Noninvasive Method For Detection Research Articles

Fluorescence sensing probe based on functionalized mesoporous MOFs for non-invasive and detection of dopamine in human fluids

Abnormal amount of dopamine (DA) in human body is closely relate to various diseases, such as Parkinson's disease, pheochromocytoma. Real-time monitoring DA is crucial for disease warning, diagnosis and treatment. Currently, most methods rely on invasive blood testing for detecting DA, which is only completed with the aid of the medical staffs in hospitals. Herein, a non-invasive fluorescence visual strategy is developed for the real-time monitoring DA, based on luminescent nanoparticles and modified mesoporous zeolite imidazole framework (ZIF-8-NH2) dodecahedrons. During the reaction process, DA is enriched through the spatial configuration of ZIF-8-NH2 and hydrogen bonding effect. The luminescence of Cr3+-doped zinc gallate (ZnGa2O4:Cr3+, ZGC) is inhibited by the photo-induced electron transfer (PET) mechanism to realize sensitively detecting DA. The intelligent sensing platform based on the designed fluorescence probe and color recognition system is structured for real-time detection of DA in urine. Furthermore, a skin-fitting hydrogel patch is prepared by combining a fluorescent probe with chitosan, which enables sensitive and accurate detection of DA in sweat without the complex sample pretreatment. The non-invasive fluorescence detection method provides an effective strategy for quantitatively monitoring DA in human fluids.

A novel, noninvasive, multimodal screening test for the early detection of precancerous lesions and colorectal cancers using an artificial intelligence–based algorithm.

3627 Background: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related mortality worldwide, and its incidence is increasing in younger populations. Detection of early-stage CRC and its precursor lesions, such as advanced adenomas (AA), is crucial for successful treatment and reduces CRC-related mortality. Although non-invasive methods for early detection are available and increase screening compliance, their performance with respect to detection of advanced adenomas and early-stage cancers is limited. Here, we describe a novel and non-invasive stool-based approach combining diagnostic biomarkers with an algorithm generated by machine learning/artificial intelligence (ML/AI) resulting in significantly improved diagnostic performance for the detection of not only CRC, but especially precancerous lesions like AA. Methods: Data were generated from a combined cohort of stool samples collected at 10 clinical sites in Europe (COLOFUTURE study) and 21 clinical sites in the US (eAArly DETECT study). The evaluable study cohort consists of 690 subjects, including 78 CRC, 146 AA, 147 with non-advanced adenoma (AD) and 319 normal colonoscopy negative control (NC) subjects (49% female, 51% male, average age 61.8 years). Results were compared with colonoscopy and pathology findings to determine sensitivity and specificity for detection of early-stage CRC and AA vs. AD and NC. Nucleic acids were extracted from stabilized stool samples using a silica bead-based extraction method. Expression of mRNA biomarkers was analyzed utilizing Real-Time PCR. Human hemoglobin was quantified using FIT. The Emerge Quantitative Evolution ML/AI platform was leveraged to develop classifiers capable of distinguishing CRC and AA from AD and NC. Results: Applying the combined approach of non-invasive diagnostic testing with an AI/ML generated algorithm, the sensitivity for detection of CRC overall was 92.3%. In addition, this method enabled AA detection with a sensitivity of 82.2%. Specificity turned out to be 90.1 % (AD+NC). Conclusions: This innovative, non-invasive, multimodal screening strategy based on self-collected stool samples which combines mRNA expression patterns and FIT analysis with an AI/ML-generated algorithm represents a substantial improvement in the effectiveness of non-invasive CRC screening. Such improvements in usability and performance leading to reliable detection of early-stage CRC and precursor lesions, such as advanced adenomas, are required for wide-spread availability and adaption of non-invasive screening tests to accepted clinically relevant usage and to prevent the development of CRC effectively.

