Diagnosis Of Disease Research Articles

Integrated Ultrasound-Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis.

The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the most promising analytical tools for point-of-care disease diagnosis. However, the low sensitivity and insufficient accuracy still limit its clinical application. In this work, a machine learning (ML)-optimized colorimetric LFA with ultrasound enrichment is developed to achieve the sensitive and accurate detection of tau proteins for early screening of Alzheimer's disease (AD). The LFA device is integrated with a portable ultrasonic actuator to rapidly enrich microparticles using ultrasound, which is essential for sample pre-enrichment to improve the sensitivity, followed by ML algorithms to classify and predict the enhanced colorimetric signals. The results of the undiluted serum sample testing show that the protocol enables efficient classification and accurate quantification of the AD biomarker tau protein concentration with an average classification accuracy of 98.11% and an average prediction accuracy of 99.99%, achieving a limit of detection (LOD) as sensitive as 10.30pgmL-1. Further point-of-care testing (POCT) of human plasma samples demonstrates the potential use of LFA in clinical trials. Such a reliable lateral flow immunosensor with high precision and superb sensing performance is expected to put LFA in perspective as an AD clinical diagnostic platform.

Hierarchically Nano-Decorated Poly(lactic acid) Nanofibers for Humidity-Resistant Respiratory Healthcare and High-Accuracy Disease Diagnosis.

The application of biodegradable and eco-friendly poly(lactic acid) (PLA) nanofibrous membranes (NFMs) toward respiratory healthcare has long been thwarted by the poor electroactivity and low surface activity of PLA. Herein, we unravel a microwave-assisted route to fabricate rod-like ZnO nanodielectrics, which were decorated with dopamine (ZnO@PDA) and anchored at the PLA nanofibers via an electrospinning-electrospray approach. The PLA/ZnO@PDA NFMs featured a substantially elevated specific surface area (up to 20.7 m2/g), increased dielectric constant (nearly 1.8) and a surface potential as high as 9.5 kV, resulting in superior air filtering performance (99.45% for PM0.3, 94.1 Pa, 32 L/min) compared with the pure PLA counterpart (90.04%, 169.0 Pa, 32 L/min). The notably increased electroactivity endowed the PLA/ZnO@PDA NFMs with significant improvements in triboelectric properties (output voltage of 11.5 V at 10 N, 0.5 Hz), laying down the cornerstone for self-powered monitoring of personal respiration. More importantly, a deep learning-assisted diagnostic system was developed based on respiration-driven signal patterns, enabling intelligent and real-time disease diagnosis with 100% accuracy for the protective membranes. The proposed hierarchical nanodecoration strategy opens up new possibilities for engendering eco-friendly nanofibers with an exceptional combination of efficient respiratory healthcare and intelligent diagnosis.

Bullous pemphigoid burden of disease, management and unmet therapeutic needs.

Bullous pemphigoid (BP) is an autoimmune blistering disease that can have a profound negative impact on quality of life. BP most often affects the elderly, a population with a high medical burden and special safety concerns. In this review, we outline the BP disease course, diagnosis, epidemiology and comorbidities, and describe tools commonly used to assess BP disease activity and severity and the impact of BP on health-related quality of life. We also outline biologic treatments currently under investigation for the treatment of BP and highlight the importance of considering safety when treating elderly patients.

Prolonged time to diagnosis of Crohn’s disease in patients with perianal fistulas negatively affects long-term outcomes

