Diagnosis Of Early-stage Disease Research Articles

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks.

Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model's architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model's accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.

Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype

E-health is being adapted in modern hospitals as a significant addition to the existing healthcare services. To this end, modern hospitals urgently require a mobile, high-capacity, secure, and cost-effective communication infrastructure. In this paper, we explore potential applications of a hybrid broadband power line communication (PLC) and Wi-Fi in an indoor hospital scenario. It utilizes the existing power line cables and Wi-Fi plug-and-play devices for indoor broadband communication. Broadband power line (BPL) adaptors with Wi-Fi outputs are used to build an access network in hospitals, particularly in areas where the wireless router signal is poor. The Tenda PH10 AV1,000 AC Wi-Fi power line adapter is a set of BPL adapters that offer operational bandwidth of up to 1,000 Mbps. These adapters are based on the HomePlug AV2 protocol and can provide a data rate up to 200 Mbps on the physical layer. An experiment using the PLC Wi-Fi kit is carried out to show that a Wi-Fi and PLC hybrid network is the best candidate to provide wide range of practical applications in a hospital including, but not limited to, telemedicine, electronic medical records, early-stage disease diagnosis, health management, real-time monitoring, and remote surgeries.

Accuracy of endobronchial ultrasound-guided transbronchial needle aspiration for staging of non-small cell lung cancer.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is routinely performed to confirm a lung cancer diagnosis and/or to clinically stage disease. EBUS-TBNA findings may be used to determine whether patients can be offered potentially curative surgery. In this study, we evaluated the reporting in our service of EBUS-TBNA cytology for early-stage (operable) non-small cell lung cancer (NSCLC), focusing on diagnostic accuracy and analyzing cases with discordant cytologic and post-surgical histopathologic conclusions. Cytology slides and cytopathology reports of 120 NSCLC patients who had undergone EBUS-TBNA and lobectomy in our hospital system between 2015 and 2021 were retrospectively reviewed. Of 290 lymph nodes (110 cases) able to be reviewed, interpretation of 48 lymph nodes was discordant with the original cytopathology report. This included 31 lymph nodes originally reported as adequate, which were found to be non-diagnostic on review. The diagnostic accuracy (benign/malignant) of lymph nodes that were sampled at EBUS-TBNA and excised at surgery was 89%. Specific examination of cases where EBUS-TBNA cytology did not reflect post-surgical findings illustrated important features and limitations of the procedure. These included potential misclassification of lymph node stations, the presence of multiple, variably involved nodes at lymph node stations, and the failure to detect small volume disease. Continuous evaluation of EBUS-TBNA performance identifies technical limitations and areas of improvement for cytopathology reporting. This is increasingly important in an era where lung cancer screening is expected to increase diagnosis of early-stage disease and with the advent of novel treatments, including non-surgical management options.

Free space whispering gallery mode microlasers as highly sensitive biosensors

High-precision biosensors for single or few molecules detection play a central role in numerous key fields, such as environmental monitoring and healthcare for early-stage disease diagnosis. In the last decade, laser biosensors have been investigated as proofs of concept, and several technologies have been proposed. Here we propose a demonstration of polymeric whispering gallery microlasers as biosensors for detecting proteins at low concentrations. Free space microlasers have the great advantage of working without any need for waveguiding for input excitation or output signal detection. The photonic microsensors can be easily patterned on microscope slides and operate in air and solution. We could detect down to 400 pg of protein without specific binding, and few tens of pg/mL with specific binding.

Choice of Nanoparticles for Theranostics Engineering: Surface Coating to Nanovalves Approach.