A novel method for chronic obstructive pulmonary disease detection using comprehensive respiratory impedance and electrocardiogram-derived respiration

Currently, pulmonary function test is the only standard method for diagnosing COPD, but it often involves high testing costs, poor compliance, and a high dependence on patient cooperation. Other new detection methods, such as CT and FOT, also have disadvantages such as radiation exposure and high usage costs. Existing non-invasive COPD detection methods consider the impact of COPD on the lungs or heart, thus they can only roughly detect COPD in individuals, without the ability to grade COPD. Therefore, this study proposed a method for COPD detection using comprehensive respiratory impedance (CRI) and electrocardio-derived respiration (EDR) signals. The method measured both respiratory impedance and ECG synchronously, and the CRI and EDR signals were obtained through an algorithm. The effects of COPD on respiratory pattern were studied by taking CRI and EDR signal morphology as objects, from which characteristic parameters were extracted and input into the supervised classifier to fuse the information of COPD on the changes of respiratory signal morphology pattern in the heart and lungs. The final result is accurate detection of normal, mild COPD and moderate COPD. The experimental results of 46 subjects showed that the accuracy, sensitivity and specificity of CRI and EDR signal characteristics for COPD detection by different classifiers were up to 93.5 %, 90.5 % and 96.5 %, and the AUC values of different classifiers were all > 0.88. Therefore, the method can not only effectively distinguish COPD patients from healthy people, but also provide differentiated detection capabilities for different COPD clinical grades, providing strong support for the early diagnosis and treatment of COPD, and can be applied to the development of portable medical devices for the detection of COPD.

Structural Defect-Enabled Magnetic Neutrality Nanoprobes for Ultra-High-Field Magnetic Resonance Imaging of Isolated Tumor Cells in Vivo.

The identification of metastasis "seeds," isolated tumor cells (ITCs), is of paramount importance for the prognosis and tailored treatment of metastatic diseases. The conventional approach to clinical ITCs diagnosis through invasive biopsies is encumbered by the inherent risks of overdiagnosis and overtreatment. This underscores the pressing need for noninvasive ITCs detection methods that provide histopathological-level insights. Recent advancements in ultra-high-field (UHF) magnetic resonance imaging (MRI) have ignited hope for the revelation of minute lesions, including the elusive ITCs. Nevertheless, currently available MRI contrast agents are susceptible to magnetization-induced strong T2-decaying effects under UHF conditions, which compromises T1 MRI capability and further impedes the precise imaging of small lesions. Herein, this study reports a structural defect-enabled magnetic neutrality nanoprobe (MNN) distinguished by its paramagnetic properties featuring an exceptionally low magnetic susceptibility through atomic modulation, rendering it almost nonmagnetic. This unique characteristic effectively mitigates T2-decaying effect while concurrently enhancing UHF T1 contrast. Under 9 T MRI, the MNN demonstrates an unprecedentedly low r2/r1 value (≈1.06), enabling noninvasive visualization of ITCs with an exceptional detection threshold of ≈0.16mm. These high-performance MNNs unveil the domain of hitherto undetectable minute lesions, representing a significant advancement in UHF-MRI for diagnostic purposes and fostering comprehensive metastasis research.

Potential non-invasive detection of lesions in broiler femur heads: application of the DXA imaging system.

Leg health is a significant economic and welfare concern for the poultry industry. Current methods of detection rely on visual assessment of the legs and gait scores and bone scoring during necropsy for full characterization. Additionally, the current scoring of femurs only examines the external surface of the femoral head. Through the use of the dual-energy X-ray absorptiometry (DXA) imaging system, we show the presence of a necrotic region in the femurs that would otherwise be considered healthy based on the current evaluation procedures. Importantly, these lesions were present in almost 60% (22 of 37) of femurs that scored normal for femoral head necrosis (FHN). Additionally, these femurs showed greater bone mineral content (BMC) relative to weight compared to their counterparts with no lucent lesions (6.95% ± 0.20% vs. 6.26% ± 0.25; p = 0.038). Identification of these lesions presents both a challenge and an opportunity. These subclinical lesions are likely to be missed in routine scoring procedures for FHN and can inadvertently impact the characterization of the disease and genetic selection programs. Furthermore, this imaging system can be used for in vivo, ex vivo, and embryonic (egg) studies and, therefore, constitutes a potential non-invasive method for early detection of bone lesions in chickens and other avian species.

