Clinical Validation Research Articles

Quantification of valvular regurgitation by transthoracic 3D high pulse repetition frequency Doppler echocardiography

Abstract Purpose To improve quantification of valvular regurgitation, a 3D high pulse repetition frequency Doppler (3D HPRFD) method was developed for regurgitant volume (RVol) estimation from transthoracic echocardiography (TTE). Although successfully applied in-vitro and in selected clinical cases, a systematic clinical validation of 3D HPRFD has not been published. Hence, our aims were to investigate 1) feasibility of 3D HPRFD and 2) correlation between 3D HPRFD and RVol estimates obtained by the 2D proximal isovelocity surface area (PISA) method and cardiac magnetic resonance (CMR) in patients with either aortic regurgitation (AR) or mitral regurgitation (MR). Methods and results We included 45 patients with AR (42% mild, 40% moderate, 18% severe) and 45 with MR (67% mild, 24% moderate, 9% severe). Median time between start of TTE and start of CMR was 1.5 hours, minimizing changes in load. Overall feasibility of 3D HPRFD was 56% in AR and 44% in MR. Feasibility was only 25% in patients with severe regurgitation. In AR, estimated RVol from 3D HPRF did not correlate with estimated RVol from PISA or CMR (Spearman rho = 0.06 (P = 0.78) and 0.04 (P = 0.4), respectively). In MR, RVol estimates from 3D HPRFD correlated with PISA (rho = 0.72, P <0.001) but not with CMR (rho = 0.31, P = 0.43). Conclusions RVol estimation by 3D HPRFD had a low feasibility, especially in severe regurgitation, and in general correlated poorly with PISA and CMR estimates. In its current state, 3D HPRFD is not ready for clinical use.

Image reconstruction impacts haemodynamic parameters derived from 4D flow magnetic resonance imaging with compressed sensing

Abstract Background Four-dimensional (4D) blood flow measurements by cardiac magnetic resonance imaging (CMR) can be used to simplify blood flow assessment. Compressed sensing (CS) can provide better flow measurements than conventional parallel imaging (PI), but clinical validation is needed. This study aimed to validate stroke volume (SV) measurements by 4D-CS in healthy volunteers and patients, while also investigating the influence of the CS image reconstruction parameter λ on haemodynamic parameters. Methods and Results Healthy participants (n=9; 20–62 years) underwent CMR with 2D, 4D-CS and 4D-PI flow. Patients (n=30, 17 with congenital heart defect; 2–75 years) had 4D-CS added to their clinical examination. Impact of λ was assessed by reconstructing 4D-CS data for 6 different λ values. In healthy volunteers, 4D-CS and 4D-PI SV differed by 0.4±6.5 ml (0.6±9.1 %; ICC 0.98), and 4D-CS and 2D flow by 0.9±7.0 ml (0.9±10.6 %; ICC 0.98). In patients, 4D-CS and 2D flow differed by –1.3±6.0 ml (-7.2±20 %; ICC 0.97). Stroke volume was not dependent on λ in patients (p=0.75) but an increase in λ by 0.001 led to increased differences between 4D-CS and 4D-PI of -0.4% (p=0.0021) in healthy participants. There were significant differences for ventricular kinetic energy (systole: p<0.0001; diastole: p<0.0001) and haemodynamic forces (systole: p<0.0001; diastole: p<0.0001), where error increased with increasing λ values in both healthy participants and patients. Conclusion Four-dimensional flow CMR with CS can be used clinically to assess SV in paediatric and adult patients. Ventricular kinetic energy and haemodynamic forces are however sensitive to the change in reconstruction parameter λ, and it is therefore important to validate advanced blood flow measurements before comparing data between scanners and centres.