Surface engineered nanoparticles (metallic and nonmetallic) have gained tremendous attention for precise imaging and therapeutics of cell/tumors at molecular and anatomic levels. These tiny agents have shown their specific physicochemical properties for early-stage disease diagnosis and cancer theranostics applications (imaging and therapeutics by a single system). For example, gold nanorods (AuNRs) demonstrate better photothermal response and radiodensity for theranostics applications. However, upon near infrared light exposure these AuNRs lose their optical property which is characteristic of phototherapy of cancer. To overcome this issue, silica coating is a safe choice for nanorods which not only stabilizes them but also provides extra space for cargo loading and makes them multifunctional in cancer theranostics applications. On the other hand, various small molecules have been coated on the surface of nanoparticles (organic, inorganic, and biological) which improve their biocompatibility, blood circulation time, specific biodistribution and tumor binding ability. A few of them have been reached in clinical trials, but, struggling with FDA approval due to engineering and biological barriers. Moreover, nanoparticles also face various challenges of reliability, reproducibility, degradation, tumor entry and exit in translational research. On the other hand, cargo carrier nanoparticles have been facing critical issues of premature leakage of loaded cargo either anticancer drug or imaging probes. Hence, various gate keepers (quantum dots to supramolecules) known nanovalves have been engineered on the pore opening of the cargo systems. Here, a review on the evolution of nanoparticles and their choice for diagnostics and therapeutics applications has been discussed. In this context, basic requirements of multifunctional theranostics design for targeted imaging and therapy have been highlighted and with several challenges. Major hurdles experienced in the surface engineering routes (coating to nanovalves approach) and limitations of the designed theranostics such as poor biocompatibility, low photostability, non-specific targeting, low cargo capacity, poor biodegradation and lower theranostics efficiency are discussed in-depth. The current scenario of theranostics systems and their multifunctional applications have been presented in this article.

Simple, sensitive, and label-free miRNA analysis through strand displacement reaction integrating with G-quadruplex-based signal generation

AbstractMicroRNAs (miRNAs) play a significant role in the pathogenesis of various diseases throughout biological processes, and the accurate detection of miRNA biomarkers holds great potential for early stage disease diagnosis and treatment. In this study, a novel method is developed to detect miRNA-21, a biomarker for drug-induced liver injury, by combining target sequence recycling with G-quadruplex-based signal production. This approach is highly sensitive and does not require the use of labels. The target sequence facilitates the cyclic exposure of G-rich regions in the detection probe by toehold-mediated strand displacement processes, with the aid of the catalytic chain. The G-quadruplex sequences that have been produced subsequently interact with thioflavin T (ThT), resulting in a significant increase in its fluorescence intensity. This enhanced fluorescence is utilized for the purpose of detecting miRNA-21, with a remarkably low detection limit of 4.4 fM. The suggested technique also allows for the very specific identification of the target miRNA-21. Due to its non-label format, excellent selectivity, and sensitivity, this technology presents a straightforward and versatile approach for detecting a wide range of biomarkers in the early phases of illness detection.

WO3 nanoparticles supported by Nb2CTx MXene for superior acetone detection under high humidity

Chemiresistive gas sensors have emerged as a promising generation of detection systems for identifying trace biomarker molecules in exhaled breath, potentially replacing conventional devices for early-stage disease diagnosis. However, their potential is currently limited by the absence of sensing materials that exhibit both humidity resistance and ultrasensitivity. In this study, we investigate the potential of WO3 nanoparticles (NPs) supported by Nb2CTx MXene (WO3/Nb2CTx) as a highly efficient material for acetone sensing. Our results demonstrate that the synergistic interplay between WO3 NPs and Nb2CTx MXene support generates an intriguing interface with chemical/electronic coupling, thereby further enhancing the gas sensing performance. The WO3/Nb2CTx sensor exhibits exceptional sensing performance for acetone, with ultrasensitivity (Ra/Rg = 2.4 at 50 ppb) and an ultrafast response time (8 s), surpassing that of WO3 alone, while maintaining excellent stability. Moreover, the sensor exhibits outstanding response intensity, rapid response time (9 s), superior selectivity, and remarkable reversibility at a relative humidity of approximately 90 %. Additionally, we evaluate the gas-sensing properties of the sensor towards simulated exhaled breath of diabetic patients. In conclusion, our study presents a novel approach that integrates NPs and MXenes, which holds promise for the development of gas sensors with resistance to humidity interference.

