Screening For Early Disease Research Articles

Novel flexible magnetoelastic biosensor based on PDMS/FeSiB/QD composite film for the detection of African swine fever virus P72 protein.

African swine fever (ASF) is a highly contagious and severe hemorrhagic disease caused by the African swine fever virus (ASFV). The continuous spread of ASFV affects the safety of the global meat supply; therefore, the establishment of sensitive and specific detection methods for ASFV has become an important hot spot in food safety. Herein, we developed a flexible magnetoelastic (ME) biosensor based on PDMS/FeSiB/QDs composite films for the detection of ASFV P72 protein. Based on the high luminescence performance of CsPbBr3 quantum dots and the excellent magnetoelastic effect of FeSiB, flexible ME biosensors convert stress signals generated by antibody-antigen-specific binding into optical and electromagnetic signals. The nanostructures covalently linked by quantum dots and PDMS provide biomodification sites for ASFV P72 antibodies, simplifying the functionalization modification process compared to the case of conventional biosensors. The deformation of the PDMS film is amplified, and the conversion of surface stress signals to electrical signals is enhanced by exposing the biosensor to a uniform magnetic field. The experimental results proved that the flexible ME biosensor has a wide linear range of 10 ng mL-1-100 μg mL-1, and the detection limit is as low as 0.079 ng mL-1. Moreover, the flexible ME biosensor also shows good stability, sensitivity and specificity, confirming the potential for early disease screening.

A Novel Terahertz Metamaterial Microfluidic Sensing Chip for Ultra-Sensitive Detection.

A terahertz metamaterial microfluidic sensing chip for ultrasensitive detection is proposed to investigate the response of substances to terahertz radiation in liquid environments and enhance the molecular fingerprinting of trace substances. The structure consists of a cover layer, a metal microstructure, a microfluidic channel, a metal reflective layer, and a buffer layer from top to bottom, respectively. The simulation results show that there are three obvious resonance absorption peaks in the range of 1.5-3.0 THz and the absorption intensities are all above 90%. Among them, the absorption intensity at M1 = 1.971 THz is 99.99%, which is close to the perfect absorption, and its refractive index sensitivity and Q-factor are 859 GHz/RIU and 23, respectively, showing excellent sensing characteristics. In addition, impedance matching and equivalent circuit theory are introduced in this paper to further analyze the physical mechanism of the sensor. Finally, we perform numerical simulations using refractive index data of normal and cancer cells, and the results show that the sensor can distinguish different types of cells well. The chip can reduce the sample pretreatment time as well as enhance the interaction between terahertz waves and matter, which can be used for early disease screening and food quality and safety detection in the future.

A dry chemistry-based self-enhanced electrochemiluminescence lateral flow immunoassay sensor for accurate sample-to-answer detection of luteinizing hormone

BackgroundColorimetric lateral flow immunoassay (LFIA) is a widely used point-of-care testing (POCT) technology, while it has entered a bottleneck period because of low detection sensitivity, expensive preparation materials, and incapable quantitative detection. Therefore, it is necessary to develop a novel POCT method that is ultrasensitive, simple, portable, and capable of accurately detecting biomarkers in biofluids daily, particularly for pregnancy preparation and early screening of diseases. ResultIn this work, a novel dry chemistry-based self-enhanced electrochemiluminescence (DC-SE-ECL) LFIA sensor is introduced for accurate POCT of luteinizing hormone (LH). The proposed DC-SE-ECL immunosensor significantly improves the detection sensitivity through the Poly-l-Lysine (PLL)-based SE-ECL probe and cathode modification of closed bipolar electrode (C-BPE). Additionally, a new type of C-BPE configuration is designed for easily performing the LFIA. And, two standalone absorbent pads are symmetrically arranged below the reporting channel of the electrode pad to decease useless residues on the detection pad, which further improves the detection performance. Under optimized conditions, the proposed LFIA sensor has a low limit of detection (9.274 μIU mL−1) and a wide linear dynamic range (0.01–100 mIU mL−1), together with good selectivity, repeatability and storage stability. SignificanceThese results indicate that the proposed DC-SE-ECL method has the potential as a new tool for detecting biomarkers in clinical samples.