Integration analysis of microRNAs as potential biomarkers in early-stage lung adenocarcinoma: the diagnostic and therapeutic significance of miR-183-3p

IntroductionLung adenocarcinoma (LUAD) poses a significant therapeutic challenge, primarily due to delayed diagnosis and the limited efficacy of existing treatments.MethodsTo understand the pathogenesis and identify diagnostic biomarkers for LUAD in the early stage, we investigated differential miRNA expression in 33 stage I LUAD patients between tumor and matched paracancerous tissues by Illumina Sequencing. Target genes of differentially expressed miRNAs were predicted using TargetScan and miRDB databases and further analyzed by GO and KEGG pathway enrichment analysis. The miRNAs expression results were verified using qRT-PCR. Additionally, we evaluated the clinical significance of miRNAs by the TCGA database. miR-183-3p was chosen for subsequent biological functional studies by cell proliferation assays, cell migration and cell invasion assays, cell apoptosis and cell cycle assays in LUAD cells. The clinical relevance target genes of miR-183-3p were predicted by TargetScan databases and bioinformatics assays. Gene-specific experimental validation was performed using qRT-PCR, western blotting and luciferase reporter assays.ResultsWe identified 36 differentially expressed miRNAs between LUAD tissues and matched paracancerous tissues. Target genes for these miRNAs revealed associations with processes and pathways such as RNA biosynthesis, intracellular signaling, protein transport, and the Ras, MAPK, and PI3K-AKT pathways. The qRT-PCR results were in alignment with the sequencing data for 19 out of these 21 miRNAs which not yet implicated in LUAD, 13 were up-regulated, 6 were down-regulated. The clinical relevance assays showed that 5 up-regulated miRNAs have diagnostic value for LUAD. miR-183-3p showed significant advantages in the result of sequencing, qRT-PCR, and clinical relevance assay. Biological functional assays showed that miR-183-3p emerged as a key regulator, promoting LUAD cell proliferation, decreasing apoptosis, and augmenting migration and invasion capabilities. The clinical relevance assays and experimental validation showed SESN1 as a clinical significance target of miR-183-3p.DiscussionOur study lays the foundation for investigating miRNAs with diagnostic significance in early-stage LUAD, pointing out that inhibition of miR-183-3p may serve as a novel therapeutic in LUAD.

A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer

Background/Objectives: Age-related macular degeneration (AMD) is a significant cause of vision loss in older adults, often progressing without early noticeable symptoms. Deep learning (DL) models, particularly convolutional neural networks (CNNs), demonstrate potential in accurately diagnosing and classifying AMD using medical imaging technologies like optical coherence to-mography (OCT) scans. This study introduces a novel CNN-based DL method for AMD diagnosis, aiming to enhance computational efficiency and classification accuracy. Methods: The proposed method (PM) combines modified Inception modules, Depthwise Squeeze-and-Excitation Blocks, and ConvMixer architecture. Its effectiveness was evaluated on two datasets: a private dataset with 2316 images and the public Noor dataset. Key performance metrics, including accuracy, precision, recall, and F1 score, were calculated to assess the method’s diagnostic performance. Results: On the private dataset, the PM achieved outstanding performance: 97.98% accuracy, 97.95% precision, 97.77% recall, and 97.86% F1 score. When tested on the public Noor dataset, the method reached 100% across all evaluation metrics, outperforming existing DL approaches. Conclusions: These results highlight the promising role of AI-based systems in AMD diagnosis, of-fering advanced feature extraction capabilities that can potentially enable early detection and in-tervention, ultimately improving patient care and outcomes. While the proposed model demon-strates promising performance on the datasets tested, the study is limited by the size and diversity of the datasets. Future work will focus on external clinical validation to address these limita-tions.

Interpretable deep learning survival predictions in sporadic Creutzfeldt–Jakob disease

BackgroundSporadic Creutzfeldt–Jakob disease (sCJD) is a rapidly progressive and fatal prion disease with significant public health implications. Survival is heterogenous, posing challenges for prognostication and care planning. We developed a survival model using diagnostic data from comprehensive UK sCJD surveillance.MethodsUsing national CJD surveillance data from the United Kingdom (UK), we included 655 cases of probable or definite sCJD according to 2017 international consensus diagnostic criteria between 01/2017 and 01/2022. Data included symptoms at diagnosis, CSF RT-QuIC and 14-3-3, MRI and EEG findings, as well as sex, age, PRNP codon 129 polymorphism, CSF total protein and S100b. An artificial neural network based multitask logistic regression was used for survival analysis. Model-agnostic interpretation methods was used to assess the contribution of individual features on model outcome.ResultsOur algorithm had a c-index of 0.732, IBS of 0.079, and AUC at 5 and 10 months of 0.866 and 0.872, respectively. This modestly improved on Cox proportional hazard model (c-index 0.730, IBS 0.083, AUC 0.852 and 0863) but was not statistically significant. Both models identified codon 129 polymorphism and CSF 14-3-3 to be significant predictive features.ConclusionssCJD survival can be predicted using routinely collected clinical data at diagnosis. Our analysis pipeline has similar levels of performance to classical methods and provide clinically meaningful interpretation which help deepen clinical understanding of the condition. Further development and clinical validation will facilitate improvements in prognostication, care planning, and stratification to clinical trials.