Abstract Background and aim This study aims to evaluate the effect of time to Crohn’s disease (CD) diagnosis on perianal fistula (PAF) outcomes in patients with a fistula as first manifesting sign. Methods In this multicenter, retrospective study, CD patients with a PAF preceding CD diagnosis between November 2015 and June 2022 were included. The primary outcome parameter was the time to CD diagnosis and its correlation with long-term outcomes. Results In total, 126 patients with a PAF prior to CD diagnosis were identified. Median time to CD diagnosis was 15.0 months (IQR 3.8-47.3). A total of 49 patients (38.9%) had a clinically closed fistula of which 21 patients (42.9%) achieved radiological healing. 25 patients (19.8%) underwent defunctioning, of which 9 patients (36.0%) needed proctectomy. Median time to CD diagnosis was shortest in patients with radiological healing (4.0 months, IQR 2.0-16.5) or clinical closure without radiological healing (11.0 months, IQR 3.0-47.8). In patients without fistula closure (n=51), median time to CD diagnosis was significantly longer compared to patients with fistula closure, 18.0 months vs 8.0 months (p=0.031). In patients who needed defunctioning, median time to diagnosis was more than twice as long compared to patients without defunctioning, 30.0 months vs 12.0 months (p=0.054). Conclusion A prolonged time to CD diagnosis in patients with a PAF as a manifesting sign is associated with worse long-term outcomes. Patients in whom radiological healing could be achieved had the shortest time to CD diagnosis, emphasizing the relevance of increased clinical awareness of underlying CD in fistula patients.

Electrochemiluminescence Biosensor Based on a Duplex-Specific Nuclease and Dual-Output Toehold-Mediated Strand Displacement Cascade Amplification Strategy for Sensitive Detection of MicroRNA-499.

The timely and accurate diagnosis of acute myocardial infarction (AMI) is of great significance to reduce mortality and morbidity associated with the condition. Herein, we developed an electrochemiluminescence (ECL) biosensor for the detection of the potential AMI biomarker microRNA-499 (miRNA-499), which was based on duplex-specific nuclease-assisted target recycling and dual-output toehold-mediated strand displacement (TMSD). First, miRNA-499 was converted into a large amount of single-stranded DNA through the DSN-assisted target recycling, which was further incubated with the DNA triple-stranded complex (S) to implement TMSD cycles. Thus, the Ru(bpy)32+-labeled signal strands were released and captured by the capture probe on the electrode surface, resulting in an intense ECL signal. Owing to the prominent cascade signal amplification, the constructed biosensor exhibited a good linear response to miRNA-499 within the range of 100 aM-100 pM with a detection limit of 69.99 aM. Furthermore, it demonstrated superior selectivity, stability, and reproducibility. In addition, the biosensor was successfully applied to detect miRNA-499 in real human serum samples, demonstrating its potential for nucleic acid detection in the early diagnosis of diseases.

Diabetes screening among women with polycystic ovary syndrome: a descriptive study of commercial claims, 2011–2019

BackgroundPolycystic ovary syndrome (PCOS) is a hormonal disorder that affects 6–12% of United States women of reproductive age. Because women with PCOS are at an increased risk of developing type 2 diabetes, clinical practice guidelines from a number of organizations (e.g. American Diabetes Association, American College of Obstetricians and Gynecologists, US Preventive Services Task Force) recommend that individuals with PCOS are routinely screened for diabetes. Guidelines further indicate that an oral glucose tolerance test (OGTT) should be used for diabetes screening in women with PCOS as opposed to an A1C or fasting plasma glucose test. The purpose of this study is two-fold: 1) to estimate rates of diabetes screening among a nationwide sample of commercially insured women with PCOS and 2) to report the percentage of women screened using each test (OGTT, A1C, fasting plasma glucose) among those who were screened.MethodsWe used the MarketScan Commercial Claims database (2011–2019) to identify a sample of women aged 18–64 years with PCOS who were free from diabetes at baseline and had ≥ 5 years of continuous enrollment in their insurance plan. PCOS was ascertained using International Classification of Disease diagnosis codes (ICD-9: 256.4; ICD-10: E28.2). Diabetes screening was ascertained using Current Procedural Terminology (CPT) codes (A1C: 8303683037; Fasting blood sugar: 82947; OGTT: 82950). Diabetes screening rates were calculated for the overall study sample as well as across subgroups defined by age, overweight/obesity, hypertension, hypercholesterolemia, and vascular disease.ResultsIn our sample of 191,110 commercially insured women with PCOS, 73.40% were screened at least once for diabetes during a five-year period. Among the women screened, 19.24% were screened using the Androgen Excess Society (AES)-recommended OGTT, 61.58% were screened using A1C, and 23.37% were screened using fasting blood sugar.ConclusionsIn a sample of commercially insured individuals spanning the timeframe 2011–2019, nearly 75% of women with PCOS complied with the ACOG screening guidelines for diabetes. Although OGTT is recommended as the preferred screening tool for women with PCOS it was less commonly used than A1C and fasting blood sugar tests.

