Universal Actuation Module and Kinematic Model for Heart Valve Interventional Catheter Robotization

Catheters have been widely used to deal with heart valve diseases. However, the diversity in handle structures and bending curvatures imposes significant complexities in safe delivery and positioning. In this letter, we designed a module for single knob actuation assembled coaxially on the catheter handle, composed of a chuck for universal clamping of diameters from 15 to 45 mm and a position-adjustable shaft to accommodate various spacing between knobs. In addition, we proposed a two-curvature with pseudo joints (TC-PJ) model for bending control of bendable sections (BSs) in catheters. The verification was decoupled into two steps based on the other three deformation patterns. Firstly, comparing the two-curvature (TC) model with pseudo-rigid-body (PRB), constant curvature (CC), and Euler spiral (ES) models to simulate planar bending and elongation, the results showed a more accurate shape representation. Then, five distinct catheters were employed to test the clamping universality of the module and tip positioning precision of the TC-PJ model which took torsion and shear strain into consideration. The root-mean-square error (RMSE) and the standard deviation (SD) of tip position and direction were analysed. Results indicated the module's suitability for clamping these catheters, with the large guide sheath exhibiting minimal position RMSE (SD) of around 0.10 (0.051) mm and 0.049 (2.15) degrees, while the puncture catheter demonstrated the highest position and direction RMSE (SD) extending to about 1.16 (0.53) mm and 0.70 (31.33) degrees, primarily attributed to the coupling of two sequential bendable components. Overall, the proposed actuation module and kinematic model showed the ability of universal manipulation and an average tip position and direction RMSE of 0.65 mm and 0.23 degrees in free space.</p

Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground

Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical solution for endowing chatbots with anthropomorphic language capabilities. However, it takes more than LLMs to enable chatbots that can act as companions. Humans use their understanding of individual personalities to drive conversations. Chatbots also require this capability to enable human-like companionship. They should act based on personalized, real-time, and time-evolving knowledge of their owner. We define such essential knowledge as the \textit{common ground} between chatbots and their owners, and we propose to build a common-ground-aware dialogue system from an LLM-based module, named \textit{OS-1}, to enable chatbot companionship. Hosted by eyewear, OS-1 can sense the visual and audio signals the user receives and extract real-time contextual semantics. Those semantics are categorized and recorded to formulate historical contexts from which the user's profile is distilled and evolves over time, i.e., OS-1 gradually learns about its user. OS-1 combines knowledge from real-time semantics, historical contexts, and user-specific profiles to produce a common-ground-aware prompt input into the LLM module. The LLM's output is converted to audio, spoken to the wearer when appropriate.We conduct laboratory and in-field studies to assess OS-1's ability to build common ground between the chatbot and its user. The technical feasibility and capabilities of the system are also evaluated. OS-1, with its common-ground awareness, can significantly improve user satisfaction and potentially lead to downstream tasks such as personal emotional support and assistance.Comment: 36 pages, 25 figures, Under review at ACM IMWU

Prediction of upcoming urinary tract infection after intracerebral hemorrhage: a machine learning approach based on statistics collected at multiple time points

PurposeAccurate prediction of urinary tract infection (UTI) following intracerebral hemorrhage (ICH) can significantly facilitate both timely medical interventions and therapeutic decisions in neurocritical care. Our study aimed to propose a machine learning method to predict an upcoming UTI by using multi-time-point statistics.MethodsA total of 110 patients were identified from a neuro-intensive care unit in this research. Laboratory test results at two time points were chosen: Lab 1 collected at the time of admission and Lab 2 collected at the time of 48 h after admission. Univariate analysis was performed to investigate if there were statistical differences between the UTI group and the non-UTI group. Machine learning models were built with various combinations of selected features and evaluated with accuracy (ACC), sensitivity, specificity, and area under the curve (AUC) values.ResultsCorticosteroid usage (p &lt; 0.001) and daily urinary volume (p &lt; 0.001) were statistically significant risk factors for UTI. Moreover, there were statistical differences in laboratory test results between the UTI group and the non-UTI group at the two time points, as suggested by the univariate analysis. Among the machine learning models, the one incorporating clinical information and the rate of change in laboratory parameters outperformed the others. This model achieved ACC = 0.773, sensitivity = 0.785, specificity = 0.762, and AUC = 0.868 during training and 0.682, 0.685, 0.673, and 0.751 in the model test, respectively.ConclusionThe combination of clinical information and multi-time-point laboratory data can effectively predict upcoming UTIs after ICH in neurocritical care