Exploring Xiaojianzhong decoction's potential in gastric cancer treatment: Integrative insights and experimental validation

Gastric cancer (GC) remains a formidable global health concern with significant morbidity and mortality rates, despite the fact that numerous advances have been made to improve conventional therapies. Xiaojianzhong decoction (XJZ), a traditional Chinese medicine, has garnered academic attention as a multicomponent, multitarget approach to managing GC. The present editorial explores the potential of XJZ in the treatment of GC through a comprehensive analysis of network pharmacology and experimental validation. Network pharmacology was used to identify key molecular targets of XJZ, including interleukin 6, prostaglandin-endoperoxide synthase 2, and matrix metalloproteinase 9, and in vitro experiments were used to confirm the efficacy of XJZ in inhibiting cell proliferation, inducing apoptosis, and modulating gene expression associated with GC progression. This editorial highlights XJZ as a promising therapeutic strategy for GC and indicates a need for further clinical exploration and validation of its efficacy.

Clinical validation of MR-generated synthetic CT by MRCAT for brain tumor radiotherapy.

MRI is an emerging modality in radiotherapy (RT). Accuracy synthetic CT is the prerequisite for implementing MR-only RT planning. This study validated the commercial algorithm of MR for calculating attenuation (MRCAT) in terms of image quality and dosimetric agreement. Brain tumor cases with 18 treated using intensity-modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT), and 15 treated using stereotactic radiosurgery (SRS) were analyzed. Synthetic CTs were resampled referencing planning CT. Treatment plan calculated on planning CT was recalculated on resampled MRCAT. Image quality of selected metrics and dosimetric agreements were assessed by dose-volume-histogram and 3D gamma analysis. For IMRT/VMAT and SRS cases, mean error were 23.42±1.05 and 28.39±3.17 HU; mean absolute error were 38.03±1.42 and 52.36±2.63 HU; root mean squared error were 89.09±6.65 and 108.38±12.23 HU; peak signal-to-noise ratio were 29.11±0.60 and 27.65±0.59dB; and structural similarity index measures were 0.88±0.00 and 0.70±0.01 respectively. No significant differences were identified for DVH metrics accounting the target coverage. Most OARs did not have significant dose deviation, except left lens with 0.70% higher in D-mean after recalculation (p<0.001). For criteria of 3mm/3%, 2mm/2%, and 1mm/1%, gamma passing rates for IMRT/VMAT were 99.92%, 99.42%, and 96.47%, while SRS were 99.86%, 99.52%, and 97.57% respectively. Correlation between passing rate and image quality metrics was established in IMRT/VMAT cases, with higher similarity yield better dosimetric agreement between planning and synthetic CT. This study has validated the MRCAT for clinical use in terms of comparable image quality and dosimetric agreement with planning CT. Further case selection and MR-compatible immobilization device would be required.

Role of circulating tumor cells in breast cancer.

Circulating tumor cells (CTCs) are tumor cells that shed from the primary tumor or metastatic loci, intravasate, and circulate in the bloodstream. CTCs have been suggested to play a major role in the metastatic spread of cancer, constantly shedding from tumors during proliferation or as a result of mechanical insults. Breast cancer (BC) is one of the most representative tumors in CTC research, with several studies conducted on its clinical validity and utility in both early and advanced BC (EBC and ABC, respectively). The assessment of the number and molecular profiles of CTCs is expected to provide a more tailored therapy for patients with BC. The detection of CTCs is usually dependent on molecular markers, and epithelial cell adhesion molecules are widely used. Although the CellSearch® technology has been widely utilized for CTC detection, recent advances have led to the development of novel detection methods, facilitating further molecular analysis. In this review, we discuss the clinical applications of CTCs, current status of research, and efforts to incorporate CTC analysis into clinical practice. Additionally, we discuss potential challenges and future directions for integrating CTC analysis into clinical practice.

