Reference Markers Research Articles

Design and manufacturing of a dynamically deformable liver phantom for radiotherapy

Phantoms representing anatomical deformations are necessary to investigate and improve dynamic treatments. In this study, we aimed to produce a deformable liver phantom by simulating respiratory motion.The dynamically DEformable Liver Phantom (DELP) is designed to create a human-specific respiratory model and to produce synchronised, repeatable motion with this model. For the deformation effect of this movement, an artificial liver was created using silicone material and mold. A stepper motor was used to compress the liver in the inferior direction according to an adjustable respiratory motion. Reference markers (fiducial) placed on the DELP helped to verify the movement and calculate the deformation. In dynamic deformation tests, the greatest amount of deformation was found in the edge region of the silicone liver. The average deformation was 3.45 ± 0.93 mm when 5 mm amplitude movement was applied and 5.98 ± 0.01 mm when 10 mm amplitude movement was applied.DELP is a deformable liver phantom with motion reproducibility. Its performance in radiotherapy application was evaluated using dosimetric equipment.

Identification of potential biomarkers of papillary thyroid carcinoma.

This study aimed to identify candidate biomarkers for papillary thyroid carcinoma using an integrative analysis of bioinformatics and machine learning (ML). The PTC datasets GSE6004, GSE3467, and GSE33630 (species: Homo sapiens) were downloaded from NCBI and analyzed using the limma package to obtain DEGs. Once DEGs were identified, GO and KEGG enrichment analyses were performed as the first step in the bioinformatics process. Subsequently, a protein-protein interaction (PPI) network was constructed according to the common genes in bioinformatics and machine learning using STRING to elucidate the important genes involved in PTC pathogenesis. In machine learning, finding genes entails feature selection to identify the key genes that distinguish biological states. Hybrid feature selection will be used for this. In the second step, the original data sets were preprocessed to detect and correct missing and noisy data; after that, all data were merged. Following performing Linear and Discriminative Hybrid Feature Selection (LDHFS) on the processed dataset, machine learning algorithms such as Random Forest (RF), Naive Bayes (NB), and Support Vector Machines (SVM) are utilized. Bioinformatics and machine learning analyses indicate that the genes RXRG, CDH2, ETV5, QPCT, LRP4, FN1, and LPAR5 are integral to the progression of thyroid cancer. This study attained the highest accuracy utilizing the RF algorithm, achieving an accuracy rate of 94.62%, a Kappa value of 91.36%, and an AUC value of 96.13%. These results offer additional evidence and confirmation for the genetic alterations of these genes. These findings may accelerate the development of prospective therapeutic and diagnostic methods in future research. Bioinformatics and machine learning techniques identified the common genes "RXRG, CDH2, ETV5, QPCT, LRP4, FN1, and LPAR5" as PTC biomarkers, providing novel reference markers for the diagnosis and treatment of PTC patients. The model is anticipated to possess significant predictive value and assist in the early diagnosis and screening of clinical PTC. These insights enhance the field of PTC management and offer guidance for future research.

Deviation parameters of intraoral scanning with and without artificial markers versus conventional impression of total, partial and congenital nasal defects: A pilot non-randomized clinical trial

PurposeThis pilot clinical trial aimed to investigate accuracy of intra-oral scanning (IOS) with and without artificial reference markers in capturing total, subtotal and congenital nasal defects. Basic proceduresThirteen patients with 3 types of nasal defects were selected and digitally scanned using an IOS with (ScM) and without artificial markers (Sc). Patients were grouped as follows; Group T (total nasal defect), Group P (partial/subtotal nasal defect), Group C (congenital defect). Silicone impressions of the defects were made and poured to get a model that was scanned using a Desktop scanner (Imp). The standard tessellation language (STL) files obtained from IOS were registered and compared to each other using a processing software. RMS, positive and negative average values were used to report deviations between the scans. Kruskal Wallis test was used to study the effect of defect type, while Friedmann was used to study the effect of impression technique. Results were considered significant at P≤.05. Main findingsAll deviation values showed statistically significant differences among the 3 studied nasal defects and among the 3 investigated impression techniques. The smallest deviation values were recorded in Sc-ScM of congenital defects (RMS= 0.13±0.04, +average=0.08±0.01, -average=-0.09±0.02), while the largest deviation values were recorded in IMP-Sc in the total defects (RMS= 0.38±0.05, +average=0.29±0.04, -average=-0.29±0.04). ConclusionsWithin the limitations of this study, it was concluded that the nasal defect type and the use of artificial markers during scanning affect the deviation parameters of the obtained scan. The combined effect of the studied parameters calls for the use of markers in when scanning total nasal defects.