Synthesis and biological evaluation of novel benzothiazole derivatives as potential anticancer and antiinflammatory agents

Introduction: Cancer, a significant global health concern, necessitates innovative treatments. The pivotal role of chronic inflammation in cancer development underscores the urgency for novel therapeutic strategies. Benzothiazole derivatives exhibit promise due to their distinctive structures and broad spectrum of biological effects. This study aims to explore new anti-tumor small molecule drugs that simultaneously anti-inflammatory and anticancer based on the advantages of benzothiazole frameworks.Methods: The compounds were characterized by nuclear magnetic resonance (NMR), liquid chromatograph-mass spectrometer (LC-MS) and high performance liquid chromatography (HPLC) for structure as well as purity and other related physicochemical properties. The effects of the compounds on the proliferation of human epidermoid carcinoma cell line (A431) and human non-small cell lung cancer cell lines (A549, H1299) were evaluated by MTT method. The effect of compounds on the expression levels of inflammatory factors IL-6 and TNF-α in mouse monocyte macrophages (RAW264.7) was assessed using enzyme-linked immunosorbent assay (ELISA). The effect of compounds on apoptosis and cell cycle of A431 and A549 cells was evaluated by flow cytometry. The effect of compounds on A431 and A549 cell migration was evaluated by scratch wound healing assay. The effect of compounds on protein expression levels in A431 and A549 cells was assessed by Western Blot assay. The physicochemical parameters, pharmacokinetic properties, toxicity and drug similarity of the active compound were predicted using Swiss ADME and admetSAR web servers.Results: Twenty-five novel benzothiazole compounds were designed and synthesized, with their structures confirmed through spectrogram verification. The active compound 6-chloro-N-(4-nitrobenzyl) benzo[d] thiazol-2-amine (compound B7) was screened through a series of bioactivity assessments, which significantly inhibited the proliferation of A431, A549 and H1299 cancer cells, decreased the activity of IL-6 and TNF-α, and hindered cell migration. In addition, at concentrations of 1, 2, and 4 μM, B7 exhibited apoptosis-promoting and cell cycle-arresting effects similar to those of the lead compound 7-chloro-N-(2, 6-dichlorophenyl) benzo[d] thiazole-2-amine (compound 4i). Western blot analysis confirmed that B7 inhibited both AKT and ERK signaling pathways in A431 and A549 cells. The prediction results of ADMET indicated that B7 had good drug properties.Discussion: This study has innovatively developed a series of benzothiazole derivatives, with a focus on compound B7 due to its notable dual anticancer and anti-inflammatory activities. B7 stands out for its ability to significantly reduce cancer cell proliferation in A431, A549, and H1299 cell lines and lower the levels of inflammatory cytokines IL-6 and TNF-α. These results position B7B7 as a promising candidate for dual-action cancer therapy. The study’s mechanistic exploration, highlighting B7’s simultaneous inhibition of the AKT and ERK pathways, offers a novel strategy for addressing both the survival mechanisms of tumor cells and the inflammatory milieu facilitating cancer progression

Loss of Setd2 Induces the Upregulation of Genes Related to Akt/Mtor Signaling Pathway

Patients with polycystic kidney disease (PKD) have a high risk of developing renal cell carcinoma (RCC). SET domain–containing 2(SETD2) is the only molecule known to regulate lysine trimethylation (H3K3me3) of histone H3 in human tissue, and SETD2 is identified as a tumor suppressor in ccRCC. Although there are some studies revealing some mechanism about PKD developing ccRCC, the underlying mechanism remains largely reported. We collected the Kidney samples from SETD2 conditional knockout mice described before (Rao, 2021) and detected the expression levels of some important genes related to Akt/mTOR signaling pathway. Besides, we found that SETD2 is closely related to Akt/mTOR signaling pathway and can be regulated by Western blot analysis, qRT-PCR and immunofluorescence. For clinical translation, the cross-talks between SETD2 and Akt/mTOR signaling may provide a potential strategy to prevent tumorigenesis in patients with ccRCC therapy

Individual Tree Segmentation from Side-View LiDAR Point Clouds of Street Trees Using Shadow-Cut