Medicinal Plants as A Natural Immunity Booster Against Covid 19: A Review

Medicinal plants have long been valued for their therapeutic properties, especially in boosting immune function. This review explores the role of medicinal plants in enhancing immunity during the COVID-19 pandemic, with an emphasis on plants known for their bioactive compounds and health benefits. A thorough search of online databases resulted in the identification of 115 published articles from 2020 to 2024, which led to the selection of 50 plants commonly used for immune support. Plants include Garlic, Ashwagandha, Neem,Turmeric, Guduchi, Holy Basil, Amla, Aloe Vera, Black Cardamom, Cinnamon, Pomegranate, Guava, Moringa, Black Cumin, Lemon, Lemongrass, Papaya, Green Tea and others. These plants contain bioactive compounds such as flavonoids, alkaloids, and terpenoids, which are believed to possess immune-boosting, anti-inflammatory, and antiviral properties. Garlic, Turmeric, and Ashwagandha are particularly noted for combating oxidative stress and supporting immune function, while Gurjo, Neem, and Amla are valued for their detoxifying and anti-inflammatory effects. These plants, traditionally used during viral infections like the flu and COVID-19, represent a fusion of ancient cultural wisdom and modern scientific research.Future research should focus on clinical validation and pharmacological studies to better understand the efficacy of these plants in immune modulation.

The Prevalence of Sickle Cell Disease in Colorado and Methodologies of the Colorado Sickle Cell Data Collection Program: Public Health Surveillance Study.

Sickle cell disease (SCD) is a genetic blood disorder that affects approximately 100,000 individuals in the United States, with the highest prevalence among Black or African American populations. While advances in care have improved survival, comprehensive state-level data on the prevalence of SCD remain limited, which hampers efforts to optimize health care services. To address this gap, the Colorado Sickle Cell Data Collection (CO-SCDC) program was established in 2021 as part of the Centers for Disease Control and Prevention's initiative to enhance surveillance and public health efforts for SCD. The objectives of this study were to describe the establishment of the CO-SCDC program and to provide updated estimates of the prevalence and birth prevalence of SCD in Colorado, including geographic dispersion. Additional objectives include evaluating the accuracy of case identification methods and leveraging surveillance activities to inform public health initiatives. Data were collected from Health Data Compass (a multi-institutional data warehouse) containing electronic health records from the University of Colorado Health and Children's Hospital Colorado for the years 2012-2020. Colorado newborn screening program data were included for confirmed SCD diagnoses from 2001 to 2020. Records were linked using the Colorado University Record Linkage tool and deidentified for analysis. Case definitions, adapted from the Centers for Disease Control and Prevention's Registry and Surveillance System for Hemoglobinopathies project, classified cases as possible, probable, or definite SCD. Clinical validation by hematologists was performed to ensure accuracy, and prevalence rates were calculated using 2020 US Census population estimates. In 2019, 435 individuals were identified as living with SCD in Colorado, an increase of 16%-40% over previous estimates, with the majority (n=349, 80.2%) identifying as Black or African American. The median age of individuals was 19 years. The prevalence of SCD was highest in urban counties, with concentrations in Arapahoe, Denver, and El Paso counties. Birth prevalence of SCD increased from 11.9 per 100,000 live births between 2010 and 2014 to 20.1 per 100,000 live births between 2015 and 2019 with 58.5% (n=38) of cases being hemoglobin (Hb) SS or HbSβ0 thalassemia subtypes. The study highlighted a 67% (n=26) increase in SCD births over the decade, correlating with the growth of the Black or African American population in the state. The CO-SCDC program successfully established the capacity to perform SCD surveillance and, in doing so, identified baseline prevalence estimates for SCD in Colorado. The findings highlight geographic dispersion across Colorado counties, highlighting the need for equitable access to specialty care, particularly for rural populations. The combination of automated data linkage and clinical validation improved case identification accuracy. Future efforts will expand surveillance to include claims data to better capture health care use and address potential underreporting. These results will guide public health interventions aimed at improving care for individuals with SCD in Colorado.