Molecular serotyping of diarrheagenic Escherichia coli with a MeltArray assay reveals distinct correlation between serotype and pathotype

ABSTRACT Diarrheagenic Escherichia coli serotypes are associated with various clinical syndromes, yet the precise correlation between serotype and pathotype remains unclear. A major barrier to such studies is the reliance on antisera-based serotyping, which is culture-dependent, low-throughput, and cost-ineffective. We have established a highly multiplex PCR-based serotyping assay, termed the MeltArray E. coli serotyping (EST) assay, capable of identifying 163 O-antigen-encoding genes and 53 H-antigen-encoding genes of E. coli. The assay successfully identified serotypes directly from both simulated and real fecal samples, as demonstrated through spike-in validation experiments and a retrospective study. In a multi-province study involving 637 E. coli strains, it revealed that the five major diarrheagenic pathotypes have distinct serotype compositions. Notably, it differentiated 257 Shigella isolates into four major Shigella species, distinguishing them from enteroinvasive E. coli based on their distinct serotype profiles. The assay’s universality was further corroborated by in silico analysis of whole-genome sequences from the EnteroBase. We conclude that the MeltArray EST assay represents a paradigm-shifting tool for molecular serotyping of E. coli, with potential routine applications for comprehensive serotype analysis, disease diagnosis, and outbreak detection.

PD-ARnet: a deep learning approach for Parkinson’s disease diagnosis from resting-state fMRI

Objective. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.Approach.This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification.Main results.Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%).Significance.The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.

Artificial intelligence methods in diagnosis of retinoblastoma based on fundus imaging: a systematic review and meta-analysis.

Artificial intelligence (AI) algorithms for the detection of retinoblastoma (RB) by fundus image analysis have been proposed as a potentially effective technique to facilitate diagnosis and screening programs. However, doubts remain about the accuracy of the technique, the best type of AI for this situation, and its feasibility for everyday use. Therefore, we performed a systematic review and meta-analysis to evaluate this issue. Following PRISMA 2020 guidelines, a comprehensive search of MEDLINE, Embase,ClinicalTrials.gov and IEEEX databases identified494studies whose titles and abstracts were screened for eligibility. We included diagnostic studies that evaluated the accuracy of AI in identifying retinoblastoma based on fundus imaging. Univariate and bivariate analysis was performed using the random effects model. The study protocol was registered in PROSPERO under CRD42024499221. Six studies with 9902 fundus images were included, of which 5944 (60%) had confirmed RB. Only one dataset used a semi-supervised machine learning (ML) based method, all other studies used supervised ML, three using architectures requiring high computational power and two using more economical models. The pooled analysis of all models showed a sensitivity of 98.2% (95% CI: 0.947-0.994), a specificity of 98.5% (95% CI: 0.916-0.998) and an AUC of 0.986 (95% CI: 0.970-0.989). Subgroup analyses comparing models with high and low computational power showed no significant difference (p=0.824). AI methods showed a high precision in the diagnosis of RB based on fundus images with no significant difference when comparing high and low computational power models, suggesting a viability of their use. Validation and cost-effectiveness studies are needed in different income countries. Subpopulations should also be analyzed, as AI may be useful as an initial screening tool in populations at high risk for RB, serving as a bridge to the pediatric ophthalmologist or ocular oncologist, who are scarce globally. What isknown Retinoblastoma is the most common intraocular cancer in childhood and diagnostic delay is the main factor leading to a poor prognosis. The application of machine learning techniques proposes reliable methods for screening and diagnosis of retinal diseases. What isnew The meta-analysis of the diagnostic accuracy of artificial intelligence methods for diagnosing retinoblastoma based on fundus images showed a sensitivity of 98.2% (95% CI: 0.947-0.994) and a specificity of 98.5% (95% CI: 0.916-0.998). There was no statistically significant difference in the diagnostic accuracy of high and low computational power models. The overall performance of supervised machine learning was best than unsupervised, although few studies were available on the second type.

