FH535 Suppresses Osteosarcoma Growth In Vitro and Inhibits Wnt Signaling through Tankyrases

Osteosarcoma (OS) is an aggressive primary bone tumor which exhibits aberrantly activated Wnt signaling. The canonical Wnt signaling cascade has been shown to drive cancer progression and metastasis through the activation of β-catenin. Hence, small molecule inhibitors of Wnt targets are being explored as primary or adjuvant chemotherapy. In this study, we have investigated the ability of FH535, an antagonist of Wnt signaling, to inhibit the growth of OS cells. We found that FH535 was cytotoxic in all OS cell lines which were tested (143b, U2OS, SaOS-2, HOS, K7M2) but well tolerated by normal human osteoblast cells. Additionally, we have developed an in vitro model of doxorubicin-resistant OS and found that these cells were highly responsive to FH535 treatment. Our analysis provided evidence that FH535 strongly inhibited markers of canonical Wnt signaling. In addition, our findings demonstrate a reduction in PAR-modification of Axin2 indicating inhibition of the tankyrase 1/2 enzymes. Moreover, we observed inhibition of auto-modification of PARP1 in the presence of FH535, indicating inhibition of PARP1 enzymatic activity. These data provide evidence that FH535 acts through the tankyrase 1/2 enzymes to suppress Wnt signaling and could be explored as a potent chemotherapeutic agent for the control of OS

Medibot for Emergency Vehicle

To create a medical robot that would be installed in an ambulance and use IoT to observe and communicate so that the patient might receive care before being brought to the hospital. In the case of a mishap, installing a finger print sensor will enable the hospital emergency room, police station, and the patient’s guardian to be informed of the unfamiliar patient’s bio-data. There are still significant problems with overpopulation and health-related illiteracy in India, and an accident-related mortality happens every minute. To build a clever smart health system, a MediBoT made up of sensors and microcontrollers is intended. It will assess the body’s condition and send information to the IoT

Exploring Explainable Artificial Intelligence for Transparent Decision Making

Artificial intelligence (AI) has become a potent tool in many fields, allowing complicated tasks to be completed with astounding effectiveness. However, as AI systems get more complex, worries about their interpretability and transparency have become increasingly prominent. It is now more important than ever to use Explainable Artificial Intelligence (XAI) methodologies in decision-making processes, where the capacity to comprehend and trust AI-based judgments is crucial. This abstract explores the idea of XAI and how important it is for promoting transparent decision-making. Finally, the development of Explainable Artificial Intelligence (XAI) has shown to be crucial for promoting clear decision-making in AI systems. XAI approaches close the cognitive gap between complicated algorithms and human comprehension by empowering users to comprehend and analyze the inner workings of AI models. XAI equips stakeholders to evaluate and trust AI systems, assuring fairness, accountability, and ethical standards in fields like healthcare and finance where AI-based choices have substantial ramifications. The development of XAI is essential for attaining AI's full potential while retaining transparency and human-centric decision making, despite ongoing hurdles

Medibot for Emergency Vehicle

To create a medical robot that would be installed in an ambulance and use IoT to observe and communicate so that the patient might receive care before being brought to the hospital. In the case of a mishap, installing a finger print sensor will enable the hospital emergency room, police station, and the patient’s guardian to be informed of the unfamiliar patient’s bio-data. There are still significant problems with overpopulation and health-related illiteracy in India, and an accident-related mortality happens every minute. To build a clever smart health system, a MediBoT made up of sensors and microcontrollers is intended. It will assess the body’s condition and send information to the IoT

Exploring Explainable Artificial Intelligence for Transparent Decision Making

Artificial intelligence (AI) has become a potent tool in many fields, allowing complicated tasks to be completed with astounding effectiveness. However, as AI systems get more complex, worries about their interpretability and transparency have become increasingly prominent. It is now more important than ever to use Explainable Artificial Intelligence (XAI) methodologies in decision-making processes, where the capacity to comprehend and trust AI-based judgments is crucial. This abstract explores the idea of XAI and how important it is for promoting transparent decision-making. Finally, the development of Explainable Artificial Intelligence (XAI) has shown to be crucial for promoting clear decision-making in AI systems. XAI approaches close the cognitive gap between complicated algorithms and human comprehension by empowering users to comprehend and analyze the inner workings of AI models. XAI equips stakeholders to evaluate and trust AI systems, assuring fairness, accountability, and ethical standards in fields like healthcare and finance where AI-based choices have substantial ramifications. The development of XAI is essential for attaining AI's full potential while retaining transparency and human-centric decision making, despite ongoing hurdles

Performance of recycled Bakelite plastic waste as eco-friendly aggregate in the concrete beams

The use of plastic waste as a partial or complete replacement for coarse aggregate in concrete mixtures has been studied in recent years. However, the quality and quantity of coarse plastic waste particles have been a challenge. This study aims to investigate the mechanical performance of concrete with Bakelite plastic waste as a partial replacement for coarse aggregate. Six different concrete mixtures with various Bakelite dosages, ranging from 0 % to 10 %, were tested. The results indicate that the addition of Bakelite plastic alters the behaviour of the concrete and reduces compressive and flexural strengths at lower dosages. The inclusion of Bakelite waste in concrete mixtures generally leads to a decrease in compressive and split tensile strength, with the exception of the mixture containing 6 % Bakelite, which showed increased strength. Although there is a slight reduction in flexural strength, Bakelite waste prevents sudden specimen breakage and maintains specimen integrity. The ultimate load capacity of reinforced concrete beams with Bakelite waste is generally lower compared to the control beam, except for the 8 % waste Bakelite beam which demonstrated a similar ultimate load capacity of 60 kN. Although managing Bakelite waste can be difficult because it can lead to the creation of microplastics in landfills over time, utilizing Bakelite waste in concrete can be a sustainable method of waste management. The innovative use of Bakelite waste as a partial replacement for coarse aggregate in concrete offers a sustainable solution to the problem of waste management and addresses the environmental concerns related to the disposal of non-biodegradable plastics. This research provides a practical solution for developing eco-friendly and cost-effective construction materials while promoting sustainable waste management practices

A Web-Based Platform for Segmentation of Abdominal Organs on CT Images

Part 10: Image UnderstandingInternational audienceWith the development trend of “Internet Plus”, medical staffs hope to change the traditional way of medical diagnosis through the Internet, and medical image processing requires a lot of data, but it is very difficult for hospitals to achieve image data sharing. Therefore, this paper designs a Web-based platform for segmentation of abdominal organs on CT images. Using the software of Xojo and Oray as the development and design of this platform, this paper studies the technology of conversion medical DICOM format image to BMP format, as well as image smoothing, edge detection, image expansion, image corrosion and liver region segmentation. The results of this paper show that the platform realized the image transformation, displaying of medical DICOM format image based on web and the segmentation of CT image of abdominal organs, which is suitable for multiple operating systems, and is convenient for hospital clinical departments to view medical images at any time, and improve the diagnostic speed and accuracy. In addition, a large number of medical image data can be collected through the platform