Case report: A novel third-generation anti-CD19/CD22 CAR T-cells combined with auto-HSCT for relapsed Burkitt lymphoma

This study explores a novel therapeutic strategy for relapsed/refractory (R/R) Burkitt lymphoma (BL) by integrating autologous hematopoietic stem cell transplantation (ASCT) with tandem anti-CD19/CD22 chimeric antigen receptor (CAR) T cell therapy. A 20-year-old Asian male with refractory BL, whose lymphoma had not responded to multiple chemoimmunotherapy regimens, received myeloablative ASCT followed three days later by infusion of a novel third-generation CAR T cells engineered with CD28 and CD3ζ signaling domains, along with a TLR2 costimulatory domain. This resulted in sustained complete remission at the 306-day follow-up, without experiencing any severe complications. This case suggests that combining myeloablative ASCT with tandem anti-CD19/CD22 CAR T cell therapy could be an effective approach for R/R BL, warranting further clinical validation.

SAS Macros for Efficient Clinical Data Validation and Cleaning

In clinical trials, ensuring the integrity and accuracy of data is critical for regulatory compliance and the validity of study outcomes. Data validation and cleaning processes are labor-intensive and prone to errors when done manually, particularly in large-scale studies. SAS macros offer a solution by automating the identification and rectification of data anomalies, ensuring consistency, and reducing human error. This white paper explores the use of SAS macros for efficient data validation and cleaning, providing detailed examples and discussing reusable macros for multi- study applications. It also presents a case study from a global cardiovascular trial and examines future trends, such as AI-assisted data cleaning within SAS environments. Keywords: SAS Macros, Data Cleaning, Data Quality Assurance, Data Integrity in Clinical Trials, Regulatory Compliance in Clinical Trials, Multi-Study Data Validation

SOCS1: A potential diagnostic and prognostic marker for aggressive gliomas and a new target for immunotherapy.

Gliomas, the most common and deadly cancers of the central nervous system, present a unique immunological barrier that severely undermines the effectiveness of immunotherapies. Suppressor of cytokine signaling 1 (SOCS1), belonging to the SOCS protein family and playing a pivotal role in various cancer treatment strategies and is abundant in high-grade gliomas. This study conducted a comparative analysis of SOCS1 and glioma immune checkpoints. It underscores the feasibility of leveraging SOCS1 as a promising diagnostic and prognostic marker for aggressive gliomas, thus offering novel targets for glioma immunotherapy. Comprehensive gene expression analyses and clinical data validations were performed across multiple databases. The expression and biological functions of SOCS1 were examined through an array of techniques including pan-cancer analysis, functional enrichment, gene set variation analysis, and immune microenvironment examination. This was done alongside a comparison of the similarities between SOCS1 and various glioma immune checkpoints. Utilizing clinical information from patients, a bespoke predictive model was developed to further corroborate the prognostic capabilities of SOCS1. The investigation revealed considerable similarities between SOCS1 and several immune checkpoints such as CTLA4, demonstrating SOCS1's role as an independent prognostic factor positively influencing glioma patient outcomes. The inclusion of SOCS1 in the developed predictive model significantly enhanced its precision. Our findings highlight SOCS1's potential as an innovative target for glioma immunotherapy, providing a novel strategy to overcome the immunological barriers posed by gliomas. Furthermore, identifying SOCS1 as a viable diagnostic marker for aggressive gliomas improves the accuracy of prognostic predictions for affected patients.

