Cost-effectiveness analysis of a whole genome sequencing test compared to the standard care among patients with hospital-acquired bacteremia

Background: Klebsiella pneumoniae carbapenemase (KPC)-producing bacteria have becoming increasingly prevalent in the US in the past decade. Klebsiella pneumoniae presents significant clinical challenges, as they are frequently misclassified and highly resistant to all commonly available antimicrobials, leading to delay in treatment and rapid spread in the hospital. With its high sensitivity and significantly reduced price, whole genome sequencing (WGS) has been considered a viable approach to help facilitate the identification of KPC-positive K. pneumonia from patient isolates from gastrointestinal endoscopy. However, evidence for its cost-effectiveness is lacking, which is of high public health significance. Objective: to compare the cost-effectiveness of WGS and the standard of care (SOC, high-level disinfection), in the detection and prevention of KPC-positive K. pneumonia. Methods: A hypothetical cohort of 1000 patients was simulated for ten years using a four-state Markov model. KPC-positive K. pneumoniae-caused infection-related healthcare costs and quality-adjusted life year (QALY) were estimated for both the WGS strategy and the SOC strategy. Results: The base case analysis showed that with the infection rate assumed to be 1% and the cost of WGS at $100 per test, the ten-year mean cost for a patient in the WGS strategy was$3,281.80 with 8.0819 QALYs gained, while the total cost for the SOC strategy was $4,583.50 with 8.0507 QALYs gained. Under these conditions, the SOC strategy was dominated by the WGS strategy. One-way sensitivity analysis and probabilistic sensitivity analysis (PSA) revealed that WGS remained economically dominant compared to SOC with the willingness-to-pay level at$50,000. Conclusion: In summary, the WGS strategy is more cost effective in identifying PC-positive K. pneumonia than SOC strategy

Graph Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering

The article introduces a new method for applying Quantum Clustering to graph structures. Quantum Clustering (QC) is a novel density-based unsupervised learning method that determines cluster centers by constructing a potential function. In this method, we use the Graph Gradient Descent algorithm to find the centers of clusters. GPU parallelization is utilized for computing potential values. We also conducted experiments on five widely used datasets and evaluated using four indicators. The results show superior performance of the method. Finally, we discuss the influence of $\sigma$ on the experimental results

PROFILIN-1 IN CAPILLARY MORPHOGENESIS OF VASCULAR ENDOTHELIAL CELLS

Vascular endothelial cells (VEC) assemble into capillary-like structures during angiogenesis, and this neovascularization process plays an important role in a wide range of physiological and pathological scenarios. Based on significant upregulation of its expression in VEC during capillary morphogenesis, profilin-1 (Pfn1 - a ubiquitously expressed actin-binding protein) was previously implicated in capillary morphogenesis of VEC. The overall objective of the present study was to investigate whether and how loss of Pfn1 function affects a) the various cellular functions that are important for capillary morphogenesis such as VEC migration, invasion and proliferation, and b) the overall capillary forming ability of VEC. Loss of Pfn1 function in VEC was achieved either by suppressing the overall expression of Pfn1 by RNA interference method or selectively abrogating specific ligand-interactions (actin, proline-rich ligands) of Pfn1 by expressing various point-mutants of Pfn1 in a near-null endogenous Pfn1 background (knockdown and knock-in approach). Loss of Pfn1 expression causes a major change in actin cytoskeleton in VEC. Particularly, there is a significant depletion of actin filaments and focal adhesions in VEC when Pfn1 expression was silenced. Silencing of Pfn1 expression also significantly impairs the migratory ability of VEC. Analyses of leading edge dynamics revealed that Pfn1 depletion results in decreased velocity and frequency of lamellipodial protrusion. Further experiments with point-mutants of Pfn1 showed that both actin and polyproline interactions of Pfn1 are required for efficient lamellipodial protrusion and overall migration of VEC. Loss of Pfn1 expression is associated with reduced dynamics of VE-cadherin dependent cell-cell adhesion, which was also found to be correlated with increased nuclear accumulation of p27 Kip1 (a major cell-cycle inhibitor) and reduced VEC proliferation. Finally, we found that loss of overall expression of Pfn1 significantly impairs collagen gel invasion and three-dimensional (3-D) capillary morphogenesis of VEC. Abolishing either of actin or polyproline interactions of Pfn1 also leads to a dramatic inhibition of capillary mophogenesis of VEC. Taken together, these results demonstrate that Pfn1 plays a critical role in capillary morphogenesis of VEC through its interactions with both actin and polyproline ligands. This study may further imply that Pfn1 could be a novel angiogenesis target

SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection

This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structures of self-augmented compound cross-domain samples to enhance the model's generalization ability. Different from DGOD trained on multiple source domains, Single-DGOD is far more challenging to generalize well to multiple target domains with only one single source domain. Existing methods mostly adopt a similar treatment from DGOD to learn domain-invariant features by decoupling or compressing the semantic space. However, there may have two potential limitations: 1) pseudo attribute-label correlation, due to extremely scarce single-domain data; and 2) the semantic structural information is usually ignored, i.e., we found the affinities of instance-level semantic relations in samples are crucial to model generalization. In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD. Specifically, our SRCD contains two main components, namely, the texture-based self-augmentation (TBSA) module, and the local-global semantic reasoning (LGSR) module. TBSA aims to eliminate the effects of irrelevant attributes associated with labels, such as light, shadow, color, etc., at the image level by a light-yet-efficient self-augmentation. Moreover, LGSR is used to further model the semantic relationships on instance features to uncover and maintain the intrinsic semantic structures. Extensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed SRCD.Comment: 10 pages, 5 figure

Subject-Independent Emotion Recognition Based on Physiological Signals: A Three-Stage Decision Method

Background: Collaboration between humans and computers has become pervasive and ubiquitous, however current computer systems are limited in that they fail to address the emotional component. An accurate understanding of human emotions is necessary for these computers to trigger proper feedback. Among multiple emotional channels, physiological signals are synchronous with emotional responses; therefore, analyzing physiological changes is a recognized way to estimate human emotions. In this paper, a three-stage decision method is proposed to recognize four emotions based on physiological signals in the multi-subject context. Emotion detection is achieved by using a stage-divided strategy in which each stage deals with a fine-grained goal. Methods: The decision method consists of three stages. During the training process, the initial stage transforms mixed training subjects to separate groups, thus eliminating the effect of individual differences. The second stage categorizes four emotions into two emotion pools in order to reduce recognition complexity. The third stage trains a classifier based on emotions in each emotion pool. During the testing process, a test case or test trial will be initially classified to a group followed by classification into an emotion pool in the second stage. An emotion will be assigned to the test trial in the final stage. In this paper we consider two different ways of allocating four emotions into two emotion pools. A comparative analysis is also carried out between the proposal and other methods. Results: An average recognition accuracy of 77.57% was achieved on the recognition of four emotions with the best accuracy of 86.67% to recognize the positive and excited emotion. Using differing ways of allocating four emotions into two emotion pools, we found there is a difference in the effectiveness of a classifier on learning each emotion. When compared to other methods, the proposed method demonstrates a significant improvement in recognizing four emotions in the multi-subject context. Conclusions: The proposed three-stage decision method solves a crucial issue which is \u27individual differences\u27 in multi-subject emotion recognition and overcomes the suboptimal performance with respect to direct classification of multiple emotions. Our study supports the observation that the proposed method represents a promising methodology for recognizing multiple emotions in the multi-subject context

Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells

BackgroundMelanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity.Materials and methodsCells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell–cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines.ResultsFollowing batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells’ proliferation, invasion, and migration.ConclusionThe differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells

Nanomolar concentration of blood-soluble drag-reducing polymer inhibits experimental metastasis of human breast cancer cells

Metastasis is the leading cause of cancer mortality. Extravasation of cancer cells is a critical step of metastasis. We report a novel proof-of-concept study that investigated whether non-toxic blood-soluble chemical agents capable of rheological modification of the near-vessel-wall blood flow can reduce extravasation of tumor cells and subsequent development of metastasis. Using an experimental metastasis model, we demonstrated that systemic administration of nanomolar concentrations of so-called drag-reducing polymer dramatically impeded extravasation and development of pulmonary metastasis of breast cancer cells in mice. This is the first proof-of-principle study to directly demonstrate physical/rheological, as opposed to chemical, way to prevent cancer cells from extravasation and developing metastasis and, thus, it opens the possibility of a new direction of adjuvant interventional approach in cancer