Centrifugo-Pneumatic Reciprocating Flowing Coupled with a Spatial Confinement Strategy for an Ultrafast Multiplexed Immunoassay.

Immunoassays serve as powerful diagnostic tools for early disease screening, process monitoring, and precision treatment. However, the current methods are limited by high costs, prolonged processing times (>2 h), and operational complexities that hinder their widespread application in point-of-care testing. Here, we propose a novel centrifugo-pneumatic reciprocating flowing coupled with spatial confinement strategy, termed PRCM, for ultrafast multiplexed immunoassay of pathogens on a centrifugal microfluidic platform. Each chip consists of four replicated units; each unit allows simultaneous detection of three targets, thereby facilitating high-throughput parallel analysis of multiple targets. The PRCM platform enables sequential execution of critical steps such as solution mixing, reaction, and drainage by coordinating inherent parameters, including motor rotation speed, rotation direction, and acceleration/deceleration. By integrating centrifugal-mediated pneumatic reciprocating flow with spatial confinement strategies, we significantly reduce the duration of immune binding from 30 to 5 min, enabling completion of the entire testing process within 20 min. As proof of concept, we conducted a simultaneous comparative test on- and off-the-microfluidics using 12 negative and positive clinical samples. The outcomes yielded 100% accuracy in detecting the presence or absence of the SARS-CoV-2 virus, thus highlighting the potential of our PRCM system for multiplexed point-of-care immunoassays.

Circulating tumor DNA (ctDNA) as a biomarker for lung cancer: Early detection, monitoring and therapy prediction.

Circulating tumor DNA (ctDNA), i.e., DNA shed from tumor cells into the bloodstream, is emerging as one of the most useful plasma biomarkers in patients with multiple types of cancer, including patients with non-small cell lung cancer (NSCLC). Indeed, NSCLC was the first malignancy in which measurement of ctDNA was approved for clinical use, i.e., mutational testing of EGFR for predicting response to EGFR tyrosine kinase inhibitors in patients with advanced disease. Although historically the gold standard method for EGFR mutational analysis required tumor tissue, the use of ctDNA is more convenient and safer for patients, results in a faster turn-around-time for return of results, provides a more complete representation of genetic alteration in heterogeneous tumors and is less costly to perform. Emerging uses of ctDNA in patients with lung or suspected lung cancer include screening for early disease, surveillance following initial treatment and monitoring response to therapy in metastatic disease. For evaluating therapy response, ctDNA appears to be especially useful in patients receiving targeted therapies against driver oncogenes or immunotherapy. Further work should not only validate these emerging findings but also aim to optimize and standardize ctDNA assays.

DGAMDA: Predicting miRNA-disease association based on dynamic graph attention network.

MiRNA (microRNA)-disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time-consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA-disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention-based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA-disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high-precision feature mining and association scoring prediction. We conducted a five-fold cross-validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1-score, which were .8986, .8869, .9115, and .8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases.

Ion-sensitive field effect transistor biosensors for biomarker detection: current progress and challenges.

The ion-sensitive field effect transistor (ISFET) has emerged as a crucial sensor device, owing to its numerous benefits such as label-free operation, miniaturization, high sensitivity, and rapid response time. Currently, ISFET technology excels in detecting ions, nucleic acids, proteins, and cellular components, with widespread applications in early disease screening, condition monitoring, and drug analysis. Recent advancements in sensing techniques, coupled with breakthroughs in nanomaterials and microelectronics, have significantly improved sensor performance. These developments are steering ISFETs toward a promising future characterized by enhanced sensitivity, seamless integration, and multifaceted detection capabilities. This review explores the structure and operational principles of ISFETs, highlighting recent research in ISFET biosensors for biomarker detection. It also examines the limitations of these sensors, proposes potential solutions, and anticipates their future trajectory. This review aims to provide a valuable reference for advancing ISFETs in the field of biomarker measurement.

