Publications by authors named "C Chiang"

BACKGROUND Studies using transgenic mouse models have demonstrated that estrogen is necessary for the development of cervical cancer, particularly in tissues responsive to estrogen. Estrogen also protects cervical cancer cells from apoptosis, suggesting its role in the survival and persistence of cancer cells. CASE REPORT An 84-year-old woman with diabetes mellitus, hypertension, and stage III chronic renal failure was diagnosed with cervical squamous cell carcinoma, FIGO stage IB2.

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Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions.

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Oral squamous-cell carcinoma (OSCC) poses significant treatment challenges due to its high recurrence rates and the limitations of current therapies. Titanium dioxide (TiO) nanoparticles are promising radiosensitizers, while bacterial outer membrane vesicles (OMVs) are known for their immunomodulatory properties. This study investigates the potential of OMV-encapsulated TiO nanoparticles (TiO@OMV) to combine these effects for improved OSCC treatment.

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The EZ DEVICE is an integrated fluorescence microflow cytometer designed for automated cell phenotyping and enumeration using artificial intelligence (AI). The platform consists of a laser diode, optical filter, objective lens, CMOS image sensor, and microfluidic chip, enabling automated sample pretreatment, labeling, and detection within a single compact unit. AI algorithms segment and identify objects in images captured by the CMOS sensor at 532 and 586 nm emission wavelengths.

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