Advancing abiotic stress monitoring in plants with a wearable non-destructive real-time salicylic acid laser-induced-graphene sensor

Drought and salinity stresses present significant challenges that exert a severe impact on crop productivity worldwide. Understanding the dynamics of salicylic acid (SA), a vital phytohormone involved in stress response, can provide valuable insights into the mechanisms of plant adaptation to cope with these challenging conditions. This paper describes and tests a sensor system that enables real-time and non-invasive monitoring of SA content in avocado plants exposed to drought and salinity. By using a reverse iontophoretic system in conjunction with a laser-induced graphene electrode, we demonstrated a sensor with high sensitivity (82.3 nA/[μmol L-1⋅cm-2]), low limit of detection (LOD, 8.2 μmol L-1), and fast sampling response (20 s). Significant differences were observed between the dynamics of SA accumulation in response to drought versus those of salt stress. SA response under drought stress conditions proved to be faster and more intense than under salt stress conditions. These different patterns shed light on the specific adaptive strategies that avocado plants employ to cope with different types of environmental stressors. A notable advantage of the proposed technology is the minimal interference with other plant metabolites, which allows for precise SA detection independent of any interfering factors. In addition, the system features a short extraction time that enables an efficient and rapid analysis of SA content.

Exhaled volatolomics profiling facilitates personalized screening for gastric cancer

Gastric cancer (GC) is one of the most fatal cancers, characterized by non-specific early symptoms and difficulty in detection. However, there are no valid non-invasive screening tools available for GC. Here we establish a non-invasive method that employs exhaled volatolomics and ensemble learning to detect GC. We developed a comprehensive mass spectrometry-based procedure and determined of a wide range of volatolomics from 314 breath samples. The discovery, identification and verification research screened a biomarker panel to distinguish GC from controls. This panel has achieved 0.90 (0.87-0.94, 95%CI) accuracy, with an area under curve (AUC) of 0.92 (0.89-0.94, 95%CI) in discovery cohort and 0.88 (0.83-0.91, 95%CI) accuracy with an AUC of 0.91 (0.87-0.93, 95%CI) in replication cohort, which outperformed traditional serum markers. Single-cell sequencing and gene set enrichment analysis revealed that these exhaled markers originated from aldehyde oxidation and pyruvate metabolism. Our approach advances the design of exhaled analysis for GC detection and holds promise as a non-invasive method to the clinic.

Agarose amplification based sequencing characterization cell-free RNA in preimplantation spent embryo medium

BACKGROUND

The cell-free RNA (cf-RNA) of spent embryo medium (SEM) has aroused a concern of academic and clinical researchers for its potential use in non-invasive embryo screening. However, comprehensive characterization of cf-RNA from SEM still presents significant technical challenges, primarily due to the limited volume of SEM. Hence, there is urgently need to a small input liquid volume and ultralow amount of cf-RNA library preparation method to unbiased cf-RNA sequencing from SEM. (75) RESULT: Here, we report a high sensitivity agarose amplification-based cf-RNA sequencing method (SEM-Acf) for human preimplantation SEM cf-RNA analysis. It is a cf-RNA sequencing library preparation method by adding agarose amplification. The agarose amplification sensitivity (0.005 pg) and efficiency (105.35 %) were increased than that of without agarose addition (0.45 pg and 96.06 %) by ∼ 90 fold and 9.29 %, respectively. Compared with SMART sequencing (SMART-seq), the correlation of gene expression was stronger in different SEM samples by using SEM-Acf. The cf-RNA number of detected and coverage uniformity of 3' end were significantly increased. The proportion of 5' end adenine, alternative splicing events and short fragments (<400 bp) were increased. It is also found that 4-mer end motifs of cf-RNA fragments was significantly differences between different embryonic stage by day3 spent cleavage medium and day5/6 spent blastocyst medium. (141) SIGNIFICANCE: This study established an efficient SEM amplification and library preparation method. Additionally, we successfully described the characterizations of SEM cf-RNA in preimplantation embryo using SEM-Acf, including expression features and fragment lengths. SEM-Acf facilitates the exploration of cf-RNA as a noninvasive embryo screening biomarker, and opens up potential clinical utilities of small input liquid volume and ultralow amount cf-RNA sequencing. (59).

