İmpedimetrik Koku Biyosensörü ile Yapay Zekâ Destekli Mantar Enfeksiyonu Tespiti

Year 2025, Volume: 1 Issue: 1, 1 - 12, 31.01.2025

Efe Kayra Soylu Soukaina Safi Mustafa Altay Atalay , Murat Peker

Abstract

Mantar enfeksiyonları oldukça yaygın olmakla birlikte, hızlı ve doğru bir tedavi yöntemi uygulanmazsa çok tehlikeli sonuçlara ve hatta ölüme bile yol açabilmektedir. Mantar enfeksiyonunu teşhis etmek için günümüzde kullanılan, yaklaşık bir ila üç gün sürebilen kültürleme gibi eski moda teşhis çözümleri hızlı ve etkili tedavi imkanını kısıtlamaktadır. Çalışmamızda hızlı, uygun maliyetli ve doğru bir şekilde mantar suşlarının tespit ve sınıflandırılmasını içeren yeni bir yöntem sunulmaktadır. Koku reseptörlerinin, koku molekülleri ile bağlanmasını taklit eden jelatin bazlı bir hidrojel kaplamanın iletken bir bakır tele uygulanmasıyla, koku tespiti için impedimetrik bir algılama konsepti oluşturulmuştur. Hidrojel elektrolit, yapısal kararlılığı için gliserol ve daha iyi iletkenliği için grafit tozu eklenerek daha da geliştirilmiştir. Sensör fabrikasyonu ise modifiye edilmiş hidrojelin, bakırdan yapılmış tellere uygulanması ile yapılmıştır. Sensör yapısı kontrollü bir ortama yerleştirilen Candida albicans, Candida glabrata ve Candida tropicalis kültür testlerinden gelen koku moleküllerine maruz bırakılmıştır. Hidrojel yapısında meydana gelen empedans değişikliklerinin Random Forest makine öğrenme algoritmasıyla sınıflandırılmasıyla %94 sınıflandırma başarısı ile enfeksiyonlar tespite dildi. Bu yeni koku biyosensörü, yerinde teşhis çağında bir devrime yol açabilecek potansiyele sahiptir. Mantar enfeksiyonlarının hızlı, basit ve hassas şekilde tespiti ve sınıflandırılması ile gereksiz tedavi maliyetleri düşürülecek ve sağlık riskleri azaltılacaktır.

Keywords

Mantar Enfeksiyonu, İmpedimetrik Biyosensör, Elektronik Burun, Yapay Zeka

AI-Assisted Fungal Infection Detection Using Impedimetric Odour Biosensors

Abstract

Yeast infections have been widely recognized and if no quick and accurate treatment method is applied, they can be very dangerous and might even turn into death. In comparison with old-fashioned diagnostic solutions such as culturing, which takes around one to three days to reveal yeast infections, rapid and effective treatment is often not initiated. In the current study a novel method is offered involving the extraction of yeast fungal strain identification in a rapid, cost-effective, and accurate way. Through the application of agelatin-based hydrogel coating that represents the way in which odor receptors attach to cells a sensing concept for impedimetric odor was constructed. The hydrogel was further improved by adding glycerol for its structural stability and graphite powder for its better conductivity. The process of making a sensor involved applying the modified hydrogel to wires made of copper. The sensor was then exposed to the odor molecules from culture tests of Candida albicans, Candida glabrata, and Candida tropicalis, which were placed in a controlled environment. Changes in impedance took place, and these measurements were analyzed using a Random Forest machine learning algorithm that helped to get 94% classification success. This new testing process may lead to a revolution in the era of clinical diagnostics. It will enable speediness, simplicity, as well as precision in the detection of yeast fungal infections, which, in turn, will decrease health risks leading to unnecessary treatment costs by approved drug companies.

Keywords

Fungal Infection, Impedimetric Biosensor, Electronic Nose, Artificial Intelligence

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