Grammaticalization of Future-Time Reference Markers in Korean and Thai: A Focus on Morphosyntax and Conceptual Motivation

Grammaticalization of Future-Time Reference Markers in Korean and Thai: A Focus on Morphosyntax and Conceptual Motivation

Ultrasensitive Electrochemiluminescence Biosensor Based on DNA-Bio-Bar-Code and Hybridization Chain Reaction Dual Signal Amplification for Exosomes Detection.

Exosomes have received considerable attention as potent reference markers for the diagnosis of various neoplasms due to their close and direct relationship with the proliferation, adhesion, and migration of tumor. The ultrasensitive detection of cancer-derived low-abundance exosomes is imperative, but still a great challenge. Herein, we report an electrochemiluminescence (ECL) biosensor based on the DNA-bio-bar-code and hybridization chain reaction (HCR)-mediated dual signal amplification for the ultrasensitive detection of cancer-derived exosomes. In this system, two types of aptamers were modified on the magnetic nanoprobe (MNPs) and gold nanoparticles (AuNPs) with numerous bio-bar-code DNA, respectively, which formed "sandwich" structures in the presence of specific target exosomes. The "sandwich" structures were separated under magnetic field, and the numerous bio-bar-code DNA were released by dissolving AuNPs. The released bio-bar-code DNA triggered the HCR procedure to produce a good deal of long DNA duplex structure for embedding in hemin, which generated strong ECL signal in the presence of coreactors for ultrasensitive detection of exosomes. Under the optimal conditions, it exhibited a good linearly of exosomes ranging from 10 to 104 exosomes particle μL-1 with limit of detection down to 5.01 exosome particle μL-1. Furthermore, the high ratio of ECL signal and minor change of ECL intensity indicated the good specificity, stability, and repeatability of this ECL biosensor. Given the good performance for exosome analysis, this ultrasensitive ECL biosensor has a promising application in the clinical diagnosis of early cancers.

An enzyme-free fluorescence sensing platform based on multiplex toehold-mediated strand displacement for point-of-care testing of single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) are biomarkers for the prediction and diagnosis of biomedical diseases, as well as important reference markers for intelligent design and breeding of animals and plants. Therefore, the development of high-sensitivity, practicability, and convenient SNPs sensing strategies and detection platforms is of great significance. In this study, we pioneered an ultra-sensitive universal sensing strategy for single base mutation detection and an enzyme-free FRET fluorescence sensing point-of-care testing (POCT) platform for SNP detection. Based on the thermodynamically regulated strand replacement mechanism, the system rationally introduced extra base mismatches and combined multiple toehold-mediated strand displacement (TMSD) reactions to achieve enzyme-free recognition of the single base mutation and double amplification of the detection signal. Finally, the biological signal of the mutant target (MT) was recognized, amplified, and transduced into a fluorescence resonance energy transfer (FRET) signal between Cy3 and Cy5 for output. Overall, the proposed biosensing strategy demonstrated high sensitivity for MT, with a detection limit as low as 0.37 fM, and could achieve detection of targets with mutant abundances as low as 0.1 %. Moreover, a portable detection system was assembled in conjunction with this sensing strategy to form a POCT platform for SNP genotyping. This POCT platform was validated to accurately identify SNPs from human cancer cells and soybean leaf genome extracts. In summary, this study will provide new insights into the design of enzyme-free biosensors for SNP detection, and will provide important technical support tools for the prevention and diagnosis of major diseases, animal and plant breeding, etc.

