Radiofrequency ablation for primary hyperparathyroidism and risk factors for postablative eucalcemic parathyroid hormone elevation.

OBJECTIVE: To investigate the efficacy of radiofrequency ablation (RFA) as a treatment option for primary hyperparathyroidism (pHPT) and risk factors for postablative eucalcemic parathyroid hormone elevation (ePTH). METHODS: This retrospective study included 51 patients with pHPT who underwent RFA. The patients were divided into the ePTH and normal PTH groups, based on the serum intact parathyroid hormone (iPTH) level one month after ablation. Serum iPTH, calcium, and phosphorus levels, and the volume reduction rates (VRR) of the parathyroid glands were compared between the groups at each follow-up point. Risk factors for ePTH at one month after ablation were examined. RESULTS: After RFA, one (2%) patient had persistent pHPT, and 50 (98%) patients were cured. The incidence rates of ePTH at 1, 3, 6, and 12 months were 48%, 30%, 20%, and 16%, respectively. Serum iPTH levels in the ePTH group were higher than those in the normal PTH group at each follow-up point (all p < 0.05), except 1 day after ablation (p > 0.05). Serum calcium and phosphorus levels, and the VRR of the glands were comparable in both groups at each follow-up point (all p > 0.05), except for calcium levels 3 days after RFA (p < 0.05). Baseline iPTH (odds ratio, 1.067; p = 0.045) and calcium (odds ratio, 3.923; p = 0.038) levels were independent risk factors for ePTH 1 month after RFA. CONCLUSIONS: RFA is safe and effective for the treatment of pHPT. Moreover, ePTH occurrence after RFA was associated with baseline iPTH and calcium levels and did not increase the risk of recurrent pHPT.

AdaSG: A Lightweight Feature Point Matching Method Using Adaptive Descriptor with GNN for VSLAM.

Feature point matching is a key component in visual simultaneous localization and mapping (VSLAM). Recently, the neural network has been employed in the feature point matching to improve matching performance. Among the state-of-the-art feature point matching methods, the SuperGlue is one of the top methods and ranked the first in the CVPR 2020 workshop on image matching. However, this method utilizes graph neural network (GNN), resulting in large computational complexity, which makes it unsuitable for resource-constrained devices, such as robots and mobile phones. In this work, we propose a lightweight feature point matching method based on the SuperGlue (named as AdaSG). Compared to the SuperGlue, the AdaSG adaptively adjusts its operating architecture according to the similarity of input image pair to reduce the computational complexity while achieving high matching performance. The proposed method has been evaluated through the commonly used datasets, including indoor and outdoor environments. Compared with several state-of-the-art feature point matching methods, the proposed method achieves significantly less runtime (up to 43× for indoor and up to 6× for outdoor) with similar or better matching performance. It is suitable for feature point matching in resource constrained devices.

Photocatalyzed cascade Meerwein addition/cyclization of N-benzylacrylamides toward azaspirocycles.

A visible-light-induced cascade Meerwein addition/cyclization of alkenes involving C-F bond cleavage was developed. This method offers a rapid access to azaspirocyclic cyclohexadienones from N-benzylacrylamides via C-F bond cleavage applying H2O as an external oxygen source, allowing for the incorporation of various aromatic moieties originating from aryldiazonium salts.

Flexible 3D reconstruction method based on phase-matching in multi-sensor system.

Considering the measuring range limitation of a single sensor system, multi-sensor system has become essential in obtaining complete image information of the object in the field of 3D image reconstruction. However, for the traditional multi-sensors worked independently in its system, there was some point in calibrating each sensor system separately. And the calibration between all single sensor systems was complicated and required a long time. In this paper, we present a flexible 3D reconstruction method based on phase-matching in multi-sensor system. While calibrating each sensor, it realizes the data registration of multi-sensor system in a unified coordinate system simultaneously. After all sensors are calibrated, the whole 3D image data directly exist in the unified coordinate system, and there is no need to calibrate the positions between sensors any more. Experimental results prove that the method is simple in operation, accurate in measurement, and fast in 3D image reconstruction.

Radical-mediated divergent cyclization of benzamides toward perfluorinated or cyanated isoquinolinediones.