Utilizing machine learning for early screening of thyroid nodules: a dual-center cross-sectional study in China.

Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators. Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits. Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models. Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These findings enhance our understanding of the key predictors of thyroid nodules and underscore the potential of machine learning in public health applications for early disease screening and prevention.

SnTe/SnSe Heterojunction Based Ammonia Sensors with Excellent Withstand to Ambient Humidities.

Non-invasive breath testing has gained increasing importance for early disease screening, spurring research into cheap sensors for detecting trace biomarkers such as ammonia. However, real-life deployment of ammonia sensors remains hindered by susceptibility to humidity-induced interference. The SnTe/SnSe heterojunction-based chemiresistive-type sensor demonstrates an excellent response/recovery to different concentrations of ammonia from 0.1 to 100ppm at room temperature. The improved sensing properties of the heterojunctions-based sensors compared to single-phased SnTe or SnSe can be attributed to the stronger NH3 adsorptions, more Te vacancies, and hydrophobic surface induced by the formed SnTe/SnSe heterojunctions. The sensing mechanisms are investigated in detail by using in situ techniques such as diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS), Kelvin probe, and a.c. impedance spectroscopy together with the Density-Function-Theory calculations. The formed heterojunctions boost the overall charge transfer efficiency between the ammonia and the sensing materials, thus leading to the desirable sensing features as well, with excellent resistance to ambient humidities.

Molecular Recognition with Macrocyclic Receptors for Application in Precision Medicine.

Macrocyclic receptors can serve as alternatives to natural recognition systems as recognition tools. They provide effectively preorganized cavities to encapsulate guests via host-guest interactions, thereby affecting the physiochemical properties of the guests. Macrocyclic receptors exhibit chemical and thermal stabilities higher than those of natural receptors and thus are expected to resist degradation inside the body. This reduces the risk of harmful degradation byproducts and ensures optimal levels of effectiveness. Macrocyclic receptors have precise molecular weights and well-defined structures; this ensures their batch-to-batch reproducibility, which is critical for ensuring quality and effectiveness levels. Moreover, macrocyclic receptors exhibit broad modification tunabilities, rendering them adaptable to various guests. Molecular recognition is the basis of numerous biological processes. Macrocyclic receptors may display considerable potential for application in diagnosing and treating diseases, depending on the host-guest recognition of bioactive molecules. However, the binding affinities and selectivities of macrocyclic receptors toward bioactive molecules are generally insufficient, which may lead to problems such as low diagnosis accuracies, off-target leaking, and interference with normal functions. Therefore, addressing the challenge of the strong and specific complexation of bioactive molecules and macrocyclic receptors is imperative.To overcome this challenge, we proposed the innovative strategies of longitudinal cavity extension and coassembled heteromultivalent recognition for application in the recognition of small molecules and biomacromolecules, respectively. The deepened cavity provides a stronger hydrophobic effect and a larger interaction area while maintaining the framework rigidity. By coassembling two macrocyclic amphiphiles into one ensemble, we achieved the desired heteromultivalent recognition. This strategy affords the necessary binding properties while preventing the requirement of tedious steps and site mismatch in covalent synthesis. Using these two strategies, we achieved specific and strong binding of macrocyclic receptors to various bioactive molecules including biomarkers, drugs, and disease-related peptides/proteins. We then applied these macrocyclic receptor-based recognition systems in biosensing and bioimaging, drug delivery, and therapeutics.In this Account, we summarize the strategies we used in the recognition of small molecules and biomacromolecules. Thereafter, we discuss their applications in precision medicine, involving the (1) sensing of biomarkers and imaging of lesion sites, which are critical in the early screening of diseases and accurate diagnoses; (2) precise loading and targeted delivery of drugs, which are crucial in improving their therapeutic efficacies and reducing their side effects; and (3) capture and removal of disease-related biomacromolecules, which are significant for precise intervention in life processes. Finally, we propose recommendations for the further development of macrocyclic receptor-based recognition systems in biomedicine. Macrocyclic receptors exhibit considerable potential for research, and continued investigation may not only expand the applications of supramolecular chemistry but also open novel avenues for the development of precision medicine.