Measurement of Si pixel sensor alignment for the ALICE ITS detector

Abstract A Large Ion Collider Experiment (ALICE) experiment is one of the four experiments at the Large Hadron Collider (LHC) designed to investigate the status of matter under very high energy densities produced during heavy-ion collisions. The ALICE inner tracking system (ITS) consists of seven concentric cylindrical layers of monolithic silicon pixel sensors known as ALICE pixel detector (ALPIDE). The sensors are used to reconstruct the paths of charged particles generated in the collisions. The sensor alignment of the detector must be adjusted to a high precision standard. The adjustment objective is to obtain a detector that can undertake high-resolution measurements. This paper introduces a method for measuring the reference markers utilized in sensor alignment determination. Markers engraved at the chip corners have been detected using the Hough transform, Canny edge detection, and template matching techniques. The distances between two markers are measured to determine the accuracy of the pixel sensor alignment before and after assembly. The proposed methods exhibit an accuracy exceeding 99% and demonstrate high speed analysis. The average processing times for detecting the circle and cross markers are 105.9 ms/image and 113.8 ms/image, respectively. The sensor alignment of the detector must be adjusted to a high precision standard. However, recent studies have shown deviations of up to 5 μ m above the desired value in the measured sensor position. Such deviations do not represent a major issue, nevertheless it is important to measure them in order to speed-up and make more accurate the recursive track-based alignment procedure used to reconstruct the position of each pixel sensor in the tracking detector. The proposed method offers a promising solution to deliver precise and rapid measurements for a large number of examined objects.

CRASH2p: Closed-loop Two Photon Imaging in Freely Moving Animals.

Direct measurement of neural activity in freely moving animals is essential for understanding how the brain controls and represents behaviors. Genetically encoded calcium indicators report neural activity as changes in fluorescence intensity, but brain motion confounds quantitative measurement of fluorescence. Translation, rotation, and deformation of the brain and the movements of intervening scattering or auto-fluorescent tissue all alter the amount of fluorescent light captured by a microscope. Compared to single-photon approaches, two photon microscopy is less sensitive to scattering and off-target fluorescence, but more sensitive to motion, and two photon imaging has always required anchoring the microscope to the brain. We developed a closed-loop resonant axial-scanning high-speed two photon (CRASH2p) microscope for real-time 3D motion correction in unrestrained animals, without implantation of reference markers. We complemented CRASH2p with a novel scanning strategy and a multi-stage registration pipeline. We performed volumetric ratiometrically corrected functional imaging in the CNS of freely moving Drosophila larvae and discovered previously unknown neural correlates of behavior.

Correlation between cerebroplacental doppler ratio and neonatal respiratory disorders: A reference marker of fetal lung maturation.

The aim of this study was to investigate the role of cerebroplacental ratio (CPR) in the final prenatal care for neonatal respiratory diseases and to analyze the risk of relevant factors associated with neonatal respiratory disorders. A prospective cohort study of 795 singleton pregnancies was conducted. The pulsatility indices (PI) of the umbilical artery (UA) and the middle cerebral artery (MCA) were measured, and the MCA to UA ratio (CPR) was determined. The severity of the case is determined by whether or not the newborn has respiratory problems. Compare the CPR correlation between the two groups and examine the illness prediction factors through a binary logistic regression method. Of the 795 participants, 124 had neonatal respiratory disorders. The mean values of CPR between neonatal respiratory diseases group and control group were 1.78±0.6, 1.97±0.9, respectively (P < 0.001). Maternal age, abortion history, cesarean section history, placental thickness, placental maturity, and amniotic fluid index (AFI) were determined to have no significant link between the two groups after comparison analysis (P > 0.05). It could be found that compared with the control group, CPR MoM indicators of neonatal respiratory distress syndrome, neonatal pneumonia and wet lung disease all show significant decreases. In binary logistic regression analysis, among the variables included in the model, CPR (OR:2.90, P = 0.015), fetal heart monitoring (OR:5.26, P < 0.001), delivery mode (OR:2.86, P < 0.001) and gestational age of delivery (OR:0.92, P < 0.001) were statistically significant in both groups. The findings of this study showed that infant respiratory problems were substantially related to CPR value. The correlation indicates that CPR was a powerful reference marker for respiratory disorders.