Ultrasensitive Detection of Dimethylamine Gas for Early Diagnosis of Parkinson's Disease Using CeO2-Coated Ti3C2Tx MXene/Carbon Nanofibers.

Parkinson's disease is a prevalent neurological disorder, with dimethylamine (DMA) recognized as a crucial breath biomarker, particularly at the parts per billion (ppb) level. Detecting DMA gas at this level, especially at room temperature and high humidity, remains a formidable challenge. This study presents an ultrasensitive chemiresistor DMA gas sensor, leveraging the CeO2-coated Ti3C2Tx MXene/carbon nanofiber (CeO2/MXene/C NFs) heterostructure to enhance dimethylamine sensing. The high conductivity of MXene, combined with C-Ti-O bonds and a sp2 hybridized hexagonal carbon structure, increases surface active sites. The presence of Ce3+ promotes the formation of surface-active oxygen species, while the MXene-CeO2 heterojunction broadens the electron depletion layer. Theoretical calculations reveal that the highest adsorption energy for DMA gas is at the Ce top site, explaining the sensor's satisfactory sensitivity, rapid response and recovery process, low detection limit (5 ppb), and high selectivity at room temperature. The Ce3+/Ce4+ dynamic self-refresh mechanism, involving surface hydroxyl elimination, enhances the sensor's performance under high-humid conditions. Clinical breath tests demonstrate the sensor's ability to distinguish between healthy individuals and Parkinson's disease patients, paving the way for developing next-generation sensors for early diagnosis of neurological disorders.

Modulation of AIE and Intramolecular Charge Transfer of a Pyrene-Based Probe for Discriminatory Detection and Imaging of Oligomers and Amyloid Fibrils.

Oligomers and amyloid fibrils formed at different stages of protein aggregation are important biomarkers for a variety of neurodegenerative diseases including Alzheimer's and Parkinson's diseases. The development of probes for the sensitive detection of oligomeric species is important for early stage diagnosis of amyloidogenic diseases. Many small molecular dyes have been developed to probe the dynamic growth of amyloid fibrils. However, there is a lack of discriminatory detection strategies to monitor the dynamics of both oligomers and amyloid fibrils based on the differential modulation of the photophysical properties of a single dye. Here we report a pyrene-based intramolecular charge transfer (ICT) dye with large Stokes shifted red-emitting aggregation induced emission (AIE) for monitoring the dynamic populations of both oligomers and fibrils during the aggregation of hen egg white lysozyme (HEWL) protein. At the early stage of protein aggregation, the accumulation of HEWL oligomers results in a rapid and substantial increase in the red AIE intensity at 660 nm. Later, as the oligomers transform into mature fibrils, the dye exhibits a distinct photophysical change. Binding of the dye to HEWL fibrils strongly suppresses the red AIE and enhances ICT emission. This is evidenced by a gradual decrease in the AIE intensity (∼660 nm) and an increase in LE (∼490 nm) and ICT (∼540 nm) emission intensities during the later stages of protein aggregation. Thus, the dye provides simultaneous measurements of the population dynamics of both HEWL oligomers and fibrils during protein aggregation based on the discriminatory modulation of AIE and ICT of the dye. The dye also enables imaging of both HEWL oligomers and fibrils simultaneously using different emission channels in super-resolution confocal fluorescence microscopy.

Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet’s robust feature extraction capabilities with ConvNeXt’s advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study’s methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models.

Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization

To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min–max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min–max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.