Near-Infrared II Semiconducting Polymer Dots: Chain Packing Modulation and High-Contrast Vascular Imaging in Deep Tissues.

Fluorescence imaging in the second near-infrared (NIR-II) window has attracted considerable interest in investigations of vascular structure and angiogenesis, providing valuable information for the precise diagnosis of early stage diseases. However, it remains challenging to image small blood vessels in deep tissues because of the strong photon scattering and low fluorescence brightness of the fluorophores. Here, we describe our combined efforts in both fluorescent probe design and image algorithm development for high-contrast vascular imaging in deep turbid tissues such as mouse and rat brains with intact skull. First, we use a polymer blending strategy to modulate the chain packing behavior of the large, rigid, NIR-II semiconducting polymers to produce compact and bright polymer dots (Pdots), a prerequisite for in vivo fluorescence imaging of small blood vessels. We further developed a robust Hessian matrix method to enhance the image contrast of vascular structures, particularly the small and weakly fluorescent vessels. The enhanced vascular images obtained in whole-body mouse imaging exhibit more than an order of magnitude improvement in the signal-to-background ratio (SBR) as compared to the original images. Taking advantage of the bright Pdots and Hessian matrix method, we finally performed through-skull NIR-II fluorescence imaging and obtained a high-contrast cerebral vasculature in both mouse and rat models bearing brain tumors. This study in Pdot probe development and imaging algorithm enhancement provides a promising approach for NIR-II fluorescence vascular imaging of deep turbid tissues.

Length-Dependent Gold Nanoparticle-Labeled Nucleic Acid Probe Based Dual Test Strip for Visualized Discrimination of Single Nucleotide Polymorphism

Single nucleotide polymorphism (SNP) is closely related to the susceptibility of various common diseases and adverse drug reactions. Conclusively, SNP detection is significant for early-stage disease diagnosis and personalized medicine. The gold nanoparticle nucleic acid strip has the advantages of low-cost and rapid visualization (within 5–10 min), and it was introduced into detecting cancer-related SNP sites (A/A, A/G, G/G) in the CYP1A1 gene. As an efficient visualized approach based on the observation of the test zone (TZ) on the strip, it is improper to develop color at the TZ toward the negative sample; otherwise, it may lead to invalid false positive results. Thus, it is crucial to avert such a situation. In this work, dual strips named A and G were prepared, which were completely complementary to the T/T site (complementary base of A/A site) and C/C site (complementary base of G/G site), respectively. Single-strand DNA (ssDNA) containing SNP sites was obtained by conventional two-step polymerase chain reaction (PCR). Then SNP sites could be directly visualized and accurately distinguished on the dual strips. Furthermore, the false positive result could be eliminated by optimizing the length of DNA modified on gold nanoparticle and the concentration of NaCl. The result for the detection of 21 human blood samples with this method was consistent with those of sequencing. The platform exhibits the benefits of rapidity, easily visual determination, is false-positive-free, and is low-cost and plays an important part in the initial screening of SNP-related diseases.

Prostate cancer: current situation in Russia and Saint Petersburg according to medical statistical indicators

Background. Prostate cancer is one of the most common malignant neoplasms in the male population worldwide with high morbidity and mortality rates.Aim. To study the main medial statistical indicators of prostate cancer in Saint Petersburg and other regions of the Russian Federation.Materials and methods. The epidemiological indicators of prostate cancer for 2012-2021 in the Russian Federation, individual federal districts and Saint Petersburg were analyzed.Results. The prevalence of malignant neoplasms of the prostate has increased in recent years. At the same time, the number of cases of early-stage disease diagnosis has increased, one-year mortality has decreased, and 5-year survival has increased, which indicates proper quality of oncological care for the population. In some federal districts, unstable or negative dynamics of indicators compared to the average Russian data are observed, which requires further analysis.Conclusion. Evaluation of statistical measures of morbidity and mortality from malignant neoplasms of the prostate gland can be used to improve diagnostic algorithms and therapeutic tactics for this pathology.

