Artificial Samples Research Articles

Evaluation of 3D-Printed Solid Microneedles Coated with Electrosprayed Polymeric Nanoparticles for Simultaneous Delivery of Rivastigmine and N-Acetyl Cysteine.

In the current study, coated microneedle arrays were fabricated by means of digital light processing (DLP) printing. Three different shapes were designed, printed, and coated with PLGA particles containing two different actives. Rivastigmine (RIV) and N-acetyl-cysteine (NAC) were coformulated via electrohydrodynamic atomization (EHDA), and they were incorporated into the PLGA particles. The two actives are administered as a combined therapy for Alzheimer's disease. The printed arrays were evaluated regarding their ability to penetrate skin and their mechanical properties. Optical microscopy and scanning electron microscopy (SEM) were employed to further characterize the microneedle structure. Confocal laser microscopy studies were conducted to construct 3D imaging of the coating and to simulate the diffusion of the particles through artificial skin samples. Permeation studies were performed to investigate the transport of the drugs across human skin ex vivo. Subsequently, a series of tape strippings were performed in an attempt to examine the deposition of the APIs on and within the skin. Light microscopy and histological studies revealed no drastic effects on the membrane integrity of the stratum corneum. Finally, the cytocompatibility of the microneedles and their precursors was evaluated by measuring cell viability (MTT assay and live/dead staining) and membrane damages followed by LDH release.

Correlation between induced polarization and sulfide content of rock samples obtained from seafloor hydrothermal mounds in the Okinawa Trough, Japan

AbstractThe physical properties of seafloor massive sulfides are crucial for interpreting sub-seafloor images from geophysical surveys, shedding light on the evolution of seafloor mineral deposits. While some studies have explored the relationship between electrical properties and the volume of conductive minerals in rocks from seafloor massive sulfide deposits, they primarily focused on artificial samples, leaving the characteristics of natural samples less understood. Moreover, there has been no comprehensive study detailing the general characteristics of electrical properties, particularly chargeability and relaxation time, in relation to the volumetric fraction of sulfides in rocks from massive sulfide mounds in typical hydrothermal areas. In this study, we employed complex conductivity measurements, elemental concentration analysis, and mineral content identification on to rock samples from the active hydrothermal zones of the Okinawa Trough in Japan. The complex conductivity observed was remarkably high, with a pronounced imaginary component and a broad frequency range. This is attributed to induced polarization extending beyond our measurement range. The rock samples were rich in conductive sulfide minerals such as pyrite, chalcopyrite, and galena. Using the Cole–Cole rock physics model, we established a correlation between rock chargeability and relaxation time coefficient with the volume fraction of conductive sulfide minerals, which deviated from previous findings. The intensity of induced polarization was notably higher than anticipated in earlier studies using artificial samples. Furthermore, we observed a distinct positive correlation between the coefficient of relaxation time and the increase in sulfide volume, likely due to the geometric characteristics of the sulfide minerals. Our findings suggest that rocks in massive sulfide mounds may generally construct sulfide clusters that lengthen the conductive path of the electrical carrier. Graphical Abstract

MaMD Analytical 1.0

We outline the functionalities and application of MaMD Analytical—a new, freely available software package for the estimation of population affinity using human cranial macromorphoscopic (MMS) traits. MaMD Analytical captures MMS scores using line drawings following the procedures outlined by Hefner and Linde (2018). MaMD Analytical generates classifications (with estimated likelihoods) into forensically significant populations using an artificial neural network and reference samples drawn from the Macromorphoscopic Databank (MaMD). Summary data (sensitivity, specificity, x-validated classification accuracies) are provided. In this article, we apply MaMD Analytical to a large sample of identified individuals not used in the original model building to assess utility and demonstrate the typical outputs for MaMD Analytical. MaMD Analytical facilitates construction of the biological profile and provides a number of safeguards in summary statistics as a valuable addition to the forensic anthropological analysis toolkit. MaMD Analytical is written in the open-source R environment integrating a previously developed artificial neural network model to estimate population affinity using well-documented and validated approaches.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease

BackgroundAlzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection. ResultsThis paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1–42 (Aβ 1–42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined “samples in, answers out” approach. SignificanceThis deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1–42, particularly in areas with low resources and limited communication infrastructure.