Advancing edema detection: Harnessing the power of machine learning and near infrared spectroscopy for cerebral and cerebellar edema assessment

Edema, characterized by brain swelling, is a common response observed in various brain injuries. Timely detection of edema is crucial to mitigate the associated risks and improve patient care. This study evaluates the efficacy of CEREBO®, a non-invasive machine learning-powered near-infrared spectroscopy (mNIRS) based device, in detecting edema. The study was conducted on 234 participants with suspected head injuries who underwent simultaneous CEREBO® scans and CT head scans. The results of the study showed that CEREBO® effectively identified edematous lobes, achieving a sensitivity of 95.7%, specificity of 97%, and accuracy of 96.9% for cases with intracranial hemorrhage (ICH). Additionally, for cases without ICH, the device exhibited a sensitivity of 100%, specificity of 97.2%, and accuracy of 97.2%. Two cases were reported where CEREBO® failed to detect edematous ICH. The study highlights the potential of CEREBO® as a valuable tool for early detection of pre-symptomatic edema and ICH, enabling timely interventions and improved patient care. The findings support the reliability of near-infrared spectroscopy as a diagnostic modality for edema.

A smartphone-based colorimetric assay using Au@Ag core-shell nanoparticles as the nanoprobes for visual tracing of fluvoxamine in biofluids as a common suicide drug

In the present study, bimetallic nanoparticles (NPs) consisting of gold (AuNPs) as the core and silver (AgNPs) as the shell have been synthesized and applied as the nanoprobe for detection of fluvoxamine (FXM) as the anti-depression drug. The physicochemical properties of the prepared citrate-capped Au@Ag core-shell NPs have been characterized by UV-Vis, Fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM) and scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) techniques. The design of the smartphone-based colorimetric FXM sensor relies on the fast hydrolysis of FXM under alkaline conditions by producing of 2-(Aminooxy)ethanamine without any significant peak at 400-700 nm. The interaction of the resulted molecule with the nanoprobe induced a red shift in the longitudinal localized surface plasmon resonance (LSPR) peak of the nanoprobe, which was accompanied by sharp and vivid color variations in the solution. A linear relationship between the absorption signal increasing by FXM concentration increasing from 1 µM to 10 µM presented a simple, low cost and minimally instrumented format for FXM quantification with a limit of detection (LOD) of 100 nM. The collected visual data with the elegant colorimetric response of the nanoprobe in the presence of FXM from Indian red to light red violet and bluish-purple color offered simple detection of FXM with the naked eye. The satisfactory results of the proposed cost-effective sensor in the rapid assay of FXM in human serum, urine, saliva and pharmaceutical samples guarantee the potential of the nanoprobe for on-site and visual determination of FXM in actual samples. The proposed sensor as the first non-invasive FXM sensor for saliva sample analysis may hold great promise to provide the technical support for the rapid and valid detection of FXM for forensic medicine and clinical organizations.

Non-invasive in-vivo glucose-based stress monitoring in plants

Plant stress responses involve a suite of genetically encoded mechanisms triggered by real-time interactions with their surrounding environment. Although sophisticated regulatory networks maintain proper homeostasis to prevent damage, the tolerance thresholds to these stresses vary significantly among organisms. Current plant phenotyping techniques and observables must be better suited to characterize the real-time metabolic response to stresses. This impedes practical agronomic intervention to avoid irreversible damage and limits our ability to breed improved plant organisms. Here, we introduce a sensitive, wearable electrochemical glucose-selective sensing platform that addresses these problems. Glucose is a primary plant metabolite, a source of energy produced during photosynthesis, and a critical molecular modulator of various cellular processes ranging from germination to senescence. The wearable-like technology integrates a reverse iontophoresis glucose extraction capability with an enzymatic glucose biosensor that offers a sensitivity of 22.7 nA/(μM·cm²), a limit of detection (LOD) of 9.4 μM, and a limit of quantification (LOQ) of 28.5 μM. The system's performance was validated by subjecting three different plant models (sweet pepper, gerbera, and romaine lettuce) to low-light and low-high temperature stresses and demonstrating critical differential physiological responses associated with their glucose metabolism. This technology enables non-invasive, non-destructive, real-time, in-situ, and in-vivo identification of early stress response in plants and provides a unique tool for timely agronomic management of crops and improving breeding strategies based on the dynamics of genome-metabolome-phenome relationships.