Microcontact-Imprinted Optical Sensors for Virulence Factors of Periodontal Disease.

Periodontitis (gum disease) is a common biofilm-mediated oral condition, with around 7% of the adult population suffering from severe disease with risk for tooth loss. Moreover, periodontitis virulence markers have been found in atherosclerotic plaque and brain tissue, suggesting a link to cardiovascular and Alzheimer's diseases. The lack of accurate, fast, and sensitive clinical methods to identify patients at risk leads, on the one hand, to patients being undiagnosed until the onset of severe disease and, on the other hand, to overtreatment of individuals with mild disease, diverting resources from those patients most in need. The periodontitis-associated bacterium, Porphyromonas gingivalis, secrete gingipains which are highly active proteases recognized as key virulence factors during disease progression. This makes them interesting candidates as predictive biomarkers, but currently, there are no methods in clinical use for monitoring them. Quantifying the levels or proteolytic activity of gingipains in the periodontal pocket surrounding the teeth could enable early-stage disease diagnosis. Here, we report on a monitoring approach based on high-affinity microcontact imprinted polymer-based receptors for the Arg and Lys specific gingipains Rgp and Kgp and their combination with surface plasmon resonance (SPR)-based biosensor technology for quantifying gingipain levels in biofluids and patient samples. Therefore, Rgp and Kgp were immobilized on glass coverslips followed by microcontact imprinting of poly-acrylamide based films anchored to gold sensor chips. The monomers selected were N-isopropyl acrylamide (NIPAM), N-hydroxyethyl acrylamide (HEAA) and N-methacryloyl-4-aminobenzamidine hydrochloride (BAM), with N,N'-methylene bis(acrylamide) (BIS) as the crosslinker. This resulted in imprinted surfaces exhibiting selectivity towards their templates high affinity and selectivity for the templated proteins with dissociation constants (K d) of 159 and 299 nM for the Rgp- and Kgp-imprinted, surfaces respectively. The former surface displayed even higher affinity (K d = 71 nM) when tested in dilute cell culture supernatants. Calculated limits of detection for the sensors were 110 and 90 nM corresponding to levels below clinically relevant concentrations.

Phenotyping of Methicillin-Resistant Staphylococcus aureus Using a Ratiometric Sensor Array.

Chemical tools capable of classifying multidrug-resistant bacteria (superbugs) can facilitate early-stage disease diagnosis and help guide precision therapy. Here, we report a sensor array that permits the facile phenotyping of methicillin-resistant Staphylococcus aureus (MRSA), a clinically common superbug. The array consists of a panel of eight separate ratiometric fluorescent probes that provide characteristic vibration-induced emission (VIE) profiles. These probes bear a pair of quaternary ammonium salts in different substitution positions around a known VIEgen core. The differences in the substituents result in varying interactions with the negatively charged cell walls of bacteria. This, in turn, dictates the molecular conformation of the probes and affects their blue-to-red fluorescence intensity ratios (ratiometric changes). Within the sensor array, the differences in the ratiometric changes for the probes result in "fingerprints" for MRSA of different genotypes. This allows them to be identified using principal component analysis (PCA) without the need for cell lysis and nucleic acid isolation. The results obtained with the present sensor array agree well with those obtained using polymerase chain reaction (PCR) analysis.

