocatalysts, enzymes possess advantages including high catalytic efficiency, strong specificity and mild reaction condition. However, most free enzymes have high requirements on the reaction environment and are easy to deactivate. Immobilization of enzymes on nanomaterial-based substrates is a good way to solve this problem. Metal-organic framework (MOFs), with ultra-high specific surface area and adjustable porosity, can provide a large space to carry enzymes. And the tightly surrounded protective layer of MOFs can stabilize the enzyme structure to a great extent. In addition, the unique porous network structure enables selective mass transfer of substrates and facilitates catalytic processes. Therefore, these enzyme-immobilized MOFs have been widely used in various research fields, such as molecule/biomolecule sensing and imaging, disease treatment, energy and environment protection. In this review, the preparation strategies and applications of enzyme-immobilized MOFs are illustrated and the prospects and current challenges are discussed.

Quantitative analysis of superb microvascular imaging for monitoring tumor response to chemoradiotherapy in locally advanced cervical cancer.

Objectives: As an ultrasound (US) image processing method, superb microvascular imaging (SMI) extracts and visualizes flow signals from vessels through advanced clutter suppression technology. We investigated the feasibility of SMI in monitoring treatment response in patients with locally advanced cervical cancer (LACC) undergoing chemoradiotherapy (CRT). Methods: Forty-nine patients underwent CRT and received SMI examination at 3 time points: before therapy (baseline), 3 weeks during, and 1 month after CRT. The maximum tumor diameter (Dmax), vascularity index (VI), and their percentage changes (ΔDmax and ΔVI) were calculated. ΔDmax was compared with MRI results as the reference standard. Results: Based on the MRI findings, 44 were classified as complete response (CR) group and 5 as partial response (PR) group. The Dmax and ΔDmax showed decrease in CR and PR groups at 3 weeks during CRT (P< 0.05), but no significant difference between the two groups (P > 0.05). Compared to the baseline, significant decrease in VI and ΔVI were observed at during and after treatment in the two groups (P< 0.05). Moreover, there were significant differences in VI and ΔVI at 3 weeks during CRT between the CR and PR groups (P< 0.05). ΔVI at 3 weeks during CRT showed a better predictive performance for responder prognosis than VI (AUC = 0.964, AUC = 0.950, respectively, P = 0.001), with a cut-off value of 41.6% yielding 100% sensitivity and 86.4% specificity. Conclusions: The SMI parameters (VI and ΔVI) have potential for monitoring treatment response in LACC.

Photocatalytic activity of attapulgite-TiO2-Ag3PO4 ternary nanocomposite for degradation of Rhodamine B under simulated solar irradiation.

An excellent ternary composite photocatalyst consisting of silver orthophosphate (Ag3PO4), attapulgite (ATP), and TiO2 was synthesized, in which heterojunction was formed between dissimilar semiconductors to promote the separation of photo-generated charges. The ATP/TiO2/Ag3PO4 composite was characterized by SEM, XRD, and UV-vis diffuse reflectance spectroscopy. The co-deposition of Ag3PO4 and TiO2 nanoparticles onto the surface of ATP forms a lath-particle structure. Compared with composite photocatalysts consisting of two phases, ATP/TiO2/Ag3PO4 ternary composite exhibits greatly improved photocatalytic activity for degradation of rhodamine B under simulated solar irradiation. Such ternary composite not only improves the stability of Ag3PO4, but also lowers the cost by reducing application amount of Ag3PO4, which provides guidance for the design of Ag3PO4- and Ag-based composites for photocatalytic applications.

Cross-View Action Recognition via Transferable Dictionary Learning.

Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

Label consistent K-SVD: learning a discriminative dictionary for recognition.

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Recognizing human actions by learning and matching shape-motion prototype trees.

A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.