Electrochemical Determination of Fentanyl Using Carbon Nanofiber-Modified Electrodes.

In this work, we report the direct electrochemical oxidation of fentanyl using commercial screen-printed carbon electrodes (SPCEs) modified with carboxyl-functionalized carbon nanofibers (fCNFs). CNFs have surface chemistry and reactivity similar to carbon nanotubes (CNTs), yet they are easier to produce and are of a lower cost than CNTs. By monitoring the current produced during the electrochemical oxidation of fentanyl, variables such as fCNF loading, fentanyl accumulation time, electrolyte pH, and differential pulse voltammetry parameters were optimized. Under an optimized set of conditions, the fCNF/SPCEs responded linearly to fentanyl in the concentration range of 0.125-10 μM, with a limit of detection of 75 nM. The fCNF/SPCEs also demonstrated excellent selectivity against common cutting agents found in illicit drugs (e.g., glucose, sucrose, caffeine, acetaminophen, and theophylline) and interferents found in biological samples (e.g., ascorbic acid, NaCl, urea, creatinine, and uric acid). The performance of the sensor was also successfully tested using fentanyl spiked into an artificial urine sample. The straightforward electrode assembly process, low cost, ease of use, and rapid response make the fCNF/SPCEs prime candidates for the detection of fentanyl in both physiological samples and street drugs.

Construction health indicator using physically-informed 1D-WGAN-GP joint attention LSTM-DenseNet method

In data-driven prognosis methods, the accuracy of predicting the remaining useful life (RUL) of mechanical systems is predominantly contingent upon the efficacy of system health indicators (HI), typically amalgamated from statistical features derived from collected signals. Nevertheless, the majority of extant HI are beset by two principal shortcomings: (1) during traditional data denoising processes, degradation information from raw data is prone to loss owing to the lack of incorporation of the true physical properties of the data; and (2) the performance evaluation of constructed HI is imbalanced due to the influence of network structures on single models, often resulting in strong performance in only one or two indicators. To overcome such shortcomings, a mechanical health indicator construction method based on physical properties was proposed, termed 1D-WGAN-GP Joint attention LSTM-DenseNet. Firstly, artificial sample data is generated by analyzing the physical properties of the original dataset, which is then used to train the 1D-WGAN-GP model to achieve data denoising. Subsequently, the fusion of the attention LSTM (A-LSTM) network and DenseNet network is utilized to extract crucial feature vectors of HI under varying health conditions from the denoised data. Finally, the extracted feature vectors are used to construct system HI using the Euclidean distance method, and these indicators are used for predicting the system’s RUL. The results indicate that the proposed method outperformed traditional methods in terms of denoising effectiveness. Further, through ablation experiment analysis, the HI constructed by the proposed method demonstrated obvious complementarity in terms of monotonicity, correlation, robustness, and comprehensive evaluation. In RUL prediction applications, the proposed method also exhibited good performance, thereby validating its effectiveness.

Colorimetric Determination of Salivary Cortisol Levels in Artificial Saliva for the Development of a Portable Colorimetric Sensor (Salitrack)

Mental illnesses, such as clinical depression, have taken an unprecedented toll on society and the economy on a global scale. The relationship between stress management and mental health decline is of utmost significance, especially as most avenues of mental health management remain inaccessible for the majority of the general public, i.e., interview-based, and face-to-face interventions or costly drug-based therapies. Cortisol, the primary stress hormone, regulates the stress response in the human body and, through persistent activation, can lead to chronic stress and mental health deterioration. Thereby, the measurement and evaluation of cortisol within saliva could harness potential developments in management and diagnostic tools to monitor physiological and psychological stress in simple point-of-care applications. The current study aims to determine the concentration of salivary cortisol in spiked artificial saliva samples using blue tetrazolium (BT) dye as a colorimetric indicator. The proposed method showcases the use of the BT dye as an effective method for the rapid measurement of salivary cortisol, with accuracy comparable to the gold-standard method for salivary cortisol analysis, enzyme-linked immunoassays (ELISAs). Finally, a prototype colorimetric sensor has been developed for point-of-care applications of stress monitoring via salivary cortisol measurement.