3D Point Cloud Object Tracking Based on Multi-level Fusion of Transformer Features

During the 3D point cloud object tracking, some issues such as occlusion, sparsity, and random noise often arise. To address these challenges, this paper proposes a novel approach to 3D point cloud object tracking based on multi-level fusion of Transformer features. The method mainly consists of the point attention embedding module and the point attention enhancement module, which are used for feature extraction and feature matching processes, respectively. Firstly, by embedding two attention mechanisms into each other to form the point attention embedding module and fusing it with the relationship-aware sampling method proposed by PTTR (point relation transformer for tracking), the purpose of fully extracting features is achieved. Subsequently, the feature information is input into the point attention enhancement module, and through cross-attention, features from different levels are matched sequentially to achieve the goal of deep fusion of global and local features. Moreover, to obtain discriminative feature fusion maps, a residual network is employed to connect the fusion results from different layers. Finally, the feature fusion map is input into the target prediction module to achieve precise prediction of the final 3D target object. Experimental validation on KITTI, nuScenes, and Waymo datasets demonstrates the effectiveness of the proposed method. Excluding few-shot data, the proposed method achieves an average improvement of 1.4 percentage points in success and 1.4 percentage points in precision in terms of object tracking

The role of potassium ion channels in chronic sinusitis

Chronic sinusitis is a common inflammatory disease of the nasal and sinus mucosa, leading to symptoms such as nasal congestion, runny nose, decreased sense of smell, and headache. It often recurs and seriously affects the quality of life of patients. However, its pathological and physiological mechanisms are not fully understood. In recent years, the role of potassium ion channels in the regulation of mucosal barrier function and inflammatory cell function has received increasing attention. In chronic sinusitis, there are often changes in the expression and function of potassium channels, leading to mucosal damage and a stronger inflammatory response. However, the related research is still in its early stages. This article will review the role of the potassium channel in the pathological and physiological changes of chronic sinusitis. The studies revealed that BK/TREK-1 potassium channel play a protective role in the nasal mucosal function through p38-MAPK pathway, and KCa3.1/Kv1.3 enhance the inflammatory response of Chronic rhinosinusitis by regulating immune cell function, intracellular Ca2+ signaling and ERK/MAPK/NF-κB pathway. Because ion channels are surface proteins of cell membranes, they are easier to intervene with drugs, and the results of these studies may provide new effective targets for the prevention and treatment of chronic sinusitis

Global model of an atmospheric-pressure capacitive discharge in helium with air impurities from 100 to 10000 ppm

Helium is a common working gas for cold atmospheric plasmas (CAPs) and this is often mixed with other gases, such as oxygen and nitrogen, to increase its reactivity. Air is often found in these plasmas and it can be either introduced deliberately as a precursor or entrapped in systems that operate in open atmosphere. In either case, the presence of small traces of air can cause a profound change on the composition of the plasma and consequently its application efficacy. In this paper, a global model for He+Air CAPs is developed, in which 59 species and 866 volume reactions are incorporated, and a new boundary condition is used for the mass transport at the interface between the plasma and its surrounding air gas. The densities of reactive species and the power dissipation characteristics are obtained as a function of air concentrations spanning from 100 to 10000 ppm. As the air concentration increases, the dominant cation changes from O2 + to NO+ and then to NO2 + , the dominant anion changes from O2 - to NO2 - and then to NO3 - , the dominant ground state reactive oxygen species changes from O to O3, and the dominant ground state reactive nitrogen species changes from NO to HNO2. O2(a) is the most abundant metastable species and its density is orders of magnitude larger than other metastable species for all air concentrations considered in the study. Ion Joule heating is found important due to the electronegative nature of the plasma, which leads to the fast decrease of electron density when the air concentration is larger than 1000 ppm. The generation and loss pathways of important biologically relevant reactive species such as O, O2 - , O3, OH, H2O2, NO, HNO2, HNO3 are discussed and differences with the pathways observed in He+O2, He+H2O, Ar+Air and pure air plasmas are highlighted. Based on the simulation results, a simplified chemistry set with 47 species and 109 volume reactions is proposed. This simplified model greatly reduces the computational load while maintaining the accuracy of the simulation results within a factor of 2. The simplified chemistry model is computationally much less intensive, facilitating its integration into multidimensional fluid models for the study of the spatio-temporal evolution of He+Air CAPs