A dual-channel biomarker detection based on spatiotemporal upconversion-linked immunosorbent assay

Multiplexed detection of different but clinically relevant low-abundance biomarkers is of paramount importance for early-stage disease diagnosis. Herein, we have developed a simple, yet versatile and sensitive upconversion-based sensing platform that leverages the excitation-controlled switchable optical properties of upconversion nanoparticles to achieve the simultaneous detection of α-fetoprotein (AFP) and human chorionic gonadotropin (hCG) biomarkers. The assay yielded a limit of detection of 7.81 pg/mL for AFP and 1.02 × 10−4 IU/mL for hCG in spiked serum. By utilizing the dual-channel luminescent signals as dual input, we have further developed an open-source software for automatic risk assessment of testicular cancer based on a dual-input logic OR gate. Due to the high optical tunability of upconversion luminescence, the technique and software present in this work should inspire new ideas for the early diagnosis of a wide range of diseases.

Single-molecule fluorescence methods for protein biomarker analysis.

Proteins have been considered key building blocks of life. In particular, the protein content of an organism and a cell offers significant information for the in-depth understanding of the disease and biological processes. Single-molecule protein detection/sequencing tools will revolutionize clinical (proteomics) research, offering ultrasensitivity for low-abundance biomarker (protein) detection, which is important for the realization of early-stage disease diagnosis and single-cell proteomics. This improved detection/measurement capability delivers new sets of techniques to explore new frontiers and address important challenges in various interdisciplinary areas including nanostructured materials, molecular medicine, molecular biology, and chemistry. Importantly, fluorescence-based methods have emerged as indispensable tools for single protein detection/sequencing studies, providing a higher signal-to-noise ratio (SNR). Improvements in fluorescent dyes/probes and detector capabilities coupled with advanced (image) analysis strategies have fueled current developments for single protein biomarker detections. For example, in comparison to conventional ELISA (i.e., based on ensembled measurements), single-molecule fluorescence detection is more sensitive, faster, and more accurate with reduced background, high-throughput, and so on. In comparison to MS sequencing, fluorescence-based single-molecule protein sequencing can achieve the sequencing of peptides themselves with higher sensitivity. This review summarizes various typical single-molecule detection technologies including their methodology (modes of operation), detection limits, advantages and drawbacks, and current challenges with recent examples. We describe the fluorescence-based single-molecule protein sequencing/detection based on five kinds of technologies such as fluorosequencing, N-terminal amino acid binder, nanopore light sensing, and DNA nanotechnology. Finally, we present our perspective for developing high-performance fluorescence-based sequencing/detection techniques.

Abstract P017: Early detection of ovarian cancer in high-risk individuals using circulating miRNAs: The MiDe study

Abstract Background: Epithelial ovarian cancers generally carry a poor prognosis due to late-stage presentation and a high rate of disease recurrence. In contrast, diagnosis of early-stage disease is associated with a high rate of durable remission. Prior studies using archival samples have suggested that a neural network analysis of circulating miRNA expression levels may be an informative biomarker for early-stage ovarian cancer diagnosis. Methods: The miRNA Detection Study (MiDe Study) is a prospective, observational clinical trial open to individuals in all 50 U.S. states. Persons with at least one ovary classified as being at high-risk for ovarian cancer, either due to known genetic predisposition or family history suggestive of possible hereditary breast and ovarian cancer syndrome or Lynch syndrome, are eligible to enroll. Study subjects will submit a blood sample every 6 months for up to 5 years. Neural network analysis of serum miRNA profiles will be correlated with the subsequent diagnosis of ovarian cancer or serous tubal intraepithelial carcinoma, either clinically or at the time of risk-reducing salpingectomy or salpingo-oophorectomy. The primary outcomes are the sensitivity and specificity of the neural network for anticipating a diagnosis of epithelial ovarian cancer or a cancer precursor within 2 years of the index blood sample. The goal recruitment is 1000 study subjects. Based on a presumed cancer incidence of 0.5-1% per year among this population, approximately 25 incident cancers or precursors are expected to be diagnosed over the timespan of the study. Results: Enrollment began in September 2019. As of August 1, 2022, 372 subjects had consented to the study. Among study subjects, 270/372 (72.5%) carry a known mutation in BRCA1/2, 63/372 (16.9%) carry a known mutation in a different high-risk gene (e.g., PALB2, BRIP1), and 39/372 (10.5%) are considered high-risk due to a mutation in a gene potentially related to ovarian cancer (e.g., ATM, CHEK2) or based on family history alone. To date, 215 (57.8%) participants have contributed at least one sample, among whom 90/215 (41.9%) of subjects have contributed multiple blood samples. Among the study subjects who have given a sample, 2/215 (0.9%) have been later diagnosed with ovarian cancer. Conclusions: The MiDe Study seeks to establish the feasibility of a circulating miRNA-based test for early diagnosis of epithelial ovarian cancer among high-risk individuals. The results of this study will clarify the performance of the neural network test among a high-risk population and inform subsequent interventional trials. Citation Format: Kevin M. Elias, Dipanjan Chowdhury, Judy Garber, Ryan C. Buehler. Early detection of ovarian cancer in high-risk individuals using circulating miRNAs: The MiDe study. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P017.