A simple and efficient copper-controlled divergent cyclization of benzamides, which leads to perfluorinated or cyanated isoquinolinediones, is developed. In the presence of AIBN, methacryloyl benzamides with perfluoroalkyl iodides undergo cascade radical addition/cyclization to afford perfluoroinated isoquinolinediones as the major product under metal-free conditions, whereas the use of CuI (10 mol%) is able to redirect the cyclization to yield isoquinolinediones bearing an α-cyano quaternary carbon center. The cyclization features controllable divergent synthesis and a broad substrate scope as well as highly practical reaction conditions, thereby making this strategy a highly attractive means to fluorinate or cyanate isoquinolinediones.

Unveiling the Stability Mechanism of Oriented Ni-Rich Layered Oxides.

Single-crystal and polycrystalline structures are the two main structural forms of the Ni-rich layered cathode for lithium-ion batteries. The structural difference is closely related to the electrochemical performance and thermal stability, but its internal mechanism is unclear and is worthy of further exploration. In this study, both polycrystalline and single-crystal LiNi0.83Co0.12Mn0.05O2 cathodes were prepared by adjusting the calcination temperature and mechanical post-treatment, respectively. Systematic comparisons were made to assess the effects of different grain structures on the electrochemical performance and thermal stability. The study revealed the superior thermal stability of monocrystalline cathodes, attributing it to oxygen vacancies and phase transitions. From the perspective of grain boundaries, it was demonstrated that the diffusion of oxygen vacancies and the reduction of Ni in polycrystalline cathodes exhibit anisotropy. This research elucidates the origins of the superior thermal stability of monocrystalline cathodes in lithium-ion batteries, providing valuable insights into battery material design.

Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.

Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell lines, CycleDNN comprises n auto-encoders. Each auto-encoder includes an encoder to convert CETSA features from one cell line into latent features in a latent space [Formula: see text]. It also features a decoder that transforms the latent features back into CETSA features for another cell line. In such a way, the proposed CycleDNN creates a cyclic prediction of CETSA features across different cell lines. The prediction loss, cycle-consistency loss, and latent space regularization loss are used to guide the model training. Experimental results on a public CETSA dataset demonstrate the effectiveness of our proposed approach. Furthermore, we confirm the validity of the predicted MS-CETSA data from our proposed CycleDNN through validation in protein-protein interaction prediction.

[Effects of Different Forms of Nitrogen Addition on Soil Physical and Chemical Properties and Microbial Community Structure of Perennial Alpine Cultivated Grassland].

This study aimed to investigate the impact of different nitrogen forms on soil physicochemical properties and microbial community structure in perennial alpine cultivated grasslands, in order to provide scientific basis for developing nitrogen addition strategies for perennial alpine cultivated grasslands. In June 2022, a 4-year-old Qinghai grassland mixed with Poa pratensis Qinghai and Festuca sinensis Qinghai was established at the Bakatai Farm in Gonghe County, Hainan Tibetan Autonomous Prefecture, Qinghai Province. The study was conducted without fertilization as a control (CK), and three different forms of nitrogen treatments were set up, namely, U:urea (amide nitrogen), A:ammonium sulfate (ammonium nitrogen), and N:calcium nitrate (nitrate nitrogen); the nitrogen application rate for each treatment was 67.5 kg·(hm2·a)-1, and the composition and diversity of soil nutrients and microbial communities under different treatments were analyzed. The results showed that the input of exogenous ammonium nitrogen significantly increased NH4+-N content, AP content, and EC; amide nitrogen input significantly increased SOC content and TN content; and nitrate nitrogen input significantly increased NO3--N content, AN content, and TC content. Exogenous nitrogen input changed the structure of soil bacterial and fungal communities, as well as the relative abundance of dominant phyla and genera, but it did not significantly affect the alpha diversity of bacterial and fungal communities. Principal coordinate analysis (PCoA) showed that different forms of nitrogen addition had a significant impact on the Beta diversity of bacterial communities, whereas the impact on fungal communities was not significant. Redundancy analysis (RDA) indicated that nitrogen addition mainly changed the composition and structure of microbial communities through soil ammonium nitrogen. Overall, ammonium nitrogen fertilizer should be given priority in the soil remediation process of perennial cultivated grasslands in the Qinghai Tibet Plateau.