Model Exploration and Application of Near-Infrared Spectroscopy for Species Separation and Quantification during Mixed Litter Decomposition in Subtropical Forests of China

Litter of different species coexists in the natural ecosystem and may induce non-additive effects during decomposition. Identifying and quantifying the origins of species in litter mixtures is essential for evaluating the responses of each component species when mixed with co-occurring species and then unraveling the underlying mechanism of the mixing effects of litter decomposition. Here, we used near-infrared spectroscopy (NIRS) to predict the species composition and proportions of four-tree species foliage mixtures in association with litter crude ash and litter decomposition time. To simulate the whole mixed litter decomposition process in situ, a controlled mixture of four tree species litter leaves consisting of 15 tree species combinations and 193 artificial mixed-species samples were created for model development and verification using undecomposed pure tree species and decomposed litter of single tree species over one year. Two series of NIRS models were developed with the original mass and ash-free weight as reference values. The results showed that these NIRS models could provide an accurate prediction for the percentage of the component species from in the litter leaf mixture’s composition. The predictive ability of the near-infrared spectroscopy model declined marginally with the prolonged litter decomposition time. Furthermore, the model with ash-free litter mass as a reference exhibited a higher coefficient of determination (R2) and a lower standard error of prediction (RMSECV). Thus, our results demonstrate that NIRS presents great potential for not only predicting the organic composition and proportion in multi-species mixed samples in static conditions, but also for samples in dynamic conditions (i.e., during the litter decomposition process), which could facilitate evaluation of the species-specific responses and impacts on the interspecific interactions of co-occurring species in high-biodiversity communities.

3D nanoprinted catadioptric fiber sensor for dual-axis distance measurement during vitrectomy

Retinal damage is a common intraoperative complication during vitrectomy, caused by a complex interplay between the suction of the vitrectome, the cut- and aspiration rate, and the distance of the instrument to the retina. To control this last factor, we developed two miniaturized fiber-optic distance sensors based on low-coherence interferometry for direct integration into the vitrectome. Both sensors have a diameter of 250 µm, which makes them compatible with a 25G vitrectome. The first sensor measures distance in the lateral direction. The second sensor is capable of simultaneously measuring distance in both the lateral and the axial direction. Axial and lateral directions correspond to the direction of the cutter port of the vitrectome and the direction along the vitrectome’s shaft, respectively. In both sensors, a free-form mirror deflects and focuses the beam in the lateral direction. In the dual-axis distance sensor, an additional lens is integrated into the free-form mirror for distance measurement in the axial direction. The beam-shaping micro-optics at the tip of the sensor fibers were fabricated through two-photon polymerization and are selectively gold coated for increased reflectivity of the mirror. Distance measurements were successfully demonstrated in artificial samples and in ex vivo pig eyes with a back-end that uses a current-tuned VCSEL as a swept-source. We experimentally demonstrate that the complete sensor system can attain a SNRmax of up to 80 dB. The small dimensions of the developed sensors make them a potential solution for various other medical applications.

WS2 nanosheets-based electrochemical biosensor for highly sensitive detection of tumor marker miRNA-4484

In this paper, a few-layer WS2 nanosheets-based electrochemical biosensor was fabricated for the highly sensitive detection of breast cancer tumor marker miRNA-4484. Firstly, few-layer WS2 nanosheets were prepared by shear stripping and characterized by SEM, TEM, AFM and UV spectrophotometer. After modification of few-layer WS2 nanosheets on the electrode surface, the miRNA probe was fixed on the few-layer WS2 nanosheets by polycytosine (PolyC). Then short-chain miRNA containing PolyC was used as the blocking agent to close the excess active sites on the surface of WS2 nanosheets to complete the fabrication of the sensor biosensing interface. Finally, the current changes caused by the specific binding of miRNA-4484 to the probe were analyzed by differential pulse voltammetry (DPV). The results showed that the sensor had a good linear relationship for the detection of miRNA-4484 in the concentration range of 1 aM–100 fM, and the detection limit was as low as 1.61 aM. In addition, the electrochemical sensor had excellent selectivity, stability and reproducibility. The artificial sample tests indicated that the developed biosensors have the potential for clinical application in the future.