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient’s breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2 fold change ⩾1 and q-values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z −242.18428 and m/z −117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.

Hollow versatile Ag@Pt alloy nanoparticles with nanozyme activity for detection and photothermal sterilization of Helicobacter pylori.

In view of a large number of people infected with Helicobacter pylori (H. pylori) with great harm followed, there is an urgent need to develop a non-invasive, easy-to-operate, and rapid detection method, and to identify effective sterilization strategies. In this study, highly specific nanoprobes with nanozyme activity, Ag@Pt nanoparticles (NPs) with the antibody, were utilized as a novel lateral flow immunoassay (LFIA). The optical label (Ag@Pt NPs) was enhanced by the introduction of the chromogenic substrate 3,3',5,5'-tetramethylbenzidine (TMB) and compared with a gold nanoparticles (Au NPs) optical label. Under the optimal condition, Ag@Pt-LFIA and TMB-enhanced Ag@Pt-LFIA for H. pylori were successfully established, two of which were over twofold and 100-fold more sensitive than conventional visual Au NP-based LFIA, respectively. Furthermore, Ag@Pt NPs with the antibody irradiated with NIR laser (808nm) at a power intensity of 550 mW/cm2 for 5min exhibited a remarkable antibacterial effect. Thenanoprobes could close to bacteria through effective interactions between antibodies and bacteria, thereby benefiting photothermal sterilization. Overall, Ag@Pt NPs provide promising applications in pathogen detection and therapeutic applications.

DNA methylation markers in esophageal cancer.

Esophageal cancer (EC) is a prevalent malignancy characterized by a low 5-year survival rate, primarily attributed to delayed diagnosis and limited therapeutic options. Currently, early detection of EC heavily relies on endoscopy and pathological examination, which pose challenges due to their invasiveness and high costs, leading to low patient compliance. The detection of DNA methylation offers a non-endoscopic, cost-effective, and secure approach that holds promising prospects for early EC detection. To identify improved methylation markers for early EC detection, we conducted a comprehensive review of relevant literature, summarized the performance of DNA methylation markers based on different input samples and analytical methods in EC early detection and screening. This review reveals that blood cell free DNA methylation-based method is an effective non-invasive method for early detection of EC, although there is still a need to improve its sensitivity and specificity. Another highly sensitive and specific non-endoscopic approach for early detection of EC is the esophageal exfoliated cells based-DNA methylation analysis. However, while there are substantial studies in esophageal adenocarcinoma, further more validation is required in esophageal squamous cell carcinoma. In conclusion, DNA methylation detection holds significant potential as an early detection and screening technology for EC.

T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study.

To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.

Early thrombus detection in the extracorporeal membrane oxygenation circuit by noninvasive real-time ultrasonic sensors

Thrombus formation in extracorporeal membrane oxygenation (ECMO) remains a major concern as it can lead to fatal outcomes. To the best of our knowledge, there is no standard non-invasive method for quantitatively measuring thrombi. This study’s purpose was to verify thrombus detection in an ECMO circuit using novel, non-invasive ultrasonic sensors in real-time, utilizing the fact that the ultrasonic velocity in a thrombus is known to be higher than that in the blood. Ultrasonic sensors with a customized chamber, an ultrasonic pulse-receiver, and a digital storage oscilloscope (DSO) were used to set up the measuring unit. The customized chamber was connected to an ECMO circuit primed with porcine blood. Thrombi formed from static porcine blood were placed in the circuit and ultrasonic signals were extracted from the oscilloscope at various ECMO flow rates of 1–4 L/min. The ultrasonic signal changes were successfully detected at each flow rate on the DSO. The ultrasonic pulse signal shifted leftward when a thrombus passed between the two ultrasonic sensors and was easily detected on the DSO screen. This novel real-time non-invasive thrombus detection method may enable the early detection of floating thrombi in the ECMO system and early management of ECMO thrombi.

Past, Present, and Future Diagnostic Methods for the Early Noninvasive Detection of Oral Premalignant Lesions: A State of the Art and Systematic Review.