Oxidative decarboxylation of mandelic acid derivative by recombinant Escherichia coli: a novel method of ethyl vanillin synthesis.

The benzoylformate decarboxylase gene (mdlC) from Pseudomonas putida was expressed in Escherichia coli BL21(DE3). The recombinant strain together with E. coli/pET30a-mdlB converted (S)-3-ethoxy-4-hydroxymandelic acid (S-EMA) into ethyl vanillin without ethyl vanillin degradation. 4 g ethyl vanillin/l was obtained from 10 g EMA/l within 12 h at 30 °C. This is the first report on the biotransformation of (S)-EMA to ethyl vanillin.

Metabonomic analysis of tumor microenvironments: a mini-review.

Metabolomic analysis is a vital part of studying cancer progression. Metabonomic crosstalk, such as nutrient availability, physicochemical transformation, and intercellular interactions can affect tumor metabolism. Many original studies have demonstrated that metabolomics is important in some aspects of tumor metabolism. In this mini-review, we summarize the definition of metabolomics and how it can help change a tumor microenvironment, especially in pathways of three metabonomic tumors. Just as non-invasive biofluids have been identified as early biomarkers of tumor development, metabolomics can also predict differences in tumor drug response, drug resistance, and efficacy. Therefore, metabolomics is important for tumor metabolism and how it can affect oncology drugs in cancer therapy.

Ultrasound-Guided Needle Release Combined with Corticosteroid Injection for the Treatment of Supinator Syndrome.

The two main branches of the radial nerve (RN) are the deep branch (DBRN) and the superficial branch (SBRN). The RN splits into two main branches at the elbow. The DBRN runs between the deep and shallow layers of the supinator. The DBRN can be easily compressed at the arcade of Frohse (AF) due to its anatomical features. This work focuses on a 42-year-old male patient who had injured his left forearm 1 month prior. Multiple muscles of the forearm (extensor digitorum, extensor digiti minimi, and extensor carpi ulnaris) were sutured in another hospital. After that, he had dorsiflexion limitations in his left ring and little fingers. The patient was reluctant to undergo another operation because he had undergone suture surgeries for multiple muscles 1 month prior. Ultrasound revealed that the deep branch of the radial nerve (DBRN) had edema and was thickened. The exit point of the DBRN had deeply adhered to the surrounding tissue. To relieve this, ultrasound-guided needle release plus a corticosteroid injection were performed on the DBRN. Nearly 3 months later, the dorsal extension in the patient's ring and little fingers was significantly improved (ring finger: -10°, little finger: -15°). Then, the same treatment was done for the second time. Nearly 1 month after that, the dorsal extension of the ring and the little finger was normal when the joints of the fingers were fully straightened. Ultrasound could evaluate the condition of the DBRN and its relationship with the surrounding tissues. Ultrasound-guided needle release combined with corticosteroid injection is an effective and safe treatment for DBRN adhesion.

Comparison efficacy of ultrasound-guided needle release plus corticosteroid injection and mini-open surgery in patients with carpal tunnel syndrome.

This retrospective study was to compare clinical outcomes of ultrasound-guided needle release with corticosteroid injection vs. mini-open surgery in patients with carpal tunnel syndrome (CTS). From January 2021 to December 2021, 40 patients (40 wrists) with CTS were analyzed in this study. The diagnosis was based on clinical symptoms, electrophysiological imaging, and ultrasound imaging. A total of 20 wrists were treated with ultrasound-guided needle release plus corticosteroid injection (Group A), and the other 20 wrists were treated with mini-open surgery (Group B). We evaluated the Boston carpal tunnel questionnaire, electrophysiological parameters (distal motor latency, sensory conduction velocity, and sensory nerve action potential of the median nerve), and ultrasound parameters (cross-sectional area, flattening ratio, and the thicknesses of transverse carpal ligament) both before and 3 months after treatment. Total treatment cost, duration of treatment, healing time, and complications were also recorded for the two groups. The Boston carpal tunnel questionnaire and electrophysiological and ultrasound outcomes at preoperatively and 3 months postoperatively had a significant difference for each group (each with P < 0.05). There were no complications such as infection, hemorrhage, vascular, nerve, or tendon injuries in both groups. Ultrasound-guided needle release and mini-open surgery are both effective measures in treating CTS patients. Ultrasound-guided needle release plus corticosteroid injection provides smaller incision, less cost, less time of treatment, and faster recovery compared with mini-open surgery. Ultrasound-guided needle release plus corticosteroid injection is better for clinical application.