Controllable Fabrication of Polypyrrole Nanopillar/Hair Arrays by Oxygen Plasma Treatment and Their Applications as Antibacterial Flexible pH Sensors

AbstractFlexible pH sensors have attracted a great deal of attention in numerous applications, such as wearable healthcare devices, smart wound bandages, and other pH monitor sensors for chemical and bioprocess control. Herein, a facile approach to fabricate conducting polymer polypyrrole nanopillar arrays is proposed, and its potential as an advanced pH sensing interface is shown. Using oxygen plasma treatment as the etching tool, polypyrrole nanopillar/hair arrays can be prepared after etching 3D polypyrrole network structures. Conductive polypyrrole nanopillar arrays possessed unique advantages in sensing: their featured nanopillars has enhanced antibacterial properties; their structures showed excellent hydrophilicity and are favorable for interaction with analytes in small‐volume liquids. A flexible polypyrrole nanopillar array‐based potentiometric pH sensor integrated with a solid‐state reference electrode is further designed and fabricated. Superior performance with a near‐Nernstian response of 60.1±1.0 mV pH−1, excellent repeatability, high stability, and reproducibility is verified in the physiological pH range. The reliabilities of flexible and small‐volume sample testing, as well as the applicability for artificial sweat sample pH sensing, are examined. Provided with the advantages of antibacterial properties and hydrophilicity, the proposed flexible pH sensor is promising for trace analysis in biological, medical, and healthcare applications.

Semiconducting polymer dots based l-lactate sensor by enzymatic cascade reaction system

Backgroundl-lactate detection is important for not only assessing exercise intensity, optimizing training regimens, and identifying the lactate threshold in athletes, but also for diagnosing conditions like L-lactateosis, monitoring tissue hypoxia, and guiding critical care decisions. Moreover, l-lactate has been utilized as a biomarker to represent the state of human health. However, the sensitivity of the present l-lactate detection technique is inadequate. ResultsHere, we reported a sensitive ratiometric fluorescent probe for l-lactate detection based on platinum octaethylporphyrin (PtOEP) doped semiconducting polymer dots (Pdots-Pt) with enzymatic cascade reaction. With the help of an enzyme cascade reaction, the l-lactate was continuously oxidized to pyruvic and then reduced back to l-lactate for the next cycle. During this process, oxygen and NADH were continuously consumed, which increased the red fluorescence of Pdots-Pt that responded to the changes of oxygen concentration and decreased the blue fluorescence of NADH at the same time. By comparing the fluorescence intensities at these two different wavelengths, the concentration of l-lactate was accurately measured. With the optimal conditions, the probes showed two linear detection ranges from 0.5 nM to 5.0 μM and 5.0 μM–50.0 μM for l-lactate detection. The limit of detection was calculated to be 0.18 nM by 3σ/slope method. Finally, the method shows good detection performance of l-lactate in both bovine serum and artificial serum samples, indicating its potential usage for the selective analysis of l-lactate for health monitoring and disease diagnosis. SignificanceThe successful application of the sensing system in the complex biological sample (bovine serum and artificial serum samples) demonstrated that this method could be used for sensitive l-lactate detection in practical clinical applications. This detection system provided an extremely low detection limit, which was several orders of magnitude lower than methods proposed in other literatures.

