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As a potentially life-threatening immune response, sepsis is often caused by the body's interaction with various microorganisms, including bacteria, viruses, and fungi. This phenomenon is one of the leading causes of death worldwide, posing numerous challenges for medical professionals. Therefore, rapid diagnosis and effective treatment implementation with appropriate antibiotics are crucial for improving treatment outcomes. However, this issue is challenging, as current molecular detection methods are mostly time-consuming, expensive, and reliant on advanced equipment and specialized personnel. Considering that every moment of delay in sepsis diagnosis and treatment increases the likelihood of death, it is imperative to utilize diagnostic devices capable of reducing the time required to identify the onset of infection until appropriate therapy is administered. In this regard, advanced testing tools capable of detecting clinically relevant biomarkers play a significant role. Electrochemical biosensors, offering advantages such as high sensitivity, rapid response, compact size, and low cost, are well-suited to meet clinical needs. This review article examines the current status of electrochemicalbased point-of-care detection platforms and their limitations for diagnosing and monitoring sepsis. It also discusses future directions and solutions to enhance and expand these technologies. [ABSTRACT FROM AUTHOR]

Dengue fever, a mosquito-borne viral infection, poses a significant global health threat, and early diagnosis is crucial for effective disease management. Human infection typically occurs through infected Aedes mosquitoes, such as Aedes aegypti or Aedes albopictus. The disease spectrum ranges from asymptomatic infection and mild febrile illness (dengue fever) to more severe conditions, such as dengue hemorrhagic fever and dengue shock syndrome. Dengue fever is characterized by severe headache, high fever, skin rash, muscle and joint pain, nausea, and vomiting. Dengue hemorrhagic fever is marked by high fever, an enlarged liver, hemorrhagic phenomena, shock, and often cardiovascular disorders. Dengue fever is transmitted through urban (human transmission cycle) and sylvatic (animal transmission cycle) cycles. The demand for early detection of this virus has increased to control the spread of infectious diseases and protect humankind from its harmful effects. Various methods can be employed for the laboratory diagnosis of this virus to detect the virus itself, viral nucleic acids, antibodies, or antigens, or a combination of these approaches. Recently, biosensors have emerged as a potential tool for detecting and quantifying viruses, offering fast detection, relative cost-effectiveness, and high sensitivity and selectivity compared to conventional diagnostic methods such as immunological and molecular techniques. Most biosensors employ electrochemical detection techniques with transducers, owing to their easy construction, low cost, ease of use, and portability. [ABSTRACT FROM AUTHOR]

A diagnostic test based on microfluidic image cytometry and machine learning has been designed and applied for accurate classification of erythrocytes and leukocytes, including a unique fully-automated 5-part quantitative differentiation into neutrophils, lymphocytes, monocytes, eosinophils, and basophils, using minute amounts of whole blood in a single counting chamber. A low-cost disposable multilayer microdevice for microfluidic image cytometry was developed that comprises a 1 mm × 22 mm × 70 μm (w × l × h) rectangular microchannel, allowing the analysis of trace volume of blood (20 μL) for each assay. Automated analysis of digitized binary images applying a border following algorithm was performed allowing the qualitative analysis of erythrocytes. Bright-field imaging was used for the detection of erythrocytes and fluorescence imaging for 5-part differentiation of leukocytes after acridine orange staining, applying a convolutional neural network enabling unparalleled speed for identification and automated morphology classification yielding 98.57% accuracy. Blood samples were obtained from 30 volunteers and count values did not significantly differ from data obtained using a commercial automated hematology analyzer. [ABSTRACT FROM AUTHOR]