Precise Method Research Articles

Bottom-Up Metasurfaces for Biotechnological Applications.

Metasurfaces are the 2D counterparts of metamaterials, and their development is accelerating rapidly in the past years. This progress enables the creation of devices capable of uniquely manipulating light, with applications ranging from optical communications to remote biosensing. Metasurfaces are engineered by rational assembly of subwavelength elements, defined as meta-atoms, giving rise to unique physical properties arising from the collective behavior of meta-atoms. These meta-atoms are typically organized using effective, reproducible, and precise nanofabrication methods that require a lot of effort to achieve scalable and cost-effective metasurfaces. In contrast, bottom-up methods based on colloidal nanoparticles (NPs) have developed in the last decade as a fascinating alternative for accelerating the technological spread of metasurfaces. The present review takes stock of recent advances in the fabrication and applications of hybrid metasurfaces prepared by bottom-up methods, resulting in disordered metasurfaces. In particular, metasurfaces prepared with plasmonic NPs are emphasized for their multifold applications, which are discussed from a biotechnology perspective. However, some examples of organized metasurfaces prepared by merging bottom-up and top-down approaches are also described. Finally, leveraging the historical disordered metasurface evolution, the review draws new perspectives for random metasurface design and applications.

Precise Camera Calibration Method Based on Eccentricity Correction of Circular Target

Abstract Acquiring high-precision and robust calibration methods for determining the intrinsic and extrinsic parameters of cameras is essential to ensuring the accuracy of visual measurements and three-dimensional reconstruction. This paper addresses the challenge of reduced calibration accuracy in camera systems caused by eccentricity errors in the extraction of circular target feature centers. We propose a precise calibration method that corrects these eccentricity errors associated with circular targets. The method begins by solving for the homography matrix of each calibration image using the coordinates of both image points and target points. Subsequently, the homography matrix is used to generate the frontal view of the circular target, enabling the precise extraction and back-mapping of the circular target's center based on this frontal view. Finally, using the corrected coordinates of the circle center and the calibration method proposed by Zhang in the paper "A Flexible New Technique for Camera Calibration", we accurately determine the intrinsic and extrinsic parameters of the camera. Simulation experiments show the proposed calibration method aligns perfectly with ground truth without noise. In practical tests, it outperformed the checkerboard and traditional circular target methods, reducing reprojection errors by 0.0295 and 0.0176 pixels, respectively. Additionally, the cross-ratio experiment showed a 0.076% reduction in error for corrected circle center points. These results highlight significant improvements in calibration accuracy, meeting high-precision needs in visual sensing.

Establishment of an indirect ELISA method for detecting bovine coronavirus antibodies based on N protein

Bovine Coronavirus (BCoV) is a significant pathogen responsible for neonatal calf diarrhea, winter dysentery in adult cattle, and bovine respiratory diseases. Infection with the virus can result in hemorrhagic diarrhea, decreased milk production, and potentially fatal outcomes in cattle, leading to considerable economic repercussions for the cattle industry. Efficient management of BCoV relies on swift and precise detection techniques. CHO cells were utilized to express a secreted recombinant nucleocapsid protein (N), whereby rabbit polyclonal antibodies (pAb) were generated through immunization. An indirect enzyme-linked immunosorbent assay (iELISA) based on N protein was established for the detection of BCoV antibodies. Reaction conditions were optimized using a checkerboard approach, with the optimal antigen concentration at 1.25 μg/mL and the optimal antibody dilution at 1:200, the cutoff value distinguishing negative and positive serum samples was 0.986. The sensitivity test indicated that this rabbit pAb had a maximum dilution of 218 within the assay range, did not cross-react with BHV-1, BVDV, BRV, and BRSV positive serum samples, and shown great specificity. The developed iELISA method and commercial kit were used to test 58 bovine serum samples, and the concordance rate was 94.83%. In summary, we have developed a cost-efficient and precise iELISA method based on N protein that serves as a useful diagnostic tool for BCoV in clinical samples and epidemiological research.