Combined Contaminant Levels from Local Harvested Food Items in the Norwegian–Finnish–Russian Border Region

This paper presents the results of a multidisciplinary study with the aim of assessing the potential combined risk from consuming locally harvested food products in the Euro-Arctic region of Norway, Finland, and Russia. The three important contaminant groups—radioactive substances, heavy metals, and persistent organic pollutants (POPs)—were measured in food samples such as berries, mushrooms, fish, birds, reindeer, and moose; they were sampled in 2013–2015. To assess the combined pollution levels and investigate the trends, similarities, and variations between different contaminant groups, subsequent multivariate statistical analysis was performed. The results showed that, in general, the levels of radioactive substances, toxic elements, and POPs were below the permitted EU maximum content in food products. However, statistical analysis revealed some correlations, similarities, and peculiarities between the accumulation of different contaminants in various species, which allowed for a better understanding of the mechanisms of accumulation and interaction between different contaminant groups. It also gave a better insight into the possible added risks and helped pinpoint species that could serve as reference markers for the accumulation of different contaminants in food. Mushrooms, fish, and reindeer were found to be important markers in the combined risk assessments for the contents of metals and radioactive substances. Further research, as well as the development of methodologies for combined assessments, are recommended.

ScAnnoX: an R package integrating multiple public tools for single-cell annotation.

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

Unveiling the Ovarian Cell Characteristics and Molecular Mechanism of Prolificacy in Goats via Single-Nucleus Transcriptomics Data Analysis.

Increases in litter size, which are influenced by ovulation, are responsible for between 74% and 96% of the economic value of genetic progress, which influences selection. For the selection and breeding of highly prolific goats, genetic mechanisms underlying variations in litter size should be elucidated. Here, we used single-nucleus RNA sequencing to analyze 44,605 single nuclei from the ovaries of polytocous and monotocous goats during the follicular phase. Utilizing known reference marker genes, we identified 10 ovarian cell types characterized by distinct gene expression profiles, transcription factor networks, and reciprocal interaction signatures. An in-depth analysis of the granulosa cells revealed three subtypes exhibiting distinct gene expression patterns and dynamic regulatory mechanisms. Further investigation of cell-type-specific prolificacy-associated transcriptional changes elucidated that "downregulation of apoptosis", "increased anabolism", and "upstream responsiveness to hormonal stimulation" are associated with prolificacy. This study provides a comprehensive understanding of the cell-type-specific mechanisms and regulatory networks in the goat ovary, providing insights into the molecular mechanisms underlying goat prolificacy. These findings establish a vital foundation for furthering understanding of the molecular mechanisms governing folliculogenesis and for improving the litter size in goats via molecular design breeding.

Accuracy of Dental Implant Placement with Dynamic Navigation-Investigation of the Influence of Two Different Optical Reference Systems: A Randomized Clinical Trial.

This randomized prospective clinical study aims to analyze the differences between the computer-assisted planned implant position and the clinically realized implant position using dynamic navigation. In the randomized prospective clinical study, 30 patients were recruited, of whom 27 could receive an implant (BLT, Straumann Institut AG, Basel, Switzerland) using a dynamic computer-assisted approach. Patients with at least six teeth in their jaws to be implanted were included in the study. Digital planning was performed using cone beam tomography imaging, and the visualization of the actual situation was carried out using an intraoral scan. Two different workflows with differently prepared reference markers were performed with 15 patients per group. The actual clinically achieved implant position was recorded with scan bodies fixed to the implants and an intraoral scan. The deviations between the planned and realized implant positions were recorded using evaluation software. The clinical examinations revealed no significant differences between procedures A and B in the mesiodistal, buccolingual and apicocoronal directions. For the mean angular deviation, group B showed a significantly more accurate value of 2.7° (95% CI 1.6-3.9°) than group A, with a value of 6.3° (95% CI 4.0-8.7°). The mean 3D deviation at the implant shoulder was 2.35 mm for workflow A (95% CI 1.92-2.78 mm) and 1.62 mm for workflow B (95% CI 1.2-2.05 mm). Workflow B also showed significantly higher accuracy in this respect. Similar values were determined at the implant apex. The clinical examination shows that sufficiently accurate implant placement is possible with the dynamic navigation system used here. The use of different workflows sometimes resulted in significantly different accuracy results. The data of the present study are comparable with the published findings of other static and dynamic navigation procedures.