Toward Digital Periodontal Health: Recent Advances and Future Perspectives

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

Harnessing the Heart’s Magnetic Field for Advanced Diagnostic Techniques

Heart diseases remain one of the leading causes of morbidity and mortality worldwide, necessitating innovative diagnostic methods for early detection and intervention. An electrocardiogram (ECG) is a well-known technique for the preliminary diagnosis of heart conditions. However, it can not be used for continuous monitoring due to skin irritation. It is well known that every body organ generates a magnetic field, and the heart generates peak amplitudes of about 10 to 100 pT (measured at a distance of about 3 cm above the chest). This poses challenges to capturing such signals. This paper reviews the different techniques used to capture the heart’s magnetic signals along with their limitations. In addition, this paper provides a comprehensive review of the different approaches that use the heart-generated magnetic field to diagnose several heart diseases. This research reveals two aspects. First, as a noninvasive tool, the use of the heart’s magnetic field signal can lead to more sensitive advanced heart disease diagnosis tools, especially when continuous monitoring is possible and affordable. Second, its current use is limited due to the lack of accurate, affordable, and portable sensing technology.

Phase 1 trial of intraperitoneal paclitaxel in patients with gastric adenocarcinoma and carcinomatosis or positive cytology.

The purpose of this phase 1 trial was to evaluate the safety and toxicity of repeated normothermic intraperitoneal paclitaxel (PTX) for patients with gastric cancer metastatic to the peritoneum. A Bayesian optimal interval design was used to prospectively identify the safety and tolerability of escalating doses of intraperitoneal paclitaxel at weekly treatments for 3 weeks, followed by a 1-week break, and then three additional treatments. The primary objective was to define the maximum tolerated dose. Secondary end points included safety, tolerability, and antitumor activity. A total of 25 patients were treated between January 2020 and April 2023. Five dose-limiting toxicities were observed at 100 mg/m2. Treatment-related grade 3-4 toxicity included leukopenia (32%) and neutropenia (32%). Seven patients required a schedule change to every other week treatments. The maximum tolerated dose for intraperitoneal PTX was 100 mg/m2. The peritoneum post-intraperitoneal PTX demonstrated progression in five (20%), stable disease in five (20%), improvement in 10 (40%), and not evaluable in five (20%). Eight patients (32%) had resolution of their peritoneal disease and seven (28%) underwent attempted resection. The median overall survival (OS) from the diagnosis of metastatic disease was 18.8 months and from the date of treatment initiation was 10.8 months. One-, 2-, and 3-year OS rates from the diagnosis of metastatic disease were 84%, 38%, and 25%, respectively. Paclitaxel may be safely used at intraperitoneal doses of 100 mg/m2. Neutropenia associated with weekly treatments was common. Peritoneal complete clinical response rates with multimodality therapy including PTX were promising.

Analyzing the performance of short-read classification tools on metagenomic samples toward proper diagnosis of diseases.

Accurate knowledge of the genome, virus and bacteria that have invaded our bodies is crucial for diagnosing many human diseases. The field of bioinformatics encompasses the complex computational methods required for this purpose. Metagenomics employs next-generation sequencing (NGS) technology to study and identify microbial communities in environmental samples. This technique allows for the measurement of the relative abundance of different microbes. Various tools are available for detecting bacterial species in sequenced metagenomic samples. In this study, we focus on well-known taxonomic classification tools such as MetaPhlAn4, Centrifuge, Kraken2, and Bracken, and evaluate their performance at the species level using synthetic and real datasets. The results indicate that MetaPhlAn4 exhibited high precision in identifying species in the simulated dataset, while Kraken2 had the best area under the precision-recall curve (AUPR) performance. Centrifuge, Kraken2, and Bracken showed accurate estimation of species abundances, unlike MetaPhlAn4, which had a higher L2 distance. In the real dataset analysis with samples from an inflammatory bowel disease (IBD) research, MetaPhlAn4, and Kraken2 had faster execution times, with differences in performance at family and species levels among the tools. Enterobacteriaceae and Pasteurellaceae were highlighted as the most abundant families by Centrifuge, Kraken2, and MetaPhlAn4, with variations in abundance among ulcerative colitis (UC), Crohn's disease (CD), and control non-IBD (CN) groups. Escherichia coli (E. coli) has the highest abundance among Enterobacteriaceae species in the CD and UC groups in comparison with the CN group. Bracken overestimated E. coli abundance, emphasizing result interpretation caution. The findings of this research can assist in selecting the appropriate short-read classifier, thereby aiding in the diagnosis of target diseases.