Ultra-Sensitive Nanoplatform for Detection of Brain-Derived Neurotrophic Factor Using Silica-Coated Gold Nanoparticles with Enzyme-Formed Quantum Dots

A fluorescence-based detection platform was developed for brain-derived neurotrophic factor (BDNF), a key biomarker of Alzheimer’s disease (AD). This platform utilizes localized surface plasmon resonance effects resulting from the interactions between silica-coated gold nanoparticles (Au@SiO2) and enzymatically synthesized quantum dots (QDs). The gold nanoparticles were silica coated via the hydrolysis of tetraethyl orthosilicate, which allowed for precise control over the distance between the nanoparticles and QDs and refined the dynamics of fluorescence quenching and enhancement. Antibody conjugation was performed via sequential amination and carboxylation, followed by EDC/NHS coupling. BDNF was detected across a range of concentrations, from 1 ng/mL to 1 ng/mL, using an alkaline phosphatase (ALP)-conjugated polyclonal antibody targeting a secondary epitope of BDNF. The enzymatic hydrolysis of p-nitrophenyl phosphate by immobilized ALP led to the formation of cadmium sulfide QDs, with the fluorescence intensity correlating directly with the BDNF concentration. This platform offers a refined and precise method for detecting BDNF and is a reliable tool for the early diagnosis of AD.

Accurate and precise in vivo liver 3D T1 mapping at 3T.

To develop an accurate and precise liver 3D mapping method using only scanner-agnostic sequences. While the spoiled gradient-recalled echo sequence is widely available on clinical scanners, variable flip angle mapping methods based on this sequence provide biased estimates, with the largest systematic error arising from inhomogeneities. To correct for this, the flip angle was mapped using a 2D gradient-echo double-angle method approach. To correct for the confounding effect of fat on liver and , Dixon and fat saturation techniques were used in combination with the variable flip angle and the map acquisitions, respectively. The and mapping methods were validated with a -phantom against gold standard methods. An intra- and inter-repeatability study was conducted at 3T in 10 healthy individuals' livers. The developed 3D mapping method achieved an excellent agreement with the gold standard, with a weighted root mean squared normalized error below 2.8%. In vivo, a median standard deviation of 31 ms and an interquartile range of [27, 39] ms was achieved across all measurements, including the intra- and inter-repeatability study data. A within-subject standard deviation for of 21 ± 5 ms had a corresponding repeatability coefficient of 60 ms. The measured values agree well with MOLLI and SASHA mapping methods, with average differences of 5%. Accurate and precise 3D liver measurements can lead the way to the wider adoption of a clinically feasible measurement as a marker of hepatic fibro-inflammation.

Deep-Optimal Leucorrhea Detection Through Fluorescent Benchmark Data Analysis.

Vaginitis is a common condition in women that is described medically as irritation and/or inflammation of the vagina; it poses a significant health risk for women, necessitating precise diagnostic methods. Presently, conventional techniques for examining vaginal discharge involve the use of wet mounts and gram staining to identify vaginal diseases. In this research, we utilized fluorescent staining, which enables distinct visualization of cellular and pathogenic components, each exhibiting unique color characteristics when exposed to the same light source. We established a large, challenging multiple fluorescence leucorrhea dataset benchmark comprising 8 categories with a total of 343K high-quality labels. We also presented a robust lightweight deep-learning network, LRNet. It includes a lightweight feature extraction network that employs Ghost modules, a feature pyramid network that incorporates deformable convolution in the neck, and a single detection head. The evaluation results indicate that this detection network surpasses conventional networks and can cut down the model parameters by up to 91.4% and floating-point operations (FLOPs) by 74%. The deep-optimal leucorrhea detection capability of LRNet significantly enhances its ability to detect various crucial indicators related to vaginal health.

