Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images

Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amounts of annotated data. To overcome this challenge, we propose a language-driven segmentation method that uses text prompt to improve to the segmentation result. Experiments on the QaTa-COV19 dataset indicate that our method improves the Dice score by 6.09% at least compared to the uni-modal methods. Besides, our extended study reveals the flexibility of multi-modal methods in terms of the information granularity of text and demonstrates that multi-modal methods have a significant advantage over image-only methods in terms of the size of training data required.Comment: Provisional Acceptance by MICCAI 202

Case report: Pediatric floating elbow fracture with monteggia-equivalent lesion, ipsilateral humeral shaft fracture, and radial nerve injury: a unique case and favorable treatment outcomes

This case report presents a rare and intricate pediatric floating elbow fracture involving a Monteggia-equivalent fracture, ipsilateral humeral shaft fracture, and radial nerve injury. The unique mechanism of injury highlights the importance of increased awareness and parental education for accident prevention. Elastic intramedullary nailing was employed for both humeral shaft and forearm fractures, leading to favorable outcomes. Despite the severity of the fractures and radial nerve injury, the prognosis was positive, with nerve function restoration and satisfactory functional recovery. However, the development of avascular necrosis of the radial head remains a challenge, emphasizing the need for further research to better understand and manage these uncommon and complex injuries

Characterizing the Penumbras of White Matter Hyperintensities and Their Associations With Cognitive Function in Patients With Subcortical Vascular Mild Cognitive Impairment

Normal-appearing white matter (NAWM) surrounding white matter hyperintensities (WMHs), frequently known as the WMH penumbra, is associated with subtle white matter injury and has a high risk for future conversion to WMHs. The goal of this study was to define WMH penumbras and to further explore whether the diffusion and perfusion parameters of these penumbras could better reflect cognitive function alterations than WMHs in subjects with subcortical vascular mild cognitive impairment (svMCI). Seventy-three svMCI subjects underwent neuropsychological assessments and 3T MRI scans, including diffusion tensor imaging (DTI) and arterial spin labeling (ASL). To determine the extent of cerebral blood flow (CBF) and DTI penumbras. A NAWM layer mask was generated for periventricular WMHs (PVWMHs) and deep WMHs (DWMHs) separately. Mean values of CBF, fractional anisotropy (FA), mean diffusivity (MD) within the WMHs and their corresponding NAWM layer masks were computed and compared using paired t-tests. Pearson's partial correlations were used to assess the relations of the mean CBF, FA, and MD values within the corresponding penumbras with composite z-scores of global cognition and four cognitive domains controlling for age, sex, and education. For both PVWMHs and DWMHs, the CBF penumbras were wider than the DTI penumbras. Only the mean FA value of the PVWMH-FA penumbra was correlated with the composite z-scores of global cognition before correction (r = 0.268, p = 0.024), but that correlation did not survive after correcting the p-value for multiple comparisons. Our findings showed extensive white matter perfusion disturbances including white matter tissue, both with and without microstructural alterations. The imaging parameters investigated, however, did not correlate to cognition

Total ginsenosides extract induce autophagic cell death in NSCLC cells through activation of endoplasmic reticulum stress.

ETHNOPHARMACOLOGICAL RELEVANCE(#br)Ginseng (Panax ginseng C. A. Mey) has been widely used in Asian countries for thousands of years. It has auxiliary anticancer efficacy and its derived preparations (e.g. Shenmai injection) are prescribed for cancer patients as Traditional Chinese Medicines clinically in China.(#br)AIM OF THE STUDY(#br)The involved adjuvant anticancer mechanisms of ginseng are still unknown. The present study evaluated the anti-cancer effect of total ginsenosides extract (TGS) and determined the anticancer mechanisms of TGS-induced cell death in human non-small cell lung cancer (NSCLC) cells.(#br)MATERIALS AND METHODS(#br)The anti-cancer effect of TGS was evaluated in NSCLC by cell proliferation assay. The autophagy flux induction of TGS were tested and validated by Western blot, immunofluorescence and transmission electron microscope. The mechanisms of TGS in inducing autophagic cell death were validated by Western blot, gene knockdown and quantitative real time PCR assay.(#br)RESULTS(#br)We found TGS could induce cell death in concentration and time dependent manners, and the cell morphology of NSCLC changed from cobblestone shape to elongated spindle shape after treated with TGS. In the study of cell autophagy, we confirm that TGS could upregulate autophagy flux and induce autophagic cell death through activation endoplasmic reticulum stress. Further investigations demonstrated this process was mediated by the ATF4-CHOP-AKT1-mTOR axis in NSCLC cells.(#br)CONCLUSION(#br)Our findings suggested that TGS could induce autophagic cell death in NSCLC cells through activation of endoplasmic reticulum stress, disclosing another characteristic of TGS-induced cell death and a novel mechanism of TGS and its derived preparations in clinical treatment of cancer patients

