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1.
Biosensors (Basel) ; 13(10)2023 Oct 13.
Article in English | MEDLINE | ID: mdl-37887119

ABSTRACT

Colorectal cancer (CRC) is a prevalent and potentially fatal disease categorized based on its high incidences and mortality rates, which raised the need for effective diagnostic strategies for the early detection and management of CRC. While there are several conventional cancer diagnostics available, they have certain limitations that hinder their effectiveness. Significant research efforts are currently being dedicated to elucidating novel methodologies that aim at comprehending the intricate molecular mechanism that underlies CRC. Recently, microfluidic diagnostics have emerged as a pivotal solution, offering non-invasive approaches to real-time monitoring of disease progression and treatment response. Microfluidic devices enable the integration of multiple sample preparation steps into a single platform, which speeds up processing and improves sensitivity. Such advancements in diagnostic technologies hold immense promise for revolutionizing the field of CRC diagnosis and enabling efficient detection and monitoring strategies. This article elucidates several of the latest developments in microfluidic technology for CRC diagnostics. In addition to the advancements in microfluidic technology for CRC diagnostics, the integration of artificial intelligence (AI) holds great promise for further enhancing diagnostic capabilities. Advancements in microfluidic systems and AI-driven approaches can revolutionize colorectal cancer diagnostics, offering accurate, efficient, and personalized strategies to improve patient outcomes and transform cancer management.


Subject(s)
Artificial Intelligence , Colorectal Neoplasms , Humans , Early Detection of Cancer , Colorectal Neoplasms/diagnosis , Microfluidics , Technology
2.
Life (Basel) ; 13(10)2023 Oct 22.
Article in English | MEDLINE | ID: mdl-37895480

ABSTRACT

Candida is the largest genus of medically significant fungi. Although most of its members are commensals, residing harmlessly in human bodies, some are opportunistic and dangerously invasive. These have the ability to cause severe nosocomial candidiasis and candidemia that affect the viscera and bloodstream. A prompt diagnosis will lead to a successful treatment modality. The smart solution of biosensing technologies for rapid and precise detection of Candida species has made remarkable progress. The development of point-of-care (POC) biosensor devices involves sensor precision down to pico-/femtogram level, cost-effectiveness, portability, rapidity, and user-friendliness. However, futuristic diagnostics will depend on exploiting technologies such as multiplexing for high-throughput screening, CRISPR, artificial intelligence (AI), neural networks, the Internet of Things (IoT), and cloud computing of medical databases. This review gives an insight into different biosensor technologies designed for the detection of medically significant Candida species, especially Candida albicans and C. auris, and their applications in the medical setting.

3.
Sensors (Basel) ; 23(17)2023 Aug 29.
Article in English | MEDLINE | ID: mdl-37687965

ABSTRACT

LoRa technology has gained popularity as one of the most widely used standards for device interconnection due to its ability to cover long distances and energy efficiency, making it a suitable choice for various Internet of Things (IoT) monitoring and control applications. In this sense, this work presents the development of a visual support tool for creating IoT devices with LoRa and LoRaWAN connectivity. This work significantly advances the state of the art in LoRa technology by introducing a novel visual support tool tailored for creating IoT devices with LoRa and LoRaWAN connectivity. By simplifying the development process and offering compatibility with multiple hardware solutions, this research not only facilitates the integration of LoRaWAN technology within educational settings but also paves the way for rapid prototyping of IoT nodes. The incorporation of block programming for LoRa and LoRaWAN using the Arduinoblocks framework as a graphical environment enhances the capabilities of the tool, positioning it as a comprehensive solution for efficient firmware generation. In addition to the visual tool for firmware generation, multiple compatible hardware solutions enable easy, economical, and stable development, offering a comprehensive hardware and software solution. The hardware proposal is based on an ESP32 microcontroller, known for its power and low cost, in conjunction with an RFM9x module that is based on SX127x LoRa transceivers. Finally, three successfully tested use cases and a discussion are presented.

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