Blue applicability grade index assessment and response surface modelling to synchronous determination of metformin hydrochloride, vildagliptin and dapagliflozin propanediol monohydrate by HPTLC method

Diabetes mellitus is major chronic disease found in human due to stressful lifestyle and bad food habit. Recently, the Central Drugs Standard Control Organization of India has approved combination of metformin hydrochloride, vildagliptin and dapagliflozin propanediol monohydrate for the management of diabetes mellitus. Numerous chromatographic methods have been published in the literature for estimation of these anti-diabetic drugs on individual basis and their combinations. But these chromatographic analyses promoted usage of toxic organic solvents which are harmful to the environment and aquatic animal lives. In addition, no chromatographic method was found in the literature for simultaneous estimation of these anti-diabetic drugs using green solvents. In recent days, white analytical chemistry approach has been introduced in the literature for development and assessment of green, accurate, precise, cost-effective and user-friendly chromatographic methods. Hence, white analytical chemistry-driven risk-based HPTLC method was developed for synchronous estimation of these anti-diabetic drugs using green solvents and response surface modelling. The analytical quality risk assessment was applied for identification of critical method variables and response variables using risk priority number ranking and filtering method. The critical method variables were linked with critical response variables using response surface modelling by Box–Behnken design. The analytical design space was navigated control strategy was framed for robust densitometric estimation of anti-diabetic drugs. The chromatographic method was validated as per ICH Q2 (R2) guideline. The developed method was applied for synchronous assay of multiple combinations of metformin, vildagliptin and dapagliflozin using single chromatographic condition to save time, cost and resources for analysis. The assay results of these anti-diabetic drug combinations were found to be complied with the respective labelled claim of the drugs. The developed and published chromatographic methods were assessed for their redness, greenness, blueness and whiteness profiles using green analytical metrics and RGB model scoring system. The proposed method was found to be green, cost-effective, robust and user-friendly for synchronous estimation of metformin, vildagliptin and dapagliflozin.

Carrier-envelope-phase-independent field sampling of single-cycle transients using homochromatic attosecond streaking

The recent development of homochromatic attosecond streaking (HAS) has enabled a novel, highly precise method for ultrafast metrology of attosecond electron pulses as well as for real-time sampling of the instantaneous field waveforms of light transients. Here, we evaluate the potential of HAS as a method for precisely sampling the field waveform of non-phase-stabilized single-cycle transients of light. We show that the extreme nonlinearity of field emission and the core properties of HAS as a field sampling technique allow one to track the waveform of a single carrier-envelope phase (CEP) setting whose field dynamics results in the most energetic electron cutoff. Our results establish HAS as a robust, compact, all-solid-state method for characterizing light fields with attosecond-level precision and as a powerful tool in light field synthesis.

Geographical Origin Traceability of Navel Oranges Based on Near-Infrared Spectroscopy Combined with Deep Learning

The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall navel oranges were selected from five major production regions in China, and the diffuse reflectance near-infrared spectrum in 4000–10,000 cm−1 were non-invasively collected. We examined seven preprocessing techniques for the spectra, including Savitzky–Golay (SG) smoothing, first derivative (FD), multiplicative scattering correction (MSC), combinations of SG with MSC (SG+MSC), SG with FD (SG+FD), MSC with FD (MSC+FD), and three combined (SG+MSC+FD). A one-dimensional convolutional neural network (1DCNN) deep learning model for geographical origin tracing of navel orange was established, and five machine learning algorithms, i.e., partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN), were compared with 1DCNN. The results show that the 1DCNN model based on the SG+FD preprocessing method achieved the optimal performance for the testing set, with prediction accuracy, precision, recall, and F1-score of 97.92%, 98%, 97.95%, and 97.90%, respectively. Therefore, NIRS combined with deep learning has a significant research and application value in the rapid, nondestructive, and accurate geographical origin traceability of agricultural products.

Lattice Vacancy-Anchored Perforation of 2D MXenes for Crafting Nanochannel Membranes with Spontaneous Multi-Level Features.