Algorithm Development and Early Performance Evaluation of a Next-Generation Multitarget Stool DNA Screening Test for Colorectal Cancer

Background and AimsThe multitarget stool DNA (mt-sDNA) assay is a non-invasive average-risk colorectal cancer (CRC) screening test. A new biomarker panel was developed for a next-generation test to improve specificity while maintaining/increasing sensitivity. We aimed first to establish an algorithm and cutoff for the next-generation mt-sDNA test and then to validate it using archived samples from the pivotal DeeP-C study (NCT01397747) of the first-generation test. MethodsAlgorithm development and cross-validation included 3011 samples from 2 specimen collection studies (NCT03821948, NCT03789162). The algorithm and cutoff were locked before validation. Validation test set samples included 57 CRC, 583 advanced precancerous lesions (APLs), and 7022 samples negative for CRC or APLs from the DeeP-C study, which prospectively enrolled average-risk, asymptomatic adults aged 50 to 84 years before screening colonoscopy. Next-generation biomarkers included methylated DNA markers LASS4, LRRC4, PPP2R5C, and ZDHHC1 (reference marker), and fecal hemoglobin. Primary validation endpoints were CRC sensitivity and specificity for the absence of advanced neoplasia. Secondary endpoints included APL sensitivity and specificity for non-neoplastic findings or negative colonoscopy. ResultsCross-validation and best-fit results from algorithm development closely matched, confirming algorithm reliability and reproducibility. For the test set, next-generation mt-sDNA test sensitivity was 93.0% (95% CI, 83.0%-98.1%) for CRC and 48.4% (95% CI, 44.2%-52.5%) for APLs. Specificity was 88.5% (95% CI, 87.7%-89.2%) for the absence of advanced neoplasia and 90.4% (95% CI, 89.5%-91.2%) for the combination of non-neoplastic findings or negative colonoscopy. ConclusionsBased on archived samples, the next-generation mt-sDNA test demonstrated promising CRC screening performance characteristics that will be further assessed in a prospective clinical validation study (BLUE-C; NCT04144738).

Standardization of flow cytometric detection of antigen expression.

Since response to antigen-based immunotherapy relies upon the level of tumor antigen expression we developed an antigen quantification assay using ABC values. Antigen quantification as a clinical assay requires methods for quality control and for interlaboratory and inter-cytometer platform standardization. A single lot of Cytotrol™ Lyophilized Control Cells (Beckman Coulter) used for all studies. The variability in antigen quantification across 4 different instrument platforms in 2 separate laboratories was evaluated. The effect of the antibody clone utilized, importance of custom 1:1 molar ratio (fluorophore to protein, F/P) verses off-the-shelf antibodies, and QuantiBrite PE calibration verses linearity calibration combined with a single point scale transformation with CD4 as reference were determined. Use of single lot control cells allowed validation of reproducibility between flow cytometer platforms and laboratories and allowed assessment of different antibody lots, cocktail preparation, and different antibody clones. Off the shelf antibody preparations provide reproducible estimates of antigen density, however custom 1:1 unimolar antibody preparations should be utilized for definitive measurement of antigen expression.Geometric Mean fluorescent Intensity (GeoMFI) was not comparable across instruments and inter-laboratory. The use of CD4 as the reference marker can minimize variability in ABC values. Comparable antigen quantification is vital in managing patients receiving antigen-based immunotherapy. If this assay is to be utilized in a clinical setting, quality control methods have to be instituted to assure reproducibility and allow validation across laboratories. We have demonstrated that use of a lyophilized cell control is highly valuable in achieveing these goals.

Interpretation of the Yak Skin Single-Cell Transcriptome Landscape.

The morphogenesis of hair follicle structure is accompanied by the differentiation of skin tissue. Mammalian coats are produced by hair follicles. The formation of hair follicles requires signal transmission between the epidermis and dermis. However, knowledge of the transcriptional regulatory mechanism is still lacking. We used single-cell RNA sequencing to obtain 26,573 single cells from the scapular skin of yaks at hair follicle telogen and anagen stages. With the help of known reference marker genes, 11 main cell types were identified. In addition, we further analyzed the DP cell and dermal fibroblast lineages, drew a single-cell map of the DP cell and dermal fibroblast lineages, and elaborated the key genes, signals, and functions involved in cell fate decision making. The results of this study provide a very valuable resource for the analysis of the heterogeneity of DP cells and dermal fibroblasts in the skin and provide a powerful theoretical reference for further exploring the diversity of hair follicle cell types and hair follicle morphogenesis.