A comprehensive characterization of cell-free RNA in spent blastocyst medium and quality prediction for blastocyst

The rate of pregnancy can be affected by many factors in assisted reproductive technology (ART), and one of which is the quality of embryos. Therefore, selecting the embryos with high potential is crucial for the outcome. Fifteen spent blastocyst medium (SBM) samples were collected from 14 patients who received in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI), seven from high-grade embryos and eight from low-grade embryos. Cell-free RNA (cf-RNA) profile of SBM samples were analyzed by RNA sequencing in the present study. It was found that a large amount of cf-RNA were released into SBM, including protein-coding genes (68.9%) and long noncoding RNAs (lncRNAs) (17.26%). Furthermore, a high correlation was observed between blastocyst genes and SBM genes. And the cf-mRNAs of SBM were highly fragmented, and coding sequence (CDS) and untranslated (UTR) regions were released equally. Two hundred and thirty-two differentially expressed genes were identified in high-grade SBM (hSBM) and low-grade SBM (lSBM), which could be potential biomarker in distinguishing the embryos with different quality as an alternative or supplementary approach for subjective morphology criteria. Hence, cf-RNAs sequencing revealed the characterization of circulating transcriptomes of embryos with different quality. Based on the results, the genes related to blastocyst quality were screened, including the genes closely related to translation, immune-signaling pathway, and amino acid metabolism. Overall, the present study showed the types of SBM cf-RNAs, and the integrated analysis of cf-RNAs profiling with morphology grading displayed its potential in predicting blastocyst quality. The present study provided valuable scientific basis for noninvasive embryo selection in ART by RNA-profiling analysis.

A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm

PURPOSE

Atherosclerosis plays a significant role in the initiation of coronary artery aneurysms (CAA). Although the treatment options for this kind of vascular disease are developing, there are challenges and limitations in both selecting and applying sufficient medical solutions. For surgical interventions, that are novel therapies, non-invasive specific patient-based studies could lead to obtaining more promising results. Despite medical and pathological tests, these pre-surgical investigations require special biomedical and computer-aided engineering techniques. In this study, a machine learning (ML) model is proposed for the non-invasive detection of atherosclerotic CAA for the first time.

METHODS

The database for study was collected from hemodynamic analysis and computed tomography angiography (CTA) of 80 CAAs from 61 patients, approved by the Institutional Review Board (IRB). The proposed ML model is formulated for learning by a one-class support vector machine (1SVM) that is a field of ML to provide techniques for outlier and anomaly detection.

RESULTS

The applied ML algorithms yield reasonable results with high and significant accuracy in designing a procedure for the non-invasive diagnosis of atherosclerotic aneurysms. This proposed method could be employed as a unique artificial intelligence (AI) tool for assurance in clinical decision-making procedures for surgical intervention treatment methods in the future.

CONCLUSIONS

The non-invasive diagnosis of the atherosclerotic CAAs, which is one of the vital factors in the accomplishment of endovascular surgeries, is important due to some clinical decisions. Although there is no accurate tool for managing this kind of diagnosis, an ML model that can decrease the probability of endovascular surgical failures, death risk, and post-operational complications is proposed in this study. The model is able to increase the clinical decision accuracy for low-risk selection of treatment options.