Two-dimensional (2D) material membranes have significant potential for selectively transporting molecules and ions, crucial for environmental and energy applications significantly. However, challenges such as complex pathways and instability due to weak interactions hinder their performance. This study takes 2D MXene (Ti3C2Tx) as a platform and proposes a lattice vacancy-anchored chemical etching method to perforate MXene nanosheets and produce acid cross-linkers simultaneously. The etching process involves H2O2 oxidation consuming Ti atoms from the lattice vacancies of MXene to create peroxo titanic acid (PTA), precisely generating nanopores in the nanosheets for additional transport pathways. At the same time, the produced PTA acts as a cross-linking agent that enhances the interaction between MXene nanosheets to form stabilized interlayer channels. This approach results in multilevel features in the MXene membrane, offering abundant vertical water channels and robust interlayer ion-sieving channels. Consequently, both permeability and selectivity improve nearly 10-fold compared to the pristine membrane, overcoming previous trade-offs. This strategy presents a precise and ingenious method for perforating 2D materials and constructing high-performance mass transport channels of 2D membranes at various levels, benefiting sustainable and highly efficient desalination processes.

Microbial analysis and antimicrobial resistance screening of drinking water in the Qassim Region of Saudi Arabia using mass spectrometry technology.

Water intended for human consumption must be devoid of harmful bacteria that can lead to waterborne illnesses. Consequently, there is a pressing need for a rapid and precise method to identify bacterial contaminants in drinking water. The objective of this study was to investigate the protein profiles of various bacterial species present in water through the application of protein fingerprinting (PF) and real-time polymerase chain reaction (real-time PCR) techniques, as well as to evaluate their antimicrobial resistance. A total of two hundred water samples were collected from five distinct locations within the Qassim region of Saudi Arabia. Bacterial isolates were identified using a protein fingerprinting analytical technique (PFAT), which was subsequently confirmed by real-time PCR. The Kirby-Bauer method was employed to assess antibiotic resistance among the bacterial isolates. Out of the 200 water samples analyzed, PFAT successfully identified 123 bacterial isolates, with the most frequently isolated species being 48 Pseudomonas aeruginosa (P. aeruginosa), 17 Staphylococcus aureus (S. aureus), and 16 Escherichia coli (E. coli). All waterborne bacterial isolates were accurately identified 100% of the time, achieving a score of 2.00 or higher. The results from real-time PCR indicated that 87.5% of P. aeruginosa isolates were positive for the oprI gene, all S. aureus isolates were positive for the nuc gene, and 93.75% of E. coli isolates were positive for the fliC gene. P. aeruginosa isolates demonstrated a high level of resistance to aztreonam (64.6%), while S. aureus exhibited significant resistance to cefoxitin and cefepime (88.24%), followed by aztreonam (82.35%) and amoxicillin-clavulanate (70.6%). E. coli isolates showed complete resistance to ampicillin (100%), with high resistance also observed against amoxicillin-clavulanate and cefoxitin (87.5%), and cefepime (81.25%). This study underscores the significance of utilizing PFAT for the microbiological identification of diverse water samples as a reliable and effective method. Furthermore, it emphasizes the necessity for regular surveillance and monitoring of antimicrobial-resistant bacteria in drinking water sources.

From Simulation to Reality: Transfer Learning for Automating Pseudo‐Labeling of Real and Infrared Imagery

Training a convolutional neural network (CNN) for real‐world applications is challenging due to the requirement of high‐quality labeled imagery. This study employs pseudo‐labeling and transfer learning, built upon a 6D pose estimation framework. A CNN trained on synthetic images predicts bounding boxes (bbox) for an object's components in a real image. With as few as four bbox predictions, the framework solves for the object's pose relative to the camera and reprojects bboxes for all components onto that image. The pose and reprojections allow filtering of bad predictions, a common issue in pseudo‐labeling. Thereby, enabling automated labeling of large datasets with minimal human intervention. Tested on color and long‐wave infrared imagery captured during December 2023 flight tests, this process demonstrates increased predictions, enhanced performance across situations, reduced reprojection error, and stabilized pose predictions. This technique is significant as it enables labeling of real‐world imagery without expensive truth systems, requiring only a camera. It supports learning and labeling of previously captured imagery without known camera calibrations, facilitating labeled data creation for impractical‐to‐simulate sensors. Ultimately, this transfer learning approach provides a low‐cost and precise method for creating CNNs trained on operationally relevant data, previously unattainable by the everyday user.

Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution

The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a computational algorithm that incorporates transcriptome size into scRNA-seq normalization and bulk deconvolution. ReDeconv introduces a scRNA-seq normalization approach, Count based on Linearized Transcriptome Size (CLTS), which corrects differential expressed genes typically misidentified by standard count per 10 K normalization, as confirmed by orthogonal validations. By maintaining transcriptome size variation, CLTS-normalized scRNA-seq enhances the accuracy of bulk deconvolution. Additionally, ReDeconv mitigates gene length effects and models expression variances, thereby improving deconvolution outcomes, particularly for rare cell types. Evaluated with both synthetic and real datasets, ReDeconv surpasses existing methods in precision. ReDeconv alters the practice and provides a new standard for scRNA-seq analyses and bulk deconvolution. The software packages and a user-friendly web portal are available.

Predictive role of circulating tumor DNA for locally advanced esophageal squamous cell carcinoma before surgery.

476 Background: A precise preoperative tumor monitoring method that reflects tumor burden during neoadjuvant treatment is required to guide individualized perioperative treatment strategies for esophageal squamous cell carcinoma (ESCC). This study examined the clinical significance of preoperative circulating tumor DNA (ctDNA) in the plasma of patients undergoing neoadjuvant chemotherapy (NAC) followed by esophagectomy. Methods: Plasma samples were collected longitudinally for ctDNA analysis as well as genomic DNA from primary lesions from patients with histologically confirmed ESCC who received neoadjuvant chemotherapy (NAC) followed by subtotal esophagectomy. Next-generation sequencing was used to identify mutations in both the plasma and primary tumors. We evaluated the relation ship between ctDNA alterations and recurrence in patients with locally advanced ESCC. Results: Pretreatment samples from 25 patients (100%) showed the same mutations in both ctDNA and primary tumors; therefore, they were classified as ctDNA-positive before treatment. In the cohort of 25 patients analyzed, those who tested positive for ctDNA after NAC had a significantly higher risk of recurrence; the 36-month recurrence-free survival rates were 92% for ctDNA-negative patients and 8% for ctDNA-positive patients (p < 0.001). Conclusions: Preoperative ctDNA status may be a promising prognostic biomarker that can be assessed before surgery in patients with ESCC who received NAC. Expanded cohort validation will allow for more personalized multidisciplinary treatment approaches for ESCC tailored to ctDNA analysis.

Application of QuEChERS for Analysis of Contaminants in Dairy Products: A Review.

The safety of dairy products is intrinsically linked to consumer health, and the exceedance of risk indicators, such as pesticide and veterinary drug residues, constitutes one of the primary issues affecting their quality and safety. To assess the safety of dairy products, it is crucial to develop accurate and reliable analytical methods for their detection. Food safety testing involving important indicators such as pesticide residues, veterinary drug residues, mycotoxins, and unapproved additives has become a pivotal requirement in the industry field. The QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) method is widely acknowledged as a food safety analysis method currently. This method can effectively extract a wide range of compound classes from diverse matrices in food safety testing, thereby enhancing the accuracy of detection. Additionally, when combined with chromatographic-mass spectrometry techniques, it can simultaneously analyze hundreds of target analytes, rendering it widely applicable in the quality and safety testing of dairy products. Although QuEChERS has rapidly developed in the field of dairy product quality and safety analysis due to its efficiency and speed advantages, certain shortcomings remain, presenting considerable room for improvement. This paper presents a comprehensive review of the utilization and research advancements of the QuEChERS technique in dairy products, with the aim of providing more precise, expeditious, and reliable methods for the safety assessment of dairy products.