Intervertebral disc degenerative disease in South Africa: a case-control analysis of selected gene variants

BackgroundIntervertebral disc (IVD) degenerative disease is a multifactorial disease for which genetics plays an integral role. Several genes, and their variants, associated with the development and progression of IVD degenerative disease have been identified. While several studies have investigated these genes in Asian and European populations, no available evidence exists for the South African population. Therefore, this study aimed to investigate these parameters.Methods and resultsBiological samples were collected in the form of buccal swabs from patients and DNA was extracted using a standard salt-lysis protocol. DNA purity and quantity was assessed by spectrophotometry, and subsequent genotyping was performed using the MassARRAY®System IPLEX extension reaction. For associations between variants and the presence of IVD degenerative disease, odds ratios (OR), confidence intervals (CI), chi-squared analysis and logistic regression was calculated. Age and sex were adjusted for, and Bonferroni’s correction was applied. This study found statistically significant associations for five of the evaluated single nucleotide polymorphisms (SNPs) with IVD degenerative disease, whereby IL-1α rs1304037 and rs1800587, ADAMTs-5 rs162509, and MMP-3 rs632478 demonstrated increased odds of a positive diagnosis for IVD degenerative disease, while decreased odds of IVD degenerative disease were seen for GDF-5 rs143383.ConclusionTo the best of our knowledge, this study represents the first of its kind to investigate the association of gene variants associated with IVD degenerative disease within the South African population. This study has shown that 5 of these gene variants were significantly associated with the presence of IVD degenerative disease, reflecting their integral roles in development and possible progression of the disease.

Rheumatic heart disease manifesting as mitral stenosis and tricuspid regurgitation in a young Asian male: a case report

Abstract Background Rheumatic heart disease (RHD) is a major global health issue because of its potential to cause heart failure. While RHD has been reported more frequently in women, this case report highlights the clinical manifestations, diagnosis, and management of rheumatic multivalvular disease in a young Asian male. Case Summary A 46-year-old Asian man with a history of percutaneous mitral valvuloplasty (PMV) was admitted with dyspnoea and generalised oedema that severely hindered his quality of life. Transthoracic and transoesophageal echocardiography revealed severe mitral stenosis (MS), left atrial appendage (LAA) thrombus, and severe tricuspid regurgitation (TR) due to RHD. Following a comprehensive evaluation by a multidisciplinary team, we advised mitral valve replacement, LAA thrombectomy, and tricuspid valve annuloplasty based on the detailed imaging of the valve anatomy and the patient's low surgical risk. Surgical intervention led to considerable improvement in the patient's symptoms, enabling a return to routine activities. Discussion This case highlights the chronic nature of RHD and its potential to cause heart failure. This report emphasises the need for regular follow-up in patients with a history of RHD to detect potential complications and ensure timely management. The successful outcome in this case underscores the importance of a team-based approach for managing complex valvular heart disease.

Robust unsupervised texture segmentation for motion analysis in ultrasound images.

Ultrasound imaging has emerged as a promising cost-effective and portable non-irradiant modality for the diagnosis and follow-up of diseases. Motion analysis can be performed by segmenting anatomical structures of interest before tracking them over time. However, doing so in a robust way is challenging as ultrasound images often display a low contrast and blurry boundaries. In this paper, a robust descriptor inspired from the fractal dimension is presented to locally characterize the gray-level variations of an image. This descriptor is an adaptive grid pattern whose scale locally varies as the gray-level variations of the image. Robust features are then located based on the gray-level variations, which are more likely to be consistently tracked over time despite the presence of noise. The method was validated on three datasets: segmentation of the left ventricle on simulated echocardiography (Dice coefficient, DC), accuracy of diaphragm motion tracking for healthy subjects (mean sum of distances, MSD) and for a scoliosis patient (root mean square error, RMSE). Results show that the method segments the left ventricle accurately ( ) and robustly tracks the diaphragm motion for healthy subjects ( mm) and for the scoliosis patient ( mm). This method has the potential to segment structures of interest according to their texture in an unsupervised fashion, as well as to help analyze the deformation of tissues. Possible applications are not limited to US image. The same principle could also be applied to other medical imaging modalities such as MRI or CT scans.