Influence of thermal maturity on carbazole distributions in coal source rocks during compaction pyrolysis experiments

Carbazole compounds are widely used in determining the direction of petroleum migration, but the effect of thermal maturity on carbazoles is still ambiguity. In this paper, using compaction pyrolysis simulation experiments, artificial mature samples with vitrinite reflectance (Ro) range from 0.38 to 3.0% were acquired. And the content and composition change characteristics of carbazole compounds were analyzed in coal source rocks. The experimental results showed that thermal maturity controls the generation of a large amount of carbazole compounds in coal rocks. Compared with the low mature stage, the content of carbazole compounds was about 10–100 times higher in the mature stage. With the increasing maturity, in the coal sample, the content of carbazole compounds showed a trend of first increasing and then decreasing. In derivatives of carbazole, the corresponding maturity for the maximum generation of ethylcarbazole (EC), dimethylcarbazole (DMCA), methylcarbazole (MCA), carbazole (CA) and benzocarbazole (BCA) performed the increasing sequence. With the increasing maturity, the relative abundance of 2-MCA, 1,7-DMCA and benzo[a]carbazole increased with the increasing maturity, while 4-MCA, 1,4-DMCA and benzo[c]carbazole gradually decreased. Benzocarbazole ratio [a]/[a] +[ c] varies only in a narrow range 0.36–0.61 in the entire maturity range, suggesting limited maturity dependence. The experimental conclusion provides more theoretical basis for future geochemical analysis using carbazole compounds.

Rapid Near-Patient Impedimetric Sensing Platform for Prostate Cancer Diagnosis.

With the global escalation of concerns surrounding prostate cancer (PCa) diagnosis, reliance on the serologic prostate-specific antigen (PSA) test remains the primary approach. However, the imperative for early PCa diagnosis necessitates more effective, accurate, and rapid diagnostic point-of-care (POC) devices to enhance the result reliability and minimize disease-related complications. Among POC approaches, electrochemical biosensors, known for their amenability and miniaturization capabilities, have emerged as promising candidates. In this study, we developed an impedimetric sensing platform to detect urinary zinc (UZn) in both artificial and clinical urine samples. Our approach lies in integrating label-free impedimetric sensing and the introduction of porosity through surface modification techniques. Leveraging a cellulose acetate/reduced graphene oxide composite, our sensor's recognition layer is engineered to exhibit enhanced porosity, critical for improving the sensitivity, capture, and interaction with UZn. The sensitivity is further amplified by incorporating zincon as an external dopant, establishing highly effective recognition sites. Our sensor demonstrates a limit of detection of 7.33 ng/mL in the 0.1-1000 ng/mL dynamic range, which aligns with the reference benchmark samples from clinical biochemistry. Our sensor results are comparable with the results of inductively coupled plasma mass spectrometry (ICP-MS) where a notable correlation of 0.991 is achieved. To validate our sensor in a real-life scenario, tests were performed on human urine samples from patients being investigated for prostate cancer. Testing clinical urine samples using our sensing platform and ICP-MS produced highly comparable results. A linear correlation with R2 = 0.964 with no significant difference between two groups (p-value = 0.936) was found, thus confirming the reliability of our sensing platform.

External Quality Assessment on Molecular Tumor Profiling with Circulating Tumor DNA-Based Methodologies Routinely Used in Clinical Pathology within the COIN Consortium.

Identification of tumor-derived variants in circulating tumor DNA (ctDNA) has potential as a sensitive and reliable surrogate for tumor tissue-based routine diagnostic testing. However, variations in pre(analytical) procedures affect the efficiency of ctDNA recovery. Here, an external quality assessment (EQA) was performed to determine the performance of ctDNA mutation detection work flows that are used in current diagnostic settings across laboratories within the Dutch COIN consortium (ctDNA on the road to implementation in The Netherlands). Aliquots of 3 high-volume diagnostic leukapheresis (DLA) plasma samples and 3 artificial reference plasma samples with predetermined mutations were distributed among 16 Dutch laboratories. Participating laboratories were requested to perform ctDNA analysis for BRAF exon 15, EGFR exon 18-21, and KRAS exon 2-3 using their regular circulating cell-free DNA (ccfDNA) analysis work flow. Laboratories were assessed based on adherence to the study protocol, overall detection rate, and overall genotyping performance. A broad range of preanalytical conditions (e.g., plasma volume, elution volume, and extraction methods) and analytical methodologies (e.g., droplet digital PCR [ddPCR], small-panel PCR assays, and next-generation sequencing [NGS]) were used. Six laboratories (38%) had a performance score of >0.90; all other laboratories scored between 0.26 and 0.80. Although 13 laboratories (81%) reached a 100% overall detection rate, the therapeutically relevant EGFR p.(S752_I759del) (69%), EGFR p.(N771_H773dup) (50%), and KRAS p.(G12C) (48%) mutations were frequently not genotyped accurately. Divergent (pre)analytical protocols could lead to discrepant clinical outcomes when using the same plasma samples. Standardization of (pre)analytical work flows can facilitate the implementation of reproducible liquid biopsy testing in the clinical routine.