Efficient Perturbation Inference and Expandable Network for continual learning.

Although humans are capable of learning new tasks without forgetting previous ones, most neural networks fail to do so because learning new tasks could override the knowledge acquired from previous data. In this work, we alleviate this issue by proposing a novel Efficient Perturbation Inference and Expandable Network (EPIE-Net), which dynamically expands lightweight task-specific decoders for new classes and utilizes a mixed-label uncertainty strategy to improve the robustness. Moreover, we calculate the average probability of perturbed samples at inference, which can generally improve the performance of the model. Experimental results show that our method consistently outperforms other methods with fewer parameters in class incremental learning benchmarks. For example, on the CIFAR-100 10 steps setup, our method achieves an average accuracy of 76.33% and the last accuracy of 65.93% within only 3.46M average parameters.

LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation.

While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-labeled datasets, which are difficult to curate due to the expert-driven and time-consuming nature of pixel-level annotations in clinical practices, and (ii) failure to generalize from one domain to another, especially when the target domain is a different modality with severe domain shifts. Recent unsupervised domain adaptation (UDA) techniques leverage abundant labeled source data together with unlabeled target data to reduce the domain gap, but these methods degrade significantly with limited source annotations. In this study, we address this underexplored UDA problem, investigating a challenging but valuable realistic scenario, where the source domain not only exhibits domain shift w.r.t. the target domain but also suffers from label scarcity. In this regard, we propose a novel and generic framework called "Label-Efficient Unsupervised Domain Adaptation" (LE-UDA). In LE-UDA, we construct self-ensembling consistency for knowledge transfer between both domains, as well as a self-ensembling adversarial learning module to achieve better feature alignment for UDA. To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images. Experimental results demonstrate that the proposed LE-UDA can efficiently leverage limited source labels to improve cross-domain segmentation performance, outperforming state-of-the-art UDA approaches in the literature.

CoIn: Correlation Induced Clustering for Cognition of High Dimensional Bioinformatics Data.

Analysis of high dimensional biomedical data such as microarray gene expression data and mass spectrometry images, is crucial to provide better medical services including cancer subtyping, protein homology detection, etc. Clustering is a fundamental cognitive task which aims to group unlabeled data into multiple clusters based on their intrinsic similarities. However, for most clustering methods, including the most widely used K-means algorithm, all features of the high dimensional data are considered equally in relevance, which distorts the performance when clustering high-dimensional data where there exist many redundant variables and correlated variables. In this paper, we aim at addressing the problem of the high dimensional bioinformatics data clustering and propose a new correlation induced clustering, CoIn, to capture complex correlations among high dimensional data and guarantee the correlation consistency within each cluster. We evaluate the proposed method on a high dimensional mass spectrometry dataset of liver cancer tumor to explore the metabolic differences on tissues and discover the intra-tumor heterogeneity (ITH). By comparing the results of baselines and ours, it has been found that our method produces more explainable and understandable results for clinical analysis, which demonstrates the proposed clustering paradigm has the potential with application to knowledge discovery in high dimensional bioinformatics data.

Ultrasound-guided needle release plus corticosteroid injection of superficial radial nerve: A case report.