Bone Health of Hemophilia Ａ Patients Using Emicizumab

Background: Hemophilia patients have been observed to exhibit a higher prevalence of osteoporosis compared to age-matched general population. However, the underlying mechanism responsible for this association remains poorly understood. One hypothesis proposes that factor VIII itself may possess additional physiological functions that contribute to bone health. Conversely, alternative viewpoints suggest that overall coagulation function or thrombin generation may play a pivotal role in maintaining optimal bone health. The introduction of emicizumab, a bispecific antibody that mimics the coagulation function of factor VIII but possesses a structurally distinct composition, presents an opportunity to shed light on this matter. Therefore, the primary objective of this study is to explore and compare the bone health status of hemophilia patients, distinguishing between those utilizing emicizumab and those who are emicizumab-naïve. Method: From June 2019 to June 2023, at the hemophilia center of National Taiwan University Hospital, additional examinations to assess the patients' bone health were performed with routine annual joint health evaluations for patients with hemophilia A (PwHA) and B (PwHB). This included the use of dual-energy X-ray absorptiometry scan (DEXA) to measure the bone mineral density (BMD) of the lumbar spine, hip, and femur. Furthermore, the serum levels of procollagen type 1 N-terminal propeptide (P1NP) and C-terminal telopeptide (CTX) were measured as reference markers for bone formation and bone resorption, respectively. The Kruskal-Wallis test was utilized to statistically evaluate the observations among the three groups. Results: The study population was divided into three distinct groups based on their hemophilia subtype and treatment: PwHA who had been utilizing emicizumab for more than one year (n=8), PwHA individuals receiving regular factor VIII treatment (n=16), and PwHB (n=10). The analysis of the collected data, as presented in the accompanying table, revealed that the three groups exhibited similar characteristics in terms of age, annual bleeding rate, and joint conditions. Moreover, no statistically significant differences were detected in the measurements of bone mineral densities (BMDs) and biomarkers. However, it is worth noting that there appeared to be a potential trend toward higher levels of P1NP in the PwHB group.In the case of PwHA patients using emicizumab, we examined the longitudinal changes in T-score of BMD over time. Notably, three PwHA patients with baseline T-scores below zero demonstrated improvements in their T-scores after one year of emicizumab treatment. Conclusion: The results of this study indicate a lack of significant disparities in bone health between individuals utilizing emicizumab and those who are not. Furthermore, our findings suggest that emicizumab usage does not lead to a decline in bone health over the observed period.

Transcription factor LHX9 (LIM Homeobox 9) enhances pyruvate kinase PKM2 activity to induce glycolytic metabolic reprogramming in cancer stem cells, promoting gastric cancer progression