Integrated Electrochemical Aptasensor Array toward Monitoring Anticancer Drugs in Sweat.

In the realm of clinical practice, the concurrent utilization of anticancer medications can enhance their overall therapeutic efficacy. However, it is crucial to acknowledge that the interactions among these anticancer drugs can potentially yield detrimental consequences on their intended outcomes. Consequently, the assessment of both anticancer potency and potential toxic side effects is greatly refined when multiple anticancer drugs are simultaneously detected and evaluated. Here, we designed a wearable electrochemical aptasensor array for monitoring multiple anticancer drugs in sweat. The integrated sensor array consists of three working electrodes modified with three different aptamers (Apt1, Apt2, and Apt3), a Au counter electrode, and a Ag/AgCl reference electrode. Molecular docking simulations were performed to show the binding affinities between three anticancer drugs and their corresponding aptamers. Various eigenvalues were derived from the square-wave voltammetry electrochemical signals, and these data sets were subjected to rigorous analysis through multivariate data analysis techniques. This analytical approach demonstrated exceptional performance by achieving flawless 100% accuracy in the precise identification of nine anticancer drugs consistently at uniform concentrations. Furthermore, the integrated wearable sensor array exhibited impressive capabilities, correctly recognizing all nine anticancer drugs with 100% accuracy and successfully distinguishing between these drugs in artificial sweat samples. The proposed sensor array presents good stability for 15 days. Flexibility tests showed stable device performance after 500 twisting cycles. This innovative wearable sensing array represents a novel approach for achieving real-time monitoring and precise adjustment of drug dosages. It offers invaluable insights for tailoring the treatment of anticancer drugs to individual patients, predicting both drug efficacy and potential adverse reactions within the field of clinical medicine.

Bimetallic FeOx-TiO2@Carbon hybrid structure materials with notable peroxidase enzyme mimics applied toone-step colorimetric detection of glucose.

FeOx-TiO2@Carbon hybrid structure materials (FeOx-TiO2@CHs) with high peroxidase (POD)-like activity have been preparedby one-pot hydrothermal method. Based on the excellent POD activity of FeOx-TiO2@CHs, one pot colorimetric detection for glucose was constructed by using TMB as substrate with the synergistic reaction of glucose oxidase; the linear range and the limit of detection (LOD) are 25 ~ 1000 and 1.77µM, respectively. Using this method, the glucose in serum real samples was detected with satisfactory results, and the results are consistent with that of the glucometer method in the hospital. The recovery in diabetic and artificial urine samples was 95.71 ~ 104.67% and 99.01 ~ 103.16%, respectively. Themechanism of the catalytic colorimetric reaction was also investigated by multiple measurements, and the results indicated that superoxide anions (O2•-) between FeOx-TiO2@CHs and substrate play a main role, but a small quantity of hydroxyl radical •OH and singlet oxygen 1O2 is also generated simultaneously. The one-pot reaction method is simple and fast; the detection process only requires a simple mixing, which is suitable for application in special environment.