A feasibility study on improving the non-invasive detection accuracy of bottled Shuanghuanglian oral liquid using near infrared spectroscopy

The ultimate goal of the study is to present a strategy to improve the accuracy of near-infrared spectroscopy detection of Shuanghuanglian oral liquid in glass bottles without damaging the primary packaging. we adopted the multi-position spectral modeling (MPSM) method to correct the spectral variation caused by the difference of bottle and measuring position, so as to improve the measurement accuracy and find the best site combination for measuring Shuanghuanglian oral liquid. Baicalin, total flavonoids and soluble solid contents were considered as the quality indicators of the oral liquid, and partial least squares (PLS) models were employed for the single-position and multi-position spectra, respectively. The root mean square error of the validation set (RMSEP) of the optimum multi-position models are 0.7412 mg/mL for baicalin, 1.1259 mg/mL for total flavonoids and 0.9491% for soluble solids contents. Compared with the traditional single-position spectral modeling method (SPSM method), MPSM method improved the prediction accuracy of baicalin, total flavonoids and soluble solid contents by 26.84%, 31.97% and 58.14% respectively. The results showed that the MPSM method can improve the measurement accuracy of bottled oral liquid and is an effective method to eliminate the uncertainty of measurement conditions.

Advanced wearable biosensors for the detection of body fluids and exhaled breath by graphene

Given the huge economic burden caused by chronic and acute diseases on human beings, it is an urgent requirement of a cost-effective diagnosis and monitoring process to treat and cure the disease in their preliminary stage to avoid severe complications. Wearable biosensors have been developed by using numerous materials for non-invasive, wireless, and consistent human health monitoring. Graphene, a 2D nanomaterial, has received considerable attention for the development of wearable biosensors due to its outstanding physical, chemical, and structural properties. Moreover, the extremely flexible, foldable, and biocompatible nature of graphene provide a wide scope for developing wearable biosensor devices. Therefore, graphene and its derivatives could be trending materials to fabricate wearable biosensor devices for remote human health management in the near future. Various biofluids and exhaled breath contain many relevant biomarkers which can be exploited by wearable biosensors non-invasively to identify diseases. In this article, we have discussed various methodologies and strategies for synthesizing and pattering graphene. Furthermore, general sensing mechanism of biosensors, and graphene-based biosensing devices for tear, sweat, interstitial fluid (ISF), saliva, and exhaled breath have also been explored and discussed thoroughly. Finally, current challenges and future prospective of graphene-based wearable biosensors have been evaluated with conclusion. Graphene is a promising 2D material for the development of wearable sensors. Various biofluids (sweat, tears, saliva and ISF) and exhaled breath contains many relevant biomarkers which facilitate in identify diseases. Biosensor is made up of biological recognition element such as enzyme, antibody, nucleic acid, hormone, organelle, or complete cell and physical (transducer, amplifier), provide fast response without causing organ harm.

Role of Conjunctival Ultraviolet Autofluorescence in Ocular Surface Squamous Neoplasia

Objective

To evaluate the adjunctive role of conjunctival autofluorescence in the management of ocular surface squamous neoplasia (OSSN).

Materials and Methods

Seventeen patients with clinically diagnosed OSSN were included. Morphological characteristics, type of OSSN, and autofluorescence photographs of the lesion were captured. Presence and area of conjunctival ultraviolet autofluorescence (CUVAF) were the main outcome measures.

Results

Overall, 17 patients with 15 (88%) primary and 2 (12%) recurrent OSSN were included. Common locations were temporal (n = 10), nasal (n = 5), and diffuse variety (n = 2). Morphologically, there were 4 (22.2%) nodular, 4 (22.2%) leucoplakic, 3 (16.7%) gelatinous, and 1 (5.5%) each of papillary, nodulo-ulcerative, and diffuse variety. Mixed morphology was present in 4 eyes (22.2%). Sixteen of 18 eyes (88.9%) with OSSN displayed autofluorescence on CUVAF images. The mean area of CUVAF was 15.82 mm² (10.77-19.59 mm²). Autofluorescence was reported in 8 eyes (44.4%) which had negative reports on impression cytology.