Objectives: To provide an in-depth analysis of noninvasive methods for the early diagnosis of oral premalignant lesions, focusing on novel biomarkers and optical technologies, and to discuss their potential in improving the prognosis of patients with oral oncological diseases. Methods: This state-of-the-art review examines various noninvasive diagnostic techniques, including the utilization of salivary microRNAs and optical technologies such as Raman spectroscopy, elastic scattering spectroscopy, diffuse reflectance spectroscopy, narrow-band imaging, autofluorescence imaging, toluidine blue staining, and microendoscopy. Results: Several noninvasive techniques have shown varying degrees of effectiveness in detecting oral cancer. Autofluorescence imaging exhibited sensitivities up to 100% but had variable specificity. toluidine blue staining reported sensitivity between 77% and 100% for high-risk lesions or cancer, with specificity around 45% to 67%. Spectroscopy techniques achieved 72% to 100% sensitivities and specificities of 75% to 98%. Microendoscopy presented a sensitivity of 84% to 95% and a specificity of 91% to 95%. Conclusion: The review highlights the strengths and limitations of each noninvasive diagnostic method and their recent advancements. Although promising results have been demonstrated, there is a need for further development of reliable strategies for early detection and intervention in oral oncology.

Reagent-Free Lactate Detection Using Prussian Blue and Electropolymerized-Molecularly Imprinted Polymers-Based Electrochemical Biosensors.

Sweat lactate, a promising biomarker for assessing physical performance and health conditions, calls for noninvasive, convenient, and affordable detection methods. This study leverages molecularly imprinted polymers (MIPs) as a synthetic biorecognition element for lactate detection due to their affordability and high stability. Traditional MIPs-based electrochemical sensors often require external redox probes such as ferricyanide/ferrocyanide in the solution to signal the binding between analytes and MIPs, which restricts their applicability. To address this, our study introduces an innovative approach utilizing a layer of Prussian blue (PB) nanoparticles as the internal redox probe on screen-printed carbon electrodes (SPCE), followed by a layer of electropolymerized MIP (eMIP) for molecular recognition, enabling reagent-free lactate detection. The real-time growth of eMIP and the processes of template elution and lactate rebinding were examined and validated using electrochemical surface plasmon resonance (EC-SPR) spectroscopy. The sensor's performance was thoroughly investigated using Differential Pulsed Voltammetry (DPV) and Electrochemical Impedance Spectroscopy (EIS) with samples spiked in 0.1 M KCl solution and artificial sweat. The developed sensors demonstrated a fast and selective response to lactate, detecting concentrations from 1 to 35 mM with a Limit of Detection (LOD) of 0.20 mM, defined by a signal-to-noise ratio of 3 in the DPV measurements. They also exhibited excellent reproducibility, reusability, and a shelf life of up to 10 months under ambient conditions. These eMIP/PB/SPCE-based lactate sensors show considerable potential as point-of-care (POC) devices for sweat lactate detection, and the technology could be adapted for reagent-free detection of a broad spectrum of molecules.

Colorectal Cancer Detection via Metabolites and Machine Learning.

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

Enhancing Mood Based Music Selection through Physiological Sensing Technology

Abstract: This research paper presents the development of an emotional sensor system that combines computer vision techniques with physiological sensors to detect and analyse facial expressions. The system utilizes a webcam for capturing facial images and integrates Arduino-based hardware components such as a pulse sensor, temperature sensor, galvanic skin sensor, and heart rate sensor for comprehensive emotional analysis. The proposed system aims to contribute to various fields such as human-computer interaction, psychology, and healthcare by providing a non-invasive and real-time method for emotion detection and monitoring.

Leveraging genomic signatures of oral microbiome-associated antibiotic resistance genes for diagnosing pancreatic cancer.