Resting-State Activity of Prefrontal-Striatal Circuits in Internet Gaming Disorder: Changes With Cognitive Behavior Therapy and Predictors of Treatment Response

Cognitive behavior therapy (CBT) is effective for the treatment of Internet gaming disorder (IGD). However, the mechanisms by which CBT improves IGD-related clinical symptoms remain unknown. This study aimed to discover the therapeutic mechanism of CBT in IGD subjects using resting-state functional magnetic resonance imaging (rsfMRI). Twenty-six IGD subjects and 30 matched healthy controls (HCs) received rsfMRI scan and clinical assessments; 20 IGD subjects completed CBT and then were scanned again. The amplitude of low-frequency (ALFF) values and the functional connectivity (FC) between the IGD group and the HC group were compared at baseline, as well as the ALFF values and FC before and after the CBT in the IGD group. Prior to treatment, the IGD group exhibited significantly increased ALFF values in the bilateral putamen, the right medial orbitofrontal cortex (OFC), the bilateral supplementary motor area (SMA), the left postcentral gyrus, and the left anterior cingulate (ACC) compared with the HC group. The HC group showed significantly increased FC values between the left medial OFC and the putamen compared with the IGD group, the FC values of IGD group were negatively associated with the BIS-11 scores before treatment. After the CBT, the weekly gaming time was significantly shorter, and the CIAS and BIS-II scores were significantly lower. The ALFF values in the IGD subjects significantly decreased in the left superior OFC and the left putamen, and the FC between them significantly increased after the CBT. The degree of the FC changes (ΔFC/Pre−FC) was positively correlated with the scale of the CIAS scores changes (ΔCIAS/Pre−CIAS) in the IGD subjects. CBT could regulate the abnormal low-frequency fluctuations in prefrontal-striatal regions in IGD subjects and could improve IGD-related symptoms. Resting-state alternations in prefrontal-striatal regions may reveal the therapeutic mechanism of CBT in IGD subjects

Towards sustainability: An assessment of an urbanisation bubble in China using a hierarchical - stochastic multicriteria acceptability analysis - Choquet integral method

Urbanisation bubbles have become an increasingly serious problem. Attention has been paid to the speed of urbanisation; however, the issue of quality has been neglected, particularly in the case of China. Therefore, the aim of this research is to evaluate China’s urbanisation bubbles by employing a hierarchical - stochastic multicriteria acceptability analysis (SMAA) - Choquet integral method. In order to highlight regional disparities, we measure the urbanisation bubbles at a provincial level. Our study aggregates the urbanisation bubble indices using the Choquet integral preference model, and considers the interactions between various indicators. Furthermore, robust ordinal regression and SMAA are applied to resolve the robustness issues associated with the entire set of weights assigned to the urbanisation bubble composite indicator. In addition, by employing a multiple criteria hierarchy process, the study aggregates urbanisation bubble indices not only at the comprehensive level, but also at the intermediate levels of the hierarchy. Our findings suggest that the ranking of urbanisation bubbles is positively related to the level of regional development. This study contributes to the evaluation of regional urbanisation and sustainable development

From physiology to pathology: the role of mitochondria in acute kidney injuries and chronic kidney diseases

Background: Renal diseases remain an increasing public health issue affecting millions of people. The kidney is a highly energetic organ that is rich in mitochondria. Numerous studies have demonstrated the important role of mitochondria in maintaining normal kidney function and in the pathogenesis of various renal diseases, including acute kidney injuries (AKI) and chronic kidney diseases (CKD). Summary: Under physiological conditions, fine-tuning mitochondrial energy balance, mitochondrial dynamics (fission and fusion processes), mitophagy, and biogenesis maintain mitochondrial fitness. While under AKI and CKD conditions, disruption of mitochondrial energy metabolism leads to increased oxidative stress. In addition, mitochondrial dynamics shift to excessive mitochondrial fission, mitochondrial autophagy is impaired, and mitochondrial biogenesis is also compromised. These mitochondrial injuries regulate renal cellular functions either directly or indirectly. Mitochondria-targeted approaches, containing genetic (microRNAs) and pharmaceutical methods (mitochondria-targeting antioxidants, mitochondrial permeability pore inhibitors, mitochondrial fission inhibitors, and biogenesis activators) are emerging as important therapeutic strategies for AKI and CKD. Key messages: Mitochondria play a critical role in the pathogenesis of AKI and CKD. This review provides an updated overview of mitochondrial homeostasis under physiological conditions and the involvement of mitochondrial dysfunction in renal diseases. Finally, we summarize the current status of mitochondria-targeted strategies in attenuating renal diseases

