A comparative study of the analgesic effects of sevoflurane and propofol in children following otolaryngology surgical procedures: A pilot study

Purpose: To determine the analgesic effects of sevoflurane (Sev) and propofol (Pro) in children whounderwent otolaryngology surgical procedures, and their post-operative conditions.Methods: A total of 62 (ASA I or ASA II) pre-medicated children who were about to undergootolaryngology surgical procedures were chosen and divided equally into Sev and Pro groups, with 31patients per group. During the surgical procedure, Sev was administered via a mask, while Pro wasgiven i.v. Each anesthesia was followed with fentanyl administration.Results: Pain scores such as verbal rating scale (VRS) and visual analogue scale (VAS) were slightlylower in Sev group than in Pro group. However, post-operative conditions such as emergence delirium(ED) and emergence agitation (EA) were significantly elevated in Sev group, when compared to Progroup (p < 0.05). In addition, patients in Sev group had higher levels of hemodynamic parameters(blood pressure), and much higher number of adverse events than those in Pro group. Thus, the overallsatisfaction score and recovery characteristics, i.e., hospitalization time and recovery were slightlybetter in Pro-anesthetized children than in those given Sev.Conclusion: These results suggest that except for pain score, Pro-anesthetized children fared better interms of speedy recovery and reduced adverse effects than those given Pro. Thus, Pro may berecommended as general anaesthetic for children undergoing otolaryngology surgical procedures.Keywords: Sevoflurane, Propofol, Pain score, Emergence agitation, Otolaryngolog

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to privacy concerns. Recent State-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs). Unfortunately, PLs suffer from accuracy deterioration caused by domain shift, and thus limit the effectiveness of the adaptation process. To address this issue, we propose a Chebyshev confidence guided SFDA framework to accurately assess the reliability of PLs and generate self-improving PLs for self-training. The Chebyshev confidence is estimated by calculating probability lower bound of the PL confidence, given the prediction and the corresponding uncertainty. Leveraging the Chebyshev confidence, we introduce two confidence-guided denoising methods: direct denoising and prototypical denoising. Additionally, we propose a novel teacher-student joint training scheme (TJTS) that incorporates a confidence weighting module to improve PLs iteratively. The TJTS, in collaboration with the denoising methods, effectively prevents the propagation of noise and enhances the accuracy of PLs. Extensive experiments in diverse domain scenarios validate the effectiveness of our proposed framework and establish its superiority over state-of-the-art SFDA methods. Our paper contributes to the field of SFDA by providing a novel approach for precisely estimating the reliability of pseudo-labels and a framework for obtaining high-quality PLs, resulting in improved adaptation performance

Facile synthesis of α-Fe2O3 micro-ellipsoids by surfactant-free hydrothermal method for sub-ppm level H2S detection

The α-Fe2O3 micro-ellipsoids were prepared using a facile hydrothermal process without any surfactant or template, and their morphological, structural and H2S sensing properties were investigated. The α-Fe2O3 showed uniform micro-ellipsoids with a long axis diameter of 1.7 μm and a short axis diameter of 1.2 μm. Detailed structural analysis confirmed that the synthesized α-Fe2O3 micro-ellipsoids were compact particles with a hexagonal structure. Gas sensor base on the α-Fe2O3 micro-ellipsoids showed excellent response, short response/recovery time (< 90 s and 30 s, respectively), low detection concentration (~ 0.5 ppm), good long-term stability and excellent selectivity towards H2S gas at the optimized operating temperature of 350 °C. The sensing mechanism of the sensor based on the α-Fe2O3 micro-ellipsoids towards H2S was discussed

CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation

While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on massive fine-annotated cell images [2] with bounding boxes, masks, and cell types for every cell in every image, to fine-tune the pre-trained model. To lower the cost of annotation, this work considers the problem of pre-training DNN models for few-shot cell segmentation, where massive unlabeled cell images are available but only a small proportion is annotated. Hereby, we propose Cross-domain Unsupervised Pre-training, namely CUPre, transferring the capability of object detection and instance segmentation for common visual objects (learned from COCO) to the visual domain of cells using unlabeled images. Given a standard COCO pre-trained network with backbone, neck, and head modules, CUPre adopts an alternate multi-task pre-training (AMT2) procedure with two sub-tasks -- in every iteration of pre-training, AMT2 first trains the backbone with cell images from multiple cell datasets via unsupervised momentum contrastive learning (MoCo) [3], and then trains the whole model with vanilla COCO datasets via instance segmentation. After pre-training, CUPre fine-tunes the whole model on the cell segmentation task using a few annotated images. We carry out extensive experiments to evaluate CUPre using LIVECell [2] and BBBC038 [4] datasets in few-shot instance segmentation settings. The experiment shows that CUPre can outperform existing pre-training methods, achieving the highest average precision (AP) for few-shot cell segmentation and detection

Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions

Image enhancement is a common technique used to mitigate issues such as severe noise, low brightness, low contrast, and color deviation in low-light images. However, providing an optimal high-light image as a reference for low-light image enhancement tasks is impossible, which makes the learning process more difficult than other image processing tasks. As a result, although several low-light image enhancement methods have been proposed, most of them are either too complex or insufficient in addressing all the issues in low-light images. In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions. Specifically, we first recognize the challenges of the need for a large receptive field to obtain global contrast and the lack of an absolute reference, which limits the simplification of network structures in this task. Then, we propose an efficient global feature information extraction component and two loss functions based on relative information to overcome these challenges. Finally, we conducted comparative experiments to demonstrate the effectiveness of the proposed method, and the results confirm that the proposed method can significantly reduce the complexity of supervised low-light image enhancement networks while improving processing effect. The code is available at \url{https://github.com/hitzhangyu/FLW-Net}.Comment: 19 pages, 11 figure

FKN Facilitates HK-2 Cell EMT and Tubulointerstitial Lesions via the Wnt/β-Catenin Pathway in a Murine Model of Lupus Nephritis

Fractalkine (FKN), also known as chemokine (C-X3-C motif) ligand 1, constitutes an intriguing chemokine with a documented role in the development of numerous inflammatory diseases including autoimmune disease. Specifically, it has been reported that FKN is involved in the disease progression of lupus nephritis (LN). The epithelial-mesenchymal transition (EMT) plays a significant role in the formation of tubulointerstitial lesions (TIL), which are increasingly recognized as a hallmark of tissue fibrogenesis after injury. However, the correlation between FKN and EMT or TIL in LN has not been determined. To investigate the potential role of FKN in EMT and TIL, MRL lymphoproliferation (MRL/lpr) strain mice were treated with an anti-FKN antibody, recombinant-FKN chemokine domain, or isotype antibody. Our results revealed that treatment with the anti-FKN antibody improved EMT, TIL, and renal function in MRL/lpr mice, along with inhibiting activation of the Wnt/β-catenin signaling pathway. In contrast, administration of the recombinant-FKN chemokine domain had the opposite effect. Furthermore, to further explore the roles of FKN in EMT, we assessed the levels of EMT markers in FKN-depleted or overexpressing human proximal tubule epithelial HK-2 cells. Our results provide the first evidence that the E-cadherin level was upregulated, whereas α-SMA and vimentin expression was downregulated in FKN-depleted HK-2 cells. In contrast, overexpression of FKN in HK-2 cells enhanced EMT. In addition, inhibition of the Wnt/β-catenin pathway by XAV939 negated the effect of FKN overexpression, whereas activation of the Wnt/β-catenin pathway by Ang II impaired the effect of the FKN knockout on EMT in HK-2 cells. Together, our data indicate that FKN plays essential roles in the EMT progression and development of TIL in MRL/lpr mice, most likely through activation of the Wnt/β-catenin signaling pathway

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Thanks to its impressive capabilities in all-round segmentation tasks and its prompt-based interface, SAM has sparked intensive discussion within the community. It is even said by many prestigious experts that image segmentation task has been "finished" by SAM. However, medical image segmentation, although an important branch of the image segmentation family, seems not to be included in the scope of Segmenting "Anything". Many individual experiments and recent studies have shown that SAM performs subpar in medical image segmentation. A natural question is how to find the missing piece of the puzzle to extend the strong segmentation capability of SAM to medical image segmentation. In this paper, instead of fine-tuning the SAM model, we propose Med SAM Adapter, which integrates the medical specific domain knowledge to the segmentation model, by a simple yet effective adaptation technique. Although this work is still one of a few to transfer the popular NLP technique Adapter to computer vision cases, this simple implementation shows surprisingly good performance on medical image segmentation. A medical image adapted SAM, which we have dubbed Medical SAM Adapter (MSA), shows superior performance on 19 medical image segmentation tasks with various image modalities including CT, MRI, ultrasound image, fundus image, and dermoscopic images. MSA outperforms a wide range of state-of-the-art (SOTA) medical image segmentation methods, such as nnUNet, TransUNet, UNetr, MedSegDiff, and also outperforms the fully fine-turned MedSAM with a considerable performance gap. Code will be released at: https://github.com/WuJunde/Medical-SAM-Adapter