Segmentation of vegetation LiDAR point clouds is an important method for obtaining individual tree structure parameters. The current individual tree segmentation methods are mainly for airborne LiDAR point clouds, which use elevation information to form a grid map for segmentation, or use canopy vertices as seed points for clustering. Side-view LiDAR (vehicle LiDAR and hand-held LiDAR) can acquire more information about the lower layer of trees, but it is a challenge to perform the individual tree segmentation because the structure of side-view LiDAR point clouds is more complex. This paper proposes an individual tree segmentation method called Shadow-cut to extract the contours of the street tree point cloud. Firstly, we separated the region of the trees using the binary classifier (e.g., support vector machine) based on point cloud geometric features. Then, the optimal projection of the 3D point clouds to the 2D image is calculated and the optimal projection is the case where the pixels of the individual tree image overlap the least. Finally, after using the image segmentation algorithm to extract the tree edges in the 2D image, the corresponding 3D individual tree point cloud contours are matched with the pixels of individual tree edges in the 2D image. We conducted experiments with the proposed method on LiDAR data of urban street trees, and the correctness, completeness, and quality of the proposed individual tree segmentation method reached 91.67%, 85.33%, and 79.19%, which were superior to the CHM-based method by 2.70%, 6.19%, and 7.12%, respectively. The results show that this method is a practical and effective solution for individual tree segmentation in the LiDAR point clouds of street trees

Effects of the Oil and Fat Feeding Period on the Nutritional Composition and Functional Properties of Eggs

Lipids are commonly incorporated into the diets of laying hens at a rate of 1% to 2% during production. However, the effects on egg quality can vary based on the source and timing of lipid addition. Hence, this experiment was conducted to investigate the impacts of adding the same concentration of soybean oil, lard, and mixed oils (1.5%) to the daily feed of layer during two feeding periods. This study aimed to assess the changes in nutritional composition and functional properties and offer valuable insights to determine suitable types of oils and fat. In this study, the experiment was conducted in two test periods, 7 days and 21 days after the addition of the lipids, to assess the effects on the nutritional composition and functional properties of eggs. The study revealed the following results: (1) Changes in the yolk’s nutritional composition. Compared to the 7-day addition period, the inclusion of lard significantly increased the unsaturated fatty acids after 21 days; (2) Changes in the albumen’s nutritional composition. Compared to the 7-day addition period, the inclusion of lard and mixed oils significantly reduced the essential and nonessential amino acids after 21 days; (3) Changes in the functional characteristics of the eggs. After 21 days of addition, the eggs from the soybean oil group exhibited significantly higher foaming and emulsifying properties compared to the groups supplemented with lard and mixed oils; (4) Changes in the antioxidant capacity of the eggs. Compared to the 7-day addition period, the inclusion of all oils and fat significantly increased the superoxide dismutase (SOD) content in egg yolk after 21 days. The aim of this experiment was to provide valuable scientific data to assist producers in making informed decisions regarding the utilization of feeding oils

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method

Characterization of RNA modifications in gastric cancer to identify prognosis‐relevant gene signatures

Abstract Background Most human genes have diverse transcript isoforms, which mainly arise from alternative cleavage and polyadenylation (APA) at 3′ ends. N7‐methylguanosine (m7G) is also an essential epigenetic modification at the 5′ end. However, the contribution of these two RNA modifications to the development, prognosis, regulation mechanisms, and drug sensitivity of gastric cancer (GC) is unclear. Methods The expression data of 2412 patients were extracted from 12 cohorts and the RNA modification patterns of 20 marker genes were systematically identified into phenotypic clusters using the unsupervised clustering approach. Following that, we developed an RNA modification model (RMscore) to quantify each GC patient's RNA modification index. Finally, we examined the correlation between RMscore and clinical features such as survival outcomes, molecular subtypes identified by the Asian Cancer Research Group (ACRG), posttranscriptional regulation, and chemotherapeutic sensitivity in GC. Results The samples were categorized into two groups on the basis of their RMscore: high and low. The group with a low RMscore had a bad prognosis. Moreover, the low RMscore was associated with KRAS, Hedgehog, EMT, and TGF‐β signaling, whereas a high RMscore was related to abnormal cell cycle signaling pathway activation. The findings also revealed that the RMscore contributes to the regulation of the miRNA‐mRNA network. Drug sensitivity analysis revealed that RMscore is associated with the response to some anticancer drugs. Conclusions The RMscore model has the potential to be a useful tool for prognosis prediction in patients with GC. A comprehensive investigation of APA‐RNA and m7G‐RNA modifications may reveal novel insights into the epigenetics of GC and aid in the development of more effective treatment strategies