Growing evidence has increasingly suggested a potential linkage between the oral microbiome and various diseases, including pancreatic ductal adenocarcinoma (PDAC). However, the utilization of gene-level information derived from the oral microbiome for diagnosing PDAC remains unexplored. In this study, we sought to investigate the novel potential of leveraging genomic signatures associated with antibiotic resistance genes (ARGs) within the oral microbiome for the diagnosis of PDAC. By conducting an analysis of oral microbiome samples obtained from PDAC patients, we successfully identified specific ARGs that displayed distinct sequence abundance profiles correlated with the presence of PDAC. In the healthy group, three ARGs were found to be enriched, whereas 21 ARGs were enriched in PDAC patients. Remarkably, these ARGs from oral microbiome exhibited promising diagnostic capabilities for PDAC (AUROC = 0.79), providing a non-invasive and early detection method. Our findings not only provide novel modal data for diagnosing PDAC but also shed light on the intricate interplay between the oral microbiome and PDAC.

An Innovative Non-Invasive Method for Early Detection and Monitoring of Acute Compartment Syndrome.

Background: Acute compartment syndrome is a major surgical emergency with complex pathophysiology and a highly unpredictable pattern of evolution. We hypothesized that the onset of acute compartment syndrome of the leg or forearm is associated with variations in the surface temperature of the distal segment (foot or hand) with a distinct pattern, which acts as an early warning sign. Materials and Methods: We developed a monitoring device that consists of two thermic sensors attached to a modular limb splint, which continuously measure the temperature difference between the proximal and distal regions of the limb (i.e., arm-hand, thigh-foot). Firstly, we investigated both the arm-hand and thigh-foot temperature gradients of hospitalized patients' healthy limbs (43 patients, 56 upper limbs, 64 lower limbs) in order to establish a baseline. Secondly, we examined the correlation between the thermic gradients and intracompartmental pressure values in compartment syndrome limbs (20 patients, 6 upper limbs, 14 lower limbs). Results: For the control group, the mean values for the normal limb thermic gradients were -0.17 °C for the upper limbs. and 0.03 °C for the lower limbs. In the impending compartment syndrome group (defined by intracompartmental pressure values), the mean index was -0.38 °C. In the fully developed compartment syndrome group, the mean value was 4.11 °C. Discussions: Analysis was performed using the ANOVA one-way statistical method. This showed significant differences between the compartment syndrome group and the impending and control groups. A decreasing trend in the thermic gradient in patients with impending compartment syndrome compared with the control group was noted. Conclusions: The thermic gradient of limbs presenting signs of impending compartment syndrome decreases as a result of the increased temperature of the distal segment. This pattern can be used as an early diagnostic method for acute compartment syndrome. This technique is non-invasive and bears no risk to the patient, allowing facile continuous monitoring during immobilization.

Microvascular Evaluation Diabetic Foot Patients with Near Infrared Spectroscopy

Peripheral Arterial Disease (PAP) requires good evaluation to reduce amputation rate. The aim of this study is to assess non-invasive detection methods in evaluating microvascular conditions in PAP. The potential use of Near Infrared Spectroscopy (NIRS) in identifying disorders in patients with PAP, either with or without DM, was also assessed. A systematic literature search was conducted through PUBMED until October 2023. Based on the literature review, it can be concluded that the use of NIRS in evaluating microvascular status in PAP is a non-invasive testing method that has expanded its role. Evaluation with NIRS provides metrics such as recovery time, deoxygenation, oxygen consumption (VO2), tissue oxygen saturation (StO2), total haemoglobin (HbT), and oxyhemoglobin area under the curve (O2 HbAUC). NIRS also has the potential to assist multidisciplinary teams in treating high-risk patients, especially in diabetic patients whose symptoms are compensated by peripheral neuropathy. This study shows that NIRS can be used as a useful tool in evaluating microvascular status in PAP, by providing important information that can be used to optimise patient care and prevent more serious complications. It is concluded that NIRS can be used as a tool to evaluate the effectiveness of treatment and prevent worsening of the condition of patients with PAP in diabetic foot.