The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data

With the rapid development of the remote sensing monitoring and computer vision technology, the deep learning method has made a great progress to achieve applications such as earth observation, climate change and even space exploration. However, the model trained on existing data cannot be directly used to handle the new remote sensing data, and labeling the new data is also time-consuming and labor-intensive. Unsupervised Domain Adaptation (UDA) is one of the solutions to the aforementioned problems of labeled data defined as the source domain and unlabeled data as the target domain, i.e., its essential purpose is to obtain a well-trained model and tackle the problem of data distribution discrepancy defined as the domain shift between the source and target domain. There are a lot of reviews that have elaborated on UDA methods based on natural data, but few of these studies take into consideration thorough remote sensing applications and contributions. Thus, in this paper, in order to explore the further progress and development of UDA methods in remote sensing, based on the analysis of the causes of domain shift, a comprehensive review is provided with a fine-grained taxonomy of UDA methods applied for remote sensing data, which includes Generative training, Adversarial training, Self-training and Hybrid training methods, to better assist scholars in understanding remote sensing data and further advance the development of methods. Moreover, remote sensing applications are introduced by a thorough dataset analysis. Meanwhile, we sort out definitions and methodology introductions of partial, open-set and multi-domain UDA, which are more pertinent to real-world remote sensing applications. We can draw the conclusion that UDA methods in the field of remote sensing data are carried out later than those applied in natural images, and due to the domain gap caused by appearance differences, most of methods focus on how to use generative training (GT) methods to improve the model’s performance. Finally, we describe the potential deficiencies and further in-depth insights of UDA in the field of remote sensing

Numerical Study on the Working Performance of a Streamlined Annular Jet Pump

To improve the working performance of the early annular jet pump (EAJP), a streamlined annular jet pump (SAJP) was proposed. The flow field and working performance of the EAJP and SAJP with an area ratio (m) of 1.75 were numerically studied and compared, separately, by using the combination of the Realizable k-ε turbulence model and the Schnerr–Sauer cavitation model. The results show that the efficiency of the SAJP is higher than that of the EAJP, when the flow ratio (q) is higher than 0.30, with a maximum increase of 1.2%. Furthermore, the high-efficiency area of the SAJP (q = 0.40~0.69) is wider than that of the EAJP (q = 0.36~0.57). There is no flow separation and low local pressure in the SAJP, due to the conjunction part of the suction chamber, throat, diffuser and outlet pipe without the structural mutation. It was found that the incipient cavitation number (σi) of the SAJP and EAJP was 0.541 and 0.578, respectively; therefore, the cavitation performance of the SAJP is better. Meanwhile, the critical flow ratio (qc) of the SAJP is 0.69, which is larger than that of the EAJP (qc = 0.57), implying that the SAJP has a wider normal working range than the EAJP. Importantly, the inception and development of cavitation appeared in the diffuser of the SAJP, different from that in the throat of the EAJP. Hence, it concluded that the cavitation in the SAJP has less influence on the flow field and working performance

MoANet: A Motion Attention Network for Sea Fog Detection in Time Series Meteorological Satellite Imagery

Sea fog detection is a significant and challenging issue in meteorological satellite imagery. Distinguishing between sea fog and low clouds is challenging due to the similar morphology and brightness characteristics of these two phenomena on the imageries. Most of the existing deep learning methods are based on a single imagery feature extraction without the time-related features in imagery sequence. Although the designed temporal models, such as temporal U-Net, expand the available features from a single imagery to the consecutive frames and introduce general temporal information, the learned motion features are not explicit and can only be implicitly learned through a large amount of data. Thus, we introduce motion features obtained from continuous temporal imagery sequences into the sea fog detection task due to the discrepancy between sea fog and other types of clouds. In this article, under the motion features acquired by Horn–Schunck (HS) optical flow method and attention mechanisms, a Motion Attention Network (MoANet) for sea fog detection is proposed, named MoANet. We performed detailed experiments on the Himawaria-8 satellite imagery data set (H-8 Dataset). The Mean Intersection over Union (MIoU) of our method reaches 81.38%, which is 6.49% higher than the single imagery method. The visualization of the results shows that MoANet has more smooth edges, as well as detects more complete area than others. Furthermore, we validate on International Comprehensive Ocean-Atmosphere Data Set (ICOADS) through contrasting visibility value to prove the practicality of the proposed method and the accuracy achieves 90.65%