Hospital recommender system

Elderly patients require more medical effort. It is clear that early-stage disease diagnosis can support time lyand appropriate treatment. But if you don’t pay attention in a timely manner, it can lead to different kinds of health problems that can lead to death. Take advantage of our recommendation system to recommend hospitals. A recommender system uses algorithms to provide product or service recommendations to users. By combining blockchain technology and machine learning models, we provide users with highly accurate recommendations. This whitepaper describes how sophisticated machine learning models and blockchain can be connected to improve recommendations, providing hospitals with higher performance and more accurate recommendations. An optimized model for recommending hospitals in a better manner is the main goal behind this paper.

Abstract P030: Prospective analysis of plasma metabolites for early detection of high-grade serous ovarian cancer

Abstract Background: Ovarian cancer has poor prognosis due to >60% of the patients being diagnosed at late-stage. However, there is currently no ovarian cancer biomarker appropriate for screening. This may be, in part, due to many prior studies using clinical samples obtained at the time of diagnosis for biomarker discovery. Here, we sought to discover novel plasma metabolomic biomarkers for ovarian cancer early detection using prospectively collected blood samples. Methods: We examined 250 plasma metabolites in 93 high-grade serous ovarian cancer (HGSOC) cases with blood drawn up to five years prior to diagnosis and 93 matched controls in the prospective Nurses’ Health Studies and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. We also measured metabolites in 40 early-stage HGSOC cases with blood collected at diagnosis and 40 matched controls in a hospital-based study. We used conditional logistic regression to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of the association between one standard deviation increase in metabolite levels and ovarian cancer risk, and used metabolite set enrichment analysis (MSEA) to identify metabolite group associations. False discovery rate (FDR) was used to correct for multiple testing. For the prospectively collected blood samples, we conducted analyses by time between blood draw and diagnosis. Results: Plasma metabolites associated with ovarian cancer in blood samples collected prospectively were different than in blood samples collected at diagnosis of early-stage disease compared to controls. There were 45 individual metabolites associated with early-stage disease (FDR<0.05), and none of these metabolites were associated with ovarian cancer in the prospectively collected blood samples (FDR<0.05). There were 10 metabolites associated with ovarian cancer diagnosed within 3 years of blood collection (p<0.05), including 7 lipid metabolites (2 lysophosphatidylcholines, 2 diglycerides, 1 phosphatidylcholine, 1 cholesteryl esters, 1 phosphatidylethanolamines) that were inversely associated with ORs ranging from 0.61-0.66. Interestingly, the direction of association remained similar when restricting to cases with blood collected up to 2 years and 1 year prior to diagnosis, and the magnitude of association generally became stronger for cases closer to diagnosis. Similarly, distinct profiles were observed in MSEA, with diglycerides being inversely associated with ovarian cancer diagnosis in prospectively collected blood samples (FDR ranging from <0.05 to <0.20) and positively associated in blood collected at early-stage disease diagnosis (FDR<0.05).Conclusion: Our results revealed plasma metabolomic profiles differ between prospectively collected blood samples up to 3 years prior to diagnosis and blood samples collected at time of diagnosis, even for early-stage disease, suggesting the use of prospectively collected blood samples may discover promising novel early detection biomarkers for ovarian cancer. Citation Format: Naoko Sasamoto, Oana A. Zeleznik, Allison F. Vitonis, Daniel W. Cramer, Julian Avila-Pacheco, Clary B. Clish, Britton Trabert, Shelley S. Tworoger, Kathryn L. Terry. Prospective analysis of plasma metabolites for early detection of high-grade serous ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P030.