Development and Efficacy Evaluation of a Novel Nanoparticle-Based Hemagglutination Inhibition Assay for Serological Studies of Porcine Epidemic Diarrhea Virus

Porcine epidemic diarrhea virus (PEDV) is a major pathogen that causes serious economic losses to the swine industry. To aid PEDV clinical diagnosis and vaccine development, sensitive and precise serological methods are demanded for rapid detection of (neutralizing) antibodies. Aiming for the development of a novel virus-free hemagglutination inhibition (HI) assay, the N-terminal region of the PEDV S1 subunit, encompassing the sialic acid-binding motif, was first expressed as an Fc-fusion protein with a C-terminal Spy Tag (S10A-Spy). The S10A-Spy protein was then presented on SpyCatcher-mi3 nanoparticles, forming virus-like particles designated S10A-NPs. Electron microscopy and dynamic light scattering analysis confirmed its topology, and the hemagglutination assay showed that S10A-NPs can efficiently agglutinate red blood cells. The HI assay based on S10A-NPs was then validated with PEDV-positive and -negative samples. The results showed that the HI assay had high specificity for the detection of PEDV antibodies. Next, a total of 253 clinical serum samples were subjected to the HI testing along with virus neutralization (VN) assay. The area under the receiver operating characteristic curve with VN was 0.959, and the kappa value was 0.759. Statistical analysis of the results indicated that the HI titers of the samples tested exhibited high consistency with the VN titers. Taken together, a novel virus-free HI assay based on the multivalent display of a chimeric PEDV spike protein upon self-assembling nanoparticles was established, providing a new approach for PEDV serological diagnosis.

In Situ Dual-targeted Drug Delivery System for Alleviating Imaging and Pathological Damage in Septic Arthritis.

Septic arthritis is a severe disease that damages articular cartilage and triggers a strong inflammatory response. Current treatments mainly depend on systemic antibiotics and lack effective intra-articular therapies, as well as standardized animal models, and precise detection methods. In this study, we present a drug delivery system responsive to the bacterial microenvironment for targeted inflammation control, along with an effective method for monitoring changes in septic arthritis in SD rats. This system consists a core with pH-sensitive metal-organic frameworks ZIF-8 loading anti-inflammatory drugs indomethacin and a shell with hybrid cell membranes from macrophages (MM) and platelets (PM), refer as MP@ZIF-8@IN. This system, which diverges from traditional treatments, enhances drug utilization, prolongs local retention, and allows for spontaneous release at the treatment site, thereby enabling the exclusive intra-articular treatment of septic arthritis. The drug delivery system inhibits the NF-κB pathway, reduces oxidative stress, and regulates macrophage polarization, preventing cartilage destruction. Additionally, in this standardized animal model utilizing the knee joints of SD rats, we have developed musculoskeletal ultrasound and magnetic resonance imaging for time-based monitoring, thus overcoming the limitation of conventional methods, which are unsuitable for soft tissue analysis. Our findings advance therapeutic strategies for septic arthritis and encourage further application of visualization techniques in related fields. STATEMENT OF SIGNIFICANCE: This study presents significant advancements in the treatment and understanding of septic arthritis. Our customized drug delivery system targets bacteria and macrophages, ensuring long-time drug retention and enhanced inflammation control, all while reducing reliance on antibiotics-an important step toward addressing antibiotic resistance. Additionally, we have refined septic arthritis animal models to establish clearer guidelines for intervention timing, grounded in clinical symptoms and imaging data. This addresses a critical gap in current research and offers a practical framework for future therapeutic approaches.

A Protocol for Determination of Proteinogenic Amino Acids in Biological Fluids by the High-Speed UHPLC-MS Method: Application on Transgenic Spontaneously Hypertensive Rat-24 Plasma and Cerebrospinal Fluid Samples.

Recently, proteinogenic amino acids have become very interesting molecules, accompanied by a large variety of metabolic processes in humans and are associated with various diseases. In the era of system biology, including a broad spectrum of associated disciplines (e.g., metabolomics, lipidomics, proteomics, etc.), the possibility of identifying trustworthy biomarkers of diseases becomes much more likely. Changes in amino acid levels in plasma, serum, or cerebrospinal fluid reflect physiological or pathological conditions and, therefore, their regular monitoring can lead to early detection of the occurrence of a disease. Therefore, the exact determination of amino acids in biological fluids is of great importance. However, it is necessary to dispose with an effective, accurate, precise, selective, and robust analytical method. This protocol describes the complex procedure of amino acid analysis based on a combination of UHPLC with single quadrupole MS. The protocol presents a highly reproducible and robust methodology that has already been established in the quality control of biopharmaceuticals and determination of proteinogenic amino acids in urine in our laboratory. Here, the application potential is extended to the most frequently investigated biological fluid, that is, plasma and to the cerebrospinal fluid, which is investigated in many neurological conditions.