Chemical Sensors for Volatile Organic Compound Detection

The detection of volatile organic compounds (VOCs) is in high demand in various fields, such as environmental pollution monitoring, early disease screening, and food freshness assessment [...]

Non-Invasive Screening for Sickle Cell Disease Using Only a Smartphone Application

Introduction Pediatric mortality due to Sickle Cell Disease (SCD) remains high and often occurs prior to diagnosis in most black and brown low- and middle-income countries (LMICs), due to the high prevalence of SCD and limited resources in these countries.To that end, we developed a low-cost non-invasive screening tool that can be widely available at the point of need. Our screening tool utilizes the power of both image analysis and predictive algorithms as a novel approach to SCD screening and diagnostics. In this new app, we have combined the quantitative analysis of fingernail pallor, an adaptation of our previously described smartphone app for estimating blood hemoglobin levels using smartphone images of the nail beds, with qualitative symptom and medical history data to compute an SCD Likelihood Score (SLS) that provides an individual's pre-diagnostic test potential for disease, a key metric in the disease screening and diagnostic process. Methods 417 subjects ages 0.5-20 years old were enrolled from Children's Healthcare of Atlanta (CHOA) hematology/oncology clinic; of these patients, 219 patients had SCD, and 198 did not. Pediatric patients were screened because they best simulated the target populations aimed for the app's use. Our analysis was restricted to the presence of specific pain, past stroke history and/or symptoms, splenic crises, neurodevelopment disorder, family history of SCD, due to their specificity to SCD and their ability to be answered without the need for resource-intensive diagnostics. The SLS was calculated by determining the presence of these medical history items and the patient's anemia status informed by the patient's most recent CBC. For each symptom a weighted score was generated by finding the difference between the percentage of subjects with SCD and those without SCD, who experienced the symptom. The presence of symptoms was binarized with 1 indicating the presence of a symptom and 0 indicating the absence of a symptom. The binary value of the symptom was then multiplied by the symptom's respective weighted value. These values were totaled and normalized to give the overall clinical profile-weighted score with the same weight as the anemia status. A receiver operating characteristic analysis was conducted to select a threshold for the SLS that provided the most accurate diagnosis in our training dataset. We incorporated this SLS model into our lab's previously described smartphone app predictive algorithm . Results A clinical assessment of the screening app was conducted at CHOA and enrolled 68 participants from both in-patient and out-patient settings. Patient hemoglobin measurements were obtained using our previously described hemoglobin screening app and patients (and their parents for those needing their assistance) were asked to provide responses to the symptom survey. Of the five medical history items queried, family history and a history of splenic crisis when present was found to contribute to 30% and 41%, respectively, of the overall clinical profile score. Whereas the presence of a history of pain, stroke history, and learning difficulties made up smaller portions of this portion of the SLS. The app performed with a sensitivity of 100% and a specificity of 75% in screening for SCD in patients <5 years of age, the target demographic for early screening. Conclusion This SCD screening application has the potential to expand and improve SCD outcomes in LMICs where access to the gold standard or any accurate diagnostic tools is limited. The app's non-invasive and easy-to-use nature and the ubiquity of smartphones globally make this screening tool a practical and feasible mode of screening. Early disease screening is paramount to survival for children with SCD and if this tool proves to be accurate in these settings, can be lifesaving for the hundreds of thousands of infants with SCD born in LMICs per year. Non-invasive modalities such as our SCD screening application can enable resource allocation and cost reduction by identifying those patients who do and do not have a high likelihood of disease, thus reserving tests for those who are high risk.