Conclusions

Conjunctival autofluorescence was seen in the majority of cases with OSSN, in spite of negative cytology reports. Our study demonstrates that CUVAF may serve as an effective ancillary, non-invasive, and resource-friendly tool for supplementing the clinical diagnosis of OSSN, especially in diffuse and recurrent lesions that are not amenable to surgical intervention.

Evaluation of TERT promoter mutations in urinary cell-free DNA and sediment DNA for detection of bladder cancer

BACKGROUND

Cell-free DNA (cfDNA) is proposed to be a valuable source of biomarkers in liquid biopsies for various diseases as it is supposed to partially originate from tumor cells. However, data about the diagnostic implications of cfDNA in urine for the detection of bladder cancer (BCa) is sparse.

METHODS

We evaluated the usability of urinary cfDNA for diagnostic purposes compared to urine sediment DNA (sDNA) in 53 BCa patients and 36 control subjects by analyzing two abundant point-mutations (C228T/C250T) in the TERT promoter using Next-Generation Sequencing.

RESULTS

Mutations were detected in 77% of the urinary sDNA compared to 63% of the cfDNA samples. Moreover, the TERT mutation allele frequencies (MAF) were highly correlated in cfDNA and sDNA. In comparison, the accuracy of the TERT assay was higher in sDNA (84%) compared to cfDNA or voided urine cytology (both 77%). Interestingly, MAFs from leukocyte-rich urines were higher in cfDNA than in sDNA, indicating a diagnostic advantage of cfDNA in such urines.

CONCLUSIONS

Urine-based mutation detection has the ability to augment and surpass voided urine cytology as the current gold-standard for the non-invasive detection and surveillance of BCa. The analysis of cell-free DNA provides no general diagnostic advantage compared to urine sediment DNA.

eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood

With the technology development on detecting circulating tumor cells (CTCs) and cell-free DNAs (cfDNAs) in blood, serum, and plasma, non-invasive diagnosis of cancer becomes promising. A few studies reported good correlations between signals from tumor tissues and CTCs or cfDNAs, making it possible to detect cancers using CTCs and cfDNAs. However, the detection cannot tell which cancer types the person has. To meet these challenges, we developed an algorithm, eTumorType, to identify cancer types based on copy number variations (CNVs) of the cancer founding clone. eTumorType integrates cancer hallmark concepts and a few computational techniques such as stochastic gradient boosting, voting, centroid, and leading patterns. eTumorType has been trained and validated on a large dataset including 18 common cancer types and 5327 tumor samples. eTumorType produced high accuracies (0.86-0.96) and high recall rates (0.79-0.92) for predicting colon, brain, prostate, and kidney cancers. In addition, relatively high accuracies (0.78-0.92) and recall rates (0.58-0.95) have also been achieved for predicting ovarian, breast luminal, lung, endometrial, stomach, head and neck, leukemia, and skin cancers. These results suggest that eTumorType could be used for non-invasive diagnosis to determine cancer types based on CNVs of CTCs and cfDNAs.

Design and testing of a low impedance transceiver circuit for nitrogen-14 nuclear quadrupole resonance

A low impedance transceiver circuit consisting of a transmit-receive switch circuit, a class-D amplifier and a transimpedance amplifier (TIA) was newly designed and tested for a nitrogen-14 NQR. An NQR signal at 1.37MHz from imidazole was successfully observed with the dead time of ~85µs under the high Q transmission (Q~120) and reception (Q~140). The noise performance of the low impedance TIA with an NQR probe was comparable with a commercial low noise 50Ω amplifier (voltage input noise: 0.25 nV/Hz) which was also connected to the probe. The protection voltage for the pre-amplifier using the low impedance transceiver was ~10 times smaller than that for the pre-amplifier using a 50Ω conventional transceiver, which is suitable for NQR remote sensing applications.