Background
Chronic musculoskeletal pain is the most common chronic pain in clinical practice and often occurs due to disease or injury. The impact of chronic pain on the entire life cycle is profound, with the Global Burden of Disease (GBD) report stating that CMP disease accounts for 17% of the number of years of disability worldwide [1] and is one of the leading causes of unemployment and early retirement [2] [3]. Chronic musculoskeletal pain (CMP) is more common in the elderly and has different effects on physical function, and psychological and social disorders. The World Health Organization (WHO) reported in 2015 that CMP diseases pose a threat to the healthy elderly population and cause a significant socio-economic burden [4]. However, due to the aging population and lifestyle changes, the prevalence of chronic musculoskeletal pain is on the rise, and CMP has become one of the most common reasons for patients to seek medical treatment [5].
As an independent disease, chronic musculoskeletal pain has its own medical definition and classification, which can be mainly divided into neuropathic (Np) pain and nociceptive (No) pain according to different etiologies. At present, pain classification mainly relies on subjective scoring systems (such as visual analog scale (VAS), etc.), which can only roughly distinguish the degree of pain, but cannot obtain objective and reliable classification results. However, different types of pain need to be treated according to their different pathogenesis. Due to the lack of reliable classification means, chronic musculoskeletal pain is often not properly differentiated and treated, which not only has a serious impact on the quality of life of patients but also increases the potential risk of drug abuse. The accurate distinction between neuropathic CMP and nociceptive CMP helps improve the efficiency of diagnosis and treatment and saves medical resources. Therefore, there is an urgent need for a chronic musculoskeletal pain classification device to assist doctors in disease diagnosis and treatment.
Overview
The expression of miRNAs is distinct in different types of CMP. This project aims to realize the programmable quantitative detection of a variety of miRNAs through the enzyme-free isothermal nucleic acid amplification technique HCR coupled with CRISPR/Cas12a technology. On this basis, a portable paper chip and incubator suitable for various application scenarios, combined with a smartphone and a classification model, are developed to realize CMP classification and assessment. For the specific design and results of each part, please refer to the pages "Results", "Hardware" and "Modeling".
Project System
Isothermal amplification and transduction of nucleic acid signals
In this system, miRNA was used as the detection target. Due to the small size of miRNAs and difficulty to detect, amplification technology is often used in miRNA detection to achieve signal amplification. HCR technology is an enzyme-free nucleic acid polymerization reaction with high compatibility and throughput and can amplify several targets to detectable levels within 30 minutes at 37℃ isothermal conditions. To realize POCT, HCR technology was used to perform isothermal amplification of target miRNAs. On this basis, the CRISPR/Cas12a detection system was introduced to assist in the specific detection of HCR amplification products, so as to generate fluorescence signals and realize signal transduction. This project makes full use of the advantages of HCR enzyme-free isothermal amplification and the high specificities of CRISPR and combines the two to form HCR-CRISPR/Cas system, which can quantitatively detect the target miRNAs with high sensitivity and specificity.
Paper-based chip system
To enable POCT detection of HCR and CRISPR reactions, we developed paper chips that are fast, low-cost, and simple to operate. The natural capillary force and pattern-based hydrophobic channel design of the paper chip can control the directional flow of liquid, and can be combined with the temperature control and fluorescence detection module to complete the detection task. The paper chip can pre-embed dried HCR reagent in the reaction pad and can detect multiple miRNA targets simultaneously by embedding different HCR probes. The operation can be completed by adding the CRISPR reagent and sample at the same time. In addition, we also verified the compatibility of the paper chip with biological reactions, HCR reagent pre-embedding, and HCR and CRISPR reactions. Paper chips are easy to operate, compact, user-friendly, and have great potential in the direction of point-of-care diagnosis.
Temperature control heating system
In order to meet the constant temperature requirement of 37℃ for the biological reaction system on the paper chip, we made the following small heating table. The heating table is mainly composed of four parts: PT1000 thermal sensor, silicone rubber heating film, TCM1040 temperature controller, and structure shell. The user presets the parameters of the temperature control module through the host computer. After entering the working state, the temperature controller controls the heating of the silicone rubber heating film. The PT1000 thermal sensor collects the temperature data of the heating film and feeds the data back to the temperature controller so that the system can maintain a constant temperature when it reaches 37℃.
Optical inspection hardware
As a good control and detection platform, smartphones have been widely used in the field of POCT. In order to realize the visualization and quantitative detection of four channels at the same time, we designed an optical hardware module combined with a smartphone. The fluorescence detection module uses an oblique light path, a smartphone flash as the light source, a camera as the optical sensor. The device can be combined with paper chips to achieve a simpler and faster detection method.
Optical inspection software
We developed an APP for the analysis of fluorescence results. Some core functions of the APP are to analyze fluorescence information and display fluorescence intensity. The user interaction interface, photo interface, and diagnostic result presentation interface are built by using Android Studio, IDEA, PyCharm, and other technical means. Image acquisition, fluorescence region selection, fluorescence intensity measurement, and detection results analysis are carried out through software programming. The detection of fluorescence intensity is realized by analyzing the G value of the picture.
Reference
[1] Vos, T. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390, 1211–1259 (2017).
[2] Schofield, D. et al. The impact of back problems on retirement wealth. Pain 153(1), 203–210 (2012).
[3] Schofield, D. J. et al. Lost productive life years caused by chronic conditions in Australians aged 45–64 years, 2010–2030. Med. J. Aust. 203, 260–260 (2015).
[4] Briggs, A. M. et al. Musculoskeletal health conditions represent a global threat to healthy aging: a report for the 2015 World Health Organization world report on ageing and health. Gerontologist 56, S243–S255 (2016).
[5] Jordan, K. P. et al. International comparisons of the consultation prevalence of musculoskeletal conditions using population-based healthcare data from England and Sweden. Ann. Rheum. Dis. 73, 212–218 (2014).
[6] Dayer, Camille Florine, et al. "Differences in the miRNA signatures of chronic musculoskeletal pain patients from neuropathic or nociceptive origins." PloS one 14.7 (2019): e0219311.
[7] Seok, Youngung, et al. "A paper-based device for performing loop-mediated isothermal amplification with real-time simultaneous detection of multiple DNA targets." Theranostics 7.8 (2017): 2220.