Clinical and Functional Analyses of an African-ancestry Gain-of-function HOXB13 Variant Implicated in Aggressive Prostate Cancer

BackgroundRecent reports have uncovered a HOXB13 variant (X285K) predisposing to prostate cancer in men of West African ancestry. The clinical relevance and protein function associated with this inherited variant are unknown. ObjectiveTo determine the clinical relevance of HOXB13 (X285K) in comparison with HOXB13 (G84E) and BRCA2 pathogenic/likely pathogenic (P/LP) variants, and to elucidate the oncogenic mechanisms of the X285K protein. Design, setting, and participantsReal-world data were collected from 21,393 men with prostate cancer undergoing genetic testing from 2019 to 2022, and in vitro cell-line models were established for the evaluation of oncogenic functions associated with the X285K protein. Outcome measurements and statistical analysisGenetic testing results were compared among patient groups according to self-reported race/ethnicity, Gleason scores, and American Joint Committee on Cancer stages using the exact test. Oncogenic functions of X285K were evaluated by RNA sequencing, chromatin immunoprecipitation sequencing, and Western blot analyses. Results and limitationsHOXB13 (X285K) was significantly enriched in self-reported Black (1.01%) versus White (0.01%) patients. We observed a trend of more aggressive disease in the HOXB13 (X285K) and BRCA2 P/LP carriers than in the HOXB13 (G84E) carriers. Replacement of the wild-type HOXB13 protein with the X285K protein resulted in a gain of an E2F/MYC signature, validated by the elevated expression of cyclin B1 and c-Myc, without affecting the androgen response signature. Elevated expression of cyclin B1 and c-Myc was explained by enhanced binding of the X285K protein to the promoters and enhancers of these genes. The limitations of the study are the lack of complete clinical outcome data for all patients studied and the use of a single cell line in the functional analysis. ConclusionsHOXB13 (X285K) is significantly enriched in self-reported Black patients, and X285K carriers detected in the real-world clinical setting have aggressive prostate cancer features similar to the BRCA2 carriers. Functional studies revealed a unique gain-of-function oncogenic mechanism of X285K protein in regulating E2F/MYC signatures. Patient summaryThe HOXB13 (X285K) variant is clinically and functionally linked to aggressive prostate cancer, supporting genetic testing for X285K in Black men and early disease screening of carriers of this variant.

Development of a High-Accuracy, Low-Cost, and Portable Fluorometer with Smartphone Application for the Detection of Urinary Albumin towards the Early Screening of Chronic Kidney and Renal Diseases.

This study presents the development of a portable fluorometer with a smartphone application designed to facilitate the early screening of chronic kidney and renal diseases by enabling the sensitive detection of urinary albumin. Utilizing a fluorescence-based aptasensor, the device achieved a linear calibration curve (0.001-1.5 mg/mL) with a linearity of up to 0.98022 and a detection limit of 0.203 µg/mL for human serum albumin (HSA). The analysis of 130 urine samples demonstrated comparable performance between this study's fluorometer, a commercial fluorometer, and the standard automated method. These findings validate the feasibility of the portable fluorometer and aptasensor combination as a reliable instrument for the sensitive and specific measurement of HSA in urine samples. Moreover, the fluorometer's portability offers potential applications in portable point-of-care testing, enhancing its utility in clinical settings for early disease screening.