Whole urine-based multiple cancer diagnosis and metabolite profiling using 3D evolutionary gold nanoarchitecture combined with machine learning-assisted SERS

To develop onsite applicable cancer diagnosis technologies, a noninvasive human biofluid detection method with high sensitivity and specificity is required, available for classifying cancer from the normal group. Herein, a three-dimensional evolutionary gold nanoarchitecture (3D-EGN) is developed by forming Au nanosponge (AuS) on a 96-well plate, followed by a decoration of Au nanoparticles (AuNPs) evolved with Au nanolamination (AuNL) for high-throughput urine sensing in liquid phase. The 3D-EGN exhibits not only strong electromagnetic field generated from numerous hotspot regions between AuNPs and further enhanced light scattering from multigrain boundaries after lamination process, but also highly volumetric field due to nanoporous structure of AuS, which is advantageous for sensitive liquid-phase SERS detection. SERS activity of the 3D-EGN platform is characterized using malachite green, showing a limit detection of 1.23 × 10−9 M in liquid phase, and excellent uniformities both within single well and well-to-well with relative standard deviation (RSD) values of about 10%. The 3D-EGN platform has been demonstrated for the detection of whole clinical human urine samples, proving effective molecular sensing in the presence of Brownian motion from liquid medium. Subsequently, cancer metabolite candidates are investigated to verify the metabolic alternations of multicancer, including pancreatic, prostate, lung, and colorectal cancers, simultaneously classifying them into five different groups, including normal with an accuracy of 95.6%, using machine-learning methods. The integration of nanomaterials with the conventional clinical platform provides rapid and high-throughput multicancer diagnostic system and opens a new era for noninvasive diseases diagnosis using clinical human biofluids.

Simplifying vein detection for intravenous procedures: A comparative assessment through near‐infrared imaging system

AbstractThe intravenous (IV) injection procedure can be a challenging task, especially for individuals with thin veins, obesity, or patients with damaged and pigmented skin. Therefore, the IV procedure necessitates a portable medical device that can be used for academic demonstrations to train medical students or by health care professionals to perform venipuncture. Vein visualization with a vein detector is principally based on the interaction of blood components with wavelengths of the electromagnetic spectrum (EMS). In this paper, we first present the process of image formation in the spectral band of the near‐infrared region (NIR) of EMS. Then, we introduce the image acquisition system with image processing to extract the veins as a noninvasive vein detection method. A Raspberry Pi (Model 4B), along with a night vision camera, serves as an image acquisition tool to capture skin area illuminated by NIR. Following this, the data is transferred to the laptop where it can be filtered and processed using Python image processing tools before being viewed on the monitor. The results achieved through the device are quite encouraging, as the image recognition between veins and adjacent tissues from the skin sample can be clearly marked. The functionality, accuracy, and simplicity associated with this vein detection system make it a potential device for IV placement and the morphological study of disease detection.

Methylation analysis of DCC gene in saliva samples is an efficient method for non-invasive detection of superficial hypopharyngeal cancer

BackgroundAdvances in upper gastrointestinal endoscopic technology have enabled early detection and treatment of hypopharyngeal cancer. However, in-depth pharyngeal observations require sedation and are invasive. It is important to establish a minimally invasive and simple evaluation method to identify high-risk patients.MethodsEighty-seven patients with superficial hypopharyngeal cancer and 51 healthy controls were recruited. We assessed the methylation status of DCC, PTGDR1, EDNRB, and ECAD, in tissue and saliva samples and verified the diagnostic accuracy by methylation analyses of their promoter regions using quantitative methylation-specific PCR.ResultsSignificant differences between cancer and their surrounding non-cancerous tissues were observed in the methylation values of DCC (p = 0.003), EDNRB (p = 0.001), and ECAD (p = 0.043). Using receiver operating characteristic analyses of the methylation values in saliva samples, DCC showed the highest area under the curve values for the detection of superficial hypopharyngeal cancer (0.917, 95% confidence interval = 0.864–0.970), compared with those for EDNRB (0.680) and ECAD (0.639). When the cutoff for the methylation values of DCC was set at ≥0.163, the sensitivity to detect hypopharyngeal cancer was 82.8% and the specificity was 90.2%.ConclusionsDCC methylation in saliva samples could be a non-invasive and efficient tool for early detection of hypopharyngeal cancer in high-risk patients.