Metabolism and spatial transcription resolved heterogeneity of glutamine metabolism in cervical carcinoma

BackgroundReprogramming of cellular metabolism is a pivotal mechanism employed by tumor cells to facilitate cell growth, proliferation, and differentiation, thereby propelling the progression of cancer. A comprehensive analysis of the transcriptional and metabolic landscape of cervical squamous cell carcinoma (CSCC) at high resolution could greatly enhance the precision of management and therapeutic strategies for this malignancy.MethodsThe Air-flow-assisted Desorption Electrospray Ionization Mass Spectro-metric Imaging (AFADESI-MSI) and Spatial Transcriptomics techniques (ST) were employed to investigate the metabolic and transcription profiles of CSCC and normal tissues. For clinical validation, the expression of ASCT2(Ala, Ser, Cys transporter 2) was assessed using immune histochemistry in 122 cases of cervical cancer and 30 cases of cervicitis.ResultsThe AFADESI-MSI findings have revealed metabolic differences among different CSCC patients. Among them, the metabolic pathways of glutamine show more significant differences. After in situ detection of metabolites, the intensity of glutamate is observed to be significantly higher in cancerous tissue compared to normal tissue, but the intensity is not uniform. To elucidate the potential factors underlying alterations in glutamine metabolism across tissues, we employ ST to quantify mRNA levels. This analysis unveils significant perturbations in glutamine metabolism accompanied by extensive heterogeneity within cervical cancer tissues. After conducting a comprehensive analysis, it has been revealed that the differential expression of ASCT2(encoded by SLC1A5) in distinct regions of cervical cancer tissues plays a pivotal role in inducing heterogeneity in glutamine metabolism. Furthermore, the higher the expression level of ASCT2, the higher the intensity of glutamate is in the region. Further verification, it is found that the expression of ASCT2 protein in CSCC tissues is significantly higher than that in normal tissues (105/122, 86.07%).ConclusionsThis finding suggests that the variation in glutamine metabolism is not uniform throughout the tumor. The differential expression of ASCT2 in different regions of cervical cancer tissues seems to play a key role in causing this heterogeneity. This research has opened up new avenues for exploring the glutamine metabolic characteristics of CSCC which is essential for developing more effective targeted therapies.

The impact of Artificial Intelligence in detecting esophageal squamous cell carcinoma: advances and challenges

This review explores the transformative role of artificial intelligence (AI), especially through deep learning (DL) and convolutional neural networks (CNNs), in the early detection of esophageal squamous cell carcinoma (ESCC). ESCC presents significant diagnostic challenges due to its aggressive nature and high mortality, often diagnosed at advanced stages. Traditional endoscopic methods, while essential, suffer from limitations like interobserver variability, especially for subtle early lesions. AI-based systems have demonstrated high sensitivity in identifying these early neoplasms, reducing diagnostic discrepancies and enhancing precision. Through a bibliographic review, the article highlights recent AI advancements, particularly in utilizing CNNs to identify early-stage ESCC. These tools have shown the potential to improve diagnostic accuracy even among less experienced endoscopists and provide critical support in clinical decision-making, including biopsy guidance and lesion mapping in chromoendoscopy and narrow-band imaging (NBI). Despite promising results, challenges persist in AI’s practical application, such as the need for extensive data for training, clinical validation, and standardization across different endoscopic equipment. The study underscores the need for large-scale multicenter studies and standardization to solidify AI’s role in ESCC diagnosis. It envisions a future where AI not only serves as a diagnostic support tool but becomes integral to endoscopic practices, requiring continued technological development, ethical considerations, and regulatory frameworks to maximize clinical benefit.

Machine Learning Techniques for Skin Fungal Infection Detection -A Review

Skin diseases are the most common than other diseases. Skin diseases may be caused by fungal infection, allergy, bacteria, viruses, etc. Fungal disease affects more than billions people worldwide. The accurately of diagnosing skin fungal infections can be challenging in their early stages or present with symptoms, which mimic other dermatological disorders. The identification and categorization of skin fungal infections could be improved by recent developments in deep learning and artificial intelligence (AI). It offers a more effective and dependable substitute for conventional diagnostic techniques. Here we tried to focus on various methods for identifying fungal skin that rely on deep learning (such as transformers, convolutional neural networks, and hybrid models), the difficulties related to data diversity and availability, going over the shortcomings of current datasets, how data augmentation and synthetic data creation which might help close these gaps. We also investigate how improving interpretability and usability can help clinical uptake of AI-based diagnostic systems. Finally, the study concludes with suggestions for further research, highlighting the revolutionary potential of deep learning in dermatology and stressing the necessity of sophisticated model architectures, a wide range of high-quality datasets, and thorough clinical validation.