Artificial Intelligence in Tongue Image Recognition

Tongue image recognition is a traditional Chinese medicine diagnosis method, which uses the shape, color, and texture of the tongue to judge the health of the human body. With the rapid development of artificial intelligence technology, the application of artificial intelligence in the field of tongue recognition has been widely considered. Based on the intelligent analysis of tongue diagnosis in traditional Chinese medicine, this paper reviews the application progress of artificial intelligence in tongue image recognition in recent years and analyzes its potential and challenges in this field. Firstly, this paper introduces three steps of tongue image recognition, including tongue image acquisition, tongue image preprocessing, and tongue image feature analysis. The application of traditional methods and artificial intelligence methods in the whole process of tongue image recognition is reviewed, especially the tongue body segmentation, and the advantages and disadvantages of convolutional neural networks are analyzed and compared. Artificial intelligence can use technologies such as deep learning and computer vision to automatically analyze and extract features from tongue images. By constructing a tongue image recognition model, tongue shape, color, texture, and other features can be accurately recognized and quantitatively analyzed. Finally, this paper summarizes the problems existing in artificial intelligence in tongue image recognition and looks forward to the future developmental direction of this field. It can promote the modernization of TCM diagnostic methods, achieve early disease screening and prevention, personalized medicine and treatment optimization, and support medical research and knowledge accumulation. However, there is still a need for further validation and practice, with a focus on patient privacy and data security.

A microneedle patch for breast cancer screening via minimally invasive interstitial fluid sampling

Early diagnosis and treatment are crucial for breast cancer patients. Interstitial fluid (ISF) shows promise for early disease screening, but its extraction for biomarker detection is challenging. To address this, we designed a microneedle (MN) patch for minimally invasive and rapid ISF extraction through materials engineering, which could improve cancer detection, ease the burden on medical resources, and increase patient compliance. Via UV crosslinking, the water absorbing moiety, acrylic acid (AA), can be polymerized and crosslinked with gelatin methacrylate (GelMA). The resulting GelMA-AA MN (G-A MN) can rapidly extract different analytes. One piece of G-A MN patch can aspirate about 1.29 mg (≈ 1.29 µL) of ISF from mice within 60 s. We further applied G-A MN for breast cancer screening in the 4T1 orthotopic mice breast cancer model and genetically engineered breast cancer model. By measuring the tumor markers carcinoembryonic antigen (CEA) and glycoantigen CA15-3, G-A MN could detect the cancer occurrence earlier than other standard screening methods, including blood tests, ultrasound, or Micro-CT. Furthermore, earlier diagnosis led to timely chemotherapy treatment and resulted in prolonged survival in the mice 4T1 breast cancer model. In conclusion, G-A MN enabled rapid ISF extraction for biomarker analysis in a minimally invasive manner. G-A MN facilitated detection outperformed other common diagnostic methods, representing a promising alternative breast cancer screening strategy.

Label correlation transformer for automated chest X-ray diagnosis with reliable interpretability.

Computer-aided diagnosis of chest X-ray (CXR) images can help reduce the huge workload of radiologists and avoid the inter-observer variability in large-scale early disease screening. Recently, most state-of-the-art studies employ deep learning techniques to address this problem through multi-label classification. However, existing methods still suffer from low classification accuracy and poor interpretability for each diagnostic task. This study aims to propose a novel transformer-based deep learning model for automated CXR diagnosis with high performance and reliable interpretability. We introduce a novel transformer architecture into this problem and utilize the unique query structure of transformer to capture the global and local information of the images and the correlation between labels. In addition, we propose a new loss function to help the model find correlations between the labels in CXR images. To achieve accurate and reliable interpretability, we generate heatmaps using the proposed transformer model and compare with the true pathogenic regions labeled by the physicians. The proposed model achieves a mean AUC of 0.831 on chest X-ray 14 and 0.875 on PadChest dataset, which outperforms existing state-of-the-art methods. The attention heatmaps show that our model could focus on the exact corresponding areas of related truly labeled pathogenic regions. The proposed model effectively improves the performance of CXR multi-label classification and the interpretability of label correlations, thus providing new evidence and methods for automated clinical diagnosis.

Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction

ObjectivesComputed tomography (CT)–based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters.MethodsA deep-learning airway segmentation model was newly trained on 24 Imaging in Lifelines (ImaLife) low-dose chest CT scans. This model was combined with an optimal-surface graph-cut for airway wall segmentation. These tools were used to calculate bronchial parameters in CT scans of 188 ImaLife participants with two scans an average of 3 months apart. Bronchial parameters were compared for reproducibility assessment, assuming no change between scans.ResultsOf 376 CT scans, 374 (99%) were successfully measured. Segmented airway trees contained a mean of 10 generations and 250 branches. The coefficient of determination (R2) for the luminal area (LA) ranged from 0.93 at the trachea to 0.68 at the 6th generation, decreasing to 0.51 at the 8th generation. Corresponding values for Wall Area Percentage (WAP) were 0.86, 0.67, and 0.42, respectively. Bland–Altman analysis of LA and WAP per generation demonstrated mean differences close to 0; limits of agreement (LoA) were narrow for WAP and Pi10 (± 3.7% of mean) and wider for LA (± 16.4–22.8% for 2–6th generations). From the 7th generation onwards, there was a sharp decrease in reproducibility and a widening LoA.ConclusionThe outlined approach for automatic bronchial parameter measurement on low-dose chest CT scans is a reliable way to assess the airway tree down to the 6th generation.Statement on clinical relevanceThis reliable and fully automatic pipeline for bronchial parameter measurement on low-dose CT scans has potential applications in screening for early disease and clinical tasks such as virtual bronchoscopy or surgical planning, while also enabling the exploration of bronchial parameters in large datasets.Key Points• Deep learning combined with optimal-surface graph-cut provides accurate airway lumen and wall segmentations on low-dose CT scans.• Analysis of repeat scans showed that the automated tools had moderate-to-good reproducibility of bronchial measurements down to the 6thgeneration airway.• Automated measurement of bronchial parameters enables the assessment of large datasets with less man-hours.

Multilayer Heterogeneous Membrane Biosensor Based on Multiphysical Field Coupling for Human Serum Albumin Detection.

A factor closely associated with renal disease status in clinical diagnosis is abnormal human serum albumin (HSA) concentration levels in human body fluids urine, serum, etc. The surface stress biosensor was developed as a new type of biosensor to detect protein molecule concentration and has a wide range of clinical applications. However, further sensitivity improvement is required to achieve higher detection performance. Herein, MXene/PDMS/Fe3O4/PDMS of the multilayer heterogeneous membrane biosensor (MHBios) based on the coupling of the magnetic field, electric field, and surface stress field was successfully developed to achieve high sensitivity HSA detection through magnetic sensitization. The modified antibody specifically binds to HSA at the AuNP layer, allowing the biosensor to convert the surface stress caused by PDMS film deformation into an electrical signal. When the biosensor was exposed to a uniform magnetic field, the conductive path of the conductive layer was reshaped further as the magnetic force amplified the deformation of the PDMS film, enhancing the conversion of biological signals to electrical signals. The results exhibited that the detection limit (LOD) of the MHBios was 78 ng/mL when HSA concentration was 0-50 μg/mL, which was markedly lower than the minimum diagnostic limit of microalbuminuria. Furthermore, the MHBios detected HSA in actual samples, confirming the potential for early disease screening.

Recent Trends in Metal Nanoparticles Decorated 2D Materials for Electrochemical Biomarker Detection.

Technological advancements in the healthcare sector have pushed for improved sensors and devices for disease diagnosis and treatment. Recently, with the discovery of numerous biomarkers for various specific physiological conditions, early disease screening has become a possibility. Biomarkers are the body's early warning systems, which are indicators of a biological state that provides a standardized and precise way of evaluating the progression of disease or infection. Owing to the extremely low concentrations of various biomarkers in bodily fluids, signal amplification strategies have become crucial for the detection of biomarkers. Metal nanoparticles are commonly applied on 2D platforms to anchor antibodies and enhance the signals for electrochemical biomarker detection. In this context, this review will discuss the recent trends and advances in metal nanoparticle decorated 2D materials for electrochemical biomarker detection. The prospects, advantages, and limitations of this strategy also will be discussed in the concluding section of this review.