Hospital Recommender System

Elderly patients require more medical effort. It is clear that early-stage disease diagnosis can support timely and appropriate treatment. But if you don’t pay attention in a timely manner, it can lead to different kinds of health problems that can lead to death. Take advantage of our recommendation system to recommend hospitals. A recommender system uses algorithms to provide product or service recommendations to users. By combining blockchain technology and machine learning models, we provide users with highly accurate recommendations. This whitepaper describes how sophisticated machine learning models and blockchain can be connected to improve recommendations, providing hospitals with higher performance and more accurate recommendations. An optimized model for recommending hospitals in a better manner is the main goal behind this paper.

Molecular Imaging for Early-Stage Disease Diagnosis.

With the development of cellular biology, molecular biology, and other subjects, targeted molecular probe was combined with medical imaging technologies to launch a new scientific disciplineof molecular imaging that is a research discipline to visualize, characterize, and analyze biological process at the cellular and molecular levels for real-time tracking and precision therapy, also termed as the medical imaging in the twenty-first century. An array of imaging techniques has been developed to image specific targets of living cells or tissues by molecular probes, including optical molecular imaging (OI), magnetic resonance molecular imaging, ultrasound (US) molecular imaging, nuclear medicine molecular imaging, X-ray molecular imaging, and multi-mode molecular imaging. These imaging techniques make the early diagnosis of various diseases possible by means of visualization of gene expression, interactions between proteins, signal transduction, cell metabolism, cell traces, and other physiological or pathological processes in the living system, which bridge the gap between molecular biology and clinical medicine. This chapter will lay the emphasis on the early-stage diagnosis of fatal diseases, such as malignant tumors, cardio- or cerebrovascular diseases, digestive system disease, central nervous system disease, and other diseases employing molecular imaging in a real-time visualized manner.

Robust nontarget DNA-triggered catalytic hairpin assembly amplification strategy for the improved sensing of microRNA in complex biological matrices.

A simple but robust fluorescence strategy based on a nontarget DNA-triggered catalytic hairpin assembly (CHA) was constructed to probe microRNA-21 (miR-21). A short ssDNA rather than degradable target miRNA was employed as an initiator. Two molecular beacons needed to assist the CHA process were simplified to avoid unfavorable nonspecific interactions. In the presence of the target, the initiator was released from a partially duplex and triggered the cyclic CHA reaction, resulting in a significantly amplified optical readout. A wide linear range from 0.1 pM to 1000 pM for the sensing of miR-21 in buffer was achieved with a low detection limit of 0.76 pM. Fortunately, this strategy demonstrated an obviously improved performance for miR-21 detection in diluted serum. The fluorescence signals were enhanced remarkably and the sensitivity was further improved to 0.12 pM in 10% serum. The stability for miR-21 quantification and the capability for the analysis of single nucleotide polymorphisms (SNPs) were also improved greatly. More importantly, the biosensor could be applied to image miR-21 in different living tumor cells with high resolution, illustrating its promising potential for the assay of miRNAs in various complex situations for early-stage disease diagnosis and biological studies in cells.