Multiplex single-cell droplet PCR with machine learning for detection of high-risk human papillomaviruses

The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence

Cervical cancer (CC) continues to be a major worldwide health concern, profoundly impacting the lives of countless females worldwide. In low- and middle-income countries (LMICs), where CC prevalence is high, innovative, and cost-effective approaches for prevention, diagnosis, and treatment are vital. These approaches must ensure high response rates with minimal side effects to improve outcomes. The study aims to compile the latest developments in the field of CC, providing insights into the promising future of CC management along with the research gaps and challenges. Integrating biotechnology and artificial intelligence (AI) holds immense potential to revolutionize CC care, from MobileODT screening to precision medicine and innovative therapies. AI enhances healthcare accuracy and improves patient outcomes, especially in CC screening, where its use has increased over the years, showing promising results. Also, combining newly developed strategies with conventional treatment options presents an optimal approach to address the limitations associated with conventional methods. However, further clinical studies are essential for practically implementing these advancements in society. By leveraging these cutting-edge technologies and approaches, there is a substantial opportunity to reduce the global burden of this preventable malignancy, ultimately improving the lives of women in LMICs and beyond.

The development of droplet-based microfluidic virus detection technology for human infectious diseases

Virus-based human infectious diseases have a significant negative impact on people's health and social development. The need for quick, accurate, and early viral infection detection in preventive medicine is expanding. A microfluidic control is particularly suitable for point-of-care-testing virus diagnosis due to its advantages of low sample consumption, quick detection speed, simple operation, multi-functional integration, small size, and easy portability. It is also thought to have significant development potential and a wide range of application prospects in the research on virus detection technology. In an effort to aid researchers in creating novel microfluidic tools for virus detection, this review highlights recent developments of droplet-based microfluidics in virus detection research and also discusses the challenges and opportunities for rapid virus detection.

Advanced technologies towards improved HPV diagnostics

Persistent infection with high-risk types of human papillomaviruses (HPV) is a major cause of cervical cancer, and an important factor in other malignancies, for example, head and neck cancer. Despite recent progress in screening and vaccination, the incidence and mortality are still relatively high, especially in low-income countries. The mortality and financial burden associated with the treatment could be decreased if a simple, rapid, and inexpensive technology for HPV testing becomes available, targeting individuals for further monitoring with increased risk of developing cancer. Commercial HPV tests available in the market are often relatively expensive, time-consuming, and require sophisticated instrumentation, which limits their more widespread utilization. To address these challenges, novel technologies are being implemented also for HPV diagnostics that include for example, isothermal amplification techniques, lateral flow assays, CRISPR-Cas-based systems, as well as microfluidics, paperfluidics and lab-on-a-chip devices, ideal for point-of-care testing in decentralized settings. In this review, we first evaluate current commercial HPV tests, followed by a description of advanced technologies, explanation of their principles, critical evaluation of their strengths and weaknesses, and suggestions for their possible implementation into medical diagnostics.

Distance-Regulated Photoelectrochemical Sensor "Signal-On" and "Signal-Off" Transitions for the Multiplexed Detection of Viruses Exposed in the Aquatic Environment

Photochemical (PEC) sensors were severely limited for multiplex detection applications due to the cross interference between multiplex signals at the single recognition interface. In this work, a distance-regulated PEC sensor was developed for multiplex detection by using an i-Motif sequence with conformational transformation activity as the signal transduction unit. Through dynamic regulation of the spatial distance between the end site of the functional sequence and the electrode material, the photogenerated electrons on the surface of the sensor were directionally transferred. Thus, a PEC sensor with "signal-on" and "signal-off" dual signal output modes was developed for simultaneous detection of multitarget molecules. Combining isothermal nucleic acid amplification, the PEC sensor constructed in this work was successfully applied to the detection of two virus (Norovirus and Rotavirus) nucleic acid sequences. Under the optimal condition, this bioassay protocol exhibits a linear range of 0.01-100 nM for both viruses with detection limits of 0.72 and 0.53 pM, respectively. In this study, a stimulus-mediated distance regulation strategy successfully addressed the transduction of multiplex detection signals at the single recognition interface of the PEC sensor. It is expected that the technical barriers to multiplex detection of PEC sensors will be overcome and the application of PEC sensing technology will be expanded in the field of environmental analysis.

Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer

Tumor-derived extracellular vesicles (EVs) are promising to monitor early stage cancer. Unfortunately, isolating and analyzing EVs from a patient's liquid biopsy are challenging. For this, we devised an EV membrane proteins detection system (EV-MPDS) based on Förster resonance energy transfer (FRET) signals between aptamer quantum dots and AIEgen dye, which eliminated the EV extraction and purification to conveniently diagnose lung cancer. In a cohort of 80 clinical samples, this system showed enhanced accuracy (100% versus 65%) and sensitivity (100% versus 55%) in cancer diagnosis as compared to the ELISA detection method. Improved accuracy of early screening (from 96.4% to 100%) was achieved by comprehensively profiling five biomarkers using a machine learning analysis system. FRET-based tumor EV-MPDS is thus an isolation-free, low-volume (1 μL), and highly accurate approach, providing the potential to aid lung cancer diagnosis and early screening.

A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples

Human papillomavirus (HPV) is a sexually transmitted virus. Cervical cancer is one of the highest incidences of cancer, almost all patients are accompanied by HPV infection. In addition, the occurrence of a variety of cancers is also associated with HPV infection. HPV vaccination has gained widespread popularity in recent years with the increase in public health awareness. In this context, HPV testing not only needs to be sensitive and specific but also needs to trace the source of HPV infection. Through machine learning and deep learning, information from medical examinations can be used more effectively. In this review, we discuss recent advances in HPV testing in combination with machine learning and deep learning.