BackgroundGlycolytic metabolic reprogramming is a phenomenon in which cells undergo altered metabolic patterns during malignant transformation, mainly involving various aspects of glycolysis, electron transport chain, oxidative phosphorylation, and pentose phosphate pathway. This reprogramming phenomenon can be used as one of the markers of tumorigenesis and development. Pyruvate kinase is the third rate-limiting enzyme in the sugar metabolism process by specifically catalyzing the irreversible conversion of PEP to pyruvate.PurposeThis study aimed to reveal the critical mediator(s) that regulate glycolytic metabolism reprogramming in gastric cancer and their underlying molecular mechanism and then explore the molecular mechanisms by which LHX9 may be involved in regulating gastric cancer (GC) progression.MethodsFirstly, we downloaded the GC and glycolysis-related microarray datasets from TCGA and MSigDB databases and took the intersection to screen out the transcription factor LHX9 that regulates GC glycolytic metabolic reprogramming. Software packages were used for differential analysis, single gene predictive analysis, and Venn diagram. In addition, an enrichment analysis of the glycolytic pathway was performed. Immunohistochemical staining was performed for LHX9 and PKM2 protein expression in 90 GC patients, and the association between their expressions was evaluated by Spearman's correlation coefficient method. Three human GC cell lines (AGS, NCI-N87, HGC-27) were selected for in vitro experimental validation. Flow cytometry was utilized to determine the stem cell marker CD44 expression status in GCSCs. A sphere formation assay was performed to evaluate the sphere-forming capabilities of GCSCs. In addition, RT-qPCR and Western blot experiments were employed to investigate the tumor stem cell markers OCT4 and SOX2 expression levels in GCSCs. Furthermore, a lentiviral expression vector was constructed to assess the impact of downregulating LHX9 or PKM2 on the glycolytic metabolic reprogramming of GCSCs. The proliferation, migration, and invasion of GCSCs were then detected by CCK-8, EdU, and Transwell assays. Subsequently, the mutual binding of LHX9 and PKM2 was verified using chromatin immunoprecipitation and dual luciferase reporter genes. In vivo experiments were verified by establishing a subcutaneous transplantation tumor model in nude mice, observing the size and volume of tumors in vivo in nude mice, and obtaining fresh tissues for subsequent experiments.ResultsBioinformatics analysis revealed that LHX9 might be involved in the occurrence and development of GC through regulating glycolytic metabolism. High LHX9 expression could be used as a reference marker for prognosis prediction of GC patients. Clinical tissue assays revealed that LHX9 and PKM2 were highly expressed in GC tissues. Meanwhile, GC tissues also highly expressed glycolysis-associated protein GLUT1 and tumor cell stemness marker CD44. In vitro cellular assays showed that LHX9 could enhance its activity and induce glycolytic metabolic reprogramming in GCSCs through direct binding to PKM2. In addition, the knockdown of LHX9 inhibited PKM2 activity and glycolytic metabolic reprogramming and suppressed the proliferation, migration, and invasive ability of GCSCs. In vivo animal experiments further confirmed that the knockdown of LHX9 could reduce the tumorigenic ability of GCSCs in nude mice by inhibiting PKM2 activity and glycolytic metabolic reprogramming.ConclusionThe findings suggest that both LHX9 and PKM2 are highly expressed in GCs, and LHX9 may induce the reprogramming of glycolytic metabolism through transcriptional activation of PKM2, enhancing the malignant biological properties of GCSCs and ultimately promoting GC progression.

CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.

Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first timeand (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.

Validity of an Artificial Intelligence-Based Application to Identify Foods and Estimate Energy Intake Among Adults: A Pilot Study

BackgroundThe commercial application Openfit allows for automatic identification and quantification of food intake through short video capture without a physical reference marker. There are no known peer-reviewed publications on the validity of this Nutrition Artificial Intelligence (AI). ObjectivesTo test the validity of Openfit to identify food automatically and semiautomatically (with user correction), test the validity of Openfit at quantifying energy intake (kcal) automatically and semiautomatically, and assess satisfaction and usability of Openfit. MethodsDuring a laboratory-based visit, adults (7 male and 17 female), used Openfit to automatically and semiautomatically record provided meals, which were covertly weighed. Foods logged were identified as an “exact match,” “far match,” or an “intrusion” using Food and Nutrient Database for Dietary Studies (FNDDS) codes. Descriptive data were stratified by meal, food item, and FNDDS group, and presented with or without beverages. Bland–Altman analyses assessed errors over levels of energy intake. Participants completed a User Satisfaction Survey (USS) and the Computer Systems Usability Questionnaire (CSUQ). Open-ended questions were assessed with qualitative methods. ResultsExact matches, far matches, and intrusions were 46%, 41%, and 13% for automated identification, and 87%, 23%, and 0% for semiautomated identification, respectively. Error for automated and semiautomated energy estimates were 43% and 33% with beverages, and 16% and 42% without beverages. Bland–Altman analyses indicated larger error for higher energy meals. Overall mean scores were 2.4 for the CSUQ and subscale means scores ranged from 4.1 to 5.5. for the USS. Participants recommended improvements to Openfit’s Nutrition AI, manual estimation, and overall app. ConclusionOpenfit worked relatively well for automatically and semiautomatically identifying foods. Error in automated energy estimates was relatively high; however, after excluding beverages, error was relatively low (16%). For semiautomated energy estimates, error was comparable to previous studies. Improvements to the Nutrition AI, manual estimation and overall application may increase Openfit’s usability and validity.This trial was registered at clinicaltrials.gov as NCT05343585.