Epigenetic Suppression of miR-137 Induces RNF4 Expression, Facilitating Wnt Signaling in Colorectal Cancer.

Colorectal cancer (CRC) is a significant health issue worldwide. Recent studies highlight the critical role of miRNAs in CRC development, particularly miR-137, which acts as a key tumor suppressor. Despite its known role, further exploration of miR-137's downstream signaling is needed to understand its biology and therapeutic potential. We examined the methylation status of miR-137 using one TCGA data and three GEO data sets. A clinical validation cohort of 78 samples was analyzed using MSP for miR-137 promoter methylation. Various in vitro molecular/cellular and animal experiments were conducted to elucidate miR-137's role in CRC. Bioinformatic analysis indicated frequent methylation of miR-137 in CRC tissues, correlating with suppressed expression. EZH2-mediated H3K27 trimethylation silences miR-137 in CRC cells by increasing chromatin compaction, reversible by EZH2 siRNA or inhibitor GSK343. miR-137 inhibits CRC cell proliferation, migration, invasion, and xenograft tumor growth, confirming its tumor-suppressive role. Using the miRWalk repository showed that miR-137 regulates the Wnt signaling pathway by reducing typical protein expression in HCT116 and SW480 cells. miR-137 directly targets RNF4, leading to its downregulation at transcriptional and protein levels, with an observed inverse correlation in CRC tissues. miR-137 accelerates c-Myc and β-catenin degradation by inhibiting RNF4, impacting protein stability and Wnt pathway inhibition. miR-137 is epigenetically silenced through DNA methylation and EZH2-mediated H3K27 trimethylation. It regulates the Wnt signaling pathway by targeting RNF4, leading to c-Myc and β-catenin destabilization. Restoring miR-137 or inhibiting RNF4 suppresses CRC cell proliferation, migration, invasion, and tumor growth, highlighting its therapeutic potential in CRC.

Experience with artificial intelligence algorithms for the diagnosis of vertebral compression fractures based on computed tomography: from testing to practical evaluation

BACKGROUND: Osteoporosis is often diagnosed at the stage with complications, i.e., low-energy fractures. Vertebral compression fractures, which are complications of osteoporosis and predictors of subsequent fractures, are often asymptomatic. Compression fractures can be found by computed tomography performed for other indications with vertebral morphometry. Approaches to using artificial intelligence algorithms designed for diagnosing vertebral compression fractures were analyzed. AIM: Testing artificial intelligence algorithms to conduct morphometric analysis of vertebrae on chest computed tomography scans and assess the possibility of their implementation in medical organizations of the Moscow Healthcare Department. MATERIALS AND METHODS: To set a clinical task for artificial intelligence algorithms, basic diagnostic requirements in the area of “vertebral compression fractures (osteoporosis)” were formulated. The testing of the artificial intelligence algorithms included the following stages: self-testing, functional and calibration testing, practical evaluation, and operation testing. The first three stages of testing were performed using previously generated datasets. At practical evaluation and operation testing, artificial intelligence algorithms analyzed the data from computed tomography performed in medical organizations. The expert group of radiologists assessed the diagnostic accuracy and functional capacity of the AI algorithms at all stages. The resulting quantitative metrics of the accuracy of artificial intelligence algorithms were compared with the required target values. RESULTS: From June 2021 to June 2022, two artificial intelligence algorithms (Nos. 1 and 2) with different methods of detecting compression fractures were tested. Both artificial intelligence algorithms successfully passed the self-testing (6 tests), functional (5 tests), and calibration (100 tests) stages. The area under the ROC curve for artificial intelligence algorithm No. 1 was 0.99 (95% CI, 0.98–1), and for artificial intelligence algorithm No. 2, it was 0.91 (95% CI, 0.85–0.96). Artificial intelligence algorithm No. 1 passed the practical evaluation stage without any significant remarks, whereas algorithm No. 2 was sent for fine-tuning. After the operation testing stage, the following accuracy metrics were obtained: the areas under the ROC curve for artificial intelligence algorithm Nos. 1 and 2 were 0.93 (95% CI, 0.89–0.96) and 0.92 (95% CI, 0.90–0.94), respectively. At all stages, both artificial intelligence algorithms demonstrated sufficient metrics for clinical validation. CONCLUSION: Artificial intelligence algorithms for the automatic diagnosis of vertebral compression fractures have been tested, demonstrating the high quality of their operation. artificial intelligence algorithms can be applied as a supplementary tool in the medical decision support system.

Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis.

Falls pose a significant public health concern, with increasing occurrence due to the aging population, and they are associated with high mortality rates and risks such as multimorbidity and frailty. Falls not only lead to physical injuries but also have detrimental psychological and social consequences, negatively impacting quality of life. Identifying individuals at high risk for falls is crucial, particularly for those aged ≥60 years and living in residential care settings; current professional guidelines favor personalized, multifactorial fall risk assessment approaches for effective fall prevention. This study aimed to explore the prognostic validity of the Fall Risk Score (FRS), a multifactorial-based metric to assess fall risk (using longitudinal real-world data), and establish the clinical relevance of the FRS by identifying threshold values and the minimum clinically important differences. This retrospective cohort study involved 617 older adults (857 observations: 615 of women, 242 of men; mean age 83.3, SD 8.7 years; mean gait speed 0.49, SD 0.19 m/s; 622 using walking aids) residing in German residential care facilities and used the LINDERA mobile health app for fall risk assessment. The study focused on the association between FRS at the initial assessment (T1) and the normalized number of falls at follow-up (T2). A quadratic regression model and Spearman correlation analysis were utilized to analyze the data, supported by descriptive statistics and subgroup analyses. The quadratic model exhibited the lowest root mean square error (0.015), and Spearman correlation analysis revealed that a higher FRS at T1 was linked to an increased number of falls at T2 (ρ=0.960, P<.001). Subgroups revealed significant strong correlations between FRS at T1 and falls at T2, particularly for older adults with slower gait speeds (ρ=0.954, P<.001) and those using walking aids (ρ=0.955, P<.001). Threshold values revealed that an FRS of 45%, 32%, and 24% corresponded to the expectation of a fall within 6, 12, and 24 months, respectively. Distribution-based minimum clinically important difference values were established, providing ranges for small, medium, and large effect sizes for FRS changes. The FRS exhibits good prognostic validity for predicting future falls, particularly in specific subgroups. The findings support a stratified fall risk assessment approach and emphasize the significance of early and personalized intervention. This study contributes to the knowledge base on fall risk, despite limitations such as demographic focus and potential assessment interval variability.

Linguistic and clinical validation of the Chinese version of the Acute Cystitis Symptom Score (ACSS) questionnaire in Taiwanese women with uncomplicated acute cystitis

Purpose: The Acute Cystitis Symptom Score (ACSS) questionnaire is designed to diagnose acute cystitis in women. By quantifying the severity of symptoms, ACSS provides objective diagnostic criteria. The aim of this study is to translate the ACSS questionnaire into a Chinese version and validate its clinical use for diagnosing acute cystitis in Taiwanese women. Materials and methods: After rigorously translating the ACSS questionnaire into traditional Chinese used by the Mandarin-speaking Taiwanese people, it was clinically validated. Women aged 20 and above with suspected acute cystitis were recruited as the patients, and healthy women undergoing health check-ups as controls. Discriminative ability was assessed by comparing ACSS scores between the 2 groups, and the optimal diagnostic cutoff was determined using receiver operating characteristics analysis. In the patient group, treatment response was evaluated as patient-reported outcome by comparing ACSS scores pretreatment and posttreatment. Results: A total of 89 and 43 participants were recruited for the patient and control groups, respectively. The total score of typical symptoms between the patient and the control groups was significantly different (P &lt; 0.001). After antibiotic treatment, the total score of typical symptoms in the patient group significantly decreased (P &lt; 0.001). Using receiver operating characteristics curve analysis, the best cutoff score for diagnosing acute cystitis was 4, with a sensitivity and a specificity of 75.3% and 95.3%, respectively. Conclusion: After clinical validation, the Chinese version of the ACSS questionnaire can now be used as a symptom-oriented diagnostic and patient-reported outcome tool for acute cystitis in the Mandarin-speaking female population in Taiwan.