Validation of a human saliva model for the determination of leachable monomers and other chemicals from dental materials

This study aimed to prove the validity of a mixture of chemicals, including salts, small organic molecules, mucin, and α-amylase, as saliva surrogate (“artificial saliva”) for assessing leakage of methacrylate monomers and other constituents from dental materials. To achieve this, we developed and validated a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the quantification of 2-hydroxyethyl methacrylate (HEMA), triethylene glycol dimethacrylate (TEGDMA), diurethane dimethacrylate (UDMA), bisphenol A glycerolate dimethacrylate (BisGMA), diphenyl(2,4,6-trimethylbenzoyl)phosphine oxide (TPO), bisphenol A (BPA), and five homologues of ethoxylated bisphenol A dimethacrylate (BisEMA EO2–6) in unstimulated and artificial saliva, and compared their concentrations in the two saliva media following either spiking with a mixture of the compounds or incubation of test specimens of printed biomaterials. Test specimens were immersed in unstimulated/artificial saliva, incubated at 37 °C for 24 h, and saliva aliquots were extracted with methanol and subsequently analyzed by LC-MS/MS. The method was validated with regard to matrix effects, linearity, selectivity, lower limits of quantification (LLOQ), precision, bias and combined measurement uncertainty (u′). The performance characteristics of the method were comparable for unstimulated and artificial saliva samples. The combined u’ for individual chemicals at a concentration of 10 × LLOQ were within the range of 5.3–14 % for unstimulated saliva and 6.9–16 % for artificial saliva, except for the BisEMA homologues. Combined u’ for the latter were 27–74 % in unstimulated saliva, and 27–79 % in artificial saliva. There was no detectable release of BPA from the test specimens, and the TPO concentrations were mainly below the LLOQ. TEGDMA and UDMA were detected in the highest quantities, and at comparable concentrations in the unstimulated and artificial saliva. For all BisEMA homologues, the release was higher in unstimulated saliva than in artificial saliva. The study showed that the artificial saliva model can be a suitable replacement for native saliva, but might underestimate leakage of more lipophilic methacrylates.

Farmland Segmentation in Landsat 8 Satellite Images Using Deep Learning and Conditional Generative Adversarial Networks

Leveraging mid-resolution satellite images such as Landsat 8 for accurate farmland segmentation and land change monitoring is crucial for agricultural management, yet is hindered by the scarcity of labelled data for the training of supervised deep learning pipelines. The particular focus of this study is on addressing the scarcity of labelled images. This paper introduces several contributions, including a systematic satellite image data augmentation approach that aims to maintain data population consistency during model training, thus mitigating performance degradation. To alleviate the labour-intensive task of pixel-wise image labelling, we present a novel application of a modified conditional generative adversarial network (CGAN) to generate artificial satellite images and corresponding farm labels. Additionally, we scrutinize the role of spectral bands in satellite image segmentation and compare two prominent semantic segmentation models, U-Net and DeepLabV3+, with diverse backbone structures. Our empirical findings demonstrate that augmenting the dataset with up to 22.85% artificial samples significantly enhances the model performance. Notably, the U-Net model, employing standard convolution, outperforms the DeepLabV3+ models with atrous convolution, achieving a segmentation accuracy of 86.92% on the test data.

Enhanced "Electronic Tongue" for Dental Bacterial Discrimination and Elimination Based on a DNA-Encoded Nanozyme Sensor Array.

Bacterial infections are the second leading cause of death around the world, especially those caused by delayed treatment and misdiagnosis. Therefore, rapid discrimination and effective elimination of multiple bacteria are of great importance for improving the survival rate in clinic. Herein, a novel colorimetric sensor array for bacterial discrimination and elimination is constructed using programmable DNA-encoded iron oxide nanoparticles (IONPs) as sensing elements. Utilizing differential interactions of bacteria on DNA-encoded IONPs, 11 kinds of dental bacteria and 6 kinds of proteins have been successfully identified by linear discriminant analysis (LDA). Moreover, the developed sensing system also performs well in the quantitative determination of individual bacteria and identification of bacterial mixtures. More importantly, the practicability of this sensing strategy is further verified by precise differentiation of blind and artificial saliva samples. Furthermore, the sensor array is used for efficiently killing multiple bacteria, demonstrating great potential in clinical prophylaxis and therapy.