BACKGROUND: The radial nerve (RN) splits into two main branches at the elbow: The superficial branch of RN (SBRN) and the deep branch of RN. The SBRN can be easily damaged in acute trauma due to its superficial feature. CASE SUMMARY: A 55-year-old male patient injured his right wrist 10 mo ago. Debridement, suturing and bandaging were performed in the emergency room. Six months after the scar had healed, he felt numbness and tingling in the dorsal surface of the thumb of the right hand. So the surgery of resection and SBRN anastomosis were performed. The pathological findings showed it as traumatic neuroma. Four months after surgery, the patient felt numbness and tingling in the right dorsal surface of the thumb again. The tenderness was marked in the operated area. Ultrasound indicated that the SBRN was adhered to the surrounding tissue. The patient refused further surgical treatment and underwent ultrasound-guided needle release plus corticosteroid injection of the SBRN. Four weeks later, the tenderness in the surgical area was reduced by 70%, the numbness in the dorsal surface of the thumb of the right hand was reduced by 40% and the nerve swelling evaluated by ultrasound was reduced. Four months passed, he did not feel any numbness or tingling sensation of his right wrist. This is the first report of ultrasound-guided needle release plus corticosteroid injection of the SBRN. CONCLUSION: Ultrasound can evaluate the condition of the RN, and the relationship with surrounding tissues. Ultrasound-guided needle release plus corticosteroid injection is an effective and safe treatment for SBRN adhesion.

Residual Channel Attention Network for Brain Glioma Segmentation.

A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of inter-class ambiguities in tumor regions. Recently, deep learning approaches have achieved outstanding performance in the automatic segmentation of brain glioma. However, existing al-gorithms fail to exploit channel-wise feature interdependence to select semantic attributes for glioma segmentation. In this study, we implement a novel deep neural network that integrates residual channel attention modules to calibrate intermediate features for glioma segmentation. The proposed channel at-tention mechanism adaptively weights feature channel-wise to optimize the latent representation of gliomas. We evaluate our method on the established dataset BraTS2017. Experimental results indicate the superiority of our method. Clinical relevance - While existing glioma segmentation approaches do not leverage channel-wise feature dependence for feature selection our method can generate segmentation masks with higher accuracies and provide more insights on graphic patterns in brain MRI images for further clinical reference.

Use of Ultrasound to Observe Mycosis Fungoides: A Case Report and Review of Literature.

BACKGROUND: Mycosis fungoides (MF) is the most common form of cutaneous T-cell lymphoma with many clinicopathological variants, thus difficult to diagnose in its early stages. CASE PRESENTATION: This case report is about a 76 years old Chinese woman presented with 2 years history of erythematous plaque on the lateral right thigh; after combining clinical manifestations with results of pathological examinations, it is consistent with the diagnosis of MF. DISCUSSION: Mycosis fungoides (MF) is the most common form of cutaneous T-cell lymphoma. The patient in this case had a long course of disease and repeated attacks. Ultrasound shows a small patch of liquid dark area of the lesion. Color Doppler image shows rich blood flow, which just looks like lacustrine. Thick and nourishing blood vessels could be seen in the depth. CONCLUSION: Our case report using ultrasound to observe MF and demonstrate that ultrasound is helpful in diagnosing and evaluating effectiveness in treating MF.

Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling.

Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph convolutions in an encoder-core-decoder architecture, extracting high-level representations from graph nodes and their neighbors. Furthermore, we efficiently aggregate intermediate features from different hierarchies to enhance the proposed model's representation capability and labeling performance. We perform extensive experiments on public datasets, in which the results prove the superiority of our approach over baselines by a clear margin. Clinical relevance- The graph convolutions and feature fusion in our approach effectively extract graph information, which provides more accurate intracranial artery label predictions than existing methods and better facilitates medical research and disease diagnosis.

Phase-Changeable Nanoparticle-Mediated Energy Conversion Promotes Highly Efficient High-Intensity Focused Ultrasound Ablation.

This review describes how phase-changeable nanoparticles enable highly-efficient high-intensity focused ultrasound ablation (HIFU). HIFU is effective in the clinical treatment of solid malignant tumors; however, it has intrinsic disadvantages for treating some deep lesions, such as damage to surrounding normal tissues. When phase-changeable nanoparticles are used in HIFU treatment, they could serve as good synergistic agents because they are transported in the blood and permeated and accumulated effectively in tissues. HIFU's thermal effects can trigger nanoparticles to undergo a special phase transition, thus enhancing HIFU ablation efficiency. Nanoparticles can also carry anticancer agents and release them in the targeted area to achieve chemo-synergistic therapy response. Although the formation of nanoparticles is complicated and HIFU applications are still in an early stage, the potential for their use in synergy with HIFU treatment shows promising results.