Significant reduction of the culturing time required for bacterial identification and antibiotic susceptibility determination by infrared spectroscopy.

Rapid testing of bacteria for antibiotic susceptibility is essential for effective treatment and curbing the emergence of multidrug-resistant bacteria. The misuse of antibiotics, coupled with the time-consuming classical testing methods, intensifies the threat of antibiotic resistance, a major global health concern. In this study, employing infrared spectroscopy-based machine learning techniques, we significantly shortened the time required for susceptibility testing to 10 hours, a significant improvement from the 24 hours in our previous studies as well as the conventional methods that typically take at least 48 hours.

Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics.

Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains.

Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning.

Bacteremia refers to the presence of bacteria in the bloodstream, which can lead to a serious and potentially life-threatening condition. In oncology patients, individuals undergoing cancer treatment have a higher risk of developing bacteremia due to a weakened immune system resulting from the disease itself or the treatments they receive. Prompt and accurate detection of bacterial infections and monitoring the effectiveness of antibiotic therapy are essential for enhancing patient outcomes and preventing the development and dissemination of multidrug-resistant bacteria.

Quick Detection of and in Patients' Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning.

Bacterial resistance to antibiotics is a primary global healthcare concern as it hampers the effectiveness of commonly used antibiotics used to treat infectious diseases. The development of bacterial resistance continues to escalate over time. Rapid identification of the infecting bacterium and determination of its antibiotic susceptibility are crucial for optimal treatment and can save lives in many cases.

Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning.

Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society.