In-vitro accuracy of the virtual patient model with maxillomandibular relationship at centric occlusion using 3D-printed customized transfer key

ObjectiveThis study aimed to create a 3D-printed customized transfer key and evaluate the accuracy of the virtual patient model with maxillomandibular relationship at centric occlusion using the transfer key.MethodsA 3D-printed transfer key was designed, combining facial and intraoral (IOS) scans. The design included components that recorded the 3D upper and lower arch at centric occlusion. The virtual patient model image was generated in-vitro using a phantom head with soft tissue simulation. Accuracy was assessed by superimposing the 3D scans with reference CBCT images and analyzing trueness and precision using root mean square (RMS) deviations.ResultsThe transfer key included an intra-oral part that acts as an anterior deprogrammer to record the relationship of two dental arches at centric occlusion (CO) and an extra-oral with a rotatable cross-shaped design with two arms for locating the facial midline and the two pupils connecting line. Superimposition demonstrated high trueness (RMS: 0.51 mm for the arch regions, 0.69 mm for the whole head region, 0.85 mm in the face region) and precision (RMS: 0.41 mm for the arch regions, 0.52 mm for the entire head, 0.63 mm in the face region) significantly (p < 0.05). Minimal deviations were observed in critical areas, including the tooth and lip position, indicating that the virtual patient model was closely aligned with the CBCT reference. The dental arches achieved the highest accuracy, while slight deviations were noted in the facial regions.ConclusionsThe 3D-printed customized transfer key effectively enhanced the virtual patient model’s accuracy, surpassing traditional trueness and precision methods. This novel approach offers a streamlined, patient-friendly solution for digital dental workflows.

RP-HPLC and MS Methods for Simultaneous Estimation of Arbutin and Quercetin from &lt;i&gt;Origanum majorana&lt;/i&gt;

Background: Arbutin (ARB) and quercetin (QUER) are two important phytoconstituents with proven anti-cancer effects. In vitro cytotoxic actions against a variety of tumor cell lines or antitumor-promoting properties have also been associated with extracts from Origanum majorana. Aim: To develop an easy, rapid, accurate, precise and reliable novel analytical method for the simultaneous estimation of quercetin and arbutin from Origanum majorana extracts and its marketed formulation. Method: The ideal chromatographic conditions for estimating arbutin and quercetin simultaneously using the RP-HPLC method are a stationary phase LichroCART C18 column (250mm × 4.0mm, 5μm), a mobile phase that contains 1% orthophosphoric acid and methanol in a 50:50% v/v ratio, a flow rate of 1 ml/min, ambient temperature operation, and a detection wavelength of 287 nm. The presence of arbutin and quercetin were qualitatively analyzed using mass spectroscopy. Results: The new RP-HPLC technique yielded a correlation value of 0.9999 and was demonstrated to be linear in the 0.5-2.5μg/ml range. Arbutin and quercetin were shown to have retention periods of 2.24 and 13.5 minutes, respectively. The amount of arbutin and quercetin in formulation were found to be 0.6484 μg and 0.1420 μg, respectively. The technique was validated using criteria such as specificity, accuracy, LOD, LOQ, precision, repeatability, stability, and system adaptability. Arbutin and quercetin were also confirmed to be present in the extract and formulation using mass spectroscopy (273 and 303 m/z values). Conclusion: Simple, fast, accurate, reliable, and economical, the suggested RP-HPLC and mass spectroscopy techniques are useful for consistently identifying arbutin and quercetin in both their prescription dose form and bulk. Major Findings: For arbutin and quercetin, the linearity was found to be between 0.5 and 2.5μg/ml, respectively. Among the extracts chloroform extract was found with separation of many components. The amount of arbutin and quercetin was determined to be 0.6484 μg and 0.1420 μg in the formulation.